MPA network design based on graph theory and emergent properties of larval dispersal

Contribution to the Theme Section Latest advances in research on fish early life stages.-- 18 pages, 8 figures, 3 tables, supplementary material https://doi.org/10.3354/meps13399Despite the recognised effectiveness of networks of marine protected areas (MPAs) as a biodiversity conservation instrument, MPA network design frequently disregards the importance of connectivity patterns. In the case of sedentary marine populations, connectivity stems not only from the stochastic nature of the physical environment that affects dispersal of early life stages, but also from the spawning stock attributes that affect reproductive output (e.g. passive eggs and larvae) and survivorship. Early life stages are virtually impossible to track in the ocean. Therefore, numerical ocean current simulations coupled with egg and larval Lagrangian transport models remain the most common approach for the assessment of marine larval connectivity. Inferred larval connectivity may differ depending on the type of connectivity considered; consequently, the prioritisation of sites for the conservation of marine populations might also differ. Here, we introduce a framework for evaluating and designing MPA networks based on the identification of connectivity hotspots using graph theoretic analysis. As a case study, we used a network of open-access areas and MPAs off Mallorca Island (Spain), and tested its effectiveness for the protection of the painted comber Serranus scriba. Outputs from network analysis were used to (1) identify critical areas for improving overall larval connectivity, (2) assess the impact of species’ biological parameters in network connectivity and (3) explore alternative MPA configurations to improve average network connectivity. Results demonstrate the potential of graph theory to identify non-trivial egg/larval dispersal patterns and emerging collective properties of the MPA network, which are relevant for increasing protection efficiencyThis study was supported by the research project Phenofish (Grant No. CTM2015-69126-C2-1-R) funded by the Spanish Ministry of Economy and Competitiveness. A.O.A. was supported by H2020 Marie Skłodowska-Curie Actions MSCA-IF-2016 (Project ID: 746361). S.d.J. was supported by H2020 Marie Skłodowska-Curie Actions MSCA-IF-2016 (Project ID: 743545). J.A. was supported by a Ramón y Cajal Grant funded by the Spanish Ministry of Science, Innovation and Universities (Grant No. RYC2018-024488-I). G.F.B. was funded by a fellowship (FPI-INIA) (CPD2015) from the National Institute for Agricultural and Food Research and Technology (INIA)With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)Peer reviewe