Edited byEcology and Conservation of Freshwater Fishes BiodiversityRafael MirandaPrinted Edition of the Special Issue Published in Waterwww.mdpi.com/journal/waterEcology and Conservation ofFreshwater Fishes BiodiversityEcology and Conservation ofFreshwater Fishes BiodiversityEditorRafael MirandaMDPI ‚ Basel ‚ Beijing ‚ Wuhan ‚ Barcelona ‚ Belgrade ‚ Manchester ‚ Tokyo ‚ Cluj ‚ TianjinEditorRafael MirandaInstitute of Biodiversity andEnvironment (BIOMA)University of NavarraPamplonaSpainEditorial OfficeMDPISt. Alban-Anlage 664052 Basel, SwitzerlandThis is a reprint of articles from the Special Issue published online in the open access journalWater (ISSN 2073-4441) (available at: www.mdpi.com/journal/water/special issues/EcologyConservation Freshwater Fishes).For citation purposes, cite each article independently as indicated on the article page online and asindicated below:LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year, Volume Number,Page Range.ISBN 978-3-0365-5420-4 (Hbk)ISBN 978-3-0365-5419-8 (PDF)Cover image courtesy of Rafael Miranda© 2022 by the authors. Articles in this book are Open Access and distributed under the CreativeCommons Attribution (CC BY) license, which allows users to download, copy and build uponpublished articles, as long as the author and publisher are properly credited, which ensures maximumdissemination and a wider impact of our publications.The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commonslicense CC BY-NC-ND.ContentsAbout the Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiPreface to ”Ecology and Conservation of Freshwater Fishes Biodiversity” . . . . . . . . . . . . ixRafael Miranda and Imanol MiqueleizEcology and Conservation of Freshwater Fishes Biodiversity: We Need More Knowledge toDevelop Conservation StrategiesReprinted from: Water 2021, 13, 1929, doi:10.3390/w13141929 . . . . . . . . . . . . . . . . . . . . . 1Ibon Tobes, Adrián Ramos-Merchante, Julio Araujo-Flores, Andrea Pino-del-Carpio, HernánOrtega and Rafael MirandaFish Ecology of the Alto Madre de Dios River Basin (Peru): Notes on Electrofishing Surveys,Elevation, Palm Swamp and Headwater FishesReprinted from: Water 2021, 13, 1038, doi:10.3390/w13081038 . . . . . . . . . . . . . . . . . . . . . 5Ana Sánchez-Pérez, Francisco J. Oliva-Paterna, Fátima Amat-Trigo and Mar TorralvaVariability in Population Traits of a Sentinel Iberian Fish in a Highly ModifiedMediterranean-Type RiverReprinted from: Water 2021, 13, 747, doi:10.3390/w13060747 . . . . . . . . . . . . . . . . . . . . . 25Shewit Gebremedhin, Stijn Bruneel, Abebe Getahun, Wassie Anteneh and Peter GoethalsScientific Methods to Understand Fish Population Dynamics and Support Sustainable FisheriesManagementReprinted from: Water 2021, 13, 574, doi:10.3390/w13040574 . . . . . . . . . . . . . . . . . . . . . 45Fátima Amat-Trigo, Mar Torralva, Daniel González-Silvera, Francisco Javier Martı́nez-Lópezand Francisco José Oliva-PaternaPlasticity in Reproductive Traits, Condition and Energy Allocation of the Non-Native PyreneanGudgeon Gobio lozanoi in a Highly Regulated Mediterranean River BasinReprinted from: Water 2021, 13, 387, doi:10.3390/w13030387 . . . . . . . . . . . . . . . . . . . . . 65Thomas P. Archdeacon, Tracy A. Diver and Justin K. RealeFish Rescue during Streamflow Intermittency May Not Be Effective for Conservation of RioGrande Silvery MinnowReprinted from: Water 2020, 12, 3371, doi:10.3390/w12123371 . . . . . . . . . . . . . . . . . . . . . 83Serena Sgarzi, Sandra Brucet, Mireia Bartrons, Ignasi Arranz, Lluı́s Benejam and AnnaBadosaFactors Influencing Abundances and Population Size Structure of the Threatened and EndemicCyprinodont Aphanius iberus in Mediterranean Brackish PondsReprinted from: Water 2020, 12, 3264, doi:10.3390/w12113264 . . . . . . . . . . . . . . . . . . . . . 101Jianhua Li, Xiaochen Chen, Xinliang Zhang, Zhenjia Huang, Liang Xiao and LiangliangHuang et al.Fish Biodiversity Conservation and Restoration, Yangtze River Basin, China, Urgently Needs‘Scientific’ and ‘Ecological’ ActionReprinted from: Water 2020, 12, 3043, doi:10.3390/w12113043 . . . . . . . . . . . . . . . . . . . . . 115Joan Manubens, Oriol Comas, Núria Valls and Lluı́s BenejamFirst Captive Breeding Program for the Endangered Pyrenean Sculpin (Cottus hispaniolensisBacescu-Master, 1964)Reprinted from: Water 2020, 12, 2986, doi:10.3390/w12112986 . . . . . . . . . . . . . . . . . . . . . 123vDani Latorre, Guillem Masó, Arlo Hinckley, David Verdiell-Cubedo, Gema Castillo-Garcı́aand Anni G. González-Rojas et al.Interpopulation Variability in Dietary Traits of Invasive Bleak Alburnus alburnus (Actinopterygii,Cyprinidae) Across the Iberian PeninsulaReprinted from: Water 2020, 12, 2200, doi:10.3390/w12082200 . . . . . . . . . . . . . . . . . . . . . 135Triantafyllia-Maria Perivolioti, Jaroslava Frouzova, Michal Tušer and Dimitra BoboriAssessing the Fish Stock Status in Lake Trichonis: A Hydroacoustic ApproachReprinted from: Water 2020, 12, 1823, doi:10.3390/w12061823 . . . . . . . . . . . . . . . . . . . . . 147Juan J. Schmitter-Soto and Roberto L. Herrera-PavónChanges in the Fish Community of a Western Caribbean Estuary after the Expansion ofan Artificial Channel to the SeaReprinted from: Water 2019, 11, 2582, doi:10.3390/w11122582 . . . . . . . . . . . . . . . . . . . . . 167Paolo Pastorino, Marino Prearo, Elisabetta Pizzul, Marco Bertoli, Danila Raffaella Franceseand Vasco Menconi et al.Hepatic Steatosis in a Bullhead (Cottus gobio) Population from a High-Mountain Lake (CarnicAlps): Adaptation to an Extreme Ecosystem?Reprinted from: Water 2019, 11, 2570, doi:10.3390/w11122570 . . . . . . . . . . . . . . . . . . . . . 185Shewit Gebremedhin, Karen Bekaert, Abebe Getahun, Stijn Bruneel, Wassie Anteneh andPeter Goethals et al.Comparison of Otolith Readability and Reproducibility of Counts of Translucent ZonesUsing Different Otolith Preparation Methods for Four Endemic Labeobarbus Species in LakeTana, EthiopiaReprinted from: Water 2019, 11, 1336, doi:10.3390/w11071336 . . . . . . . . . . . . . . . . . . . . . 197viAbout the EditorRafael MirandaPh.D. graduated in 1998, and performs his research activity in the field of Hydrobiology andAnimal Ecology, especially dealing with Freshwater Fishes and Management and Conservationof Wildlife in the Iberian Peninsula and Latin America. He is a professor and researcher in theDepartment of Environment Biology at the University of Navarre.Currently, his research projects aim to analyze the implications of the knowledge of biodiversityfor the management and conservation of protected areas, in collaboration with some Latin-Americaninstitutions. He is a member of the External Affairs Committee of the American Fisheries Society(AFS) and the treasurer of the Iberian Ichthyologic Society (SIBIC). Dr. Miranda has publishedmore than 90 papers in scientific international journals, related to conservation, hydrobiology, andlimnology, especially on the ecological analysis of human impacts.viiPreface to ”Ecology and Conservation of FreshwaterFishes Biodiversity”Freshwater fishes are the most diverse vertebrate group, with almost 36,000 species describedso far, and more species are being discovered all the time, evenly distributed between marine andfreshwater habitats. Freshwater ecosystems serve as a habitat for more than 18,000 fish species,occupying less than 1% of the Earth’s surface. Among all ecosystems, inland waters are one of themost affected. Wetlands are disappearing three times faster than forests, and freshwater populationsdecrease faster than terrestrial biodiversity. Nowadays, freshwater fishes may be considered the mostthreatened vertebrate group.Understanding the ecological subjects, environmental necessities, and pressures of freshwaterfishes remains a key concern of their conservation biology. This reprint explores the relationshipsbetween environmental issues, freshwater fish biodiversity, and human impacts from differentperspectives, but always focuses on the conservation biology of species and ecosystems.A change in mindset is needed to protect biodiversity in the upcoming years. Conservation planshave failed because our current knowledge is deficient and needs to be improved. We need countriesto commit to protecting biodiversity and develop realistic targets that can be met while compromisingwith conflicting needs and interests. The articles included in this reprint emphasize the necessityof having more knowledge to develop conservation strategies. Future conservation targets may beadvanced in part based on the knowledge provided by these papers and similar studies to ensure thelong-term protection of freshwater fish and other life forms.Rafael MirandaEditorixwaterEditorialEcology and Conservation of Freshwater Fishes Biodiversity:We Need More Knowledge to Develop Conservation StrategiesRafael Miranda 1,2,* and Imanol Miqueleiz 1Citation: Miranda, R.; Miqueleiz, I.Ecology and Conservation ofFreshwater Fishes Biodiversity: WeNeed More Knowledge to DevelopConservation Strategies. Water 2021,13, 1929. https://doi.org/10.3390/w13141929Received: 2 July 2021Accepted: 9 July 2021Published: 13 July 2021Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).1 Biodiversity Data Analytics and Environmental Quality Research Group,Department of Environmental Biology, University of Navarra, 31009 Pamplona, Spain;imiqueleiz@alumni.unav.es2 Biodiversity and Environment Institute, 31009 Pamplona, Spain* Correspondence: rmiranda@unav.esFreshwater fish represent one-fourth of all vertebrate species, despite freshwateroccupying less than 1% of the Earth’s surface [1]. They are among the most threatenedvertebrates, as they are especially vulnerable to human alterations resulting from speciesintroduction, overexploitation, fragmentation, the degradation of continental watercourses,and climate change [2]. Furthermore, freshwater fish show high levels of endemism becauseof the particular characteristics of their aquatic ecosystems and their evolutionary isolation;their loss could have irreparable consequences.Knowledge regarding the conservation status and ecology of freshwater fish is lessthan that for terrestrial vertebrates due to biases in conservation research and managementtoward more charismatic species [1]. Research into the ecological subjects, environmentalnecessities, and pressures of freshwater fishes is considered crucial to develop effectivemanagement measures for freshwater ecosystems. Understanding these environmentalfeatures remains a key concern of freshwater fish’s conservation biology.This Special Issue of Water explores the relationships of environmental issues, freshwa-ter fish biodiversity, and human impacts from different perspectives, but always focused onthe conservation biology of species and ecosystems. This Special Issue comprises thirteenpapers with contributions from seventy-one authors, including research studies from verydiverse ecosystems, places, and countries, from the most extensive basins, such as MiddleRio Grande in New Mexico (USA) [3] or the Yangtze River Basin (China) [4], to remote areasin Andean Amazon piedmont (Peru) [5] or Lake Tana (Ethiopia) [6]. Analyzed ecosystemsinclude basins, rivers and streams, lakes, and ponds from high mountain to estuarineplaces, from pristine to highly altered environments.The articles include eleven research articles, one review and a short communication.The nature of studies included in this Special Issue is diverse, considering a variety ofscientific approaches in a mixture of field topics. One of the published papers considersmethodological approaches to studying freshwater fishes and their conservation status.The authors propose a methodological study and improvement on age estimation for fourspecies of Labeobarbus genus using otoliths as a crucial tool for conserving these species.According to the authors, this kind of study is very relevant to Africa, particularly Ethiopia,in order to develop effective management strategies to conserve endemic species [6].Similarly, Gebremedhin et al. review scientific methods, concepts, and processes related tostock assessment and population dynamics in Africa [7].Three published papers present results on fish distribution, ecosystem explorations, andecological features in unique places, using many different techniques. Perivolioti et al. [8]conduct hydroacoustic monitoring of the Lake Trichonis (Greece) fish diversity, with the aim ofoffering an updated assessment of this unique ecosystem and the associated endemic species,with a focus on management and conservation.Tobes et al. [5] study the distribution of fish communities related to the environmentalvariables of the Alto Madre de Dios River, a poorly studied Andean–Amazon watershed1Water 2021, 13, 1929of southern Peru. Results show a significant shift in fish diversity regarding altitude,separating headwater and middle-lowland communities. In the light of this scenario whereno Andean–Amazon Basin will remain untouched, the studied basin still preserves healthyecosystems, showing excellent environmental quality overall. This condition makes thebasin a perfect candidate for serving as a reference basin for these endangered ecosystems.Schmitter-Soto et al. [9] investigate changes in a fish community during a long-term period,from 1999–2001 to 2015–2018. The results show that changes may be due to morphologicalchanges in the channel. Still, other threats could condition these changes: illegal fishingoutside the bay, and erosion in the innermost part, impacting native habitats. Li et al.analyze fish distribution, influencing factors, and habitat requirements in the East TiaoxiRiver (a major tributary of the renowned Yangtze River). Results show differences in fishmanagement and several severe threats to their conservation. Complete and continuousscientific research of fish diversity is crucial in order to develop efficient conservation andrestoration plans in the Yangtze River Basin [4].Several papers in this Special Issue explore, in more detail, applications of ecologicaland biological studies to improve monitoring of environmental changes and impacts onfreshwater fishes. Sometimes biological studies permit us to detect reactions to environmen-tal changes. Pastorino et al. [10] describe liver alterations observed in a bullhead population(Cottus gobio Linnaeus, 1758) from a mountain lake as an adaptation to extreme ecosystemsand adverse conditions. Other studies highlight the interaction between ecological stressfactors and biological traits and the use of sentinel species for the long-term monitor-ing of environmental status. This is the case with the study of Sánchez-Pérez et al. [11],where they analyze biological traits such as the growth, size structure, and somatic con-dition of the Southern Iberian barbel (Luciobarbus sclateri (Günther, 1868)) in a stressedMediterranean river.Some studies are focused on severely imperiled species, such as the Rio Grande SilveryMinnow (Hybognathus amarus (Girard, 1856)), catalogued as Endangered by the IUCN RedList of threatened species; the Spanish toothcarp (Aphanius iberus Valenciennes, 1846),catalogued as Endangered; or the Pyrenean Sculpin (Cottus hispaniolensis Bacescu-Master,1964), included in the Spanish Catalogue of Threatened Species as Endangered [3,12,13].Archdeacon et al. analyze the inefficiency of a specific conservation tool, the rescue ofthe Rio Grande Silvery Minnow during streamflow intermittency. Restoring natural flowregimes is the more effective action for species threatened by streamflow intermittency;re-establishment of the biological processes under which fishes evolved advances theconservation of this species [3].Other species threatened by water quality have been studied in the Iberian Peninsula.Sgarzi et al. [13] examine abiotic and biotic factors that could influence the size structureand density of Spanish toothcarp in Mediterranean brackish ponds. They suggest thatachieving a better pond ecological status may be necessary to conserve this endangeredfish. Manubens et al. [12] describe the ex situ conservation plan for the endemic and rarePyrenean sculpin (Cottus hispaniolensis Bacescu-Mester, 1964). The captive breeding processincludes six consecutive phases: nesting behavior, courtship, egg fixation, parental care(incubation), hatching, and survival during juvenile development. The management planimplemented for this project has probably allowed the main impediments described inother similar programs to be largely overcome.Conversely, some studies in this Special Issue are biological and ecological analysesof exotic invasive species, as the Pyrenean Gudgeon (Gobio lozanoi Doadrio & Madeira,2004) in a Mediterranean river or the bleak (Alburnus alburnus (Linnaeus, 1758)) in theIberian Peninsula. Both studies are focused on the significant role of plasticity in thesuccess of these invasive alien species. Latorre et al. [14] assess the variability in dietarytraits of the bleak in the Iberian Peninsula and compare the dietary characteristics of thisspecies among the main Iberian rivers and a native bleak population from France. Similarly,Amat-Trigo et al. [15] evaluate other biological traits in addition to the diet. The results ofboth studies suggest that this wide interpopulation variability will contribute to the species’2Water 2021, 13, 1929successful establishment throughout Mediterranean Europe, posing a severe risk to nativefish fauna.All the articles included in this Special Issue point in the same direction. The currentstatus of freshwater fish must be brought to our attention in order to design and implementeffective management measures to conserve freshwater fish. While we should maintainour interest in learning and know more about the conservation status of the imperiledfreshwater fish, policy and administrations should invert inefficient plans in order to revert,or at least reduce, the current crisis in biodiversity.A change in mindset is needed to protect biodiversity in the upcoming years. Con-servation plans have failed because our current knowledge is deficient and needs to beimproved. We need countries to commit to protecting biodiversity and develop realistic tar-gets that can be met while compromising with conflicting needs and interests. The articlesincluded in this Special Issue emphasize the necessity of having more knowledge to de-velop conservation strategies. Future conservation targets may be advanced in part basedon the knowledge provided by these papers and similar studies to ensure the long-termprotection of freshwater fish and other life forms [1].Funding: This research received no external funding.Conflicts of Interest: The authors declare no conflict of interest.References1. Miqueleiz, I.; Böhm, M.; Ariño, A.H.; Miranda, R. Assessment Gaps and Biases in Knowledge of Conservation Status of Fishes.Aquat. Conserv. 2020, 30. [CrossRef]2. Barbarossa, V.; Bosmans, J.; Wanders, N.; King, H.; Bierkens, M.F.P.; Huijbregts, M.A.J.; Schipper, A.M. Threats of Global Warmingto the World’s Freshwater Fishes. Nat. Commun. 2021, 12, 1–10. [CrossRef] [PubMed]3. Archdeacon, T.P.; Diver, T.A.; Reale, J.K. Fish Rescue during Streamflow Intermittency May Not Be Effective for Conservation ofRio Grande Silvery Minnow. Water 2020, 12, 3371. [CrossRef]4. Li, J.; Chen, X.; Zhang, X.; Huang, Z.; Xiao, L.; Huang, L.; Kano, Y.; Sato, T.; Shimatani, Y.; Zhang, C. Fish Biodiversity Conservationand Restoration, Yangtze River Basin, China, Urgently Needs ‘Scientific’ and ‘Ecological’ Action. Water 2020, 12, 3043. [CrossRef]5. Tobes, I.; Ramos-Merchante, A.; Araujo-Flores, J.; Pino-del-Carpio, A.; Ortega, H.; Miranda, R. Fish Ecology of the Alto Madre deDios River Basin (Peru): Notes on Electrofishing Surveys, Elevation, Palm Swamp and Headwater Fishes. Water 2021, 13, 1038.[CrossRef]6. Gebremedhin, S.; Bekaert, K.; Getahun, A.; Bruneel, S.; Anteneh, W.; Goethals, P. Torreele Comparison of Otolith Readabilityand Reproducibility of Counts of Translucent Zones Using Different Otolith Preparation Methods for Four Endemic LabeobarbusSpecies in Lake Tana, Ethiopia. Water 2019, 11, 1336. [CrossRef]7. Gebremedhin, S.; Bruneel, S.; Getahun, A.; Anteneh, W.; Goethals, P. Scientific Methods to Understand Fish Population Dynamicsand Support Sustainable Fisheries Management. Water 2021, 13, 574. [CrossRef]8. Perivolioti, T.-M.; Frouzova, J.; Tušer, M.; Bobori, D. Assessing the Fish Stock Status in Lake Trichonis: A Hydroacoustic Approach.Water 2020, 12, 1823. [CrossRef]9. Schmitter-Soto, J.J.; Herrera-Pavón, R.L. Changes in the Fish Community of a Western Caribbean Estuary after the Expansion ofan Artificial Channel to the Sea. Water 2019, 11, 2582. [CrossRef]10. Pastorino, P.; Prearo, M.; Pizzul, E.; Bertoli, M.; Francese, D.R.; Menconi, V.; Mugetti, D.; Bozzetta, E.; Varello, K. Hepatic Steatosisin a Bullhead (Cottus Gobio) Population from a High-Mountain Lake (Carnic Alps): Adaptation to an Extreme Ecosystem? Water2019, 11, 2570. [CrossRef]11. Sánchez-Pérez, A.; Oliva-Paterna, F.; Amat-Trigo, F.; Torralva, M. Variability in Population Traits of a Sentinel Iberian Fish in aHighly Modified Mediterranean-Type River. Water 2021, 13, 747. [CrossRef]12. Manubens, J.; Comas, O.; Valls, N.; Benejam, L. First Captive Breeding Program for the Endangered Pyrenean Sculpin(Cottus hispaniolensis Bacescu-Master, 1964). Water 2020, 12, 2986. [CrossRef]13. Sgarzi, S.; Brucet, S.; Bartrons, M.; Arranz, I.; Benejam, L.; Badosa, A. Factors Influencing Abundances and Population SizeStructure of the Threatened and Endemic Cyprinodont Aphanius iberus in Mediterranean Brackish Ponds. Water 2020, 12, 3264.[CrossRef]14. Latorre, D.; Masó, G.; Hinckley, A.; Verdiell-Cubedo, D.; Castillo-García, G.; González-Rojas, A.G.; Black-Barbour, E.N.; Vila-Gispert, A.; García-Berthou, E.; Miranda, R.; et al. Interpopulation Variability in Dietary Traits of Invasive Bleak Alburnus alburnus(Actinopterygii, Cyprinidae) Across the Iberian Peninsula. Water 2020, 12, 2200. [CrossRef]15. Amat-Trigo, F.; Torralva, M.; González-Silvera, D.; Martínez-López, F.J.; Oliva-Paterna, F.J. Plasticity in Reproductive Traits,Condition and Energy Allocation of the Non-Native Pyrenean Gudgeon Gobio lozanoi in a Highly Regulated Mediterranean RiverBasin. Water 2021, 13, 387. [CrossRef]3waterArticleFish Ecology of the Alto Madre de Dios River Basin (Peru):Notes on Electrofishing Surveys, Elevation, Palm Swampand Headwater FishesIbon Tobes 1,2 , Adrián Ramos-Merchante 3, Julio Araujo-Flores 4,5,6 , Andrea Pino-del-Carpio 2, Hernán Ortega 7and Rafael Miranda 2,*Citation: Tobes, I.;Ramos-Merchante, A.; Araujo-Flores,J.; Pino-del-Carpio, A.; Ortega, H.;Miranda, R. Fish Ecology of the AltoMadre de Dios River Basin (Peru):Notes on Electrofishing Surveys,Elevation, Palm Swamp andHeadwater Fishes. Water 2021, 13,1038. https://doi.org/10.3390/w13081038Academic Editor: José LuisSánchez-LizasoReceived: 8 February 2021Accepted: 7 April 2021Published: 9 April 2021Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).1 Centro de Investigación en Biodiversidad y Cambio Climático (BioCamb), Facultad de Cienciasdel Medio Ambiente, Universidad Tecnológica Indoamérica, Quito EC170103, Ecuador; ibonto-bes@uti.edu.ec2 Biodiversity Data Analytics and Environmental Quality Research Group, Department ofEnvironmental Biology, University of Navarra, 31009 Pamplona, Spain; apino@alumni.unav.es3 Department of Integrative Science, University of Huelva, Campus Universitario El Carmen,Avda. Andalucí s/n, 21071 Huelva, Spain; ramosadrian24@gmail.com4 Centro de Innovación Científica Amazónica, Jr Cajamarca Cdra 1, Puerto Maldonado 17001, Peru;araujojm@wfu.edu5 Center for Energy, Environment, and Sustainability, Department of Biology, Wake Forest University,1834 Wake Forest Rd, Winston-Salem, NC 27109, USA6 Colección Científica de Ictiología, Universidad Nacional Amazónica de Madre de Dios,Av. Jorge Chávez 1160, Puerto Maldonado 17001, Peru7 Departamento de Ictiología, Museo de Historia Natural de la Universidad Nacional Mayor de San Marcos,Lima 15072, Peru; hortega.musm@gmail.com* Correspondence: rmiranda@unav.es; Tel.: +3448-425600Abstract: Our study analyzes the distribution of fish communities related to the environmentalvariables of the Alto Madre de Dios River, an Andean-Amazon watershed of southern Peru, between300 and 2811 m a.s.l. within the Manu Biosphere Reserve. We provide new ecological and diversitydata on fishes for these poorly studied rivers and new data for palm swamp habitats. With electricfishing techniques, we collected a total of 1934 fish specimens belonging to 78 species, 42 generaand 15 families. To assess main patterns of diversity we combined SIMPER and ANOSIM withcanonical correspondence analysis to obtain an overview of the community structure of fish andtheir distribution related to aquatic habitats. Our results show an important shift on fish diversity at700 m a.s.l. separating headwater and middle-lowland communities. Electrofishing was a hindrancedue to the depth, flow and low conductivity of the rivers, but also allowed us to capture fish notobserved with other techniques. We also compared the use of elevation with slope as an alternativevariable for statistical analysis. Our results show that slope offers a solid and equivalent explanationfor fish distribution variability, avoids redundance, and instead of giving geographical data offersecologically solid information.Keywords: Tropical Andes; Manu Biosphere Reserve; Astroblepus; Trichomycterus; Mauritia flexuosa1. IntroductionFreshwater ecosystems are often referenced among those that are most altered andthreatened by anthropogenic impacts [1] and their fauna is at greater risk than any otheranimal and plant groups [2]. In this respect, precise knowledge of ecological aspectslike species distribution and requirements are a key point for conservation strategies,especially when the focal species are threatened or endangered [3]. Unfortunately, ourlimited taxonomic knowledge and incomplete information on species distributions forbroad territories is very high and represents an insurmountable obstacle for documentationof imperilment and extinction of freshwater biodiversity [4]. In particular, fish assemblagevariations in mountain streams of the Andes are poorly understood [5,6].5Water 2021, 13, 1038The Tropical Andes is regarded as the richest of the 25 recognized global biodiversity“hotspots”. It includes Andean ecosystems above 500 m a.s.l., extending from Chile andArgentina, through Bolivia, Peru, Ecuador, to Colombia and Venezuela. This hotspotsupports an estimated 45,000 plant and 3400 vertebrate species (excluding fishes), whichrepresents about 15% and 12% of all globally known species, respectively, being nearly halfof them endemic to the area [7].Peru is home to 1064 fish species [8], more than the 7% of all the globally knownfreshwater species. Most of them (more than 800) are found in the Amazonian Basin.Only during the first decade of the XXI century, 155 new species were named in Peruand the experts estimate the total number of Peruvian freshwater fishes at approximately1200 species [8]. However, as seen before, there have been few ecological surveys focusingon fish and the majority point out the uneven level of knowledge for this group of verte-brates [9,10], with some taxa itemized at the species level, whereas others routinely arecatalogued at family level, order or even phylum [4].Reviewing research on the Department of Madre de Dios where our study was carriedout (Figure 1), Pitman et al. [11] reported that all the scientific studies generated for thedepartment found that only 2.8% of the analyzed manuscripts focus on fish or hydrobiology.Only lichens received less attention than fish. Other research works regarding plants ormammals comprise 21.3% and 16.6% of the total scientific literature, respectively.Streams of the Tropical Andes are also ecologically important as the headwaters ofthe megadiverse lowland river systems in South America [12]. They are responsible ofdelivering major loads of inorganic sediment and organic carbon to lowlands [13] and playa key role in the ecological processes along the Andes-to-Amazon fluvial continuum [14].Furthermore, the Andean flank of the Amazon hosts the highest biodiversity rates and hasbeen least affected by historical climate variability and land use [15].However, the Andean-Amazonian piedmont is a rapidly changing landscape, partof the “arc of deforestation” [16], caused foremost by the expansion of cattle and soybeanproduction [17]. The recent growth of human populations, the exploitation of naturalresources and the proliferation of hydroelectric dams are leading to extensive reductions inhabitats and subsequent impacts on rivers [18–20]. Fortunately, many opportunities forprotecting these habitats yet exist, particularly in Peru, where entire river systems are stillrelatively intact and where there are few large dams and other major structural changes toriver channels [21].Our study area, the Alto Madre de Dios River Basin, in the south west of Peru, is analmost pristine basin. It is located in the Andean-Amazonian piedmont, in the transitionzone of the Manu Biosphere Reserve, bordered on the northwest by the Manu NationalPark, and on the southeast by the Amarakaeri Communal Reserve, two important protectedareas of the Peruvian Amazon. This region has long been known to tropical biologists as aregion high in species diversity, as well as some of the world’s largest expanses of pristinetropical forest [22].Given the pessimistic predictions for the sustainability and conservation of the Andeanbiodiversity due to environmental alterations [23,24], studies on the relationships amongbiotic and abiotic factors and the abundance and distribution of Andean fish are critical todocument the primary characteristics of the original communities and thereby contributeto the delimitation of appropriate conservation areas and/or to recovery strategies.One of the most frequent variables used for studying ecology and biodiversity distri-bution patterns is elevation. Ecologist all around the globe have demonstrated the strongcorrelation between altitude and changes in community composition, for plants [25], in-sects [26], amphibians [27], birds [28], and other organisms. Elevation is also commonlyused for studying freshwater biodiversity distribution patterns along the watersheds [29],and together with the distance to the mouth or the distance to the source, they are amongthe most repeated variables for analyzing fish distribution patterns [10,30]. Nevertheless,the use of those geographical variables can be problematic. We aspire to discuss it andevaluate the use of slope as an alternative environmental variable.6Water 2021, 13, 1038Figure 1. Sampling sites in the Alto Madre de Dios River, Peru (white circles).In the present study we examine the freshwater ecology for the Alto Madre de DiosRiver focusing on its ichthyofauna and evaluate the environmental integrity of the studiedecosystems. Our specific objectives are to (1) describe spatial variation of fish assemblageof an unstudied Andean-Amazonian stream, (2) to identify patterns of association betweenfish assemblages and habitat variables, and to (3) discuss the use of elevation for ecologicaland fish distribution analysis.2. Materials and Methods2.1. Study AreaThe Manu National Park has been considered one of the world’s most importanttropical protected areas since its creation in 1973 (Shepard et al., 2010). Located in thesouthern Peruvian Amazon rainforest constitutes the core of the Manu Biosphere Reserve7Water 2021, 13, 1038and an IUCN World Heritage Site [31]. The southern buffer zone of Manu Biosphere Re-serve includes the Alto Madre de Dios basin (11◦00′–13◦30′ S, 73◦30′–68◦30′ W) (Figure 1).The river flows, south to north, for 275 km through the rainforest of Cusco and Madrede Dios Departments, draining an area of approximately 1600 km2. The altitude in thebasin varies from up to 3500 to 300 m a.s.l. in the Manu River junction, spanning fiveAndean vegetation zones: puna (4500–3500 m), upper cloud forest (3500–2500 m), lowercloud forest (2500–1000 m), piedmont (1000–400 m) and lowlands (400–50 m) [32]. Ourhighest sampling site was located at 2411 m a.s.l. while to lowest was at 398 m a.s.l., 80 kmsdownstream. Rivers flow through deep and narrow valleys and steep slopes from thesource to around 700 m a.s.l., where the valleys open to wide and flat lowland floodplains.Flow regime is highly responsive to rainfall and presents the greatest discharge period fromNovember to April, decreasing progressively in the dry season (from May to October). Therivers remain turbid through the year, with highest sediment loads during the rainy season.Human population density in the Alto Madre de Dios valley is low, with scatteredsettlements that are under the administration of the village of Pilcopata in the District ofKosñipata (pop. 4790 in 2007, according to the Statistical National Institute of the PeruvianGovernment), Department of Cusco.Nowadays, most of the land is still covered by primary Amazonian forest, althoughsome areas have been deforested and are used for cattle raising. Tourism facilities—lodgesand a network of forest trails—are used by a relatively low number of tourists in comparisonwith other lodges in Madre de Dios area [22].2.2. Field Sampling and AnalysisOur survey was conducted on twenty-two sampling sites in June 2012, during the dryseason (Figure 1). The following environmental variables were recorded at each site usinga multiparametric probe (Hanna Instrument, HI 98129 Combo Waterproof, Woonsocket,RI, USA): conductivity (µS/cm), water temperature (◦C), and pH. Characterization ofhabitat structure was done using transects according to Armantrout (1998), and includeddepth (m), width (m), water velocity (m·s−1), percent tree canopy shading and domi-nant substrate categorized as fines (<2 mm), gravels (~2–64 mm), pebbles (~64–256 mm),boulders (>256 mm), or bedrock and concrete. Additionally, two habitat quality indiceswere measured: Qualitative Habitat Evaluation Index—QHEI (Rankin, 1989), and Andeanadapted version of the riparian forest quality index QBR (Acosta et al., 2009) (Table 1).QHEI jointly considers different habitat parameters, such as bottom substrate and embed-dedness, instream structure, velocity and depth regime, canopy cover, channel alterationand pool/riffle or run/bend ratios. QBR index includes aspects of the riparian forest suchas total riparian vegetation cover, cover structure, cover quality and channel alterations.Fish sampling was carried out in wadeable stream stretches (<1 m of height) byelectrofishing surveys, using a backpack electrofishing gear (Hans Grassl model IG200/2D,300–600 V, 0.2–2 A). Surveys was conducted following a single-run depletion methodologyand estimating fish abundance based on catch per unit effort (CPUE) (Meador, McIntyre &Pollock, 2003). Collected fish were anesthetized and subsequently counted, measured tothe nearest 0.1 cm total length (TL), weighed with a digital scale to an accuracy of 0.05 g andreleased after the survey, except for some voucher specimens kept to confirm identification.Voucher specimens were deposited in the fish collection of the Natural History Mu-seum of National University of San Marcos (Lima, Peru). In the laboratory, fish werepreserved in alcohol (75%) and identified by Ana María Cortijo, Jessica Espino and HernánOrtega, members of the Department of Ichthyology of Museum. Scientific names werevalidated according to W. N. Eschmeyer’s Catalog of Fishes [33].8Water 2021, 13, 1038Table 1. Sampling sites and environmental variables in Alto Madre de Dios River, Peru.Code River Place Date Alt pH Ta Tw Cd Wh Dh Vc QBR QHEI01R Aguas Calientes 13 June 2012 398 7.95 24.3 21.7 105 18.2 25.1 0.51 100 7202R Shintuya 12 June 2012 424 8.07 24.4 24.4 99 11.2 13.7 0.23 90 7203A Anaconda 13 June 2012 428 7.37 26.4 21.4 119 3.45 16.8 0.05 95 6104Q Mascuitania 14 June 2012 459 6.82 29.0 25.4 44 7.86 25.6 0.25 85 6705Q Puente a km 2,5 14 June 2012 462 8.03 26.1 22.4 169 5.54 18.7 0.15 100 7406R Carbón 11 June 2012 499 8.78 26.3 25.3 102 14.4 34.5 0.41 100 8507R Pilcopata 10 June 2012 511 6.94 24.8 25.0 119 15.7 15.6 0.04 100 7408R Piñi-Piñi 7 June 2012 518 6.86 22.3 22.0 131 6.86 44.1 0.19 100 8809Q Villa Carmen 7 June 2012 518 7.20 22.3 20.6 126 4.51 5.11 0.13 100 6210R Hospital 8 June 2012 547 7.63 23.1 19.9 26 28.3 36.6 0.74 100 8411R Tono 8 June 2012 551 7.70 19.1 18.5 50 6.94 22.1 0.30 100 6812R Salvación 11 June 2012 558 7.26 24.0 23.2 33 4.42 30.1 0.39 100 6613R Queros 9 June 2012 587 8.00 20.0 18.3 61 10.4 39.5 0.31 80 8214R Sabaloyacu 9 June 2012 593 7.70 19.7 21.2 44 22.9 31.0 0.43 100 8715A Aguajal 17 June 2012 622 5.77 23.9 22.0 26 2.49 57.4 0.04 50 6316R Asunción 10 June 2012 720 7.40 26.4 19.3 65 5.16 19.9 0.32 100 7817Q Quitacalzon 16 June 2012 1064 7.83 20.9 18.7 52 4.07 31.4 0.45 100 8818R Kosñipata 16 June 2012 1347 7.53 22.2 16.0 89 6.42 24.4 0.15 95 7019R San Pedro 16 June 2012 1394 7.81 20.1 18.5 43 7.74 49.4 0.48 95 8820R Unión 17 June 2012 1673 7.72 20.2 15.3 64 8.41 32.0 0.72 75 6621Q Wayquecha 18 June 2012 2218 7.40 17.3 11.4 51 3.99 30.2 0.83 100 8422Q Pacchayoc 18 June 2012 2411 4.41 16.1 11.7 188 6.11 23.9 0.89 100 82Alt: Altitude (m), Ta: Air temperature (◦C), Tw: Water temperature (◦C), Cd: Water conductivity (µS·cm−1), Wh: Mean width (m), Dh: Meandepth (cm), Vc: mean water velocity (m·s−1), QBR: riparian forest quality index and QHEI: qualitative habitat evaluation index.2.3. Data AnalysisFrom fish community compositional data, richness (Margalef’s index R = (S − 1)/ln N),and diversity (Shannon–Wiener index H’ = −∑pi log2pi, and Simpson’s index D = ∑pi2)measures were calculated, where pi is the proportion of species “i” at a given site, N is thetotal number of collected specimens, and S is the number of species [34].First, polynomial regressions were set among the mentioned indices and elevation,distance to the mouth or slope. We wanted to describe and compare patterns of diversityconsidering these three variables related to location along the basin.Second, several multiparametric statistical approaches were used to establish the mainspatial patterns in the fish community composition [35]. To detect patterns among fishcommunities, we used a permutation-based test with a nonparametric one-way analysis ofsimilarity (ANOSIM) using a Bray–Curtis similarity index based on abundance data [36,37].The ANOSIM statistic compares the mean of ranked dissimilarities among groups to themean of ranked dissimilarities within groups. An R-value close to 1 suggests dissimilarityamong groups while an R value close to 0 suggests an even distribution of high and lowranks within and among groups. Post-hoc tests with Bonferroni correction in p-values(which were multiplied by the number of comparisons) are done. This correction is veryconservative (produces large p-values). The sequential Bonferroni option does not outputcorrected p-values, but significance is decided based on step-down sequential Bonferroni,which is slightly more powerful than simple Bonferroni. If ANOSIM revealed a statisticallysignificant result, the relative contribution of each species to group dissimilarities wasquantified using a similarity percentage analysis (SIMPER), with a cut-off criterion of90% (Bray–Curtis similarity index), to identify subsets of the most important species [36].SIMPER calculates the average Bray–Curtis dissimilarity among all pairs of inter-groupsamples, expressed in terms of the average contribution from each species. Previously,we created three categorical variables (type of mass of water (river vs. stream), elevation(upstream-downstream 700 m), and stream order). Fish assemblages were comparedconsidering these three categories.For the choice of linear or unimodal analysis, a preliminary DCA (Detrended Cor-respondence Analysis) was performed [38]. This analysis evaluates the species turnover(the length of gradients) through the first DCA axis. The criterion for choosing linear vs.unimodal ordinations models is to obtain a SD < 3 [39]. Our results showed a SD > 39Water 2021, 13, 1038because of the large number of species and their frequency of occurrence. To examinerelationships between community composition and several environmental variables aCanonical Correspondence Analysis (CCA, Unimodal response; length of gradient SD > 3)was used through the program CANOCO 4.5 [39,40] based on DCA results. To reduce thenumber of graphics and considering the similar patterns shown, altitude was selected overdistance to mouth (r = 0.964, p-value < 0.01) as representant of the geographical variables,to compare it with slope. Two parallel CCA were carried out with altitude or slope askey variables related to location along the headwater-downstream gradient. This directgradient analysis technique provides ordination axes linearly related to the explanatoryvariables. The main result is a scatterplot ordination diagram displaying the pattern offish assemblage variation along the environmental variables shown as vectors. Vectordirection and length indicate the relative magnitude and influence of a particular variableon fish assemblages. The significance of the analyses was assessed by a permutation testwith 1000 random permutations. Habitat structure and water quality variables were alsoincluded in the analysis. To improve linearity, environmental variables were log trans-formed. Proportional data and abundance fish data were transformed (arcsine and roottransformed, respectively). Peebles was removed to avoid a high variance inflation factor(VIF) [41].All multiparametric analysis were done for species that were captured in more thantwo sites (i.e., 10% occurrence) and were performed using the R project software ver-sion 3.2.3 [42] with the package ”vegan” version 2.3–3 [43] except in the case of CCA whichhad been developed in CANOCO 4.56 [40].Finally, fish species have been classified according to their trophic specialization [6,44,45],with the aim to analyze the distribution of these trophic groups in the elevation gradient.3. ResultsA total of 1934 specimens were collected, and 78 fish taxa were identified belonging to43 genera and 14 families (Dataset S1). Twenty-seven species were undetermined, ten wereidentified as species affinis, and 41 species were correctly identified. Characids were themost diverse family with 43% of the species, followed by loricarids (14%), trichomycterids(8%), astroblepids, cichlids and heptapterids (7% each one) and crenuchids (3%). Theremaining fish belonged to ten families with only one represented species.Regarding diversity indices, values presented a marked altitudinal pattern. Shannon–Wiener and Margalef’s indices showed negative correlations with altitude, distance to themouth and slope, whereas Simpson’s index showed positive correlation (Figure 2). Site13 presented the highest diversity and hosted alone 28% of the total fish species (22 species).On the other hand, highest sites, 21 and 20, only had two species (Astroblepus sp. andTrichomycterus sp.). Finally, site 15 significantly different from the rest of the samplingpoints. It was a swamp ecosystem isolated from the main river and due to its uniqueenvironmental characteristics, it was discarded for subsequent analysis.Results of one-way ANOSIM showed significant differences in community compo-sition as indicated the high R values (global R: 0.786, p < 0.001) with the cut-off point at700 m a.s.l. This makes fish composition upstream and downstream 700 m a.s.l. welldistinguishable. Mass of water and stream order showed not significant differences (globalR: 0.04 and R: 0.22, respectively; p > 0.05). The SIMPER procedure indicated that using threespecies was possible to explain more than 38% of variation among elevation categories.Astroblepus, Trichomycterus and Knodus were the most important species explaining thevariation in community composition (Table 2).10Water 2021, 13, 1038Figure 2. Estimates of the fish community diversity indices along the Alto Madre de Dios River, regarding to elevation,distance to the mouth and slope of sampling points. *** significative coefficient of determination (R2).Table 2. Relative abundance of taxa presents under (U) and above (A) 700 m of elevation. Taxaare ordered by their overall weight in the SIMPER analysis (taxa showed with a cut-point of 90%of contribution). Percent of contribution of each taxon accord to SIMPER is show for each group(%contri). In the last row richness of each category of groups are shows.Elevation-700 mTaxa % Contri. U AAstroblepus 18.36 1.03 9.00Trichomycterus 10.76 0.78 5.78Knodus 8.91 3.92 -Chaetostoma 7.67 3.54 1.93Creagrutus 6.35 3.09 -Bryconamericus 6.01 1.84 2.64Ceratobranchia 5.88 3.22 -Astyanax 5.26 2.45 -Ancistrus 4.57 2.48 0.28Bujurquina 3.42 1.73 -Hemibrycon 3.36 1.63 -Characidium 3.01 1.33 0.28Aphyocharax 2.67 1.52 -Rineloricaria 2.36 1.05 -Hoplias 1.96 0.87 -Richness 24 611Water 2021, 13, 1038The relationship between fish and environmental variables are shown in Figure 3 andTables 3 and 4. The importance of environmental variables is indicated by the marginaleffect values (λA): for both analysis conductivity (0.22), boulders (0.25), water velocity(0.25) and water temperature (0.38) were most significant. The variables with the highestmarginal effect were slope (0.44) for the first CCA (Table 3a) and elevation (0.59) for thesecond CCA (Table 3b).λ −1.0 1.0−0.61.0AncistruAphyochaAstrobleAstyanacAstyanaxBryconamBujurquiCeratobrChaetostCharacidChasmocrCorydoraCreagrutCrenicicCrossoloHemibrycHopliasKnodusMoenkhauProdontoRhamdia.RineloriSerrapinTrichomySlopepHTemperatureConductivityWidthDepthWater velocityFinesGravelsBouldersShade8−1.0 1.5AncistruAstrobleAstyanacAstyanaxBryconamBujurquiChaetostCharacidChasmocrCorydoraCreagrutCrenicicCrossoloHopliasKnodusMoenkhauProdontoRineloriSerrapinTrichomyElevationpHTemperatureConductivityWidth DepthWater velocityFinesGravelsBouldersShade8HemibrycCeratobrAphyocha2218 m a.s.l.398 m a.s.l.Altitudea bFigure 3. Triplot of results of canonical correspondence analysis carried out with slope (a) or elevation (b) as key variablesrelated to location, showing site scores (circles), the environmental variables (vectors) and fish abundances (triangles) on thefirst two canonical axes. The code for site scores and environmental variables refers to sampling sites according to Table 1.Table 3. Results of canonical correspondence analyses (CCA) carried out with slope (a) or elevation (b) as key variablesrelated to location along the headwater-downstream gradient, showing canonical coefficients and weighted intrasetcorrelation coefficients of explanatory variables with the first two axes of the CCA. Importance of environmental variablesusing marginal effects are shown λA.(a) Slope Marginal Effects Intraset Correlations Regression/Canonical CoefficientsGroups λA CCA1 CCA2 CCA1 CCA2Slope 0.44 0.786 −0.261 0.755 −0.114Water temperature 0.38 −0.715 −0.304 −0.418 −0.433Water velocity 0.25 0.517 −0.370 0.047 0.002Boulders 0.25 0.554 0.118 0.006 0.111Conductivity 0.22 −0.374 0.472 0.007 0.987Depth 0.19 0.382 −0.216 −0.076 0.377Fines 0.15 −0.250 0.368 −0.149 0.282Width 0.14 −0.291 −0.243 −0.132 0.064pH 0.13 0.014 −0.415 −0.090 −0.662Gravels 0.07 0.220 −0.252 0.018 0.236Shade 0.07 0.041 −0.214 −0.266 −0.289(b) Elevation Marginal Effects Intraset Correlations Regression/Canonical CoefficientsGroups λA CCA1 CCA2 CCA1 CCA2Elevation 0.59 0.963 0.081 1.069 0.077Water temperature 0.38 −0.730 −0.213 0.189 −0.330Water velocity 0.25 0.510 −0.427 0.074 −0.032Boulders 0.25 0.563 0.051 0.091 −0.010Conductivity 0.22 −0.354 0.526 −0.068 1.064Depth 0.19 0.364 −0.259 −0.080 0.402Fines 0.15 −0.236 0.400 0.045 0.287Width 0.14 −0.289 −0.205 −0.084 0.184pH 0.13 −0.018 −0.403 0.164 −0.787Gravels 0.07 0.198 −0.276 −0.035 0.261Shade 0.07 0.034 −0.199 −0.083 −0.25012Water 2021, 13, 1038Table 4. Summary statistics table for CCA ordination presented, with slope (a) or elevation (b) askey variables.(a) Slope CCA Axis 1 2 3 4Eingenvalues 0.579 0.264 0.167 0.126Species-environmental correlations 0.956 0.924 0.884 0.950Cumulative percentage varianceof species data 27.9 40.6 48.7 54.7of species-environmental relation 38.4 55.9 67.0 75.4(b) Elevation CCA Axis 1 2 3 4Eingenvalues 0.597 0.263 0.170 0.132Species-environmental correlations 0.976 0.918 0.903 0.931Cumulative percentage varianceof species data 28.8 41.5 49.7 56.0of species-environmental relation 38.8 55.9 67.0 75.5For (a) Significance of the axis by the Monte Carlo permutation test is given by F = 3.10 (p < 0.05). All canonicalaxes were significant. Values in bold indicate significant difference at p < 0.05. For (b) Significance of the axis bythe Monte Carlo permutation test is given by F = 3.23 (p < 0.05). All canonical axes were significant. Values inbold indicate significant difference at p < 0.05.Outcomes were equivalent using slope and elevation as main variables, therefore, onlyresults for CCA with slope are described next. The inertia in the assemblage compositionin the studied sites was 0.579 for axis 1, 0.264 for axis 2, 0.167 for axis 3 and 0.126 foraxis 4. The CCA ordination revealed strong relationships between species abundancesand measured environmental variables explaining 72.6% of species distribution. The firstcanonical axis (axis 1) accounted for 38.4% of the variation in the data set, the second axis(axis 2) accounted for 17.5% of the variation in the data set. An unrestricted Monte Carlopermutation test indicated that all canonical axes were significant (p < 0.05).First Canonical Correspondence Analysis CCA1 eigenvalue accounts 0.579 and it iscomposed by slope, boulders and water velocity on the positive side and water temperatureon the opposite side (Figure 3a). First Canonical Correspondence Analysis was interpretedas a longitudinal gradient from high to lowlands with sites above 700 m a.s.l. beingdistributed on the right area of the graph (except for site 12, slightly right from the axis)and sites below that altitude spread in the left area. Second Canonical CorrespondenceAnalysis CCA2 (eigenvalue = 0.264) is dominated by conductivity on the positive sideand pH and water temperature on the negative side. Second Canonical CorrespondenceAnalysis was interpreted as hydrochemical parameters (Table 3a; Figure 3a).On the right area of the graph (Figure 3a) a first species group composed by Trichomyc-terus (Figure 4) and Astroblepus genera (Figure 5) was separated from the remaining and itwas positively related to headwaters showing distinctive features: high velocities of water,big boulders and lower temperatures. Bryconamericus genera species were also related tothese headwater streams, although they do not appear so high in the altitudinal gradient.A second group of fish represented by Serrapinnus, Prodontocharax, Astyanax, Astyanacinusand Knodus genera, located on the top-left area of the graph, was positively related to stillwaters with low velocities, higher conductivities and fine substrates (mainly sands). Athird cluster, located on the top-left area of the graph, included Rineloricaria, Chaetostoma,Aphyocharax, Hemibrycon, Ancistrus, Creagrutus and Crossoloricaria. They were related toopen places with highest water temperatures and wide riverbeds. The rest of the generawere distributed between these two groups along the first axis on the left of the graph,including Hoplias, Rhamdia, Chasmocranus and Characidium.Attending to fish distribution and trophic specialization [6] along the altitudinalgradient (Figure 6), no fish were found above 2200 m a.s.l. Astroblepus and Trichomycteruswere the only genus found on highest reaches (above 1400 m a.s.l.), feeding exclusivelyon invertebrates. Even though they appear tightly related to headwaters, they werefound all along the basin with occasional and scarce occurrence also in lowland streams.At 1400 m a.s.l. three new genera appeared: one invertivorous Bryconamericus, and the13Water 2021, 13, 1038first two herbivore-detritivores Chaetostoma and Ancistrus genera. Next invertivorousCharacidium genus was found below 1100 m. Above the 700 m other three invertivorousgenera were found: Rhamdia, Creagrutus and Chasmocranus. The rest of the fish taxa werefound below 622 m, representing most of the diversity. The first piscivorous species,Hoplias malabaricus and Crenicichla semicincta were found at this altitude. Figure 4. Species and morphotypes of genus Trichomycterus collected in the upper Madre de DiosRiver. (A,B) Trichomycterus sp.3 collected on Américo stream (21Q), (C) Trichomycterus sp.2 collectedon Unión River (20R), (D) Trichomycterus sp.2 collected on San Pedro River (19R), (E) Trichomycterussp.3 collected on Quitacalzon stream (17Q), (F,G) Trichomycterus sp.5 collected on Quitacalzon stream(17Q), (H) Trichomycterus sp.4 collected on Salvación River (12R), (I) Trichomycterus sp.6 collected onKosñipata River (16R), and (J) Trichomycterus sp.1 collected on Queros River (13R).14Water 2021, 13, 1038Figure 5. Species and morphotypes of genus Astroblepus collected in the upper Madre de Dios River.(A) Astroblepus sp.2 collected on Américo stream (21Q), (B) Astroblepus sp.1 collected on Unión River(20R), (C) Reddish specimen of Astroblepus sp.1 collected on Unión River (20R), (D) Astroblepussp.1 collected on San Pedro River (19R), (E) Astroblepus mancoi collected on San Pedro River (19R),(F) Astroblepus sp.2 collected on San Pedro River (19R), (G) Astroblepus sp.1 collected on Quitacalzonstream (17Q), (H) Astroblepus aff. longifilis collected on Kosñipata River (16R), (I) Astroblepus mancoicollected on Queros River (13R), and (J) Astroblepus aff. trifasciatus collected on Salvación River (12R).15Water 2021, 13, 1038Figure 6. Elevational ranges and trophic specializations of fish species occurring at elevations >400 m a.s.l. in tributary andmain-channel sites within the Alto Madre de Dios watershed, south-eastern Peru.4. DiscussionA gradual increase in species richness and diversity is expected along longitudinalgradients in lotic systems, associated with changes in resource availability, channel mor-phology, flow regime and substrate type [46,47]. Addition of species is usually relatedto an increase of habitats motivated for the structural diversification of the environmentdownstream [48]. Our study confirms this pattern (Figure 2), with the only exception ofone sampling point 15, that does not fit the trend due to its low diversity values, explainedby its peculiar features as discussed below.All the studied environmental variables were correspondent with the values reportedby other works for natural and unaltered streams of this territory [6,30,49]. Quality indicesof riparian and fluvial habitat (QBR and QHEI) got very high scores in all the samplingpoints, highlighting the good environmental quality, with some remarkable exceptions(Table 1). Riparian forest (and adjacent jungle) on the right bank of the sampling point 13,in the Queros River, was lacking due to deforestation for extensive cattle raising.Besides, sampling point 15, located at 622 m a.s.l., showed significant differencesboth in environmental and diversity features, as already mentioned. This site belongsto a special type of aquatic ecosystem of Amazon forest known as aguajal. They areback swamp forests, defined as forest on low-lying areas outside of streams courses, onlyconnected to them during high flood season. They are usually dominated by palms(Mauritia flexuosa), although other tree species like figs (Ficus species) can be abundantas well [50]. They also tend to be clear and acidic, in contrast to the higher turbidity andcircumneutral pH of the streams around [51,52]. This aguajal was confined by extensivecattle pastures surrounding it and was used as drinking reservoir for cattle, showing aremarkable eutrophication. Sampling point 15 presented the described characteristics16Water 2021, 13, 1038and showed low habitat quality indices (Table 1) and low diversity of fishes (Figure 2),with only three extant species: Moenkhausia oligolepis (70 specimens), Crenicichla semicincta(three specimens) and Hoplias malabaricus (one specimen). However, diversity mightincrease in the rainy season, when this habitat is connected to the river and other speciespresent in the river could occupy this place. Species of genera Astroblepus, Trichomycterus,Bryconamericus, Ceratobranchia, Creagutus and Rhamdia are present in the nearest samplingpoints (Sites 14 and 16).Because of its special and no-comparable characteristics, this peculiar sampling pointhas been excluded for the general comparative analysis. Although the importance of theaguajales has been widely recognized because of their ecological and social relevance [53,54],no fish species records are available on the literature. Our fish and ecological data throwsome light on these poorly studied freshwater habitats.When analyzing richness, diversity and dominance in relation with position fromheadwaters-downstream indicated by elevation, distance to mouth and slope, results weresolid and similar for the three variables (Figure 2). The Canonical Correspondence Analysesthrough the marginal effect values (λA) highlighted principal role of elevation (0.59) or slope(0.44) for explaining fish distribution along the basin (Table 3). The resulting graphics werealso analogous when using both variables (Figure 3). Elevation and distance to mouth arewidely used for multivariate analysis [55,56]. Nevertheless, they do not give environmentalinformation, they are geographical variables and indicate spatial position. Although theyshow strong correlation with ecologic changes, their use for ecological analysis togetherwith environmental variables may be problematic. Instead, slope is a purely environmentalvariable that measures a geophysical characteristic of the site. Furthermore, it is stronglyrelated to position along the basin but is independent from other variables, something thatavoids “noise” on multivariate analyses. After validating its robustness for explaining fishdistribution as well as elevation, we recommend the use of slope as alternative variable toelevation or distances to mouth/source.Fish community composition along the basin presented two distinct groups with aclear cut-off point occurring at 700 m a.s.l as indicated by the ANOSIM, separating moun-tainous fish from piedmont communities. The boundary between both zones is markedby geomorphological changes on the basin, when it shifts from the steep mountainousstreams flowing through narrow valleys, to flat and wide floodplains where river channelswiden. As pointed out by SIMPER procedure Astroblepus and Trichomycterus are the mostdistinctive species from the mountainous streams, while Knodus was the most importantspecies from lowlands explaining the variation in community composition (Table 2).Fish abundance and distribution in response to environmental variables was rep-resented using the CCA analysis and showed similar results using slope or elevation(Figure 3). The observed distribution of species along the longitudinal gradient of the basinwas comparable to other fish assemblages found in rivers with similar characteristics inadjacent areas [10,30,49]. This biotic zonation corresponds to discontinuities in river geo-morphology or abiotic conditions and are usually related to smooth transitions of abioticfactors contributing to nested patterns of assemblage composition along the altitudinalgradient [57]. The graph highlights this zonation, distributing headwater sites and theirfish assemblages on the right of the diagram, related to fast running waters, steep slope,boulders and low water temperatures, opposed to middle and lowland streams on the leftof the graph related to higher water temperatures, wider streams, low velocities, higherconductivity and fine substrates.Genera Astroblepus and Trichomycterus co-occur in high reaches, being the uniquespecies present on the sites above 1400 m a.s.l. These genera are adapted to headwaters,related to rocky substrates and cold, clean and well oxygenated water (Figure 3). They arebenthonic species without scales and a powerful sucker mouth or opercular odontodes [58].Nevertheless, they occasionally occur in lower areas, although their distribution is usuallylimited to elevations greater than 400 m.a.s.l. (Lujan et al., 2013). Bryconamericus, a rheophilegenus, is the next genus joining the headwater assemblage at 1400 m a.s.l. (Figure 6).17Water 2021, 13, 1038They prefer areas of moderate to strong current and present even in the torrential flows,occupying intermediate places between mountain upstream and jungle downstream [30,59].We also found some Chaetostoma and Ancistrus fishes at these reaches, although theyappeared closely related with lower middle section streams (Figure 3).We found most of the species related to these middle-low reaches, distributed alongthe left area of the CCA (Figure 3): habitats with fine substrata, very slow water velocities,mild slope and more conductivity, related with Serrapinus, Prodontocharax, Astyanancistrus,Astyanas, Knodus and Moenkhausia, on the top-left area of the diagram (Figure 3b) [60]; otherlotic sites presenting the widest habitats with highest temperature and higher pH, weredistributed on the bottom left of the diagram, related with Aphyocharax and Hemibrycongenera characids and loricarids like Rineloricaria, Chaetostoma and Ancistrus [47,59,61].We also observed a remarkable zonation related to trophic niches (Figure 6). Headwa-ters were dominated exclusively by invertivorous Astroblepus (Figure 5) and Trichomycterus(Figure 4) fishes due to the scarcity of other food resources in these clean waters [9,58].Although the first herbivore-detritivore Chaetostoma and Ancistrus individuals were foundstarting at 1400 m a.s.l., they were more abundant below 1000 m a.s.l., once the rivercarries enough organic matter coming from the surrounding forest offering a new trophicniche [9,49]. The first piscivorous species, Hoplias malabaricus and Crenicichla semicincta,were distributed around 600 m a.s.l. and below, first found at site 15, the aguajal, where theMoenkhausia oligolepis community was very abundant providing enough biomass to feed thepredators. This distribution pattern is according with the observations of Lujan et al. [6].According to some ecological studies and inventories carried out in this area [8,62],more than 130 species of freshwater fishes have been reported for the Alto Madre de DiosBasin. In the present study, only 78 species were collected, due to two main reasons: (1) thehighest biodiversity is found on the lowest reaches of the basin and our lowest samplingwas at 398 m a.s.l.; (2) electrofishing technique has some limitations on these ecosystems.Regarding electrofishing, only wadeable stretches were sampled, whereas largercourses or deep stretches were avoided. This limitation directed our sampling efforts tosmaller rivers and tributaries where the electrofisher gained efficiency [63]. Although thisrepresents a significant impediment for carrying out a complete biological survey, alsobrings up new opportunities. Most of the ichthyofaunal studies undertaken in Amazoniahave focused on the large rivers and commercially valuable species, therefore small andnoncommercial fishes and secondary streams have been usually overlooked [64]. Guidedby our limitations, part of our surveying efforts was concentrated on this poorly studiedecosystems and species.Besides the restrictions for choosing samplings sites, fish resistance to electrofishingwas significant. This resistance was favored by low water conductivity and temperature,high velocities, shelter’s abundance or low visibility [65]. Furthermore, capture efficiencydepended on the mobility of species: little benthic fish were collected easily, in contrast tolarger, strong-swimming species that escaped the electrofishers range. However, accordingto other authors [65,66] electrofishing is the best sampling method when your objectivesare to estimate and quantify freshwater fish populations in streams and wadable riversand to correlate these abundances with environmental features, habitat characteristics,hydrochemical parameters or other ecological measures. Besides, although electrofishingsurveys have been widely achieved on the world, they have been scarce in remote placeslike the Alto Madre de Dios Basin due to the more complex logistics.If we match our results with the survey carried out with seine nets by Araújo-Floresin 2013 [62] we find remarkable differences. Comparing the total 44 species reported byAraújo-Flores [62] with our 78 species only 17 of them were captured by both studies.Nevertheless, survey of Araújo-Flores focused on two rivers and its tributaries (Pilcopataand Piñi-Piñi Rivers), distributing ten sampling sites in a smaller area with a limitedaltitudinal range (500–600 m a.s.l.). If we compare our data for the three sites coincidingwith the survey of Araújo-Flores (sites 7, 8 and 9), although our sampling effort was lower(3 vs. 10 sites) and our captured diversity smaller (26 vs. 44 species), we collected 15 species18Water 2021, 13, 1038missing in the survey of Araújo-Flores [62]. Therefore, although electrofishing is not aseffective in tropical streams as it is for temperate streams, it may be more effective forcapturing some species that seem to be underestimated by other techniques. Consequently,as we focused on understudied small rivers and streams, catching poorly known smallfish, using an uncommon technique for tropical freshwaters, our study presents new andrelevant taxonomic and ecological information, providing data for some fish never capturedbefore by previous surveys in the area.Although our results highlight the good conservation status of the Alto Madre deDios Basin, the territory endures severe threats: logging [22,31], gold mining [67], climatechange [15,23], fossil fuel extraction [68], and hydropower projects [20,23]. The develop-ment and implementation of management plans is crucial for anticipating and mitigatingfuture impacts. Nevertheless, the principal threat for this peculiar area, with high endemicbiodiversity and very good conservation status, is the lack of knowledge regarding itsfauna and flora. Studies of the fish fauna are critical to document the primary characteris-tics of the original communities and thereby contribute to the delimitation of appropriateconservation areas and/or to recovery strategies for degraded streams [69]. Therefore,Peruvian freshwater fish faunal inventory (including the Alto Madre de Dios River Basin)is a priority [8,69]. There are many remote areas, with poor accessibility, where knowledgeof freshwater fish fauna is negligible and for most fish taxa basic taxonomic work is stillrequired, not only for this area, but for all the Amazon and adjacent basins [8]. Besides,there is even less information, in some cases null, on the ecology of many species. Thisknowledge gap highlights the necessity of basic taxonomic works and the creation of fieldguides providing new research projects an essential tool [70]. Fortunately, there are someremarkable guides for the Madre de Dios Basin [52,61] and adjacent areas [71,72] that offerinestimable material about freshwater fishes in this region, although, the information inthese guides is incomplete for many species. In this sense, the lack of basic knowledgemay hamper the development of community-level analyses [3,73]. Although fish diversityfor the hotspot is unclear yet, some papers estimate fish richness for the Andean regionmore than 600 species [69], with more species found at low elevations compared to higherreaches [5]. Around 2700 species have been recognized for the hotspot inside the AmazonBasin territory [74]. In contrast to richness, endemism tends to increase at higher elevationsand particularly concentrates in isolated patches of habitat such as valleys and mountaintops [7]. An IUCN report evaluated fish fauna conservation status for the Tropical An-des [75] considering 666 endemic species: 13 spp. critically endangered; 33 spp. vulnerable;36 spp. near threatened; 341 spp. least concern; 215 spp. data deficient. One of the mainconclusions points out the significant information gap: 32% of the evaluated fish werepoorly known.Concerning this taxonomic constraint, freshwater fishes of high headwaters demandspecial attention. All of them are Andean species and live on high altitudes, areas wherehuman settlements and their subsequent impact has a longer and more intense presence,compared with Amazonian streams [23]. We found five different species of Astroblepus(Figure 5), two of them undetermined, five species of Bryconamericus, and six species ofTrichomycterus all undetermined (Figure 4). Astroblepids and trichomycterids are typicallyrestricted in their geographical distribution and endemic to single or adjacent river sys-tems of the Andes, and their taxonomy is poorly known and in constant revision [76–78].Advances on the taxonomic and ecological knowledge of these high mountain species,related to tropical montane cloud forests, are mandatory for the design of conservation andmanagement plans in the area.According to Ortega et al. [8], there is not an official national Peruvian red list offreshwater fishes because of this lack of knowledge (despite several attempts and proposals).Although conservation lists have their limitations and critics, they represent an essentialtool required for protecting biodiversity [79]. Therefore, it is imperative to continue withthe study of freshwater fish species in Peru with the aim of improving management actionsand conservation plans.19Water 2021, 13, 1038In the light of this scenario where no Andean-Amazon Basin will remain untouched,the Alto Madre de Dios River Basin still preserves healthy ecosystems, with mild humanimpacts affecting some stretches, but showing a good environmental quality overall. There-fore, this makes the basin a perfect candidate for being preserved and considered as areference basin for these seriously endangered ecosystems.Author Contributions: Conceptualization, R.M. and I.T.; methodology, R.M., I.T. and A.R.-M.; formalanalysis, A.R.-M. and I.T.; investigation, R.M., I.T., A.P.-d.-C. and J.A.-F.; data curation, I.T. and R.M.;writing—original draft preparation, I.T. and R.M.; writing—review and editing, R.M., I.T., A.P.-d.-C.,A.R.-M., J.A.-F. and H.O.; visualization, J.A.-F. and I.T.; supervision, A.P.-d.-C. and R.M.; projectadministration, R.M. and A.P.-d.-C.; funding acquisition, R.M. All authors have read and agreed tothe published version of the manuscript.Funding: This research was funded by Government of Spain, Ministry of Foreign Affairs andCooperation, AECID (Code A1/040396/11) and Asociación de Amigos Universidad de Navarra.Institutional Review Board Statement: Not applicable.Informed Consent Statement: Not applicable.Data Availability Statement: The data presented in this study are available on request from thecorresponding author. The data are not publicly available due to authors are preparing a dataset ofthese and another fish data from the Andean rivers that will be published in the next months.Acknowledgments: Invaluable field assistance and friendship was provided by Américo Quispe. Weare grateful for the unreserved cooperation of Hugo Pepper, who provided logistic and data support.Robin Van Loon helps us on the English revision. We are grateful to Miles Silman for their usefulcomments. We thank the members of the Department of Ichthyology of MUSM for their help andassistance. The Asociación para la Conservación de la Cuenca Amazónica (ACCA), kindly providedvaluable information, collaboration and lodging during our fieldwork. We also wish to thank RonaldMendoza for helping to prepare the map figure.Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the designof the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, orin the decision to publish the results.References1. Gozlan, R.E.; Britton, J.R.; Cowx, I.G.; Copp, G.H. Current Knowledge on Non-Native Freshwater Fish Introductions. J. Fish Biol.2010, 76, 751–786. [CrossRef]2. Ricciardi, A.; Rasmussen, J.B. Extinction Rates of North American Freshwater Fauna. Conserv. Biol. 1999, 13, 1220–1222. [CrossRef]3. Miqueleiz, I.; Böhm, M.; Ariño, A.H.; Miranda, R. 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Water 2021, 13, 747. https://doi.org/10.3390/w13060747Academic Editor: HeikoL. SchoenfussReceived: 9 February 2021Accepted: 3 March 2021Published: 10 March 2021Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).1 Departamento de Zoología y Antropología Física, Facultad de Biología, Campus de Espinardo, Universidadde Murcia, 30100 Murcia, Spain; fjoliva@um.es (F.J.O.-P.); torralva@um.es (M.T.)2 Department of Life and Environmental Sciences, Faculty of Science and Technology, Bournemouth University,Poole, Dorset BH12 5BB, UK; fatima.amat@um.es* Correspondence: ana.sanchez15@um.esAbstract: Human pressures on water resources have been suggested as a driver of biological traitsthat induce changes in native fish populations. This study highlighted the interplay between envi-ronmental stress factors, mostly related to flow regulation, and the longitudinal river gradient inbiological traits such as the growth, size structure and somatic condition of a sentinel fish, Luciobarbussclateri. We found an increase in size-related metrics and somatic condition at population levelsassociated with downstream reaches, although fragmentation and habitat alteration, flow regimealteration and the abundance of non-native fish were also significantly involved in their variability.Age-related parameters and growth were only explained by flow regime alterations and the abun-dance of non-native fish species. The high plasticity observed in L. sclateri population traits suggeststhat this is a key factor in the species adaptability to resist in a strongly altered Mediterraneanriver basin. However, the interplay of multiple stressors plays an important role in fish populationdynamics and could induce complex responses that may be essential for long-term monitoring insentinel species.Keywords: sentinel species; longitudinal gradient; human impacts; flow regime alteration; non-native fish; fragmentation; habitat alteration; Mediterranean rivers; Segura River basin1. IntroductionFreshwater ecosystems are considered among the most altered as a consequence of thehistorical pressure of human activities [1,2]. Hydraulic management to take advantage ofwater resources and the effects of climate change are inducing quantitative and qualitativechanges in river systems [3,4]. These changes imply hydro-morphological, chemical andbiological alterations which affect the freshwater fauna [2,5]. Hydraulic management isespecially intense in Mediterranean regions where water resources are scarce [6]. Rivers insemi-arid regions, such as the Iberian Peninsula, are heavily impacted by the constructionof a large number of dams and weirs [7,8].Mediterranean regions are characterized by marked seasonality and inter-annualvariability with severe periods of floods and droughts [9,10]. The native freshwater faunais adapted to such natural variability and displays great resistance and resilience [11–13];however, it is considered especially sensitive to human impacts [14,15]. Human pressuresare particularly severe in Mediterranean regions and they coincide with high naturalvariability, causing severe alterations to fluvial ecosystems [8,16,17]. Despite the highadaptability of freshwater fauna, the expected increase in human impacts under future sce-narios of global climate change could increase its vulnerability to such pressure, especiallyin Mediterranean regions [11,18,19].The flow regime is considered one of the main driving forces of freshwater ecosystems,determining the structure and ecological dynamic of rivers [19,20]. In the Iberian Peninsula,the alteration of the natural flow regime by dam regulation is one of the most important25Water 2021, 13, 747stress factors that negatively affects the native fish fauna [21–23]. The effects of flow regimealteration have been widely documented in Mediterranean fish populations [24–27]. Inaddition, flow management infrastructures cause loss of connectivity and habitat alterationsrelated to the fragmentation process (e.g., increase in lentic habitats, changes in waterquality), which could facilitate the establishment and spread of non-native fish species,altering the composition of the community [28,29]. However, the effects of these stressfactors have been poorly studied in Mediterranean rivers [30–32].The Segura River basin is located in the southeast of the Iberian Peninsula and ischaracterized by a marked environmental variability and displays a wide variety of humanimpacts along its longitudinal gradient [33,34]. Furthermore, this basin shows a well-documented range of impacts on the natural flow regime [35,36] and is considered one ofthe most regulated Mediterranean river systems [37]. Therefore, the Segura River offers anopportunity to study the effect of multiple human-induced stressors along an intensivelyaltered basin. The fish assemblage in the Segura River basin is characterized by a low num-ber of species where Luciobarbus sclateri (Günther, 1868) appears to be dominant [38]. Thisnative species is an endemic potamodromous fish considered a sentinel species in the south-ern Iberian Peninsula [12], and its biology and ecology have been well documented [39–41],being the unique native fish widely distributed in the Segura River basin [42]. Therefore, L.sclateri populations in the Segura River could be considered a useful tool through which toassess the intra-specific variability along a wide longitudinal gradient strongly affected byenvironmental alterations mainly related to flow regulation. However, few authors havedescribed the effects of multiple stressors on its populations, and nothing is known aboutits intra-specific variability along longitudinal gradients [43–45].The main objective of the present study was to assess the variability of L. sclateripopulation traits in relation to different environmental stressors along the longitudinalgradient of a highly regulated river system. Our hypothesis was that L. sclateri wouldexhibit high phenotypic plasticity that enables populations to survive along the longitudinalgradient of a highly impacted river basin. L. sclateri has been evaluated as near threatened(NT) on a regional scale due to the intensification of human pressures which caused adecrease in habitat quality (mainly pollution and flow regime alteration) and an increasein the establishment and spread of several non-native fish species [46]. Furthermore, inrecent years, a severe decline in L. sclateri populations has been documented in the SeguraRiver basin [38,47]. The use of different biological traits provides ecological insights intohow populations respond to multiple stress factors, allowing us to understand how species’traits could predispose species to local extinctions [48] and to establish more successfulmanagement and recovery programs [8,49].2. Materials and Methods2.1. Study Area and Sampling DesignThis study was conducted in the Segura River basin, situated in the semi-arid Mediter-ranean climatic zone in the southeast of the Iberian Peninsula (Figure 1). The river basindrains an area of 18,870 km2 and is characterized by a strong climatic and altitudinalgradient, and significant annual and inter-annual natural variation in the flow regime [6].Multiple human activities—primarily related to agriculture, but also to electricity genera-tion and human supply—have developed in the study area [33,34,37]. Irrigation accountsfor 90% of the water demand and is considered the main pressure on the water resources.A total of 33 dams (>10 m height and >1 hm3 of reservoir) and 170 smaller obstacles existalong the longitudinal gradient of the river with a capacity of regulation of approximately1200 hm3. Since 1979, this basin has received an external water transfer from the Tajo Riverwith an average of 350 hm3year−1, so the storage capacity increased by around 140% of thenatural input (871 hm3year−1) [37,50]. Furthermore, local agricultural practices add an ar-tificial source of pollutant discharge (mainly phosphates and nitrates) into the river [51,52].As a result, the fluvial and riparian habitats of the Segura River basin have been severelyaltered [33,53], in addition to strong modifications in the natural flow regime [54].26Water 2021, 13, 747●−Figure 1. Study area. Location of the Segura River basin in the southeast of the Iberian Peninsula. Sampling sites are marked(•): TAI (Taibilla River), TUS (Tus River), MU (Mundo River) and SE (Segura River). Names of large reservoirs are betweenparentheses [].We sampled a total of 25 sites in fluvial reaches distributed along upstream–downstreamgradients from the upper Segura main stem (SE) to the middle Segura (195 km), from theupper Mundo River (MU) to the Mundo–Segura confluence (51 km) and in two uppertributaries (Tus and Taibilla) (Figure 1). The range in altitude is 112–809 m.a.s.l. in the studyarea; the water conductivity ranged from 316 to 1303 µs cm−1 and the range of the meanannual water temperature was 13.9–16.9 ◦C during the study period. The distribution ofsampling sites reflects the different hydrological flow regimes present in the study area.Sampling sites placed in areas with the most natural flow regime (e.g., MU01 and SE01;Figure 1) were characterized by a strong seasonal variation, alternating summer droughtsand spring/autumn short-time flow peaks (Figure 2a). An impact gradient on the naturalflow regime in the study area was described by Amat-Trigo [35] and Amat-Trigo et al. [36].Impacted areas exhibit a seasonal inversion of the natural flow pattern (high flow levels inspring and summer, low flow levels in autumn and winter) due to the water demand foragricultural practices. Furthermore, impacted areas can be characterized by two extremeflow impacts: reaches downstream of the Cenajo reservoir (sampling sites SE06, SE07 andSE09; Figure 1) showed a high level of contingency and low variability, but also low pre-dictability (Impact 1, Figure 2b), while other reaches (e.g., sampling sites MU09, SE12 andSE13; Figure 1) were characterized by more stable and high levels of base flow throughoutthe year, high values of temperature and spell peaks, in addition to the inversion in flowseasonality (Impact 2, Figure 2c).27Water 2021, 13, 747−Figure 2. Flow variation (mean daily discharge; m3 s 1) at three representative fluvial sectors in thestudy area. Repre-sentative flow regimes were measured at three gauging stations: (a) Natural flowregime at the upper part of the Mundo River, (b) Impact 1 just downstream of the Cenajo reservoir,and (c) Impact 2 downstream of the La Mulata dam (see location in Figure 1). Flow discharge datawere obtained from the Segura Hydrographic Confederation.The fish assemblage in the study area is composed of both native and non-nativespecies [38]. Non-native species are dominant (90% of total species richness) and L. scla-teri is the only widely distributed native species in the study area. The most abundant speciesare cyprinids: the native L. sclateri and the non-native species Pseudochondrostoma polylepis(Steindachner, 1864), Gobio lozanoi (Doadrio and Madeira, 2004) and Alburnus alburnus (Lin-naeus, 1758). The non-natives Cyprinus carpio (Linnaeus, 1758) and Lepomis gibbosus (Linnaeus,1758) are locally abundant. Furthermore, the natives Squalius pyrenaicus (Günther, 1868) andSalmo trutta (Linnaeus, 1758), as well as the non-natives Gambusia holbrooki Girard 1859, On-corhynchus mykiss (Walbaum, 1792), Micropterus salmoides (Lacépède, 1802), Sander lucioperca(Linnaeus, 1758) and Esox lucius (Linnaeus, 1758), are present in the study area.2.2. Environmental VariablesWe described a total of ten environmental variables and gradient descriptors in thestudy area (Table 1). Water conductivity was measured in situ using a multi-parameter (340iWTW), and ecological status was assessed according to the EU Water Framework Directive,Fluvial Habitat Index (IHF) [55], Riparian Quality Index (RQI) [56] and altitude [57]. TheLongitudinal Connectivity Index (ICL) [58] and free reach (length of reach available for freemovement) were calculated using databases available online from the official monitoringservice of the Segura Hydrographic Confederation (CHS) (https://www.chsegura.es/chs/cuenca/restauracionderios/obstaculos/visorjs.html; accessed on 12 September 2018).28Water 2021, 13, 747Water temperature and daily river discharge data were also obtained from the CHS onlinedatabases with data gathered by gauging stations distributed in the study area (https://www.chsegura.es/chs/cuenca/redesdecontrol/estadisticashidrologicas/; accessed on12 September 2018). Mean monthly values of water temperature for 2009 and 2010 werecalculated. Mean daily discharge (m3 s−1) over a 16-year period (1994–2010) was usedto calculate the mean daily base flow (MDBF) as the total base flow component of thehydrograph divided by the number of recording days, and flow variability as the dailyrange between Q10% and Q90% discharge divided by the median value. These two flowmetrics (MDBF and flow variability) were calculated using time series analysis (TSA) ofthe River Analysis Package (RAP version 3.0.7) [59].Table 1. Environmental variables and gradient descriptors measured or calculated at sampling sites. Altitude (meters abovesea level), ecological status (categorized as: 1 = high; 2 = good; 3 = moderate; 4 = poor), Fluvial Habitat Index (IHF) andRiparian Quality Index (RQI) (%), conductivity (µS cm−1), free reach (km), Longitudinal Connectivity Index (ICL), watertemperature (◦C), mean daily base flow (MDBF) (m3 s−1) and flow variability ((Q10% − Q90%)/median).SamplingSiteAltitudeEcologicalStatusIHF RQI ConductivityWaterTemperatureFreeReachICL MDBFFlowVariabilityMU01 560 2 72 55 602 14.7 4.13 255 1.59 −2.44MU02 540 2 81 85 619 14.7 2.43 255 1.59 −2.44MU03 520 2 81 85 619 14.7 5.39 255 1.59 −2.44MU04 480 3 69 43 639 14.7 3.25 205 8.58 −1.71MU05 460 3 75 53 674 14.7 2.02 205 8.58 −1.71MU06 430 3 70 64 688 14.7 3.67 205 8.58 −1.71MU07 396 3 72 46 724 14.7 3.79 205 8.58 −1.71MU08 354 3 70 34 1303 14.7 2.29 205 8.58 −1.71MU09 350 4 67 39 1109 15.4 3.54 138 14.04 −1.62SE01 860 3 78 94 364 14. 3 25.88 84 2.91 −3.14SE02 685 1 61 98 387 14.3 32.33 84 2.91 −3.14SE03 491 1 63 87 390 14.3 12.58 84 2.91 −3.14SE04 470 1 67 78 340 13.8 30.02 0 4.14 −2.58SE05 452 1 68 80 401 13.8 30.02 0 4.14 −2.58SE06 432 1 78 98 401 13.8 30.02 0 4.14 −2.58SE07 363 2 67 77 447 14.1 7.73 126 1.77 −13.10SE08 325 2 73 66 492 14.1 5.96 126 1.77 −13.10SE09 306 2 58 69 579 14.1 1.7 126 1.77 −13.10SE10 290 2 62 32 756 15.9 7.18 126 14.04 −1.62SE11 260 3 57 44 803 15.9 10.64 90 14.04 −1.62SE12 200 2 66 70 861 16.4 4.98 106 14.22 −1.55SE13 148 2 58 45 1139 16.4 6.88 365 15.14 −1.47SE14 112 4 64 36 1157 16.8 4.36 365 2.46 −3.31TAI 640 2 61 43 566 14.7 15.67 58 - -TUS 809 1 84 65 432 14.5 12.81 76 1.59 −2.442.3. Fish Sampling and Population TraitsSites were sampled using electrofishing (working voltage between 200 and 350 V,2–3 A), following the CEN standard protocol [60]. Each sampling site was consideredan independent population, taking into account the distance between sampling sites, thepresence of non-passable barriers (dams and weirs) and the biological characteristics of L.sclateri. Fish were collected in 100-m-long wadable sections blocked by nets that acted asbarriers. Fish sampling sessions were carried out in 30–45 min. Sampling was conductedduring October–November 2009 to prevent the capture of spawning fish [45] and to avoidvariation in body condition due to gonad development [39,43,61]. Fish manipulation wascarried out following the European Union Directive 2010/63/UE on the protection ofanimals used for scientific purposes and it was not necessary to obtain authorization fromthe research ethics commission. In concordance with administrative permits, a total of 1529specimens of L. sclateri were caught, processed in the field and returned to their habitat.Fork length (FL ± 0.1 cm) and weight (TW ± 0.1 g) were measured. A subsample of scales(611 specimens) was taken and cleaned later in a laboratory to determine the age accordingto Herrera et al. [40] and Torralva et al. [45].29Water 2021, 13, 747L. sclateri populations were evaluated at sampling sites to assess intra-specific variabil-ity. The fish population traits we studied were relative abundance, size-related parameters,age-related parameters, relative growth rates and somatic condition. Relative abundancewas measured as the number of L. sclateri individuals caught per hour (catch per uniteffort, CPUE) in a standardized sample area without significant differences in habitatcomplexity and, thus, assuming that catch efficiency remains constant. Size-related param-eters included mean, maximum and range of fork length (FL), and a size diversity indexcalculated as a Shannon-Wiener modification using the number of size classes grouped in2 cm length ranges. Age-related parameters included mean, maximum and range of age,determined for a subsample of L. sclateri scales. Back-calculated lengths were estimated bythe Fraser-Lee equation following the methodology used by Torralva et al. [45] and Miñanoet al. [62] based on the counting and measuring of scales’ annuli, and checked accordingto Musk et al. [63]. Proportions (%) of back-calculated lengths for each age class wereobtained from the Walford method [64] and used to calculate the mean individual growthindex (GI). Relative growth rates at the site level were estimated from the mean individualgrowth index (GI). The methodology used to calculate the growth index (GI) is detailedin Masó et al. [65] and Amat-Trigo et al. [66], which followed the Hickley and Dexterprocedure [67]. We used the mean values of GI at age 1 year, age 2 years and maturity(individuals older than 2 years) according to the age of maturity previously establishedfor the species in the Segura River basin [43,45,61]. Somatic condition was expressed aspredicted values of log-transformed weight (mean value at sampling site) obtained fromthe application of univariate analysis of covariance (ANCOVA) using total weight (WT) asthe dependent variable and fork length (FL) as the covariate; differences in variation weretested by ANOVA and Tukey’s HSD post hoc tests [68]. Individuals with an FL less than 75mm were considered juveniles [43]. Bivariate relationships between population traits wereanalyzed using Spearman’s rank correlations. Statistical analysis was performed with theSPSS software package v. 24.2.4. Effect of Environmental Stress Factors on Population TraitsWe conducted a model selection analysis based on the Akaike Information Criterionwith a correction for small samples sizes (AICc) [69] to determine the stressors associatedwith the variability in L. sclateri population traits. To establish model ranking, the MuMIn(Multi-Model Inference) R Package was used [70]. Stress factors were obtained by thereduction in transformed environmental variables and gradient descriptors (log for numericvariables, arcsine square root for proportions) using principal component analysis (PCA)with varimax rotation [71] using R package psych. Spearman’s rank correlation analysiswas used to test the redundancy between the variables. Principal component axes scoreswere used as stressors. The ratio of the abundance of non-native fish species (abundance ofnon-native species/total abundance) was also considered a stressor. General linear modelresults (GLMs) for the best models (∆AICc < 2.0) were used to describe the response ofL. sclateri biological traits to the stress factors. These analyses were performed in the Rstatistical environment (Version 1.40.4).3. Results3.1. Relative Abundance and Population TraitsThe abundance of L. sclateri showed high spatial variation, ranging from 13.33 to 150.00catches per unit of effort (CPUE, Supplementary Table S1). The highest value was found inthe Taibilla tributary (sampling site TAI) and the lowest was found at SE04, downstream ofthe Fuensanta reservoir (Figure 1).The mean size fork length in the whole study area was 18.3 ± 6.55 cm FL, with themaximum value detected at MU03 (58.2 cm). The maximum validated age was 15+ years,with individuals this old detected at sampling sites SE04, SE07 and SE09; however, themean age at the population level was 4.9 ± 1.43 years. The population size/structurediffered among sampling sites, although a polymodal distribution pattern was evident30Water 2021, 13, 747in most sampling sites along the longitudinal gradient (Figure 3). Both size and ageparameters displayed lower values in headwaters (Figure 3 and Table S1), with maximumfish sizes below 30 cm (FL), lower size ranges and lower size diversity index values (Figure3a,b,c). The other sampling sites had specimens longer than 40 cm (FL), with higher valuesat sampling sites just downstream of non-passable obstacles (e.g., SE07, Figure 3) andupstream of large reservoirs (e.g., MU03, Figure 3).ΔFigure 3. Size distribution (2 cm fork length (FL) groups) of L. sclateri at sampling site. Plot distribution: (a) Mundo River(MU) sampling sites, (b) sampling sites placed from Segura (SE) riverbed to Mundo River confluence, (c) sampling sites attributaries Tus and Taibilla (TAI) Rivers and (d) sampling sites from Segura–Mundo confluence to the latest downstreamsampling site (SE14).The growth index (GI) displayed high variability for individuals of each age classbetween sampling sites. The mean GI value at age 1 year across the study area was 66.74 ±1.28, with a maximum value of 82.47 detected at sampling site SE01, while the mean valueof GI at age 2 years was 39.34 ± 1.13, with a maximum of 53.52 at sampling site MU09. Formature fish, the mean GI value in the study area was 17.69 ± 1.57, with a maximum of32.53 detected at site MU05. The lowest GI values were detected in the TUS tributary (age1 year = 45.72; age 2 years = 19.26; mature = 0.03).Somatic condition was higher in mature individuals and also showed greater vari-ability among sampling sites. The mean of predicted values of log-transformed weight formature individuals was 1.87 ± 0.03, with a maximum value of 2.43 at sampling site MU04,whereas the mean value for immature individuals was 0.63 ± 0.03, with a maximum of 0.90at sampling site SE10. Generally, lower values of somatic condition were detected in head-waters for both mature (TUS = 1.28) and immature (SE01 = 0.44) individuals. Populationtraits at the site level are shown in Table S1 (Supplementary Material). The relationshipsamong population traits are presented in Table S2 (Supplementary Material).3.2. Environmental FactorsThe first three PC axes obtained from the dimension reduction of environmentalvariables and gradient descriptors explained 79.7% of the total variance (Table 2). PC1 wasassociated with habitat alteration and fragmentation, which were directly related to poorecological status and low RQI, high values of conductivity and water temperature and lowconnectivity (high values for ICL and low values of free reach). PC2 was associated withthe longitudinal gradient, which was directly related to high altitude, IHF and low water31Water 2021, 13, 747temperatures. PC3 was associated with flow regime alteration, which was directly relatedto flow variability and MDBF.Table 2. Loadings and proportions of variance extracted by PCA of environmental variables withvarimax rotation. Loadings > 0.50 are marked in bold.Env. Variables PC1 PC2 PC3Ecological status 0.817 −0.150 0.202Fluvial Habitat Index (IHF) 0.081 0.882 0.036Riparian Quality Index (RQI) −0.648 0.427 −0.286Altitude −0.339 0.832 −0.071Free reach −0.909 0.052 0.087Longitudinal connectivity (ICL) 0.869 0.021 −0.068Conductivity 0.750 −0.443 0.393Water temperature 0.520 −0.534 0.466Mean daily base flow (MDBF) 0.170 −0.473 0.700Flow variability −0.038 0.164 0.950Proportion of variance 0.366 0.241 0.1913.3. Effects of Environmental Factors on Population TraitsTable 3 displays the best models obtained from the model selection analysis basedon AICc. The longitudinal gradient (PC2) was significantly linked with size-related pa-rameters and somatic condition metrics. The gradient from the upper sampling sites tothe downstream sites was associated with increment in population traits including thesize range, mean and maximum size, size diversity index and somatic condition metric(longitudinal gradient axis, Figure 4). The longitudinal gradient interplayed with otherstressors such as the non-native fish species, showing a significant effect on size rangeand maximum size (size-related parameters, Table 3). In addition, this environmentalfactor interplayed with the flow regime alteration (PC3), displaying a significant effect onmean size (size-related parameters, Table 3) and somatic condition for mature individuals(somatic condition, Table 3). Finally, the longitudinal gradient also interplayed with habitatalteration and fragmentation (PC1), displaying a significant effect on the size diversityindex (size-related parameters, Table 3) and somatic condition in immature individuals(somatic condition, Table 3).Table 3. General linear models (GLM) results for the best models obtained by model selection analysis based on the AkaikeInformation Criterion with a correction for small samples sizes (AICc). GLM analysis was conducted for each populationtrait: CPUE, size- and age-related parameters, growth and somatic condition. (• p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001).SIZE-RELATED PARAMETERSSize rangeLongitudinal gradient + Non-native speciesEstimate Std. Error t value Pr(>|t|)(Intercept) 26.264 5.239 5.013 5.81 × 10−5 ***Longitudinal gradient −4.660 1.921 −2.426 0.024 *Non-native species 18.278 8.792 2.079 0.050 •Mean sizeLongitudinal gradient + Flow regime alterationEstimate Std. Error t value Pr(>|t|)(Intercept) 18.420 1.114 16.537 1.62 × 10−13 ***Longitudinal gradient −3.097 1.119 −2.768 0.0115 *Flow regime alteration 2.578 1.117 2.307 0.0313 *32Water 2021, 13, 747Table 3. Cont.Maximum sizeLongitudinal gradient + Non-native speciesEstimate Std. Error t value Pr(>|t|)(Intercept) 29.456 5.913 4.981 6.27 × 10−5 ***Longitudinal gradient −4.812 2.168 −2.220 0.038 *Non-native species 23.839 9.924 2.402 0.026 *Size diversity indexLongitudinal gradient + Habitat alteration and fragmentationEstimate Std. Error t value Pr(>|t|)(Intercept) 2.231 0.051 43.977 <2 × 10−16 ***Longitudinal gradient −0.023 0.051 −4.434 0.0002 ***Habitat alteration and fragmentation 0.117 0.056 2.098 0.048 *ABUNDANCE (CPUE)CPUENullEstimate Std. Error t value Pr(>|t|)(Intercept) 61.525 7.786 7.902 5.3 × 10−8 ***AGE-RELATED PARAMETERSAge rangeFlow regime alterationEstimate Std. Error t value Pr(>|t|)(Intercept) 8.765 0.519 16.900 4.36 × 10−14 ***Flow regime alteration −1.586 0.520 −3.048 0.006 **Mean ageNon-native speciesEstimate Std. Error t value Pr(>|t|)(Intercept) 3.237 0.749 4.324 2.73 × 10−4 ***Non-native species 2.985 1.245 2.397 0.025 *Maximum ageNon-native speciesEstimate Std. Error t value Pr(>|t|)(Intercept) 7.206 1.658 4.345 2.59 × 10−4 ***Non-native species 5.060 2.759 1.834 0.080 •SOMATIC CONDITIONImmature somatic conditionLongitudinal gradient * Habitat alteration and fragmentationEstimate Std. Error t value Pr(>|t|)(Intercept) 0.642 0.023 28.162 9.97 × 10−14 ***Longitudinal gradient −0.119 0.028 −4.291 0.0007 ***Habitat alteration and fragmentation −0.015 0.028 −0.552 0.589L. gradient: H. alter. and fragment. 0.110 0.043 2.578 0.022 *Mature somatic conditionLongitudinal gradient + Flow regime alterationEstimate Std. Error t value Pr(>|t|)(Intercept) 1.869 0.058 32.211 <2 × 10−16 ***Longitudinal gradient −0.188 0.058 −3.228 0.004 **Flow regime alteration 0.107 0.058 1.844 0.080 •33Water 2021, 13, 747Table 3. Cont.GROWTHGrowth index (GI) at age 1NullEstimate Std. Error t value Pr(>|t|)(Intercept) 66.676 2.387 27.930 <2 × 10−16 ***Growth index (GI) at age 2Flow regime alterationEstimate Std. Error t value Pr(>|t|)(Intercept) 40.724 1.723 23.639 <2 × 10−16 ***Flow regime alteration 3.607 1.728 2.087 0.049 *Growth index (GI) for matureFlow regime alterationEstimate Std. Error t value Pr(>|t|)(Intercept) 18.165 2.042 8.894 9.73 × 10−9 ***Flow regime alteration 5.614 2.049 2.740 0.012 *In addition to the interplay with the longitudinal gradient, the flow regime alteration(PC3) was linked to variability in the age range (age-related parameters, Table 3) and thegrowth index (GI) for mature fish and fish aged 2 years (growth, Table 3). An increase inthe PC3 axis implied high flow variability and base flow (Impact 2, Figure 2c) and wasassociated with increased mean size and somatic condition of mature individuals, andGI at age 2 years and for mature individuals (flow regime axis, Figure 4). In contrast, adecrease in the PC3 axis implied low base flow and variability (Impact 1, Figure 2b) andwas associated with a decreased age range (flow regime alteration axis, Figure 4).In addition to the interplay with the longitudinal gradient, non-native fish specieswere linked to the variability in the mean and maximum age (age-related parameters,Table 3). An increased relative abundance of non-native fish species was associated withan increase in these two age-related parameters (non-native fish species axis; Figure 4).Finally, habitat alteration and fragmentation (PC1) were linked to the size diversityindex (size-related parameters, Table 3) and somatic condition of immature individuals(somatic condition, Table 3) and always exhibited an interplay with the longitudinalgradient. An increase in habitat alteration and fragmentation was associated with increasedsize diversity index (habitat alteration and fragmentation axis, Figure 4). Furthermore, theonly interaction found in this study occurred between this stressor and the longitudinalgradient, which was associated with increased somatic condition for immature individuals(longitudinal gradient axis, Figure 4). Finally, the model selection analysis did not find anystress factors among those considered in this study to explain the variability in the growthindex (GI) of fish at age 1 year or abundance (CPUE).34Water 2021, 13, 747Figure 4. Cont.35Water 2021, 13, 747Figure 4. Relationship between environmental factors (longitudinal gradient, habitat alteration and fragmentation, flowregime alteration and non-native fish species) and L. sclateri population traits. Significant results (GLM p-value < 0.05)are represented with marked trend line on the plot. In x-axes, arrows pointing down indicate decrease in environmentalvariables (i.e., water temperature), and arrows pointing up indicate increase in environmental variables (i.e., altitude) andalso increase in non-native abundance ratio.36Water 2021, 13, 7474. DiscussionIn this study, we analyzed the variation in population traits of L. sclateri in response toenvironmental factors in the fluvial reaches of the Segura River basin. Our results confirmsignificant variability in population traits along the longitudinal gradient due to the effectof human impacts, mostly those related to flow regulation such as fragmentation andhabitat alteration, flow regime alteration and the relative abundance of non-native fishspecies. The use of biological traits related to size and age, relative abundance, growth andsomatic condition allowed us to identify the population-level responses of this sentinelIberian fish to different stressors and to verify the complex effect of multiple stressors in aheavily modified Mediterranean-type river.The longitudinal gradient is a key factor in the structure and dynamic of freshwaterecosystems, so it is essential to consider its effects in the assessment of multiple stres-sors in river systems [72–75]. Environmental conditions along the longitudinal gradientdetermine the availability of resources such as food, refuges and breeding areas [34,76].Consequently, a marked effect of the spatial variation on fish populations was expected,especially in Mediterranean-type rivers which exhibit a strong climatic and altitudinalgradient [10,77,78]. The increment of resources downstream along natural river systemspromotes large sizes and a wider range of size classes in the fish fauna [73,76,79]. We foundsignificant variability in the size parameters and somatic condition of L. sclateri popula-tions associated with spatial variation, with an increase in these biological traits along thelongitudinal gradient. The size population structure displayed a polymodal pattern, withhigh variability among sampling sites. The size distribution results highlighted a lack ofsome size groups, and this was more evident in sampling sites downstream of reservoirs.Small and medium-sized individuals were scarce downstream of operational dams (i.e.,at sites MU09, SE04, SE09 and SE11). The GLM results (Table 3) showed that size andsomatic condition parameters were significantly associated with the longitudinal gradient.In fact, habitat alteration and fragmentation, flow regime alteration and relative abundanceof non-native fish species were all related to the variability of population parameters,reflecting the severe alterations that have affected the Segura River basin [34,37].Human impacts increase along the longitudinal gradient as a consequence of thegreater accessibility to water resources [77]. As a result, the interplay among spatialvariation and human stress factors shaped the environmental conditions that act as a “filter”of biological traits [80]. The selected traits determine the biological responses of freshwaterfauna to cope with altered conditions [18,75,81]. The Segura River basin shows a stronginfluence of human alterations mainly related to agricultural supply [33,37]. Agriculturalpractices involve strong hydraulic management that results in a loss of connectivity (habitatfragmentation), flow regime alteration and water quality and habitat degradation andencourages the establishment of non-native fish species [18,32,82,83]. Our results show theinterplay between environmental stress factors, mostly those related to flow regulation,and the longitudinal gradient in some biological traits such as size and somatic condition.In addition, we found that age and growth variations were significantly associated withthe isolated effect of the flow regime alteration and the relative abundance of non-nativefish species. This finding suggests that the magnitude of human impacts in the study areacould be masking some ecological responses to longitudinal gradients [84,85].The flow regime is considered the main driver of freshwater ecosystems, defining thestructure, function and dynamic of rivers, and affecting the individual fitness and growthrate of fish populations [80,86,87]. We found significant relationships between flow regimealteration and some population traits of L. sclateri including mean size, age range, somaticcondition for mature individuals and growth variability. Our results show an increasein these population traits associated with fluvial sectors that had a high level of baseflow throughout the year (reflected as Impact 2 in Figure 2) and a flow regime pattern thatreduced the strong seasonal variability of Mediterranean-type rivers. Although these fluvialsectors display an inversion in flow seasonality related to agricultural water demands,they also provide an increased availability of water, refuge and food resources [25,76,88].37Water 2021, 13, 747Furthermore, high flow levels promote changes in body shape and muscle development,which induce better swimming performance and increased somatic condition [89,90], afinding that was previously documented in the Segura River basin [43]. In contrast, weobserved a decrease in population traits (mean size, age range, somatic condition formatures and growth variability) associated with strong inversion in flow seasonality andbase flow reduction (Impact 1 in Figure 2). We found this flow pattern in sampling sitesdownstream of the Cenajo reservoir, a consequence of the operating characteristics of itsdam [34,35,54]. The extreme hydrological conditions caused by this type of water regulationresult in a poor-quality habitat, especially for adult barbels [91], and are considered alimiting factor for the growth of barbel species in the Iberian Peninsula [25,92]. These twocontrasting results in the response of fish populations under different hydrological flowpatterns have been described in other Iberian rivers as well [25,93].Non-native fish species tend to be dominant in human-altered ecosystems, such asthe Mediterranean rivers, where more stable environmental conditions that result fromflow regulation measures encourage their establishment and spread [82,83,94]. Iberianfish communities have exhibited significant changes over recent decades as a consequenceof the introduction of a wide range of non-native fish species [95–97], and these changesare especially evident in the Segura River basin [38]. The negative responses of nativefish populations associated with the presence and abundance of non-native species inthe fish assemblage are well documented [75,98,99]. Our results show an increase in themaximum size and size range, and the mean and maximum age of L. Sclateri populationsassociated with a higher ratio of non-native fish abundance. The proliferation of non-nativespecies is generally a result of changes in the environmental conditions caused by flowregulation [4,8]. The lack of small size classes of fish at sampling sites placed downstreamof reservoirs where the presence of non-natives is favored (i.e., SE04 in Figure 3) suggestsan effect of the fish assemblage composition on the structure of L. sclateri populations. Ingeneral, predation by non-native fish could affect the population structures and dynamicsof native fish species in the Iberian Peninsula [95]. Some studies from other Iberian riversconfirmed the inclusion of different barbel species in the diet of top predator fish such asE. lucius [100,101] and S. Lucioperca [102]. Predatory fish (E. lucious, S. lucioperca and M.salmoides) showed a higher occurrence in the lower reaches of the study area. They couldbe inducing higher predation pressure on certain size classes of L. sclateri and thereforeaffecting the population size structure as Bravo et al. [103] showed in the Palancar River,where M. salmoides predation was directly related to the lack of 0+ individuals of dominantspecies such as L. sclateri.The results of this study highlight relevant associations between human impacts, mostof which were related to flow regulation, and the population traits of L. sclateri along alongitudinal gradient, providing insights into the population-level responses of this sentinelIberian fish to environmental conditions at the site level. The key role of the longitudinalgradient in driving the increase in human impacts as a result of greater accessibility relatedto the lower reaches of rivers is evident, since most of the stressors were related to thespatial variation, so this dependence makes it difficult to interpret the effect of isolatedstressors. In addition, there is a wide variety of human impacts present in the study areathat were not considered in this study. For example, pollution could be driving the responseof L. sclateri to environmental conditions [32], or predatory mammals (Lutra lutra), whosepredation on L. sclateri in the Segura River basin was recently confirmed [104].The ability of this species to adapt to changes in local conditions has been shownby the variation in population size/structure along the longitudinal gradient. Althoughhuman impacts exerted significant effects on the biological traits we evaluated, our findingsalso suggest that the wide inter-population plasticity displayed by L. sclateri may be a mech-anism for this species to successfully inhabit a highly modified Mediterranean-type river.Cyprinids in general show great adaptability to environmental alterations [12,25,92,105],and L. sclateri showed a tolerance to the effects of flow regulation previously studied in thesame river basin [45,61].38Water 2021, 13, 747In recent decades, declines in fish populations have been documented for severalIberian fish species and there have been drastic reductions in fish species that were previ-ously widely distributed in the study area. Native fish species are vulnerable to the rapidincrease in human pressure on the water resource; this is especially so in Mediterraneanareas where an increase in the magnitude of extreme weather events is expected underclimate change scenarios [106]. Therefore, the use of well-known and widely distributedsentinel species, such as L. sclateri, may prove a useful tool to increase the knowledge of theadaptability and population responses to gradients of single and multiple stressors, whichis essential to establish and improve management actions to protect native fish species.Supplementary Materials: The following are available online at https://www.mdpi.com/2073-4441/13/6/747/s1, Table S1: Population traits of L. sclateri at sampling site. Table S2. Coefficients ofSpearman rank correlation between population traits.Author Contributions: Project administration, F.J.O.-P. and M.T.; conceptualization, A.S.-P., F.J.O.-P.and M.T.; methodology and data collection, A.S.-P., F.J.O.-P., F.A.-T. and M.T.; formal analysis, A.S.-P.;writing—original draft preparation, A.S.-P.; writing—review and editing, A.S.-P., F.J.O.-P., F.A.-T. andM.T. All authors have read and agreed to the published version of the manuscript.Funding: Financial support was provided by the Fundación Séneca (project 08728/PI/08); RegionalAgency of Science and Technology, Autonomous Government of Murcia. A.S.P. was supported by apre-doctoral contract (FPU14/03994) from the Spanish Ministry of Science and Education.Institutional Review Board Statement: Not applicable.Informed Consent Statement: Not applicable.Data Availability Statement: The data presented in this study are available within the article orsupplementary material.Acknowledgments: We would like to thank all the members of the UMU research group for theirhelp and support with the field surveys.Conflicts of Interest: The authors declare no conflict of interest. They have no known competingfinancial interests or personal relationships that could influence this paper. 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Shifts in the Climate Space of Temperate Cyprinid Fishes Due to ClimateChange Are Coupled with Altered Body Sizes and Growth Rates. Glob. Chang. Biol. 2016, 22, 3221–3232. [CrossRef] [PubMed]106. IPCC. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable LandManagement, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems; Shukla, P.R., Skea, J., Calvo Buendia, E., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D.C., Zhai, P., Slade, R., Connors, S., van Diemen, R., et al., Eds.; Intergovernmental Panelon Climate Change: Geneva, Switzerland, 2019.43waterReviewScientific Methods to Understand Fish Population Dynamicsand Support Sustainable Fisheries ManagementShewit Gebremedhin 1,2,* , Stijn Bruneel 1 , Abebe Getahun 3, Wassie Anteneh 4 and Peter Goethals 1Citation: Gebremedhin, S.; Bruneel,S.; Getahun, A.; Anteneh, W.;Goethals, P. Scientific Methods toUnderstand Fish PopulationDynamics and Support SustainableFisheries Management. Water 2021,13, 574. https://doi.org/10.3390/w13040574Academic Editor: Rafael MirandaReceived: 31 December 2020Accepted: 19 February 2021Published: 23 February 2021Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).1 Department of Animal Sciences and Aquatic Ecology, University of Ghent, 9000 Ghent, Belgium;stijn.bruneel@ugent.be (S.B.); peter.goethals@ugent.be (P.G.)2 Department of Fisheries, Wetlands and Wildlife Management, Bahir Dar University, P.O. Box 5501,6000 Bahir Dar, Ethiopia3 Department of Zoological Sciences, Addis Ababa University, P.O. Box 1176, 1000 Addis Ababa, Ethiopia;abebe12002@yahoo.com4 Department of Biology, Bahir Dar University, P.O. Box 79, 6000 Bahir Dar, Ethiopia; wassie74@gmail.com* Correspondence: shewitlove@gmail.comAbstract: Fisheries play a significant role in the livelihoods of the world population, while thedependence on fisheries is acute in developing countries. Fisheries are consequently a criticalelement for meeting the sustainable development (SDG) and FAO goals to reduce poverty, hungerand improve health and well-being. However, 90% of global marine fish stocks are fully or over-exploited. The amount of biologically unsustainable stocks increased from 10% in 1975 to 33% in2015. Freshwater ecosystems are the most endangered ecosystems and freshwater fish stocks areworldwide in a state of crisis. The continuous fish stock decline indicates that the world is still farfrom achieving SDG 14 (Life Below Water), FAO’s Blue Growth Initiative goal and SDG 15 (Lifeon Land, including freshwater systems). Failure to effectively manage world fish stocks can havedisastrous effects on biodiversity and the livelihoods and socio-economic conditions of millions ofpeople. Therefore, management strategies that successfully conserve the stocks and provide optimalsustainable yields are urgently needed. However, successful management is only possible whenthe necessary data are obtained and decision-makers are well informed. The main problem for themanagement of fisheries, particularly in developing countries, is the lack of information on the pastand current status of the fish stocks. Sound data collection and validation methods are, therefore,important. Stock assessment models, which support sustainable fisheries, require life history traitsas input parameters. In order to provide accurate estimates of these life history traits, standardizedmethods for otolith preparation and validation of the rate of growth zone deposition are essential.This review aims to assist researchers and fisheries managers, working on marine and freshwaterfish species, in understanding concepts and processes related to stock assessment and populationdynamics. Although most examples and case studies originate from developing countries in theAfrican continent, the review remains of great value to many other countries.Keywords: life history traits; methods comparison; population imbalance; stock assessment1. IntroductionHuman connections to fisheries have developed over thousands of years, underliningthe notable contribution of fish and fisheries to human well-being. Globally, millions ofpeople directly or indirectly depend on fisheries for their employment, income and foodsecurity [1–3]. This dependence is acute in developing countries, particularly for poor andmarginalized people [4,5]. The opening up of global markets for fish and fisheries productshave created multiple opportunities to increase employment and income from fisheries [6].Total employment in fisheries grew increasingly from 28 million in 1995 to 39 million in2010 [3] (Figure 1). In 2016, more than 40 million people were involved in fisheries, with79% and 13% of these 40 million people living in Asia and Africa, respectively [3]. The45Water 2021, 13, 574involvement of people in fisheries in developing countries has been growing steadily,while it has been declining in most developed countries [7], indicating that fisheries play asignificant role in developing countries. Whereas men are primarily involved in fishing,women are heavily involved in fisheries-related activities such as processing and trade [8].In developing countries, particularly in Asia and Africa, millions of women are involved infish processing, marketing, making and repairing nets, making baskets, pots, and baitinghooks [7]. Figure 1. The global role of the fisheries sector for employment (Source: [3]).Fisheries have an important economic contribution worldwide. Fisheries products areamong the most traded food items and play a vital role in the global and local economy.For example, in 2016, approximately 60 million tonnes of fish and fish products (35% ofglobal fish production) entered international trade in various forms [3]. This representsa total increase of 245% compared to 1976, but if we consider the trade in fish for humanconsumption alone the increase is more than 500% [3]. The value of global fish productsalso grew significantly from USD 8 billion in 1976 to USD 152 billion in 2017 [3]. This valuesurpasses the combined value of the net exports of rice, coffee, tea, tobacco and meat of thatsame year [3]. Fisheries play an important role in the national economies of many develop-ing countries through the generation of foreign exchange derived from international trade.Fish production exports from developing countries account for approximately 60% of thetotal fish production being traded internationally [7]. Fish trade by developing countriesincreased from less than USD 4 billion in 1980 to USD 18 billion in 2001 [9]. Thus, fisheriesare playing an increasingly important role in the national economy of many developingcountries. Additionally, since the majority of the people involved in fisheries are fromdeveloping countries, fisheries are essential to keep households and communities out ofpoverty and improved fisheries management has the potential to further reduce poverty.The rural poor and marginalized people employed in fisheries could use the income earnedfrom the sector to buy basic needs for living and to send their children to school. Fisherieshave several valuable socio-cultural aspects. Understanding the socio-cultural values46Water 2021, 13, 574associated with fish and fisheries is, therefore, vital for effective management of the re-sources [10]. Ignoring these values could reduce the social acceptability of the managementoptions [11].Of the seven billion people in the world, one billion are officially designated asstarving [12] and two billion people suffer from micronutrient deficiency [13]. Althoughthe improvement of agriculture practices has been identified as essential to overcomethe looming food security crisis, fisheries can also make a significant contribution [14].Fish is an important and affordable source of protein, essential micronutrients, and fattyacids, especially for people in developing countries [15,16]. Fish consumption has beenassociated with various human benefits such as child mortality reduction, and maternalhealth improvement [7]. The annual global fish production for human consumption hasincreased from 67% of the total fish production in the 1960s to 88% in 2016 [3]. Fishaccounted for 17% of animal protein consumed by the world population [3], the majority ofwhich was consumed in low-income food-deficit countries [1,17,18]. Generally, fisheries areimportant to address hunger, micronutrient deficiencies and food insecurity [1], underliningits vital role in meeting the sustainable development goals (SDGs 1 = no poverty, 2 = zerohunger, 3 = good health and well-being) and FAO’s goal of a world without hungerand malnutrition.Despite the significant contribution of fisheries to livelihoods, employment and in-come, many of the global fish stocks have been declining drastically. Successful manage-ment of the fish resources is therefore crucial. Failure to effectively manage world fishstocks can have disastrous effects on biodiversity and the livelihoods and socio-economicconditions of millions of people who are strongly dependent on these resources. Man-agement strategies that successfully conserve the stocks and provide optimal sustainableyields are urgently needed. Successful management is possible when the necessary datasuch as age, growth, mortality and maximum yield are obtained, processed and interpretedand decision-makers are well informed. Fisheries managers, particularly from developingcountries, are faced with many challenges due to lack of information on the past and currentstatus of fisheries and the fish stocks. Although stock assessment modelling is necessary (i)to provide answers to questions about the current status of the stock, (ii) to predict the effectof current and future management measures and (iii) to support sustainable fisheries byproviding fisheries managers with necessary advice to make informed decisions (Figure 2),such studies are limited in developing countries. To understand the factors affecting fishpopulation imbalance, a good understanding of the wide range of age determination andvalidation techniques is required. They provide valuable input parameters for furtherstock assessment evaluation. However, the majority of young researchers in developingcountries have limited skills and knowledge on how to select and prepare ageing hard struc-tures and to validate measurements. Although there have been several well-documentedmethodological studies in developed countries that can be used as a reference, most ofthem are not open access. This hinders fish resources managers and young researchers indeveloping countries from accessing these documents. Therefore, the aim of this study is toprovide an accessible review to fisheries managers and young researchers from developingcountries. The review focuses on the factors that affect fish population imbalance, the differ-ent ageing hard structures, optimal otolith preparation and age validation techniques andtheir limitations and advantages. The review provides essential information to illustratethe need for reliable methods for life history trait estimation and evidence-based fisheriesmanagement. Therefore, this review aims to assist researchers and fisheries managers,working on marine and freshwater fish species, in understanding concepts and processesrelated to stock assessment and population dynamics. Although most examples and casestudies originate from developing countries in the African continent, the review remains ofgreat value to many other countries.47Water 2021, 13, 574 Figure 2. The process of fisheries data collection for age determination methods and stock assessment modelling to provideimportant advice to develop regulations and policies for sustainable fisheries management.2. Global Fish Stock StatusGlobal fish stocks have been declining drastically. This decline has been attributedto several pressures, particularly the rapid increase of fishing efforts to feed the rapidlyincreasing human population [3]. Currently, 90% of global marine fish stocks are fully orover-exploited [3]. The size of biologically sustainable marine fish stocks (i.e., maximallysustainably fished and underfished stocks) decreased from 90% in 1975 to 67% in 2015,while the size of biologically unsustainable stocks (i.e., overfished stocks) increased from10% in 1975 to 33% in 2015 [3] (Figure 3). Since there are no new fishing grounds to beexploited [3,19] and the current world human population is predicted to exceed nine billionby 2050, there will likely be more pressure on the stocks. Figure 3. The trend of the global marine fish stock (source: [3]).48Water 2021, 13, 574Freshwater ecosystems, one of the most important life-support systems on Earth, arethe most endangered ecosystems in the world [20]. Freshwater ecosystems are vulnerableto changes in the basin [21,22] originating from agriculture, mining, urbanization anddam and diversion weirs construction [23]. Anthropogenic pressures cause changes in thephysical, chemical and/or biological components of the freshwater ecosystems when thecarrying capacity of ecosystems decreases below the ability to absorb stress. Freshwater fishstocks are in a state of crisis worldwide [24]. The perceived increase is caused by changes inthe monitoring and measuring rather than actual changes in fisheries production [3]. Thisis corroborated by the drastic decline in abundance and diversity of the different freshwaterfish species due to the increased anthropogenic pressures [20,25–33]. Furthermore, ≥65%of the inland water habitats are moderately or highly threatened [27], suggesting that someof the fish species inhabiting these systems are at high risk of extinction. According toWorld Wide Fund for Nature (WWF [34]), the current rate of fish population decline infreshwater systems is two times the rate for marine systems.There are many examples of excessive stressors with negative consequences. Forexample, the catches, diversity and composition of the fish communities, particularly themost commercially valuable species, in many African lakes such as Lake Malawi, Lake Tan-ganyika, Lake Victoria and Lake Tana have markedly declined due to overexploitation, ille-gal fishing, the introduction of exotic species and environmental degradation [31,33,35–38].A recent assessment by the International Union for Conservation of Nature (IUCN) revealedthat 9% of the 458 fish species inhabiting Lake Malawi are at high risk of extinction [39].Studies indicated that in Lake Malawi, long-living, slow-growing and late-maturing specieshave been depleted [40–44] while the biomass of the endemic Oreochromis species (chambo),has been declining rapidly [45,46]. There are strong signs of overfishing for chambo, themost valuable component of the lake fishery [47]. The chambo fishery in Lake Malawi hasdeclined from 5000 tonnes per year in 1992 to less than 2000 tonnes per year in 1999 [48].Similarly, in response to intensified pressures, like eutrophication and overfishing, fishstocks in Lake Victoria have changed both in composition and abundance [49]. Currently,more than 76% of the fish species in Lake Victoria face extinction [50]. The abundanceand diversity of fish species in Lake Turkana drastically declined due to degradation ofthe littoral habitats and flood pulse breeding areas caused by upstream development andclimate change [51]. Fisheries in the major river basins such as the Zambezi River systemhave experienced drastic declines in catch rates, changes in fish communities and lossof valuable species [52]. Most of the fish communities in the inland waters of Ethiopiaare showing signs of overfishing [25,53–55]. Anthropogenic pressures in and around theinland waters negatively affect the survival of fish species in Ethiopia. For example, theabundance and size of the fish populations in Lake Tana have been declining drasticallydue to overexploitation, agriculture and dam constructions [31–33]. As a result, the catchper unit effort (CPUE) of the endemic Labeobarbus in the lake decreased from 63 kg/trip in1991–1993 [56] to 2 kg/trip in 2016–17 [55]. Currently, five Labeobarbus species are alreadyreported as IUCN red-listed species [57]. This number will likely increase even more in thenear future as the present status of many species have not been evaluated yet. Furthermore,the predominance of small-sized species with little economic importance over large-sizedspecies with high economic importance has been reported [53]. This suggests overfishingof the system. Due to overfishing, the proportion of large and valuable species decreasesin favour of small and less valuable species [58]. Therefore, the continuous fish stockdecline, particularly in developing countries indicates that the world is far from achievingthe SDGs. Especially, SDGs target 14.4 to end illegal, unreported and unregulated (IUU)fishing by 2020 will not be met at all. Additionally, the world human population is rapidlyincreasing, leading to a higher demand for fish, which poses a hurdle for the FAO’s BlueGrowth Initiative goal. This goal aims to maximize the goods and services provided bythe different ecosystem types without compromising the social and economic benefits thesystems offer [3].49Water 2021, 13, 5743. Dynamics in Fish Population Size and Life History Traits3.1. Factors Affecting Fish Population ImbalanceFish population dynamics are primarily affected by three factors: (1) recruitment,(2) growth and (3) mortality rates [59]. The recruitment is defined as the number ofindividuals born within a given period. Growth is the increase in length and weight ofthe individuals of a population in a given period of time, and mortality is the numberof individuals removed from the population within a given period of time (Figure 4).Recruitment and growth increase the fish population in number and biomass, whilemortality, due to fishing and/or natural causes, decreases the population both in numberand biomass. Illegal, unreported and unregulated (IUU) fishing increases fishing mortalityand has adverse effects on the abundance and size composition of fish populations. Fishersoften target the spawning biomass, causing high mortality rates which in turn leads todrastic reductions in the abundance of recruits and mega-spawners. Furthermore, smallmesh sizes, typically used for illegal fishing, are known to have negative effects on the sizedistribution and maturation of fish [60,61]. For example, the majority of the specimens ofLabeobarbus species in Lake Tana caught using ≤8 cm mesh size gillnets had fork lengthless than the size at first maturation (FL50%) [55]. The dominance of immature individuals(1 year).Difficulty to identify apost-mark annulus±1 year >1Minimal excludingcost of taggingcruiseMarginalincrement analysisand Edge analysisValidates frequency ofgrowth incrementsformation.Applicable tofast-growing or youngfish.A year-round sample isrequired.±1 year >100Minimal otherthan a fishcollectionRadiometricValidates absolute age.Applicable to any recentlycollected data.Suitable to long-lived fishCan only distinguishbetween widelydivergent age estimates±25–50% 10–50~$1000 per agecategoryRadiocarbonValidate both absolute ageand frequency of growthincrements.Suitable for long lived fish.Does not require recentlycollected sample.Some of the fish in thesample must be hatchedbefore 1965.±1–3 years 20–30$700–$1000 perotolith55Water 2021, 13, 5745.1. Mark-Recapture of Chemically Tagged FishAt the moment this method is one of the best and most cost-effective methods availableto validate the rate of growth zone formation [79]. It can be applied through variousmethods such as injection, immersion and feeding. Injection is the most common techniquefor tagging wild fish [148–150]. Fish species that are captured from the wild are injectedwith calcium-binding chemicals such as oxytetracycline (OTC), alizarine, calcein andstrontium immediately at the time of tagging [125]. These chemicals are incorporatedinto otoliths shortly after injection. The permanent mark is visible under fluorescent lightin the growth zone being formed at the time of tagging [79]. The rate of growth zonedeposition can be determined based on the number of growth zones deposited distallyto the mark in the recaptured fish and the time at liberty. If the difference in the timeof injection and liberty is one year and one growth zone is deposited during this time,it means that the studied fish species deposited one growth zone per year. However, iftwo growth zones are deposited, it means the rate of growth zone deposition is biannual.This method has been applied to validate the periodicity of growth zone deposition inseveral fish species [141,143–145,151–154]. The growth zones being validated are formedwhile the fish is growing in the natural environment. This method is time-consuming,technically difficult to apply and the recovery rates of the tagged fish are usually low [155].Additionally, since the numbers of growth zones formed after tagging are low, a wrongconclusion can be made on the rate of growth zone deposition, if one of the growth zonesis misinterpreted.5.2. Marginal Increment AnalysisMarginal increment analysis (MIA, linear-circular model) is the most widely usedvalidation method due to its modest sampling requirements and low cost [79]. The MIAis based on the observed incremental patterns of growth zones throughout the year andassumes that the outermost increment displays a yearly sinusoidal cycle when plottedagainst months of capture [156,157]. It uses the ratio of the width of the last growing zoneand the width of the last fully completed growth zone (MIR) as a dependent variable andmonths of capture as an independent variable [158–160]. The marginal increment ratio(MIR) is, therefore, computed as follows [161].MIR =R − rnrn − rn−1(3)where R is the distance from the core to the outermost of the edge, rn is the distance fromthe core to the end of the growing zone and rn-1 is the distance from the core to the end ofthe last fully formed growth zone.When the MIR value is equal to one, it indicates the completion of growth zoneformation. Although the MIA is a useful method, especially when supported by othervalidation methods [159], it is also susceptible to bias and misinterpretation if not appliedrigorously [79]. The approach has several limitations. The extended time of samplecollection (monthly at least for one year), high possibility to collect small sample sizeper size classes within each month, difficulties to objectively classify the edge types andsubstantial inter-individual variation in marginal increment appearance [79,147]. Theselimitations are more pronounced in older fish where growth increments become very thinand packed together [118,162]. Therefore, when MIA is applied as age validation, thefollowing protocols should be applied. (1) samples must be completely randomized whenassigned to the examiner, (2) a minimum of two complete cycles need to be examined,in accordance with accepted methods for detecting cycles, and (3) the results must beinterpreted objectively [79]. All the described protocols and encountered limitations forthis technique here are also applicable for the edge analysis approach described below [79].56Water 2021, 13, 5745.3. Edge AnalysisSimilar to the MIA, edge analysis (EA, binary-circular model) is also based on ex-amination of the marginal increments. Its dependent variable is binary, the otolith edgetypes either opaque or translucent, while the month of capture is the independent variable.Analysis of the EA can, therefore, verify the hypothesis that growth zone deposition iseither annual or biannual. This approach assumes that the density of the outermost margin(i.e., proportion of the translucent zone) exhibits a sinusoidal cycle when plotted againstthe months of capture [79]. Several researchers found this approach useful for validatingthe periodicity of growth zone deposition [89,143,161,163]. For example, the Edge analysisrevealed an annual growth zone deposition for Labeobarbus platydorsus in Lake Tana [140](Figure 5). Although the EA approach is cheap and logically simple, it is susceptible to biasand misinterpretation if not applied rigorously [79]. Figure 5. The proportion of asteriscus otoliths with a translucent growth zone on the edge for Labeobarbus platydorsus basedon samples collected between May 2016 and April 2017 in Lake Tana, Ethiopia [140]. The bar graph denotes the proportionof the translucent growth zone and the open dot line represents the predicted model results. The numbers above the bars inthe no cycle model are total sample size and the same sample size is used for the other models. The annual cycle mode bestfit the data.57Water 2021, 13, 5746. ConclusionsFisheries management strategies must be developed to ensure that stocks are harvestedat sustainable levels. Fisheries managers rely on age estimates to develop effective andsustainable management options. Accurate and precise age estimates can be obtained if andonly if an appropriate otolith preparation method is described and the rate of growth zonedeposition is properly validated. Age estimates combined with data such as fish length,weight and reproductive condition can be used to describe the structure and dynamics ofthe population considered to comprise the harvested stock. For example, longevity andgrowth rates are estimated using length and age data, while the combination of sex andreproductive condition with growth data are used to describe the age-fecundity relationshipand sex-specific growth. Mortality rates are also computed by combining age estimateswith counts of the number of fish per age class in a sample. These analyses provideresearchers and fisheries managers with a range of information to derive sustainableharvest strategies through stock assessment evaluations. In order to avoid complete stockcollapse, fisheries catch should not exceed the maximum sustainable yield of the stock(MSY). The MSY is an important tool to quantify the goal of a fishery and allows fisheriesmanagers to evaluate the performance of the fishery. The comparison of the assessed stateof the fish stock with the values of the fisheries reference points such as MSY supports themanagers to make informed decisions. Thus, fisheries reference points should be calculatedas correctly as possible. The most popular and widely used model to estimate the MSY isthe yield-per-recruitment model, introduced by Beverton and Holt [164]. Understandingthe population dynamics, age determination techniques, and the estimation of life historytraits allow policymakers and fisheries managers to optimize future conservation strategies(Figure 6). Furthermore, monitoring and evaluating the effects of the major pressures suchas pollution, habitat degradation and over utilization of aquatic resources is vital to provideinsights into the changes of aquatic ecosystems and indicate their status (Figure 6). Figure 6. Schematic representation of fish community monitoring, fish stock assessment and environmental modelling todevelop science-based fisheries management.58Water 2021, 13, 574Author Contributions: S.G. conceived the main idea and wrote the manuscript. S.B., A.G., W.A. andP.G. reviewed and edited the manuscript. All authors have read and agreed to the published versionof the manuscript.Funding: This work was supported by the Critical Ecosystem Partnership Fund (CEPF) and specialresearch fund (BOF), Ghent University.Institutional Review Board Statement: Not applicable.Informed Consent Statement: Not applicable.Data Availability Statement: Not applicable.Conflicts of Interest: The authors declare no conflict of interest.References1. Welcomme, R.L.; Cowx, I.G.; Coates, D.; Béné, C.; Funge-Smith, S.; Halls, A.; Lorenzen, K. Inland capture fisheries. Philos. Trans.R. Soc. B Biol. Sci. 2010, 365, 2881–2896. [CrossRef] [PubMed]2. Lynch, A.J.; Cooke, S.J.; Deines, A.M.; Bower, S.D.; Bunnell, D.B.; Cowx, I.G.; Nguyen, V.M.; Nohner, J.; Phouthavong, K.; Riley, B.The social, economic, and environmental importance of inland fish and fisheries. Environ. Rev. 2016, 24, 115–121. [CrossRef]3. FAO. The State of World Fisheriess and Aquaculture 2018-Meeting the Sustainable Development Goals, Room, Italy. 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On the Dynamics of Exploited Fish Populations; Fisheries Investigation Series 2; UK Ministry of Agriculture:London, UK, 1957; Volume 19, p. 540.64waterArticlePlasticity in Reproductive Traits, Condition and EnergyAllocation of the Non-Native Pyrenean Gudgeon Gobio lozanoiin a Highly Regulated Mediterranean River BasinFátima Amat-Trigo 1, Mar Torralva 1, Daniel González-Silvera 2 , Francisco Javier Martínez-López 2and Francisco José Oliva-Paterna 1,*Citation: Amat-Trigo, F.;Torralva, M.; González-Silvera, D.;Martínez-López, F.J.;Oliva-Paterna, F.J. Plasticity inReproductive Traits, Condition andEnergy Allocation of the Non-NativePyrenean Gudgeon Gobio lozanoi in aHighly Regulated MediterraneanRiver Basin. Water 2021, 13, 387.https://doi.org/10.3390/w13030387Academic Editor: José Maria SantosReceived: 18 December 2020Accepted: 29 January 2021Published: 2 February 2021Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).1 Department of Zoology and Physical Anthropology, Faculty of Biology, University of Murcia,30100 Murcia, Spain; fatima.amat@um.es (F.A.-T.); torralva@um.es (M.T.)2 Department of Physiology, Faculty of Biology, University of Murcia, 30100 Murcia, Spain;danielgs@um.es (D.G.-S.); javmaraq@um.es (F.J.M.-L.)* Correspondence: fjoliva@um.esAbstract: The invasion success of non-native fish, such as Pyrenean gudgeon Gobio lozanoi in severalIberian rivers, is often explained by the expression of its life history traits. This study provides the firstinsights into the reproductive traits, fish condition, and energy allocation (protein and lipid contentsof tissues) of this species, along a longitudinal gradient in one of the most regulated river basins in theIberian Peninsula, the Segura river. Larger sizes of first maturity, higher fecundity and larger oocyteswere found in fluvial sectors with the most natural flow regimes, characterised by a low base flowwith high flow peaks in spring and autumn. A delay in the reproductive period, lower fish conditionand no differences in sex-ratio were observed in fluvial sectors with a high increase in base flow andnotable inversion in the seasonal pattern of flow regime. Lipid contents in the liver and gonads werestable during the reproductive cycle and decreases in muscle were noted, whereas ovarian and liverproteins increased. In relation to energy allocation for G. lozanoi, an intermediate energy strategy wasobserved between income and capital breeding. Our results support the hypothesis that the highplasticity of G. lozanoi population traits plays a significant role in its success in a highly regulatedMediterranean river basin. Understanding the mechanisms by which flow regulation shapes fishpopulations in Mediterranean type-rivers could inform management actions.Keywords: energy allocation; fecundity; flow regulation; Mediterranean-type river cyprinids;invasive fish1. IntroductionFlow regulation is one of the most widespread anthropogenic alterations in naturalaquatic ecosystems and plays an important role in habitat development, food sourcesavailability and the distribution of organisms [1,2]. There are many studies that confirm theimpact of flow regulation (i.e., dams and weirs) on the structure and functioning of rivers,and in particular, how they affect populations of fish worldwide [3–5]. Stream flow is afactor which has been considered as an important force shaping fish population traits [6,7]and life-histories [8,9], and several flow alteration studies have already shown significanteffects on population traits such as, for instance, growth and maturation [5], changes inthe timing of spawning and spawning areas [10,11], recruitment failure [12,13] and evenchanges in reproductive traits [3,14].In relation to reproductive strategies, nutrient acquisition and energy allocation toreproduction are essential for energy balance in order to meet survival, growth and repro-duction demands and, consequently, to develop the most competitive strategy [7]. Thus,the management of energy reserves and allocation during the reproduction process deter-mines the reproductive strategy [15]. Fish species that can use the energy previously stored65Water 2021, 13, 387in tissues for the development and maturation of reproductive features have been referredto as capital breeders [16,17], and this strategy is typical of total spawners or species withsynchronous oocyte development [15]. Alternatively, income breeding strategists includespecies that are not able to store energy and where reproduction success is determined bythe environmental resources at the time of reproduction [18]. This strategy is more com-mon in many small, batch-spawning fishes with asynchronous oocyte development [15].Between these two extreme strategies, some species show intermediate characteristics ofboth energy allocation strategies [19–21].This study is focused on the reproductive strategy and energy allocation dynam-ics during the reproductive cycle of the Pyrenean gudgeon, Gobio lozanoi Doadrio andMadeira 2004 (Actinopterygii, Cyprinidae; Supplementary Material 1, Figure S1), which isan endemic species from the Iberian Peninsula and the south of France [22]. The specieshas been translocated into several Iberian catchments as live bait for angling and, nowa-days, is widely distributed across the Iberian Peninsula, with established populationsin many rivers [23,24]. Some authors consider this species as having a high capacityto spread and as being able to behave invasively, increasing its density rapidly and oc-cupying new habitats [24]; a process which is probably favoured by river regulationand artificial impoundments [25]. It has already been suggested that this non-nativespecies may have potential impacts on the environment and native species throughoutseveral Iberian basins [23,25–27]. Some examples include interspecific competition for foodresources [28,29] or disease transmission [30].Freshwater biotas are especially vulnerable to new invasive fish, particularly in areaswith high endemism, such as the Mediterranean basins [26,31,32]. The non-native pop-ulations of G. lozanoi have been previously classified as opportunistic strategists (sensuWinemiller and Rose [33]), but also as intermediate strategists because they use strategiesranging from periodic to opportunistic [34]. Thus, non-native populations of G. lozanoiare characterised by early maturity, low fecundity, multiple spawnings per year and havea long reproductive span [34]. However, there is a scarcity of studies that have dealtwith the biology and reproductive traits of non-native populations of G. lozanoi [35–37].Consequently, the negative effects of the species on native fish may not yet have been fullyelucidated. In addition, no studies exist that have included a physiological approach toenergy allocation dynamics in reproductive strategies.A greater understanding of the phenotypic plasticity involved in the adaptationof non-native fishes to local conditions is an important tool for control programs [38].According to Ribeiro and Leunda [39], there is a clear need for biological information aboutG. lozanoi population traits across the Iberian Peninsula and especially in its non-nativeriver basins, which could be an important knowledge gap hampering effective controland management. Moreover, the life history variability of fish seems to play a key role indriving invasion success and significant intraspecific plasticity has often been observedin the process of acclimatization to new habitats [40,41]. However, nothing is knownabout the intraspecific variability of G. lozanoi along gradients in the same watershed orin terms of comparisons between populations located at different flow regimes. Takinginto account that reproductive investment can be understood as the result of the energybalance between survival, growth and reproduction demands in order to achieve the mostcompetitive strategy [7,15], the goal of this study was to analyse the reproductive traitsand the energy balance of G. lozanoi in an invaded Mediterranean basin. The two mainhypotheses proposed were: firstly, the reproduction strategy could show inter-populationplasticity due to different flow scenarios and it is expected to be closer to an opportunisticstrategy in fluvial sectors with the most unpredictable flow regimes. Secondly, energyallocation mechanisms should be closer to income breeding strategies according to itsreproductive traits. For this purpose, the following specific objectives have been proposed:(a) to describe the reproductive and fish condition cycles of G. lozanoi in five fluvial sectors;(b) to describe the energy allocation (proteins and lipids contents) among tissues in this66Water 2021, 13, 387target fish; and (c) to analyse the relationships among reproductive traits, fish conditionand patterns of energy allocation.2. Materials and Methods2.1. Ethical InformationThe care and use of experimental animals complied with University of Murcia andSpanish Law 32/2007 and RD 53/2013 animal welfare laws, guidelines and policies,as approved by Ministry of the Presidency, Relations with the Courts and DemocraticMemory. The specific permit AUF20150077 was approved by the Regional Ministry ofWater, Agriculture and Environment of Murcia and Castilla-La Mancha and it allowed usto sacrifice the non-native species of the Segura River Basin.2.2. Study AreaThis study was conducted in the upper and middle parts of the Segura River Basin(drainage area of 18,870 km2), a highly regulated river located in the southeast of the IberianPeninsula (Figure 1). The Segura River Basin is characterised by a typical Mediterraneanclimate with a pronounced spatial and seasonal hydrological variability. Currently, thisbasin is highly regulated in terms of irrigation supply and human water demands, whichhave greatly modified the natural flow regime, resulting in changes in flow magnitude anda reverse seasonal flow pattern in some areas [42,43]. Supplementary Material 1 providesan accurate description about the flow regime characteristics of the sampled streams.Figure 1. Sampling sites location for Gobio lozanoi in the Segura River basin at south-eastern IberianPeninsula, Spain.Sampling fluvial sites were selected following flow regime criteria. They were locatedalong the longitudinal gradient of the basin in different hydrological sectors separatedby large dams (Figure 1; Table 1). The flow characteristics ranged from natural (TUS)to reverse flow regimes (SE3 and SE4) (Supplementary Material 1, Figure S1 and TableS1). Each sampling site was characterised by the following six environmental variables(Table 1): altitude (Alt) (meters above sea level), ecological status sensu EU Water Frame-67Water 2021, 13, 387work Directive (Status) (with the following categories: 1 = high; 2 = good; 3 = moderate;4 = poor), conductivity (µS cm−1), Fluvial Habitat Index (FHI) [44], Riparian Quality Index(RQI) [45] and 2015 mean monthly temperature (◦C). These six selected environmentalvariables are among the ecological drivers that play a significant role in the freshwaterfish ecology, and also in biological invasion processes by fishes, of the Mediterranean-typerivers [39,40].Table 1. Habitat variable values of each sampling site where Gobio lozanoi populations were assessed in the SeguraRiver Basin.Sampling Site Code Latitude LongitudeAltitude(m.a.s.l.)StatusConductivity(µs cm−1)IHF RQIWater Tempeture(◦C, ± 95% Cl)Tus TUS 38◦24′40.5′ ′ N 2◦19′01.3′ ′ W 809 1 399.44 ± 9.89 84 65 15.63 ± 2.77Camping SE1 38◦17′48.0′ ′ N 2◦24′42.3′ ′ W 685 1 374.33 ± 8.06 61 98 14.71 ± 2.15Letur SE2 38◦24′31.9′ ′ N 2◦06′33.5′ ′ W 460 1 362.62 ± 4.21 68 80 13.04 ± 1.59Bajo Cenajo SE3 38◦21′50.9′ ′ N 1◦46′17.2′ ′ W 363 2 383.28 ± 3.73 67 77 12.49 ± 1.28Hoya García SE4 38◦14′30.6′ ′ N 1◦32′35.7′ ′ W 200 2 686.19 ± 55.20 66 70 16.25 ± 2.01Altitude (meters above sea level), ecological status (1–4); water conductivity (±0.1); Fluvial Habitat Index (IHF); Riparian Quality Index(RQI), and water temperature (Mean and 95% Cl Confidence limits).Since first recorded in the upper region in the 1980s, G. lozanoi has been registered influvial sectors and reservoirs along the Segura River Basin [23,27]. The ichthyofauna ofthis basin is characterised by low species richness and the fish assemblage composition isdominated by non-native species [27]. The studied species share resources and habitatswith native cyprinids, such as the southern iberian barbel Luciobarbus sclateri (Günther,1868) and the south iberian chub Squalius pyrenaicus (Günther, 1868), and with several non-native fish to the basin, such as the pumpkinseed Lepomis gibbosus (Linnaeus, 1758), bleakAlburnus alburnus (Linnaeus, 1758), common carp Cyprinus carpio (Linnaeus, 1758), iberianstraight-mouth nase Pseudochondrostoma polylepis (Steindachner, 1864), northern pike Esoxlucius (Linnaeus, 1758), pike-perch Sander lucioperca (Linnaeus, 1758) and largemouth blackbass Micropterus salmoides (Lacepèd, 1802).2.3. Field Sampling and Laboratory ProceduresFish were collected by electrofishing (1800 W DC generator at 200–300 V, 2–3 A) duringa one-year study period (January–December 2015). One fisherman with an electric dip-net,supported by two assistants each with a non-electric dip-net, removed fish following azigzagging and upstream direction of each sampling stretch (100 m long), which wasblocked off with barrier nets (samplings were carried out between 10 a.m. and 4 p.m.).Samples were taken once every two weeks in spring and summer, and monthly during therest of the year (Supplementary Material 1, Table S2). A total of 2333 G. lozanoi were caught(TUS: 437; SE1: 478; SE2: 385; SE3: 485 and SE4: 548) and, in accordance with Spanishregulations, immediately sacrificed in a water tank with an overdose of anaesthetic solution(1:10 solution of clove oil dissolved in ethanol 70%), before being placed on ice and thenthey were stored at −20 ◦C in the laboratory.Fork length (LF ± 1 mm), total and eviscerated masses (MT and ME ± 0.1 g) andorgan masses (hepatic and gonad, MH and MG ± 0.001 g) of a subsample of 1982 fish wererecorded (TUS: 382; SE1: 403; SE2: 365; SE3: 366 and SE4: 466). Gonads were visuallyinspected for sex determination (male, female or immature), and also to determinatethe reproductive stage (i.e., quiescence, maturation, spawning and postspawning). Asubsample of 133 mature specimens (110 females, TUS: 20; SE1: 25; SE2: 24; SE3: 17 andSE4: 24, and 23 males, TUS: 13 and SE4: 10), with fork lengths ranging from 7.2 to 11.2 cmwas used to estimate fecundity, oocyte size and physiological macronutrients (proteinand lipid content in tissues). Due to protein and lipid quantification methods, there werenot enough testis masses to perform physiological analysis in every sampling site. Toquantify protein and lipid content in the muscle, liver and gonad, samples were weighedand protein levels were determined using the method of Bradford (1976) [46] and expressed68Water 2021, 13, 387as percentages. Total lipids were extracted following the method of Folch et al. (1957) [47].Samples were weighed and homogenized in 5 mL of chloroform/methanol (2:1 v/v) andwashed with KCl (0.88% w/v). The weight of lipids was determined gravimetrically afterevaporation of the solvent and expressed as percentages. Finally, these fish were aged,counting true annuli from scales taken between the lateral line and dorsal fin origin.Ovarian development and fecundity were studied using the gravimetric method [48].To make sure that the ovary was homogenous in structure (number and size of oocytes),small portions of anterior, middle and distal parts were compared, and no significantdifferences were found (ANOVA, p > 0.05). Therefore, all oocytes present in a subsamplefrom the mid-region of the right ovarian lobe (5% of the total weight of the gonad) wereplaced in Gilson liquid, shaken periodically to soften gonadal tissue and to disperse oocytes,washed with distilled water and preserved in 70% ethanol for following analyses [37].Image processing program ImageJ v1.80 (available at https://imagej.nih.gov/ij/) was usedto count and measure oocytes. Fecundity was determined in 39 mature females caught fromApril to July. Fecundity was analysed at three levels: potential (FecPOT), absolute (FecABS)and batch fecundity (FecBAT). These levels were determined by counting the total numberof opaque and vitellogenic oocytes, total number of vitellogenic oocytes and total number ofvitellogenic oocytes of the last mode representing size before spawning, respectively [48,49].Oocyte size at each level of fecundity (ØPOT, ØABS, ØBAT) and maximum diameter (ØMAX)were assessed.2.4. Statistical AnalysesSex-ratio was analysed for the whole population and in every sampling site. The de-gree of significance of the obtained results was established in χ2 at a p-value ofp < 0.05. Linear regressions of fecundity to fork length were fitted by least-squares methodto log-transformed data.Analyses of length-mass relationships were performed to study temporal variation insomatic condition (SC), hepatosomatic condition (HC) and gonadal activity (GSI) usingthe predicted values of ME, MH and MG from analysis of covariance, respectively. Thestatistical approach included the application of a covariance analysis (ANCOVA) usingME, MH and MG as dependent variables, LF as the covariate (log-transformed data) andreproductive stage (quiescence, maturation, spawning and postspawning stage) as a factor.The analysis was developed by sampling site and sex separately. Differences betweendependent–covariate relationships were tested to check that the covariate by-factor interac-tion (homogeneity of slopes) was significant (p < 0.05). If the covariate by-factor interactionwas not significant, standard ANCOVA was applied to obtain predicted values (predictedME, MH and MG values). When differences were found, a post hoc Bonferroni test formultiple comparisons was performed. Student’s t-test was used to evaluate differences infish conditions (somatic and hepatosomatic condition), gonadal activity, and protein andlipid content between sexes.Analyses of variance (ANOVA) were performed to determine differences in proteinand lipid content among the different temporal phases and to evaluate differences amongsampling sites at each reproductive stage in fish conditions, gonadal activity, fecundity,oocyte diameter and percentage of proteins and lipids in tissues, followed by the TukeyHSD (honestly significant difference) test post-hoc comparisons if significant differencesamong populations were found. When data did not show homogeneity of variances,Welch’s analysis of variance (ANOVA) followed by T3 of Dunnett for pairwise multiplecomparisons were used. The non parametric tests of the Kruskal–Wallis H-test and Mann–Whitney U-test were used when data did not fit normal distribution. Relations betweenfish condition, gonadal activity, fecundity, oocyte diameter and percentage of proteins andlipids by tissue were analysed using Spearman’s correlation coefficients.Size of first maturity was estimated after running binary logistic regressions (immature-mature individuals) for each sampling site by sex (Supplementary Material 2). Differences69Water 2021, 13, 387in first maturity among sampling sites were tested using generalised estimating equations(GEE), with binomial errors and the logit link function.3. Results3.1. Reproductive Cycle and Temporal Variation in Fish ConditionThe results of the ANCOVA test to estimate the effects of the factor on the LF-ME,LF–MH and LF–MG relationships are shown in the Supplementary Material 3, Tables S3and S4. In both sexes, significant differences were observed among reproductive stagesin the five sampling sites for fish SC, HC and GSI (Figure 2; Supplementary Material 3,Tables S3 and S4).Figure 2. Temporal variation in gonad activity (predicted MG values, MG is gonad mass) along thestudy period for the five studied populations (TUS, SE1, SE2, SE3 and SE4) for both sexes of Gobiolozanoi. The lines represent the adjusted model Loess for each population.The reproductive cycle was fitted by the ANCOVA predicted MG values as a Gona-dosomatic index (GSI) showing significant temporal differences in the gonadal activity(Figure 2). Both sexes showed a similar reproductive cycle in which four temporal stageswere identified based on the GSI values (Figure 2): (1) the quiescence stage, with low valuesof GSI in winter; (2) the maturation stage, when GSI values rise up steeply—especiallyin March—and reach the maximum values at the beginning of May in females (exceptfemales from SE2) and also in males from TUS, SE3 and SE4, however, in males from SE1and SE2, maturation was observed in late May; (3) the spawning stage, when GSI valuesare steady or decreasing moderately until late summer or early fall, and (4) the regressionstage or postspawning, when GSI continues to decrease and reaches minimum values(Figure 2). The female gonadosomatic index was significantly higher than the male’s in allreproductive stages (Student’s t-test; quiescence stage: t = −14.56 p < 0.001; maturationstage: t = −28.97 p < 0.001; spawning stage: t = −20.21 p < 0.001; postspawning stage:t = −11.54 p < 0.001). Significant differences in the gonadal activity among reproductivestages were found in the total population (Figure 3) and when sampling sites were analysedindividually for both sexes (Table 2). The GSI was significantly different among samplingsites for the total of fish (both males and females) (females Kruskal–Wallis, χ2 = 46.17,p < 0.001; males Kruskal–Wallis, χ2 = 20.09, p < 0.001), SE1 and SE2 populations showinghigher GSI values and SE4 the lowest values in both sexes (Table 2).70Water 2021, 13, 387Figure 3. Mean predicted MG, ME and MH values by ANCOVA (LF as covariate) in each reproductive stage (quiescence,maturation, spawning and postspawning) for both sexes of Gobio lozanoi. Letters show significant differences (Welch’sanalysis of variance p < 0.05 and T3 of Dunnett post hoc tests) among reproductive stages in females (capital letters) and inmales (lowercase letters).Table 2. Mean predicted ME, MH and MG values by ANCOVA (LF as covariate) in each reproductive stage for both sexes ofGobio lozanoi. ANOVA results of comparison of somatic condition (SC), hepatosomatic condition (HC) and gonad activity(GSI) among reproductive stages in each sampling site are showed and significant p-values are included. Codes of samplingsites (TUS, SE1, SE2, SE3, and SE4) from the Segura River Basin were included.SamplingSiteReproductive Stages ANOVATotal Quiescence Maturation Spawning Postspawning df F pFEMALESSCTUS 0.651 0.569 0.679 0.683 0.629 3 4.851 0.004SE1 0.696 0.635 0.736 0.686 0.689 3 5.124 0.002SE2 0.634 0.664 0.625 0.605 0.665 3 0.908 0.443SE3 0.488 0.503 0.480 0.466 0.511 3 0.348 0.791SE4 0.583 0.543 0.598 0.567 0.638 3 4.656 0.004HSTUS 0.043 0.024 0.059 0.051 0.021 3 20.996 <0.001SE1 0.051 0.036 0.074 0.040 0.025 3 43.062 <0.001SE2 0.044 0.037 0.041 0.057 0.043 3 1.603 0.199SE3 0.025 0.020 0.027 0.029 0.023 3 1.888 0.135SE4 0.034 0.028 0.044 0.032 0.026 3 6.309 0.001GSITUS 0.179 0.057 0.293 0.205 0.058 3 61.079 † <0.001SE1 0.225 0.099 0.352 0.212 0.074 3 107.369 † <0.001SE2 0.196 0.142 0.188 0.323 0.098 3 33.643 <0.001SE3 0.161 0.084 0.206 0.229 0.058 3 49.019 <0.001SE4 0.140 0.063 0.234 0.153 0.054 3 146.528 † <0.001MALESSCTUS 0.697 0.641 0.747 0.725 0.586 3 3.409 0.022SE1 0.745 0.671 0.821 0.756 0.620 3 6.767 <0.001SE2 0.741 0.779 0.686 0.775 0.776 3 1.920 0.133SE3 0.610 0.578 0.621 0.585 0.690 3 1.407 0.249SE4 0.638 0.631 0.628 0.636 0.700 3 2.171 0.101HSTUS 0.031 0.034 0.036 0.030 0.021 3 1.561 0.206SE1 0.030 0.044 0.036 0.025 0.018 3 11.160 <0.001SE2 0.048 0.053 0.048 0.044 0.044 3 0.437 0.727SE3 0.026 0.023 0.028 0.021 0.037 3 4.472 0.007SE4 0.025 0.033 0.025 0.020 0.032 3 6.724 0.001GSITUS 0.040 0.017 0.063 0.041 0.012 3 37.531 <0.001SE1 0.052 0.020 0.085 0.050 0.014 3 52.044 <0.001SE2 0.054 0.038 0.057 0.087 0.026 3 11.223 <0.001SE3 0.040 0.019 0.055 0.042 0.031 3 52.816 <0.001SE4 0.037 0.016 0.050 0.032 0.017 3 46.862 † <0.001† Means no normalised data and Kruskal–Wallis analysis of variance.The somatic condition (SC) and hepatosomatic condition (HC) varied over the repro-ductive cycle with the exception of SC of males (Figure 3), and they showed significantdifferences among reproductive stages in both sexes in most sampling sites when theywere analysed individually (Table 2). Male SC was significantly higher than for the fe-males in most reproductive stages (Student’s t-test; quiescence stage: t = 3.83, p < 0.001;71Water 2021, 13, 387maturation stage: t = 4.01, p < 0.001; spawning stage: t = 4.29, p < 0.001), whereas femaleHC showed higher values than the males’ during maturation and at spawning (Student’st-test; maturation stage: t = −7.13, p < 0.001; spawning stage: t = −5.49, p < 0.001). Fishconditions in all males showed significant differences among sampling sites (SC ANOVA,F(4, 598) = 15.56, p < 0.001; HC Welch ANOVA, F(4, 236.26) = 15.24, p = 0.001), SE3 andSE4 showed the lowest SC and HC values (Table 2). In all females, fish conditions alsoshowed significant differences among sampling sites (SC Welch ANOVA, F(4, 327.68) = 39.99,p < 0.001; HC Kruskal–Wallis, χ2 = 73.99, p < 0.001), SE1 showed the highest SC and HSvalues, while SE3 and SE4 showed the lowest in both conditions (Table 2).3.2. Population Structure and Reproduction TraitsGobio lozanoi fish ranged from 1.8 cm to a maximum LF of 12.3 cm (a male caught in SE3).Total males (LF 7.6 ± 1.6 cm) were significantly longer than females (LF 7.1 ± 1.5 cm) (Stu-dent’s t-test, t = 5.55, p < 0.001). Both sexes show significant differences among samplingsites in the total data (females ANOVA, F(4, 922) = 16.99, p < 0.001; males Welch ANOVA,F(4, 308.18) = 2.57, p = 0.038). Shorter females were found in TUS (6.5 ± 0.2) and SE3(6.8 ± 0.2) and larger ones in SE1, SE2 and SE4 (7.5 ± 0.2, 7.4 ± 0.2 and 7.4 ± 0.2,respectively), while in males individuals in SE2 (7.9 ± 0.3) were larger than in TUS(7.2 ± 0.3).The overall sex-ratio (696 males, 928 females) was significantly skewed towardsfemales (χ2 = 33.14, p < 0.001) in the whole study period, with females being significantlymore abundant in all sampling sites with the exception of SE3, which did not showdifferences between males and females (χ2 = 1.43, p = 0.23).Length at first maturity in males ranged between 3.55 cm LF in SE3 and 6.26 cmLF in SE2, while female range was between 4.28 cm LF in SE3 and 6.60 cm LF in TUS.Above these lengths all individuals were considered mature (Figure 4). However, onlysignificant differences in length at first maturity were found among sites in males (GEE:Wald-χ2(4) = 13.57 p = 0.009), finding significantly larger fish at first maturity in TUS, SE1and SE2 populations (Figure 4).Figure 4. Predicted probability of maturity according to fork length for females and males for thefive studied populations (TUS, SE1, SE2, SE3 and SE4) of Gobio lozanoi.Oocytes larger than 0.25 mm in diameter were considered opaque and all oocytesabove 0.55 mm of diameter were vitellogenic. Fecundity data from each sampling site aresummarised in Table 3. No significant differences were found in fecundity and oocyte diam-eters by age (ANCOVA, p > 0.05) and fork length was not significant as a covariable whenoocyte diameters were analysed, which indicates no effect of fish size on egg diameters inthe studied fish. Significant differences were found in potential (ANCOVA F(1, 4, 39) = 3.27,p = 0.023) and absolute fecundity (ANCOVA F(1, 4, 39) = 2.90, p = 0.037) among samplingsites. SE1 showed the highest number of oocytes and SE3 showed the lowest ones at agiven length (Bonferroni post hoc: p = 0.023 and p = 0.037, respectively). Only the diam-72Water 2021, 13, 387eter of batch fecundity showed significant differences among sampling sites (ANCOVAF(1, 4, 37) = 9.96, p < 0.001). Batch oocyte diameters in TUS and SE1 populations were largerthan in SE2, SE3 and SE4 (Bonferroni post hoc: p < 0.001) (Figure 5).Table 3. Mean, minimum and maximum values of potential fecundity (FecPOT), absolute fecundity (FecABS) and batchfecundity (FecBAT) of Gobio lozanoi. Linear regression of fecundities and fork length (LF) are shown (significant p-values areincluded). Codes of sampling sites (TUS, SE1, SE2, SE3, and SE4) from the Segura River Basin were included.SitePotential Fecundity Absolute Fecundity Batch FecundityMean Min Max Mean Min Max Mean Min MaxTUS 239.3 89 401 152.4 71 250 65.8 37 101LogFecPOT = −3.64+ 6.30 LogLF LogFecVIT = −1.61 + 3.987 LogLF LogFecBAT = −1.942 + 3.988 LogLFr2 = 0.82 p = 0.001 r2 = 0.54 p = 0.015 r2 = 0.72 p = 0.004SE1 311.25 111 461 194.58 78 324 79 33 152LogFecPOT = −4.337 + 7.045 LogLF LogFecVIT = −3.783 + 6.266 LogLF LogFecBAT = −4.511 + 6.628 LogLFr2 = 0.62 p = 0.007 r2 = 0.56 p = 0.013 r2 = 0.70 p = 0.003SE2 248.13 136 389 168.5 83 317 85 42 140LogFecPOT = −2.023 + 4.634 LogLF LogFecVIT = −3.482 + 5.974 LogLF LogFecBAT = −3.444 + 5.575 LogLFr2 = 0.75 p = 0.012 r2 = 0.77 p = 0.010 r2 = 0.63 p = 0.018SE3 117.4 41 181 79 36 103 56 35 74LogFecPOT = −3.085 + 5.509 LogLF LogFecVIT = −0.861 + 1.154 LogLF LogFecBAT = −4.143 + 6.165 LogLFr2 = 0.94 p = 0.033 r2 = 0.34 p = 0.417 r2 = 0.86 p = 0.073SE4 220.25 70 427 132.5 41 252 85.5 37 124LogFecPOT = −3.671 + 6.115 LogLF LogFecVIT = −3.942 + 6.172 LogLF LogFecBAT = −2.105 + 4.119 LogLFr2 = 0.91 p = 0.044 r2 = 0.95 p = 0.023 r2 = 0.77 p = 0.121Figure 5. Estimated marginal means (by ANCOVA) ± IC 95% at 9.0 cm of fork length for oocytenumber and diameter of opaque plus vitelogenic oocytes (potential fecundity; white bars), and ofvitelogenic oocytes (absolute fecundity; grey bars) and oocytes of batch fecundity (dark grey bars).Letters show significant differences (ANCOVA, Bonferroni post hoc tests) among sampling sites.3.3. Protein and Lipid ContentsSignificant differences in the percentages of proteins and lipids were found in tissuesduring the whole period studied (Table 4). Females showed maximum protein values in thegonads and maximum lipid values in the liver, while males presented highest protein andlipid values in the liver (Table 4). Comparisons between sexes revealed that females showedsignificantly higher protein and lipid percentages than males in the muscles (Student’st-test, t = −4.46, p < 0.001; t = −3.97, p < 0.001, respectively), while the percentage of proteinin the liver was higher in males (Student’s t-test, t = 4.51, p < 0.001). In the gonads, the ovaryprotein content was higher (Student’s t-test, t = −9.75, p < 0.001), but the testis showedhigher values of lipid content (Student’s t-test, t = 3.14, p = 0.005).Percentages of protein and lipid content in tissues showed significant differencesduring the reproductive cycle and the lipid–protein dynamic was different between sexes(Figure 6). The lipid percentage in the muscle decreases from quiescence to spawningstages in both sexes (ANOVA: female F(3, 106) = 11.26, p < 0.001; male F(3, 19) = 6.33, p = 0.008)73Water 2021, 13, 387(Figure 5). In the liver, protein percentages in females reached higher values at spawning(ANOVA F(3, 104) = 5.32, p = 0.002), but no differences were found in the percentages of lipidcontents in this tissue (Figure 6). No significant differences were found in the percentagesof protein and lipid contents in the liver in males during the reproductive cycle. Theprotein percentage in the ovary increased until the spawning stage (ANOVA F(3, 103) = 8.73,p < 0.001), but decreased in testis (ANOVA F(2, 19) = 9.670, p = 0.001), whereas no significantdifferences were found in gonadal lipids during the reproductive cycle (Figure 6).Table 4. Percentage of lipids and proteins in each tissue of Gobio lozanoi. All p-values in Kruskal–Wallis test are significant(<0.001).Muscle % Liver % Gonad % Kruskal–Wallisn Range Mean n Range Mean n Range Mean χ2Female Protein 110 4.18–11.10 6.95 108 4.10–15.14 9.69 107 0.28–25.59 9.99 86.016Lipid 108 0.40–1.35 0.73 105 0.52–18.79 6.94 109 1.05–9.09 3.00 265.237Male Protein 23 3.49–8.18 5.67 23 9.26–18.13 12.51 23 0.16–8.99 2.68 54.916Lipid 23 0.39–0.97 0.58 23 2.45–22.42 7.18 21 1.84–8.73 4.65 46.484n, Number of individuals, range of minimum and maximum values and Kruskal–Wallis test.Figure 6. Mean and ± IC 95% percentages of proteins and lipids in muscle, liver and gonads byreproductive stages. White bars represent female values and grey bars represent the male ones.The letters show significant differences (ANOVA, p < 0.05) among reproductive stages by post hoccomparison Tukey test. Capital letters for female data and lower case letters for male data.74Water 2021, 13, 3873.4. Fish Metrics RelationshipsFish conditions (SC and HC) and GSI were positively correlated with fecundity andthe percentage of ovary proteins. SC had a positive relationship with batch oocyte diam-eter only, whereas HC and GSI were positively correlated with potential, absolute andmaximum oocyte diameters (Table 5). Fish conditions and GSI were positively correlatedwith the percentage of ovary protein, whereas SC and HC had a negative relationship withthe percentage of muscle lipids. Moreover, the percentages of ovary proteins and lipidswere positively correlated with absolute fecundity and oocyte diameters (ØPOT, ØABS,ØBAT, ØMAX). Absolute oocyte diameter (ØABS) was positively related to the percentage ofproteins in the liver and negatively related to the percentage of muscle lipids (Table 5).Table 5. Correlation matrix of fish somatic condition (SC), hepatosomatic condition (HC), gonad activity (GSI), fecundity,oocyte size and proteins and lipids percentages by tissues in females of Gobio lozanoi. Spearman’s coefficient above thediagonal and p-values below the diagonal.SC HC IGS FecPOT FecABS FecBAT ØPOT ØABSSC - 0.713 ** 0.426 ** 0.491 ** 0.386 ** 0.749 ** 0.032 0.155HC <0.001 - 0.816 ** 0.415 ** 0.635 ** 0.755 ** 0.465 ** 0.472 **IGS <0.001 <0.001 - 0.267** 0.611 ** 0.748 ** 0.570 ** 0.470 **FecPOT <0.001 <0.001 0.005 - 0.695 ** 0.744 ** 0.079 0.136FecABS <0.001 <0.001 <0.001 <0.001 - 0.768 ** 0.732 ** 0.680 **FecBAT <0.001 <0.001 <0.001 <0.001 <0.001 - 0.015 0.058ØPOT 0.744 <0.001 <0.001 0.413 <0.001 0.924 - 0.925 **ØABS 0.138 <0.001 <0.001 0.192 <0.001 0.720 <0.001 -ØBAT 0.023 0.246 0.520 0.009 0.002 0.279 0.013 <0.001ØMAX 0.141 <0.001 <0.001 0.016 <0.001 0.184 <0.001 <0.001% PMUS 0.581 0.870 0.860 0.368 0.387 0.470 0.760 0.959% PLIV 0.888 0.695 0.675 0.006 0.216 0.050 0.460 0.036% POVA 0.015 <0.001 0.003 0.072 <0.001 0.739 <0.001 <0.001% LMUS 0.005 0.025 0.178 0.669 0.365 0.852 0.250 0.001% LLIV 0.139 0.754 0.303 0.099 0.104 −0.910 0.576 0.380% LOVA 0.707 0.847 0.533 0.450 0.020 0.813 0.003 <0.001ØBAT ØMAX % PMUS % PLIV % POVA % LMUS % LLIV % LOVASC 0.347 * 0.142 −0.054 0.014 0.234 * −0.267 ** −0.146 −0.037HC 0.186 0.556 ** 0.016 0.038 0.435 ** −0.215 * −0.031 −0.019IGS 0.103 0.627 ** −0.017 0.041 0.282 ** −0.130 −0.101 −0.060FecPOT 0.401 ** 0.228 * 0.087 −0.259 ** 0.173 0.041 0.161 0.073FecABS 0.466 ** 0.780 ** 0.088 −0.126 0.467 ** −0.092 0.167 0.233 *FecBAT 0.173 0.212 0.116 −0.308 * −0.054 −0.030 −0.018 −0.038ØPOT 0.383 * 0.874 ** −0.029 0.071 0.587 ** −0.111 0.055 0.565 **ØABS 0.570 ** 0.922 ** −0.005 0.216 * 0.682 ** −0.326 ** 0.093 0.661 **ØBAT - 0.896 ** −0.190 0.107 0.384 ** −0.234 −0.030 0.284ØMAX <0.001 - −0.034 0.054 0.605 ** −0.174 0.132 0.583 **% PMUS 0.234 0.724 - −0.070 −0.054 0.096 0.175 0.134% PLIV 0.507 0.574 0.466 - 0.119 −0.255 ** −0.279 ** 0.030% POVA 0.013 <0.001 0.574 0.217 - −0.213 * 0.143 0.368 **% LMUS 0.141 0.060 0.317 0.007 0.026 - 0.240* −0.083% LLIV 0.855 0.179 0.073 0.004 0.146 0.013 - 0.141% LOVA 0.025 0.001 0.162 0.753 <0.001 0.389 0.148 -FecPOT, potential fecundity; FecABS, absolute fecundity; FecBAT, batch fecundity; ØPOT, oocyte size at potential fecundity; ØABS, oocyte sizeat absolute fecundity; ØBAT, oocyte size at batch fecundity; ØMAX, maximum oocyte size; % PLIV, percentage of proteins in liver; % PMUS,percentage of proteins in muscle; % PGON, percentage of proteins in gonads; % LLIV, percentage of lipids in liver; % LMUS, percentage oflipids in muscle; % LGON, percentage of lipids in gonads; ** significance level of p < 0.01; * significance level of p < 0.05.4. DiscussionReproductive cycles of freshwater fish depend on a set of environmental factors andrheophilic fish, such as the target species, usually need flow requirements to activatemigration processes, gonadal maturation and spawning success [50]. Reproduction is75Water 2021, 13, 387related to stream flow, photoperiod and temperature cues [8,51] and there must be optimalconditions for all these variables to coincide in time for gonadal activation to begin. Similartemporal dynamics of the gonadosomatic index were found in another studied populationof G. lozanoi in an upper fluvial sector of the Segura River Basin [37]. However, in othernon-native populations of G. lozanoi located more to the north of the Iberian Peninsula,where environmental factors are different, shorter maturation and spawning periods wereobserved [35,36].During this study, several intraspecific differences in reproductive traits among pop-ulations inhabiting different hydrological sectors have been observed. Indeed, temporaldynamics of the gonadosomatic index showed two different patterns: one of them in-creased steeply, reaching a peak GSI value in April and May, just in the most upper sites,and the other one showed a slight GSI increase until June and July. The maturation delay,found mainly in SE2 and SE3 fluvial sectors, could be related to the lack of flow cues, suchas spring peak flows present in TUS and SE1. Hydrological sector SE2 did not show anyhigh flow peak during the year and SE3 is located right below the Cenajo dam which startsto release water in March, while other hydrological sectors showed high flows in earlyFebruary (Supplementary Material 1; Figure S1). Thus, the increase in flow stability orreduction in natural flow disturbances, together with an imbalance between temperaturesand flow peaks may be affecting the onset of the gonadal activation [8,52]. Furthermore, thespawning delay observed below the Cenajo dam (SE3) and the disruption of temperatureincrease (due to hypolimnetic cold water selective releases from the reservoir) may causegonadal regression or failed oocyte development [9], which could explain the lower GSIvalues observed in fish inhabiting this fluvial sector.Fish conditions (somatic and hepatosomatic) showed different patterns between sexes,suggesting that condition investment was not the same for both sexes during the reproduc-tive cycle. The SC patterns of both sexes were not observed in other non-native populationsof G. lozanoi in the Iberian Peninsula, which showed two peak values of somatic condi-tion at the beginning and at the end of spawning, with minimum values in October [35].Temporal dynamics of the HC of females were similar to the GSI pattern, increasing at thebeginning of the activation stage and decreasing at the end of spawning. In indeterminatebatch spawners such as G. lozanoi [35,36], oocyte recruitment is continuous during thespawning season and high liver mass (high HC) could confirm an intense liver activity forvitellogenesis, while in determinate batch spawners or total spawners the hepatosomaticactivity decreases during the reproductive cycle due to the completion of vitellogenesisprior to spawning [53,54]. Moreover, fish conditions showed differences among differentflow regimes during the reproductive cycle. In fact, only in the hydrological sector whereflow was constant all year round (SE2) was no significant variability observed for theseparameters. No drastic flow events and homogenization of flow conditions may favourthe stability of fish investment because fish inhabiting unstable environments with sea-sonal flash floods may require high levels of energy reserves, such as high somatic andhepatosomatic conditions; a high investment to increase reproductive success [8,55,56].On the other hand, the unnatural concurrence of very high flows during the reproductiveevent (maturation and spawning) implies energy redistribution in fish between survivalsin suboptimal environments and reproductive investment [7], which could explain thelower values of fish conditions and GSI in these hydrological sectors (SE3 and SE4).Previous studies of G. lozanoi in the Iberian peninsula observed maximum size rangesbetween 10.1 to 14.0 [23], whereas the population studied here showed a maximum length(FL) of 12.3 cm. The smallest individuals were observed in sampling sites located in themost natural flow conditions (TUS), and the largest ones were observed in SE4 and SE2.Larger maximum sizes of fish populations usually correlate with more stable environments,where abiotic fluctuations, such as flow peaks, are less significant and food sources are morereadily available, so lower mortality rates can be observed [57]. Furthermore, low-flowperiods, typical of dry summers in the Mediterranean basins, reduce food and habitat76Water 2021, 13, 387availability and may be affecting growth rates as has been observed in other Mediterraneancyprinids [8].Hydrologic conditions are, for sure, one of the main drivers responsible for shapingthe reproductive success and reproductive strategy used [6]. In this study, the analysedreproductive traits showed significant intraspecific variability which could be related toflow conditions at the basin scale. There were a higher number of females in all populations,apart from directly below the Cenajo dam (SE3). Thus, very high flows could be increasingfemale mortality rates after reproductive investment, which was higher than male invest-ment; this has been recorded in other non-native populations of G. lozanoi in the IberianPeninsula [36]. In addition, variability in sexual maturity is a compensatory populationresponse to different environmental factors [58,59]. Many studies relate high mortalityin populations to early maturation in order to compensate the decrease observed in thenumber of adults and maximize the egg production capacity [59,60]. In general, sexualmaturity of non-native populations of G. lozanoi in the Iberian Peninsula was reached at ayoung age and most of the individuals aged 2+ or greater than 7.0 cm in fork length weremature [35–37]. In this study, the shortest mature males were 3.55 cm (LF) and femaleswere 4.28 cm (LF), representing the smallest sizes found in the whole Peninsula. Highlydisturbed areas or extreme environmental conditions and unregulated flow conditions (nat-ural hydrological sectors in this study) are expected to be associated with early maturation,which is typical of opportunistic strategies [3,33]. However, in this study, a shorter lengthof first maturation was observed in flow regulated sectors, where natural disturbances arebuffered, although some other environmental perturbations may be acting as well.The studied population showed a lower absolute fecundity compared to other pop-ulations of G. lozanoi in the Iberian Peninsula [35,36]. In spite of this, oocyte diameters(between 0.84 and 0.92 mm) were larger than in other Iberian populations, which scatteredoocyte diameters of 0.76, 0.76 and 0.73 mm in the Matarraña, Moros and Ucero rivers,respectively [35,36]. These results could indicate that a trade-off between egg size andfecundity is established [58]. Furthermore, higher fecundity and larger oocytes were ob-served in more natural flow areas, while populations inhabiting more altered flows andwith reverse regimes showed a lower number and size of oocytes. Production of largeroocytes could be a compensatory strategy to produce larger larvae which will be moreresistant to low flow stress factors in dry summers, while the production of a high numberof eggs can ensure the survival of the species against the high mortality rates of eggsand larvae in areas with very variable and unpredictable flows [58,59]. On the one hand,the first hypothesis of this study suggested an opportunistic life-history strategy (smallsize, short longevity, early maturity, low fecundity, multiple spawnings per year and longreproductive periods) (sensu Winemiller and Rose, 1992 [33]) in more natural flow condi-tions, because this strategy is more associated with Iberian native and non-native speciesinhabiting unregulated Mediterranean rivers with strong seasonal flow patterns [61]. Onthe other hand, characteristics closer to a periodic life-history strategy (large body size,late maturation, high fecundity and a reduced spawning period) would be expected inmore predictable and hydrologically stable environments. However, later maturity anda higher number and size of oocytes were observed in more natural flow sectors; latermaturity, higher fecundity and small oocytes were found in the most stable flow sector,and populations with earlier maturity and lower number and size of oocytes were foundin hydrological sectors with reverse flow regimes. We hypothesised that our studiedpopulations would show intermediate characteristics between opportunistic and periodiclife-history strategies, as previously described [34]. The results were not conclusive enoughto establish strong correlations between the life strategy of G. lozanoi and flow conditions.Although reproductive strategies and reproductive success are directly related to hydro-logic conditions [6], the flow effect is probably not strong enough to drastically changethe reproductive strategy of populations of G. lozanoi that live in the same basin and mayshare the same genetic pool. The intraspecific variability observed in some reproductivetraits suggests a certain degree of effect caused by flow regimes, however, its plasticity77Water 2021, 13, 387allows the species to survive flow regulation events at several scales, as well as to resist thelong-term environmental stress typical of Mediterranean-type rivers [62].Percentages of lipid contents in liver and gonads were stable during the reproductive cyclein both sexes. The increase in protein contents in females’ livers at the beginning of reproductioncould suggest synthesis activity in the liver of the yolk and eggshell proteins which aretransported to the ovarian tissues for oocyte vitellogenesis and maturation [58,63]. Ovarianprotein content also increased during the reproductive cycle in response to the oocytedevelopment and maturation [64,65]. In females, during gonadal maturation ovarian andliver lipid contents did not vary and protein content increased in both tissues. This couldindicate that enough energy was available during the spawning to develop gonads andstore energy in the liver. Additionally, in the absence of other energy sources, feedingduring the reproductive season might provide energy for vitellogenesis [66–68]. Thereproduction investment of studied populations strongly depends on food availabilityand provides rapid transport from ecosystem productivity to reproduction, which mayallow continuous adjustments of the reproductive effort to food intake [15,69]. Thesefindings provide evidence of the energy intake of G. lozanoi during the reproductive seasonwhich is typical of income breeders. However, the decrease in lipid percentage content inmuscles during the reproductive cycle in females could be suggesting energy allocationto reproduction from muscle. The use of muscle tissue as an energy source has beendocumented for several fish, such as salmonids [70]. This pattern could suggest that G.lozanoi exhibits an intermediate strategy (income–capital breeding strategy) in which storedenergy in muscles is also required for reproduction [71,72].Higher fecundity and oocyte diameter were correlated with better somatic and hep-atosomatic condition, as well as gonadosomatic index, suggesting fish condition plays animportant role during recruitment and development of oocytes [73]. Moreover, proteinsand lipids of ovary tissue also showed a positive correlation with absolute fecundity andoocyte sizes supporting the fact that a greater mobilization of macronutrients to gonadsfavours recruitment and oocyte quality. Thus, food availability has an important influenceon reserves of protein and lipid in the tissues and there is a food-dependent variation instored lipid energy which affects the reproductive potential of individual fish [16,74].5. ConclusionsIn summary, flow conditions have an important effect on some reproductive traits ofG. lozanoi, reflected as intraspecific variations in the most of studied parameters. However,this intraspecific variability was not conclusive enough to classify populations either asopportunistic strategists in unpredictable flow sectors, or as periodic strategists in areasthat show stable flow regimes. Moreover, protein and lipid contents in tissues during thereproductive cycle provide some insight into the energy allocation during the reproductionof this species, which suggests that current food intake is the main—but not only—energysource for this species to reproduce, since they also use part of the energy supplementthey store. Hence, G. lozanoi can be classified as an income–capital breeder. Comparativestudies of reproductive traits and energy balance are a powerful approach to understandinglife-history trade-offs of species, and they may serve as excellent models for studies ofplasticity and adaptation of breeding strategies to new environmental conditions in exoticspecies. Further studies are needed to increase the knowledge about phenotypic plasticityin species that may be potential invaders since the lack of information could be hidingnegative effects on other species, as well as on the environment.Supplementary Materials: The following are available online at https://www.mdpi.com/2073-4441/13/3/387/s1. Supplementary Material 1: Flow Characterisation of Sampling Sites. Supplemen-tary Material 2: Immature-Mature Determination. Supplementary Material 3: ANCOVA Results.Supplementary Material 4: Picture of the target species.78Water 2021, 13, 387Author Contributions: F.A.-T. study design, data collection, data analysis and manuscript prepa-ration; M.T. resources, study design, manuscript preparation and review; D.G.-S. data collectionand data analysis; F.J.M.-L. resources and data analysis; F.J.O.-P. conceptualization, resources, studydesign, data collection, data analysis, manuscript preparation and review. All authors have read andagreed to the published version of the manuscript.Funding: Financial support was partially provided by LIFE+ Segura Riverlink (Project LIFE12ENV/1140). F.A.T. held a doctoral fellowship (FPU13/00235) from the Spanish Ministry of Education.Institutional Review Board Statement: Ethical review and approval were waived for this study, dueto the care and use of experimental animals complied with University of Murcia and Spanish Law32/2007 and RD 53/2013 animal welfare laws.Informed Consent Statement: Not applicable.Data Availability Statement: Data is contained within the article or supplementary material.Acknowledgments: The authors would like to thank J.M. Zamora, A. Zamora, J. Franco, A. Guerreroand A. Sánchez for their participation in fish collecting and E. Martínez (EMC I Traducciones) andCatherine Gutmann Roberts for English review. We also are grateful to L. Gabaldón and E. Aledo forhelping in the processing of sampling permits.Conflicts of Interest: The authors declare no conflict of interest. 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Wiegand, M.D. Composition, accumulation and utilization of yolk lipids in teleost fish. Rev. Fish Biol. Fish. 1996, 6, 259–286.[CrossRef]71. Zudaire, I.; Murua, H.; Grande, M.; Pernet, F.F.; Bodin, N.N. Accumulation and mobilization of lipids in relation to reproductionof yellowfin tuna (Thunnus albacares) in the Western Indian Ocean. Fish. Res. 2014, 160, 50–59. [CrossRef]72. Henderson, B.A.; Morgan, G.E. Maturation of walleye by age, size and surplus energy. J. Fish Biol. 2002, 61, 999–1011. [CrossRef]73. Henderson, B.A.; Trivedi, T.; Collins, N.C. Annual cycle of energy allocation to growth and reproduction of yellow perch. J. FishBiol. 2000, 57, 122–133. [CrossRef]74. Henderson, B.A.; Wong, J.L. Control of lake trout reproduction: Role of lipids. J. Fish Biol. 1998, 52, 1078–1082. [CrossRef]81waterArticleFish Rescue during Streamflow Intermittency MayNot Be Effective for Conservation of Rio GrandeSilvery MinnowThomas P. Archdeacon 1,* , Tracy A. Diver 1 and Justin K. Reale 21 U.S. Fish & Wildlife Service, New Mexico Fish & Wildlife Conservation Office, Albuquerque, NM 87131,USA; tracy_diver@fws.gov2 U.S. Army Corps of Engineers, Albuquerque District, Albuquerque, NM 87131, USA;justin.k.reale@usace.army.mil* Correspondence: thomas_archdeacon@fws.govReceived: 20 October 2020; Accepted: 28 November 2020; Published: 1 December 2020Abstract: Streamflow intermittency can reshape fish assemblages and present challenges to recoveryof imperiled species. During streamflow intermittency, fish can be subjected to a variety of stressors,including exposure to crowding, high water temperatures, and low dissolved oxygen, resulting insublethal effects or mortality. Rescue of fishes is often used as a conservation tool to mitigate thenegative impacts of streamflow intermittency. The effectiveness of such actions is rarely evaluated.Here, we use multi-year water quality data collected from isolated pools during rescue of Rio Grandesilvery minnow Hybognathus amarus, an endangered minnow. We examined seasonal and diel waterquality patterns to determine if fishes are exposed to sublethal and critical water temperatures ordissolved oxygen concentrations during streamflow intermittency. Further, we determined survivalof rescued Rio Grande silvery minnow for 3–5 weeks post-rescue. We found that isolated pooltemperatures were much warmer (>40 ◦C in some pools) compared to upstream perennial flows,and had larger diel fluctuations, >10 ◦C compared to ~5 ◦C, and many pools had critically lowdissolved oxygen concentrations. Survival of fish rescued from isolated pools during warmer monthswas <10%. Reactive conservation actions such as fish rescue are often costly, and in the case of RioGrande silvery minnow, likely ineffective. Effective conservation of fishes threatened by streamflowintermittency should focus on restoring natural flow regimes that restore the natural processes underwhich fishes evolved.Keywords: thermal stress; salvage; climate change; drought; hypoxia1. IntroductionIntermittency is a common and natural condition for many of the world’s streams [1]. However,many regions of the world are predicted to experience decreased precipitation, altering historicalstreamflow regimes [2]. Currently, climate change coupled with human-mediated water abstractionhas already increased the frequency of streamflow intermittency in some areas [3,4], threateningendemic fishes [5]. These species now face increasing water temperatures and risk of stranding inisolated pools [6–8]. Some stream temperatures are projected to exceed the thermal critical maxima formany fishes [9], which may lead to temperature-dependent mortality, population declines, or othersublethal effects [10–12]. Exposure to intermittent habitats and elevated water temperatures canreshape fish assemblages [13,14], which may result in shifts towards more homogenous assemblagesdominated by extremophile species [15]. Understanding the long-term consequences of drought onfish communities, including the effects of water temperature and streamflow intermittency, will likelybe an ever-increasing challenge for native fish conservation [16,17].83Water 2020, 12, 3371Streamflow intermittency can directly impact fish communities when historically perennial systemsbecome disconnected, forcing individuals to seek refuge in more contracted and often less hospitablehabitats [14,15,18]. The Middle Rio Grande (MRG) covers ~330 km through central New Mexico, USA(MRG; Figure 1). This reach is affected by seasonal and supra-seasonal drought that often results instreamflow intermittency. Historically, the MRG was a large, wide, and shallow river dominated bysandy substrates with considerable intra-annual variation in flows with peak runoff driven by springsnowmelt and low summer flows [19]. Historical accounts of streamflow intermittency prior to the1890s occurred, but intermittency was not observed annually [19]. Low-flows and intermittency becamemore common in the MRG in the 1900s as more water management infrastructure was constructedin New Mexico and Colorado [19]. Indeed, the MRG fish assemblage formerly included species likeshovelnose sturgeon Scaphirynchus platorhynchus and American eel Anguilla rostrata that would beintolerant of frequent drying [20]. More recently, frequent supra-seasonal drought [21], declines insnowpack [22,23], and human-mediated water abstraction of up to 95% [24,25] in the MRG Basin haveresulted in long periods of summer streamflow intermittency, e.g., >100 days and >80 km in extremeyears, averaging around 38 days and 35 km annually [18].Figure 1. Areas of streamflow intermittency (crosshatched areas) in the Middle Rio Grande Basin ofNew Mexico, where temperatures of isolated pools were collected from 2011 to 2016. Circles representmetropolitan areas, squares are surface flow diversion structures, and the triangle is a streamflow andwater quality gauging station.84Water 2020, 12, 3371The MRG is designated as a critical habitat for the only remaining wild population of theendangered Rio Grande silvery minnow Hybognathus amarus, a small-bodied member of the familyCyprinidae [26,27]. Rio Grande silvery minnow were once widespread throughout the mainstem RioGrande and Pecos Rivers, from north central New Mexico to the Texas coast, but now occurs onlyin the MRG [28,29]. Rio Grande silvery minnow exhibits an opportunistic life-history strategy [30].Fishes with this life-history strategy are typically small, short-lived, have high reproductive output,and maintain high demographic resilience [31]. Rio Grande silvery minnow have a relatively shortlifespan, with few living > 2 years [32]; they reach maturity in the first year of life [33] and haverelatively high fecundity for their body size [34]. Rio Grande silvery minnow spawn non-adhesive,neutrally buoyant ova directly into the water column [35], a mode of reproduction found in severalother cyprinid species in the Great Plains of the western United States [36]. For Rio Grande silveryminnow, annual reproductive success is highly variable and is tied to high flows in spring [18,33,37],likely through creation of nursery habitats for larvae and juveniles [38,39]. Rio Grande silvery minnowwere listed as endangered in 1994. Prior to Rio Grande silvery minnow being listed as endangered,four other species of minnow with similar reproductive biology were extirpated from the MRG duringthe 20th century [28].Rio Grande silvery minnow are directly impacted by intermittent streamflow. During periodsof river drying, Rio Grande silvery minnow are forced into isolated pools [37]. Compared toother intermittent streams in the American Great Plains region [15,40] and around the world [41],isolated pools in the MRG are shallow (<0.6 m) and short-lived, with only a small percentagepersisting > 4 days [37]. Globally, isolated pools can last for weeks to years and allow the persistenceof fishes and other organisms [42–44]. Isolated pools that support fish can be important for fishes thatinhabit seasonally intermittent streams by providing habitat patches that reduce mortality comparedto areas with no surface water [45]. However, in parts of the MRG, the river channel is perched aboveirrigation canals built for downstream water deliveries [46], which has resulted in rapid onset ofintermittent conditions and evaporation of isolated pools due to a lack of connection to groundwater.In the absence of fish rescue, stranding in isolated pools in the MRG would result in almost certainmortality of fishes during summer months [37]. Thus, immediate rescue of Rio Grande silvery minnow,i.e., collection from isolated pools and translocation to areas with perennial surface flow, is performedbefore complete desiccation of isolated pools occurs as a conservation action to mitigate for the negativeeffects of streamflow intermittency.Fish rescue and translocation are commonly used for mitigating the negative effects of strandingand streamflow intermittency [8,47,48]. However, rescue efforts are costly and rarely evaluated foreffectiveness [8]. Exposure to stressors prior to translocation, as well as capture and transport stress,may limit survival after rescue. Evaluation of rescue efforts has focused on salmonids and the economiccost–benefit of translocating stranded fish [49,50]. Benefits to the population, simulated or realized,require that rescued fish survive to reproduce [50]; however, this assumption is likely both speciesand season dependent. Within the MRG, rescue and translocation of Rio Grande silvery minnowstranded during streamflow intermittency has been used as a management action for conservationsince the early 2000s [18]. Fish are rescued and transported to areas with perennial flow each dayafter intermittency begins. However, the short-term survival of these fish after rescue and, ultimately,the effectiveness of rescue for conservation of the species is unknown. Many factors may decrease theeffectiveness of fish rescue in the MRG. Pools may experience extreme temperature or dissolved oxygenfluctuations during streamflow intermittency in summer months [51]. Even though fish are typicallyrescued and translocated within 24 h of being stranded, they are likely exposed to high, and possiblylethal, temperatures, hypoxic conditions, and other stressors, such as crowding and predation prior torescue. All of these factors may reduce the short-term survival of Rio Grande silvery minnow rescuedfrom isolated pools.Acute temperature and dissolved oxygen tolerances for larval, juvenile, and adult Rio Grandesilvery minnow have been quantified in a laboratory setting [52]. Temperature tolerance was determined85Water 2020, 12, 3371through the incipient lethal temperature technique [52]. This method requires an abrupt transfer offish to temperatures above or below the acclimation temperature, and lethal thresholds are determinedwhen 50% mortality occurs within a set timeframe [53–55]. Acute dissolved oxygen tolerance wasdetermined similarly [52]. For adult Rio Grande silvery minnow, the 50% lethal temperature thresholdwas 32.8 ◦C within a 24-h period; however, mortality began to occur at temperatures > 30 ◦C. Juvenileshad higher 50% lethal temperature thresholds of 36.7 ◦C, with mortality beginning at about 34.5 ◦Cwithin a 24-h period. For dissolved oxygen, juveniles experienced 50% mortality at 0.7 mg/L with nomortality at concentrations > 1.9 mg/L. For adults, these limits were 0.8 mg/L for 50% mortality and1.1 mg/L for no acute mortality. While these types of tests can be critical for defining critical limits,the effects of chronic exposure to sublethal or fluctuating water temperatures or hypoxic conditionsremains largely unknown. Chronic exposure to high, sublethal temperatures can affect metabolicrates [56], slow growth, and reproduction, and can alter behavior and increase stress and susceptibilityto disease in other fishes [12]. Chronic hypoxia can also cause stress, affect swimming performanceand metabolic functions [57], and result in transgenerational reproductive impairment [58]. Thus,understanding both the risk and effects of exposure to poor water quality may help managers determinethe value of fish rescue and improve our understanding of the effects of rising water temperatures onfish assemblages [16].Rescue efforts remain a major management strategy for Rio Grande silvery minnow duringperiods of streamflow intermittency and have been employed every year since 2009 [37]. In order toevaluate the severity of thermal conditions fish are exposed to prior to rescue, we used multi-yeardata collected during fish rescue to estimate hourly and seasonal changes in pool temperaturesduring streamflow intermittency. We compared this to the thermal regime of an upstream perennialreach. We also examined the number of Rio Grande silvery minnow exposed to elevated watertemperatures or hypoxic conditions prior to rescue to assess the extent to which individuals in thisendangered population are subjected to damaging water quality conditions. Finally, we estimatedpost-rescue survival of Rio Grande silvery minnow collected from isolated pools formed duringstreamflow intermittency as a preliminary evaluation of the effectiveness of fish rescue. Our resultswill help provide realistic estimates of the water quality conditions fishes are exposed to duringstreamflow intermittency, which will inform future studies examining lethal and sublethal effectsof water temperature and hypoxia. Further, our results will inform conservation actions aimed atmitigating the effects of streamflow intermittency and stranding.2. Materials and MethodsDuring irrigation season, March through November, surface water is diverted at multiple diversiondams within the MRG. During warm summer months, surface flow diversions can result in streamflowintermittency in areas below the Isleta and San Acacia Diversion dams (Figure 1). Typically, flows aredecreased over several days and constant bypass is held at a diversion dam. Intermittent sections expandover these days, then remain relatively constant until further streamflow diversion is needed, reducingflows, or precipitation increases flows. Intermittency occurs in a mid-reach pattern, with perennialareas upstream and downstream of drying [59]. The pattern of drying is due to upstream diversions,irrigation return flows, channel perching, and groundwater influences. Multiple drying and wettingcycles can occur annually due to monsoon rain events or irrigation demands [18]. Observations ofthe channel conditions are made daily during this period of time; crews are dispatched to rescue fishwhen new areas of isolated pools form. The amount of newly dewatered channel is variable, rangingfrom 0.1 km up to 8 km [37], resulting in up to 130 new isolated pools per day. Fish rescue occurredwithin one day of streamflow intermittency, usually the morning through afternoon after isolatedpools formed. During our study period from 2011 to 2016, we visited all isolated pools that occurreddue to streamflow diversions. We recorded counts of Rio Grande silvery minnow collected in eachpool, dissolved oxygen, temperature, maximum depth (0.1 m), date, and time of day. Fishes weresampled using 3.0 m by 1.0 m seines (3.2 mm mesh size). All Rio Grande silvery minnow collected86Water 2020, 12, 3371were transported in ~130-L fiberglass tanks mounted on all-terrain vehicles. We supplied pure oxygento transport tanks to maintain ~100% saturation. Fish were released the same day in nearby areas withsurface flows that were not expected to become intermittent. Depending on the number of pools thatformed, fish spent 1 to 6 h in transport tanks before being released.We measured water temperature (±0.01 ◦C) and dissolved oxygen (±0.01 mg L−1) of all pools witha multi-parameter probe (YSI 556 MPS; Yellow Springs Institute) where maximum depth was recordedat the time of fish rescue. We were not able to collect temperature or dissolved oxygen at every pooldue to equipment malfunction or data recording errors. We assumed isolated pools were thermallyunstratified because most were <0.60 m in depth [37]. Previous studies support this assumption:in 2007, continuous (e.g., 15-min intervals) water quality monitoring conducted in five isolated poolsover several days showed less than a 1 ◦C difference between surface temperature and temperatureabove the substrate [60].We set a range of water temperature criteria based on preliminary laboratory data for Rio Grandesilvery minnow [52] and similar species found in North America [55,61]. Based on these studies,we classified temperatures into four broad categories: <30 ◦C—no adverse effects, 30–33 ◦C—minimaladverse effects, including loss of equilibrium and muscle spasms, >33–36 ◦C—lethal for adults andsublethal for juveniles, and >36 ◦C—lethal for all age classes. These broad categories represent acontinuum of possible adverse effects, but chronic, sublethal effects of elevated water temperatureon Rio Grande silvery minnow are unknown. In order to place these effect thresholds into contextwith water temperatures experienced during flowing conditions, we compiled a 15-min intervaltemperature record over the same time period on the MRG at Alameda Gauging Station (Figure 1)from two locations c. 400 m from one another. Specifically, records collected by the University ofNew Mexico [62] and the U.S. Geological Survey (Gage No. 08329918) were combined to minimizetemporal gaps. This location is within the occupied Rio Grande silvery minnow critical habitat thatalso maintains perennial surface flow. Similarly, we defined broad categories for dissolved oxygen:>2.0 mg L−1—no adverse effects, 1.0–1.9 mg L−1—minimal mortality for adults but potentially lethalfor juveniles, and <1.0 mg L−1—potentially lethal for both adults and juveniles.We estimated average pool temperatures or stream temperatures by date and time of day from2011–2016. We used a linear mixed-effects model with a sinusoidal response. We used both firstand second harmonic sine and cosine terms for time of day (period = 24) and ordinal day of year(period = 365) to model the cyclical annual and diel temperature fluctuations. We treated these ascontinuous fixed effects and year as a random effect to predict the mean temperature of isolated poolsover time. We used the same model on each water temperature recorded at the Alameda GaugingStation and independently of isolated pools. Statistical models were run in the program R version 4.0.2using the package “lme4” [63,64].We also estimated post-rescue survival of Rio Grande silvery minnow. Rather than immediatelyreturning fish to perennial areas, we retained smaller samples of Rio Grande silvery minnow fromisolated pools and held them in sterile laboratory conditions for 5 to 7 weeks. Additional fish werecollected from areas with surface flow to serve as control groups (3 replicates, 311 total fish) andcompared to those collected in isolated pools (9 replicates, 2289 total fish) from March through August in2018 and June through August in 2020 (Table 1). Fish spent approximately one hour longer in transporttanks—compared to those released directly in areas with surface flows—while being transported backto laboratory aquaria. Fish were acclimated to water in aquaria by slowly replacing tank water withaquarium water over 15–45 min, which was similar to fish released in areas with surface flow. Fish wereheld indoors in a 5100-L indoor recirculating system consisting of seven individual fiberglass tanks,one of which functioned as a sump and contained filtration and aeration equipment to maintain oxygenlevels at 100% saturation. Each tank measured 180 × 75 × 60 cm. The system was filled with municipalwater passed through a reverse-osmosis filter. Water temperature in tanks was not continuouslymonitored and fluctuated with ambient indoor air temperatures. However, discrete temperaturemeasurements in holding tanks were between 19 and 23 ◦C. Water drained through a standpipe to a87Water 2020, 12, 3371sand filter and ultraviolet sterilizing filter. Flow to each tank was approximately 1000 L/h. This systemhas held a variety of fish species with minimal mortality of control groups [65,66]. We fed fish flakefood specifically designed for Rio Grande silvery minnow [67] twice daily to satiation, and excess foodand waste were siphoned from tanks twice per week for the duration of the experiment. We recordedmortality daily.Table 1. Dates, stream conditions, number, and survival of Rio Grande silvery minnow rescued in theMiddle Rio Grande, New Mexico.Date Conditions Number Survival26 March 2018 Continuous 102 76.424 April 2018 Continuous 67 1009 May 2018 Continuous 142 73.93 April 2018 Intermittent 250 24.410 April 2018 Intermittent 300 43.78 July 2018 Intermittent 226 2.64 August 2018 Intermittent 267 6.611 June 2020 Intermittent 45 8.912 June 2020 Intermittent 290 1.414 June 2020 Intermittent 101 8.914 July 2020 Intermittent 389 5.47 August 2020 Intermittent 421 5.03. ResultsWe sampled 7597 pools during the six-year study period. We measured temperature in 6780 poolsand dissolved oxygen in 5555 pools. Pool temperatures at the time of rescue ranged from 6.4 to 41.0 ◦C.Dissolved oxygen ranged from 0.01 to 21.35 mg L−1. Across all days, pools were hottest each dayfrom ~13:00 to 18:00 (Figure 2). Isolated pools rarely exceeded effect levels of >30 ◦C prior to 1000 h.The model results (Table A1) show that the estimated mean water temperatures began exceeding 30 ◦Cin mid-June and persisted through August (Figure 3), with the largest daily duration at or above thistemperature occurring in August. The highest mean pool temperatures occurred in August between~14:00 and 16:00 (Figure 3). Isolated pools had diel water temperature fluctuations of >10◦C, with thesepools cooling at a faster rate in evenings compared to rates of warming in the mornings (Figure 3).In the perennial flow reach, maximum estimated mean water temperatures were <28 ◦C and exhibiteddiel fluctuations of ~5 ◦C (Figure 3).Dissolved oxygen was also variable among pools. Generally, dissolved oxygen in the poolsdecreased as temperature increased. However, values above and below the temperature-dissolvedoxygen solubility curve [68] (Figure 2), suggesting other physical (e.g., groundwater contributions)and biological controls (i.e., gross primary productivity and ecosystem respiration), are influencing thedissolved oxygen among pools.From 2011 to 2016, we rescued 32,951 Rio Grande silvery minnow from pools where watertemperature was measured. The majority of fish were juveniles (63.9%). A similar number ofboth juveniles (N = 10,041) and adults (N = 10,251) were collected in pools that were <30 ◦C(5688 isolated pools), which represented the majority (61.6%) of rescued fish. We found asubstantial portion (10,895 juveniles, 1179 adults) of Rio Grande silvery minnow in pools abovethe ‘no adverse effects’ threshold of >30 ◦C (643 isolated pools). Of those, 351 isolated pools had watertemperatures > 33 ◦C and contained 85 juveniles and 311 adults. Ninety-nine isolated pools had lethalwater temperatures ≥ 36 ◦C and contained four juveniles and 74 adults.88Water 2020, 12, 3371 pools above the ‘no adverse effects’ threshold ≥Figure 2. Water temperature of isolated pools by time of day (A) and dissolved oxygen by watertemperature (B) that formed during streamflow intermittency in the Middle Rio Grande, New Mexico,June through October from 2011 to 2016. Individual pools are represented by gray dots andbox-and-whisker plots depict the median (bar), interquartile range (box), points within 1.5 timesthe interquartile range (whiskers), and outliers (black dots). The dashed trendline represents the effectof temperature on solubility of oxygen in freshwater (chlorine and salinity = 0 ppm) at 760 mm Hg [68].The majority of isolated pools (N = 5059) had dissolved oxygen concentrations ≥ 2.0 mg L−1and contained the majority of Rio Grande silvery minnow: 9960 juveniles and 3383 adults. Fewerisolated pools had dissolved oxygen concentrations between 1.0 and 2.0 mg L−1 (N = 272) and had364 juveniles and 1051 adults. Finally, we found 224 isolated pools with <1.0 mg L−1 of dissolvedoxygen and containing three juveniles and 323 adults.Survival of Rio Grande silvery minnow rescued from isolated pools was generally poor. Fishrescued during surface flow conditions early in the year had higher survival than those rescued duringstreamflow intermittency, ≥74% compared to <50% (Figure 4). Further, among groups rescued fromisolated pools, those collected earlier in the year had 3 to 20 times higher survival compared to thoserescued in June and later (Table 1).89Water 2020, 12, 3371≥ 2.0 mg L−−−≥Figure 3. Heat map showing estimated mean temperature of isolated pools (A) or areas with perennialflow (B) from June through October, 2011 to 2016 in the Middle Rio Grande, New Mexico, by time ofday and date, with cooler temperatures shown in blue.−−Figure 4. Daily cumulative survival of Rio Grande silvery minnow captured during surface flows(solid lines) or during streamflow intermittency (dashed lines) and held in captivity in 2018 and 2020.90Water 2020, 12, 33714. DiscussionAs expected, we found a substantial effect of time of day and ordinal day on pool temperature,with the hottest recorded water temperatures occurring in the late afternoon of June through August.Pool temperatures regularly exceeded levels found in an upstream section of the MRG that maintainscontinuous streamflow, suggesting that Rio Grande silvery minnow are exposed to unusually hightemperatures during streamflow intermittency. Indeed, the maximum water temperature we recordedfrom isolated pools was >10 ◦C higher than the maximum temperature recorded in the connectedupstream reach. Although these water temperature comparisons are somewhat confounded dueto their spatial separation, it is likely that Rio Grande silvery minnow would experience lower andmore stable water temperatures if continuous streamflow was maintained downstream. However,during the summer of 2018, diel fluctuations of >10 ◦C and values that regularly exceeded 30 ◦C wereobserved during low-flow but continuous conditions (~0.03 m3 s−1) on the MRG between 3 and 10 kmdownstream of San Acacia Diversion Dam [51]. Although continuous flows alone likely do not providethermal refuge, they can allow individuals the opportunity to seek refuge in upstream areas belowdiversion dams or near irrigation return outfalls, which may be cooler and have dissolved oxygenconcentrations > 2.0 mg L−1 [51], whereas the opportunity to do so during intermittency is eliminated.Although they are difficult to implement in a water-scarce basin, Rio Grande silvery minnow and otherfishes in the MRG would benefit from development of water-use strategies that enhance connectivityamong habitats during all seasons, allowing fish greater access to refuges during warm periods.Relatively few Rio Grande silvery minnow were exposed to lethal water temperatures at the timeof collection. Exposure to lethal water temperatures within isolated pools was likely limited becausethe majority of rescue efforts took place rapidly (within 24 h of drying) and during the early hours ofthe day before pool temperatures increased above critical thresholds. A slower response to new areasof drying or increased rates of channel drying would lead to more individuals being exposed to lethaltemperatures. While the acute effects of water temperature on mortality are relatively well understood,the effects of chronic exposure to sublethal temperatures and rapid fluctuations in temperature on fishesis largely unknown [12,55]. We found that a large number of rescued fish experience both sublethaltemperatures and relatively large temperature fluctuations. The effects of sublethal temperatureexposure on Rio Grande silvery minnow have not been evaluated, but laboratory studies conductedon the regionally similar endangered loach minnow (Tiaroga cobitis Family: Cyprinidae) showed thatsublethal static temperatures of 28 and 30 ◦C reduced growth rates compared to fish held at 25 ◦C [69].These reduced growth rates suggest that fish experienced chronic stress at these thermal thresholds.Although lethal temperature tolerances have been evaluated for Rio Grande silvery minnow at varyinglife stages, it is unknown how chronic temperature stress affects this species.The ability of an organism to respond to thermal change likely depends on the magnitude of thetemperature shift, the frequency of thermal change, and the ability of individuals to acclimate to constantor fluctuating diurnal cycles [70]. While it is common for streams to experience daily fluctuationsof ~4◦C [12,71,72], our model results show that, on average, isolated pool temperatures fluctuatedby >10 ◦C within a relatively short 8 to 12 h period. Our estimated diel fluxes are consistent withprevious studies where temperature loggers were deployed in isolated pools within the MRG [51,60].Growth rates in salmonids are reduced when subjected to sublethal, dynamic water temperatureregimes [72,73], though these have focused on much lower temperature changes than what Rio Grandesilvery minnow experience in isolated pools.The observed dissolved oxygen values within the pools can largely be attributed to temperaturecontrols of the solubility of oxygen in the water [68]. An inverse relationship between temperatureand dissolved oxygen has been observed during short-term (3 days) deployments of high-frequencysensors within isolated pools in the MRG [51]. The reduction of biological controls on dissolvedoxygen, via a reduction in rates of gross primary productivity and ecosystem respiration, has beenobserved in other rivers following pool isolation [74]. However, we observed that dissolved oxygenvalues elevated above the temperature–dissolved oxygen solubility curve indicate that inputs from91Water 2020, 12, 3371primary production [75] could be contributing, as considerable periphyton biomass was observedin a small number of pools during rescue and during summer low-flow conditions in the MRG [76].The dissolved oxygen concentrations observed below the temperature–dissolved oxygen solubilitycurve may be a result of deep groundwater inputs within the MRG [77–79], upwelling of hyporheicwaters [80], or high rates of heterotrophic metabolism within the hyporheic zone [81].Regardless of the mechanism controlling diel dissolved oxygen dynamics, we observedthat 496 pools that contained 367 juvenile and 1387 adult Rio Grande silvery minnow were<2.0 mg/L, whereas locations below diversion dams and irrigation return drains during low-flowconditions provided more suitable dissolved oxygen conditions for Rio Grande silvery minnow [51].Although periods of acute hypoxia have also been observed within the MRG during periodsof connectivity [51,82,83], periods of acute hypoxia in isolated pools have been observed ingroundwater-dominated headwater streams [15,84]. However, native cyprinid species with lowtolerance to hypoxia in the laboratory have been found to persist in isolated pools [15]. This variationin laboratory and field observations suggests that hypoxic conditions may not result in mortality ofnative species within isolated pools, but may favor non-native extremophile fishes [15]. Non-lethaleffects of short-term hypoxia include transgenerational reproductive impairments [58], swimmingperformance, and behavior [85,86]. As a result, a higher threshold for sub-lethal effects of hypoxia onfish is recommended [87], and we propose that it should be further evaluated and implemented forRio Grande silvery minnow.Despite relatively few Rio Grande silvery minnow being exposed to elevated water temperatures,survival of rescued fish was context dependent. Survival was low in the weeks following captureand transport to a laboratory setting for fishes rescued in June through August. The markedly higheroverall survival of Rio Grande silvery minnow collected under lotic conditions compared to isolatedpools and lower survival of fishes collected in summer months demonstrates the cumulative stressorsof confinement, temperature, and otherwise declining water quality conditions through the year duringstreamflow intermittency [51]. In both 2018 and 2020, almost no young-of-year fish were collecteddue to low spring runoff, resulting in failed recruitment [33]. Body condition, age, and season canall affect thermal tolerance in fish [88]. Possibly, adult fish are in poor condition after spawning andmay have died regardless of exposure to high water temperatures. Future research should focuson both improving survival after rescue and examining differential effects on survival of adults andyoung-of-year. We also stress that these are likely conservative estimates of post-rescue survival: Fishwere held in sterile, predator-free conditions, with optimal water quality, and were provided access tofood resources.As river drying occurs, it is likely that increased temperatures and reduced flows limit the abilityof individuals to seek thermal refuge. Thus, it is likely that Rio Grande silvery minnow are exposedto lethal and sublethal temperatures for several days prior to stream intermittency and could bephysiologically compromised before being rescued [51]. Handling and transport alone are significantstressors on healthy Rio Grande silvery minnow [89]. During rescue, mitigating stress to increaseoverall survival would prove difficult to implement. Fish transported in live wells mounted on theback of all-terrain vehicles cannot reasonably be acclimated to the broad daily temperature rangefrom which Rio Grande silvery minnow are collected. The inability to properly acclimate fish duringrescue along with additional handling stress may further limit the ability of individuals to surviveonce translocated. Water in holding tanks is generally around 23 ◦C early in the day and increasesto about 27 ◦C at the time of release. Given the large number of pools that can be encountered daily(>100) and the long distances covered (>10 km), acclimating fish from individual pools would notbe feasible. Rescuing a smaller number of Rio Grande silvery minnow per day with the intentionof increasing post-rescue survival may be possible. However, this may be ineffective, as pool sizedoes not necessarily predict numbers of stranded Rio Grande silvery minnow [37]. Rescuing fishprior to drying, while increasing survival, is also more difficult, as sampling efficiency is much lowercompared to collecting from isolated pools. Further, this period of time overlaps with Rio Grande92Water 2020, 12, 3371silvery minnow spawning [33]. Handling of fish during that time could not only affect their abilityto spawn but would not likely prevent their offspring from being stranded in isolated pools, as theyproduce neutrally-buoyant, non-adhesive eggs that passively disperse downstream [35,90].Given that Rio Grande silvery minnow are a relatively short-lived species that rarely live longerthan two years in the wild, the effects of streamflow intermittency on survival and growth warrantconcern. Immediate effects of river drying include direct or indirect mortality, which could reducepopulation size. Further, little is known about the effects of chronic stress, including sublethal stress andits effects on reproduction. Increased mortality of young life stages or reproductive impairment couldreduce demographic resiliency and eliminate reproductive contributions in subsequent years. Coupledwith a 90% reduction in geographic range and fragmented habitats in the remaining population [26,90],streamflow intermittency further hinders recovery of Rio Grande silvery minnow, an outcome likelyto be shared by many similar species with opportunistic life histories unless effective conservationactions are implemented [91,92].Recovery of Rio Grande silvery minnow and other imperiled freshwater fishes will dependon proactive actions as opposed to reactive actions. Our results show that only a small portion offishes rescued from isolated pools survive in the short term and that rescue is likely ineffective atmitigating the negative effects of streamflow intermittency on the population over the long term.For Rio Grande silvery minnow, rescue and translocation represent a conservation trap, in whichconcerted conservation efforts to offset species’ declines result in actions that are perpetuated andunsustainable in the long term [93]. While fish rescue and translocation may be useful in dire conditions,their use as a regular conservation action to offset widespread, frequent streamflow intermittency hashindered recovery of Rio Grande silvery minnow. Rather than reactively rescuing stranded fish duringintermittency, conservation actions need to examine ways to limit the ultimate causes of streamflowintermittency in order to achieve recovery goals. Such proactive efforts face multiple challenges in theMRG, chiefly declining precipitation [23,94] and surface flows [25,95] coupled with over-appropriationof water [96]. However, proactive actions are likely more effective than reactive actions. Proactiveconservation actions may be less expensive over the long term and may prevent “conservation-reliantspecies” [93,97,98].Climate change and increased human demands for freshwater will likely increase the frequency,duration, and spatial extent of streamflow intermittency, resulting in more fish stranding, greaterfragmentation of habitats, and higher water temperatures. It is possible that similar rescue efforts willbecome more common in other systems. Thus, the effectiveness of rescue efforts must be evaluated.Population simulations may prove useful for modeling the overall population-level impact of fishrescue; however, the effect is likely minimal unless post-rescue survival can be increased. Arguably,the most prudent management action should be to maintain a known level of base flow that maintainssuitable water quality, which, in turn, supports the survival of fishes and does not induce unnecessarystress. Recovery of threatened fishes will be difficult in the absence of proactive conservation effortsdesigned to promote self-sustaining populations.93Water 2020, 12, 3371Appendix ATable A1. Model parameters relating temperature of isolated pools or stream temperatures in aperennial section of the Rio Grande, New Mexico, to time of day and day of year in a linearizedsinusoidal mixed-effects model.Model Parameter Estimate SE t-ValueIsolate PoolsIntercept 16.9809 1.2183 13.938sine time −5.1871 0.3277 −15.83cosine time −8.495 0.8906 −9.539sine time (2nd harmonic) 1.2147 0.2123 5.722cosine time (2nd harmonic) −1.537 0.3304 −4.652sine day 6.1214 1.0853 5.64cosine day −5.3918 1.1174 −4.825sine day (2nd harmonic) 4.4314 0.5364 8.262cosine day (2nd harmonic) −1.926 0.1974 −9.758Perennial FlowIntercept 14.17188 0.103947 136.34sine time −1.55107 0.00554 −279.97cosine time −0.55226 0.005598 −98.65sine time (2nd harmonic) 0.479119 0.005553 86.29cosine time (2nd harmonic) 0.169554 0.005589 30.34sine day −3.68166 0.005618 −655.37cosine day −9.53207 0.005798 −1643.96sine day (2nd harmonic) 1.024666 0.005507 186.05cosine day (2nd harmonic) −0.83944 0.005737 −146.32Author Contributions: Conceptualization, T.P.A. and J.K.R.; Methodology, T.P.A.; Software, T.P.A.; Validation,T.P.A., T.A.D., and J.K.R.; Formal Analysis, T.P.A.; Investigation, T.P.A., T.A.D., and J.K.R.; Resources, T.P.A.; DataCuration, T.P.A.; Writing—Original Draft Preparation, T.P.A.; Writing—Review and Editing, T.A.D. and J.K.R.;Visualization, T.P.A.; Supervision, T.P.A.; Project Administration, T.P.A.; Funding Acquisition, T.P.A. and J.K.R. Allauthors have read and agreed to the published version of the manuscript.Funding: This research and APC was funded by the U.S. Bureau of Reclamation and U.S. Army Corps of Engineers.Acknowledgments: We are grateful to W. Jason Remshardt, Tristan Austring, Judith Barkstedt, Matthew Nolan,Kjetil Henderson, and Rebecca Cook for their assistance with field efforts and data collection. We thank NickWhiterod, Martinique Chavez, and two anonymous reviewers for providing constructive reviews that improvedthis manuscript. All fish were handled under U.S. Fish and Wildlife Service permit TE-676811-11 and NewMexico Game and Fish Department permit 1776. The views presented here reflect those of the authors and do notnecessarily reflect the views of the U.S. Fish and Wildlife Service or the U.S. Army Corps of Engineers.Conflicts of Interest: The authors declare no conflict of interest.Data Availability Statement: Data used in this manuscript are available at http://dx.doi.org/10.17632/bz3dvhf95s.1.References1. Acuña, V.; Datry, T.; Marshall, J.; Barceló, D.; Dahm, C.N.; Ginebrada, A.; McGregor, G.; Sabater, S.; Tockner, K.;Palmer, M.A. Why should we care about intermittent waterways? Science 2014, 343, 1080–1081. [CrossRef][PubMed]2. Milly, P.; Dunne, K.; Vecchia, A. Global pattern of trends in streamflow and water availability in a changingclimate. Nature 2005, 438, 347–350. [CrossRef] [PubMed]3. Falke, J.A.; Fausch, K.D.; Magelky, R.; Aldred, A.; Durnford, D.S.; Riley, L.K.; Oad, R. The role of groundwaterpumping and drought in shaping ecological futures for stream fishes in a dryland river basin of the westernGreat Plains, USA. 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This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).99waterArticleFactors Influencing Abundances and Population SizeStructure of the Threatened and EndemicCyprinodont Aphanius iberus in MediterraneanBrackish PondsSerena Sgarzi 1,* , Sandra Brucet 1,2,*, Mireia Bartrons 1 , Ignasi Arranz 1,3, Lluís Benejam 1 andAnna Badosa 11 Aquatic Ecology Group, University of Vic—Central University of Catalonia, 08500 Barcelona, Spain;mireia.bartrons@uvic.cat (M.B.); ignasi.arranz-urgell@univ-tlse3.fr (I.A.); lluis.benejam@uvic.cat (L.B.);anna.badosa@uvic.cat (A.B.)2 Catalan Institution for Research and Advanced Studies (ICREA), 08010 Barcelona, Spain3 Laboratoire Evolution et Diversité Biologique (EDB UMR 5174), Université de Tolouse, CNRS, IRD, UPS,118 Route de Narbonne, F-31062 Tolouse, France* Correspondence: serena.sgarzi@uvic.cat (S.S.); sandra.brucet@uvic.cat (S.B.);Tel.: +34-93-881-5519 (S.S. & S.B.)Received: 29 October 2020; Accepted: 17 November 2020; Published: 21 November 2020Abstract: Aphanius iberus is an endemic cyprinodontoid fish species of Mediterranean ponds indanger of extinction. In this study, we studied some abiotic and biotic factors that can influenceA. iberus’s size structure and density in Mediterranean brackish ponds. We sampled fish using fykenets in 10 ponds of Empordà (Spain) during the spring season. Our results showed that a betterecological status (according to the Water Quality of Lentic and Shallow Ecosystems (QAELS) index),pond’s depth and pond’s isolation (reflected by an increase in total nitrogen) were related to largerindividual sizes and more size-diverse populations. Increasing the salinity is known to help theeuryhaline A. iberus acting as a refuge from competitors. Nevertheless, our results showed that higherconductivities had a negative effect on A. iberus’s size structure, leading to a decrease in the mean andmaximum size of the fish. Fish abundance (expressed as captures per unit of effort (CPUE)) seemedto increase with increasing the pond’s depth and total nitrogen (the latter reflecting pond isolation).In conclusion, our results suggest that achieving a better pond ecological status may be important forthe conservation of endangered A. iberus, because better size-structured populations (i.e., larger meanand average lengths) were found at higher water quality conditions.Keywords: Mediterranean ponds; fish; Aphanius iberus; size structure; ecological status1. IntroductionAquatic ecosystems of the Iberian Peninsula are a hotspot for endemic freshwater fish fauna; still,most of the fish species are critically threatened by habitat destruction, intensive agricultural activitiesor the introduction of exotic species [1,2]. The Spanish toothcarp (Aphanius iberus, Valenciennes, 1846) isa small cyprinodont (up to 6 cm in length) endemic from the Eastern Mediterranean lowland waters ofthe Iberian Peninsula [3–5] and in danger of extinction [6,7]. As other cyprinodonts, it is characterizedby fast growth, early maturity, high reproductive effort and multiple spawnings [8,9], which implies ashort longevity (age up to two+). A. iberus is an eurytherm and euryhaline species, well-adapted tochanges in environmental conditions [10] such as sudden alterations in temperature and salinity dueto marine intrusions or freshwater floodings [11]. This cyprinodont originally inhabited a wide range101Water 2020, 12, 3264of lowland waterbodies, but now, its geographical distribution is limited to brackish and hypersalinecoastal waterbodies [1,5,8,12] due to habitat degradation (e.g., intensive agriculture, water pollutionand wetland desiccation) and the introduction of invasive species, which usually act in an additivemanner, since habitat degradation facilitates biological invasions [13,14]. The high degree of isolationamong the remaining populations also poses a threat to their conservation, as they show higher ratesof extinction than populations in well-connected locations [5].Some studies have shown that the abundance and size structure of A. iberus depends on theecological status of the ponds, with larger individuals and higher densities found in ponds with ahigher water quality [15]. Indeed, in Italy, another species of the Aphanius genus (Aphanius fasciatus)has been proposed as an indicator of the ecological status of salt marshes [16], suggesting that thosefish are sensitive to changes in the ecological status of their environments. More confined and lessaccessible ponds also seem to host populations of A. iberus more abundant and stable over time [17].Another variable that may influence the density and size structure of A. iberus is the pond morphometry(area and depth), because it has a strong impact on the structural complexity and niche availability,as has been found for other fish species and communities [18–20]. However, to our knowledge, therehave been not studies in this respect.The Eastern mosquitofish (Gambusia holbrooki) [21,22] is an invasive species that very often interactswith A. iberus, because both share similar habitats [23] and compete for the same resources [24,25].Both fish species are zooplanktivorous, but G. holbrooki consumes mainly cladocerans, ostracods andcopepods [26,27], and A. iberus prefers harpacticoid copepods, copepod nauplii and detritus [24].Sometimes, G. holbrooki can act in an aggressive way against A. iberus, and this behavior seems to beinversely proportional to the salinity, as well as its ability to capture prey [28]. Young individuals ofA. iberus have been found to capture less prey in the presence of conspecific adults and G. holbrooki,suggesting both strong intraspecific and interspecific competition [12,29]. Currently, A. iberus hasdisappeared from fresh and oligohaline waters, and its habitat is restricted to salt marshes, coastallagoons and river mouths [24,30], where the invasion success of the mosquitofish is limited due to thehigh salinity fluctuations [24,31,32]. Nevertheless, although A. iberus tolerates high salinity conditions,its metabolism may be affected when the salinity levels in the location are high [33–35]. Physiologicalfunctions such as oxygen consumption, critical swimming speed and routine activity level show ageneral decrease at the extreme salinity in Aphanius dispar [36], although the spawning efficiency seemsnot to be significantly affected by the changes in salinity [37].Identifying the key factors that influence the population structure of A. iberus is relevant todevelop efficient conservation and management plans for this endangered species. The body size ofA. iberus has been used to assess growth-related parameters, such as age [8], fecundity and sexualmaturity [38], as well as ammonia excretion rates [39]. Although, in Mediterranean brackish ponds,trophic interactions are very often body size-dependent [40–42], studies about the size structure ofA. iberus and the factors that determine it are scarce.The present study aims to identify the factors influencing the abundances and population sizestructure of A. iberus in the north-east of the Iberian Peninsula in late-spring (i.e., when this speciesfinishes the first period of annual reproduction). Specifically, we assessed whether abiotic factors(i.e., conductivity, nutrient concentrations and pond morphology); the ponds’ ecological status; foodresource availability (zooplankton biomass) and the presence of the main competitor, Gambusia holbrooki,are correlated with the size structure and abundance of this endangered species in 10 coastal brackishand hypersaline ponds. We assessed the size structure using the size diversity index [43], in additionto several size metrics, such as the maximum size, mean size and size range.We hypothesized that a good ecological status, together with a larger pond dimension (depthand area), would increase the possibilities to find well size-structured populations with a higher sizediversity, as well as higher densities of fish, as these two factors are supposed to set good conditionsfor the fish growth. Concerning conductivity, we expected a decrease in the size-related variables ofA. iberus, because the high conductivity negatively affects its metabolism. In contrast, locations at102Water 2020, 12, 3264higher conductivity levels could host higher A. iberus densities, because high conductivity may preventthe colonization of invasive species, such as G. holbrooki. We also hypothesized that the presence ofG. holbrooki (main competitor of A. iberus) would lead to lower densities of A. iberus, as G. holbrooki havebeen observed to outcompete A. iberus [30]. Finally, we would expect that the abundance of A. iberuswould be negatively correlated with the zooplankton biomass due to fish predation on zooplankton.2. Materials and Methods2.1. Study AreaThe studied ponds are located in two protected areas of the Empordà coastal wetlands (Figure 1),between 42◦01′42” N–3◦11′18” E and 42◦15′58” N–3◦08′17” E of the Ter River Basin (NE IberianPeninsula). Eight of the ten ponds were located in the “Aiguamolls de l’Empordà” Natural Park,and two were located south in the “El Montgrí, Illes Medes i el Baix Ter” Natural Park (some examplesof individual ponds are shown in Figure S1. The climate is Mediterranean, with hot, dry summersand mild, wet winters. The hydrology of these Mediterranean coastal wetlands is characterized bya prolonged confinement period during warm seasons without water inputs, followed by irregularflooding events (i.e., rainfall or marine intrusions during sea storms, the latter no more often than twicea year) [44]. Hydrological connections among ponds and/or to rivers and the sea take place only duringsuch irregular flooding events [45]. The studied ponds are characterized by their shallowness andcover a range of morphometry (e.g., pond area and depth) and conductivity [46]. Concentrations ofinorganic nutrients (nitrates and phosphates) in late-spring are low due to the scarceness of waterinputs, but concentrations of total nutrients, especially total nitrogen, are high due to a concentrationeffect produced by a high evaporation rate [44,45,47]. In these confined coastal environments, nitrogen,rather than phosphorous, usually limits the primary production [41,48,49]. For more details about theenvironmental characteristics and planktonic composition of the studied ponds, see [46]. −−≥CBAABFranceSpain10 km500 m1 kmMediterranean seaFigure 1. Location of the study region: Empordà coastal wetlands (Ter River Basin in the NE of theIberian Peninsula) (C), with the detailed geographical position on the studied ponds in red (A,B).Eight of the studied ponds were located in the “Aiguamolls de l’Empordà” Natural Park (A), and twowere located south in the “El Montgrí, Illes Medes i el Baix Ter” Natural Park (B). This map wasproduced with the online software ArcGIS (version 10.5.1, 2017, ESRI Environmental System ResearchInstitute, Redlands, CA, USA) (https://www.arcgis.com).103Water 2020, 12, 3264Fish community in the studied ponds is mainly composed by Aphanius iberus and the invasivefish species Gambusia holbrooki [46]. Whereas the former is, overall, more abundant in ponds of highersalinity, the latter is more abundant in oligohaline ponds [46]. The rest of the community is composedby the Atherina boyeri (Risso, 1810), Pomatoschistus sp. (Gill, 1863), Mugil cephalus (Linnaeus, 1758),the marine fish Solea solea (Queusel, 1806), Anguila anguila (Linnaeus, 1758) and the invasive Lepomisgibbosus (Linnaeus, 1758), although the last three species are very scarce (see [46] for more details onthe relative abundance of the fish species).2.2. Field Sampling and AnalysisThe 10 ponds studied were sampled once during the end of the spring season (from May to earlyJune 2016). Conductivity (mS·cm−1) was measured using a multiparameter probe (Hanna Instruments,Woonsocket, RI, USA). Total area (m2) of each pond was estimated by using the “Google MapsArea Calculator Tool” [50] (reference 58, old paper), while the mean water column depth (cm) wascalculated from in situ repeated measures obtained with a two-meter rule. Total nitrogen (mg·L−1) wasmeasured according to Koroleff, 1973 [51], adapted by Seal Analytical to an integrated system of a CFA(Continuous Flow Analysis) digester.Two ecological indices related to the pond’s ecological status were used in each pond: (1) theECELS (Conservation Status of Lentic and Shallow Ecosystems) estimates the conservation status oflentic ecosystems based on morphological aspects, type of aquatic vegetation and human impacts [52].The ECELS categories range from bad (0–30 out of 100), deficient (30–50 out of 100), mediocre (50–70 outof 100), good (70–90 out of 100) and very good (90–100 out of 100), and (2) the QAELS (Water Qualityof Lentic and Shallow Ecosystems) index evaluates the water quality based on the compositionof microcrustacean assemblages and taxonomic richness of aquatic insects and crustaceans in theMediterranean wetlands [53]. The QAELS categories range from bad (<0.46), deficient (0.46–0.55),mediocre (0.55–0.62), good (0.62–0.72) and very good (≥0.72). The QAELS index was calculated afterthe observation of macroinvertebrate samples under optic microscope and a stereoscope. Samples wereobtained through a dip net (mesh size 250 µm) following standard protocols [53].Zooplankton samples were taken from each pond by mixing subsamples from five differentsites in order to overcome the expected patchy distribution of plankton. Five liters of mixed watersamples were filtered through a 50-µm mesh size net and preserved in 4% Lugol’s acid solution.Zooplankton individuals collected (including rotifers, copepods and cladocerans) were counted,identified and measured using a stereoscope and an inverted microscope (Utermöhl method), as wasdescribed in [46]. To estimate the zooplankton biomass, the total length (µm) of the first 100 individuals(when possible) was measured assuming that all individuals were equally distributed in the observedsample. Individual biomasses were then calculated using approximation to shape formulas.Fish were caught by fyke nets set for 24 h, a common and widely used method in coastallagoons [17,54,55]. Fyke nets consisted of a semicircular entrance ring followed by three smallercircular rings surrounded by a net (3.5-mm mesh) with two consecutive funnels (120 mm of funneldiameter, 1050 cm2 of interception area, 98 cm of length, 30 cm of height and 95 cm of wing length).The total number of fyke nets set in each pond varied according to its area and depth (Table S1). A totalof 49 fyke nets were set in all the ponds. All captured fish were sexed (except juveniles < 13 mm),measured for total length (mm) and released. We measured all the individuals of Aphanius iberus ineach sample, in order to minimize the error estimation.2.3. Aphanius iberus Abundance and Size StructureIn each pond, the Aphanius iberus abundance was calculated by dividing the total captures bythe number of fyke nets set in each pond (captures per unit of effort; CPUE). The size structure ofA. iberus in each fyke net was assessed using four size-based metrics: (1) the maximum size, (2) meansize (computed as the geometric mean), (3) size range (the difference between the maximum and theminimum size) and (4) size diversity index (µ). For each fyke net, the size diversity was calculated104Water 2020, 12, 3264using individual size (i.e., length) measurements, as proposed by [43]. Size diversity is based on theShannon–Wiener diversity index [56] adapted for a continuous variable, such as body size. This indexis the continuous analog of the taxonomic Shannon diversity index, and it produces values in a similarrange to those of the Shannon index. In our case, it integrates the amplitude of the length range andrelative abundance of the different lengths. Thus, the high values of the size diversity would indicate ahigh diversity of sizes with an equitable numerical frequency of sizes along the distributions [57,58].In contrast, the low values of size diversity (rarely taking negative values) would indicate a lowdiversity of fish sizes with an inequitable numerical frequency of sizes along the distribution [43].2.4. Data AnalysisWe used mixed linear models (MLMs) to test the effects of abiotic and biotic factors on theabundance and size metrics of A. iberus. We considered captures of each fyke net as an observation unit(N = 49), and “pond” was introduced as a random effect to deal with pseudoreplication. As predictorvariables, we considered conductivity, pond area and mean water depth, total nitrogen, ECELSand QAELS indexes (as estimates of ecological status) and zooplankton biomass (as food resourceavailability). Pearson’s r index revealed correlations among some of those variables. We appliedBonferroni correction to counteract the multiple comparisons issue and, finally, removed the variablesthat were highly correlated (>0.6). As G. holbrooki was not present in all the ponds (it was absentin 7 ponds out of 10), we could not include its abundance in our MLMs. Instead, we performed anANOVA, comparing the situations “with” and “without” the presence of G. holbrooki for the abundanceand for each of the size-related variables of A. iberus considered in this study.We looked for the most parsimonious model from the full models by using a stepwise (backward)selection. The most parsimonious model was chosen using the Akaike information criteria (lowestAIC), which represents the best at explaining the data with the lowest combination of variables. We alsocalculated the standardized (beta) coefficients for the significant predictors included in the best modelsby using the R package “QuantPsyc” version 1.5 [59]. Predictors were previously checked for normalityand homogeneity of variance, and, if variables did not meet the assumptions, base 10 logarithmictransformations were applied. Additionally, a visual inspection of the residual plots was done todetect any violation of the regression assumptions. In order to improve homoscedasticity, we usedthe function “varPower” of the package “nlme” [60]. For the creation of the boxplots, we used the“ggplot2” package [61]. All analyses were done with the software R version 3.4.2 (R core Team, 2017,Boston, MA, USA).3. Results3.1. Description of the Local Characteristics in the Mediterranean PondsThere were wide ranges of environmental and ecological conditions across the study area (Table 1).Abiotic and biotic factors measured, as well as ECELS and QAELS indexes of ecological status,are shown in Table 1. The 10 studied ponds during spring showed conductivity values rangingfrom 10.7 mS·cm−1 to 69.10 mS·cm−1 and mean water column depths ranging from 16 cm up to150 cm. The ponds differed quite a lot in their areas, with values ranging from 147.90 m2 to 68,150 m2.Total nitrogen, which includes organic and inorganic nitrogen compounds, showed the lowest valueat 55.58 µmol·L−1 and the highest value at 234.40 µmol·L−1. The zooplankton biomass ranged from1.13 µg·L−1 up to 4840.48 µg·L−1. Regarding the ponds’ ecological status, ECELS index values rangedfrom 43 to 98, indicating a “bad” and “mediocre” status, respectively. Whereas the QAELS indexranged from 0.25 to 0.56, also indicating a “bad” and a “mediocre” status, respectively (Table S1). As anaverage, ponds showed a “deficient” status according to the mean index value.105Water 2020, 12, 3264Table 1. Mean, standard deviation (SD), minimum and maximum values of the abiotic and bioticfactors and ecological status indexes measured in the study ponds (N = 10). ECELS: ConservationStatus of Lentic and Shallow Ecosystems and QAELS: Water Quality of Lentic and Shallow Ecosystems.Mean SD Minimum MaximumMean water column depth (cm) 59.30 41.93 16.00 150.00Pond area (m2) 10,873.60 20,463.50 147.90 68,150.00Conductivity (mS·cm−1) 46.14 17.63 10.07 69.10Total nitrogen (µmol·L−1) 92.01 51.63 58.55 234.40Zooplakton biomass (µg·L−1) 498.17 1313.92 1.13 4840.48ECELS index 77.00 18.70 43.00 98.00QAELSe 2010 index 0.47 0.12 0.25 0.563.2. Variation of the Population Structure of A. iberus across Mediterranean PondsThe mean, standard deviation and minimum and maximum values calculated for the differentsize metrics of A. iberus, as well as the abundance (expressed as CPUE), are shown in Table 2.Maximum length values ranged from 19 mm to 54 mm, while the mean length had a minimum of16 mm and a maximum of 41 mm. This last value coincided with the maximum value of the lengthrange, whereas the minimum length range value was 6 mm. Size diversity showed a wide range ofvalues, from 0.27 to 2.34. A. iberus abundance (in CPUE) also varied largely among fyke nets, from twoindividuals to 525 individuals.Table 2. A. iberus size metrics and abundance (CPUE) obtained/computed per each sample (N = 49).The descriptive statistics are the mean, standard deviation (SD), minimum and maximum.Mean SD Minimum MaximumAphanius iberus maximum length (mm) 41.08 8.06 19.00 54.00Aphanius iberus mean length (mm) 29.21 5.36 16.00 41.00Aphanius iberus length range (mm) 21.67 7.38 6.00 41.00Aphanius iberus size diversity 1.15 0.36 0.27 2.34Aphanius iberus capture per effort unit (CPUE) 59.49 91.68 2.00 525.003.3. Main Drivers Affecting the Fish Population Size Structure and DensityThe MLMs identified the most important drivers influencing the fish population size structureand density across all ponds. The most parsimonious significant models for each fish metric mentionedabove (Table 2) as dependent variables are shown in Table 3. The results showed that the maximumlength was negatively related to the conductivity and zooplankton biomass but positively related tothe total nitrogen and QAELS index. The zooplankton biomass was the predictor with the strongesteffect on the maximum length of A. iberus (Table 3).Concerning the mean length of the fish, the MLM model showed similar results as whenconsidering the maximum length of A. iberus as a response variable. The mean length of A. iberussignificantly decreased with the increasing conductivity and zooplankton biomass but showed apositive relation with the ecological quality index QAELS.106Water 2020, 12, 3264Table 3. Results of the linear mixed models (N = 49 fyke nets) showing the predictor variables thatsignificantly relate with Aphanius iberus size metrics and abundance (expressed as CPUE). Only the mostparsimonious significant models were shown for each response variable. For each model, the intercept(estimated and standard error, S.E.), beta coefficients (standardized), t-value, significance (p-value) anddegrees of freedom (df) are also reported.Response Variable Predictor Estimate S.E.BetaCoefficientst-Value p-Value dfAphanius iberus MAXIMUMlengthConductivity −19.49 4.69 −0.47 −4.15 <0.01 5Log Total Nitrogen 2325.09 564.20 0.45 4.12 <0.01 5QAELS index 3894.26 690.27 0.59 5.64 <0.01 5Zooplankton biomass −0.22 0.05 −0.69 −4.51 <0.01 5Aphanius iberus MEAN lengthConductivity −0.15 0.05 −0.42 −3.35 0.02 6QAELS index 31.94 6.76 0.55 4.73 <0.01 6Zooplankton biomass −0.01 −0.01 −0.59 −4.01 0.01 6Aphanius iberus lengthRANGELog Pond Mean Depth 14.45 5.36 0.42 2.70 0.03 7Log Total Nitrogen 36.88 8.93 0.48 4.13 <0.01 7Aphanius iberus SIZEDIVERSITYConductivity <0.01 <0.01 0.29 2.21 0.06 7Log Total Nitrogen 0.93 0.37 0.30 2.51 0.04 7Aphanius iberus capture pereffort unit (CPUE)Log Pond Mean Depth 1.46 0.57 0.55 2.56 0.04 7Log Total Nitrogen 2.15 0.97 0.36 2.23 0.06 7The length range of A. iberus was found to be positively related to the pond mean depth andtotal nitrogen. Beta coefficients of this model (0.42 and 0.48) showed similar effects of both predictorvariables on the length range. The same results were found for fish density, with the CPUE positivelyrelated to the mean depth and total nitrogen (Table 3), suggesting that a higher number of individualsinhabited more nutrient-rich and larger ponds. However, the pond mean depth exhibited a strongercorrelation on the fish density than the total nitrogen (beta coefficients of 0.55 and 0.36, respectively;Table 3).Concerning the size diversity, it was positively related to the total nitrogen and only slightlyrelated with the conductivity (Table 3). In this case, the beta coefficients for the two main driverswere similar (0.29 for conductivity and 0.30 for total nitrogen; Table 3). Finally, the ECELS index andpond area were the only variables not retained in any of the models selected (see the full models inSupplementary Table S2).3.4. Influence of the Presence of G. holbrooki on the Size Structure and Density of A. iberusBoxplots showed the CPUE and size metrics of A. iberus in the presence and absence of G. holbrookiin the pond (Figure 2). Overall, the presence of G. holbrooki in the pond did not significantly modify thesize structure and density of A. iberus (p-values > 0.39; Figure 2). However, the mean size of A. iberuswas significantly higher when G. holbrooki was present in the pond (Figure 2), indicating an unexpectedincrease of body size with the presence of the main competitor.107Water 2020, 12, 3264 ∗ Figure 2. Boxplots showing the distribution of Aphanius iberus capture per unit of effort (CPUE) andsize-related variables according to the presence (“with Gambusia”) or absence (“without Gambusia”)of Gambusia holbrooki. Significant differences are marked with the asterisk symbol (*).4. DiscussionOur results suggested that both the maximum and mean sizes of A. iberus increased with theincreasing pond’s water quality (QAELS index) and decreased with the increasing conductivity andzooplankton biomass. The size range, maximum size, size diversity and CPUE of A. iberus werepositively related to the nutrient concentration (i.e., total nitrogen), while the size range and CPUEwere also larger in deeper ponds. In contrast to our hypothesis, the presence of G. holbrooki seemed notto affect negatively the population structure of A. iberus.We found larger maximum and mean lengths of A. iberus in locations with better water quality(i.e., the QAELS index). These results support a previous study on multiple water bodies in thesouthernmost distribution area of A. iberus, showing its preference for ponds of better ecologicalstatus [15]. Although the results showed that the average of the studied locations had a deficientecological status, higher values of the QAELS index are usually associated with the predominance oflarge zooplankton (such as big copepods) over small rotifers (that are more linked to eutrophic andhypoxic conditions; [53,54,62]. Adults of A. iberus (with larger body lengths) are usually associatedwith glasswort habitats (highly productive and occasionally inundated environments) where bigzooplankton is more abundant, and this may support the positive relationship between A. iberus sizeand the ecological status of the ponds found in our study. In contrast, younger and smaller individualspositively select more eutrophic algal mats, associated with a bad ecological status, where small rotifersdominate [24].The total nitrogen was found to be related with the CPUE, as well as with all size metrics, expectthe mean length. In Mediterranean salt marshes, the concentration of total nitrogen in the wateris an indicator of a pond’s confinement or isolation [44], and during the late-spring and summerseason, the total nitrogen is more concentrated because of evaporation processes [47]. Our results arein accordance with previous studies that showed that, in more confined and less accessible ponds,108Water 2020, 12, 3264the populations of A. iberus are more abundant and stable over time, probably due to a lack of externalperturbations, such as isolation from invasive species [17] or fewer entries of freshwater inputs. Sincenitrogen is typically the limiting nutrient in these ponds, a higher total nitrogen concentration could alsobe associated with higher production rates of A. iberus, which, in turn, may favor larger populations.Our results also showed, as expected, a negative relationship among conductivity and themaximum and mean sizes of A. iberus. In high saline habitats, conductivity can act as a “refuge” for theA. iberus to avoid the colonization of less salt-tolerant fish species, such as G. holbrooki [63]. However,high salinity levels may also have negative effects on the metabolism of cyprinodontoids [5,36], becausethe energy used for osmoregulation is not available for their growth performance and survival [33–35].This could explain the presence of smaller fish in ponds at higher levels of conductivity.In our study, the A. iberus CPUE, along with the A. iberus size range, were positively related tothe pond depth. Similarly, studies from European wetlands and lakes found that wider and deeperwaterbodies hosted greater biomass and sizes of fish [17,64], with the consequent higher probability tofind a wider range of fish sizes [20]. Additionally, individuals of A. iberus trapped in brackish pondsdue to competition exclusion and habitat degradation can, in some cases, reach unnaturally highdensities [17].Our results also showed that the zooplankton biomass is negatively correlated to the A. iberusmaximum and mean sizes. Other studies on A. iberus observed that both juveniles and adults of thisspecies have similar food preferences, as they mainly feed on harpacticoids, copepods and nauplii,detritus and diptera larvae [24]. Still, smaller individuals prefer feeding on small-sized prey, whilelarger fish show a greater preference for large-sized prey [24]. Larger individuals have higher feedingrates [34,65]. Thus, the presence of larger fish (expressed by higher mean and maximum sizes) mayimply a lower zooplankton biomass, as it increases the consumption rates with the fish body sizes.In addition, previous studies observed that, when the potential resource availability is low, the fish sizedistribution tends to be more diverse, suggesting that competitive interactions for resources promotediversification by size [19,66,67].As for the influence of G. holbrooki on A. iberus abundance and size structure, the results suggestedthat both the CPUE and size metrics of A. iberus were not affected by the presence of this allochthonefish. Only the mean size seemed to be slightly positively affected by the presence of the competitor.This result differed from our expectations, in which A. iberus would be smaller and less abundant inthe presence of G. holbrooki. This apparent inconsistency could be explained by the fact that G. holbrookiwas found just in few of the studied ponds, the ones with lower conductivity, and what we observedcould be an indirect effect of environmental conditions that favor A. iberus development more thanthe effect of direct competition. Thus, the low number of ponds with G. holbrooki in our study did notunable us to derive strong conclusions about the influence of the G. holbrooki presence on A. iberusabundance and size structure.In conclusion, our results suggest that the ponds’ ecological status (as shown by the QAELS index),depth, conductivity and nutrient concentrations are key variables that determine the variations of thesize structure and abundance of A. iberus in Mediterranean brackish ponds. Achieving a better pondecological status seems to be important for the conservation of endangered A. iberus, because bettersize-structured populations (i.e., larger mean and average lengths) are found at higher water qualityconditions. In addition, a pond’s isolation may also be an advantage to preserve A. iberus populations.Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4441/12/11/3264/s1:Figure S1: title, Table S1: Main geographic and morphometric characteristics of the studied ponds, along with thenumber of traps used and the ecological status values (QAELS and ECELS) for each pond, Table S2: Results ofthe MLMs (N = 49) showing the predictor variables that affect size-related variables and abundance (CPUE) ofAphanius iberus. Both Full models and Best models are presented. For each one, intercept (estimate and standarderror, S.E.), Beta coefficients (standardized), t-value, significance (p-value), and df are shown.109Water 2020, 12, 3264Author Contributions: Conceptualization: S.B., A.B. and S.S.; sampling: S.S., A.B., L.B., M.B., I.A. and S.B.;statistical analysis: S.S.; writing—original draft preparation: S.S.; methodology: S.B., S.S., M.B., I.A., L.B., A.B. andwriting—review and editing: S.B., S.S., M.B., I.A., L.B., A.B. All authors have read and agreed to the publishedversion of the manuscript.Funding: This research was performed within the framework of the “SOS Fartet Project” supported by FundaciónBiodiversidad and is also funded by a grant from the Spanish Ministry of Science, Innovation and Universities (grantno. RTI2018-095363-B-I00). S.B. was partially supported by a grant from the Deutsche Forschungsgemeinschaft(DFG) (grant no. Me 1686/7-1).Acknowledgments: We thank Sergi Romero, director of “Aiguamolls de l’Empordà” Natural Park, and Marc Marífrom “El Montgrí, les Illes Medes i el Baix Ter” Natural Park for the facilities and authorizations to developthe project. We are also thankful to the researchers and undergraduate students who helped during sampling:S. Carrasco, P. Ortega, M. Carol, P. Antoni, M. Omer, O. Vigil, and E. Corella. Thanks also to Z. Ersoy for hersupport with the chlorophyll-a and statistical analyses.Conflicts of Interest: The authors declare no conflict of interest. The funding sponsors had no role in the designof the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decisionto publish the results.References1. Doadrio, I. Atlas y Libro Rojo de los Peces Continentals de España; Ministerio del medio ambiente: Madrid,Spain, 2001.2. Vilà, M.; García-Berthou, E. Monitoring biological invasions in freshwater habitats. 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This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).113waterCommunicationFish Biodiversity Conservation and Restoration,Yangtze River Basin, China, Urgently Needs‘Scientific’ and ‘Ecological’ ActionJianhua Li 1, Xiaochen Chen 2,* , Xinliang Zhang 3, Zhenjia Huang 2, Liang Xiao 2,Liangliang Huang 4 , Yuichi Kano 5 , Tatsuro Sato 5, Yukihiro Shimatani 5and Chunpeng Zhang 61 Key Laboratory of Yangtze Water Environment, Ministry of Education, Tongji University, Shanghai 200092,China; leejianhua@tongji.edu.cn2 Fujian Provincial Engineering Research Center of Rural Waste Recycling Technology, College ofEnvironment and Resources, Fuzhou University, Fuzhou 350108, China; zhuang@fzu.edu.cn (Z.H.);n190627056@fzu.edu.cn (L.X.)3 Crawford School of Public Policy, Australian National University, Canberra, ACT 2601, Australia;adam.zhang@alumni.anu.edu.au4 College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China;llhuang@glut.edu.cn5 Department of Urban and Environmental Engineering, Graduate School of Engineering, Kyushu University,Fukuoka 819-0395, Japan; kano@species.jp (Y.K.); sato@civil.kyushu-u.ac.jp (T.S.);shimatani@civil.kyushu-u.ac.jp (Y.S.)6 Key Laboratory of Groundwater Resources and Environment (Ministry of Education), Jilin University,Changchun 130021, China; zhang_cp@jlu.edu.cn* Correspondence: chenxiaochen@fzu.edu.cnReceived: 23 September 2020; Accepted: 27 October 2020; Published: 29 October 2020Abstract: Although many significant policies and legislation have been put forth by the Chinesegovernment aiming toward “ecological civilization” for the aquatic environment, in practice,the situation is still undesirable. A pioneering multi-year study has since been conducted onthe East Tiaoxi River (a major tributary of the renowned Yangtze River), focusing on fish distribution,influencing factors and habitat requirements, and river health as well. This revealed certain keyspecies, hotspot protection areas, negative impacts of local anthropogenic activities, and inappropriateperceptions and practices of conservation and restoration. We found that not all fish species wereequally conserved and that the supposedly ecological engineering measures were initiated withoutregard for ecological integrity. Our exemplary study appreciates scientific basis and truly ecologicalnotion, and urgently advocates comprehensive and continuous basin-scale scientific investigation offish biodiversity, and “ecological river”-oriented conservation and restoration action for the YangtzeRiver Basin and broader areas of China.Keywords: conservation; ecological integrity; fish biodiversity; restoration; river basin management;Yangtze River1. IntroductionThe number and biodiversity of fish are the most significant indicators of the health of a waterbody.In China, there are more than 1100 species of freshwater fish, 70% of which inhabit the rivers [1,2].Although the total number is notable among worldwide statistics, the biodiversity has been continuouslydeclining with increasing anthropogenic and economic development. In fact, as early as 1998, 92 species115Water 2020, 12, 3043of the freshwater fish were listed in the China Red Data Book of Endangered Animals: Pisces [3],which shows that four species unfortunately have gone extinct and 28 species have become endangered.The loss of biodiversity greatly exceeds the world average [4]. It is the adverse anthropogenicimpacts on the freshwater ecosystem that must first be to blame. Both notorious industrialisation andurbanisation are major hindrances to sustainable development in China. Against the backgroundof abrupt population expansion, many irrational activities associated with urbanisation have ledto deterioration of the aquatic environment, such as unsustainable exploitation of water resources,improper river modification, pollutant discharge and exotic species invasion.In 2018, China’s 13th National People’s Congress inserted the term “ecological civilization” into thecountry’s constitution for the very first time, indicating that the notion has now been raised up tothe national strategy level as a main focus paralleling economic and social development [5]. In thefield of aquatic environment, Chinese government has established a series of policies aimed towardbalanced water resources exploitation and aquatic ecosystem protection and restoration. In April2015, the State Council of China promoted the “Action Plan for Prevention and Control of WaterPollution,” a milestone also known as the “Ten-point Water Plan,” the primary objective of whichwas to substantially improve the country’s water quality and aquatic ecological situation by the year2020 [6]. More recently, legislation also has been enhanced. In 2018, the revised Water PollutionPrevention and Control Law came into force, aiming at boosting aquatic environmental protectionand restoration from a legal standpoint [7]. The effects of water quality on aquatic biodiversity havebeen comprehensively reviewed, and accordingly a series of protection areas have been establishedthroughout the country. All these actions demonstrate that the priority of public policy has shiftedfrom mainly social and economic progress to also include environmental conservation for healthyaquatic ecosystems and sustainable hydrologic cycles.However, the practical situation suggests an aquatic ecological deficit would remain. What isworse, knowledge of the ecosystemic constitution of freshwater fish (including migratory fish) of riverbasins over much broader areas of China, as well as specific factors influencing the biodiversity, is farfrom sufficient. The reported monitoring data over the past few decades were either not comprehensiveor lacked continuity. As a result, the conservation and restoration of aquatic ecosystems have beenperformed nationwide without a reliable scientific basis and have even tended to be too formalisticand/or disorganised. To make contributions for sound river basin management in China, a pioneeringstudy on the ecological situation (especially the fish biodiversity) was initiated in the Yangtze RiverBasin. The necessity and significance of the scientific basis and the truly ecological notion would beemphasized. Besides, the comprehensive and continuous basin-scale scientific investigation, as well asthe “ecological river” concept-oriented conservation and restoration action, would be timely advocated.2. Materials and MethodsAs shown in Figure 1, the Yangtze River originates from glaciers on the Qinghai–Tibet Plateauat its westernmost point, heads toward the East China Sea and eventually enters the Pacific Ocean.Along the way, it flows across the southwestern and central parts of China before reaching its outletin Shanghai (the economic centre of the country). With a length of 6300 km and a catchment areaof 1.8 million km2, the Yangtze River is the third largest river in the world. Having the biggestdischarge volume, it also supports the world’s largest hydroelectric power station, i.e., the ThreeGorges Dam. Designated as part of the latest national development strategies, the Yangtze RiverEconomic Belt includes about 40% of the country’s population and aggregate economy and is on theway of high-speed development. In its development planning outline issued in 2016, “ecology first”and “green development” form basic principles. Unfortunately, the Yangtze River Basin had longbeen sustaining extremely severe destruction of aquatic habitats [8]. In a multi-year study undertakenby the China Europe Water Platform (CEWP) project, a China–Austria cooperating project, etc.,representative tributaries of the lower basin of the Yangtze River were thoroughly and continuouslyinvestigated. The main study area was the East Tiaoxi River Basin, one of the best epitomes of the116Water 2020, 12, 3043environmental issues of the Yangtze River Basin (Figure 1). With a mainstream length of 152 km and acatchment area of 2267 km2, the East Tiaoxi River is the largest river flowing into Lake Taihu, the thirdlargest freshwater lake in the Yangtze River Basin. This lake with scenic beauty has been drawingfocuses of worldwide researchers, ironically due to its notorious chronic water pollution problemssuch as eutrophication [9]. Figure 1. Study area and sampling sites.Regarding fish species and communities, the joint research focused primarily on the following:(1) spatiotemporal distribution, (2) natural and anthropogenic influences, (3) habitat requirements ofcertain key species and (4) river health assessment based on fish-based index of biological integrity (FIBI).Regarding FIBI system development, 24 metrics were selected as candidate metrics, which coveredfive attributes of fish assemblage, i.e., species richness and composition, trophic composition,tolerance/intolerance, reproductive guilds, and fish abundance and health condition. Two FIBI systemswere then established for the upper reaches (eco-zone of evergreen broad-leaf forest) and middle-lowerreaches (eco-zone of urban and suburban agricultural areas), respectively. The detailed systemcomposition, as well as the criteria of metric scoring and assessment, was described by Huang et al. [10]Geographic Information System (GIS) was one of the major tools used to facilitate the entire research.Detailed field investigation was carried out at 55 sampling sites of the East Tiaoxi River Basin (Figure 1),by wading in the upper reaches (reservoirs excluded) and by boat in the middle-lower reaches,respectively. In the shallow reaches fish were sampled using specific backpack electrofisher at the mainhabitat units such as riffle, glide and pool, while in the middle-lower reaches local boat electrofisherwas used. The samples were identified to species level, followed by data processing using methodssuch as sample-based rarefaction curve and cluster analysis to reveal fish distribution patterns [11–13].The spatiotemporal variation of fish communities and the associations of fish density and richnesswith environmental variables (influencing factors) were also investigated using methods such asnon-metric multidimensional scaling and canonical correspondence analysis based on fish abundancedata [11,13–15]. Note that the major natural factors focused on included distance to source, altitude,stream width, stream velocity, stream depth and pH, and the major anthropogenic factors coveredresidential area, revetment, farmland and factory.3. ResultsIn this study, we recorded 84 species of fish, 35 of which were endemic. The main results arepresented in Table S1, Figures S1 and S2. Inspiringly, certain endangered species in the neighbouringcountry Japan were found to be locally flourishing (Figure 2). Owing to the devastation of habitats as wellas the blockage of breeding migration routes, the once common Leptobotia tchangi Fang, 1936 has almostdisappeared and hence was called a “natural monument” by the Japanese Ministry of the Environment.117Water 2020, 12, 3043For similar anthropogenic reasons, Vanmanenia stenosoma Boulenger, 1901 and Acheilognathus chankaensisDybowski, 1872 are both at risk of extinction in Japan. The detailed investigation of their habitatrequirements (stream depth, stream velocity, pebble size, etc.) provided valuable information forfurther protection and restoration initiatives in both China and Japan. Taking Leptobotia tchangi forinstance, it was found that its density had positive correlation with stream velocity, while its body sizepositively correlated with pebble size (see Figures S3 and S4). On the other hand, the once-famous Coiliaectenes Jordan & Seale, 1905 of high culinary and economic value now can rarely be observed, as thedeterioration of water quality, devastation of habitats and overharvesting in the Yangtze River led tothe drastic decline of this diadromous fish in the past few decades [16]. Meanwhile, another varietyfeaturing freshwater migration—Coilia nasus taihuensis Yuan, Lin, Liu & Qin, 1977—has emerged inLake Taihu and tributaries of the East Tiaoxi (Figure 2), reminding people of the dramatic ecologicalchange induced by human activities. Furthermore, according to the results of river health assessment(Tables S2 and S3), 24 out of 45 monitoring sites were determined to be “healthy” or “fair,” while therest were either “poor,” “very poor” or “null.” Based on the strategy of identifying conservationpriority areas first, 21 locations with high fish biodiversity were determined to be hotspot protectionareas, including the habitats of all the above-mentioned key species (see Figure 2). GIS-based toolssignificantly increased the accuracy of protection activities. Figure 2. Map of fish biodiversity hotspots and influencing factors for East Tiaoxi River.By comprehensively researching the influences on the richness of fish species, natural factorswere identified, including distance to source, altitude, stream width, stream velocity, stream depthand pH (Table S4, Figure S5). More importantly, the study also revealed the impacts of anthropogenicactivities (Figure S6, Table S5), especially the negative influences (Figure 2). In addition to commonadverse effects such as from water pollution and ship disturbance, we found the impact of localprivatised stone mining to be non-negligible. With no regard for the aquatic environment, the stonepowder and other residuals from mining and transportation freely entered the adjacent waterbodies,which seriously suffocated the fish by getting stuck in their gills. Besides, other ubiquitous practicessuch as river training works and water pollution control projects have been continuously convertingriver environments from their original states to artificial states, thereby causing serious loss of thebasic ecological functions of the rivers. The entire study area has locally been subjected to variousnegative impacts by human activities, such as the expansion of floodways, constrictions caused bybridge crossings, river realignments due to cutoffs and restoration activities, changes in resistance118Water 2020, 12, 3043of dikes, weirs and riprap, river diversion for water intakes, dams and grade control structures,sediment supply from deforestation, increased runoff due to impervious cover, increased nutrient loadsand aggravated bank erosion (see Figure 3 for examples). From one October to the next, engineeringconstruction had degraded a hotspot with 15 fish species and 216 individuals to a silent-spot withonly 6 species and 20 individuals in just a year. Only where the aquatic habitats were maintainedin a good state and rich with hydrophytes was satisfactory fish biodiversity observed (Figure 2).Ironically, the “submerged plants” area would have been reclaimed without this study at river basinscale. The flora presents relevant ecological significance, including the ability to mitigate negativeimpacts resulting from stone mining. However, it was completely neglected in previous conservationand restoration schemes, as regular surveys on water quality and fish biodiversity were conductedrandomly and only at the local level, thus failing to draw the whole picture. Figure 3. Threat to riverine fish biodiversity of Yangtze River posed by engineering construction.We have also noticed inappropriate perceptions and practices of protection and restoration,which require urgent correction. For example, only fish regarded as flagship species and/or included inthe national protection list draw broad attention, such as Acipenser sinensis Gray, 1835 and NeophocaenaPalmer, 1899. Normal fish species, including ones presented in Figure 2, are usually ignored. However,based on the theory of a healthy ecosystem as well as the experience and lessons of many developedcountries, all the species within a specific ecosystem must be equally conserved, as they all interrelateand interdepend. Swayed by the impractical expectation of instant effects as well as influenced by theurbanisation tendency in river basin planning, various kinds of supposedly ecological engineeringmeasures are prevailing in riparian areas. Energy-consuming modes of wastewater treatment plants andfacilities have been transplanted to the riparian areas. Since they fulfil the dream of ameliorating waterpollution overnight, such palliatives make it seem to be quite desirable for local government to handlethe inspection and assessment based on directives from higher authorities. Also, popular artificialwetlands have been constructed here and there, with the main focus being on water purificationand aesthetics rather than biodiversity conservation. Even worse, a large amount of money hasbeen invested to the artificial landscape projects with no true ecological idea involved, such as ahydrophilic platform (Figure 4). These supposedly ecological measures swiftly satisfy human desiresbut go against the laws of nature. With dramatically changed land cover and land use in the riparianareas, the vulnerable habitats for fish and other living creatures are apt to be even further degraded.According to the theory of ecological integrity, it is a natural and undisturbed state with physical,chemical and biological integrity that should be the right goal of ecological restoration [17].119Water 2020, 12, 3043 Figure 4. Hydrophilic platform, an example of supposedly ecological measures.4. DiscussionThe master plan for the development of the Yangtze River Economic Belt has become one ofChina’s principal national strategies. Because the primary goal of water-related environmental policiesin China has fundamentally changed as conservation and restoration gain top priority, some measureshave been taken as essential, such as a 10-year ban on fishing in the Yangtze River starting in 2020.However, this case study revealed that current practices apparently fail to keep pace with the centralgovernment’s directives. The present situation in the Yangtze River Basin is still sounding the alarm andcausing critical concern among the public. As the biodiversity of freshwater fish keeps on decreasingmainly due to anthropogenic impacts, it is apparent that there has been a serious lack of scientific basisand truly ecological action for sound river basin management.Comprehensive and continuous scientific investigation at river basin scale is proposed to trackriverine fish biodiversity and to further understand the influencing factors. We believe that accurateidentification of ecological protection red lines and relevant hotspot protection areas, more targetedaction plans and technical guidelines for normalising conservation and restoration activities would beachieved only on the basis of data from scientific study in accordance with the theory of ecologicalintegrity. The results of this exemplary study reveal significant insight into the highly anticipatednationwide practices. To support the initiative, we recommend that the relevant governmentbodies—such as the Ministry of Science and Technology and the affiliated National Natural ScienceFoundation of China—set up special funds for long-term fundamental research on the ecologicalenvironment and biological resources of the Yangtze River Basin. All involved local authoritiesshould also take responsibility for routine environmental monitoring and assessment. Further,we advise that the supposedly ecological engineering measures for conservation and restoration,together with the destructive pollution control and treatment practices, be strictly prohibited by policyand institutional means [18], which recently have received positive reply from the prime minister.Meanwhile, scientific data and the know-how for effective environmental conservation and restorationmust be delivered to and shared among all the stakeholders, especially the government decision-makersin this Chinese case. From the viewpoint of ecological integrity, the real concept of “ecological river”needs to be advocated and put into practice, which aims at recovering the original structure andecological functions of a river and hence make it regain the resilience and stability in order to maintainacceptable quality under long-term or sudden disturbance [13]. Last but not least, to ensure the successof the whole cause, more stringent legislation and regulations must be enacted and enforced.Water resources management is the epitome of China’s environmental issues, and significantaquatic biodiversity loss in the major river basin shows the severity of the challenges. Heading towardsustainable development, China must make its great power accountable and lead the way by taking“scientific” and “ecological” action.120Water 2020, 12, 3043Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4441/12/11/3043/s1,Figure S1: Fish species composition in the East Tiaoxi River [13], Figure S2: Fish species richness in each streamorder of the East Tiaoxi River [13], Figure S3: The relation between Leptobotia tchangi’s density and stream velocityat different sampling sites [11], Figure S4: The relation between Leptobotia tchangi’s body size and pebble size atdifferent sampling sites [11], Figure S5: Canonical Correspondence Analysis (CCA) of environmental factors andfish assemblage [11], Figure S6: Impacts of anthropogenic activities to non-parametric multidimensional scaling(NMDS) ordination of fish assemblages [11], Table S1: The list of fish species and their distribution in the EastTiaoxi River [13], Table S2: Criteria for river health assessment based on ecological integrity of fish in the EastTiaoxi River [11], Table S3: River health condition based on Fish Integrated Biotic Index [11], Table S4: Matrix ofSpearman rs correlation coefficients of environmental variables [11], Table S5: One-way ANOSIM for analysingthe impacts of human activities on fish communities [11].Author Contributions: Conceptualization, J.L.; methodology, J.L.; formal analysis, L.H., Y.K., T.S. and Y.S.;investigation, L.H., Y.K., T.S. and Y.S.; writing—original draft preparation, X.C. and X.Z.; writing—review andediting, J.L., X.C., Z.H., L.X. and C.Z.; supervision, J.L. and X.C.; project administration, J.L. and X.C.;funding acquisition, J.L. and X.C. All authors have read and agreed to the published version of the manuscript.Funding: This research was funded by China Europe Water Platform (CEWP) project; China–Austria JointInnovative and Technological Research Project, grant number 2016YFE0115800; Japanese Global COE Program(Center of Excellence for Asian Conservation Ecology as a Basis of Human-Nature Mutualism); China MajorScience and Technology Program for Water Pollution Control and Treatment, grant number 2017ZX07205;Shanghai Institute of Pollution Control and Ecological Security; CSC-DAAD PPP Programmes for Project-RelatedPersonal Exchange, grant number [2019]12039; and Research Funds of Jinjiang Fuzhou University Science Park,grant number 2019-JJFDKY-07.Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of thestudy; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision topublish the results.References1. 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This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).122waterArticleFirst Captive Breeding Program for the EndangeredPyrenean Sculpin (Cottus hispaniolensisBacescu-Master, 1964)Joan Manubens 1,*, Oriol Comas 1, Núria Valls 1 and Lluís Benejam 21 Associació de Defensa i Estudi de la Fauna i Flora Autòctona (ADEFFA), Camadoca, Santa Maria de Merlès,08517 Catalonia, Spain; a.adeffa@gmail.com (O.C.); hola@adeffa.cat (N.V.)2 Aquatic Ecology Group, University of Vic—Central University of Catalonia, Vic, 08500 Catalonia, Spain;lluis.benejam@uvic.cat* Correspondence: joanmanubensgil@gmail.comReceived: 28 September 2020; Accepted: 22 October 2020; Published: 24 October 2020Abstract: The strong decline of freshwater fish species in Europe implies that further ex-situconservation plans should be implemented in the near future. The present study reflects ourexperience with the Pyrenean sculpin (Cottus hispaniolensis Bacescu-Mester, 1964)—a small cottidendemic to the Hispano-French Garona River basin. In recent years, the Spanish Pyrenean sculpinpopulation has reached a limit situation. Because of that, the non-profit association ADEFFA—withsupport from the public administration—started the first captive breeding program for this speciesin 2006. Fourteen years later, this study presents the results and evaluates the different steps ofthe program, with the aim of discussing and improving the ex-situ conservation plans for this andother cold freshwater species. There is a description and a comparison between six consecutivephases during the captive breeding process: nesting behaviour, courtship, egg fixation, parental care(incubation), hatching and survival during juvenile development. The purposes of this projectare to: (1) identify the most determining phases for a successful captive breeding; (2) identify thefactors that had a major influence to the success of the critical phases; and (3) increase the numberof the offspring. This study is based on thirty-three wild individuals collected from Garona River(Val d’Aran, Spanish Pyrenees). During the program, twelve couples spawned in captive conditions,with around 2300 eggs laid. Eight couples bred successfully, with 751 hatched individuals and608 juveniles reared. The analysis of each step of the captive breeding does not reveal significantdifferences between phases, so it can be concluded that they are all critical at the same level. In theliterature, similar study-cases of captive breeding programs identify incubation and survival phasesas the most critical. Consequently, the management made for this project has probably allowed toovercome in part the main impediments described in other similar programs.Keywords: threatenedspecies; conservationplanning; climatechange; captivebreeding;Cottushispaniolensis1. IntroductionFreshwater ecosystems are some of the most threatened ones in the world. Over 37% of Europeanfreshwater fishes are threatened—which is one of the highest threat levels in any major taxonomicgroup [1]. Especially the Iberian Peninsula is experiencing a critical conservation status of itsichthyofauna, with 52% of species now catalogued as critically endangered, endangered or vulnerable,according to the IUCN criteria. This condition is related to a high degree of endemism (among nativespecies, 65% are endemic to the Iberian Peninsula) and a long history of human impact in theseareas [2,3].123Water 2020, 12, 2986In Europe freshwater species of the genus Cottus (family Cottidae, order Scorpaeniformes) arefound in a variety of cold-water habitats such as well-oxygenated headwaters, lakes and channels.They are generally nocturnal and feed on wide variety of benthic invertebrates. The egg-layingperiod occurs between March and April (when the water temperature rises above 12 ◦C). The eggs areadhesive, laid in a compact clutch on the ceiling of small cavities, so they need gravel or rocky riverbeds. Females spawn once a year and most individuals spawn for several years. Males guard the eggsuntil hatching, while they do not feed and may lose 20% of their body weight [4–6].Recently, a total of 16 Cottus species have been described in Europe [4,5,7]. Several of thesespecies are endemic of certain river basins and isolated in their particular habitats, as ecologicalbarriers do not allow them to migrate across main rivers [4,5]. These speciation events occurreddue to glacial periods [8,9]. These range-restricted species have been reported as endangered duringthe past few years, mostly due to habitat fragmentation [10–13]. The habitat fragmentation isaggravated by the intense human pressure, with additional threats such as habitat reduction causedby urban and agricultural pollution; water extraction for agriculture and damming; or unnaturalflow regimes resulting from flow regulations which meet electrical power demands—often resultingin low water levels, followed by instantaneous high flow regimes [6,10,11,13]. This impact hasreduced the distribution areas into smaller and isolated populations, which face serious risks suchas environmental changes, demographic stochasticity, natural catastrophes and reduced geneticvariability. Therefore, the combination of these threats leads to population decline and, eventually,to extinction [14–16].This study focuses on the Pyrenean sculpin (Cottus hispaniolensis Bacescu-Mester, 1964), a smallcottid (up to 100–150 mm, total length) endemic to the Hispano-French Garona River basin andrestricted to the southern part of the drainage—in the central part of the Pyrenean mountain range [4,5].This species shows a discontinuous presence along its distribution range [17], with an apparent (butunknown) degree of isolation.Freyhof & Kottelat [18] catalogued the Pyrenean sculpin as of Least Concern according to the IUCNguidelines. Afterwards, Doadrio et al. [19] reported an accentuated population decline and proposed tocatalogue it as Critically Endangered (B1bc+2bc), which was supported again by Sousa-Santos et al. [13].The Pyrenean sculpin is included in the Annex II of the European Habitats Directive 92/43/EEC [20] andin the Annex I (for endangered species) of the Spanish National Catalogue of threatened species [21].In the French Red List, it is included as Data Deficient [6].During the summer of 2013, there was a devastating flood in the Garona River—caused byheavy rainfall and a big fast snow melt—with an estimated return period (RP) of 30–50 years [22–24].Changes in the river course and the subsequent arrangement by the machinery led the Pyreneansculpin populations to a limit situation: only 16 individuals were captured during several samplingsalong most of the Spanish distribution area in 2014 [17].The delicate situation of the Pyrenean sculpin has been known for a long time. The ConselhGenerau d’Aran (local administration) and the LIFE LimnoPirineus project (NAT/ES/001210) have beenworking with the species. They have been sampling the population of the Pyrenean sculpin since 2001,in different points all along the Spanish part of the Garona and its tributaries, in order to determineits size, density and trends. Further actions need to be implemented—such as genetic studies [17].Knaepkens et al. [25] have suggested that in-situ actions are required to increase the population sizeand range of endangered populations. It is therefore necessary to restore the longitudinal functionalityof rivers and to guarantee its dilution capacity, by: improving water management policies—in orderto mimic the natural hydrologic regime; improving the connectivity with well-designed fish ladders;and reducing pollution—in order to improve water and ecosystem quality [2,13].When endangered species are incapable of surviving in their natural habitat despite all thein-situ efforts, and effective alternatives are unavailable in the short term, ex-situ conservation canplay a crucial role [26–28]. The strong decline of freshwater fish species implies that further ex-situconservation plans should be implemented in the near future [2]. However, it is important to underline124Water 2020, 12, 2986that captive breeding is a last resort and it should not be a long-term conservation strategy whichexcludes other in-situ actions [26,28–30], because it is typically associated with many limitations suchas high economic costs, diseases, adaptation to captivity, relaxation of adaptation, inbreeding and lossof genetic diversity—with consequent inbreeding depression and fitness reduction [26–29,31–33].In 2006, the non-profit association ADEFFA, with the support from the public administration,started the first captive breeding program for this species in Camadoca wildlife centre (Santa Maria deMerlès, Barcelona). The main purposes of the breeding program were to complete the ex-situ breedingcycle, to maintain a genetic stock, and to study the biology, reproduction and behaviour of the Pyreneansculpin. Other actions such as habitat restoration, population studies in-situ, environmental educationand divulgation were also implemented to complement the breeding program [34].This study describes the results and the evaluation of fourteen years of captive breeding of thePyrenean sculpin, as an ex-situ conservation program. It describes and compares six consecutivebreeding phases during the breeding period: nesting behaviour, courtship, egg fixation, parental care(incubation), hatching and survival during juvenile development, in order to: (1) detect which one ofthese phases of the reproductive cycle is most determining with respect to the success of the captivebreeding; (2) observe the variation into this critical phase/s and the factors influencing its success;(3) determine key factors that influence the success of the captive breeding and the number of offspringproduced; and, finally, (4) develop possible improvements for the critical steps of the ex-situ program.The results of other captive breeding programs with cold freshwater species have shown that theincubation and the juveniles’ survival are the most critical phases [35–37]. Therefore, the hypothesisis that these two phases could be the most determining ones to the success of the captive breedingprogram for the Pyrenean sculpin.2. Materials and Methods2.1. Brood-Stock Transportation and MaintenenceThirty-three wild samples of Pyrenean sculpin (18 males and 15 females) were collected along theSpanish part of the Garona River (Figure 1) by electrofishing during spring in 2006, 2009, 2014, 2017and 2018, with the collaboration of the Conselh Generau d’Aran forestry guards. Breeders collected in2014 were maintained in captivity until 2015 breeding season. Fish total length ranged from 70 mm to120 mm and weight ranged from 9 g to 26 g at the time of collection. They were carried in 25 L plastictanks with oxygenation, inside isolation boxes, to the fauna conservation centre Camadoca (Santa Mariade Merlès, Barcelona). The care and use of all specimens had complied with local animal welfare laws,with the corresponding administration permissions. The facility counts with two 450 L rectangularglass tanks for the breeders without direct natural illumination and another 250 L rectangular glasstank for the juveniles and hatched larvae (rearing tank). Since 2015, there is a new and more efficientfacility to improve the maintenance, with four more 250 L rectangular glass tanks for the breedersand direct natural illumination (from a window). In 2018, the frontal part of these tanks was coveredpartially in order to reduce the direct illumination from the window. The bottom of all the tanks(except the rearing tank) is covered with a gravel layer. Each tank is provided with stones or curvedclay tiles as a substratum on which the fish can hide or make nest to spawn. Before every stockingevent, water is changed, all the material is renewed and tanks are cleaned and disinfected.Each tank counts with mechanic filter system (50 L/h), water cooling system equipped withthermostat (except in 2006) and continuous aeration. The rearing tank counts with an externalUV-irradiation lamp (without direct contact with the tank). A single 40 W fluorescent suspended at20 cm above the water surface of each tank provides a natural photoperiod (13L:11D). Except in 2006(when there wasn’t cooling system), water temperature is maintained between 8 ◦C and 10 ◦C for20–40 days after the stocking. At this time, it is gradually increased to 11 ◦C. Adults are fed everyevening with alive Artemia sp. nauplii, chironomids, tubifex and other freshwater macroinvertebratesad libitum. Juveniles are first fed with decapsulated Artemia sp. during two weeks approx.125Water 2020, 12, 2986 Figure 1. Map of the Garona drainage in Val d’Aran (Catalonia, Spain) and its main tributaries (left)and map of the Garona River basin (right).2.2. Reproductive Cycle and Data AnalysisIn order to evaluate which phases of the captive breeding are the most critical (with the objectiveof improving the breeding techniques effectiveness), the process is divided in six phases (1 = “nesting”;2 = “courtship”; 3 = “fixation”; 4 = “incubation”; 5 = “hatching”; 6 = “survival”). The description ofeach phase and the success criteria used are attached in Table 1, in concordance with [38]. Each phase isevaluated with success (yes = 1) or failure (no = 0), depending on whether the couple finishes the phasesuccessfully. It cannot be either success or failure if the previous phase is failure. Then, the numberof couples that succeed in a phase is divided by the initial number of couples (percentage of successfrom initial couples) and by the number of couples that succeeded in the previous phase (percentage ofsuccess from the previous phase). These percentages of the phases considered to be most critical willbe analysed.Table 1. Description of each phase and the success criteria used. The male naturally guards the eggsuntil the hatching (ca. 30 days), but most times it happens that the clutch is removed from the breedingtank to be put it in the rearing one.Phase Name Description Success CriteriaNesting behaviourIn males: when the yellow margin of the first dorsalfin contrasts against the black overall coloration.If male constructs and guards the gravel cavity (thecave) under the stones or tiles.In females: when ovulation process occurs. If female is gravid (full of completed eggs).Courtship (sexualbehaviour)When the male attracts the female to the cave and‘hugs’ her during hours until laying finishes.If sexual behaviour occurs (external and internalcourtship) and the eggs are fertilized.FixationWhile female is laying, eggs are fixed by gelatinousmatrix in an oval compact clutch.If the clutch is fixed on the ceiling of the cave (fixationsubstrate is indicated to evaluate this phase).Parental care(incubation)The male ventilates (fanning activity) and guardsthe eggs from predators and infections.If the male takes care of the eggs at least some days(number of days are indicated to assess this success).HatchingWhen larvae go out of the egg (it takes hours).They are little mobile and feed on the vitelline sackfew days until they become juveniles.If at least one individual hatches. It is assessed withthe hatching percentage.(Nlaid eggs/Nlive hatched alevins)SurvivalSurvival during the juvenile development, 3months after the hatching.If at least one juvenile survives. It is assessed withthe survival rate.Each couple is also categorized by two additional variables, in order to control its effects uponsuccess in the subsequent analysis and distinguish them from the phases effects upon success:couple type (1 = “first year in captivity”; 2 = “one year in captivity at least”; 3 = “born in captivity”;4 = “combination of 2 and 3”) and breeding year (1 = “2006”; 2 = “2009”; 3 = “2014”; 4 = “2015”;5 = “2017”; 6 = “2018”). Then, a logistic regression is performed to test the relation between the binary126Water 2020, 12, 2986outcome (“Success”) and the categorical variables (“Phase”, “Type” and “Year”) with a GeneralizedLineal Model (GLM, family= binomial, link= logit) in R i383. Four different GLM models are performedto evaluate whether success is related to the different fixed factors (phase, type or year). The firstone, considers the interaction between all three factors; the second and the third one, considers theinteraction between two of them (the phase with the other two factors); and the last one, considers onlythe phase. Finally, the most parsimonious model is chosen, by comparing the Akaike InformationCriteria (AIC). Significance level for this study is set to 5%.During the six breeding seasons performed since 2006, twenty-two couples are formed includingfive individuals (one male and four females) born in captive conditions (Table 2). There are three malespresent in two couples (14–15, 16–17 and 19–20). Breeders of 2014 are the same individuals in 2015,but any combination of individuals in the couples is the same.Table 2. Final results summary per year with total number of breeders present in the season andnumerical code of each formed couple. Couples 14–15, 16–17 and 19–20 are formed with the same threemales and different females. Where F = female, M = male. Apart from the success (Yes or No) it isindicated in parentheses the fixation substrate, the number of days during which the male takes care ofthe eggs, the hatching rate and the survival rate. † Individuals maintained at least one year in captivity.‡ Individuals born in captivity. § Only female nesting behaviour is considered as success.YearNo.BreedersCodeCoupleReproductive Phases No.FitnessNesting Courtship Fixation Incubation Hatching Survival2006 41 Yes Yes Yes (stone) Yes (21 days) Yes (49%) Yes (14%) 142 No - - - - - -2009 83 Yes Yes Yes (stone) Yes (4 days) No (0%) - -4 Yes Yes Yes (stone) Yes (3 days) No (0%) - -5 No - - - - - -6 (F ‡, M ‡) Yes § Yes Yes (glass) No - - -2014 77 Yes Yes Yes (tile) Yes (12 days) Yes (73%) Yes (94%) 1708 No - - - - - -9 No - - - - - -2015 810 (F †, M †) Yes Yes Yes (stone) Yes (7 days) Yes (7%) Yes (100%) 411 (F †, M †) No - - - - - -12 (F †, M †) Yes Yes No - - - -13 (F ‡, M †) Yes § Yes No - - - -2017 914 Yes Yes Yes (tile) Yes (11 days) Yes (36%) No (0%) -15 (F ‡) Yes Yes Yes (tile) Yes (11 days) Yes (29%) Yes (100%) 1616 Yes No - - - - -17 Yes Yes Yes (tile) Yes (5 days) Yes (41%) Yes (93%) 7518 No - - - - - -2018 919 (F ‡) Yes Yes Yes (stone) Yes (5 days) No (0%) - -20 Yes Yes Yes (stone) Yes (9 days) Yes (9%) Yes (14%) 221 Yes Yes Yes (tile) Yes (22 days) Yes (56%) Yes (95%) 21222 Yes Yes Yes (tile) Yes (13 days) Yes (60%) Yes (96%) 115% initial success 73% 68% 59% 55% 41% 36%% phase success 73% 94% 87% 92% 75% 89%3. ResultsThe results of GLM analysis show that proportion of success and failure is not significantly differentamong phases of the reproductive cycle (Table 2, Figure 2). Any of the four GLM analysis performeddoes not show a statistically significant relationship. The best performed model is the one that considersthe binary outcome (success) and the effect of different phases (p-value = 0.595; d.f. = 5; AIC = 72.91).As it is shown in Table 3, models including the effect of the couple type or the year have a higher AICvalue, so it can be considered that the best model has to exclude these effects. Anyway, as no model hasa significance level low enough, the success is not significantly different between phases.Considering that all phases are critical at the same level (no differences among phases), it isanalysed the success percent and the variation observed into all of them. The lowest percent observed127Water 2020, 12, 2986is in nesting behaviour (73% of the couples), including two couples (6 and 13) in which males did notpresent nesting behaviour (these couples continued to the next phase and completed it successfully).So, nesting behaviour is observed only in 27 breeders from the 45 present in the program (60%). On theother hand, 94% of the couples with success in the first step go on courtship and spawn—the higherpercent observed. Only couple 16 failed in this phase (the female laid the clutch without interactionwith the male). During 2017 and 2018, there were three males that spawned with two females (Table 2).Egg fixation was completed in 87% of the couples in the last step: six fixed the clutch on the stone,six on a tile and one on the glass (couple 6). Couple 13 did not fix it. Female of couple 12 died in thisphase without laying. The death cause determined in the necropsy was an ovarian obstruction. 92% ofthe fixed clutches were incubated at least few days for the male (parental care). Only two clutches(couple 1 and 21) were incubated until the hatching and the other ten were situated in controlledconditions (in rearing tanks) at 5–13 days after fixation. The second lowest percent is observed inhatching, with 75% of success of the incubated clutches. Moreover, the hatching rate is very low manytimes (with a reduced number of hatched larvae). Survival during juvenile development is a success89% of the times, with only one failure.From the eight couples that had brood with success, only four (7, 17, 21 and 22) had reared aconsiderable number of juveniles. In 2006 (couple 1), there was high mortality due to an infection.The diagnostic analysis—both of breeders and eggs—carried out in 2006 by doctor Francesc Padrós(Servei de Diagnòstic Patològic de Peixos, UAB) shows that infections during parental care were causedby fungus of the Saprolegnia genus. During the rearing in 2006, an Ichthyophthirius multifiliis infection(white spot disease) was also detected. Since 2015, it is observed that couples situated in the newfacilities did not succeeded (11, 12, 13, 18 and 19) or had the lowest fitness number (10 and 20). 𝐸𝑥𝑖𝑡 ~ 𝑃ℎ𝑎𝑠𝑒 𝑇𝑦𝑝𝑒 𝑌𝑒𝑎𝑟𝐸𝑥𝑖𝑡 ~ 𝑃ℎ𝑎𝑠𝑒 𝑇𝑦𝑝𝑒𝐸𝑥𝑖𝑡 ~ 𝑃ℎ𝑎𝑠𝑒 𝑌𝑒𝑎𝑟𝐸𝑥𝑖𝑡 ~ 𝑃ℎ𝑎𝑠𝑒 Figure 2. Number of successes (1) and failures (0) in each phase (1 = “nesting”; 2 = “courtship”;3 = “fixation”; 4 = “incubation”; 5 = “hatching”; 6 = “survival”).Table 3. Results of the performed GLM analysis, with the factors considered for each model, its p-value(from a Chi square test), its freedom degrees and its AIC value.Model p-Value d.f. AICExit ~ Phase + Type + Year 0.442 12 82.232Exit ~ Phase + Type 0.817 8 77.976Exit ~ Phase + Year 0.769 9 79.094Exit ~ Phase 0.595 5 72.913128Water 2020, 12, 2986As general results of the breeding program, twelve couples spawned in ten nests witharound 2300 eggs laid (mean = 212; SD = 118), 751 individuals hatched and 608 juveniles reared.Individuals (juveniles and breeders) of the captive breeding seasons of 2014, 2015 and 2017 werereleased in different points along the Garona River and its tributaries in collaboration with the LIFELimnoPirineus project (NAT/ES/001210), a Fecsa ENDESA project and the Conselh Generau d’Aran,in order to support declining populations. There was no post-releasing monitoring program.4. DiscussionAs significant differences on the success among phases cannot be determined, the conclusionis that there is no phase more related to success than others. However, in most of the captivebreeding programs with cold freshwater species, it is widely known that incubation and juveniles’survival are the most critical phases on its management. This is the case of the well-known salmonidshatcheries [35,39–41]. In the same way, it is also observed in little literature on the ex-situ breeding ofthe genus Cottus [36,37]. A comparison between the bibliography study-cases and results obtainedduring this project, leads us to discuss possible success/failure causes, factors that influence successand possible improvements for each phase. The aim is to improve program’s management efficacyand finally to collaborate with the species conservation.4.1. Analysis of Variation into PhasesReferring to nesting behaviour, factors that cause differences between couples are unknown.They may be intrinsic factors to the funding individuals (age, size, sexual maturity, life history, etc.) orthey may also be factors related to the species’ reproductive behaviour (social behaviour, sex ratio,relative size, etc.)—all of them related to mating success in behaviour studies [42–46], so specificbehaviour studies (ex-situ and in-situ) should be developed in order to improve captive breedingeffectiveness [47]. However, it is observed that there is a limiting factor in this step. The number ofgravid females marks the number of couples that can breed, as far as one male can breed with morethan one female. For that reason, it would be recommendable to increase the ratio of females permale in the event that there are more females than males in the brood-stock or if males do not presentnesting behaviour. The female of the only couple that failed during courtship (couple 16) was biggerthan the male, so relative size could be taken into account in future behaviour studies (also suggestedby Brown [48] and Bisazza and Marconato [42]). Fixation is probably related to the type of breedersand the time during which they are maintained in captivity, as far as the female of couple 12 did notspawn the previous year (probably due to the ovarian obstruction). Fixation success is also probablyrelated to behaviour, because when males failed in the first step, this phase was never completedcorrectly (if the clutch was fixed, it was fixed on the glass). Until further behavioural studies still lack,these types of breeders in the program should be avoided. However, the rate of couples that finallyfixed the egg-laying (50%) is considerably higher than the 20% described in Piccinini et al. [37].When fixation is completed properly, parental care also succeeds (except couple 6, commentedabove). Even though, success and quality of successive phases indicate that major problems occurduring parental care, due to stress and infections. For its importance, those key factors will be discussedlatter in detail. This tendency during parental care—as said before—coincides with problems describedin the two study-cases of Cottus species breeding. However, they describe this phase as critical,together with survival—a phase described as successful in the present study. There is only one case inwhich this phase failed (couple 14), due to a human error in the maintaining. White spot disease problemwas practically solved during the following breeding seasons—as the results show—working on thesame key factors affecting incubation discussed below. In fact, after 2006, survival is considerably highercomparing with Vught et al. [36]. They describe problems in intensively larvae rearing with tanks.129Water 2020, 12, 29864.2. Key Factors Affecting SuccessThere are some principal factors considered to be important for the success of the captive programthat have influenced its development. These factors are stress, infections, type of breeders and timeduring which they are maintained in captivity.It is well-known that stress negatively affects captive breeding [49]. During the first season of theprogram, remarkable territorial behaviours with aggressivity between males were observed. In order toavoid these stress inputs, since 2006, males are situated in different tanks during breeding period. It isalso important to minimize noise in facilities and handling during all breeding phases. During 2009,some stress factors that could be related to the breeding failure were observed (illumination andnoise due to an extensive monitoring with research and education purpose). Moreover, there aremany factors such as illumination conditions that have to be considered as stress factors affectingreproduction, as indicate Raghavan et al. [50]. In that way, some of the differences between facilities(such as background coloration, illumination type or intensity) could affect reproduction success,as observed with couples situated in the new facilities. In fact, with a little modification done during2018 by reducing the impact of overall illumination inside tanks, an improvement in the couple’sbehaviour situated there (19 and 20) was noted. Although it has to be studied in detail, any relationwith smaller tank sizes in the new facilities could be discarded, because other breeding programs suchas Vught et al. [36] describe a much larger brood fish density per tank.Saprolegnia sp. infection is the main cause of problems in the incubation of several captivebreeding programs and hatcheries, and it has a direct effect in their success [35,37,39–41]. The malePyrenean sculpin naturally takes care of the clutch in order to protect it from fungal infections,but their appearance is still inevitable. With the management developed, there are three key factorsused to control its expansion. The first one is temperature. In the first year, when there were notcoolers, the water temperature was higher during summer, rising at 16 ◦C—when average temperaturedoes not reach 11 ◦C during June in Garona River. Coldwater fish are more resistant to infection atlower temperatures [40]. Conversely, higher temperatures increase Saprolegnia’s growth rate [39,51]and favour rapid appearance of fungus while incubating. This can be prevented by maintainingtemperature at a relatively low level (considering 11 ◦C as optimum for that species). The second keyfactor is feeding. High level of organic particles and nitrogenous waste in the water may predisposethe fish to saprolegniasis [40] and reduce efficiency of disease treatments [52]. Although it is importantto feed with organisms similar to the ones that we find in the species natural habitat [29], feeding withorganisms from totally different environments may be safer to prevent infections, especially duringincubation. It has been shown that high salinity prevents saprolegniasis [40,53] and withe spotdisease [54], which could imply that the Artemia sp. is a less probable vector. For that reason, since 2009,during critical periods, we feed exclusively with Artemia sp. Finally, the third key factor: the number ofdays and the kind of treatment during incubation in controlled conditions (artificial incubation) areprobably highly related to the clutch degree of affectation. It has been observed in some occasions (e.g.,couple 14 and 15) that if the male incubates during a longer time, the number of viable eggs can beconsiderably reduced due to a longer exposition to infection. This situation may be also related tofilial cannibalism [55]. Otherwise, when the infection is detected in its start (e.g., couple 17) it can besuccessfully stopped with antifungal treatments in controlled conditions [40,52]. Although the risk ofmanipulation, infections have been controlled in a more efficient way by removing the clutch from thebreeding tank and putting it in the rearing tank in controlled conditions (with also different antifungaltreatments, not tested in this study).The last factor to discuss is the captive breeding method employed. During the captive program,it was observed that captive born females could breed successfully, and wild breeders could bemaintained (with breed success as well) more than a year in captivity. However, in order to avoidproblems related to captivity, it is important to choose a qualitatively and quantitatively appropriatenumber of breeders. Thus, the genetic diversity of the captive stock can be maximized [56,57]. It isalso important to consider population genetics structure (variability within and between populations),130Water 2020, 12, 2986with the aim of maintaining offspring genetic variability [2,29]. Supportive breeding method is basedon the introduction of new genotypes that increase variability and fitness [33,58]. This method raisesthe demographic size in the post-releasing censuses, but it may result in genetic risks which would alsoimply problems in terms of fitness reduction if we do not work with some important parameters [10].In addition, captive populations are frequently established when wild populations of the species are atrisk or have already suffered significant reductions in population size, which limits the number ofindividuals that can be collected, with a small fraction of the genetic diversity [59]. In fact, in somecases, it is not possible to achieve a level of genetic diversity high enough to obtain a self-sufficientcaptive population without compromising wild populations [60]. In such a case, it is fundamental tochoose the most appropriate method of intervention [33].With the aim to avoid the accumulation of all these problems related to the conditions in captivityover time and to reduce the cost of this complex maintenance, as well as to reduce the risk ofaccidents (like the one of the death female in 2015), it is recommendable to opt for a system thatreduces the duration of the breeding period—as pointed out by Philippart [29]—with wild adultsthat are sexually mature and that have been collected just before or during the reproduction period.Besides, considering the limited number of individuals of the species of study (due to threat level andthe current situation of the population), it could be positive to return both adults and offspring to wildpopulation after the breeding (as has been done in 2017 and 2018). Thus, the total number of breedersof the breeding program will progressively increase over the necessary seasons and it will not have toomuch impact on the current population.Precisely, due to this limitation, it has been impossible to carry out replies or different treatmentsof the factors previously discussed. It has been done this way to avoid risks for breeders and offspring.When the species status improves, it will be possible to develop experimental designs that betterexplain the influence of the different factors, and it would underline possible differences betweenphases (that are not significant in the current model).5. ConclusionsIt has not been possible to: (1) determine the critical phases of the reproductive cycle undercaptive conditions—as the expected differences between phases are not significant. Despite that fact,(2) observing variation into phases, many factors that might be related to its success are detected—suchas the ratio of females per male, relative size between individuals of the couple and proper courtship.Avoiding stress factors (by reducing illumination and noise); controlling infections by keepingtemperature at 11 ◦C with coolers, by providing accurate feeding (e.g., Artemia sp.) during criticalperiods, and by incubating artificially as soon as diseases appear; and reducing as much as possiblethe period in captivity of the brood-stock and offspring are (3) described as key factors determiningbreeding success and number of offspring produced. Working upon the factors as soon as they havebeen detected (improving progressively the program) has probably (4) allowed to overcome mainimpediments of the most critical steps of the reproductive cycle described in other similar programs.It is true that it has been impossible to carry out replies or different treatments of the factors previouslydiscussed. When the species status improves, it will be possible to develop experimental designs thatbetter explain the influence of the different factors, and it would underline the possible differencesbetween phases (that are not significant in the current model). So, as a general conclusion, the initialaim of the captive breeding program (i.e., to complete the ex-situ breeding cycle) is accomplished withencouraging results in fishes’ maintenance, that may be a little step forward for the captive breeding ofall endangered Cottus species and its long-term conservation.Author Contributions: Conceptualization, J.M., O.C., N.V. and L.B.; Data curation, J.M., O.C. and N.V.;Formal analysis, J.M. and L.B.; Funding acquisition, O.C. and N.V.; Investigation, J.N. and L.B.; Methodology, J.M.,O.C., N.V. and L.B.; Project administration, O.C. and N.V.; Resources, O.C. and N.V.; Software, J.M.; Supervision,O.C., N.V. and L.B.; Validation, O.C., N.V. and L.B.; Visualization, O.C., N.V. and L.B.; Writing—original draft, J.M.;Writing—review & editing, J.M. All authors have read and agreed to the published version of the manuscript.131Water 2020, 12, 2986Funding: Financial support was provided by Fundación Biodiversidad del Ministerio para la TransiciónEcológica, grant number FBCA2015CCHMT and FBCA2018CCHGP; Fundació Zoo de Barcelona, grant numberBPZB2015CCH; and Obra Social “la Caixa”, grant number OSC2018CEAC.Acknowledgments: This project has been possible thanks to I. Afonso (Biodiversity Service, Conselh Generaud’Aran); rural guards from Vall d’Aran, who collaborated with the broodstock collection; rural guards fromCatalunya Central (particularly to D. Flores); technicians from the Servei de Fauna of the Departament de Territori iSostenibilitat of the Generalitat de Catalunya; and finally, to F. Padrós (UAB), for his assessment and his pathologicreports. We are also grateful for the collaboration of E. Valbuena-Ureña, J. Mainé, M. Vilaró, J. Casas, S. Arjó, J.Sañé, Q. Pou, M. Ventura, and X. Bayer, P. Guasch, J. Guasch and C. Guasch from Estudi Divulgació Natura (EDN).Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of thestudy; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision topublish the results.References1. Freyhof, J.; Brooks, E. European Red List of Freshwater Fishes; Publications office of the European Union:Luxembourg, 2011.2. Maceda-Veiga, A. Towards the conservation of freshwater fish: Iberian Rivers as an example of threats andmanagement practices. Rev. Fish Biol. Fish. 2013, 23, 1–22. [CrossRef]3. Miranda, R.; Pino-Del-Carpio, A. Analysing freshwater fish biodiversity records and respective conservationareas in Spain. J. Appl. Ichthyol. 2016, 32, 240–248. [CrossRef]4. Freyhof, J.; Kottelat, M.; Nolte, A. Taxonomic diversity of European Cottus with description of eight newspecies (Teleostei: Cottidae). Ichthyol. Explor. Freshw. 2005, 16, 107–172.5. Kottelat, M.; Freyhof, J. 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Comptes Rendus Biol. 2003, 326, 22–29. [CrossRef]Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutionalaffiliations.© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).134waterArticleInterpopulation Variability in Dietary Traits ofInvasive Bleak Alburnus alburnus (Actinopterygii,Cyprinidae) Across the Iberian PeninsulaDani Latorre 1, Guillem Masó 1,2, Arlo Hinckley 3,4 , David Verdiell-Cubedo 1,Gema Castillo-García 5 , Anni G. González-Rojas 5, Erin N. Black-Barbour 5,Anna Vila-Gispert 1, Emili García-Berthou 1 , Rafael Miranda 6 , Francisco J. Oliva-Paterna 7,Ana Ruiz-Navarro 7, Eduardo da Silva 8, Carlos Fernández-Delgado 9 , Julien Cucherousset 10,José M. Serrano 3 and David Almeida 1,5,*1 GRECO, Institute of Aquatic Ecology, University of Girona, 17003 Girona, Spain;danilatorre1@gmail.com (D.L.); guillem.maso@gmail.com (G.M.); verdiell@um.es (D.V.-C.);anna.vila@udg.edu (A.V.-G.); emili.garcia@udg.edu (E.G.-B.)2 Department of Biodiversity and Ecological Restoration, Instituto Pirenaico de Ecología (IPE-CSIC),22700 Jaca, Spain3 Department of Biodiversity, Ecology and Evolution, Complutense University of Madrid,28040 Madrid, Spain; arlo@ebd.csic.es (A.H.); jomserra@ucm.es (J.M.S.)4 Conservation and Evolutionary Genetics Group, Estación Biológica de Doñana-CSIC, 41092 Sevilla, Spain5 Department of Basic Medical Sciences, USP-CEU University, 28925 Alcorcón, Madrid, Spain;g.castillo6@usp.ceu.es (G.C.-G.); ag.gonzalezrojas@usp.ceu.es (A.G.G.-R.); e.black@usp.ceu.es (E.N.B.-B.)6 Department of Environmental Biology, University of Navarra, 31080 Pamplona, Spain; rmiranda@unav.es7 Department of Zoology and Physical Anthropology, University of Murcia, 30100 Murcia, Spain;fjoliva@um.es (F.J.O.-P.); anaruiz@um.es (A.R.-N.)8 Department of Anatomy, Cell Biology & Zoology, University of Extremadura, 06006 Badajoz, Spain;edasilva@unex.es9 Department of Zoology, University of Córdoba, 14071 Córdoba, Spain; ba1fedec@uco.es10 CNRS, Université Toulouse III Paul Sabatier, ENFA, UMR 5174 EDB (Laboratoire Évolution & DiversitéBiologique), 31062 Toulouse, France; julien.cucherousset@univ-tlse3.fr* Correspondence: david.almeidareal@ceu.esReceived: 13 June 2020; Accepted: 4 August 2020; Published: 5 August 2020Abstract: The bleak Alburnus alburnus is native to most of Europe. This cyprinid fish is a successfulinvader in the Iberian Peninsula. No studies exist on its foraging strategies on a large scale for thisecoregion. The aim of the present study was to compare dietary traits of invasive bleak among themain Iberian rivers and a ‘reference’ native bleak population from France. Bleak were sampled duringMay–June 2019 from the Iberian Rivers Ebro, Tagus, Guadiana, Segura and Guadalquivir and theRiver Saône (France). Diptera larvae and zooplankton were common food categories in the RiverSaône. Insect nymphs were more important in the River Ebro. The intake of plant material was higherin the River Tagus. Flying insects were more consumed in the River Guadiana. Nektonic insectswere important in the River Guadalquivir. Detritus was a frequent food category for all populations,in terms of occurrence and mass. Dietary parameters followed a unimodal response in relation to thelatitudinal gradient, with the maximum values for the Tagus and Guadiana populations. Overall,results suggest that this wide interpopulation variability will contribute to the species’ successfulestablishment throughout Mediterranean Europe, which poses a serious risk to its highly valuablenative fish fauna.Keywords: gut content analysis; Mediterranean rivers; prey richness; trophic niche breadth; Spain135Water 2020, 12, 22001. IntroductionThe disruptive effect of fish invasions is of particular conservation concern in the Iberian Peninsula,which is rich in endemisms. Indeed, >50% of native fish species are unique to this region [1,2], but theproportion of non-native fishes (>30%) continues to increase [2,3]. The bleak Alburnus alburnus (L., 1758)is a cyprinid species native to most of Europe, from the northern Pyrenees to the Urals. In its nativearea, bleak inhabit lakes or still-waters in medium/large rivers and feed chiefly on zooplankton [4–6].In the Iberian Peninsula, this species was mainly introduced in reservoirs during the 1990s as a ‘foragefish’ for non-native piscivorous fishes, such as northern pike Esox lucius L., 1758, largemouth bassMicropterus salmoides (Lacepède, 1802) or pikeperch Sander lucioperca (L., 1758) [7]. Since its introduction,the bleak has displayed a strong invasive character throughout Spain and Portugal, taking advantageof anthropogenic disturbances on Mediterranean freshwater ecosystems [8–10]. Moreover, this speciesthreatens several Iberian fishes, mainly through hybridization, trophic competition and behavioralinterference (i.e., aggression) [3]. This is because Iberian fish communities have low diversity andare poorly adapted to a such invasion level of non-native fishes [1], which usually displace nativespecies from available resources. As an example, direct observations by snorkeling have clearly shownthat foraging behavior of endemic Ebro nase Parachondrostoma miegii (Steindachner, 1866) is disturbedby bleak [11]. All this information on bleak traits (e.g., spread capacity, environmental impacts)allows to consider this species as a clear invasive fish of high ecological risk in Iberia [2,12]. However,the available information on bleak dietary traits in the Iberian Peninsula corresponds to a few particularsites only [13,14], with no study comparing data at the scale of this entire ecoregion. This would be ofparticular interest to reveal macroecological patterns (e.g., latitudinal gradients). Indeed, dietary traitsof non-native fish can show wide variability under contrasting ecological conditions [14–16] and thishas implications from a conservation perspective. Thus, a better understanding of dietary data canbe used by policy-makers and environmental managers to assist monitoring programs in identifyingwhich other areas are likely to be colonized by this invasive species [2]. Therefore, the information inthe present paper is highly relevant to understand invasion features of this fish species throughoutIberian freshwaters.Consequently, the aim of the present study was to assess the interpopulation variability indietary traits of invasive bleak across the Iberian Peninsula. For this purpose, bleak populations werecompared among the main rivers within this region, along with a native bleak population from France.Specifically, diet composition was analyzed by means of two overall indices: percentages of occurrenceand ingested mass; and four dietary parameters were examined: ingested mass, prey richness, trophicdiversity and trophic niche breadth.2. Materials and Methods2.1. Study AreasFive main Iberian rivers were sampled for bleak along a latitudinal gradient (from North to South):Ebro (41◦47′30” N–1◦05′24” W and 41◦26′39” N–0◦28′26” W), Tagus (39◦49′07” N–4◦20′25” W and39◦58′12” N–4◦42′17” W), Guadiana (38◦59′58” N–5◦51′52” W and 38◦50′18” N–6◦13′53” W), Segura(38◦06′05” N–1◦17′50” W and 38◦04′54” N–0◦53′37” W) and Guadalquivir (37◦37′01” N–5◦35′45” Wand 37◦30′46” N–5◦56′38” W). These geographic coordinates correspond to two sampling sites perriver (first coordinates are for sites located upstream). For comparative purposes, we surveyed a mainriver within the bleak’s native range close to the Pyrenees, i.e., historically a potential ‘donor region’for non-native fish introductions to the Iberian Peninsula along the so-called ‘Perpignan–Barcelonacorridor’ (see Clavero and García-Berthou [17] for details on invasion routes). Thus, the River Saône(Rhône drainage, eastern France) was selected as a ‘reference’ population, although only one site(47◦02′13” N–5◦06′52” E) was finally surveyed. In any case, a comparable number of bleak specimens(200 individuals, see below) were collected from the River Saône and data from the two sampling sitesin Iberian rivers were pooled for statistical analyses, as no ‘site effect’ was found (see Data Analyses136Water 2020, 12, 2200below). All the study rivers were selected because they represent large water courses (i.e., >500 kmriver length), where bleak are widely distributed and reach relatively high abundances within the localfish assemblages. A final key point for selection of these particular Iberian rivers was that bleak weresequentially introduced into these different catchments and thus, their populations will potentiallyreflect specific ‘invasion stages’ on the examined dietary traits (e.g., Závorka et al. [18]). Specifically,bleak were introduced into the study rivers in the following years (first mention) [7]: Ebro in 1992,Guadiana in 1999, Segura in 2004, Tagus in 2005 and Guadalquivir in 2006. The climate in the study areaof the River Saône is Temperate Oceanic (800–1000 mm of mean annual rainfall, 10–13 ◦C mean annualtemperature) [19], the most common climatic conditions for the bleak’s native range in Europe [6].The climate regime for the study Iberian rivers is typical Mediterranean, with rainfall concentrated inautumn–winter (≈500 mm) and intense summer drought (<100 mm). The mean annual temperatureranges between 15–18 ◦C. The lowest temperatures occur in winter (down to −5 ◦C) and the highestin summer (>35 ◦C) [20]. The bed geomorphology was similar between rivers, mainly consisting ofpebbles and gravel, although the fraction of silt was slightly higher in the River Saône.2.2. Field and Laboratory WorkFish were collected from May to June 2019, just before the spawning period of bleak for each studyarea (authors, pers. obs.), thereby avoiding any effect of the ‘reproductive status’ on foraging habits.Moreover, the year 2019 is considered to have been hydrologically ‘average’ in the study areas [19,20].As a result, the effects of particular dry or wet years on dietary traits are avoided within our study.Sampling sites (n = 11, two per Iberian river plus one in the River Saône) were selected to encompasssimilar environmental conditions. Specifically, sites were located in well-regulated middle reachesof the main channel, where water level fluctuations are controlled throughout the year, avoiding theeffects of strong increases/decreases in river discharge. Water velocity was registered in each site tocheck the values were similar among populations for a better comparability. These habitat conditionsare representative of large rivers in both the native and Iberian ranges. Also, sampling sites werelocated in the vicinity of similar surroundings (e.g., land use for agricultural exploitation) and farfrom the influences of main tributaries and towns. Sites were separated >50 km within each Iberianriver to ensure the data were more representative from the study areas and also to minimize datadependence among sampling sites per river. Given that water temperature has a strong influence onfish metabolism and food digestion, this parameter was measured per site to check that variabilitywas in accordance with the study latitudinal gradient (i.e., colder waters to the North and warmerwaters to the South). A variety of consistent sampling protocols was followed by wading and fromboats according to the European legislation (CEN/ISO Standards, EC Directive 2014/101/UE [21]).This allowed obtaining a representative sample of bleak across the broadest possible body size rangefrom each river. Catch methods consisted of following a zigzagging and upstream direction in bothbanks at each site (100-m river length) by electrofishing (2000 W pulse DC generator at 200–250 V,2–3 A, 30 min per bank), dip nets (1.5-m-long pole, 30-cm-diameter net, 10-mm mesh size), seine nets(20 × 2 m, 10-mm mesh size) and gill nets (20 × 1 m, 10-mm mesh size, 50% hanging ratio, 1.5-m deep).All surveys followed the same sampling protocols (e.g., proportional effort in terms of people andtime) to ensure comparability among the study rivers. Additionally, a professional fisherman sampledfor bleak in the River Saône. To encompass the existing environmental variability, fish were collectedfrom all meso-habitats present in the study rivers (e.g., runs, pools, shallows), as bleak can be foundunder contrasting river conditions in the Iberian Peninsula [10,14].After each survey was concluded, bleak were immediately euthanized by immersion in anoverdose solution of anesthetic (MS-222) for 15 min, followed by severance of the spinal cord. Fish werestored on ice during transport to the laboratory (<2 h within the same sampling date). Individualsof the remaining fish species were identified and counted. Native species were kept in a tank withsupplied oxygen (two battery-operated aerators with portable pumps) until fully recovered beforebeing released. Other non-native species were euthanized (see details above). All field procedures137Water 2020, 12, 2200complied with animal use and care regulations of Europe (specific licenses were granted for scientificfield research). Fishes were collected by trained personnel and thus, no adverse effects were caused onthe wildlife/habitat in the sampling sites, with all native fish being fully recovered.On arrival at the laboratory, bleak individuals (n = 1200, 100 ind. × 2 sites × 5 Iberian rivers+ 200 ind. × 1 site from the River Saône) were measured for standard length (SL,±1 mm). This particularfish length was selected because it avoids ‘noise’ given by variation of caudal fin length not related tobody size (e.g., wounds and cuts in the fish skin and rays). Specifically, bleak size ranged between40–195 mm SL. Fish were dissected to examine the sex and collect the digestive system. Only theanterior one-third of the intestinal tract was preserved in 4% formalin for subsequent examination toavoid food remains severely digested (see a similar procedure in Latorre et al. [13]). Food items wereidentified to the lowest possible taxonomic level (e.g., Tachet et al. [22]) using a dissecting microscope(40×) and weighed using an electronic balance (±0.1 mg).2.3. Data AnalysesPreliminary analyses did not find any difference between the sexes for the examined dietaryvariables (all p-values > 0.05). Consequently, this categorical factor was not included in subsequentdata analyses to simplify the analytical models and thus, increase the statistical power of the remainingsources of variation, which would otherwise be seriously compromised (see a similar procedure inAlcaraz and García-Berthou [23]). Given that percentages of empty stomachs were similar acrosspopulations (25–35%), this parameter was not statistically analyzed. In addition, data were pooled perIberian river because the effect of ‘sampling site’ within each water course was not significant for anydietary variable (all p-values > 0.05), after performing Generalized Linear Mixed Models (GLMMs)with ‘site’ as the random factor (see a comprehensive review of this statistical technique in Johnson etal. [24]). This analytical approach (i.e., pooled data) considerably improved the clarity of results andalso facilitated biologic interpretation.Two overall dietary indices were calculated (omitting empty guts) and expressed as a percentagefor each food category (e.g., Latorre et al. [13]): ‘occurrence’ (frequency of fish guts in which a particularfood category occurred relative to the total number of fish individuals) and ‘ingested mass’ (frequency ofthe mass of a particular food category relative to the total ingested mass in all the examined intestinaltracts). Both of these percentages indicate whether a given food item is commonly eaten within thepopulation and whether this food category is energetically important to the population. Furthermore,four dietary parameters were also calculated for each fish (e.g., see Almeida et al. [16] for details on theparticular formulae): ingested mass (mg), prey richness (S), trophic diversity (Shannon index, H’) andtrophic niche breadth (standardized Levin’s index, B). These parameters were selected as differentmeasures of trophic plasticity to avoid potential bias of using only one parameter. Consequently,any macroecological pattern (e.g., latitudinal gradient) in dietary traits will be much clearer revealed ifprofiles among rivers are similar for several parameters.Given that previous studies revealed significant relationships between dietary parameters andbleak size (an approach of age for fish) in Iberian waters (e.g., Almeida et al. [14]), linear regressionswere used, which showed similar slopes among the populations (all p-values > 0.05), with low andpositive values. Consequently, analysis of covariance (ANCOVA) was used to reveal significantdifferences between populations (i.e., rivers) for the four dietary parameters. The effect of body sizewas controlled by using SL as the covariate. ANCOVAs were followed by a post hoc Tukey–Kramerhonestly significant difference (HSD) test. Data were transformed by using ln (x + 1). Assumptions ofnormality of residuals and homogeneity of variances were verified through residual plots. Statisticalanalyses were performed with R version 3.1.3 [25]. The significance level was set at α = 0.05. SequentialBonferroni corrections were performed for every set of multiple tests.138Water 2020, 12, 22003. ResultsDiptera larvae (e.g., Chironomidae and Simuliidae), zooplankton (e.g., Cladocera water fleas)and detritus were the most frequent food categories for the reference native population (i.e., theRiver Saône), both in terms of occurrence and ingested mass (Table 1). The Ebro population showeda similar diet composition as per native bleak, although Ephemeroptera and Plecoptera nymphswere also important prey items. For the rest of study rivers, percentages of food categories werehighly variable (Table 1). More in detail to be highlighted per river: vegetation and other benthicinvertebrates (e.g., freshwater snails) were frequent in the River Tagus; flying insects (e.g., wasps,mosquitoes or butterflies) and Diptera larvae were important in the Guadiana and Segura populations,respectively; benthic invertebrates (e.g., Diptera larvae, insect nymphs, Mollusca) and nektonic insects(e.g., water boatmen or predatory beetles) were very important in terms of ingested mass (>40% and≈20%, respectively) for the River Guadalquivir. Detritus was an important food category for all bleakpopulations: 21–42% in occurrence and 13–27% as ingested mass (Table 1).Table 1. Diet composition of bleak Alburnus alburnus in the study rivers. Percentages of occurrences(Oc., %) and ingested masses (Mass, %) are presented.River: Saône Ebro Tagus Guadiana Segura GuadalquivirFood Category Oc. Mass Oc. Mass Oc. Mass Oc. Mass Oc. Mass Oc. MassAlgae and plant debris 3 <1 2 5 29 17 5 3 3 <1 8 9Zooplankton a 50 17 40 15 11 8 1 <1 5 3 10 6Ephemeroptera and Plecoptera nymphs 5 9 33 21 5 10 20 23 7 21 5 12Odonata nymphs 1 2 11 6 1 5 5 9 1 <1 1 3Diptera larvae 69 39 27 15 3 15 3 1 59 31 3 16Trichoptera larvae 6 1 5 <1 1 <1 15 13 6 11 1 5Other benthic invertebrates b 12 4 3 3 53 13 1 <1 12 1 71 14Nektonic and neustonic insects c 1 <1 1 <1 1 <1 1 <1 1 <1 20 19Flying insects d 3 <1 5 8 35 7 61 23 3 19 – –Terrestrial arthropods e 1 <1 1 <1 – – 1 9 1 <1 7 2Detritus 31 27 25 26 42 24 24 18 31 13 21 14a Acari, Cladocera and Copepoda; b Mollusca, Oligocheta and Coleoptera larvae; c Corixidae, Gerridae andDytiscidae adults; d Hymenoptera, Diptera and Lepidotera; e Araneae and Formicidae.Regarding dietary parameters and after accounting for fish length, significant differences werefound between bleak populations for ingested mass (F5,812 = 32.94, p < 0.001). Four distinct groupswere found (from low to high adjusted mean): Saône/Segura (≈12 mg), Guadalquivir (≈25 mg),Guadiana (≈40 mg) and Tagus (almost 60 mg) (Figure 1a). Differences were also found for preyrichness (F5,812 = 18.68, p < 0.001). Two river groups were found: Ebro/Saône (S ≈ 3–4 prey items)and Guadiana/Guadalquivir/Tagus (S ≈ 5 prey) (Figure 1b). Significant differences were foundbetween populations for trophic diversity (F5,812 = 19.76, p < 0.001). Two river groups were found:Saône/Ebro/Guadalquivir (H’ ≈ 1) and Guadiana/Tagus (H’ > 1.5) (Figure 1c). Significant differenceswere also observed between rivers for trophic niche breadth (F5,812 = 20.49, p < 0.001). Two distinctgroups were found: Saône (B ≈ 1.4) and Tagus/Guadiana (B > 2); while the remaining three riversshowed intermediate B values (1.6–1.7) (Figure 1d).139Water 2020, 12, 2200were found (from low to high adjusted mean): Saône/Segura (≈12 mg), Guadalquivir (≈25 mg), Guadiana (≈40 mg) and Tagus (almost 60 mg) (Figure 1a). Differences were also found for prey ≈≈≈≈ (a) (b) Figure 1. Cont.140Water 2020, 12, 2200 (c) (d) et al. [4]; Vašek and Kubečka [5]), whereas bleak diet has received less attentiFigure 1. Comparison of dietary parameters for bleak Alburnus alburnus among the study rivers. Resultsare adjusted means ± SE (ANCOVA, covariate: SL). Letters above bars indicate significant differencesbetween bleak populations (Tukey’s HSD tests, p < critical p-value from Bonferroni correction). (a):ingested mass. (b): prey richness. (c): trophic diversity. (d): trophic niche breadth.4. DiscussionsGiven that the bleak mainly inhabits stillwaters in its native area, this species is well-adapted toplay an ecological role as an openwater feeder, with diet being chiefly based on zooplankton [4,5].Similarly, planktonic Crustacea were very important for native bleak in the River Saône. However,the main food categories were Diptera larvae and detritus (i.e., benthic food items) for this referencepopulation. This discrepancy between scientific literature and the present results for the native areawas probably because most studies on bleak have been performed in lakes and reservoirs (e.g., Vinniet al. [4]; Vašek and Kubečka [5]), whereas bleak diet has received less attention in flowing waters.141Water 2020, 12, 2200However, even in lentic environments, bleak can modulate this zooplanktivorous strategy at eachparticular habitat patch to increase resource partitioning and consequently, reduce competition withcoexisting cyprinid fishes, such as roach Rutilus rutilus (L., 1758) or freshwater bream Abramis brama(L., 1758) [4,5]. Dietary indices were more similar between the River Saône and the River Ebro, the(geographically) closest study area to the reference population. This was also observed by Latorreet al. [26]) for other biologic traits (i.e., growth and reproduction), which was explained in termsof climatic and hydrological ‘proximity’. However, the intake of Diptera larvae and insect nymphswas different between the Saône and Ebro populations. In the River Saône, particle size was finer(see Methods section), with these bed conditions being very suitable for Diptera larvae (J. Cucherousset,pers. obs.), whereas substrate was coarser in the River Ebro (see Methods section), providing a highavailability of nymphs (R. Miranda, pers. obs.). Other food items were prominent among the studypopulations, including a wide variety of trophic resources in terms of contrasting ecological features:vegetation, neuston, nekton and benthos. Thus, consumption of algae and plant material was higherin the River Tagus. In this respect, Vinni et al. [4] demonstrated that intra- and mainly inter-specificcompetition were key causes to understand this result (i.e., high use of vegetation) in the native area.In relation to this finding, Tagus River showed the highest value for ingested mass, resulting in onlyone ‘statistical group’. This may compensate a higher proportion of vegetation in the diet (see Resultsabove). As a support for this interpretation, Latorre et al. [26] did not find any effect of this ‘lownutritious’ feeding type (vegetation) on the growth rate in the River Tagus, with this populationshowing the highest values. Particularly for detritus, bleak showed a high consumption in the studyIberian rivers, which was also found elsewhere within this ecoregion, from streams to reservoirs [13,14].As a potential explanation, bleak may accidentally ingest plant material and detritus while feedingon sheltered invertebrates (D. Verdiell-Cubedo, pers. obs.). In the Guadiana population, bleak tookadvantage of prey from the water surface, such as fallen flying arthropods (D. Almeida, pers. obs.),which results in a low effort in terms of energy investment. Bleak caught prey from the water columnin the River Guadalquivir, with this population displaying a ‘costlier’ foraging alternative, as nektonicinsects (e.g., adult Dytiscidae beetles) require higher pursuit and capture efforts. However, moreimportant, bleak fed on a great variety of benthic prey, from insect nymphs and larvae to mollusksand annelids, in all river populations. This finding indicates an elevated capacity of this fish to usefood resources apparently less suitable to its morphologic adaptations (e.g., conspicuous superiormouth). Overall, these results suggest that bleak could deeply change the foraging strategy, from a‘pelagic’ to a ‘benthic’ feeder, even within the same population (see Results for benthos and nektonin the River Guadalquivir). This capacity may aid bleak to better thrive in contrasting Iberian rivers,where zooplankton availability may be a more limited trophic resource [14]. Such a significant dietaryshift has been also observed in the Iberian Peninsula for other invasive fishes adapted to a more stablehydrological regime in their native ranges, although they are phylogenetically and geographically very‘distant’ to the bleak (e.g., North American centrarchids, see Almeida et al. [16]).Regarding dietary parameters, variation ranges for the four examined predictors were similar asper other study areas in the Iberian Peninsula (Latorre et al. [13] in the NE; Almeida et al. [14] in the SW).The present data were obtained from large rivers and the just mentioned studies included streams andreservoirs. Thus, these overall results indicate that Iberian bleak populations possess a high capacity ofadapting to the particular habitat conditions where this invasive fish inhabits. No clear pattern wasobserved for any dietary parameter in relation to the year of introduction, which was in accordancewith Latorre et al. [26] for growth and reproduction traits. This lack of relationship is probably becausethe establishment stage was reached in a few years after bleak introductions (e.g., Bøhn et al. [27]).As an example, a rapid and wide establishment of this species has been recently demonstrated in theRiver Segura [10]. Nevertheless, it must be clearly stated that data were limited (n = five Iberian rivers).However, unlike the year of introduction, the foraging strategy appeared to show a slight relationship withthe latitudinal gradient, specifically a unimodal response [28], with Guadiana and mainly Tagus reachingthe maximum values for all dietary parameters. In addition, the Tagus population showed the highest142Water 2020, 12, 2200backcalculated lengths at all ages and growth index among the same study rivers (see Latorre et al. [26]).A potential explanation is that bleak may be more favored by a moderate Mediterranean climate incentral Iberia during the prespawning period (warmer) than temperate conditions in the native area(colder). Yet, bleak are affected by slightly more ‘severe’ ecological conditions in southern Iberia,e.g., oxygen level was lower because of higher temperature (physicochemical data not shown, but inaccordance with the expected latitudinal gradient) [29]. More in detail, these ‘benign’ conditions incentral Iberia may improve the physiological status (e.g., metabolic rate) of bleak, which facilitatesthis fish species to use alternative prey and widen its diet, with the corresponding increase in thetrophic variability. In contrast to this ‘generalist’ strategy, more northerly and southerly populations(i.e., distant from the optimum in terms of habitat quality, see theoretical aspects in Oksanen andMinchin [28]) displayed a more ‘specialist’ strategy, reducing the complexity of diet composition.Nevertheless, these ecological interpretations must be understood with caution, as similarly to theprevious statement, data were limited to a few locations (n = five Iberian rivers) and moreover, theassessed latitudinal amplitude was relatively narrow (only 10◦ of Latitude, 37–47◦ N).Ecological responses such as dietary traits usually show wide variability in non-native species wheninvading new habitats, with this being particularly clear in freshwater fishes [13,15,16]. In the IberianPeninsula, invasive fishes usually display wide trophic plasticity under contrasting environmentalconditions, which contributes to improve their invasion process [3,13,14]. Accordingly, dietary traitswere clearly variable among the study rivers, indicating a wide ‘flexibility’ in bleak foraging strategiesdependent on particular river conditions (i.e., habitat heterogeneity, food supply). Studies on bleakdiet exist across Iberia in which resource availability were not assessed [13,14]). However, otherstudies on feeding habits of invasive fishes truly estimated food supply as a measure of trophicresources in this ecoregion (e.g., Almeida et al. [16,30]), comparing this availability to the use (i.e., bleakdiet), and specifically calculating electivity indices (i.e., selection). The present study did not assessresource availability because of technical restrictions (in terms of materials and personnel) to properlyquantify the biomass of plant, benthos and plankton in large rivers, which poses a strong studylimitation. Thus, abundance of contrasting food items is a key factor to be analyzed for future researchon bleak invasion. Similarly—as per other biologic attributes (e.g., growth and reproduction)—thehigh interpopulation variability is considered a mechanism for bleak to successfully invade novelMediterranean freshwater ecosystems, from streams to large rivers [8,10,26]. Indeed, Iberian endemicspecies are specialized to narrow trophic niches [1] or alternatively, these fishes may display highplasticity to the natural variability in Mediterranean rivers. Irrespective of this specific mechanism, thelow level of coevolutionary competition is a potential cause why Iberian fish fauna is not adapted tostrong invasive competitors, such as bleak. This is one of the main reasons to consider this fish speciesas a bioinvader of high risk in the Iberian Peninsula (see Table 1 in Almeida et al. [2]). Consequently,environmental managers should apply urgent conservation measures to control bleak populationsacross Iberian freshwaters. First, these measures should focus on the target species (i.e., fish culling).Second—and according to other studies on bleak in Iberian waters [9]—actions on aquatic habitatsshould be implemented to restore the ‘Mediterranean conditions’ of natural flow regimes that benefitnative fish communities and hamper invasive species.Author Contributions: Conceptualization, D.L., G.M., A.V.-G. and D.A.; methodology, D.V.-C., E.G.-B., J.C., E.d.S.,C.F.-D., R.M., F.J.O.-P., A.R.-N., J.M.S.; software, D.L., G.M. and D.A.; validation, D.L., G.M., D.A.; formal analysis,D.L., G.M., A.H. and D.A.; investigation, D.L., G.M., A.H. and D.V.-C.; resources, D.V.-C., E.G.-B., J.C., E.d.S.,C.F.-D., R.M., F.J.O.-P., A.R.-N., J.M.S.; data curation, D.L., G.M., G.C.-G., A.G.G.-R., E.N.B.-B.; writing—originaldraft preparation, D.L., G.M., A.V.-G. and D.A.; writing—review and editing, D.V.-C., G.C.-G., E.N.B.-B., A.G.G.-R.,E.G.-B., J.C., E.d.S., C.F.-D., R.M., F.J.O.-P., A.R.-N., J.M.S.; visualization, D.L., G.M., G.C.-G., E.N.B.-B., A.G.G.-R.,A.V.-G. and D.A.; supervision, A.V.-G. and D.A.; project administration, D.A.; funding acquisition, E.G.-B., J.C.,E.d.S., C.F.-D., R.M., F.J.O.-P., A.R.-N., J.M.S. All authors have read and agreed to the published version ofthe manuscript.Funding: This research was partly supported by the Spanish Ministry of Education (FPU 13/03291), the SpanishMinistry of Economy and Competitiveness (projects CGL2015-69311-REDT and CGL2016-80820-R) and theGovernment of Catalonia (ref. 2014 SGR 484).143Water 2020, 12, 2200Acknowledgments: Arlo Hinckley held a ‘collaboration’ grant from the Complutense University of Madrid (Spain).Gema Castillo-García held a ‘pre-graduate’ grant from the Universidad San Pablo-CEU (Spain). The anonymousreviewers improved the manuscript.Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of thestudy; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision topublish the results.References1. Reyjol, Y.; Hugueny, B.; Pont, D.; Bianco, P.G.; Beier, U.; Caiola, N.; Casals, F.; Cowx, I.; Economou, A.;Ferreira, T.; et al. 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This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).145waterArticleAssessing the Fish Stock Status in Lake Trichonis:A Hydroacoustic ApproachTriantafyllia-Maria Perivolioti 1,*, Jaroslava Frouzova 2,3, Michal Tušer 2 and Dimitra Bobori 11 Department of Zoology, School of Biology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;bobori@bio.auth.gr2 Institute of Hydrobiology, Biology Centre of the Czech Academy of Sciences,370 05 České Budějovice, Czech Republic; jaroslava.frouzova@hbu.cas.cz (J.F.);michal.tuser@hbu.cas.cz (M.T.)3 Institute for Environmental Studies, Faculty of Science, Charles University, Benátská 2,128 43 Prague, Czech Republic* Correspondence: triaperi@bio.auth.grReceived: 13 April 2020; Accepted: 23 June 2020; Published: 25 June 2020Abstract: Fish stock monitoring is an important element for the sustainable management of inlandwater resources. A scarcity of data and the lack of systematic monitoring for Lake Trichonis precludesan up-to-date assessment. To assess the current status of pelagic fish stock, a hydroacousting surveywas conducted for the first time in Lake Trichonis, Greece. In October 2019, the lake was acousticallysurveyed with two, horizontally and vertically mounted, 120 kHz transducers during day and night.A decreasing gradient in pelagic fish density from the western to the eastern shores of the lake wasobserved. Fish density was significantly higher in the intermediate layers of the water column, in theeastern region, compared to the western region. The lake appears to host primarily communities ofsmall-sized fish (TL: 0–5 cm), whereas larger fish (TL: 5–50 cm) are a small minority of the total fishstock. The overall average estimated fish length was approximately 2.4 cm. The adoption of routineinland fish stock monitoring through hydroacoustic methods could be a promising step in the effortto improve the understanding of unique inland water ecosystems with minimum impact on endemicspecies, as well as to mitigate human impact and achieve long-term sustainable management.Keywords: acoustics; fish density; fish distribution; Mediterranean lake1. IntroductionWithin Europe, in order to eliminate or mitigate freshwater degradation, the Water FrameworkDirective (WFD) calls for all the natural aquatic ecosystems to achieve a “good” ecological status.To date, inland fisheries management is focused on improving the aquatic environment for biodiversity,allowing the sustainable exploitation of the resources and verifying conservation and protection offish and fisheries [1]. However, the methodologies adopted by researchers for the monitoring offreshwater fish stocks, such as gillnetting, are often unable to support an ecosystem–based managementapproach [2], as they fail to comprehensively address the spatiotemporal variability of fish stocks [3],require high effort [4] and often result in a reduction of fish biomass [5].In this context and considering the proposed WFD goals, there is a strong demand for standardized,reliable, reproducible and non-invasive methods for routine monitoring programs. Satellite-basedEarth Observation data provide safe and cost-effective approaches for the systematic monitoring ofwater bodies [6] and fish environmental preferences but fail when it comes to the direct detectionof fish [7]. On the other hand, hydroacoustic methods have been applied in all kinds of aquaticecosystems in order to acquire detailed information about aquatic fauna, and especially about fish [8].147Water 2020, 12, 1823In freshwater ecosystems, hydroacoustic methods are used for assessing fish biomass, fish behaviour,stock abundance and fish size distribution [8]. The most widely used scientific echosounders infreshwaters operate at single frequencies of 38, 70, 120 and 200 kHz [9]. The main advantage ofhydroacoustics is the capability to remotely sample large volumes of water in a relatively short amountof time, providing high spatial resolution in both the horizontal and the vertical domain. In addition,acoustic measurements are non-invasive and non-extractive [8,10]. Moreover, the technologicalimprovements in this field and their increased precision have contributed to their extensive use inboth marine and freshwater ecosystems [11–13]. Although there are several limitations of currenthydroacoustic methods, e.g., lack of species identification, difficulties in shallow waters, and limitationsin noisy environments, and despite the considerable need for further research, the application ofhydroacoustics is based on a well-established, standardized methodology for inland fishery productionassessment and management [14].Geographically, hydroacoustics research in freshwater ecosystems has so far been concentrated inNorth America and Northern and Central Europe (83% of the studies reviewed in [15]). Although theMediterranean Basin is found to be globally important for its freshwater biodiversity [16], few studieshave been published with respect to the application of hydroacoustic methods (e.g., [17]). In Greece,hydroacoustics research has so far been applied in marine systems, mostly focusing on the applicationof acoustic techniques to monitor marine fisheries resources (e.g., [18,19]). Nevertheless, hydroacousticresearch on Greek inland waters has not been conducted before.Inland fishery production in Greece is impacted by overfishing and habitat alteration, as well aspollution [20]. Professional fisheries are operated primarily in large lakes. Lake Trichonis, the largestnatural lake in Greece, bears very high economic, cultural and ecological significance. The lakeis important for its fish fauna since 20 species have been recorded in its waters, 11 of which areendemic (see detailed list in [21,22]), among which there are unique species such as Economidichthystrichonis. The species present in the lake are classified into 8 different systematic families, which,ecologically, span all available ecological niches (herbivores, benthivores, planktivores and carnivores).Among them, there are carnivorous species such as Anguilla anguilla and Silurus aristotelis that feedat night, predominantly on fish [23,24]. The majority of fish species inhabiting Lake Trichonisbelong to Cyprinidae and spawn during spring to early summer [25–28]. Moreover, small gobies arereproducing in the same period [25]. Additionally, the lake belongs to one of the most importantfishing attractions of the country. In fact, a land-locked, not overexploited population [29] of Atherinaboyeri is also present in the lake, which offers, with the exploitation of other species such as Rutiluspanosi and Scardinius acarnanicus, economical support for professional fishers. According to the localFisheries Department, the primary catch composition for the period of 1989–1998 is made up of smallspecies. In particular, in October 1989, A. boyeri (Lmax = 13.5 cm [30]; Lm = 5.8 cm [31]) comprisedapproximately 64%, while Tropidophoxinellus hellenicus (Lmax= 9.3 cm [31]) and Scardinius acarnanicus(Lmax = 35.4 cm [32]; Lm = 14–18 cm [31]) contributed each 17% of fisheries production (calculated asthe percentage, per weight, of all catches). In 1990, 47% of the production consisted of T. hellenicus(Lmax = 9.3 cm [31]), while 22% of production was R. panosi (SL = 12.0–14.0 cm [31]). In 1998, 56%of the annual production of the lake consisted of Atherina boyeri while 20% of the production wasScardinius acarnanicus (Lmax = 35.4 cm [32]; Lm = 14–18 cm [31]). Other species (Silurus aristotelis,Luciobarbus albanicus, Carassius gibelio, Tropidophoxinellus hellenicus) comprised approximately 3% of thetotal production. A similar species composition was found to occur during the contemporary fishingcatches (local professional fishermen, pers. comm).Despite its significance, because of limited staff, management authorities are having a hardtime monitoring fish harvests of individuals for the prevention of illegal fishing in Lake Trichonis.Furthermore, the lack of comprehensive long-term studies has resulted in the limited availability ofinformation about fish stock status in the lake. Although historical data exist as a result of researchprojects carried out by universities, institutes and organizations, it is scarce and, in practice, the statusof inland fisheries is largely unknown. Taking all the aforementioned into account, it is important to148Water 2020, 12, 1823adopt a standardized, effective and non-invasive method for the assessment and monitoring of inlandfish stocks.Hydroacoustic techniques could serve as a non-destructive tool for inland fish monitoring.The purpose of this study is to use a hydroacoustic method for obtaining a detailed overview ofthe freshwater fish stock status in Lake Trichonis and highlight the hindrances and improvementsthat need to be considered, in order to adopt this methodology as a common practice in Greek lakes.In particular, we aimed to (i) quantify freshwater fish biomass and fish density, (ii) assess fish sizedistributions, and (iii) study the variation of fish vertical distribution in relation to water abioticparameters. Therefore, despite ongoing progress in hydroacoustics, the present work comprises afirst-step towards the attempts that should be made for fish stock assessment in larger biogeographicalscales [15], and especially in the Mediterranean countries, where high levels of endemicity are observed.2. Study AreaLake Trichonis, located in the central-western part of Greece, has a surface area of 96.9 km2,a maximum length of 19 km, and is the deepest (maximum depth of 58 m, average depth of 30 m)natural lake in Greece (Figure 1). The trophic status of the lake is characterized as oligotrophic [33].Lake Trichonis belongs to the Natura 2000 network established under the Habitats Directive 92/43/EECand is characterized as an area with high potential research value [34]. Responsible for the protection,management and monitoring of the lake is the Management Authority of Messolonghi Lagoon.Water 2020, 12, 1823 3 of 21 adopt a standardized, effective and non-invasive method for the assessment and monitoring of inland fish stocks. Hydroacoustic techniques could serve as a non-destructive tool for inland fish monitoring. The purpose of this study is to use a hydroacoustic method for obtaining a detailed overview of the freshwater fish stock status in Lake Trichonis and highlight the hindrances and improvements that need to be considered, in order to adopt this methodology as a common practice in Greek lakes. In particular, we aimed to (i) quantify freshwater fish biomass and fish density, (ii) assess fish size distributions, and (iii) study the variation of fish vertical distribution in relation to water abiotic parameters. Therefore, despite ongoing progress in hydroacoustics, the present work comprises a first-step towards the attempts that should be made for fish stock assessment in larger biogeographical scales [15], and especially in the Mediterranean countries, where high levels of endemicity are observed. 2. Study Area Lake Trichonis, located in the central-western part of Greece, has a surface area of 96.9 km2, a maximum length of 19 km, and is the deepest (maximum depth of 58 m, average depth of 30 ) natural lake in Greece (Figure 1). The trophic status of the lake is characterized as oligotrophic [33]. Lake Trichonis belongs to the Natura 2000 network established under the Habitats Directive 92/43/EEC and is characterized as an area with high potential research value [34]. Responsible for the protection, management and monitoring of the lake is the Management Authority of Messolonghi Lagoon. Figure 1. Depiction of the study area and lake bathymetry (derived from the digitization of 1:50,000 scale topographic maps, which constitute an appropriate reference source for such geomorphometric information [35–36]) with the hydroacoustic survey and the sampling points overlaid. Figure 1. Depiction of the study area and lake bathymetry (derived from the digitization of 1:50,000 scaletopographic maps, which constitute an appropriate reference source for such geomorphometricinformation [35,36]) with the hydroacoustic survey and the sampling points overlaid.149Water 2020, 12, 18233. Materials and Methods3.1. Hydroacoustic Survey DesignThe hydroacoustic survey was carried out in Lake Trichonis during the period of 3–10 October2019. The pattern of the survey transects was designed in such a way, as to include fish communitiesalong areas of identical or similar depths (i.e., along depth contours) (Figure 1). Horizontal and verticalrecordings were carried out simultaneously using a suitable arrangement.In order to assess the variation in diurnal estimates of fish biomass and distribution, the surveywas conducted during both day and night [37]. The day survey commenced one hour after localsunrise, whereas the night survey commenced one hour after local sunset. Considering the highsensitivity of horizontal recording to the noise introduction into the hydroacoustic records, the surveywas carried out on windless days. In total, two hydroacoustic surveys were needed to cover the wholelake; one during daytime and one during nighttime. The total length of each survey was ~40 km,resulting in a coverage coefficient of 4 per survey [38,39]. Surveys were grouped per time of day,into daytime and nighttime surveys.A Simrad EK60 echosounder with a frequency of 120 kHz, equipped with two simultaneouslyoperating transducers (SIMRAD ES120-7C), was used to collect the acoustic data. The transducers weremounted at a depth of about 1 m, at the front of the boat. The first transducer was vertically orientedand the second one horizontally oriented, enabling fish detection in the surface zone. The horizontaltransducer was oriented perpendicularly with respect to the vertically oriented transducer and wasfacing towards the side of the vessel (perpendicular to the sailing direction). The echosounder wasdriven by the Simrad ER software. The ping interval was set to 0.2 s. Each transducer transmitted2–3 pings per second, alternating between transducers (known as multiplexing), for a total ping rate of5–6 pings per second. During surveying, the boat maintained a constant speed of 6 km/h and a volumeof 3.7 × 106 m3 of water was explored. For the full calibration of the system, a standard copper sphere of23 mm in diameter was used and gains were calculated according to Demer et al., 2015 [40]. The spherewas placed at approximately 9.4 m of depth from the transducer, which was aimed vertically towardsthe sphere. The calibration was performed before starting with the first day survey. The effect ofbackground (passive) noise was removed from all files by subtracting passive listening data collectedbefore the survey. The background noise was checked by stopping pulse transmission and putting theequipment into a passive listening mode. Passive listening was performed on a windless day beforethe acoustic survey.Survey positioning was assisted by a Garmin GPSMAP 60CSx GPS receiver (Geolocation/horizontalaccuracy of 3–5m—95%).3.2. Hydroacoustic Data ProcessingAcoustic raw data were converted to compatible formats and processed using the Sonar5 Prosoftware (CageEye A/S, Oslo, Norway). Fish density values (number of individuals per hectare) werecalculated using the SV/TS scaling method [8], which uses volume back-scattering strength, SV, and themean target strength (TS) to calculate fish density.Acoustic biomass was expressed as the volume backscattering strength (SV), which is the equivalentlogarithmic measure of the volume backscattering coefficient (sV) obtained through echo-integration [8].SV has been found to be a good expression of biomass distributions, especially for small targets [8].Mean target strength (TS) was estimated based on single echo detections (SED) [41]. In order toexpress TS into total length measurements (cm) rather than dB, empirical TS-length relationships wereused. Equations for a 120 kHz echosounder given by [42] in the case of vertical and [43] in the case ofhorizontal recordings were applied. Fish Total Length distribution was categorized into 50 classes by1 cm step, starting from zero.During the data post-processing, the threshold for target strength (TS) was set to −65 dB and forSV to −71 dB, in order to cover the entire possible size spectrum of fish targets [44]. The collected data150Water 2020, 12, 1823were visually examined to remove subsets with noise and non-fish targets, in order to improve thesignal-to-noise ratio of the dataset. A layer of 0.5 m above the detected bottom layer was excluded inorder to eliminate the effects of bottom back-scattering. A surface layer of 4 m was applied in horizontaland vertical recordings in order to exclude the near field. Each transect in both the horizontal and thevertical survey was divided into 200-m segments. In order to assess the vertical distribution of fish, thewater column recorded in the vertical survey was divided into 1-m thick depth layers. The verticaldata were processed up to a depth limit equal to the maximum depth up to the bottom, dependingon the position of the segment within the transect. The horizontal data were processed up to a rangeequal to the maximum depth of the lake. The surface layer ranging from 0–4 m was covered by thehorizontally-aimed transducer and compensates for the lack of data in the near field of the verticaltransducer. Data recorded horizontally were processed using deconvolution procedure. Deconvolutionis a procedure employed in the processing of horizontally recorded data and is especially used for thedetermination of target size. It is based on stochastic assumptions of random aspect orientation [45].The data were taken along the transect, which was divided in segments. In charts of verticaldistribution, the horizontal layer was taken to describe the entire surface layer and is representedby a single depth range. The rest of the vertical layers have been accordingly matched and aligned,below the horizontal layers. The overall arrangement represents a division of the total insonifiedlake volume in cells, each of which spans 200 m × 1 m (along-transectx vertically), with a variableathwart dimension for each depth, as a result of the conically-shaped beam. Exceptionally, the cells ofthe surface layer were sized 200 m × 4 m (along-transect x vertically). The data were either summedor averaged vertically (for vertical analyses) and averaged or summed horizontally (i.e., along thetransect) for horizontal or lengthwise analyses.Due to the potential effects of multiple scattering and acoustic shadowing caused by verydense aggregations of fish on TS and the consequent density estimates, additional measures werechecked and examined. These include the share of single echoes (the ratio between volumebackscattering strength resolved as single echoes and the total volume backscattered strength) andthe Sawada index NV (the number of fish per acoustic sampling volume where TS is estimated) [46](see Supplementary Materials).3.3. Environmental ParametersIn situ measurements of physico-chemical parameters were conducted during the day survey inLake Trichonis at three sampling stations (Table 1 and Figure 1). In order to estimate the temperature(◦C) and dissolved oxygen (DO) (mg/L) vertical profiles, the measurements were carried out at 1-mdepth intervals using a YSI multiparameter probe (YSI Incorporated, Ohio, USA).Table 1. Locations of the 3 sampling stations in Lake Trichonis.Lake Location Latitude (deg) Longitude (deg) Sampled Depth Range (m)Sampling point S1 38.58625 21.48000 22Sampling point S2 38.57250 21.48301 34Sampling point S3 38.53671 21.61033 503.4. Statistical AnalysisThe calculated parameters were mapped along the transect, as well as depth-wise, and wereexplored for differences between day and night. Mean SV (dB), share of single echoes (%) and fishdensity (inds./ha) were averaged per layer, mapped by depth and compared for differences betweenday and night. Cross-correlation analysis was performed on the average fish density depth-wisedistributions using depth-lag to determine the mean depth difference between day and night fishdensity distributions. Fish density was explored for spatial distribution differences, while it was alsocolor-mapped both along transect and depth-wise, in the West-East direction, in order to visualize the151Water 2020, 12, 1823variation in both dimensions at the same time, where transect mileages were matched to lake regionsbased on the transect path. Finally, fish density was also summed depth-wise at each segment andinterpolated spatially to create total-water-column fish density maps for day and night.To study fish density in combination with size composition, the targets were separated in twoclasses, ≤5 cm (small fish) and >5 cm (large fish) and density was re-calculated for small and large fish,respectively, using the corresponding target count ratios. The target count proportions for small andlarge fish were also mapped independently as stacked absolute values vs. depth (excluding layerswith a total of <100 targets), separately for day and night, to study variations due to time of day, depthand size class simultaneously. Targets that were classified in 1 cm sized classes were separated byorientation (surface layer vs. vertical data layers) and time of day (daytime vs. nighttime) and wereweighed by average class size, to calculate weighted average fish sizes. Specifically, the middle of eachclass was multiplied by the total target count of the class; the products were summed and divided bythe total targets from all classes.The segments of the transect closest to each sampling point were determined, and fish densitywas determined by depth for each of those segments (summing fish density values for all layers).Thereafter, the average fish density based on these segments was determined. A circle of specific radiuswas used to locate the closest segments, and segments were included wherever they intersected thiscircle. For two of the points, a radius of 1 km was used, whereas a radius of 2.5 km was used for thethird point, because no segment was close enough to intersect a circle of 1 km radius around that point.Data parsing and post-processing was carried out using custom scripts in "R" (R DevelopmentCore Team, 2020), as well as LibreOffice Calc and Microsoft Excel. Thematic maps were extractedusing ARCGIS and QGIS. Interpolation for the creation of Raster Grids from the scattered processedhydroacoustic data was carried out using Multilevel B-splines [47]. Comparisons between serieswere made using pairwise t-tests (or 1-sigma confidence intervals where noted), and distributionswere compared using Kolmogorov–Smirnov tests. For all tests, a significance level a = 0.05 was used.In some results, marginal hypothesized differences were also explored as the mean difference leadingto a p-value approximately equal to a.4. Results4.1. Acoustic Biomass and Fish DensityAcoustic biomass detected per sampling unit (Figure 2) did not exhibit diurnal heterogeneities.During the day, the average SV per depth layer was −65.9 ± 9.1 dB, ranging from −87.6 dB to −49.4 dB,while during the night, the average SV per depth layer was −66.1 ± 10.1 dB, ranging from −83.8 dB to−49.3 dB. No significant difference was found (p = 0.92). Concerning the vertical distribution, duringthe day and night survey, the highest biomass was observed in the layer 16–30 m, (average value−55.8 dB in day survey and −55.6 dB in night survey). The coefficient of variation (CV) revealedgreater heterogeneity in the vertical distribution during night rather than during the day survey(CV = 15% against 13%).The share of single echoes did not exhibit large fluctuations comparing between acquisition times(Figure 3). Specifically, the day average was 17 % ± 20.2 %, ranging from 0 % to 91.9 %, while the nightaverage was 15 % ± 11.3 %, ranging from 0 % to 44.8 %. Day-to-night difference was not statisticallysignificant (p = 0.39). Most of the values were relatively low, typically less than 40% in most layers,during both day and night. The layers close to the surface (<8 m depth) were exceptions to thisobservation, with the night measurements exhibiting a share of single echoes up to approximately 45%at night versus approximately 90% during the day for those layers.Fish density layer average was approximately 1226 ± 1608 inds./ha, ranging from 8 to 7984 inds./haduring the day. During the night, the fish density layer average was 3473 ± 3994 inds./ha, rangingfrom 20 to 16,384 inds./ha. The vertical distribution of fish density was significantly different betweenday and night (p < 0.001). An assumed mean difference of 1250 inds./ha less during the day produces152Water 2020, 12, 1823a p-value of 0.05 for the t-test, meaning that this is a marginal difference that could be statisticallyaccepted. Moreover, average fish density is more than double at night than during the day in thesurface layer (0–4 m) and 5 times higher at 15 m (Figure 4). Application of a cross-correlation modelbetween day and night vertical distributions and using depth as the lag parameter, reveals an averagedepth-wise offset of ~9 m between the two distributions, with a maximum correlation coefficient of0.724 at dh = 9 m (Figure 5). This means that the distributions are very similar but translated withrespect to each other by approximately 9 m of depth.Water 2020, 12, 1823 7 of 21 Figure 2. SV (dB) vertical distribution during the day and night survey in Lake Trichonis. The share of single echoes did not exhibit large fluctuations comparing between acquisition times (Figure 3). Specifically, the day average was 17 % ± 20.2 %, ranging from 0 % to 91.9 %, while the night average was 15 % ± 11.3 %, ranging from 0 % to 44.8 %. Day-to-night difference was not statistically significant (p = 0.39). Most of the values were relatively low, typically less than 40% in most layers, during both day and night. The layers close to the surface (<8 m depth) were exceptions to this observation, with the night measurements exhibiting a share of single echoes up to approximately 45% at night versus approximately 90% during the day for those layers. Figure 2. SV (dB) vertical distribution during the da and night surv y in Lake Trichonis.Water 2020, 12, 1823 8 of 21 Figure 3. Differences in share of single echoes (%) between different layers as well as measurement acquisition time (daytime, nighttime). Fish density layer average was approximately 1226 ± 1608 inds./ha, ranging from 8 to 7984 inds./ha during the day. During the night, the fish density layer average was 3473 ± 3994 inds./ha, ranging from 20 to 16384 inds./ha. The vertical distribution of fish density was significantly different between day and night (p < 0.001). An assumed mean difference of 1250 inds./ha less during the day produces a p-value of 0.05 for the t-test, meaning that this is a marginal difference that could be statistically accepted. Moreover, average fish density is more than double at night than during the day in the surface layer (0–4 m) and 5 times higher at 15 m (Figure 4). Application of a cross-correlation model between day and night vertical distributions and using depth as the lag parameter, reveals an average depth-wise offset of ~9 m between the two distributions, with a maximum correlation coefficient of 0.724 at dh = 9 m (Figure 5). This means that the distributions are very similar but translated with respect to each other by approximately 9 meters of depth. Figure 3. Differences in share of single echoes (%) between different layers as well as measurementacquisition time (daytime, nighttime).153Water 2020, 12, 1823 Figure 4. Vertical distribution of average density (inds./ha) for each water column layer during dayand night surveys.Water 2020, 12, 1823 10 of 21 Figure 5. Cross-correlation between day vs. night, using depth (h) as the lag parameter. A depth difference of dh = ~9 m produces the maximum correlation between day and night fish density distributions. Total water column fish density exhibited significant differences between the shallow and the deep areas of the lake, separating at a longitude decimal degree of 21.55 (p < 0.0001, Figure 6). Average fish density during the day was 41925 ± 18735 inds./ha in the shallow areas (western side of the lake) and 32424 ± 13548 inds./ha in the deep areas of the lake (eastern area) (total water column sums). During the night, fish density was 174986 ± 69521 inds./ha in the western region and 127930 ± 79934 inds./ha in the eastern region (total water column sums). An assumed mean day fish density difference of 8880 inds./ha between regions produces a marginal value of p = 0.05, while for the night, a marginal p = 0.05 value is produced by an assumed mean night fish density difference of ~45750 inds./ha. The fish density depth-wise and spatial distribution along the transect, as well as the differences between day and night are shown in detail in Figures 7 and 8. A horizontally interpolated spatial distribution of fish density is shown in Figures 9 and 10. Figure 5. Cross-cor elation betwe day vs. ni ht, using depth (h) as the lag parameter.A depth difference of dh = ~9 m produces the maximum correlation between day and night fishdensity distributions.Total water column fish density exhibited significant differences between the shallow and the deepareas of the lake, separating at a longitude decimal degree of 21.55 (p < 0.0001, Figure 6). Average fishdensity during the day was 41,925 ± 18,735 inds./ha in the s allow ar as (western side of the lake) and32,424 ± 13,548 inds./ha in the deep areas of the lake (eastern area) (total water column sums). Duringthe night, fish density was 174,986 ± 69,521 inds./ha in the wes ern r g on and 127,930 ± 79,934 inds./hain the eastern region (total water column sums). An assumed mean day fish density difference of8880 inds./ha between regions produces a marginal v lue of p = 0.05, while for the night, a marginalp = 0.05 value is produced by an assumed mean night fish density difference of ~45,750 inds./ha. Thefish density depth-wise and spatial distribution along the transect, as well as the differences betweenday and night are shown in detail in Figures 7 and 8. A horizontally interpolated spatial distributionof fish density is shown in Figures 9 and 10.154Water 2020, 12, 1823Figure 6. Total water column fish density vs. longitude of averaged-cell midpoint. Significantlydifferent fish density clusters are visible between the regions for both day and night. Figure 7. Color map of daytime fish density depth-wise and along transect variations. Figure isspatially referenced with respect to the lake.155Water 2020, 12, 1823Figure 8. Color map of nighttime fish density depth-wise and along transect variations. Figure isspatially referenced with respect to the lake.Water 2020, 12, 1823 13 of 21 Figure 9. Fish density (inds./ha) distribution during the day survey in lake Trichonis. Figure 10. Fish density (inds./ha) distribution during the night survey in lake Trichonis. Figure 9. Fish density (inds./ha) distribution during the day survey in lake Trichonis.156Water 2020, 12, 1823Water 2020, 12, 1823 13 of 21 Figure 9. Fish density (inds./ha) distribution during the day survey in lake Trichonis. Figure 10. Fish density (inds./ha) distribution during the night survey in lake Trichonis. Figure 10. Fish density (inds./ha) distribution during the night survey in lake Trichonis.4.2. Fish Size Composition and DistributionIndividual fish lengths, converted from TS data, ranged up to 50 cm. The length of most of thedetected fish is not larger than 5 cm (Figures 11 and 12). Smaller fish, with lengths less than 5 cmdominate the total counts and observed fish density diminishes as target size increases. After separatingby orientation (surface layer vs. vertical data layers) and time of day and weighing for class size,weighted mean fish size for the surface layer was ~1.6 cm for the day survey and ~2 cm for the nightsurvey, while for the layers from 4 m to the bottom, mean fish size was ~4.5 cm for the day survey and2.9 cm for the night survey.Water 2020, 12, 1823 14 of 21 4.2. Fish Size Composition and Distribution Individual fish lengths, converted from TS data, ranged up to 50 cm. The length of most of the detected fish is not larger than 5 cm (Figures 11 and 12). Smaller fish, with lengths less than 5 cm dominate the total counts and observed fish density diminishes as target size increases. After separating by orientation (surface layer vs. vertical data layers) and time of day and weighing for class size, weighted mean fish size for the surface layer was ~1.6 cm for the day survey and ~2 cm for the night survey, while for the layers from 4 m to the bottom, mean fish size was ~4.5 cm for the day survey and 2.9 cm for the night survey. Figure 11. The size (total length, cm) and Target Strength (dB) distribution of pelagic fish recorded during night and day vertical surveys in Lake Trichonis. Figure 12. The size (total length, cm) and Target Strength (dB) distribution of pelagic fish recorded during night and day horizontal surveys in Lake Trichonis. Small fish density during the day was 922 ± 1324 inds./ha, while during the night, it was 3068 ± 3496 inds./ha. Correspondingly, large fish density during the day was 304 ± 463 inds./ha, whereas during the night, large fish density was 405 ± 808 inds./ha. Small fish density was significantly larger during the night (p < 0.001), with a marginal p = 0.05 being produced by an assumed mean difference equal to 1220 inds./ha (of size < 5 cm). Large-fish density distribution by depth was not significantly different between day and night (Figure 13). Horizontal layer fish density exhibits fish density values more than an order of magnitude higher than the vertical layers, for both day and night (Figure 14). Figure 1 . The size (total length, cm) and Target Strength (dB) distribution of pelagic fish recordedduring night and day vertical surveys in Lake Trichonis.157Water 2020, 12, 1823Water 2020, 12, 1823 14 of 21 4.2. Fish Size Composition and Distribution Individual fish lengths, converted from TS data, ranged up to 50 cm. The length of most of the detected fish is not larger than 5 cm (Figures 11 and 12). Smaller fish, with lengths less than 5 cm dominate the total counts and observed fish density diminishes as target size increases. After separating by orientation (surface layer vs. vertical data layers) and time of day and weighing for class size, weighted mean fish size for the surface layer was ~1.6 cm for the day survey and ~2 cm for the night survey, while for the layers from 4 m to the bottom, mean fish size was ~4.5 cm for the day survey and 2.9 cm for the night survey. Figure 11. The size (total length, cm) and Target Strength (dB) distribution of pelagic fish recorded during night and day vertical surveys in Lake Trichonis. Figure 12. The size (total length, cm) and Target Strength (dB) distribution of pelagic fish recorded during night and day horizontal surveys in Lake Trichonis. Small fish density during the day was 922 ± 1324 inds./ha, while during the night, it was 3068 ± 3496 inds./ha. Correspondingly, large fish density during the day was 304 ± 463 inds./ha, whereas during the night, large fish density was 405 ± 808 inds./ha. Small fish density was significantly larger during the night (p < 0.001), with a marginal p = 0.05 being produced by an assumed mean difference equal to 1220 inds./ha (of size < 5 cm). Large-fish density distribution by depth was not significantly different between day and night (Figure 13). Horizontal layer fish density exhibits fish density values more than an order of magnitude higher than the vertical layers, for both day and night (Figure 14). Figure 12. The size (total length, cm) and Target Strength (dB) distribution of pelagic fish recordedduring night and day horizontal surveys in Lake Trichonis.Small fish density during the day was 922 ± 1324 inds./ha, while during the night, it was3068 ± 3496 inds./ha. Correspondingly, large fish density during the day was 304 ± 463 inds./ha,whereas during the night, large fish density was 405 ± 808 inds./ha. Small fish density was significantlylarger during the night (p < 0.001), with a marginal p = 0.05 being produced by an assumed meandifference equal to 1220 inds./ha (of size < 5 cm). Large-fish density distribution by depth was notsignifica tly diff rent between day and night (Figure 13). Horizontal layer fish density exhibits fishdensity values mor than n order of magnitude higher than the ve tical layers, for both day and night(Figure 14). Figure 13. Vertical distribution of density (inds./ha) of the detected target size classes (small: <5 cm,large: 5–50 cm) during the day and night surveys in Lake Trichonis.4.3. Temperature and Oxygen StratificationThe water temperature in the surface layer varied between 23.5 and 24.7 ◦C in the three samplingpoints. In the eastern region (deepest point), a thermocline at around 18–28 m was recorded, whiletemperature was almost constant in the hypolimnion (11.6–11.9 ◦C). During the survey, differences intemperature and oxygen profiles were observed between the western and eastern region (Figure 15).In the deep point of the western region, the thermocline was recorded at around 15–31 m, while thetemperature in the hypolimnion was 12.7 ◦C. In the shallow point of the western region, temperature158Water 2020, 12, 1823was almost constant up to a depth of 14 m, after which it exhibited a noticeable gradual drop, reachingits lowest value (14.3 ◦C) at 22 m. Figure 14. Proportional fish distribution by size, depth and time of day. Layers with <100 detectedindividuals excluded for clarity. Figure 15. Variations of density (inds./ha), dissolved oxygen (mg/L) and water temperature (◦C) withdepth (m) at the three different sampling locations of Lake Trichonis ((A). Sampling point S1 (B).Sampling point S2 (C). Sampling point S3).The horizontal variations of DO concentrations in the epilimnion from the eastern to the westernregion of the lake were weak. In the eastern region (deepest point) the DO concentration remained atapproximately 8.3 mg/L in the upper 17 m mixing layer and significantly increased to 10–11 mg/L at adepth of 18–26 m. An anoxic layer was recorded after a depth of 30 m, as DO concentration fell below2 mg/L [48]. Namely, the recorded DO values ranged from 1.82 to 0.15 mg/L. In the shallow point ofthe western region, the DO concentration was higher (approximately 10.5 mg/L) at around 16–22 m159Water 2020, 12, 1823of depth than in the surface layer (approximately 8.3 mg/L). In the deep point of the western region,the highest concentration of DO (approximately 11.9 mg/L) was recorded at a depth around 15–27 m.An anoxic layer was also recorded after the depth of 32 m.The vertical distribution of fish during the day survey varied according to thermal stratificationand seemed to roughly follow the vertical variations of DO concentrations (Table 2). Based on theresults of correlations between fish density and dissolved oxygen, it appears that as the depth of thesampled water column increases, a strong correlation manifests as the relationship between these twoparameters. An apparent rise in correlation coefficient absolute values was observed between fishdensity and dissolved oxygen, especially around the area of the deepest point of the lake, whereasanoxic conditions occurred in the bottom layers.Table 2. Correlation coefficients between fish density (inds./ha) and DO (mg/L)/Temperature (◦C) forthe three sampling points in Lake Trichonis.Sampling Point S1 Sampling Point S2 Sampling Point S3TemperatureDissolvedOxygenTemperatureDissolvedOxygenTemperatureDissolvedOxygenR −0.56 0.54 −0.52 0.53 0.018 0.61R2 0.31 0.29 0.27 0.28 0.00032 0.37p-value 0.013 0.018 0.0026 0.0023 0.91 5.9 × 10−6DF 17 17 29 29 45 455. DiscussionVolume backscattering strength (SV), was used in this study as a proxy variable for fish biomass [8].The calculation of biomass based on acoustic recordings requires knowledge of an overall fish SL–W(Standard Length to Weight) relationship, particularly for a mixed-species community, as well as TSestimates for fish fauna [49]. Given that this was the first hydroacoustic survey in Lake Trichonis,it was not feasible to obtain the aforementioned values. SV values were compared among the differentlayers of the water column [50]. The average SV values in Lake Trichonis −66.1 dB in the night and−65.9 dB in the day were slightly lower than in other 18 European lakes (on average −62.8 dB) [51].The recorded range of SV values in the different layers was also comparable with values of monomicticalpine lakes with similar morphology (ranges from −62.3 to −67.4 dB in different layers and lakes) [9]and with the results of Cech et al. [52], who studied distributions of juvenile fish (range of SV wasmeasured from −71.3 to −45.7 dB). Similarly to this last study, in Lake Trichonis, fish formed more orless dense layers; the type of aggregations is probably dependent on the kind of species.Fish density was found to be lower in the deep areas (eastern area) than the shallow areas(western area) of the lake. Additionally, fish density was higher in intermediate depth locations,indicating that fish targets are generally distributed in the water column at larger depths. An offsetbetween day and night was observed in the vertical distribution of fish, estimated at approximately9m (fish were located deeper during the day). Fish were more closely aggregated during the day andmore dispersed at night. Vertical migration behavior with diel periodicity is well described [53] and islinked to predation avoidance and reduction of competition [54].A statistically significant difference was observed in vertical distribution between day and nightin the pelagic zone. The overall recordings revealed a relatively high degree of fish aggregation(low share of single echoes), especially during daytime, which, in turn, makes it harder to discern andcount single targets and decreases the reliability of density estimations. According to Appenzellerand Leggett [55], the estimation of the number of targets can be biased towards smaller values whenfish form dense aggregations, due to acoustic shadowing. Precise determination of fish target countsshould coincide with the time of their maximal dispersion [37]. The share of single echoes did notpresent significant changes in depth distribution between day and night. Nevertheless, during thenight, significantly larger fish densities were observed at smaller depths, thereby observing a wider160Water 2020, 12, 1823size spectrum of fish living in the lake. In this respect, it may suggest that night hydroacoustic surveysin Lake Trichonis could be more suitable. Nonetheless, we recommend that diel differences be furtherinvestigated by carrying out additional day-night comparisons, also using fish catches. The use ofhydroacoustics in a future setting over the same study area would be useful to investigate the possiblebias and aggregation-related behavior and simultaneously also the true size composition by the use ofappropriate fishing gear.In relation to higher fish densities, values of the Sawada index were also taken into account toconsider reliability of the density estimates. In most of the analyzed cells, values of NV were belowthe commonly accepted limit of 0.1 by Parker-Stetter et al. [56], only exceeding 0.1 in the layers withthe densest aggregations, with a few or no single echo detections. Nevertheless, TS distribution wasexamined. There was no statistically significant difference in the two distributions (TS for all NV andTS only for NV < 0.1) in the lake, neither for the day (K-S D-value = 0.2143, p = 0.9205), nor for thenight (K-S D-value = 0.167, p = 0.9985) (for details, see Supplementary Materials).The majority of all detected targets have a small size, while larger targets only comprise a minimalfraction. The presence of small-sized fish [21] is also a reason for the threshold used in this study, whichmay seem to be lower than other hydroacoustic studies using a frequency of 120 kHz. The lake hosts20 fish species [21], most of which are of small size, such as the big-scale sand smelt Atherina boyeri,which occupies the pelagic regions of the lake and its population is extremely abundant, supporting avaluable commercial purse-seines fishery [29]. It is important to note that the targets observed couldnot be other than fish, as no big zooplankton, which could overlap with small fish, was observed inthe lake [22], nor invertebrates [57] could be expected in this autumn time. Nevertheless, we cannotexclude the possibility of juvenile fish being mixed into the composition of the recorded populations.However, due to the lack of catch data in the present study, as well as due to the lack of correspondinghistorical data, it is not possible to identify the smaller fish target species, thus precluding a conclusiveinterpretation of the results. Consequently, provided that the detected fish target species composition,biology and behavior are not known, further investigation into the variations in acoustic parametersacross an annual cycle is warranted [58]. It is also necessary to use control catches to enable properinterpretation of acoustic targets.A general estimation of the fish length, even roughly, is crucial to the understanding of anaquatic ecosystem [9]. Fish length from TS data can be calculated based on an equation that expressesthis relationship [8]. The most important factor affecting acoustic estimates appears to be theappropriate relationship between the fish length and TS for a given population. Equations of Love [42]and Frouzova et al. [43] were derived from different fish species and size ranges, and therefore,the estimation of fish length based on those equations ought to be further assessed with data fromcatches regarding their use for the endemic species of the lake.The vertical distribution of the fish in the lake appears to have been primarily driven by watertemperature and oxygen stratification. In the present study, the surface layer density does not seemto be correlated to dissolved oxygen and water temperature, but other factors, such as predationrisk, may play an important role. Similarly to Doulka and Kechayias [59], Lake Trichonis showeda minimum saturation value of dissolved oxygen in the lower hypolimnion in autumn. However,in contrast to the study of Doulka and Kechayias [59], in the present study anoxic conditions wererecorded in the lake’s hypolimnion, rendering the DO concentration a limiting factor for the verticaldispersion of fish. As a result, in the vertical domain, fish distribution was highly correlated withoxygen stratification, i.e., it decreased as the dissolved oxygen also decreased. This correlation betweendensity and dissolved oxygen is well established, while the relation can be explained, in part, by the factthat there is a natural threshold of oxygen, beyond which survival very quickly becomes non-viable [60].It is also important to note that the presence of larger abundances of large fish in intermediate layers isin agreement with the results of Breitburg et al. [61], according to which the response of predatorsand prey to oxygen distribution variations strongly influenced the spatial focus of trophic interactions.161Water 2020, 12, 1823Consequently, the metalimnion in Lake Trichonis is the most productive depth stratum, due to theintense variation of abiotic and biotic variables [59].The methodology presented and the results obtained through this research were the first step topresent and analyze the challenges for optimizing a hydroacoustic approach for monitoring freshwaterfish stock in Greek lakes. This methodology could be incorporated in the decision-making processtowards the improvement of the implementation of the EU Water Framework Directive 2000/60.The use of hydroacoustic techniques can eliminate some distortions traditionally contained in censusesand studies of fish fauna in lakes. Lake Trichonis is traditionally exploited by commercial fishermen,most of which use purse-seine fishing. The knowledge of feeding and nursery grounds where fish aregathered during their vertical and horizontal movements through the day will help and support localauthorities and contribute to decision making in applying restrictions for a more sustainable fisheriesmanagement. The potential adjunct usage of common fishing gear might not be able to adequatelyreveal the potentially essential role of small fish in the open waters of Lake Trichonis. As a result,the proposed methodology can provide new insights for enhancing fisheries monitoring.Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4441/12/6/1823/s1.Figure S1: Empirical distribution functions for TS, categorized in 2-dB bins separately for day and night, as well asfor the sets of all sampled cells, and of only cells with NV < 0.1. Figure S2: Sawada NV index vertical distributionwith 1-sigma confidence intervals (left) and day-night difference, i.e., day values minus night values (right).Author Contributions: Conceptualization, T.-M.P. and D.B.; methodology T.-M.P., M.T. and J.F.; formal analysis,T.-M.P.; investigation, T.-M.P., M.T. and J.F.; data curation, T.-M.P., M.T. and J.F.; writing—original draft preparation,T.-M.P.; writing—review and editing, M.T., J.F. and D.B.; visualization, T.-M.P.; supervision, M.T., J.F. and D.B.;funding acquisition, T.-M.P. All authors have read and agreed to the published version of the manuscript.Funding: This research is co-financed by Greece and the European Union (European Social Fund–ESF) throughthe Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context ofthe project “Strengthening Human Resources Research Potential via Doctorate Research–2nd Cycle (MIS–5000432),implemented by the State Scholarships Foundation (IKY). The hydroacoustic equipment used and J. Frouzovaand M. Tušer were partly supported by the Czech Science Foundation (project P504/12/1186), the SoWaResearch Infrastructure funded by MEYS CZ grant LM2015075, program “Project of Large Infrastructure forResearch, Development, and Innovations” and Operational Programme Research, Development and Education(CZ.02.1.01/0.0/0.0/16_013/0001782).Acknowledgments: The authors would like to thank Jan Kubečka, Vladislav Draštík and Antonios Mouratidisfor their invaluable comments and suggestions, which have been of significant help for improving the quality ofthis study. Last but not least, the authors would like to thank George Perivoliotis for providing a robust adaptableframe construction and helping during the fieldwork.Conflicts of Interest: The authors declare no conflict of interest.References1. Arlinghaus, R.; Mehner, T.; Cowx, I.G. Reconciling traditional inland fisheries management and sustainabilityin industrialized countries, with emphasis on Europe. Fish Fish. 2002, 3, 261–316. [CrossRef]2. 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Breitburg, D.L.; Rose, K.A.; Cowan, J.H., Jr. Linking water quality to larval survival: Predation mortality offish larvae in an oxygen-stratified water column. Mar. Ecol. Prog. Ser. 1999, 178, 39–54. [CrossRef]© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).165waterArticleChanges in the Fish Community of a WesternCaribbean Estuary after the Expansion of an ArtificialChannel to the SeaJuan J. Schmitter-Soto * and Roberto L. Herrera-PavónEl Colegio de la Frontera Sur, Av. Centenario km 5.5, Chetumal 77014, Quintana Roo, Mexico;rherrera@ecosur.mx* Correspondence: jschmitt@ecosur.mx; Tel.: +52-983-835-0440 (ext. 4302)Received: 30 October 2019; Accepted: 2 December 2019; Published: 6 December 2019Abstract: Increased connectivity between coastal lagoons and the sea is expected to entail a greaterproportion of marine species in the former. Chetumal Bay, estuary of the Hondo river into theCaribbean, had a limited access to the sea until the opening of the Zaragoza Canal. We soughtchanges in the fish community from 1999–2001 (just after an expansion of the canal) to 2015–2018.The same fishing gear was used, in the same localities, during all seasons. Total fish abundanceand mean local richness decreased, although total abundance increased in the polyhaline zone.Diversity was greater in the oligohaline zone in 1999–2001, and in the mesohaline zone in 2015–2018.Three guilds were absent in 2015–2018: Medium-sized herbivores, large piscivores, and medium-sizedplanktivores. Abundance of small benthivores decreased by decade; medium-sized piscivores andsmall planktivores became more abundant in 2015–2018 in the polyhaline zone. These changes maybe due to the opening of the channel, but illegal fishing outside the bay may explain the decrease injuveniles of large piscivores, and erosion in the innermost part may be destroying important habitats.Our findings can be a reference for similar situations, as coastal development and climate changeinteract and affect tropical estuaries.Keywords: ichthyofauna; trophic guilds; salinity-tolerance; connectivity; Chetumal Bay; Mexico1. IntroductionChetumal Bay (known as Corozal Bay in Belize) is the estuary of the Hondo River and minoraffluents, connecting them to the Caribbean Sea, at the border between Mexico and Belize. This largewater body (about 3500 km2 in area) is protected by both countries [1]. For thousands of years,the narrow and winding natural passage of Bacalar Chico was the connection of the Mexican part of thebay to the Caribbean Sea; a much wider opening exists near the Belize–Guatemala border, about 300 kmsouth. However, between 1999 and 2004 an artificial channel was dredged and expanded for navigation,the Zaragoza Canal, opened initially in 1901, but never made deep enough for larger vessels. This directcommunication started having a strong influence on the abiotic and biotic conditions of the system,including the intrusion of corals and other reef organisms to formerly brackish areas of the bay [2].Many of the fishes found in this system are important resources. Most relevant for recreationalfisheries in Belize and Mexico are bonefish (Albula vulpes), but also permit (Trachinotus falcatus),snook (Centropomus undecimalis), and tarpon (Megalops atlanticus) [3]. Other species are fishedmostly for local markets: Snappers (Lutjanus spp., especially L. griseus and L. apodus), mojarras(Gerres cinereus, Eugerres plumieri), mackerels (Scomberomorus maculatus and other species), and barracuda(Sphyraena barracuda) [4]. Other mojarras (Eucinostomus spp.) are also abundant, as are needlefishes(Strongylura spp.), pupfishes (Cyprinodontidae), flatfishes (several families), sea catfishes (Ariidae),167Water 2019, 11, 2582gobies (Gobiidae), puffer (Sphoeroides testudineus), and, under the influence of freshwater, also cichlids,poeciliids, and tetra (Astyanax bacalarensis), among others [5]. A recent addition to this ichthyofauna isan exotic invader, lionfish (Pterois volitans) (unpublished observation).Qualitative and quantitative data on the ichthyofauna of Chetumal Bay are available from the timejust before the expansion in depth and width of the Zaragoza Canal [5,6]. Our aim in this paper is toevaluate ichthyological changes almost two decades later, in terms of composition, diversity (includingrichness, evenness, beta-diversity), abundance of species, trophic guilds, and salinity-tolerancecategories, maximum length, and distribution through the salinity gradient of the bay, from the rivermouth to the Zaragoza Canal. We discuss possible processes to account for the observed patterns.2. Materials and Methods2.1. Study AreaThe region has a warm humid climate, with summer rains and an average temperature greaterthan 26.5 ◦C, with an annual precipitation of 1000–1500 mm; the bay is shallow, its maximum depth 5 m(mean 3.2 m), with scattered, much deeper, sinkholes [7]. Most of the bay is mesohaline (13 to 22 psu),with freshwater conditions occurring near the mouth of the Hondo River, and marine salinity close tothe Zaragoza Canal [8]. Salinity varies seasonally, increasing in the dry season (February to April) anddecreasing during the rains (May to October) and during the colder “north winds” season (Novemberto January). However, three areas can be recognized year-round, and we term them here Oligohaline,Mesohaline, and Polyhaline (Figure 1). Except in winter, southeasterly trade winds predominate,with a mean wind speed of 3.1 m·s−1 [7].Tides are semidiurnal, with excursions ≥ 0.5 m but, nevertheless, inducing inward or outwardcurrents at the Zaragoza Canal and minor channels. Water temperature fluctuates between 22 ◦C at thepeak of the north-winds season and 31 ◦C in August [7]. Bottoms are mostly sandy or sandy-muddy,except near rocky points, usually without vegetation but often with seagrass and algae. Mangroverims the coast, with Avicennia germinans reaching heights of 10 m [9], and occasional patches of exoticCasuarina equisetifolia trees.There are no human settlements in the northern half of the bay. Chetumal City lies at themouth of the Hondo river (Figure 1), and just to the south is the Belizean town of Sarteneja.Pesticides from agricultural fields along both banks of the river and organic matter from Chetumalrepresent environmental stressors [10], although largely confined to the river mouth by windsand currents.168Water 2019, 11, 2582Figure 1. Study area, the northern (Mexican) part of the Bay of Chetumal (known as Bay of Corozalin Belize). Localities: 1 Cayo Chelem; 2 Zaragoza Canal; 3 Dos de Abril; 4 Mala Noche; 5 PuntaJas; 6 Mainada; 7 Punta Calentura; 8 Punta Flor; 9 Dos Hermanos; 10 Mogote Barlovento; 11 SieteEsteros; 12 Tollocan; 13 Río Krik; 14 Cayo Venado; 15 Punta Pol Box; 16 Punta Lagarto; 17 Isla Tamalcab(leeward); 18 Isla Tamalcab (windward); 19 Punta Catalán; 20 UQROO; 21 ECOSUR. Abbreviations forsalinity zones: O—oligohaline; M—mesohaline; P—polyhaline (see text). Map by Janneth Padilla.2.2. Field and Laboratory WorkWe sampled the bay for fishes at the same 21 localities explored by us about 15 years before [6](Figure 1); the same procedure described below was applied. Expeditions took place in April 13–15(dry season) and July 6–8, 2016 (wet season), and in April 23–24, 2018 (same as in 1999–2001), and morethan 13,000 specimens were collected from 162 samplings, using the same fishing gear and effort(a beach seine, 20 m long, 1.2 m tall, 1 cm diagonal mesh, 2–5 seinings per site, until no new specieswere found, each seining lasting ca. 3 min). The contagious distribution of some species could bea source of heteroscedasticity even within one same site, but usually abundant species continued to bepresent in a similar proportion in all subsequent seinings.Fish were identified and counted in situ and then freed; voucher specimens were kept onlyfor those species which could not be determined in the field, such as specimens of Eucinostomus,which were deposited in the fish collection of ECOSUR (acronym ECO-CH). Abundance was controlledby the number of seinings per site/date, as catch per unit effort (CPUE). Collected specimens weremeasured (standard length) to the nearest millimeter with a vernier caliper or an ichthyometer.Composition (i.e., the species list) included data from the literature [11,12], revision of collectionspecimens, and observations or captures outside seinings.169Water 2019, 11, 25822.3. Data AnalysisWe compared the following variables between decades (1999–2001 vs. 2015–2018) and by salinity areas(Oligohaline, Mesohaline, and Polyhaline—hereafter zones O, M, P): (a) composition (i.e., presence/absenceof species); (b) richness; (c) diversity, both alpha and beta; (d) abundance (i.e., CPUE) of dominant species;(e) abundance by guild (see below); (f) abundance by salt-tolerance category [13]: marine stenohaline,marine euryhaline, estuarine resident, freshwater primary or secondary; (g) maximum observed lengthof selected species; (h) frequency (i.e., proportion of localities where present); (i) environmental abioticvariables, mainly temperature, salinity, and wind at the time of capture. We did not compare biotic data bymonth because an earlier analysis found no seasonal difference in 1999–2001 [6]. Differences in salinity(refractometer, to 1 psu), temperature (digital thermometer, to 1 ◦C) and wind force (Beaufort scale) wereexamined controlling by season (see Study Area) and time of day (morning 07–10:59, noon 11–14:59,afternoon 15–19 h: time when sampling started). Fourteen guilds were defined as combinations of feedinghabits and three body-size categories [14–16].In addition to analyzing pooled data, the variables listed above were examined separately bysalt-tolerance category of the fishes. Abundance was log-transformed to achieve homoscedasticity andnormality of the data. Only species that made up 75% of total abundance were analyzed separately.The test used was a two-way ANOVA (by decade and by salinity area), with an interaction term;post-hoc comparisons were performed using Tukey Honest Distance. Frequencies were compared bya Kolmogorov–Smirnov test (KS). Diversity (H’n, Shannon index, in nats) and its components (richness,as number of species; J’n, Pielou’s equity) were calculated by locality and controlled by rarefaction.Beta-diversity as turnover was explored via the Sørensen index and plotted in the triangular graphsuggested by Koleff et al. [17]. The software used to analyze data and prepare graphs was R [17],with p < 0.05 (instances where p > 0.05 but p < 0.10 are also mentioned and discussed); the package“vegan” was used for diversity analyses [18].We sampled under Permit PPF/DGOPA-053/15 from the Comisión Nacional de Pesca (the MexicanCommission for Fisheries), with further authorization by the protected area “Bahía de ChetumalSantuario del Manatí”. Accession to our database will be provided during review.3. ResultsSampling effort, with emphasis on the mesohaline zone (samplings: 140 O, 261 M, 215 P) due to thegreater number of localities (5 O, 8 M, 7 P), did not vary between decades (KS, D = 0.67). Every localitywas sampled on average 29 times between both decades. The mean number of fish by species persampling was 9; the maximum, 294.Salinity varied from 1 to 36 psu (mean 10.9 psu); water temperature, 25–34 ◦C (mean 29 ◦C).Wind force in the Beaufort scale was 1 to 4 (mode 2). Controlling by season, salinity varied significantlybetween decades (F = 10.9), changing from a mean of 10.4 psu in 1999–2001 to 11.8 psu in 2015–2018.Controlling by season and by time of day, water temperature was also significantly different betweendecades (F = 6.9), from 29.2 ◦C to 28.7 ◦C, as well as wind force (F = 21.6), from 1.9 to 2.4 in theBeaufort scale.General abundance of fish decreased from 1999–2001 to 2015–2018, except in zone P (F = 5.82 bydecade, F = 8.05 for the interaction decade:zone; Figure 2, Appendix A). By zone, an “estuary effect” isapparent, with greater abundance in zone P than in O, but greater in O than in M (F = 5.97).There were few changes in composition (Appendix B). The most conspicuous novelty is thelionfish Pterois volitans, absent in 1999–2001, now confirmed in the zone P, closest to the ZaragozaCanal. Among the species that were captured in 1999–2001 but not in 2015–2018 are A. vulpes,Ariopsis assimilis, Centropomus undecimalis, Jenkinsia lamprotaenia, Opsanus beta, and Opisthonema oglinum;however, all of them are still present in the area, as observed outside our seinings. On the contrary,species that were not seined in 1999–2001 but that were collected in 2015–2018 include A. bacalarensis,Corvula sanctaeluciae, Hypanus spp., Diapterus auratus, and Lutjanus spp. Juveniles of Trachinotusfalcatus, not found in the quantitative samplings in 1999–2001, were captured in all three salinity170Water 2019, 11, 2582zones in 2015–2018. Three species of Strongylura were seined in 1999–2001, but in 2015–2018 only S.notata. Also exclusive to the older decade were usually reef-dwelling species, as Holocentrus rufus andSparisoma rubripinne, freshwater secondary species, as Poecilia kykesis, and others (Syngnathus spp.,Selene vomer, Haemulon spp., Chaetodipterus faber, Chilomycterus schoepfi, Bothus ocellatus, and Acanthurusspp.). One case of concern was Gobiosoma yucatanum, the only species endemic to Chetumal Bay [19,20],which we found in 1999–2001, but not in the most recent expeditions.Figure 2. General abundance of fishes by salinity area and decade in Chetumal Bay. Abundance(CPUE) log-transformed. Posthoc significant differences between M:1999 and M:2015, P:1999 andM:2015, M:2015 and P:2015, and O:2015 and P:2015. Abbreviations for salinity zones: O—oligohaline;M—mesohaline; P—polyhaline; “1999” is 1999–2001 and “2015” is 2015–2018.Changes in dominance were clearer than changes in composition. Atherinomorus stipes andHarengula jaguana were always abundant and frequent, the former especially in the windward(sandy) side of mangrove islands, whereas Floridichthys polyommus, in both decades, predominatedin the leeward (silty) side. Bairdiella ronchus and Cyprinodon artifrons decreased (in frequency,3 to 1); in 1999–2001, C. artifrons was more abundant than the ecologically similar Jordanella pulchra,which increased in frequency from 1 to 3. Concerning another ecologically similar pair, Eugerres plumieriwas more abundant than Gerres cinereus in 1999–2001 (especially in zone O), but not in 2015–2018(G. cinereus increased in frequency from 2 to 3). The five species of Eucinostomus kept their relativeabundances similar, except that E. melanopterus was not seined in 1999–2001.Few species displayed significant changes by decade or salinity zone (Table 1). Most increasesoccurred in zone P, most decreases in zone M. The flatfish Achirus lineatus decreased in abundance in allthree salinity zones; among zones, it preferred O to P, in spite of being a marine species. The silversideAtherinomorus stipes was almost absent from zone O back in 1999–2001, and in 2015–2018 it preferred Pto M. As stated above, E. plumieri decreased, especially in zone O, which it used to prefer in 1999–2001.The needlefish Strongylura notata became more abundant in zone P in 2015–2018.171Water 2019, 11, 2582Table 1. Fish species whose abundance changed significantly by decade (1999–2001 vs. 2015–2018)and/or between salinity zones (Oligohaline, Mesohaline, Polyhaline) in Chetumal Bay.Species F DifferenceAchirus lineatus 12.70 Decreased by decade at all zonesAchirus lineatus 3.41 Greater abundance in O than in PAtherinomorus stipes 4.97 Decreased by decade in MEugerres plumieri 6.11 Decreased by decade in O and M, not in PGerres cinereus 3.44 Decreased by decade in MHarengula jaguana 7.97 Greater abundance in P in 2015–2018Strongylura notata 2.47 Greater abundance in P in 2015–2018Diversity was not uniform by locality. In 1999–2001 most of the sites with greater diversitybelonged in zone O, such as Punta Catalán (H’n = 2.47 nats) and UQROO (2.14 nats), same as equity(J’n = 0.84, 0.97, respectively) and richness (up to 19 spp.), whereas in 2015–2018 most diverse localitieswere in zone M, e.g., Siete Esteros (2.32 nats) and Punta Flor (2.19 nats), same as equity (0.90, 0.88,respectively) and richness (up to 14 spp., although Chelem, in zone P, reached 15 spp.).Total diversity or equity did not differ between decades (t = 0.36 and t = 1.10, respectively),but richness yes (t = 2.30), decreasing from a mean of 11.8 spp. in 1999–2001 to 9.3 spp. in 2015–2018.Beta-diversity (turnover) did not differ between decades, although the graph shows a somewhatgreater dispersion in 2015–2018 than in 1999–2001 (Figure 3). Turnover varied slightly between decades,from 0.78 to 0.79, and nestedness from 0.06 to 0.08.Figure 3. Beta-diversity of fishes in Chetumal Bay by decade: (a) 1999–2001; (b) 2015–2018. Circles arepairwise comparisons of species turnover derived from the Sørensen formula, where a’ are sharedspecies, b’ are species exclusive to the focal locality and c’ are species exclusive to the compared locality.The most frequent guilds were benthivores, small and medium-sized, followed by smallplanktivores and medium-sized piscivores. No large benthivores occurred in our samplings in1999–2001 and three guilds were absent in 2015–2018: Medium-sized herbivores, large piscivores,and medium-sized planktivores. Although medium-sized and small benthivores always predominated,the proportions for the 14 guilds changed significantly between decades (χ2 = 106.75; Table 2). However,significant changes in abundance by guild between decades and/or zones occurred only for smallbenthivores (F = 5.27: decreased by decade), medium-sized piscivores (F = 4.03: greater in zone P thanzone M in 2015–2018), and small planktivores (F = 8.00: greater in zone P than in zones M and O in2015–2018, mostly due to juveniles of H. jaguana).172Water 2019, 11, 2582Table 2. Fish guild occurrences in Chetumal Bay in 1999–2001 and 2015–2018. Figures are numberof samplings.Guild 1999–2001 2015–2018Large benthivores 0 4Middle-sized benthivores 63 46Small benthivores 157 97Medium-sized detritivores 8 1Small detritivores 8 6Medium-sized herbivores 1 0Small herbivores 9 3Medium-sized omnivores 3 2Small omnivores 11 12Large piscivores 1 0Medium-sized piscivores 45 42Small piscivores 1 6Medium-sized planktivores 1 0Small planktivores 63 26By salinity-tolerance, the most frequent species were marine euryhaline, followed by residentestuarine, freshwater, and very few marine stenohaline, the latter only in the most saline localitiesof zone P. Freshwater fishes appeared not only in zone O, but also in stream mouths within zone M(not only secondary freshwater species, but also primary, i.e., A. bacalarensis), especially during therainy season. The proportions did not change between decades (KS, D = 0.25). Marine euryhalinespecies decreased between decades in zones O and M, but increased in zone P (F = 9.54; Figure 4a).Estuarine species decreased in zones M and P, and increased in zone O, but, in spite of that trend,they always tended to prefer areas of greater salinity (F = 2.85, p = 0.06; Figure 4b).Figure 4. Abundances of salinity-tolerance groups of fishes by decade and salinity area in Chetumal Bay.Abundance (catch per unit effort (CPUE)) log-transformed; (a) marine euryhaline fishes; (b) estuarineresident fishes. Posthoc significant differences for (a): M:2015 against M, O, and P:1999 and P:2015; P:2015against O:2015; (b) P vs. O and M. Abbreviations for salinity zones: O—oligohaline; M—mesohaline;P—polyhaline; “1999” is 1999–2001 and “2015” means 2015–2018.Only two species exhibited significant differences in maximum length observed in seinings.Strongylura notata decreased in size between decades from 268.9 to 181.7 mm (F= 6.15). Eucinostomus gulaincreased (F = 8.49), but only in zone O, from a mean maximum length of 56.4 mm in 1999–2001 to87.3 mm in 2015–2018; by zones, only in 2015–2018, it was larger in zone O than in zone P (50.8 mm),whereas in 1999–2001 there was no spatial difference in size. Another mojarra, E. argenteus, also seemedto increase marginally in size in all zones, from 42.4 mm in 1999–2001 to 50.0 mm in 2015–2018 (F = 3.6,p = 0.07).173Water 2019, 11, 25824. DiscussionGiven that our study features no parallel “control” for the “impacted” area, ours is not a trueB.A.C.I. (Before/After-Control/Impact) design [21]. Moreover, the “before” data belong in fact toa period after the initial enlargement of the canal. Nevertheless, we expected the expansion of theZaragoza Canal after 1999–2001 to induce changes in composition and diversity of fishes in ChetumalBay, some of them perhaps positive for the environmental integrity (greater access of marine fishes,at least to zone P, and better development of sea grasses due to increased salinity [22]), others probablynegative, not just because of the entrance of lionfish and sargasso (unpubl.), but also due to the methodsrequired for the expansion. The bottom of the canal is mostly bedrock, so explosives are neededto deepen it, resulting in sediment suspension likely affecting habitats in the bay and adjacent reef;moreover, widening the canal implies destroying mangrove, an important habitat for fishes within andoutside the bay [23].The increase in abundance between decades specific to the polyhaline zone, as well as the increaseof the guilds of medium-sized piscivores and small planktivores, and the group of marine euryhalinefishes, were in line with our expectations, given the expansion of Zaragoza Canal and its interactionwith other factors (discussed below). When an artificial channel was opened in the El Carmen-Machonacoastal lagoon system in the southern Gulf of Mexico, the composition of the estuary included moremarine and fewer estuarine species over the course of one decade [24].Large schools of the sardine Harengula jaguana, a planktivore that we classified as “small” becausewe captured mostly juveniles, occurred in zone P in 2015–2018 apparently as often as they do in theadjacent coast, usually over seagrass meadows (pers. obs.), whereas in former decades they weremore abundant in zones O and M (Table 1, Appendix A). In addition, the oligohaline zone was themost diverse area in 1999–2001, but the mesohaline zone was most diverse in 2015–2018, also shiftingdominance towards the seaward opening of the system. Notwithstanding, no general change incomposition or diversity was apparent, and several trends that may cause concern were detected, e.g.,decreases in total abundance, mean local richness, and number of guilds.Excessive illegal fishing outside the bay may be one factor that helps explain the decrease injuveniles of large piscivores. The negative trend for this guild exists as well in the reefs adjacent toChetumal Bay [21]. The probable shift in dominance from E. plumieri to G. cinereus may also reflect thefact that the former is preferred as a fishery resource in the bay [4].The increased size of E. gula, and perhaps also E. argenteus, especially in zone O, could bea by-product of the decline in abundance of other benthivores in that area, possibly because of lesscompetition. In contrast, S. notata became smaller in length in zone P, where diversity increased.Body size strongly influences trophic level and habitat use of fishes [25].The changes in zone P may reflect the salinity increase in Chetumal Bay during the last decade,a process that had been noted already by other researchers [26]. However, the reason for this salinizationis not only the expansion of Zaragoza Canal: There is a general trend in the region for the dry season tobe longer, thus increasing evaporation and decrease freshwater input from rains (Carrillo, pers. comm.).As for the lower local water temperature in the face of increasing regional heat [27], the increasedwind force may offer a straightforward explanation, although an interacting factor could be the canalexpansion itself, i.e., greater tidal movement of water, fewer shallow sites with slow-moving watersusceptible to heating.The differences in freshwater and estuarine species are more difficult to explain, except forthe catfish A. assimilis, which in 1996 suffered a massive mortality that was attributed to pollutionand specific pathogens [28]; the species seemingly has not recovered ever since. The interdecadaldecrease for the guild of small benthivores, many of them estuarine, especially strong in zone M, is notexplainable by predation, because medium-sized and large piscivores also declined. We speculatethat this decline could be accounted for, at least in zone O, by the loss of habitat due to the continuederosion of the western coast of the bay. This process that has been ongoing for centuries, as attested by174Water 2019, 11, 2582Classic Mayan fishery structures that should have been in contact to the shore, but now are severalmeters away from the coast [29].The man-made opening of seaward channels strongly alters benthic communities of plants andinvertebrates in lagoons [30] by promoting the entrance of marine organisms, including stony corals [2],into Chetumal Bay. This obviously affects herbivore and benthivore species, also accounting for thespread of the invasive lionfish [31]. The zooplankton can be affected as well, especially if the salinityand the trophic state of the estuary change [32]. However, as stated above, we are cautious to ascribethis change solely to the expansion of the canal, given the mentioned regional trend towards lessprecipitation in the region, related to global climate change.On the other hand, and although we did not detect any interdecadal difference in beta-diversity,the increased abundance in zone M of freshwater species, even primary, and of marine species in zoneO, may signal a faunistic homogenization of the bay. In fact, part of our original rationale for definingthese three areas was to follow approximately the isolines of 9 psu, upper salinity tolerance of theprimary freshwater characid A. bacalarensis and lower limit for juveniles of the marine euryhalinesnapper L. apodus, and 19 psu, upper bound for the secondary freshwater molly P. mexicana [33],and yet, in 2015–2018, many of these species were recorded outside their preferred salinity area.As observed by Rahel [34], “[h]abitat and flow homogenization are major drivers of biotichomogenization.” At a planetary level, biotic homogenization implies invasion by nonnative species [35],a process enhanced by global warming [36]. Locally, however, impact factors, such as hurricanes,can also make the fauna more homogeneous [37]. In any case, it is clear that the canal expansion is notan isolated factor for this process.The expansion of the Zaragoza Canal may be ethically ambiguous. On the one hand, it favorsthe entry of invaders and could induce faunal homogenization, as well as the probable loss of suchimportant guilds as large piscivores. On the other hand, making the opening wider and deeper mightplay a positive role for the seasonal migratory movements of bonefish [12], barracuda [23], and otherspecies between the Caribbean sea and the bay. However, if habitat destruction is not controlled,especially the flats and mangrove cays that are favorite feeding grounds for bonefish and permit,the damage would be both ecological and socioeconomical, by impinging detrimentally on such speciesthat support an important fishery in Belize and Mexico [3].In spite of the methodological limitations of this study, and perhaps the idiosyncratic nature ofChetumal Bay and its artificial canal, our findings can be a useful reference for similar sites elsewhere.For example, coastal works in Spain’s Mar Menor [38] have brought positive changes (increased benthicbiodiversity due to greater availability of hard bottoms), but also negative ones (altered sedimentquality and vegetation cover), and canals are globally a major pathway for biological invasions [39].Examining a variety of outcomes for comparable situations should be necessary for environmentalmanagers and ecologists alike.Author Contributions: Conceptualization, J.J.S.-S.; methodology, J.J.S.-S. and R.L.H.-P.; software, J.J.S.-S.; validation,J.J.S.-S. and R.L.H.-P.; formal analysis, J.J.S.-S.; investigation, J.J.S.-S. and R.L.H.-P.; resources, J.J.S.-S. and R.L.H.-P.;data curation, J.J.S.-S.; writing—original draft preparation, J.J.S.-S.; writing—review and editing, J.J.S.-S. and R.L.H.-P.;visualization, J.J.S.-S.; supervision, J.J.S.-S.; project administration, J.J.S.-S.; funding acquisition, J.J.S.-S.Funding: This research was funded by the Consejo Nacional de Ciencia y Tecnología (Mexican council for scienceand technology), grant number 242558.Acknowledgments: Janneth Padilla prepared the map. In addition to the authors, field workers included FernandoAguilar, Juan Aguilar, Rudy Castellanos, Felipe Martínez, Norman Mercado, Erica Pimentel, Áxel Schmitter andMartha Valdez. The anonymous reviewers improved the manuscript.Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of thestudy; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision topublish the results.175Water 2019, 11, 2582Appendix AQuantitative sampling: Fish species of Chetumal Bay by salinity zone (Oligohaline, Mesohaline,Polyhaline) and decade. Figures are mean abundances (CPUE).Species Zone 1999–2001 2015–2018Anchoa cayorum 0.5 0.5O 0 0.5P 0.5 0Achirus lineatus 68.3 13.4M 45.4 2.0O 19.0 9.7P 3.8 1.7Albula vulpes 3.5 0P 3.5 0Anchoa colonensis 0.3 0M 0.3 0Anchoa cubana 19.2 10.0M 3.0 0O 11.7 0P 4.5 10.0Ariopsis assimilis 2.3 0M 1.0 0O 1.3 0Astyanax bacalarensis 0 45.2M 0 38.0O 0 1.2P 0 6.0Atherinomorus stipes 2306.3 590.7M 1542.2 90.8O 167.7 0.3P 596.5 499.5Bairdiella ronchus 43.3 2.0M 21.5 0O 21.3 0P 0.5 2Bathygobius curacao 0 1.2M 0 1.2Bothus ocellatus 0.3 0P 0.3 0Canthigaster rostrata 0 0.3P 0 0.3Centropomus undecimalis 1.0 0M 1.0 0Chriodorus atherinoides 9.8 2.5M 8.2 0P 1.7 2.5Corvula sanctaeluciae 0 0.7P 0 0.7Cyprinodon artifrons 45.2 6.0M 1.5 0.7O 0 1.5P 43.7 3.8176Water 2019, 11, 2582Species Zone 1999–2001 2015–2018Diapterus auratus 0 23.3P 0 23.3Eucinostomus argenteus 34.2 63.6M 9.5 3.3O 4.5 0.5P 20.2 59.8Eucinostomus gula 269.8 53.8M 38.8 12.3O 9.5 5.0P 221.5 36.5Eucinostomus harengulus 15.7 30.7M 3.3 11.2O 0.3 1.0P 12 18.5Eucinostomus jonesii 257.5 72.8M 155.3 13.8O 45.3 34.1P 56.8 24.8Eucinostomus melanopterus 0 1.7M 0 0.3P 0 1.3Eugerres plumieri 78.4 7.8M 23.6 1.0O 52.3 2.9P 2.5 3.8Floridichthys polyommus 133.4 266.7M 78.3 34.4O 12.5 5.5P 42.7 226.8Gerres cinereus 1.8 31.2M 1 4.3O 0 20.3P 0.8 6.5Gobiosoma yucatanum 1.5 0O 1.5 0Gambusia sexradiata 0.5 0O 0.5 0Gambusia yucatana 9.8 1.5M 8.5 1.0P 1.3 0.5Harengula clupeola 9 30P 9 30Harengula humeralis 6 0.3P 6 0.3Harengula jaguana 174.7 149.0M 37.3 1.0O 74.3 0P 63.0 148.0Hippocampus erectus 0.3 0P 0.3 0Hypanus americanus 0 0.3M 0 0.3177Water 2019, 11, 2582Species Zone 1999–2001 2015–2018Hyporhamphus roberti 1.5 0.25M 0 0.25O 1.5 0Jenkinsia lamprotaenia 2.5 0P 2.5 0Jordanella pulchra 8.0 8.9M 8.0 2.0O 0 6.25P 0 0.7Lophogobius cyprinoides 13.8 2.8M 7.3 0O 6.5 2.8Lutjanus griseus 0 13.3P 0 13.3Mayaheros urophthalmus 4.2 1.5M 4.2 0O 0 1.5Monacanthus tuckeri 0 0.3M 0 0.3Oligoplites saurus 1.2 0.3M 0.7 0P 0.5 0.3Oostethus lineatus 0 0.3M 0 0.3Opisthonema oglinum 9.8 0O 5 0P 4.8 0Opsanus beta 1.7 0O 1.7 0Paraclinus fasciatus 0.5 0O 0.5 0Poecilia mexicana 20.2 0.4M 19.2 0O 2.0 0.4Sphoeroides testudineus 36.0 47.8M 6.0 27.4O 25.2 11.7P 4.8 8.7Sphyraena barracuda 24.8 12.0M 8.8 4.6O 5.2 3.5P 10.8 4.0Strongylura marina 0.5 0P 0.5 0Strongylura notata 51.9 150.5M 25.3 27.2O 5.8 1.5P 20.8 121.8Strongylura timucu 1.5 0M 1.5 0Syngnathus floridae 0.25 0M 0.25 0178Water 2019, 11, 2582Species Zone 1999–2001 2015–2018Syngnathus scovelli 0.5 0O 0.5 0Trachinotus falcatus 0 1.2M 0 0.3O 0 0.5P 0 0.3Trichromis salvini 0 3.5O 0 3.5Vieja melanurus 2.0 0O 2.0 0Appendix BQualitative records: Fish species of Chetumal Bay by salinity zone (Oligohaline, Mesohaline,Polyhaline) and decade (before 2005 vs. 2006 up to now). Voucher numbers (at ECO-CH, except onefrom the Colección Nacional de Peces, acronym CNPE) or literature sources (in italics) given, when thespecies was not recorded in this study. Records not assignable with precision to one of the three salinityzones, or known only from ichthyoplankton, not considered.Species OBefore 2005MP O2006 up toNow MPAcanthurus bahianus 4222Achirus lineatus X X X X X XAetobatus narinari pers. obs.Albula vulpes 4221 X [12] [12]Anchoa cayorum X XA. colonensis XA. cubana X X X XArchosargusprobatocephalus4219A. rhomboidalis 4217Ariopsis assimilis X X 4261Astyanax bacalarensis 7815 1301 X X XAtherinomorus stipes X X X X X XBagre marinus 3143Bairdiella ronchus X X X XBathygobius curacao XBothus ocellatus XCanthigaster rostrata XCaranx latus 4214 XCentropomusundecimalis4216 XChaetodipterus faber 4213Chilomycterus schopfi 4047Chriodorus atherinoides X X 8020 XConodon nobilis CNPE 3221Corvula sanctaeluciae 1703 XCribroheros robertsoni XCyprinodon artifrons X X X X XD. guttata 4194179Water 2019, 11, 2582Species OBefore 2005MP O2006 up toNow MPDiapterus auratus XEcheneis neucratoides 2785 XElops saurus [38]Epinephelus itajara pers. obs. pers. obs.Eucinostomus argenteus X X X X X XE. gula X X X X X XE. harengulus X X X X X XE. jonesii X X X X X XE. melanopterus X XEugerres plumieri X X X X X XFloridichthyspolyommusX X X X X XGambusia sexradiata XG. yucatana X X X XGerres cinereus X X X X XGobiosoma yucatanum XGymnothorax funebris pers. obs.Haemulon sciurus pers. obs.Harengula clupeola 2061 X XH. humeralis X 7806 XH. jaguana X X X X XHippocampus erectus XHolocentrus rufus 4224Hypanus americanus XHyporhamphus roberti X XJenkinsia lamprotaenia XJordanella pulchra X X X XKyphosus incisor pers. obs.Lachnolaimus maximus pers. obs.Lobotes surinamensis 4218Lophogobius cyprinoides X X XLupinoblennius vinctus 4134Lutjanus analis [38]L. apodus 4236L. cyanopterus pers. obs.L. griseus 4384 XL. jocu [39]L. mahogoni pers. obs.L. synagris XMayaherosurophthalmusX X XMegalops atlanticus 3139 4381Monacanthus tuckeri 7838Mugil cephalus pers. obs.Narcine brasiliensis pers. obs.Ocyurus chrysurus [39]Oligoplites saurus X X XOostethus lineatus XOpisthonema oglinum X XOpsanus beta XParaclinus fasciatus X 3169Poecilia kykesis 1246P. mexicana X X XPristis sp. pers. obs. pers. obs.180Water 2019, 11, 2582Species OBefore 2005MP O2006 up toNow MPPterois volitans pers. obs.Scomberomorusmaculatus4768 pers. obs.Selene vomer 4215Sparisoma viride pers. obs.Sphoeroides testudineus X X X X X XSphyraena barracuda X X X X X XStrongylura marina XS. notata X X X X X XS. timucu 5063 X 8025Styracura schmardae 3799Symphurusdiomedeanus4137Syngnathus floridae XS. scovelli XTrachinotus falcatus X X XTrichromis salvini XVieja melanurus X 4302References1. 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This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).183waterArticleHepatic Steatosis in a Bullhead (Cottus gobio)Population from a High-Mountain Lake (Carnic Alps):Adaptation to an Extreme Ecosystem?Paolo Pastorino 1,2,* , Marino Prearo 2, Elisabetta Pizzul 1, Marco Bertoli 1,Danila Raffaella Francese 2, Vasco Menconi 2, Davide Mugetti 2, Elena Bozzetta 2and Katia Varello 21 Department of Life Sciences, University of Trieste, via Giorgieri 10, 34127 Trieste, Italy; pizzul@units.it (E.P.);marco.ber3@gmail.com (M.B.)2 Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148,10154 Torino, Italy; marino.prearo@izsto.it (M.P.); danilaraffaella.francese@izsto.it (D.R.F.);vasco.menconi@izsto.it (V.M.); davide.mugetti@izsto.it (D.M.); elena.bozzetta@izsto.it (E.B.);katia.varello@izsto.it (K.V.)* Correspondence: paolo.pastorino@izsto.it; Tel.: +39-011-268-6295Received: 4 November 2019; Accepted: 4 December 2019; Published: 5 December 2019Abstract: The aim of this study was to describe liver alterations observed in a bullhead (Cottus gobio)population from a high-mountain lake (Dimon Lake; 1857 m a.s.l.) located in Carnic Alps (NortheastItaly). Two fish sampling campaigns (summer and autumn) were performed in 2017 to explore thepossible causes of this phenomenon. In addition, to investigate the typical liver features of C. gobio,control specimens were captured from Degano Creek in the same seasons. Total length, weight,and liver weight were recorded in fish from both sampling sites. In addition, Fulton’s condition factor(K) and hepatosomatic index (HSI) were calculated. Stomach contents were also analyzed. Liverfrom each specimen was sampled, and histological examination was carried out. Liver steatosis (L)(nuclear displacement and cytoplasm vacuolization) were histologically evaluated by assigning asemiquantitative severity score. A significant difference in L was found between Dimon Lake andDegano Creek in both summer (Mann–Whitney test; p = 0.0001) and autumn (Mann–Whitney test;p < 0.0001). Regarding HSI, a significant difference was also recorded between Dimon Lake andDegano Creek in summer (Mann–Whitney test; p < 0.0001) and also in autumn (Mann–Whitney test;p < 0.0001), but no seasonal change in K values was recorded between the two sites in both summer(Mann–Whitney test; p = 0.8589) and autumn (Mann–Whitney test; p = 0.6415). A significant positivecorrelation between HSI and L was found (ρS 0.573). The causes of this abnormality might be relatedto adaptation by the fish to the high-altitude environment, accumulating lipids in the liver to tacklethe adverse season, which lasts for 6 months of the year. Nonetheless, our study does not precludesteatosis induced by a nearly exclusive chironomids diet in bullhead from Dimon Lake.Keywords: adaptation; alpine lakes; Cottus gobio; hepatic steatosis1. IntroductionAlpine lakes are extreme ecosystems with harsh climatic conditions. Though located in remoteareas, they may be affected by local anthropogenic impacts, such as fish introduction. Most alpinelakes were, in fact, originally fishless, isolated ecosystems. Owing to the relatively small numberof native species, the lakes have low resilience to disturbances and can be especially sensitive tothe introduction of alien fish for recreational fishing. During the greater part of the year, snow andice cover prevents sunlight from penetrating the lake water column [1]. Without the penetration of185Water 2019, 11, 2570light, photosynthesis is not possible, and the lake waters remain in a long period of darkness [2],becoming a heterotrophic system isolated from the surrounding area. In early summer, when the snowmelts, the lakes quickly shift from extremely low to extremely high solar irradiance correlated withaltitude. Low oxygen availability and low temperatures make life at such altitudes challenging, thoughmany species have adapted successfully via physiological changes. Unlike short-term acclimatization(immediate physiological response to a changing environment), high-altitude adaptation meansirreversible, evolved physiological responses to high-altitude environments, associated with behavioraland genetic changes.Published data on fish adaptation to high-altitude environments are scarce. For example,some studies have focused on metabolic rate, as reported in highland westslope cutthroat trout(Oncorhynchus clarkii) as compared with introduced lowland rainbow trout (Oncorhynchus mykiss) inthe Oldman River basin (Canada): Fish at high altitudes have a lower metabolic rate [3]. Kang andco-workers [4] investigated the genetic mechanism of high-altitude adaptation in Creteuchiloglanismacropterus (Siluriformes) and found that many genes were involved in energy metabolism and hypoxia,which might be an important resource for studying adaptations to a high-altitude environment. Li andco-workers [5] characterized the mitochondrial genomes of two species (“morphologically primitive”clade and “morphologically specialized” clade) of schizothoracine fishes. They reported evidencefor positive selection acting on the protein-encoding genes in the mitochondrial genomes of the“morphologically specialized” clade, implying a possible genetic basis for high-altitude adaptation inthis derived lineage of cypriniform fish.Other studies on macrobenthic invertebrates in streams have showed a decrease in species richnesswith increasing altitude due to lower oxygen saturation rather than low temperature [6]. These factorsmay reduce productivity in high-altitude ecosystems, meaning there will be less energy available forconsumption, growth, and activity, which provides a survival advantage to fish with lower metabolicdemands. During 2017, two fish sampling campaigns were conducted in a high-mountain lake (DimonLake) located in the Carnic Alps (northeast Italy). Liver examination revealed alterations in specimensof the bullhead (Cottus gobio). The aim of this study was to describe the liver alterations observed inC. gobio and discuss the possible causes of this abnormality.2. Materials and Methods2.1. Study SitesThe main study site was Dimon Lake (46◦34′05.4′′ N; 13◦03′45.8′′ E), a high-mountain lake locatedin the Carnic Alps (municipality of Ligosullo, Udine Province, Friuli Venezia-Giulia Region, northeastItaly) at 1857 m a.s.l. The Carnic Alps are among the most remote areas in Italy. Anthropic impacts arevery limited, except for pasturing activity. Dimon Lake is a typical glacial-origin lake, classified as aSite of Community Interest and Special Areas of Conservation (SCI/SAC-IT3320002 “Monti Dimone Paularo”). Originally a fishless lake, fish were introduced for recreational fishing in the past [7].The lake lies on sandstone and volcanic rocks; it measures 376 m in perimeter, 0.6 ha in surface area,and has a maximum depth of 4.27 m. For the purposes of comparison in this study, C. gobio individualswere captured in Degano Creek (46◦26′21.9′′ N; 12◦52′43.6′′ E), a sub-alpine watercourse near themunicipality of Raveo (520 m a.s.l.), about 40 km from Dimon Lake, with scarce human impacts(Figure 1). The comparison of lake vs. stream populations was dictated by the absence of C. gobio inother high-altitude lakes in northeast Italy.186Water 2019, 11, 2570Figure 1. Location of fish sampling sites in Friuli Venezia-Giulia, Italy. Degano Creek (pictured on theleft) and Dimon Lake (pictured on the right).2.2. Fish SamplingIn 2017, two fish sampling campaigns (July and October) were performed. These two monthswere chosen because of accessibility, which is usually limited by snowfall from early November toJune. Fish were sampled using an electrofishing boat in both the littoral and the deep zones (maximumdepth 4.27 m). The fish were identified and measured for length (TL; cm) and weight (W; g). Fulton’scondition factor (K) was also calculated for each specimen. The K factor was derived from the followingformula [8]:K = (W/TL3) × 100,where W is the weight (g) and TL is the total length of the fish (cm). A total of 35 fish (15 in thesummer and 20 in the autumn campaign) were sampled, euthanized with an overdose of tricainemethanesulfonate MS-222 (50 mg kg−1), and then necropsied. The liver from each specimen wasweighted to calculate the hepatosomatic index (HSI) using the formula [9]:HSI (%) = (LW/FW) × 100,where LW is the liver weight (g) and FW is the whole weight of the fish (g). Liver was sampledfor histological evaluation. Only larger fish were selected in order to have enough material for allhistological analyses (see Section 2.4). Moreover, as control fish, we also performed two samplingcampaigns in the same periods (July and October) using electrofishing in Degano Creek. Only 10individuals both for summer and autumn were captured to avoid putting pressure on the wild nativepopulation. TL, W, and LW were determined and HSI and K were calculated. Fish sampling campaignswere performed by permission of Ente Tutela Patrimonio Ittico del Friuli Venezia-Giulia (authorizationn. 11/DIR/17/01/2017).2.3. Analysis of Stomach ContentsIn both sampling campaigns the stomach contents of the fish from Dimon Lake were analyzedto obtain more detailed information about their diet. The stomachs were preserved in alcohol 70%and the content was identified by optical microscope (Olympus BX40, Olympus, Tokyo, Japan) andstereomicroscope (Zeiss Stemis V8, Zeiss, Shangkeheng, Germany). An attempt was made to identifythe ingested prey to the lowest recognizable taxa, but accurate identification was often not possible dueto digestion of the prey. Stomach content analysis was also performed on the fish from Degano Creek.In order to describe the diet, the prey frequency of occurrence (Fi) [10] was calculated for both seasons.187Water 2019, 11, 25702.4. Histological EvaluationA total of 35 liver samples were partly fixed in 10% neutral buffered formalin and partly frozen.The fixed tissues were dehydrated in a gradual series of alcohols and embedded in paraffin. The paraffinblocks were cut into 4 ± 2 mm sections and stained with hematoxylin and eosin (HE) for microscopicevaluation. Histological changes (nuclear displacement and cytoplasm vacuolization) to defineliver steatosis were evaluated and a semiquantitative severity score was assigned: 0—not observed;1—mild; 2—moderate; 3—severe [11]. Histochemical stains, Periodic-acid Schiff (PAS) to detectmucopolysaccharides and Sudan III to detect lipids, were also performed [12]. Slides were evaluatedmicroscopically at increasing magnification (10×, 20×, 40×) on a Zeiss Axio Scope.A1 microscope.Samples were considered positive for the presence of red-purple glycogen or orange lipidic deposits inhepatocyte cytoplasm for PAS and Sudan III stain, respectively.2.5. Statistical AnalysisThe Wilcoxon test was performed to compare (a) the liver steatosis score (L), (b) the hepatosomaticindex (HSI), and (c) the K values between seasons in Dimon Lake and in Degano Creek. Mann–WhitneyU tests were used for pairwise comparison of L, HSI, and K values of C. gobio in Dimon Lake vs.Degano Creek in both summer and autumn. Spearman’s rank correlation coefficient (ρS) was usedto test correlations between L, W, HIS, and K. Differences were considered significant with a p value<0.05. All statistical analyses were performed using GraphPad Prism version 8.0.1 (GraphPad Software,San Diego, CA, USA).3. Results3.1. Fish Sampling and Anatomopathological FeaturesValues of TL, W, K, LW, and HIS for C. gobio captured in Dimon Lake and Degano Creek arereported in Tables 1 and 2, respectively.Regarding Dimon Lake, the average TL for C. gobio captured during the summer campaign was13.88 ± 1.12 cm, with an average W of 31.20 ± 7.54 g. The average TL of individuals captured duringautumn was 13.25 ± 1.37 with an average W of 26.70 ± 6.89 g. K values ranged between 1.15 ± 0.12in summer and 1.14 ± 0.15 in autumn. LW ranged between 0.85 ± 0.14 in summer and 0.82 ± 0.24 inautumn. HSI ranged between 2.80 ± 0.38 in summer and 3.08 ± 0.39 in autumn (Table 3). At necropsy,the livers appeared variably pale to yellowish in color and friable in consistency; no visible lesions inthe other visceral organs were observed (Figure 2).In Degano Creek, the average TL of individuals captured during summer was 14.24 ± 0.86 cm withan average W of 33.60 ± 6.15 g. Instead, during autumn, individuals showed a TL of 12.36 ± 1.76 cmwith a W of 23.10 ± 9.04 g. K values ranged between 1.16 ± 0.14 in summer and 1.17 ± 0.14 in autumn.LW ranged between 0.54 ± 0.12 in summer and 0.36 ± 0.14 in autumn. HIS ranged between 1.60 ± 0.15in summer and 1.60 ± 0.10 in autumn (Table 3). No lesions in organs were observed, liver included(Figure 3).Statistical analysis showed no seasonal differences in the liver steatosis score in Dimon Lake(Wilcoxon test; p = 0.6719), but a significant difference was found between Dimon Lake and DeganoCreek in both summer (Mann–Whitney test; p = 0.0001) and autumn (Mann–Whitney test; p < 0.0001)(Table 3). Regarding HSI, no seasonal differences were observed in Dimon Lake (Wilcoxon test;p = 0.1173), but a significant difference were recorded between Dimon Lake and Degano Creek insummer (Mann–Whitney test; p < 0.0001) and also in autumn (Mann–Whitney test; p < 0.0001) (Table 3).No seasonal change in K values was recorded in Dimon Lake (Wilcoxon test; p = 0.5696), not evenbetween Dimon Lake and Degano Creek in both summer (Mann–Whitney test; p = 0.8589) and autumn(Mann–Whitney test; p = 0.6415). A significant positive correlation between HIS and L was found(ρS 0.573).188Water 2019, 11, 2570Table 1. Total length (TL), weight (W), liver weight (LW), hepatosomatic index (HSI), Fulton’s conditionfactor (K), and liver steatosis score (L) of Cottus gobio (N) captured during summer and autumn inDimon Lake.Season N TL (cm) W (g) LW (g) HSI K LSummer(July)1 14.0 29 0.803 2.77 1.06 22 15.3 38 0.927 2.44 1.06 13 15.3 37 0.925 2.50 1.03 24 14.9 29 0.913 3.15 0.88 25 13.1 27 0.867 3.21 1.20 36 15.4 46 1.090 2.37 1.26 07 14.5 41 0.914 2.23 1.34 28 13.5 32 0.877 2.74 1.30 39 13.2 26 0.874 3.36 1.13 310 15.0 39 0.905 2.32 1.16 211 13.4 31 0.964 3.11 1.29 312 13.5 27 0.877 3.25 1.10 213 12.2 20 0.594 2.97 1.10 314 12.5 25 0.767 3.07 1.28 315 12.4 21 0.540 2.57 1.10 1Autumn(October)16 14.3 37 1.424 3.85 1.27 317 12.3 19 0.507 2.67 1.02 018 14.5 37 0.884 2.39 1.21 319 12.4 26 0.946 3.64 1.36 220 12.0 17 0.530 3.12 0.98 321 14.5 31 0.973 3.14 1.02 322 15.5 33 0.861 2.61 0.89 223 15.0 32 0.896 2.80 0.95 124 15.5 39 1.197 3.07 1.05 325 13.5 23 0.655 2.85 0.93 326 14.5 34 1.231 3.62 1.12 327 13.0 26 0.738 2.84 1.18 228 13.5 28 0.717 2.56 1.14 229 11.0 20 0.578 2.89 1.50 230 12.5 24 0.838 3.49 1.23 331 13.5 27 0.896 3.32 1.10 332 11.8 19 0.606 3.19 1.16 333 12.0 20 0.674 3.37 1.16 334 11.7 19 0.589 3.10 1.19 335 12.0 23 0.718 3.12 1.33 3Figure 2. Macroscopical aspect of liver from Cottus gobio captured in Dimon Lake during autumn 2017.It appears pale to yellowish in color and friable in consistence (green circle).189Water 2019, 11, 2570Figure 3. Macroscopical aspect of normal liver (green circle) of Cottus gobio captured in Degano Creekduring autumn 2017.Table 2. Total length (TL), weight (W), liver weight (LW), hepatosomatic index (HSI), Fulton’s conditionfactor (K), and liver steatosis score (L) in individuals of Cottus gobio (N) captured during summer andautumn in Degano Creek.Season N TL (cm) W (g) LW (g) HSI K LSummer(July)1 13.5 27 0.407 1.509 1.10 02 14.2 40 0.768 1.920 1.40 13 15.4 38 0.566 1.490 1.04 04 14.9 31 0.478 1.543 0.94 05 15.1 37 0.661 1.789 1.07 16 13.1 25 0.390 1.560 1.11 17 14.5 41 0.598 1.458 1.34 08 13.5 32 0.530 1.657 1.30 09 13.2 26 0.429 1.652 1.13 110 15.0 39 0.575 1.475 1.16 0Autumn(October)11 11.2 16 0.282 1.766 1.14 012 13.2 26 0.402 1.547 1.13 013 10.7 15 0.244 1.631 1.18 014 14.1 38 0.622 1.639 1.36 115 13.0 24 0.416 1.735 1.09 016 10.6 14 0.216 1.542 1.14 017 10.7 16 0.253 1.584 1.29 118 14.4 28 0.462 1.651 0.93 019 10.7 17 0.251 1.479 1.39 120 15.0 37 0.532 1.437 1.10 0Table 3. Mean and standard deviation of total length (TL), weight (W), liver weight (LW), hepatosomaticindex (HSI), Fulton’s condition factor (K) and median values of liver steatosis score (L) in individuals ofCottus gobio (N) captured during summer and autumn in Dimon Lake and Degano Creek. Lowercaseletters indicate statistically significant differences between sites in summer (a) and autumn (b) (p <0.05).Site Season N TL (cm) W (g) K LW (g) HSI LDimonSummer 15 13.88 ± 1.12 31.20 ± 7.54 1.15 ± 0.12 0.85 ± 0.14 2.80 ± 0.38 a 2 aAutumn 20 13.25 ± 1.37 26.70 ± 6.89 1.14 ± 0.15 0.82 ± 0.24 3.08 ± 0.39 b 3 bDegano Summer 10 14.24 ± 0.86 33.6 ± 6.15 1.16 ± 0.14 0.54 ± 0.12 1.60 ± 0.15a 0 aAutumn 10 12.36 ± 1.76 23.1 ± 9.04 1.17 ± 0.14 0.36 ± 0.14 1.60 ± 0.10 b 0 b190Water 2019, 11, 25703.2. Stomach Contents AnalysisThe analysis of the stomach contents showed that C. gobio from Dimon Lake fed chiefly onChironomidae larvae in both summer (96.18%) and autumn (98.30%). Other taxa were present inextremely low proportions (Chironomidae pupae: 3.18% in summer and 0.79% in autumn; Tricoptera:0.64% in summer and 0.39% in autumn; Tricoptera Limnephilidae: 0% in summer and 0.52% in autumn).Stomach content analysis of the Degano Creek fish revealed Tricoptera (42.63% in summer; 34.03% inautumn), Ephemeroptera (7.5% in summer; 5.8% in autumn), and Plecoptera (4.1% in summer and2.5% in autumn).3.3. Histological AnalysisHistology of the liver samples of C. gobio from Dimon Lake showed a range of cytoplasmvacuolization of hepatocytes from mild (score 1) to severe (score 3) in 33 specimens, with multifocalto diffuse localization (Figure 4). Only two specimens showed no hepatic lesions (score 0). Nucleardisplacement and cytoplasm vacuolization are depicted in Figure 5. Table 1 presents the liver steatosisscore (L) for each specimen examined. Table 3 presents median values of L for both seasons.Sudan III was positive in all the samples with a high vacuolization score (2–3) (Figure 6a). PAS stainpositivity was inversely proportional to the histological score (Figure 6b). Fish with grade 3 showedonly rare residual glycogen deposits in hepatocytes.Finally, individuals used as the control and sampled from Degano Creek showed no hepatocytevacuolization (score 0; Figure 4a) in both seasons, except a few cases with a score of 1. Median valuesof L are also reported in Table 3.Figure 4. Liver sections of Cottus gobio (HE): (a) no vacuolization observed in hepatocytes (control);(b) mild vacuolization; (c) moderate vacuolization; (d) severe, diffuse vacuolization.191Water 2019, 11, 2570Figure 5. Liver section of Cottus gobio (HE) from Dimon Lake: detail of Figure 4d with nucleardisplacement (black arrow) and cytoplasm vacuolization (asterisk).Figure 6. Liver sections of Cottus gobio from Dimon Lake. (a) Sudan III stain; orange lipidic deposits inhepatocyte cytoplasm of a subject with a high vacuolization score (3); (b) Periodic-acid Schiff (PAS)stain: red-purple residual glycogen deposits in hepatocyte cytoplasm of bullhead classified as grade 3.4. DiscussionHigh-altitude ecosystems are rare and important habitats that host endemic and endangeredspecies characterized by specific adaptations to extreme conditions. These ecosystems are often subjectto direct anthropic pressure, such as the introduction of alien species. For example, fish speciesintroduced for recreational fishing in originally fishless alpine lakes in Italy [13] have become a seriousthreat to aquatic biodiversity, especially for amphibian communities. Species (predators, such assalmonids, but also forage fish) reared in aquaculture facilities were transported by fishermen orfishing associations, placed in these environments, and forced to live in oligotrophic waters withscarce prey [14]. Bullhead (Cottus gobio) naturally inhabits stony-bottom, oxygen-rich, cool-waterstreams and rivers. The altitude ranges from high-altitude streams to lowland rivers. Cottus gobio is a192Water 2019, 11, 2570retiring fish [15]. This relates to its high vulnerability to predation; for this reason, cover and shade areextremely important habitat features for bullhead. Generally, the bullhead diet is based on benthicinvertebrates and its feeding activity is concentrated around dawn and dusk because it is susceptibleto predators during the day. While autochthonous to the Padano-Veneto district (Friuli-Venezia Giuliaincluded) [16], the species was introduced accidentally with brook trout (Salvelinus fontinalis) to DimonLake in the 1980s [17]. S. fontinalis is presumably no longer present in the lake since we caught none.Our study started with the aim of exploring the fish community of the lake. After discoveringbullhead specimens with pronounced steatosis, we decided to investigate this abnormal condition andpose a hypothesis for its cause. Stomach contents analysis was performed to understand nutritionalrequirements, trophic and energy dynamics, food webs, food chains, material, and energy transfersbetween and within ecosystems [18]. This is an important part of studying feeding habits and,in general terms, a necessary step in research into the more complex questions of freshwater fishecology. We analyzed the stomach contents of C. gobio sampled during two seasons both in DimonLake and Degano Creek to determine whether there were differences in diet across the two seasons [18].Stomach content analysis showed that C. gobio from Dimon Lake feed mostly on Chironomidae larvae.This could be justified by the relative, great abundance, and almost exclusive presence of Chironomidaelarvae in the lake [19], and it could also be explained by the feed ecology of this species, since thistaxon is most easy to prey than other aquatic macroinvertebrates. In fact, C. gobio is a stationary benthicfeeder, which shows a well-developed homing instinct [20]. Indeed, Hirudinea and Oligochaeta taxaare also present in this lake [19], but more endobenthic than Chironomidae, thus more difficult tohunt, justifying the bullhead preference for Chironomidae. Generally, C. gobio prey on a wide range ofinsect larvae as Plecoptera, Trichoptera, Ephemeroptera, and Diptera [21–24], as recorded in DeganoCreek. Nevertheless, bullhead was found to be predominantly food generalists, with an ability toshift their feeding strategy towards specialization when exposed to environmental disturbances [25].Comparison of the histological evaluations on fish from the two different sites (lake vs. stream) showedthat only C. gobio from Dimon Lake was affected by medium-severe (score 2–3) liver steatosis. Lipidaccumulation in the hepatocytes in this species or in fish living in other alpine lakes has never beenreported in the literature to date. Our study is the first report of this phenomenon. Two hypotheses forthe causes of these histological alterations can be offered. The first posits for adaptation of the fish tothe winter season, which in this type of environment lasts for 6 months of the year. Our results clearlyreveal that HSI values were significantly lower in Degano Creek compared to Dimon Lake in bothseasons. Furthermore, a slight increase in the hepatosomatic index was recorded in autumn comparedto summer in Dimon Lake. In this situation, it can be assumed that the bullhead from Dimon Lakeaccumulates lipids in the liver and then use them as fuel for energy in the adverse season (winter).Adaptive processes are often based on biochemical and physiological adaptations. Lipids play akey role in biochemical adaptation in animals, especially in aquatic organisms, and they are of pivotalimportance for long-term energy storage. Adaptations provide an effective response to stressors (e.g.,changes in water temperature or oxygen). In some cases, fish may be able to adapt by changing theirbehavior, but more often it is through physiological adaptations, especially at the cell membrane level.Temperature, for instance, can be considered a stressor that acts to establish a new balance betweenthe environment and the biochemical properties of cell membranes. Fish respond to environmentaltemperatures by activating a suite of compensatory mechanisms operating mainly at the cellular andsubcellular levels of organization [26]. For example, carps progressively cooled from 30 ◦C to 23, 17,and 10 ◦C showed substantial changes in the fatty acid composition of the endoplasmic reticulumphospholipids, with increasing proportions of unsaturated fatty acids and changes in the activity ofother biosynthetic enzymes [27,28].Animals develop a complicated metabolic system to adapt to changes in nutritional states.In nature, when food is plentiful, animals eat and store the excess energy as lipids. This is a survivalstrategy for animals when food is not readily available [29]. Lipids are the most important biochemicalcompounds of fish [30]. Fish store lipids in muscles and liver [31] in contrast to mammals, which193Water 2019, 11, 2570store lipids in adipose tissue. Most of these lipids are transferred to different parts of the body forvarious physiological actions [32]. In some fish species, extensive lipid infiltration into the liveris not considered pathological, since the liver works as a major lipid storage organ, especially inautumn [33,34]. However, in our study, we found no significant statistical difference in liver alterationsbetween summer and autumn seasons. Kandemir and Polat [31] investigated seasonal and monthlyvariations in the amount of total lipid and fatty acids in the muscle and liver of reared rainbowtrout (Oncorhynchus mykiss). Lipid levels were higher in summer, autumn, and winter than in spring.Furthermore, the amount of total lipid and fatty acid was higher in the liver than in muscle tissue.Active fish store their lipids in muscle tissues, but fish living at the bottom store their lipids in theliver [35], as seen in the Cottus gobio in our study. These considerations seem to corroborate ourhypothesis. However, values of K did not show significant difference between fish from Dimon Lakeand Degano Creek, indicating a robustness and good state of nutrition of both populations [36]. In thiscase, the use of Fulton’s condition factor did not allow us to predict the lipid accumulation, as alsopreviously suggested [37].The second hypothesis rests on the diet of bullhead, and consequently the lipid content ofchironomids, which are the only trophic resource available for this species. Bogut and co-workers [38]showed how Chironomus plumosus larvae represent a potential suitable natural component of farm fish,with a crude fat content of 1.3% and 9.7% in fresh larvae and dry matter, respectively. Timon-David [39]reported a similar percentage of lipids (8.3%) for adults of Chironomus spp. and larva. Lipids arehigh-energy nutrients and typically make up about 7% to 15% of a fish diet [40]. Nevertheless, an adultdiet of Chironomid larvae may induce liver steatosis in reared male Nothobranchius furzeri [41]. For thisreason, we cannot exclude that the presence of only chironomids as feed could be the cause of thesteatosis observed in the bullhead samples.Steatosis can be also induced by environmental contaminants, such as cadmium [42], but weexcluded this possibility since Cd concentrations in bullhead from Dimon Lake were under the limit ofquantification (0.02 mg kg−1) as reported by Polazzo [19].Morphological modifications, such as hepatocyte vacuolization, may also be interpreted as aresponse to a pathological process caused by infections or parasites [43,44], but we did not find bacteriaor parasites in the fish during a survey performed in the same animals as reported by Pastorino andco-workers [7].Further investigations are needed to explore this phenomenon, for example, by analyzing thecomposition of the total body fat in chironomids present in the lake or by determining the lipid profilein the livers of bullhead specimens.All organisms need to adapt to their habitat if they are to survive. Our results suggest that fishmay also implement strategies, such as physiological changes (lipid accumulation) or changes in feedpreferences, to survive in adverse conditions.Author Contributions: Conceptualization, P.P. and K.V.; Data curation, P.P. and K.V.; Investigation, P.P., E.P.,M.B., D.R.F., V.M., D.M. and K.V.; Methodology, P.P., M.P., M.B., D.R.F., V.M., D.M. and E.B.; Software, K.V.;Writing—original draft, P.P. and K.V.; Writing—review and editing, M.P., E.P., E.B. and K.V.Funding: This research received no external funding.Acknowledgments: Authors express their special thanks to Geostudio RC (Marco Rosa Clot and Luca Rosa Clot)and Ente Tutela Patrimonio Ittico Friuli Venezia-Giulia for its support during fish sampling campaigns and theMunicipality of Ligosullo (UD) for the hospitality.Conflicts of Interest: The authors declare no conflict of interest.194Water 2019, 11, 2570References1. Felip, M.; Wille, A.; Sattler, B.; Psenner, R. 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This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).196waterArticleComparison of Otolith Readability andReproducibility of Counts of Translucent ZonesUsing Different Otolith Preparation Methods for FourEndemic Labeobarbus Species in Lake Tana, EthiopiaShewit Gebremedhin 1,2,*, Karen Bekaert 3, Abebe Getahun 4, Stijn Bruneel 1 ,Wassie Anteneh 5, Peter Goethals 1 and Els Torreele 31 Department of Animal Science and Aquatic Ecology, University of Ghent, 9000 Ghent, Belgium2 Department of Fisheries, Wetlands and Wildlife Management, Bahir Dar University, Bahir Dar 6000, Ethiopia3 Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), 8400 Ostend, Belgium4 Department of Zoological Sciences, Addis Ababa University, Addis Ababa 1000, Ethiopia5 Department of Biology, Bahir Dar University, Bahir Dar 6000, Ethiopia* Correspondence: shewitgebremedhin.kidane@ugent.be; Tel.: +25-192-051-8412Received: 10 May 2019; Accepted: 24 June 2019; Published: 28 June 2019Abstract: The analysis of fish age data is vital for the successful conservation of fish. Attempts todevelop optimal management strategies for effective conservation of the endemic Labeobarbus speciesare strongly affected by the lack of accurate age estimates. Although methodological studies arekey to acquiring a good insight into the age of fishes, up to now, there have not been any studiescomparing different methods for these species. Thus, this study aimed at determining the best methodfor the endemic Labeobarbus species. Samples were collected from May 2016 to April 2017. Asteriscusotoliths from 150 specimens each of L. intermedius, L. tsanensis, L. platydorsus, and L. megastomawere examined. Six methods were evaluated; however, only three methods resulted in readableimages. The procedure in which whole otoliths were first submerged in water, and subsequentlyplaced in glycerol to take the image (MO1), was generally best. Except for L. megastoma, this methodproduced the clearest image as both the coefficient of variation and average percentage error betweenreaders were lowest. Furthermore, except for L. megastoma, MO1 had high otolith readability and nosystematic bias. Therefore, we suggest that MO1 should be used as the standard otolith preparationtechnique for the first three species, while for L. megastoma, other preparation techniques shouldbe evaluated. This study provides a reference for researchers from Africa, particularly Ethiopia, todevelop a suitable otolith preparation method for the different tropical fish species.Keywords: count bias; growth zone counts; precision; otolith preparation methods1. IntroductionThe endemic Labeobarbus species in Lake Tana are the only remaining known intact speciesflock of large cyprinids in the world [1]. Labeobarbus species are the most economically andecologically important fishes in Lake Tana. However, because of their migratory and spawningaggregation behaviours [2] and specialized endemic characteristics [3], Labeobarbus populations arehighly susceptible to illegal fishing and environmental degradation. As a result, their abundancehas markedly declined [4–7] and six of the seventeen species have already been listed in the IUCN(International Union for Conservation of Nature) Red List as threatened species [8,9]. Therefore, theconservation of these species is urgently needed.197Water 2019, 11, 1336In this regard, the local fisheries authorities have taken the responsibility to conserve these species.However, their attempts are hampered by a lack of information. Although there have been reports onsome biological aspects and anthropogenic pressures [2,3,10–17], little is known about the life-historytraits of the endemic Labeobarbus species in Lake Tana. Information about age, age at maturity, growth,mortality, and longevity of Labeobarbus spp., which are important indicators of the health of fishstock [18], is virtually non-existent.To understand the current stock status of the Labeobarbus species, investigation of their life-historytraits is crucial. In studying the life history traits of the Labeobarbus species, knowledge of theage structure of fish populations, which is the basis of quantitative stock assessments, is vital [19].Age structures allow the determination of the age at maturity, growth and mortality rates, which areessential input parameters for stock assessment models [19]. Inaccurate age estimations result in biasedstock assessment outputs, which in turn lead to incorrect implementation of fisheries managementstrategies [20,21]. Several hard structures such as otoliths, scales, vertebrae, and fin rays can be usedfor fish age determination, although otoliths and scales are most frequently used. Because of theirnon-lethal nature, scales have long been considered the most efficient and practical structures for agedetermination, but later, several studies have revealed them to be inaccurate [22–25]. The limitationsof scales to reveal all growth zones, particularly for slow-growing and older fish, have been proven byvarious studies [19,24,25]. Even though they require sacrificing the fish, otoliths generally provideprecise and accurate counts of growth zones [26].As it is well known that the most adequate otolith preparation methods differ from one speciesto another, a description of the most suitable method is indispensable for precise and accurate ageestimation. In developed countries, validated age determination methods have been establishedfor many fish species. However, this has not been the case in developing countries like Ethiopia.This study is part of a large study about the stock assessment of the endemic Labeobarbus species inLake Tana. The aim of the study is to compare different otolith preparation methods in order to selectthe method with the best contrast between translucent and opaque growth zones. Therefore, this studyis a prerequisite to undertake more detailed studies on age validation, growth, and mortality. Thefindings of this study are the first attempts to describe the most suitable method for the four Labeobarbusspecies, and these will be adopted as a standard protocol in further age validation studies.2. Materials and Methods2.1. Study AreaLake Tana is situated on the basaltic plateau of the north-western Ethiopian highlands at 12◦ N,37◦15′ E and at about 1800 m above sea level (Figure 1). Lake Tana, covering an area of approximately3050 km2, is Ethiopia’s largest lake and contains half of the country’s surface freshwater. The climate istypical for a semi-arid region close to the equator. Lake Tana, as a tropical lake, has a relatively lowtemperature ranging from 20 to 27 ◦C [27].198Water 2019, 11, 1336 Figure 1. (a) The location of Ethiopia and Lake Tana in Africa, (b) the location of Lake Tana in Ethiopia,and (c) a map of the Lake Tana watershed showing the four study sites.2.2. Sampling DesignFour sampling sites were systematically distributed over the lake and together represent theopen-water zone of the whole lake. For site selection, we considered the cardinal directions north, south,east, and west of the lake; presence of the major tributaries, which are an ideal breeding grounds of themigratory Labeobarbus species in the lake; and the major fishing grounds. Sampling was conductedeach month from May 2016 to April 2017 at all four sites. We used 10 multi-mesh gill nets consisting of6, 8, 10, 12, and 14 stretched mesh sizes and monofilament gill nets of 4 and 6 cm stretched mesh size.Multifilament gill nets were set for 16 h, around 16:00 to 08:00, and were lowered to the bottom usinganchors. The monofilaments were set during the daytime from 08:00 to 10:00. The identification ofLabeobarbus spp. to species level using keys took place immediately after capture [11]. The majority ofthe Labeobarbus spp. caught by gill net died, but those that were caught without major injury werequickly identified and returned to the lake. Fork length of each specimen (to the nearest 0.1 cm) andtotal weight (to the nearest 0.1 g) were measured in the field using a measuring board and a precisionbalance. After dissection, the gonad maturity of each specimen was determined using a seven-pointmaturity scale [28] and each fish was sexed. For growth zone counts, four species—L. intermedius,L. tsanensis, L. platydorsus, and L. megastoma—were selected. Species selection was based on theirprevalence in the commercial catch. All specimens selected for growth zone counts were beheadedand samples were transported using an ice-box to the fisheries laboratory in Bahir Dar University forotolith extraction.2.3. Otolith Preparation MethodsOf the three otolith pairs within the Cyprinidae family, asteriscus otoliths are the largest andhave the most suitable hard structure for aging [29–31]. A total of 150 asteriscus otolith pairs werecollected from each of the four Labeobarbus species. Otoliths were cleaned by removing the attached199Water 2019, 11, 1336tissues and were preserved in 45% glycerol using labelled micro-centrifuge tubes. In order to ascertainthe best method to obtain a clear view of the core, edge, and growth increments, we tried out anumber of different otolith preparation methods. These preparation methods included (1) wholeotoliths submerged in glycerol and water and (2) otoliths that were sectioned and stained. A detaileddescription of the different otolith preparation methods is given in the following sections. The leftotolith was generally used, but if it was broken or lost, the right otolith was used instead.2.3.1. Whole Otolith Submerged in Glycerol and WaterThe effect of glycerol and water to enhance the visibility of the growth zones was evaluated. Wholeotoliths were first immersed in glycerol at different time intervals. For example, the whole otolithswere immersed in glycerol for 2, 4, 6, 8, 12, 14, and 16 hours and the visibility of the growth zones ateach time interval was examined. Similarly, the same otoliths were submerged in water following thesame procedures used for glycerol. In order to remove the effect of glycerol, otoliths were rinsed usingacetone, cleaned, and dried prior to submergence in water. The combined effect of glycerol and waterwas also evaluated. The whole otoliths were first submerged in glycerol before transferring them to awater medium to take the image for growth zone count, or vice versa. Whole otoliths were placed in ablack bowl filled with glycerol or water using forceps and an image was taken using an AxioCam MRccamera attached to a stereo-microscope using reflected light at 10X magnification.2.3.2. Sectioned and Stained OtolithsPrior to sectioning, whole otoliths were cleaned using water, dried, and embedded in black resin.A mould with six rows was used to embed the otoliths. The black resin was first poured on each rowand then, depending on the size of the otolith, 9–10 whole otoliths were placed on each row usingforceps. The embedded otoliths were allowed to dry for 12 h. After fully drying, a top layer was addedand allowed to harden for an extra 12 h before sectioning. Finally, an approximately 0.45–0.50 mmthick transverse section was cut through the nucleus using a high-speed saw rotating at 3000 rpm(ATM BRILLANT250).After sectioning, otoliths were stained using 100% of a neutral red solution and the enhancementof growth zone visibility was checked at 1-minute intervals for 30 min. As this trial did not give a goodresult, we also tried to stain the otoliths using 50% of the solution. Moreover, as an alternative to theneutral red solution, alizarin solution was used. Using this solution, otoliths were stained for 30 minand growth zone enhancement was checked at 1-minute intervals. Additionally, otoliths were alsostained for 1 h without interruption. Besides the sectioned otoliths, we also stained the whole otolithsusing both solutions. Both reflected and transmitted light was used to take images of the sectionedand stained otoliths at 16X magnification.2.3.3. Preliminary Evaluation of Otolith Preparation TechniquesA preliminary evaluation was made based on the clarity and contrast of the images (Figure 2).Of all the examined otolith preparation methods, three methods did not produce distinguishablegrowth zones. More specifically, (1) the method using sectioned otoliths, (2) the method using stainedotoliths, and (3) the method using otoliths submerged in water and remained in the water to take theimage. Thus, these methods were omitted at this stage and otolith preparation was continued usingonly the three methods that produced distinguishable growth zones, core, and edge. These methodsare the following: (1) method 1: whole otoliths were first submerged in water for 16 h and the imagewas taken while the otolith was placed in glycerol (MO1), (2) method 2: whole otoliths were firstsubmerged in glycerol for 14 h and the image was taken while the otolith was placed in water (MO2),and (3) method 3: whole otoliths submerged in glycerol and remained in glycerol to take image (MO3).For MO1, whole otoliths submerged in water for about 16 h resulted in clear images, whereas forMO2 and MO3, whole otoliths immersed in glycerol for about 14 h produced relatively good images.The data from these three methods were retained for data analysis200Water 2019, 11, 1336 Figure 2. Otoliths images prepared by the three different aging methods for L. intermedius (a),L. tsanensis (b), L. platydorsus (c), and L. megastoma (d). MO1, MO2, and MO3 represent aging methodone, two, and three, respectively. Red dots are a number of growth zones.Otolith preparation and examination was undertaken in the Flanders Research Institute forAgriculture, Fisheries, and Food (ILVO) in Belgium. Growth zones were annotated on the digital otolith201Water 2019, 11, 1336image using SmartDots software (www.smartdots.ices.dk). Growth zones were visible as translucent(dark) and opaque (light) zones when examined under reflected light. Counts of translucent zoneswere made along an anterio-dorsal transect situated on the otolith rostrum. All otoliths were examinedby two experienced readers unfamiliar with these species and working independently. Readers had noprevious knowledge on the date of capture, size, and sex of the fish. Each reader read each otolithonce and the reader’s confidence to otolith readability was assigned as follows: easily readable (Q1),moderately readable (Q2), or unreadable (Q3).3. Statistical AnalysisTranslucent Zone Count Precision and BiasBoth statistical and graphical methods were used to compare the bias and precision betweenreaders and methods. The precision between readers and methods was measured using the averagepercentage error (APE) [32], coefficient of variation (CV) [33], and percentage of agreement (PA).The APE and CV are the most suitable and statistically sound measurements of precision [19]. AlthoughPA can be used as an index of precision, it is often not considered as a suitable measure as it widelyvaries among species as well as among ages within a species [32]. The PA is the ratio of the number ofagreements between the paired readings to the total number of readings made by the two independentreaders. The APE and CV were computed by the following formula:APE =100NN∑J−11RR∑i−1∣∣∣Xij −Xj∣∣∣Xj, (1)CV =100NN∑J−1√∑Ri=1(Xij−Xj)2R−1Xj, (2)where N is the number of fish whose otoliths were read, R is the number of times otoliths were read,Xij is the ith read for the jth fish, and Xj is the average estimated growth zone count of the jth fish.Count-bias plots were produced to visualize the deviation of readings between the methods andreaders from the 1:1 equivalence line [34]. Although none of the three methods was validated, otolithimages from MO1 were clearer and sharper, which would likely lead to fewer errors in growth zonecount. Additionally, the precision of MO1 was better than the other methods for all four species.On this basis, we assumed that growth zone counts from MO1 are reliable and, therefore, we comparedgrowth zone counts from MO2 and MO3 with those from MO1, which is considered as a referencecount. Similarly, growth zone counts from MO2 were compared with those from MO3, because theprecision for MO3 was better than for MO2. The null hypothesis that the mean of the none-referencecounts did not significantly differ from the reference counts for each reference count was tested using apaired t-test. The mean difference of the growth zone counts assigned by the two readers for eachmethod was determined using an independent t-test. One-way analysis of variance (ANOVA) wasused to determine if there were significant differences among mean growth zone counts derived fromthe three methods. The null hypothesis that there is no symmetric difference in growth zone countsbetween readers and methods was tested using a Bowker’s chi-square symmetry test [35].In order to reveal if the difference between the growth zone counts of the two readers and amongmethods was consistent, a linear regression was computed. The slope and intercept of the regressionline were tested for significant difference from 1 and 0, respectively. For the regression analyses betweenreaders, growth zone counts from reader 1 (R1) and reader 2 (R2) were used as an independent and adependent variable, respectively. As both readers had relatively the same otolith reading experienceand both of them were unfamiliar with these species, we randomly chose growth zone counts fromR1 as the independent variable. The mean of the paired reads was used for the regression analyses202Water 2019, 11, 1336between methods. For the regression analyses between MO1 and the other methods, growth zonecounts from MO1 were used as an independent variable, while growth zone counts from MO3 wereused as an independent variable for the regression analysis between MO2 and MO3.The null hypothesis that the mean difference between paired growth zone counts is zero wastested using a paired t-test. Otolith readability based on the confidence rankings assigned by the tworeaders was tested using a Mann–Whitney U test. A Kruskal–Wallis test was used to determine otolithreadability among methods. Confidence rankings from R1 were used for this analysis. The pairwisecomparison of the otolith readability between methods was made using Dunn’s test with p-valuesadjusted with the Benjamini–Hochberg method. When a reader’s confidence to otolith readability wasassigned as Q3 (an unreadable), it was discarded from the analysis to ensure equal sample sizes forthe three methods. The periodicity of growth zone deposition was validated and the growth zonedeposition rate in asteriscus otolith of the studied species was proven to be annual [36]. Data analysiswas undertaken using R software (version 3.5.0, R Developer Core Team, R Foundation for StatisticalComputing, Vienna, Austria).4. Results4.1. Length FrequencyFish size ranged from 10–65 cm fork length (FL) for L. intermedius, 12–45 cm FL for L. tsanensis,12–65 cm for L. platydorsus, and 12–57 cm for L. megastoma. The majority of the specimens of L. intermedius,L. tsanensis, and L. platydorsus were within the length groups of 15–20 cm (18 cm mid-length) and20–25 cm (23 cm mid-length), while specimens with a fork length of more than 33 cm of were rarelycaught (Figure 3). For L. megastoma, a high length frequency was recorded for specimens that wereincluded within the length groups of 25–30 cm (28 cm mid-length) and 30–35 cm (38 cm mid-length),whereas specimens larger than 38 cm were rare (Figure 3). Figure 3. Length frequency distribution of the four dominant Labeobarbus spp. in Lake Tana.203Water 2019, 11, 13364.2. Otolith ReadabilityFor both readers, otoliths prepared using MO1 resulted in higher readability than the othermethods for the four species (Figure 4). Readability of the growth zones of otoliths prepared usingMO2 and MO3 was inconsistent. It was often difficult to delineate the growth zones around most ofthe sections, which likely led to misinterpretations. Regarding the otoliths prepared using MO1, bothreaders were able to read the growth zones (Q1 and Q2) in 100% of the samples for L. intermedius andL. tsanensis and in 99% of the samples for L. platydorsus and L. megastoma, while there was a considerablenumber of unreadable (Q3) otoliths prepared by MO2 and MO3 (Figure 4). A Mann–Whitney U testindicated that the confidence rankings between readers for the three methods were not significantlydifferent for L. intermedius and L. tsanensis (Figure 4). However, for L. platydorsus and L. megastoma,confidence rankings between readers for MO1 were significantly different. Confidence rankings amongmethods showed a significant difference (Table 1). For all four species, confidence rankings for MO1were significantly different from the other methods, while there was no significant difference betweenMO2 and MO3 (Table 1). Therefore, the high level of readers’ confidence for MO1 likely led to a reliablecount of growth zones with lower error levels. Figure 4. The bar graphs indicate the percentage of otoliths at each level of otolith readability(Q1 = readable, Q2 =moderately readable, and Q3 = unreadable). R1 refers to reader 1 and R2 refers toreader 2. The Mann–Whitney U test, which compares the confidence rankings between readers foreach method, is also included.204Water 2019, 11, 1336Table 1. Comparison of reader confidence among the three methods for the four endemic Labeobarbusspecies in Lake Tana. Q1 = readable, Q2 =moderately readable, and Q3 = unreadable. Numbers referto the number of otolith under each readability level. Different superscript letters for the methods foreach species indicate a significant difference. Confidence rankings from reader 1 (R1) were used forthis analysis.Aging MethodsReaders’ Confidence Kruskal–Wallis Rank Sum TestQ1 Q2 Q3 Chi-Square df p-ValueL. intermediusMO1a 119 25 026.25 2 0.000MO2b 85 58 5MO3b 88 58 2L. tsanensisMO1a 112 25 09.69 2 0.008MO2b 104 41 4MO3b 99 46 4L. platydorsusMO1a 97 35 127.10 2 0.000MO2b 70 52 28MO3b 84 41 25L. megastomaMO1a 95 38 123.97 2 0.000MO2b 72 57 18MO3b 74 57 164.3. Precision and Bias between ReadersAge composition of the studied fish species based on the three methods did not exhibit largevariation (Figure 5). Although growth zone counts distribution derived from MO3 was only slightlywider for L. intermedius, L. tsanensis, and L. platydorsus, while MO1 and MO2 resulted in the samegrowth zone counts distribution. For L. megastoma, growth zone counts distribution derived fromMO1 was slightly wider than the other methods. Growth zone counts of 4 and 5 were dominant forL. tsanensis and L. platydorsus, while growth zone counts of 5 and 6 were dominant for L. intermedius,and 3–5 for L. megastoma. The relationship between length and growth zone counts for the four studiedspecies is depicted in Figure 6.The reproducibility and uncertainty of the growth zones counts from all three methods wereevaluated by means of CV, APE, and PA with respect to comparisons between readers. The CV andAPE values between readers were generally low for all methods for L. intermedius, L. tsanensis, andL. platydorsus, while these were relatively high for L. megastoma (Table 2). MO1 resulted in more preciseage readings for L. intermedius, L. tsanensis, and L. platydorsus than did MO2 and MO3. This is indicatedby the low CV and APE values (Table 2). For L. megastoma, MO1 and MO3 exhibited equal precision,while MO2 yielded relatively low precision (Table 2). In all three methods, the CV and APE valueswere the lowest for L. intermedius and generally the highest for L. megastoma. Growth zone countassignments by the two readers had a relatively poor percentage of agreement for all three methodsand four species (Table 2). Compared with the PA of the other methods (Appendices Figures A1and A2), the PA between readers for MO1 was relatively high for L. intermedius (69%), L. tsanensis(60%), and L. platydorsus (68%). Additionally, the PA for MO1 at age 1 and ≥2 absolute differences,respectively, was low for L. intermedius (25%, 6%), L. tsanensis (34%, 5%), and L. platydorsus (29%, 3%)(Figure 7a,c, Figure 8a, histograms in the y-axis), while these values were relatively high for MO2 andMO3 (Appendices Figures A1 and A2, histograms in the y-axis). However, for L. megastoma, the PAat age zero and 1 absolute differences was relatively the same for all three methods (Figure 8c and205Water 2019, 11, 1336Appendices Figure A2, histograms in the y-axis). The PA for this species was low compared with theother species. Figure 5. A number of individuals with the same number of growth zones in their otoliths examinedusing the three methods: MO1, MO2, and MO3 for the four dominant Labeobarbus species in Lake Tana.Mean of the paired reads from the two independent readers was used. The dotted black, red, and greylines indicate the trend in the distribution of translucent growth zones counts for MO1, MO2, andMO3, respectively. ≥Figure 6. Relationship between fork length and the count of the number of translucent zones in theotoliths for the four studied Labeobarbus species in Lake Tana.206Water 2019, 11, 1336Table 2. Average percentage error (APE, %), coefficient of variation (CV, %), and percentage ofagreement (PA, %) to determine the precision of age readings. n = sample size and R = numberof readings.Between ReadersSpeciesMO1 MO2 MO3n R CV APE PA n R CV APE PA n R CV APE PAL. intermedius 144 2 4 3 69 139 2 7 5 57 144 2 6 4 64L. tsanensis 137 2 7 5 60 143 2 9 6 56 143 2 9 6 56L. platydorsus 133 2 7 5 68 119 2 9 6 55 123 2 9 6 62L. megastoma 133 2 11 8 45 125 2 13 9 42 123 2 11 8 47Between MethodsMO1 vs MO2 MO1 vs MO3 MO2 vs MO3L. intermedius 128 2 3 2 77 128 2 4 3 67 128 2 3 2 77L. tsanensis 129 2 4 3 73 129 2 5 4 71 129 2 4 3 78L. platydorsus 104 2 5 4 71 104 2 5 4 75 104 2 3 2 81L. megastoma 99 2 4 3 71 99 2 4 3 77 99 2 4 3 79Overall, the mean non-reference growth counts did not significantly differ from each correspondingreference counts for all four species (paired t-test, p > 0.05). However, the mean difference betweennon-reference and reference growth zone counts (y-axis) plotted against the reference counts (x-axis)exhibited a minimal relative bias from the line of the agreement for each method and species (Figure 7a,cand Figure 8a,c and Appendices Figures A1 and A2). For MO1, the mean non-reference growth zonecounts for the young specimens of L. intermedius, L. tsanensis, and L. platydorsus were only slightlyhigher than the reference counts, but the reverse was true for the old specimens (Figure 7a,c, Figure 8c).However, for L. megastoma, mean non-reference growth zone counts for both the young and oldspecimens were higher than the reference counts (Figure 8c). In general, for L. intermedius, L. tsanensis,and L. platydorsus, differences from the line of the agreement were relatively small for MO1 (Figure 7a,c,Figure 8c) compared with the other methods (Appendices Figures A1 and A2).4.4. Precision and Bias among Aging MethodsThe precision of age readings derived from the three methods for the four Labeobarbus species wasgenerally good (Table 2). The lowest CV and APE values were computed from MO1 versus MO2 andMO2 versus MO3 for L. intermedius, L. tsanensis, and L. megastoma, while these values were low onlybetween MO2 and MO3 for L. platydorsus (Table 2). The PA values between MO2 and MO3 were slightlyhigher than the other methods (Table 2). For all comparisons between methods, the discrepancy at azero difference was high for all four species (Figure 7b,d and Figure 8b,d, and Appendices Figure A1 &Table A1, right, histograms in the y-axis), although the discrepancy at age 1 absolute difference wasalso high in some cases.Even though there was no significant difference between the mean non-reference growth zonecounts and each corresponding reference count (paired t-test, p> 0.05), the age bias plots for comparisonof the three methods revealed a discrepancy from the line of agreement (Figures 7 and 8 and AppendicesFigures A1 and A2, right). For most species, MO2 and MO3 underestimated the growth zone counts ofold individuals relative to MO1. These trends were also apparent in plots of MO2 and MO3 counts ofgrowth zones. Differences between the mean non-reference growth zone counts and the reference countincreased with the number of growth counts for all three methods and four species. The discrepancybegan at count 4 for L. megastoma, at count 6 for L. platydorsus, and at count 8 for L. intermedius andL. tsanensis (Figures 7 and 8 and Appendices Figures A1 and A2, right). This suggests that the level ofcount precision among methods could be lower when counting growth zone from old fish.207Water 2019, 11, 1336 Figure 7. Mean (dots) and 95% confidence intervals of differences in counts of growth zones derivedfrom the best method (MO1) between two independent readers at the readings for the first readerand difference between methods (MO1 and MO2) at the readings for MO1 for L. intermedius (a &b) and L.tsanensis (c & d) in Lake Tana. The horizontal solid line is the 1:1 equivalence line andmarginal histograms are for counts of the first reader (top) and differences in counts between readersand methods.208Water 2019, 11, 1336 Figure 8. Mean (dots) and 95% confidence intervals of differences in counts of growth zones derivedfrom the best method (MO1) between two independent readers at the readings for the first readerand difference between methods (MO1 and MO2) at the readings for MO1 for L. platydorsus (a &b) and L. megastoma (c & d) in Lake Tana. The horizontal solid line is the 1:1 equivalence line andmarginal histograms are for counts of the first reader (top) and differences in counts between readersand methods.4.5. Statistical Comparisons of Growth Zones Counts between Readers and MethodsA one-way ANOVA test revealed no significant difference among mean growth zone countsderived from the three methods for all four species (L. intermedius, F(2, 381) = 0.04, p = 0.97; L. tsanensis,F(2, 384) = 0.23, p = 0.80, L. platydorsus, F(2, 309) = 0.05, p = 0.95; and L. megastoma, F(2, 294) = 0.48,p = 0.62). Except for L. megastoma, growth zone counts between readers for the three methods and threespecies were not significantly different (Table 3). This suggests that there was no significant count biasbetween readers, except for L. megastoma. Moreover, for L. intermedius, L. tsanensis, and L. platydorsus,there was no evidence of systematic disagreement for growth zone counts assigned between R1 andR2 using MO1 and MO3, while this was significantly different for L. megastoma (Table 4). However,the growth zone counts assigned between R1 and R2 using MO2 revealed symmetric bias for all four209Water 2019, 11, 1336species (Table 4). No systematic bias in growth zone counts of the different methods was found, exceptthose between MO1 and MO2 for L. megastoma (Table 4).Table 3. The mean number of growth zones counted by reader 1 (R1) and reader 2 (R2) for each of thethree methods of four Labeobarbus species in Lake Tana.MethodsReadert-Value df p-Value 95% Confidence IntervalsR1(Mean) R2 (Mean)L. intermediusMO1 5.9 6.1 −0.61 285 0.54 −0.59–0.31MO2 5.8 6.1 −1.45 276 0.15 −0.81–0.12MO3 5.8 6.1 −1.22 277 0.22 −0.75–0.17L. tsanensisMO1 4.7 4.9 −0.75 272 0.46 −0.64–0.29MO2 4.5 4.8 −1.06 284 0.29 −0.66–0.20MO3 4.8 4.8 −0.28 284 0.78 −0.50–0.38L. platydorsusMO1 4.4 4.5 −0.39 263 0.70 −0.41–0.27MO2 4.1 4.3 −1.45 236 0.15 −0.56–0.08MO3 4.2 4.3 −0.94 244 0.35 −0.50–0.18L. megastomaMO1 4.1 4.7 −2.97 259 0.003 −1.01–(−0.20)MO2 3.8 4.6 −3.42 229 0.000 −1.20–(−0.32)MO3 4.0 4.6 −3.03 233 0.003 −1.09–(−0.23)Table 4. Analysis of Bowker’s chi-square symmetry test, comparing pairs of growth zone countsbetween readers and methods applied to the four Labeobarbus species in Lake Tana. “ns” refers to nosignificant difference. “*” and “**” refers to significant difference at p = 0.01 and 0.001, respectively.Between ReadersSpeciesMO1 MO2 MO3df Chi-sq p df Chi-sq p df Chi-sq pL. intermedius 17 19 ns 19 28 ** 15 24 nsL. tsanensis 14 22 ns 15 30 * 15 21 nsL. platydorsus 10 19 ns 10 20 * 10 15 nsL. megastoma 15 58 ** 17 59 ** 13 58 **Between MethodsMO1 vs. MO2 MO1 vs. MO3 MO2 vs. MO3L. intermedius 9 7 ns 10 8 ns 10 9 nsL. tsanensis 13 14 ns 16 12 ns 13 15 nsL. platydorsus 9 15 ns 11 10 ns 4 6 nsL. megastoma 8 16 * 7 11 ns 7 9 nsThe regression and paired t-test analyses between readers and methods showed significantdifferences in some cases, generally indicating minimal bias between readers and methods (Appendix C).For MO2 and MO3, the slope and intercept of the linear regression between readers were significantlydifferent from 1 and 0, respectively, for all four species, with the exception of the intercept forL. megastoma. Similarly, the mean difference of the paired readings for MO2 and MO3 showed asignificant difference from 0 for the four species. However, for MO1, the slope and intercept of the linearregression between readers did not significantly differ from 1 and 0, respectively, for L. intermedius andthe slope was not significantly different from 1 for L. megastoma. Additionally, the mean differenceof the paired readings obtained from MO1 was not significantly different from 0 for L. platydorsus.210Water 2019, 11, 1336Between readers, the mean paired difference for the four species was generally small for MO1 (<0.18),compared with MO2 and MO3.With respect to the methods, the slope and intercept of the linear regression between MO1 andMO2 showed a significant difference from 1 and 0, respectively, for L. tsanensis, L. platydorsus, andL. megastoma. Similar results were observed from the regression analyses between MO1 and MO3for L. tsanensis and L. platydorsus and between MO2 and MO3 for all four species. The comparisonsbetween methods did not reveal significant mean paired differences from 0 for most of the cases(Appendix C).5. Discussion and ConclusionsAge estimation based on the interpretation of growth zones usually involves judgment andsubjective interpretation [19]. Errors in age determination are mainly related to the inconsistentperiodicity of growth zones and systematic errors due to the validity of the otolith preparation method,interpretation problems of the aging structure, or experience level of age readers [21,37,38]. The lattercould be minimized by describing a precise and accurate otolith preparation method [19]. The precisionof otolith preparation methods can be computed using graphical and statistical methods, while avalidated method for all age groups in the population is required to estimate accuracy [39]. Becausevalidating all the proposed otolith preparation methods for all different age groups in the population islogistically challenging, it may be more efficient to select the most accurate technique first. The bestmethod should, therefore, be validated for the rate of growth zone formation.The process of growth zone formation is affected by a combination of factors including physical,chemical, environmental, and physiological aspects. Depending on these factors, different otolithpreparation techniques may be considered as optimal [40]. Consequently, optimal otolith preparationtechniques differ markedly among species. In order to assist the fisheries biologists and managers inselecting the best technique, we compared different otolith preparation techniques for the Labeobarbusspecies in Lake Tana. Selection of the appropriate otolith preparation method is dependent on thebalance between readability (ease of annulus interpretation) and consistency (repeatability of growthzone counts) of the aging structures [41]. In this study, selection of the most suitable otolith preparationmethods, out of the six evaluated methods, was undertaken based on clarity and contrast of theimages. These methods that produced distinguishable growth zones, core, and edge were selected andcompared to find the optimal method. Three methods including (1) the method using sectioned otoliths,(2) the method using stained otoliths, and (3) the methods using whole otoliths submerged in water andremained in the water to take the image did not produce readable structures and were omitted from thecomparison, while the other three methods (MO1, MO2, and MO3) produced distinguishable growthzones, core, and edge. Similar to the findings of this study, problems with sectioned asteriscus otolithsto produce clear and readable growth zones were reported in previous studies [31,42–46]. Higherreadability and consistent age estimates were produced from whole asteriscus otoliths immersed inmethyl salicylate than sectioned otoliths [43,45]. Furthermore, both whole and sectioned asteriscusotoliths from Cyprinus carpio resulted in similar interpretation [47], but the authors noted that sectionedasteriscus otoliths obscured the visibility of opaque zones close to the margin.A comparison of growth zone counts from the three suitable otolith preparation methods withinthis study revealed that MO1 produced readable and consistent results for L. intermedius, L. tsanensis,and L. platydorsus in Lake Tana. Concerning the reproducibility of growth zone counts derived fromMO1 by the two readers, the CV and APE values for L. intermedius, L. tsanensis, and L. platydorsuswere lower than the average values in the literature (CV 7.6%, APE 5.5%) [19], while these valuescalculated from MO2 and MO3 were slightly higher for most species. For L. megastoma, the CV andAPE values between readers for the three methods were relatively high compared with average valuesin the literature. With respect to the methods, the CV and APE estimates were lower than the averagevalues in the literature for the four species. The precision of the comparison between methods wasgenerally higher than the precision of the readers. Additionally, except for L. megastoma, the count211Water 2019, 11, 1336bias plots between readers for MO1 revealed less bias relative to MO2 and MO3. Furthermore, MO2and MO3 underestimated actual growth zone counts, particularly for old individuals, relative to MO1.This is most likely because of the poor clarity of the images, suggesting that using these methods forprecise and accurate growth zone counts of the Labeobarbus species is difficult. In contrast, the precisionestimates and count bias plots indicated that MO1 provides consistent counts of growth zones forL. intermedius, L. tsanensis, and L. platydorsus. These results sustain the choice of this method for agedetermination of these species.The suitability of MO1 for growth zone counts of the three Labeobarbus species was furthersupported by the statistical comparisons and by the assigned scores for readability, which were bestfor MO1. Except for L. megastoma, there was no evidence of a symmetric difference between R1 andR2 for MO1 and MO3, while this was significantly different for MO2 for all species. Although theregression and paired t-test analyses revealed significant difference for some of the cases, the slope andintercept of the linear regression for MO1 were much closer to 1 and 0, respectively, compared with theother methods. Moreover, the mean difference was smaller than 0.18 for MO1, while this differencewas generally higher for the remaining methods. Otoliths prepared using MO1 were perceived tohave good readability, as indicated by the quality scores given by readers, showing that growth zoneinterpretation was relatively easy for this method. Both readers felt growth zones on otoliths preparedby MO1 were better defined and relatively easy to interpret and had higher confidence than in growthzone counts they made from MO2 and MO3. More than 99% of the otolith samples prepared usingMO1 were readable at AQ1 and AQ2 confidence rankings.The underestimation of the growth zone counts, particularly for the older fish using MO2and MO3 relative to MO1, occurs because of the presence of false rings, difficulty in following thegrowth zones around the otolith, and invisibility of growth zones at the edge of the otolith leadingto misinterpretation of the growth zones. Imprecise and inaccurate interpretation of growth zonesleads to incorrect estimation of growth and mortality parameters, which in turn may have drasticimplications for fish stock assessment and fisheries management. The information generated willbe useful to fisheries managers and researchers to select the most appropriate otolith preparationmethod for age determination of the selected fish species. Therefore, as MO1 resulted in less error, weconclude that this method should be accepted as standard protocol for preparation of the otolith ofL. intermedius, L. tsanensis, and L. platydorsus for reading. Concerning L. megastoma, more research isneeded before a conclusion can be drawn regarding the most suitable method. Description of the bestotolith reparation method is the first step in the extraction of useful information about a fish. Althoughthere have been efforts to compare different otolith preparation methods for some of the South Africanfish species [43], a lot still needs to be done to ascertain optimal method development and methodselection for the tropical freshwater fish species. The aim of this study is to fill this gap by describingthe best method for the Labeobarbus species in Lake Tana and providing a reference for researchersfrom Africa, particularly Ethiopia, to develop or select suitable otolith preparation methods for thedifferent tropical fish species.Author Contributions: S.G. conceived the main idea, collected and analyzed the data, and wrote the manuscript.K.B., A.G., S.B., W.A., P.G., and E.T. reviewed and edited the manuscript.Funding: This research was funded by the Critical Ecosystem Partnership Fund (CEPF) and by the special researchfund (BOF), Ghent University.Acknowledgments: We would like to thank Ilse Maertens and Martine Moerman, Flanders Research Institute forAgriculture, Fisheries, and Food (ILVO), Belgium, for their valuable support in otolith preparation and reading.We are grateful to ILVO for providing us the SmartDots software.Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of thestudy; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision topublish the results.212Water2019,11,1336AppendixA FigureA1.Mean(points)andrange(95%confidenceinterval)ofdifferencesingrowthzonecountsderivedfromMO2(a)andMO3(b)betweentwoindependentreadersatthecountsforthefirstreader(left)anddifferencebetweenmethodsatthecountsforMO1(c)andatthecountsforMO3(d)forL.intermediusandL.tsanensisinLakeTana( right).Thehorizontalsolidlineisthe1:1equivalencelineandmarginalhistogramsareforgrowthzonecountsofthefirstreader(top)anddifferencesingrowthzonecountsbetweenreadersandmethods.213Water2019,11,1336AppendixB FigureA2.Mean(points)andrange(95%confidenceinterval)ofdifferencesingrowthzonecountsderivedfromMO2(a)andMO3(b)betweentwoindependentreadersatthecountsforthefirstreader(left)anddifferencebetweenmethodsatthecountsforMO1(c)andatthecountsforMO3(d)forL.platydorsusandL.megastomainLakeTana( right).Thehorizontalsolidlineisthe1:1equivalencelineandmarginalhistogramsareforgrowthzonecountsofthefirstreader(top)anddifferencesingrowthzonecountsbetweenreadersandmethods.214Water 2019, 11, 1336Appendix CTable A1. Statistical tests (regression and paired t-test) for the detection of bias of growth zone countsof the endemic Labeobarbus species between readers and methods.StatisticsReaders and Methods PairsReader 1 vs.Reader 2 (MO1)Reader1 vs.Reader 2 (MO2)Reader 1 vs.Reader 2 (MO3)MO1 vs.MO2MO1 vs.MO3MO2 vs.MO3L. intermediusRegressionSlope 0.97 ± 0.03 0.92 ± 0.04 0.95 ± 0.04 0.99 ± 0.02 1.00 ± 0.03 0.92 ± 0.02p 0.392 0.033 0.248 0.660 0.856 0.000Intercept 0.31 ± 0.20 0.84 ± 0.23 0.57 ± 0.26 0.03 ± 0.15 −0.01 ± 0.21 0.42 ± 0.15p 0.137 0.000 0.029 0.826 0.979 0.005Paired t-testMeandifference−0.139 −0.345 −0.286 0.031 −0.031 −0.063p 0.027 0.000 0.000 0.482 0.608 0.184L. tsanensisRegressionSlope 0.92 ± 0.03 0.89 ± 0.04 0.87 ± 0.04 0.90 ± 0.03 0.90 ± 0.04 0.91 ± 0.03p 0.012 0.007 0.000 0.002 0.009 0.002Intercept 0.56 ± 0.16 0.72 ± 0.19 0.68 ± 0.18 0.39 ± 0.17 0.54 ± 0.21 0.29 ± 0.15p 0.000 0.000 0.000 0.021 0.010 0.060Paired t-testMeandifference−0.175 −0.231 −0.063 −0.116 0.031 0.147p 0.006 0.002 0.372 0.063 0.682 0.007L. platydorsusRegressionSlope 0.83 ± 0.04 0.81 ± 0.56 0.87 ± 0.05 0.79 ± 0.04 0.82 ± 0.05 0.90 ± 0.03p 0.000 0.000 0.004 0.000 0.000 0.002Intercept 0.09 ± 0.17 1.02 ± 0.24 0.73 ± 0.20 0.91 ± 0.20 0.81 ± 0.22 0.43 ± 0.15p 0.000 0.000 0.000 0.000 0.000 0.006Paired t-testMeandifference−0.068 −0.235 −0.163 0.058 0.019 −0.038p 0.273 0.001 0.012 0.368 0.774 0.374L. megastomaRegressionSlope 1.04 ± 0.04 1.17 ± 0.06 1.12 ± 0.05 0.89 ± 0.03 0.95 ± 0.03 0.88 ± 0.03p 0.378 0.006 0.018 0.000 0.145 0.000Intercept 0.45 ± 0.19 0.12 ± 0.25 0.18 ± 0.21 0.30 ± 0.15 0.12 ± 0.16 0.41 ± 0.15p 0.020 0.618 0.391 0.048 0.460 0.007Paired t-testMeandifference−0.609 −0.760 −0.658 0.212 0.101 −0.111p 0.000 0.000 0.000 0.000 0.049 0.033References1. 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