THE SURFACE WATERS ACIDIFICATION PROJECT PALAEOLIMNOLOGY PROGRAMME : MODERN DIATOM/LAKE-WATER CHEMISTRY DATA-SET A.C. Stevenson 1, S. Juggins2, H.J.B. Birks2, D.S. Anderson3, N.J. Anderson4, R.W. Battarbee5, F. Berge6, RB. Davis3, R.J. Flower5, E.Y. Haworth7, V.J. Jones5, J.C. Kingston8, A.M. Kreiser5, J.M. Line9, M.A.R. Munro5, I. Renberg10• 1 Department of Geography, University of Newcastle upon Tyne, Newcastle upon Tyne, NEI 7RU, U.K. 2 Botanical Institute, University of Bergen, Allegaten 41, N-5007 Bergen, Norway. 3 Department of Botany, Deering Hall, University of Maine, Orono, Maine 04469, U.S.A. 4 Danish Geological Survey, Thoravej 8, DK-2400, Copenhagen NV, Denmark. 5 Palaeoecology Research Unit, Department of Geography, University College London, 26 Bedford Way, London, WCIH OAP, U.K. 6 2920 Leira, Valdres, Norway. 7 Institute of Freshwater Ecology, Ferry House, Ambleside, Cumbria, LA22 OLP, U.K. 8 Department of Biology, Queen's University, Kingston, Ontario K7L 3N6, Canada. 9 University of Cambridge Computer Laboratory, Pembroke Street, Cambridge CB2 3QG, U.K. 10 Dcpartmenl of Ecological Botany, University of UmeA, S-901 87 UmeA, Sweden. ISBN. 1 871275 05 9 1991 ENSIS Publishing London CONTENTS Preface Introduction Field and laboratory methods Sample collection and diatom preparation Chemistry and other environmental features of the lakes sampled Diatom taxonomy and nomenclature Introduction Slide exchange and analytical quality control Initial identification of problems a) Nomenclature b) Splitting and amalgamation of taxa c) Identifications Applying and refining the SW AP taxonomic code Protocols for handling unknowns Problems within the genus Aulacoseira Diatom database Numerical analysis of the chemical and other environmental data Basic statistics Principal components analysis Numerical analysis of the diatom data Basic statistics Detrended correspondence analysis Canonical correspondence analysis Reconstruction of lake surface-water chemistry from diatom assembiages Diatom indicator taxa Conclusions Bibliography Appendix A The SW AP taxonomic guide - revised 1989 Appendix B List of diatoms in the SW AP training sets and their optima for pH, DOC, and total Al estimated by weighted averaging regression 1 3 3 3 4 11 11 12 12 12 12 12 14 16 18 18 19 19 30 38 38 39 42 52 57 63 64 68 78 PREFACE In 1983, when the Surface Waters Acidification Programme (SWAP) was announced, we were asked to design and implement a palaeolimnology sub-project involving scientists from Sweden, Norway, and the UK. Our aim was to reconstruct the acidification history of a range of sites in the three countries and to identify and evaluate the various alternative causes of lake acidification. The results of the project have been published recently (Battarbee et al. 1990, Renberg and Battarbee 1990). Although a comprehensive range of palaeolimnological methods and approaches was used in the study we recognised diatom analysis as central to the entire project. We consequently committed considerable effort to improving our diatom methodology and we were especially concerned with the pursuit of a common approach to diatom taxonomy and pH reconstruction. This effort centred on the creation and analysis of a large data-set of surface-sediment diatom assemblages and associated environmental variables from 170 sites representing the full range of lake types in the acid-sensitive and acidified regions of the three countries. We achieved taxonomic harmonisation by agreeing on taxonomic distinctions in workshop discussions and by testing and reinforcing the decisions we took by "blind" analysis of quality control test slides from the calibration set (Munro et al. 1990). For pH reconstruction we achieved a common methodology by using the calibration data-set along with the help and inspiration of Cajo ter Braak to develop a new, statistically robust technique for pH reconstruction (Birks et al. 1990a), which is implemented by the program WACALIB (Line and Birks 1990). Most recently this approach has been extended for dissolved organic carbon (DOC) and total aluminium reconstructions (Birks et al. 1990b). This paper describes the calibration data-set, summarises the taxonomic decisions taken, and presents an initial statistical analysis of both the diatom and environmental data. It also includes a summary of the approach taken in the project for water chemistry reconstruction and lists potentially good diatom indicators for pH, DOC, and total aluminium. The Palaeolimnology Programme within SW AP owes thanks to the many participants in Norway, Sweden, and the UK, to the SW AP Management Committee, and to the sponsors, the Central Electricity Generating Board (UK) and the National Coal Board (UK). At this final point we are especially indebted to Tony Stevenson, Steve Juggins, and John Birks for the time and acumen they have devoted to this ultimate stage of our combined work. We would also like to thank Hazel Juggins for her help with preparing text and camera-ready copy, Sylvia Peglar for her assistance with diagram preparation, Simon Patrick of ENSIS for organising publication, and Cajo ter Braak for providing a pre-release version of CANOCO 3.10 and for his invaluable statistical advice throughout. R.W. Battarbee, London I. Renberg, UmeA INTRODUCTION The development of a large calibration, training set of over 170 surface-sediment diatom assemblages and associated environmental variables for the Surface Waters Acidification Programme (SW AP) Palaeolimnology Sub-Project necessitated the amalgamation of several existing regional training sets from Scotland, England, Wales, Norway, and Sweden so that the samples would represent a wide range of water chemistry, catchment sensitivity to acidic deposition, and pollution loadings. This paper documents the SW AP training set, both its diatom assemblages and the associated chemical and physical variables, outlines the methods used in sampling, diatom quality control, and taxonomic harmonisation, and provides a summary of the major patterns of variation in both the environmental and the diatom data using a range of numerical procedures. The joint variation of the diatoms in relation to the environmental variables in the training set is explored using multivariate direct gradient analytical techniques. Finally the ability of the training set to reconstruct lake-water pH, total aluminium, and dissolved organic carbon (DOC) is evaluated, and the possible use of diatoms as "indicator species" is investigated. FIELD AND LABORATORY METHODS Sample coUection and diatom preparation Surface-sediment samples were usually taken from the deepest point in each lake using modified Kajak, Hongve, or mini-Mackereth corers operated from boats or from the frozen lake surface. In a few cases the deepest point was not the optimal location and an alternative sampling location was selected nearby. In all cases the top 0.5 cm was used for diatom preparation following the procedures outlined in Stevenson et al. (1987). At least 500 valves were counted from each sample or, if planktonic diatoms were common, at least 300 valves of periphyton taxa were counted. The diatom/water chemistry training set used within the SW AP project is derived from five regional data-sets, some of which were already extant at the commencement of the project (Table 1). A total of 178 samples derived from 170 sites is included in the data-set. Table I Origin of samples by region and analyst 1. Sweden provided by I. Renberg & NJ. Anderson 2. Norway - provided by F. Berge, D.S. Anderson, & R.B. Davis 3. Scotland - provided by R.J. Flower 4. Wales provided by R.J. Flower 5. Lake District - provided by E.Y. Haworth Number of samples 30 51 60 32 5 Table 2 presents a summary of the site data including the country-specific short code, grid reference, latitude and longitude, lake area, and forest status. The variables forested, unforested, conifer forest, and deciduous forest are nominal (presence/absence) variables. The percentage afforestation is the approximate percentage of the lake's catchment that is afforested, usually by planted conifers. This variable is only relevant for the Welsh, Scottish, and English sites. Sites are individual lakes, and there are some lakes with replicate samples collected at different times. Figure 1 shows the site locations in relation to current sulphur deposition loadings. Chemistry and other environmental features of the lakes sampled Water samples were collected from each lake at either the outflow or the lake centre using acid-washed plastic bottles and kept cool and in the dark until analysis. Most lakes were sampled at three to four monthly intervals over the period 1985 - 1988 and the results presented here are arithmetic means except for pH which is a geometric mean. In some cases, especially for some remote sites and lakes with difficult access, the chemistry is based on one sample. As far as possible a standardised water-analytical procedure was used between all laboratories involved in the SW AP project. The participating laboratories included: i) Scotland ii) Wales iii) England Department of Agriculture and Fisheries for Scotland, Pitlochry (R. Harriman) Solway River Purification Board, Dumfries (D. Tervet) Welsh Water, Swansea (R. West) Institute of Freshwater Ecology, Ambleside (D.W. Sutcliffe) •a1 lsoline·s of S deposition Location of site INSET 1 74• 0 67 e117 •108 111•• 93 e70 ':,~~o 104.°164 106° ~~: 07 • •114 •155 101• .751 3 • ~53•168 0119 166• 0109 •47 •72 •81 078 • 57 .ss 084 •49 158• •100 •76 •63 0 5 lui,' Figure 1 Map of the 170 sites where surface-sediments for diatom analysis and samples for chemistry were obtained. Superimposed are the sulphur deposition isolines derived from Eliassen et al (1988). Site numbers follow Table 2. Table 2 Details of the 170 lakes from which the 178 diatom surface assemblages were collected. Site numbers refer to the numbers on Figure 1. Forestry status refers to: P = forestry present, C = conifer forest present, D = deciduous forest present, % = approximate percentage of catchment afforested. Coulltry codes are N = Norway, SCO Scotland, CYM = Wales, ENG= England, S = Sweden. Sita Site Site Name Country Grid Grid Easting Northing Latitude Longitude Altitude Forest Status Ma.xirnurri Lai-a:: g <( 0:: i _j A a b b d e vommamtaxa Lingmoor Tarn ~c h taxonomic ~,f>-t\lS \,,I.),~ "'o"'" ~b 0 10 20 30 0 10 20 30 1------g is c11:1mJl.'!e occurrence. The agreements resulting from the discussion of these and other taxa were circulated to all the workshop participants in a SW AP taxonomic guide (Appendix A). Decisions on taxonomy and nomenclature were based on the Checklist of British Diatoms (Hartley 1986) which formed the framework for A Coded Checklist of British Diatoms (Williams et al. 1988). The SWAP taxonomic guide also included agreements on definitions for boundaries between certain species and their varieties. If possible, published descriptions were referred to or failing this, the criteria for identification were agreed between the analysts. Applying and refining the SWAP taxonomic guide The next stage in the harmonization of diatom taxonomy was to put the protocols agreed at the 1987 workshop to the test. Three slides from lake-sediment samples representing the range of pH values encountered within SW AP ( one sample from each of the ranges pH < 5 .0, pH 5 .0 - 6.0, and pH > 6.0) were circulated to all SW AP diatomists. This time, count sheets were not included so the slides were counted without prior knowledge of the diatom assemblages. The results were discussed at a diatom taxonomy workshop in July 1988. Many potential problems had been avoided by following the protocols agreed at the previous workshop. However, due to the inclusion of taxa additional to those already encountered, some further problems were raised concerning nomenclature, splitting/amalgamation, and identification. Figure 3 shows the predominant taxa from one slide, Botungen. In this slide the use of both Cyclotella comta and C. radiosa was identified as a problem in nomenclature and C. radiosa HAkansson was adopted then as the valid name for this taxon. Also, the four forms of Tabellariaflocculosa had been split by each diatomist using different criteria. In this case, no common set of criteria could be agreed upon for consistently separating the forms so it was agreed to amalgamate the longer forms of this species into a T.flocculosa aggregate category within the SW AP data-set. Similarly, it was agreed to combine Achnanthes minutissima and A. microcephala for the purposes of data analysis within SW AP, although with these and all other combinations of taxa made, it was agreed that diatomists could retain the original distinctions in their own data-sets, if they wished. In addition this workshop produced a guide for the classification of unknown diatoms. I-Cl) ::i <( z V <( VI ~ 0 10 20 0 10 20 0 10 20 0 10 20 30 40 0 C 1---d---, . '" ,. ~ ~ R R ~ t:; ::i <( z <( L L I I L b t ' ' I ' 0 10 20 0 10 20 0 10 20 30 40 0 10 20 0 10 0 10 e e, nrohlem.i;; e, b u«.mrnrium taxa in the OEUl'Ellf:W. sliiJe in 1988. scale is 'f.:€1fUi1~€1 ffU'ff'l'6/F"lrll Protocols for handling unknowns There are two main groups of unknown diatom~: 1) those that occur infrequently and cannot be allocated a name due to damage or defom1ation or are too rare to justify detailed taxonomic work, and 2) those that occur frequently enough to be identified as a discrete taxon but no published description of the diatom can be found. Within SW AP it was agreed to name unknown diatoms in group 1 as follows: i) Diatoms that cannot be identified to genus level due to breakage, being in girdle view or being partly obscured on the slide should be called "unknown pennate", "unknown centric", or simply "unknown". ii) Diatoms that cannot be identified to species level due to the reasons listed above should be allocated a genus if possible. For example, Achnanthes spp., Pinnularia spp., "unknown Naviculaceae", or "unknown Achnanthes!Navicula"'. iii) Unknown diatoms occurring infrequently which do not resemble a particular species but can be allocated to a genus should also be classified as in ii) above. iv) Diatoms which do not exactly fit a description for a taxon but have some features in common with that taxon and that do not occur often enough to warrant classifying them separately from the taxon they resemble, should be put in a "cf" category. For example, in a count of 500 valves there may be several valves of a diatom which closely resembles Achnanthes marginulata but which do not fit the published descriptions of the species. In this case, if the diatoms have not been observed elsewhere it may be convenient to put these valves into Achnanthes cf marginulata rather than list them individually as in group 2. In group 2 unknown diatoms that occur with sufficient regularity or in sufficient numbers to be identifiable as a discrete taxon should be named using [ ]. The square brackets can contain anything that readily identifies that diatom to the diatomist who finds it. The name of the "type" site for the diatom and the initials of the diatomist making the identification should appear as the "authority" after the name. Some examples are given below: Unknown [sp.1 Stauroneis/Navicu/a] Loch Doilet AK In this case the diatom could not be placed in either genus so the two possible genera are included within the brackets. The diatom was first noted at Loch Doilet by Annette Kreiser (AK). Achnanthes [sp.2 nodosa] Loch Chon AK Here the diatom is clearly an Achnanthes. The information in the brackets shows that it is the second unknown Achnanthes named at this site and that A. nodosa is the closest published description to it. Aulacoseira [sp.1 PIRLA distans var. 5] Llyn Tecwyn RJF This name indicates that the diatomist (R.J. Flower) has found a similar unknown (Aulacoseira distans var. 5) in the PIRLA (1984-1986) diatom iconograph. Pinnularia [sp.2 small, capitate] Llyn Berwyn AK In this case the unknown does not resemble any other known or published taxon so other memory-joggers are included in the name. Information contained within the square brackets only has significance for the diatomist who put it there, unless a full description has been circulated by the diatomist. This is particularly important in the case of a name containing a reference to a published description or illustration. The reference given does not necessarily accurately describe the diatom in question. This allows a diatomist to call an unknown Eunotia, Eunotia [PIRLA sp.13] because it exactly matches PIRLA Eunotia sp.13 (which is not an officially published name so has to remain in square brackets) but also enables another diatomist to use the same name for a diatom from a different site which only resembles PIRLA Eunotia sp.13 in certain respects. Clearly in cases like this the two occurrences should be listed separately with the different site names and the diatomist's initials, indicating that they may be different tax.a, unless circulated descriptions confirm that they are the same taxon. Descriptions of important unknowns were circulated to other diatomists to help prevent the use of different names for the same taxon. A list of the final taxonomic decisions made for the SW AP training set is given in Appendix A. As a result of the 1988 workshop, a revised edition of the SW AP taxonomic guide was circulated to all SW AP diatomists. It was also decided to distribute a reference set of the surface-sediment (training data-set) slides to each SW AP laboratory so that particular unknown or problematic taxa could be readily examined by diatomists with reference to their own material. Problems within the genus Aulacoseira A major problem encountered at the 1988 workshop was one of identification within the genus Aulacoseira (Figure 3b). This genus presents particular taxonomic problems due to the difficulties of matching side (girdle) views of the diatom valves with front (valve) views. Difficulties with identification of the rarer and smaller taxa within the genus, such as those within the A. subarctica I subborealis group remained unresolved. As a result of the 1988 workshop it was decided to concentrate on the taxonomy of the Aulacoseira genus at the next workshop. Three samples containing Aulacoseira were selected from SW AP sites and slides were circulated to all SW AP diatomists. Three hundred valves of Aulacoseira were counted from each slide and the results discussed at the diatom taxonomy workshop in February 1989. There were no problems with the most abundant Aulacoseira taxa, such as A. lirata and A. distans var. nivalis. For A. lirata var. alpigena a description was agreed (see Appendix A). Work is continuing on the A. subarctica I A. subborealis group. For the purposes of SW AP, it was decided to amalgamate these taxa into a A. [subarctica agg.] category. Although the analysis of surface and core samples had been completed by this stage, it was agreed to recount samples containing the problematic Aulacoseira taxa since considerable revision of their taxonomy had occurred as a result of the 1989 workshop. This series of workshops and quality-control exercises enabled all the diatom data generated within SW AP to be compatible between laboratories. Nomenclature follows Williams et al. (1988) but names for taxa or amalgamated groups of taxa found only within SW AP are also used. This standardization of the diatom data was essential before the data could be stored and manipulated in the computerized data-base. DIATOM DATABASE The diatom database (DISCO) at University College London (Munro et al. 1990) combines archives of diatom counts, chemical analyses, and catchment descriptions for SW AP sites. It uses the commercial program ORACLE and the standard database language SQL. The database includes a diatom checklist which allocates a code to each taxon on the list using an alphanumeric code to represent the original name of the taxon. Diatomists submitting counts to the database provided an outline code dictionary for their data. The dictionary was checked to ensure that it contained valid checklist codes, and was compared to previous lists from the same laboratory. The counts were then converted to percentages of the total number of diatoms counted in each sample; any amalgamations and other re-definitions of taxa were performed. The database also provided a list of the more frequently occurring taxa for each group of samples, defined as those present in at least two of the samples and having a frequency of more than 1 % in at least one sample. These lists were compared to help identify unresolved taxonomic problems. The SW AP training data-set was created by merging full sets of surface-sediment percentages in this way for the regional data-sets from Scotland, Wales, English Lake District, Norway, and Sweden. All the aggregates to genus level and above (e.g. Navicula spp.) were deleted and the remainder of the list was used to select the taxa to be exported to the data-analytical and calibration programs. NUMERICAL ANALYSIS OF THE CHEMICAL AND OTHER ENVIRONMENTAL DATA The training data-set consists of 170 lakes with diatom, chemical, and other environmental data. Of these, eight have a replicate set of diatom and chemical variables (collected in 1987 from Round Loch ofGlenhead, Loch Howie, Loch Grannoch, Loch Skerrow, Loch Dee, Loch Skae, Loch Woodall, Loch Whinyeon; Stevenson et al. 1989) to give a basic 178 sample data-set. These replicates differ in collection date by 3 - 4 years. Figure 1 illustrates the geographical coverage of the 170 SW AP training lakes. Overall, the samples are derived from lakes with varying geologies giving varying sensitivities to acidification and from a range of different pollution settings. Land-use within the modem data-set varies from upland farming with some improvement to unimproved moorland and forest (both natural and planted). In general, the majority of the UK sites are derived from unforested catchments while the Scandinavian sites are predominantly forested. Basic statistics The available mean chemical data for all 178 samples with associated diatom counts are tabulated along with the available basic catchment vegetation and site physical variables in Table 4. Frequency histograms of the distribution of selected chemical and site physical variables are presented in Figure 4. Summary statistics for selected variables are given in Table 5 as mean, median, standard deviation, minimum, maximum and lower and upper quartiles. Basic scatter plots for the major chemical determinands are shown in Figures 5 and 6. Table 4 Summary of the available chemical data for the 170 lakes from which the 178 sample diatom I chemistry SWAP training-set is derived Sample Site pH Cond DOC Ca Mg Na K S04 Cl Alkal N03 TON Altot Zn Mn F'e Si02 Number Code Number Code us cm-1 mg 1-1 ---------------------------- ueq 1-1 ---------------------- -------------------- ug 1-1 -------- ------ -------- ------ ---•-·-- ------ ------ ------ --·---- ------ ------ -----1 1. 21 2 L2 4.5 58.0 L9 70.0 61. 9 241.4 12.0 142.0 239. 2 0. 0 27.5 477. 5 299.0 23.7 75 .0 73.3 1000 2 10.21 3 10.2 5.3 42.6 4.1 127.9 92 .1 132.9 15.7 174.9 138.2 7.5 20.2 577. 5 176.4 22. 3 31. 5 55.0 2425 3 11 1 1 4.9 17.6 66.9 21.4 4 .1 Bl. 2 19.7 227.0 4 111.11 4 111. l 6.5 16.8 2.5 65.3 27.0 69.7 6.0 22.6 71.3 22.8 3.4 112.5 41. 9 4 .0 15.0 66.7 5 113. 21 5 113. 2 6.4 25.1 2. 8 104.6 34.0 105.8 4.9 27.1 114. 4 102.2 2. 7 192.5 37.1 2.7 2.5 23.3 6 115.11 6 115. l. 5.7 33.2 5.0 46.1 54.9 193. 2 6.8 40.8 217 .2 14.2 2.5 210.0 68. l 3.3 10.5 133 .3 7 12 .11 8 12.1 5.2 12.1 1.4 32.0 15.0 30.1 3.8 51.6 23.2 0. 0 11. 3 232. 5 95.8 10.8 21.8 40.8 775 8 121 7 :2 4.8 26.6 14.5 32 .1 119. 2 5.1 45.8 138.2 90.0 9 15 .11 9 15.1 5.4 20.3 4.6 69.0 31. l 46.3 8.2 97.0 39.9 0.0 8.8 332.5 175.7 23.6 86.3 141.4 2450 10 17.21 10 17 .2 4.9 44.7 5.7 114 .4 74.3 168. 8 8. 3 167. 2 145.5 0.0 6.3 367.5 168. 6 25.7 38.0 140.0 11 18.11 12 18.1 5.4 1.2.9 . 9 34.8 15.6 39.1 6.7 42.6 40.4 18.5 8.3 118. 8 43.6 10.6 20.5 98.0 1000 12 181 11 18 6.3 31.7 79.8 47.7 156.6 3.8 75.0 158.0 35.0 13 19.21 13 19.2 6.3 10.4 1.5 55.7 11. 9 29.4 4.9 34.9 18.8 31. 4 1. 2 105.0 28.9 8.6 7.0 53.4 1450 14 2 .11 15 2.1 5.0 47.8 1.5 67.9 67.9 246.8 8.4 114. 5 243.7 0.0 10.6 280.0 125.0 9.0 11.0 20.0 1550 15 20 .11 16 20.1 6.6 13.7 1.8 99.7 13.2 25.3 5.7 40.0 14. 4 37.7 1.8 142.5 26.4 8.8 3.0 28.8 975 16 21 14 2 4.5 37.2 32.4 34.6 85.3 8.4 114. 5 90.3 321.0 }~ 21.11 17 21.1 6,0 24.3 s.o 112. 4 36.2 40.6 6.3 94.0 27.8 0.0 3.3 185.0 107.6 12.2 24.0 192. 0 3225 3 .11 18 3.1 4.6 35.0 3. 8 23.8 28.4 89.0 3.3 70.8 96.8 0.0 24.3 150.0 8.3 12 .o 35.0 l 025 19 3. 511 19 3.51 7.0 47.0 3.7 369.3 156.2 39. 5 264.0 40.0 20 34.11 20 34.1 5.0 9.5 . 6 6.2 9.0 30.7 2.4 18.7 27.3 0.0 4.3 36.7 12.3 7.5 41.7 300 21 37 .11 21 37.1 6.5 16.8 4.9 89.5 54.3 27.2 3.1 57.5 15.2 1.1 220.0 16. ·, 9.0 11. 0 84.0 1500 22 4 .11 22 4.1 4.4 32.6 4.0 24. 5 24.0 68.2 6.2 94.2 68.9 0.0 18.6 637. 5 212.9 18.6 lo. 3 108.0 525 23 42 .11 23 42.1 5.1 17.0 1. 2 15.9 21. 5 83.5 3.2 27.7 83.8 0.0 2.1 140.0 62. 4 8.4 7. 5 47.0 650 24 44.21 24 44.2 6. 6 12.9 1. 4 71. 4 51.0 16.6 2.3 52.1 10.4 0.0 . 7 20.0 5.0 1.5 35.5 325 25 49.11 25 49.1 4.8 27 .3 • 8 20.5 33.0 136.8 4.1 49.1 133. 2 0.0 11. 0 203.8 59.7 10.5 7.7 30.5 300 26 5.11 26 5.1 4. 6 39.9 63 .2 60.5 121. 6 8.4 145.8 128.8 0.0 6.8 320.0 27.0 27. 5 466.7 1625 27 58.11 27 58.1 5. 7 17.6 1. 9 26.3 27.5 88.6 4.7 33.3 102.2 0.0 2. 4 83.8 52.4 6.8 5.3 39.4 28 59.11 28 59.1 5.5 10.4 • 7 43.8 7.8 26 .1 4. 0 40.2 18.2 0.0 5.1 162.5 37.9 6.8 3.0 45.4 550 29 6. 21 30 6.2 4.6 22.9 3.1 26.2 21. 2 47.8 4.3 73.6 46.7 0.0 10.2 375.0 226.2 12.8 17 .0 90 .0 950 30 601 31 60 4.8 15.2 2.7 31. 4 19.7 29.1 2.6 57.3 24.0 • 7 221.5 31 61 29 6 4. 6 30.3 1.5 10.0 14. 6 46.6 2.0 34.8 53.9 159.0 32 65.21 32 65.2 6.2 21.2 l. 3 36.1 32.2 117. 6 5.5 34.8 123.7 16. 4 1. 3 125.0 21. 7 13 .o 6.0 103.3 1550 33 66 .11 33 66.1 6.0 7.4 1.0 21.2 9.9 30.4 3.1 20.8 28.2 0.0 2.9 8.7 2 S 41. 7 600 34 71 34 7 5.7 45.3 129.7 69.1 151.4 15.9 156.2 158.0 117 .0 35 8.21 35 8.2 4.9 12.9 24.5 14.0 30.3 2.2 43.7 31. 9 0.0 3.3 110 .0 3. 0 8.0 98.3 1075 36 80 .11 36 80.1 1.7 41.1 2.7 47.6 49.1 192. 5 8.6 105.0 i79.4 0.0 10. 7 460.0 198 .0 17 .o 25.0 80.0 37 81. 21 37 81.2 4.5 33.3 5.4 45.4 38.0 108.4 6.8 105. 0 109.4 0.0 10.9 452.5 235.0 15.3 29.0 100.0 1700 38 82 .11 38 82.1 4. 6 21. 4 5.0 27.8 19.2 44. 9 2.7 56.8 48.3 0.0 5.0 245.0 l. 60. 4 11.0 10.3 147.0 1175 39 83 .11 39 83.1 4.9 16.2 1.1 10.1 19.3 65.7 2.6 31.2 75.2 0. 0 7.8 158.8 1 OB .3 9.4 5.0 4 71. 2 350 40 86.11 40 86.1 4.9 22.6 5.5 86.5 28.4 37.7 5.6 105.8 37.9 0.0 6.5 343.8 169.6 19.2 84.0 199. 0 2000 41 87.21 41 87.2 5.1 12.8 2.8 39.3 16.0 29.8 3.0 56.2 24.8 0.0 4. 6 230.0 74.0 14. 0 16.0 46.7 1100 42 88.21 42 88.2 5.3 9.0 1.0 24.1 9.9 21.3 2.8 38.2 17.8 0.0 8.3 123.3 14.0 17 .0 60.0 900 43 89.11 43 89.1 5. 8 16,7 1.0 47.3 27 .1 67.0 3.9 42.6 71. 7 0.0 6.5 132.5 23.0 5.2 8.3 24.0 750 14 9.11 44 9.1 4.7 20.1 2.2 22.0 16.8 32.3 5.1 72.9 27.9 0.0 9.5 370.0 111.3 14.0 103.3 127.5 475 45 ACH1 45 ACH 5.1 32.3 2.8 37.5 28.0 192.5 5.5 42.7 212.3 4.3 1. 7 l7. 0 46 ARRl 46 ARR 6.2 35.0 2.2 40.4 49.4 193 .o 13.6 43.0 219.8 56.9 3. 6 26.0 47 ARTHl 47 ART!! 7,1 101.1 4.6 395. 7 166.6 328.4 29.9 244.8 428.6 315. 9 44. 9 250.0 133.3 6.0 22.5 16.0 1650 48 BAREl 48 BARE 6.7 147.3 6.6 241.5 207.2 369. 8 16.0 261. 3 684.7 279.3 20.4 180.0 73.3 4.0 46.5 66.0 2639 49 BARLl 49 BARL 6.4 34.3 2.1 116 .1 55.8 127.9 5.4 69.0 148.l 97.9 53.3 24. 5 3.5 43.0 300 50 BERl so BER 4.3 58.3 41.1 50.0 261. 5 6.5 165. 4 234.2 .1 125.4 18.2 480.4 229.4 468 51 BODGl 51 BODG 6.5 75.7 1. 9 214.4 99.4 267.5 9.3 175.7 291. 5 103.4 400.0 54.3 44.0 19.0 18.7 733 52 BODLl 52 BODL 5.4 31.8 1. 2 48.7 45.3 150.1 5.9 72.6 1"76.3 3.2 200.0 52.3 17. 7 31.3 9.3 350 53 BRECl 53 BREC 6.6 77.0 189.2 116.8 262 .1 5.0 188.4 437.3 83.4 15.3 90.0 235.0 13.5 17.0 180.0 1550 54 BUGEl 54 BOGE 4.8 52.8 4. 6 46.8 51. 2 152.0 5.1 78.9 232.7 3.5 117 .0 26.0 25.8 383.8 650 55 BURNMTl 55 BORNMT 6.1 45.7 • 2 87.5 61. 5 183.0 9.5 89.0 203.0 11~0 12.0 1930 56 BYCHl 56 BYCH 6. 4 55.3 1.8 154.2 115. 9 228.5 10.0 111.6 261. 0 144.8 200.0 l 5. 0 55.0 12.5 16.3 700 57 CFYNl 57 CFYN 5.5 26.8 1. 2 35.3 27.5 108.4 5.5 48.8 112 .8 8.3 33.5 15.3 17.3 20.0 200 sample Site pH Cond DOC Ca Mg Na K S04 Cl Alkal N03 TON Altot Zn Mn fe Si02 Number Code Number Code us cm-1 mg 1-1 ---------------------------- ueq 1-1 ---------------------- -------------------- uq l-1 -------- -------- ------ -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ----···- ------58 CHNl 58 CHN 5.1 38.5 2.3 78.5 48.5 173.5 7.5 85.8 207.0 4.4 16.8 93 .0 59 CLONl 59 CLON 6.9 152.8 6. 6 416. 5 337.3 671.7 40.8 352.4 838.7 360.7 64.2 158.3 3.0 123.5 116.0 2850 60 CLYDl 60 CLYD 6.1 29.3 . 5 55.9 48.0 123.9 10.6 65.8 134.0 33.9 200.0 16.3 13.7 6.3 5.0 475 61 CONl 61 CON 4.8 33.0 2.1 41. 8 36.3 134.6 6.6 69.7 155.2 0.0 65.8 29.5 93.8 82.3 250 62 CORI 62 COR 5.3 17 .8 . 2 28.8 19.8 67.0 7.8 47.3 81. 7 13. 7 17.0 150.3 63 CWBYl 63 CWBY 5.2 38.0 1. 4 55.7 48.8 181. 6 8.2 99.7 183.4 2.4 250.0 131.0 38.5 54. 8 20.3 550 64 DEVOKEl 64 DEVOKE 6.1 57.8 . 8 116. 0 83.0 229.0 10.5 126.0 269.0 15.0 18.0 1500 65 DIWAl 65 DIWA 5.7 27.3 1.0 46.3 39.1 129.9 6.4 62.3 14 i. 0 11. 3 46.7 16.7 12.B 11.3 525 66 ,DOil 66 DOI 5. 9 43.B 2.4 54.0 60.5 238.7 12.5 70.6 277.0 15.4 6.4 54. 7 67 DOONl 67 DOON 5.2 47.0 3.7 69.4 77.5 193.3 10.4 116. 4 225.4 31.4 15.1 100.0 125.0 68 DU!ll 68 DUH 5.1 20.0 1.4 30.0 24.0 82.3 6.0 59.8 76.B 2.1 13.0 137. 7 69 DULl 69 DUL 5.2 35.4 1. 6 44.0 54.3 168 .1 8.1 83.0 189.8 . 4 400.0 107.2 22 .o 30.0 12.8 669 70 ENOl 70 ENO 4.5 32.7 1.1 12.5 23.4 86.1 5.6 50.7 136. 2 0.0 7.3 105.0 74.9 10~5 : 2. 4 22.9 393 71 EUNl 71 EUN 5.0 22.3 • 5 29.7 26.3 82.3 5.7 62.3 75.7 0.0 27.0 256.3 72 FERNl 72 FERN 6.8 119. 8 8.7 369.5 231.1 385.1 54.1 .283. 2 4 98 .1 303.2 91. 5 170.0 173.3 6.5 601.5 837.5 7000 73 FHil 73 fHI 5.3 52.4 1.0 51. 2 73.0 277.8 12.8 106.7 301.7 1. 4 12.7 255.4 74 FINLl 74 FINL 5.8 49.6 3.6 80.4 68.7 247.8 12.7 112. 2 246.1 52.3 4.2 20.0 312.5 2.0 45.0 87.0 1750 75 FLEl 75 FLE 4.5 39.8 4.0 28.8 30.0 95. 2 5.0 79.5 133.9 0.0 12.7 133.5 22.0 1 90. 8 70.8 596 76 GARNl 76 GARN 6.3 36.8 1.3 112. 9 46.1 153 .0 9.0 90.0 176 .3 41. 9 33.3 9.8 4.3 9.0 325 77 GEIRl 77 GEIR 6.8 75.0 1. 7 226.1 111. 7 253.6 10.9 196. 8 296. 2 138.2 400.0 36.0 719.0 13.0 31. 5 950 78 GLASl 78 GLAS 6.2 28.3 . 5 71.2 43.4 130.l 7.8 57.3 126.9 33.4 366.7 13 .0 18.5 3.S 850 79 GLFRl 79 GLFR 6.9 99.0 5.7 390. 7 143.1 372.8 35.8 93.7 479.5 394.2 26.0 8.0 58.0 80 GLYNl 80 GLYN 4. 9 32.8 1.2 39.7 58.9 150.6 7.4 92.9 148.l 0.0 90.3 16.7 69. 5 59.0 400 81 GODl 81 GOD 5.7 60.5 1. 6 102.1 99.5 297. 5 6.7 128.9 338.5 26.6 350.0 68.0 40.0 20.5 18.5 767 82 GREENTl 82 GREENT 5.2 44.7 .1 47.0 57.5 181. 5 6.0 83.5 203.5 0.0 10.0 117. 0 143 0 83 GULSPETl 83 GULSPET 4.8 38.9 340.0 84 GWYNl 84 GWYN 6.7 35.5 1.1 135.0 47.0 142.7 6.1 72.9 141.1 100.1 200.0 23.5 12.5 11.0 6.0 550 85 GYNl 85 GYN 5.1 33.6 3.6 41.4 54.7 141.1 7.2 79.3 164. 2 5.7 62.6 26.8 130.8 214. 6 788 86 HARRl 86 HARR 5.0 36.2 1. 7 43.8 44.4 147.8 6.2 87.9 173.9 16.7 21. 0 150.0 340.0 11 ~ 5 34. 5 48.5 2 900 87 HIRl 87 HIR 4.8 44.1 46.3 50.0 170.1 8.3 120.5 216.7 0.0 88.5 20.6 161. 5 60.6 264 88 HOLETl 88 HOLET 4.5 21. 2 1. 6 26.0 36.0 121. 9 4.7 74.6 154.4 14.0 142.0 149.0 89 HOLMEVl 89 HOLMEV 4.7 19.1 21.3 11. 7 26.8 5.4 56.5 28.5 90 HOWil 90 HOWI 5. 6 67.7 3.4 150 .3 99.0 250.5 8.5 179.6 341.1 25.5 4 2. 6 360.0 230.7 18.0 27. 5 24. 5 3200 91 HOWI2 90 HOWI 5.6 56.0 130.7 85.6 201.8 5.4 185.3 289.4 5.3 350.0 111.0 14.0 31.0 28.0 2100 92 INVAl 91 INVA 6.6 70.8 7.6 180.l 135.3 202.2 10.9 174. 9 262.3 97.7 17. 6 160.0 135 .0 10.5 159.0 205.5 2100 93 IRDl 92 IRD 5.3 40.0 1.0 60 .4 57.6 194. 3 6.1 95.4 197. 5 2.0 250.0 65.5 12. 5 28.0 9.5 450 94 KIRRl 93 KIRR 5.1 49.7 37.2 64. 6 225.6 6.0 79.2 341. 4 16.7 3. 2 20.0 253.5 5.5 119. 0 319.5 600 95 LAGl 94 LAG 5.2 30.8 2.4 60.5 47.9 165. 2 4.2 63 .1 198. 5 1. 3 200.0 78.3 12.5 48.8 46.8 323 96 LAil 95 LAI 5.4 24.8 3.1 41.3 31.0 132.0 6.3 45.5 130 .0 12.8 2.8 24.3 97 LARl 96 LAR 4. 9 135.4 5. 8 63. 4 188.8 1004.0 24.4 120.8 947.0 0.0 2.7 61. 3 98 LCSLl 97 LCSL 5.2 36.5 . 5 35.4 54.2 183.8 9.9 75.2 176.3 0.0 300.0 102.5 21. 0 37.5 9.8 1350 99 LCSUl 98 LCSU 5.4 33.0 . 5 35.9 56.6 185.3 10.9 83.6 155.2 3.8 400.0 62. 5 11. 5 30.0 5.5 1350 100 LDEl 99 LDE 5.3 33.5 2.5 38.7 38.6 98. 8 8.9 62.4 154.4 42.8 12.3 78.7 7.0 38.8 35.6 1128 101 LDE2 99 LDE 5.7 22.0 38.9 38.7 85.7 4.4 54.6 83.2 2. 9 40.0 94. 0 6.0 10.0 61.0 1100 102 LENYl 100 LENY 6.2 44. 5 2.4 139.0 62.3 178.7 6.3 104.6 190. 5 52.8 366. 7 36.5 10.8 4.0 11.8 325 103 LGRl 101 LGR 4. 6 41. 8 5.5 31. 7 30.6 106.9 5.5 75.3 190.9 0.0 11.1 140.0 193. o 17. 5 167. 2 70.6 11 57 104 LGR2 101 LGR 4.8 47.0 35.4 39.5 134.8 5.1 80.4 151. 5 0.0 20.0 1 900 105 LJOSVl 102 LJOSV 4.4 42.0 . 5 14.6 39.4 138. 4 4.4 78.6 180.6 21. 0 250.0 145.5 106 LLDUl 103 LLDU 5.8 56 .3 1. 7 127.4 86.1 250.5 9.8 118 .8 291. 5 48.7 300.0 41.0 10.0 8.0 48.7 533 107 LLGHl 104 LLGH 4. 6 40.4 3.4 36 .1 44.6 157 .3 6.6 88.7 198 .1 11. 4 5.3 80.0 146.7 10.0 32.0 106.5 1700 108 LODl 105 LOD 5.5 29.0 2.8 33.2 40.2 163 .0 8.0 39. 5 185.4 8.1 2.5 43.3 109 LOWTl 106 LOWT 5.0 43.0 . 7 48.0 46.5 159 .o 6.0 80.5 172.0 0.0 18.0 80.0 ll 20 110 MABEl 107 MABE 5.3 83.0 10.5 104.4 129.6 288.8 10.1 160.7 329.9 24.1 9.1 90.0 242.5 11.0 36.0 7 68. 0 2900 111 MACAl 108 MACA 5.0 38.3 3.7 36.3 49.6 164. 3 8.2 78.4 198 .1 15.7 1.0 170.0 6.5 1 ll .0 265.0 1600 112 MANNl 109 MANN 6.4 89.3 4.0 285.8 175.6 319.5 18. 7 195.6 381. 5 165.6 46.6 200.0 206.7 8.0 51.0 203.5 4150 113 MINNI 110 MINN 5.2 40.0 2.5 45.1 50.2 157.0 7. 5 97.2 184. 9 44.0 16.7 80.0 417. 5 11.0 87.0 419. 0 3050 114 MOANl 111 MOAN 5.1 60.0 5.7 82.7 89.3 257.7 7.3 159.6 334.6 13. 6 4.0 30.0 250.0 14. 5 207.0 573.5 2250 115 MUCKl 112 MUCK 5. 4 62.6 9. 7 90.6 98.5 259.l 9.3 113. 7 319.6 43. 7 0.0 262. 5 11. 0 87.0 419.0 2450 116 NAGAl 113 NAGA 5.0 21.3 . 8 29.8 34.3 87.3 7.3 64.8 76.5 0.0 22.3 129. 0 117 OCHil 114 OCHI 6.0 71.1 9.9 134.6 142 .2 278.6 15.1 165 .1 407.9 60.3 11. 9 30.0 185.0 8.0 22.0 224.0 1300 118 PARCl 115 PARC 6.8 81.0 1.1 208.6 159.7 336.2 9.7 234.0 394.9 112. 5 300.0 59.0 730.0 43.0 11.0 1300 Sample Site pH Cond DOC Ca Mg Na K S04 Cl Alkal N03 TON Altot Zn Mn Fe SiO2 Number Code Number Code us cm-1 mg 1-1 ---------------------------- ueq 1-1 uq l·-1 -------- -------- ------ -------·- ------ ------ --·---- ------ ------ ------ ------ ------ ------ -----·-· ------- ------ ------ ------119 PENRl 116 PENR 5.0 32.3 3.4 49.5 53.1 148 .o 6.2 80.J 148.l 3.7 250.0 135. 7 ·,2 .3 95.8 104. 5 500 120 RIECl 117 RIEC 5.3 ~3.3 5.1 51.3 62.8 176.0 7.1 96.4 227.4 4 5 1 l.O 10.0 177.5 3.0 44. 0 15.0 1500 121 RLGHl 118 RLGH 4.7 33.5 2.5 20.9 29.1 100.4 5.0 60.8 170. 9 0.0 4.8 70.0 109.4 11. 5 42.2 36.3 538 122 RLGH2 118 RLGH 4.8 25.0 19.5 27.1 91.3 4.1 60.8 107.2 a.a 156.0 7.0 27. 0 111.0 500 123 RONAl 119 RONA 6.5 98.3 246.8 175.2 430.9 22.6 183.3 598 .1 138. 0 7. 9 110. 0 130. 0 5.0 41.0 53.0 1950 124 R\01 120 R\0 6.7 27.4 4.8 66.4 35.9 42.5 7.8 24.1 120.1 1. 4 170.0 33.3 68. 0 179 125 S101 122 510 6.7 31.1 7.3 144.0 72.0 14.0 82.0 21. 0 119. 0 2.3 309. 0 106.0 l3 .0 200.0 2760 126 S11 121 Sl 6.8 62.3 9.1 371. 0 88.0 15.0 165 .0 82.0 23 9. 0 2.7 431. 0 62.0 18.0 89.0 1130 127 S111 123 S11 5.3 .64 .3 11. 6 178.0 102. 0 20.0 224.0 206.0 3.0 5.6 581.0 302.0 160. 0 735.0 1270 128 S121 124 S12 6.5 25.2 4.7 119 .0 33.0 10.0 92.0 24.0 65.0 1. 4 262.0 39.0 8.0 32.0 1340 129 S131 125 513 5.2 44.0 8.4 105.0 68.0 14.0 151.0 129.0 2.0 3.1 470.0 135.0 169. 0 710 .o 1130 130 5141 126 Sl4 6.4 27. 6 7.5 120 .o 50.0 12.0 BLO 27.0 72.0 2 3 330.0 94. 0 22.0 224.0 2900 131 5151 127 Si5 7.3 76.9 7.3 605.0 69.0 37.0 55.0 48.0 619. 0 • 8 654.0 38.0 44.0 30.0 540 132 S161 128 S16 6.4 44.4 8.8 175.0 87.0 20.0 149.0 93 .o 73.0 4. 7 498.0 113. 0 58.0 220.0 1230 133 S171 129 S17 5.8 78.7 3.0 258.0 117 .0 23.0 340.0 233.0 13.0 3.9 340.0 112. 0 70.0 35.0 890 134 5181 130 518 5.8 67.3 5.4 189.0 99.0 16.0 240.0 230.0 17 .0 4.5 578.0 91.0 157. 0 290.0 620 135 S191 131 S19 5.0 58.3 12.1 97.0 95.0 12.0 149.0 244.0 1.0 3. 4 51 7. 0 275.0 67.0 529.0 1390 136 S201 133 S20 5.5 22.8 6.1 63.0 34.0 13.0 84.0 49.0 1.0 2.2 34 0. 0 119 .0 59.0 241. 0 660 137 S21 132 S2 7.2 140.8 12.7 938.0 214.0 35.0 227.0 161.0 903. 0 6. 9 722.0 75.0 30.0 260.0 2810 138 S211 134 S21 4.5 139 S221 135 S22 4.4 83.0 2.1 63.0 91.0 12.0 205.0 348.0 0.0 9.6 332.0 546.0 102.0 66.0 410 140 S241 136 S24 6.0 35.6 7.3 140.0 53.0 11.0 133.0 79.0 21. 0 5.9 396. 0 110.0 25.0 156.0 1230 141 S251 137 S25 7.0 66.5 5.3 377.0 79.0 11. 0 178.0 127 .o 237.0 5.6 362.0 66.0 12.0 51.0 1140 142 S261 138 S26 6.6 95.8 17. 0 4 64. 0 137.0 24.0 237.0 265.0 220.0 16. 6 982.0 140.0 306.0 4 87 .0 2820 143 S271 139 S27 5.1 28.5 12.5 87.0 47.0 10.0 101.0 54.0 o.o 2 .1 409.0 280.0 66.0 783.0 2260 144 S281 140 S28 4.9 24.4 6.6 59.0 33.0 11.0 92.0 42.0 0.0 5. 9 353.0 176. 0 71.0 2 67. 0 1560 145 S291 141 S29 5.0 26.8 4.3 62.0 37.0 10.0 112.0 52.0 0.0 4. 9 303.0 139.0 121.0 67.0 no 146 S301 143 S30 5.5 26.3 8.1 90.0 49.0 12.0 99.0 48.0 5.0 2. 6 386. 0 141. 0 59.0 367 .0 1000 147 S31 142 S3 6.7 34.6 6.1 163 .o 61.0 13.0 108.0 58.0 82.0 3.4 31 9. 0 54. 0 28.0 50.0 1130 148 S311 144 S31 5.9 51. 2 8.0 146.0 94 .o 13.0 168 .o 168 .o 18.0 4.8 425.0 201.0 35.0 98.0 1990 149 S41 145 S4 5. 8 36.4 11.6 140.0 79.0 15.0 137 .o 57.0 20.0 4. 9 486.0 171.0 68.0 312. 0 1690 150 S51 146 S5 6.3 69.4 6.3 257.0 182.0 24.0 285.0 112 .0 146.0 6.2 489.0 33.0 26.0 99.0 360 151 S61 147 S6 6.0 66.9 14.9 183.0 83.0 19.0 126. 0 279.0 59.0 3.7 648. 0 202.0 37.0 425.0 1360 152 S71 148 S7 6.5 42.6 15.4 221.0 78.0 10.0 149.0 56.0 68.0 3.0 489.0 152.0 30.0 4 24. 0 2380 153 S81 149 S8 6.7 25.3 3.4 129.0 59.0 9.0 28.0 39.0 147.0 . 6 240.0 18.0 9.0 79.0 1960 154 S91 150 S9 6.2 82.7 14.3 312.0 161.0 15.0 313 .0 207.0 58.0 4.1 737.0 151.0 167 .o 4 63. 0 1460 155 SCOATTl 151 SCOATT 5.0 42.8 . 5 38.0 50.0 166.0 8. 5 62.0 196.0 o.o 24.0 223.0 1370 156 SKAKl 152 SKAK 5.8 64.0 146.0 97.9 245.4 3.8 171.8 327.2 58.6 3. 2 80.0 205.0 12.5 6.5 29.5 1050 157 SKAK2 152 SKAK 5.8 49.0 125.7 78.2 163 .1 3.1 139.9 205.6 41. 9 10.0 63. 0 14.0 24.0 73.0 700 158 SKEl 153 SKE 5.1 51.4 6.5 112. 9 72.0 186.4 8.1 134.3 256.6 62.8 9.0 290 .o 5.0 56.0 143.0 159 SKE2 153 SKE 5.1 36.0 66.4 52.6 140. 9 3.8 72.7 141. 0 0.0 30.0 187.0 8.0 73.0 119. 0 1300 160 5KOM.AKV1 154 SKOM.AKV 4.7 19.7 16.4 23.1 80.4 3.5 41.0 83.9 0.0 120.5 161 STROl 155 STRO 4.8 49. 4 5.8 89.4 63. 6 204. 5 4.7 147. 9 226.7 37.7 2. 7 20.0 4 65. 0 10.0 136.0 269.0 3800 162 TANNl 156 TANN 5.0 45.0 1.7 35.2 64. 6 232.2 11.8 91.0 218.3 . o. 0 19. 0 197.6 163 TEANl 157 TEAN 5.7 16.3 110.0 255.0 981.0 23.0 233.0 1160. 0 12.0 7.0 164 TECWl 158 TECW 6.1 59.0 1.7 157.2 93 .o 262.6 9.1 163. 7 282.1 36.5 500.0 26.0 7.0 16 .0 27.5 200 165 TINKl 159 TINK 6.0 25.3 3.6 78.0 35.0 101.0 6.7 62.3 101.7 24.2 4.0 24. 2 166 TROOl 160 TROO 5.0 39.8 2.7 43.3 50.1 166. 6 8.7 96.2 233.9 11. 4 9. 7 50.0 241.7 10.0 32.0 102.0 2050 167 UAil 161 UAI 5.8 40.5 .4 69.0 59.5 191.0 10.0 82.0 215.0 14.8 10. 0 112. 5 168 UISl 162 UIS 6.2 33.6 4.8 67.8 49.2 191. 2 16.0 46.8 166. 8 60. 8 2.3 34.0 169 URRl 163 URR 6.8 59.3 166. 5 129.9 181.8 11.3 148.7 247.3 60.5 14.7 130.0 227. 7 6.3 23.7 .l 94. 0 1933 170 VALl 164 VAL 4.7 33.1 2.6 17.7 23.8 93.1 5.5 57.1 171.1 0.0 9.8 130.0 126.5 11.0 33.8 31.0 495 171 VEREVl 165 VEREV 4. 5 64.3 802.0 172 WHINl 166 WHIN 6.9 80.0 316.1 116.0 278.4 15.5 140.6 363. 9 305.7 6.1 120.0 215.0 4.0 15.5 38.5 1100 173 WHIN2 166 WHIN 6.9 60.0 292 .4 86.4 182.7 6.9 90.0 197. 4 158.8 50.0 32.0 3.0 11.0 48.0 500 l 74 WHITl 167 WHIT 7.0 154 .o 244 .6 157.2 305.6 16. 4 161. 4 640.0 415.5 5.9 30.0 70.7 3.5 33.2 52.6 2292 175 WOODl 168 WOOD 6.8 98.4 5.8 324 .8 239.0 325.7 16.5 190 .1 397. 7 278.2 69.9 440.0 138.3 7.5 48.0 111. 0 3450 176 W00D2 168 WOOD 6.9 77.0 309. 4 198 .3 204.4 14.1 119.3 207.0 203.8 190.0 78.0 5.0 146.0 2!'13.0 2900 1 77 YBil 169 YBI 5.1 34.0 1. 2 53 .1. 57.1 195. 7 8.1 100.4 225. 4 .3 200.0 134 .8 24.1 60.1 19.4 641 178 YGADl 170 YGAD 5.7 32.0 1.8 79.3 42.l 156.7 8.6 73.6 169.3 29.7 62.0 9.0 19.0 33.0 450 Table 5 Summary statistics for selected environmental variables in the SWAP training set (Stan. dev. = standard deviation, min.= minimum, max.= maximum, quar. = quartile, N = number of determinations, afforest= afforestation) Stan. Lower Upper N Mean Median dev. Min. Max. Quar. Quar. pH 178 5.59 5.40 0.77 4.33 7.25 4.96 6.25 Conductivity 177 44.5 37.2 27.6 7.4 154.0 26.5 57.0 DOC 148 3.9 2.8 3.5 0.1 17.0 1.4 5.5 Ca 175 109.5 66.4 120.2 6.2 938.0 37.5 134.6 Mg 174 67.5 51.1 52.8 7.8 337.3 33.0 85.7 Na 144 171.8 156.7 138.0 16.5 1004.0 88.7 204.4 K 174 9.8 8.0 7.4 2.0 54.1 5.5 11.8 S0 4 175 106.9 90.0 64.4 18.7 352.4 60.8 145.8 Cl 175 187.4 164.2 161.6 10.4 1160.0 79.0 233.0 N0 3 126 10.4 6.2 13.3 0.0 91.5 3.1 12.4 TON 117 271.7 245.0 183.4 10.0 982.0 130.0 380.5 Alkalinity 161 57.3 12.8 116.5 0.0 903.0 0.0 58.8 Total Al 173 130.9 112.0 106.2 12.0 802.0 54.2 175.9 Zn 109 26.2 11.0 96.3 2.0 730.0 7.3 17.3 Mn 136 55.0 30.0 78.9 1.5 601.5 12.8 66.8 Fe 139 143.4 70.6 175.7 3.5 837.5 33.0 199.0 Si0 2 137 1329.9 1128.0 991.7 179.0 7000.0 550.0 1725.0 Altitude (m) 176 349.2 270.5 267.4 10.0 1246.0 165.3 488.7 % afforest 112 22.3 0.0 33.0 0.0 100.0 0.0 50.0 Max Depth (m) 176 15.9 13.5 10.8 LO 61.0 9.0 20.0 Figure 4 shows that the majority of the sites are acidic, oligotrophic, and clearwater with low modal pH (5.0 - 5.4), conductivity (25 - 35 µS cm·1), calcium (< 50 µeq 1'1), and alkalinity (< 50 µeq 1'1) classes. As might be expected from this distribution the majority of the lakes are slightly coloured with a modal DOC range of 1 - 3 mg 1'1• Strong colour is restricted to a few sites, especially in northern Scotland and a large number of sites from Sweden (e.g. Hagasjon, V.'l.gsjoama). Throughout the acid end of the data-set, aluminium levels are high (modal class 50 - 150 µg 1'1 total Al). Sodium and chloride are in approximate marine proportions (data-set Na/O = 0.92 ± 0.21; marine ratio = 0.85) throughout the data-set (Figure 6) indicating a contribution predominantly from rainfall. A few lakes from coastal locations such as Loch na 'Larach and Loch Teanga in Scotland have very high conductivities, sodium, and chloride levels. A number of lakes subject to past and current mining activities in their catchments have high trace-metal concentrations, e.g. Llyn Geirionydd and Llyn y Pare (Zn >700 µg 1'1). 1.07 0.9~ OB-'s 0.7-'E ~ o.e--;;; ls. 0.5-:S 0.4-g 0.3-n:: . 02-' I 0.1~ I I 1.07 0.9~ ! 0.8~ 's 0.7~ tJ I o.e~ a, I ~ 0.6: 6 0.4-i "" l ~ 037 n.. :1 02 0.1 42 5.4 6.6 78 pH ~ I I I I b I r= r= I 0 300 600 900 1200 Malrlty (fsVO 0 120 240 380 ~ 30 25 0 L20 ~ I 115 i r- 10 5 140 120 100 80 0 ~ I ~60 I ~: 50 40 0 30 ~ 20 10 1.07 0.9-j OBJ '.§ I tJ 0,77 j o.e~ "' I ~ 0.5i ,§ 0.47 g 0.3; ct 02 0.1 1.0 0.9 OB '.§ 0.7 tJ t 0.6 CJ) ~ 0.5 i 0.4 0.3 n:: '5 02 0.1 1.0 0.9 OB 0.7 1 0.8 ., ~ 0.5 ~ 0.4 ~ 0.3 ct 02 0.1 0 0 0 60 120 160 Oond.JclMty (µSiem) 300 600 900 Oald.m (p3q,1) 300 600 900 1200 16CX) Socl.m (fsVO 50 40 30 ~ 10 70 60 60 0 40 ~ ~30 20 10 70 :~ 40 30 20 10 Figure 4 Frequency histograms for major chemical determinands and catchment variables, showing the proportion of different classes for the major variables. '5 u 02 0.1 1.J 0. i I 0.8-l ! i 0.7--, I 0.6-; I UJ ! ~ 05! ~ 0.4~ ~ 0.3~ d: I ! 02-j I 0.1 0.1 n I , ' ' ! ! ' 0 0 0 20 40 60 80 Potassun (fl3CVO 600 1200 1600 400 800 1200 1600 Total orgaric rllrogen (mg/I) Figure 4 Continued. 80 70 0 60 ~ 50 40 30 20 10 60 50 40 30 20 10 25 20 15 10 5 0 ~ ; 1.0 0.9 0.8 0.7 i 0.6 (JJ ~ 0.5 ,§ 0.4 ~ 0.3 d: 02 0.1 1, 0.9 0.8 's Q.7 u I 0.6 "' ~ 0.5 i 0.4 0.3 d: 02 0.1 1. 0.9 0.8 '5 u 0.7 t o.e U} ~ 0.5 $ 0.4 § 0.3 d:: 02 0.1 0 0 0 6 120 240 360 81..ipha te (fl3CVO 400 800 1200 1600 Iron (mg/1) 12 18 24 30 r: ~ r 20 I r 10 I r 120 100 0 80 ~ I 60 40 20 60 50 40 ~ 30 20 10 1.07 1.0 0.9--' 0.9 0.8- OB '5 0.7- '5 D.7 1:' 1:' "' I i:'. 0.6- 0.6 40 0 CTl 0 lo ~ lfJ 0.5 ~ qj 0.5- r, 40 ~ 0. I 30 2 0.4- ~ 0.4 30 t5 0.3- ~ 0.3 20 0. 0 cl:: 20 [L 0.2- 0.2 10 10 0,1- 0.1 0 300 600 900 0 3000 6000 90CO Totai Al (µg/1) Sliicate (mg/I) 'l 1.0 0.9 09 0.8 0.8 ~ 120 '5 0.7 0.7 'E ! 100 i (•_:: i< ... ,, . ,, :· .. . ,. .,, ,, .. :" ;/?:<. ,,~·-::r._\\·.··.··. L __ _ · ... ·. : .. . ):U(::_.· . •:· ··. •.· .. : .. ~i..•.:· · .. • .• ... ,;., Calcium · .. · . ~(,i· .. ... · · .. ·. - ... . ~-... ·. ·Ji5,r,, •• ·• Dissolved Organic Carbon :· •.,. .. ii/\/ . Total Aluminium Figure 5 Matrix of uniformly scaled scatter plots for major chemical determinands, showing the patterns between pairs of chemical determinands. i·=r· . ~ eoo ,. r_··-1 E =L ::; c., ro 200 • .. ' .u .).-~=:-.::· .: ·-~ .... .. i. . ·('It ... ~,.:-. 0 ~ ...... ___ 4 5 6 7 8 pH 1000 --800 ~ rr Q) ::i 600 >-.... C 400 co ~ 200 <( 0 ..... .:.:'lo:". .___....__., ... • ~· ,._· _...,;;;,:_,.·_,_·'.'l__: _L __ __, 4 5 e 7 8 pH pH 20 16 ---0) E 10 u 0 0 6 .... •, •I l•,,, 0 4 6 6 7 pH 16CXl ~-------------:::::: --rr Q) 1CXXJ ::::L 0) -0 500 ·;::: 0 .c u ·:;. -~:;:-i J I i ' I 0 "'-------L--------'-----0 /500 1CXXJ 1 /500 sodium lµeq/li Figure 6 Scatter plots between selected chemical determinands. The correlation matrix (Table 6) of environmental variables, with the significance levels adjusted for varying sample numbers, shows expected patterns of correlation between the variables. pH is positively correlated with both alkalinity and calcium levels (see also Figures 6 and 7), and negatively correlated with total aluminium. Altitude is negatively correlated with conductivity, DOC, magnesium, sodium, and potassium. The most acid sites are also the highest. Lake depth appears to have little correlation with lake-water chemistry. Table 6 Matrix of product-moment correlations between environmental variables pH Cond DOC Ca Mg Na K S0 4 Conductivity 0.423* DOC 0.260 0.442* Ca 0.705** 0.714** 0.531* Mg 0.528** 0.856** 0.455* 0.704** Na 0.275 0.706** 0.361* 0.511* 0.823** K 0.494* 0.712** 0.495* 0.749** 0.755** 0.680** S0 4 0.333* 0.764** 0.509* 0.629** 0.820** 0.672** 0.64** Cl 0.211 0.718** 0.138 0.295 0.788** 0.959** 0.517* 0.570* N0 3 0.117 0.446 * -0.039 0.277 0.514* 0.310* 0.474* 0.410* TON 0.107 0.198 0.542* 0.438* 0.209 0.003 0.423* 0.400* Alkalinity 0.681 ** 0.674** 0.354* 0.914** 0.600* 0.424* 0.692** 0.400* Total Al -0.437* 0.139 0.279 -0.097 0.079 0.080 0.025 0.271 Zn 0.191 0.146 -0.128 0.159 0.155 0.164 0.019 0.256 Mn -0.108 0.319* 0.395* 0.161 0.290 0.301* 0.430* 0.394* Fe -0.082 0.205 0.708** 0.145 0.234 0.191 0.303* 0.308* Si0 2 0.267 0.454* 0.435* 0.376* 0.523* 0.433* 0.496* 0.419* Altitude -0.186 -0.576** -0.470* -0.374* -0.560* -0.559* -0.449* -0.571* Unforested -0.118 -0.298 -0.503* -0.308* -0.249 -0.072 -0.188 -0.407* Forested 0.118 0.298 0.503* 0.308* 0.249 0.072 0.188 0.407* Conifer F. 0.099 0.295 0.530* 0.303* 0.249 0.064 0.196 0.400* Deciduous F. 0.063 -0.001 -0.107 0.007 -0.011 0.025 -0.036 0.012 %afforest. 0.067 0.366* 0.279 0.252 0.261 0.165 -0.012 0.446* Max Depth -0.165 -0.394* -0.312* -0.280* -0.337* -0.299 -0.303* -0.332* Table 6 Continued. Cl NO3 TON Alkal Tot. Al Zn N0 3 0.375* TON -0.157 0.025 Alkalinity 0.276 0.277 0.227 Total Al 0.137 0.191 -0.010 -0.153 Zn 0.123 -0.027 0.185 0.090 -0.121 Mn 0.260 0.461* 0.165 0.081 0.252 -0.035 Fe 0.081 0.102 0.235 0.015 0.443* -0.090 SiO2 0.348* 0.587* -0.021 0.343 0.386* -0.042 Altitude -0.498* -0.171 -0.183 -0.269 -0.195 -0.072 Unforested -0.042 -0.058 -0.184 -0.239 -0.323* -0.103 Forested 0.042 0.058 0.184 0.239 0.323* 0.103 Conifer F. 0.038 0.058 0.163 0.240 0.344* 0.105 Deciduous F. 0.013 0.040 -0.013 -0.090 -0.012 %afforest. 0.253 0.109 -0.177 0.192 0.428* 0.217 Max Depth -0.274 -0.102 -0.174 -0.232 -0.118 -0.055 Mn Fe SiO2 Altit Unfor Forest Conif Dri1 %Aff Fe 0.517* SiO2 0.385* 0.466* Altitude -0.221 -0.250 -0.354* Unforested -0.189 -0.272 -0.357* 0.503* Forested 0.189 0.272 0.357* -0.503* -1.000** Conifer F. 0.202 0.300* 0.393* -0.468* -0.964** 0.964** Deciduous F. -0.052 -0.107 -0.137 -0.113 -0.098 0.098 -0.170 %afforest. 0.151 0.149 0.355* -0.419* -0.769** 0.769** 0.799** -0.070 Max Depth -0.231 -0.208 -0.159 0.347* 0.042 -0.042 -0.032 -0.037 0.171 (* = significant at p:s;0.05, ** = significant at p:s;0.01; all significance tests have been adjusted for varying sampling size) (Max = maximum, afforest. = afforestation, F. = forest) Principal Components Analysis Principal components analysis (PCA) was used to summarise the major patterns of variation within the environmental data. Since not all determinands were available for all samples or all sites, a reduced data-set of 157 samples with nine chemical variables (pH, Cl, SO4 , conductivity, Mg, K, Ca, alkalinity, total Al) in common was selected for PCA. Figure 7 is a PCA correlation biplot derived from a PCA of the correlation matrix of the untransformed chemical data. The individual sample scores and variable loadings for this analysis are listed in Table 8. The seven catchment and physical variables (maximum depth, altitude, unforested, forested, forested with deciduous trees, forested with coniferous trees, percentage afforested) were included as passive variables (ter Braak 1987). In a PCA correlation biplot, variables with high positive correlations generally have small angles between their biplot arrows. Variables with long arrows have high variance and are often the most important within the data (Jongman et al. 1987). The PCA and all the other ordinations presented here were implemented by means of the computer program CANOCO 3.10 (ter Braak 1987, 1990). Table 7 Eigenvalues and cumulative variance accounted for in a PCA of the 157 sample x 9 chemical variable data-set Axis 1 Axis 2 Axis 3 Axis 4 Eigenvalue 0.608 0.184 0.082 0.047 Cumulative variance represented 60.8% 78.1% 86.3% 91.0% The variance within the data-set is efficiently captured with over 78% accounted for by the first two components. The first axis (60.8%) is effectively contrasting lakes with high pH/alkalinity and associated variables (Ca, K, Mg, S04, Cl) with acid sites, such as Bliksvatn (82.1 on Figure 7) and the Round Loch of Glenhead (RLGH), positioned to the left-hand side of the PCA biplot. Alkaline sites such as Loch Fem (FERN), Loch Clonyard (CLON), and Edasjon (S2) are located on the right-hand side. Lake altitude and maximum depth, included as passive variables, are negatively correlated with this set of variables. Axis two (18.7%) is more complex and may reflect contrasts in aluminium, with high aluminium sites such as Loch Stroan (STRO), Loch Minnoch (MINN), and Harsvatten (S22), located at the positive end of axis 2 and low aluminium sites such as V. Helgtjamen (S15), Flickersjon (S8), and Loch Whinyeon (WIDN) located at the negative end of axis 2. The contrast in the relative sizes of axes 1, 2, and 3 (eigenvalues 0.608, 0.184, 0.082, respectively) suggests that there is only one overriding gradient of variation in the chemical data, namely axis 1 reflecting the strong gradient from high to low pH, alkalinity, calcium, conductivity, etc. There is very little scatter of sites on axis 2, with the exception of two Swedish sites (S22, S15), and axis 2 may be primarily influenced by extreme values in these sites. N 1:/l •l'""I ~ ~ < u p,.,i 4 --7 3 Cl S0 4 Tot Al 2 ' ' S22 Cond srno Mg 1 MINN . . . . ... • CLON . . .. . • • J' @ 1//; RLGH • • • _. • FERN ... '• 0 82.1 • :'"·:..: , ... :: ,;-••i • .. ®t ,, -. ffi K • •• f& ' . . -1 ~8X Dep WHIN S8 • S2 1 Unfor Alt S15 -2 Alkalinity Ca pH -3 -2 -1 0 1 2 3 4 5 PCA . 1 axis Figure 7 Principal components correlaaon biplot of the 157 sample x 9 chemical variable data-set. Catchment and other environmental variables are analysed as passive variables. The individual sample scores and variable loadings are listed in Table 8. See Table 2 for site codes. Abbreviations: tot = total, a.ff= afforestation, for = forested, coni = conifer, cond = conducavity, alt = altitude, unfor = unforested, decid = deciduous, max dep = maximum depth, PCA = principal components analysis. i i 3 2 • S22 Tot Al . I . I. Cl j 7 i i ! i 1 I ! ! . . . . . . . so • CLON ~ 0 -1 -2 -3 -2 Max dep Unfor / Alt . . . .. . . . .. 1:. ... -1 4 • FERN Alkalinity .. pH • S2 0 1 2 3 4 PCA axis 1 Figure 8 Principal components correlation biplot of the 138 x 10 chemical variable data-set. Catchment and other environmental variables are analysed as passive variables. The individual sample scores and variable loadings are listed in Table 8. See Table 2 for site codes. Abbreviations follow the capdon for Figure 7. 5 Table 8 Sample scores and environmental variable loadings for the PCAs of the 157 and 138 sample data-sets. 157 sample data-set 138 sample data-set Axis 1 Axis 2 Axis 3 Axis 4 Axis 1 Axis 2 Axis 3 Axis 4 Variable loadings pH 0.6219 -0.6453 0.0120 -0.2047 0.8556 -1.3713 0.1815 -1.0496 Conductivity 0.9326 0.2283 0.2078 0.0417 U999 0.5589 0.5208 0.4665 DOC 0.5742 0.1637 -0.6619 -0.3308 Ca 0.8858 -0.3118 -0.2258 0.1607 1.1294 -0.6969 -1.0525 -0.0215 Mg 0.9344 0.1599 0.2020 -0.1072 1.2026 0.4038 0.5040 -0.4662 K 0.8770 -0.0480 -0.0444 0.1254 1.0880 -0.0879 -0.5255 0.6240 S04 0.8187 0.3257 -0.0409 -0.2284 1.0163 0.8415 -0.2766 -2.0332 Cl 0.6516 0.4073 0.5819 -0.0345 0.8965 0.9998 1.7918 0.9214 Alkalinity 0.7929 -0.4151 -0.1132 0.3971 1.0413 -0.8933 -0.8445 1.4136 Total Al 0.0727 0.8320 -0.3688 0.2874 0.0411 1.9092 -1.7487 0.3041 Altitude -0.5546 -0.3304 0.0320 0.1753 -0.6978 -0.7510 -0.1018 0.4185 Unafforested -0.3599 -0.2127 0.4031 0.1217 -0.4360 -0.4583 1.0890 1.1294 Forested 0.3532 0.1582 -0.3994 -0.1579 0.4281 0.3382 -0.9932 -1.1553 Conifer F. 0.3535 0.1799 -0.4311 -0.1460 0.4249 0.3797 -1.0689 -1.0496 Deciduous F. -0.0109 -0.0777 0.1182 -0.0360 -0.0037 -0.1594 0.3041 -0.3359 % afforested 0.1730 0.2895 0.1796 0.0949 0.2839 0.7094 0.4663 -0.1474 Max. depth -0.4215 -0.1567 0.0221 0.0360 -0.4922 -0.3865 0.0565 0.0582 Sa.mple scores Sample Sample number code 1.21 -0.0778 0.8051 -0.2540 0.1288 -0.1045 1.8021 0.1107 1.4889 2 10.21 0.1347 0.2803 -0.2154 -0.2352 0.2067 0.6454 -0.3514 -0.1510 4 111.11 -0.5203 -0.6220 0.0846 -0.0426 -0.6633 -1.3766 0.0209 -0.4591 5 113.21 -0.3311 -0.6494 0.0801 0.0843 -0.4264 -1.4435 0.0973 0.0432 6 115.11 -0.3576 -0.1833 0.2784 0.1290 -0.3857 -0.3111 0.2554 -0.6728 7 12.11 -0.8007 -0.2170 -0.0976 -0.0105 -1.0225 -0.4400 -0.2066 0.3170 9 15.11 -0.4983 0.0154 -0.2968 -0.1136 -0.5550 0.1197 -0.9998 -0.0099 10 17.21 -0.0946 0.3418 -0.1329 -0.2082 -0.0278 0.8559 -0.5559 -0.4970 11 18.11 -0.7096 -0.3968 0.0083 0.0355 -0.9317 -0.8625 0.1247 0.2634 13 19.21 -0.6519 -0.6538 0.0102 -0.0858 -0.8519 -1.4595 -0.0519 -0.2365 14 2.11 -0.1635 0.2589 0.1619 0.0323 -0.2397 0.5766 0.8460 0.2405 15 20.11 -0.5231 -0.7463 -0.0291 -0.1375 -0.6855 -1.6774 -0.1813 -0.3511 17 21.11 -0.3888 -0.2969 -0.1631 -0.2522 -0.4202 -0.5953 -0.8573 -0.7865 18 3.11 -0.6712 0.1895 -0.0664 0.1180 -0.7878 0.5414 -0.3380 0.3774 20 34.11 -0.9605 -0.3494 0.0670 0.1053 -1.2460 -0.7249 0.2044 0.3554 22 4.11 -0.6503 0.3787 -0.2868 0.1007 -0.7483 0.9669 -0.7877 0.8015 23 42.11 -0.8082 -0.2254 0.0993 0.1231 -1.0416 -0.4487 0.2927 0.3031 24 44.21 -0.5293 -0.6663 0.0963 -0.3085 -0.7050 -1.5149 0.1319 -0.9994 25 49.11 -0.6803 -0.0738 0.1764 0.1440 -0.8954 -0.1214 0.6529 0.3881 26 5.11 -0.2779 0.7136 -0.4504 -0.0404 27 58.11 -0.6639 -0.3422 0.1443 0.0336 -0.8493 -0.7257 0.2818 -0.2048 28 59.11 -0.8041 -0.4441 0.0072 -0.0329 -1.0535 -0.9688 0.0682 0.0830 Table 8 Continued. Sample Sample 157 sample data-set 138 sample data-set number code Axis 1 Axis 2 Axis 3 Axis 4 Axis 1 Axis 2 Axis 3 Axis 4 36 80.11 -0.3468 0.3951 -0.0976 0.0927 -0.4216 0.9309 0.0348 0.7039 37 81.21 -0.5138 0.4470 -0.2886 0.0708 -0.5439 1.1348 -0.9367 0.5331 38 82.i 1 -0.8190 0.1137 -0.1834 0.1122 -0.9353 0.4145 -0.8947 0.3161 39 83.11 -0.8496 -0.0893 -0.0089 0.1400 -L0918 -0.1449 0.1095 0.6483 40 86.11 -0.5708 0.1122 -0.2986 -0.0855 -0.6179 0.3841 -1.1531 0.0054 41 87.21 -0.8060 -0.2399 ·0.0522 -0.0250 -0.9913 -0.4492 ·0.3648 -0.0590 42 88.21 -0.8767 -0.1849 -0.1530 0.0301 -1.1266 -0.3753 -0.2655 0.6134 43 89.11 -0.6516 -0.4567 0.1478 -0.0561 -0.8631 -1.0111 0.3977 -0.2920 44 9.11 -0.7794 -0.0082 -0.1318 0.0376 -0.9688 0.0636 -0.3294 0.4745 45 ACH1 -0.5458 -0.1944 0.3591 0.1904 -0.6816 -0.3544 0.7492 -0.2603 46 ARR1 -0.1503 -0.4188 0.2994 0.1599 -0.2201 -0.9462 0.8654 -0.1588 47 ARTH1 1.9557 -0.1288 -0.1294 -0.0003 2.4230 -0.4717 0.7026 0.9739 48 BARE1 2.0155 0.3022 0.7497 0.0215 2.5513 0.5461 2.3842 -0.9427 49 BARL1 -0.1217 -0.4914 0.1029 -0.0388 -0.1886 -1.1362 0.3952 -0.0741 50 BER1 -0.2164 0.4815 0.1495 -0.0327 51 BODG1 0.6487 -0.1912 0.2500 -0.2507 0.7506 -0.5489 1.1684 -0.5334 52 BODL1 -0.4228 -0.1463 0.2338 0.0147 -0.5723 -0.3198 0.7780 -0.1397 53 BREC1 0.7226 0.3983 0.1553 -0.2181 54 BUGE1 -0.3387 0.2175 0.2122 0.1656 -0.3653 0.5938 0.3290 -0.1236 56 BYCH1 0.4403 -0.4079 0.3146 -0.0403 0.4874 -1.0017 1.2266 -0.2317 57 CFYN1 -0.5799 -0.3119 0.1832 0.0449 -0.7675 -0.6768 0.5518 -0.0501 58 CHN1 -0.3094 0.0335 0.1518 0.0671 -0.4002 0.1070 0.5503 0.0901 59 CLON1 3.3234 0.6187 0.4663 0.0291 4.1512 1.1346 2.6187 0.0634 60 CLYD1 -0.2599 -0.4656 0.2058 -0.0444 -0.4007 -1.0915 0.8243 -0.2172 61 CON1 -0.5274 -0.0207 0.1590 0.1160 -0.6694 0.0172 0.4662 0.1460 62 COR1 -0.6436 -0.0682 -0.1516 0.1124 -0.8630 -0.1582 0.0683 1.0511 63 CWBY1 -0.3145 0.1367 0.0501 0.0182 -0.4271 0.3057 0.5005 0.3498 65 DIWA1 -0.4448 -0.2909 0.1878 -0.0152 -0.6077 -0.6592 0.6540 -0.1577 66 0011 -0.0727 -0.1556 0.3546 0.0965 -0.1142 -0.3530 1.0410 -0.3889 67 DOON1 -0.0530 0.1689 0.1099 0.0206 -0.0424 0.4142 0.3693 -0.0980 68 DUH1 -0.6675 -0.0240 -0.1115 0.0593 -0.8581 -0.0204 -0.0715 0.6351 69 DUL1 -0.3529 0.0687 0.1318 0.0564 -0.4702 0.1686 0.6074 0.1876 70 ENO1 -0.6896 0.0231 0.1372 0.2178 -0.8942 0.1143 0.5543 0.6335 71 EUN1 ·0.6716 0.2998 -0.3587 0.1176 -0.8791 0.6754 -0.3281 1.5963 72 FERN1 2.6373 0.2218 -0.1908 0.1500 3.3830 0.3834 0.3597 0.7305 73 FHl1 -0.0010 0.5469 -0.0284 0.1450 -0.0468 1.1734 0.7316 1.0814 74 FINLi 0.0874 0.4776 -0.2927 0.1005 0.1368 1.0523 -0.2826 1.0908 75 FLE1 -0.5811 0.2110 0.0069 0.1396 -0.6730 0.5915 ·0.1534 0.2702 76 GARN1 ·0.1034 -0.4075 0.1835 -0.0995 -0.1871 -0.9628 0.7200 -0.3330 77 GEIR1 0.8166 -0.2764 0.2518 -0.3151 0.9452 -0.7734 1.2701 -0.6088 78 GLAS1 -0.3134 -0.5226 0.2104 -0.0607 -0.4669 -1.2166 0.7768 -0.2761 79 GLFR1 1.7961 -0.6129 0.0994 0.7303 2.2468 -1.4500 0.9890 2.0303 80 GLYN1 -0.4122 0.0630 0.1096 0.0077 -0.5522 0. 1587 0.5769 0.1784 81 GOD1 0.1861 0.0916 0.4417 -0.0686 0.1812 0.1527 1.5325 -0.5483 82 GREENT1 -0.3159 0.1186 0.1662 0.0559 -0.4659 0.2356 0.9708 0.4482 84 GWYN1 -0.0713 -0.6325 0.1290 -0.0876 -0.1576 -1.4887 0.5878 -0.1191 85 GYN1 -0.4027 -0.0564 0.1938 0.0370 -0.4767 -0.0479 0.3134 -0.4099 86 HARR1 -0.3980 0.6127 -0.3964 0.1517 -0.5042 1.3708 ·0.3754 1.7484 87 HIR1 -0.2860 0.1947 0.1888 0.0218 90 HOWl1 0.3916 0.5300 0.0309 -0.1519 0.4974 1.1404 0.5481 -0.D251 Table 8 Continued. Sample Sample 157 sample data-set 138 sample data-set number code Axis 1 Axis 2 Axis 3 Axis 4 Axis 1 Axis 2 Axis 3 Axis 4 95 LAG1 -0.4552 -0.0583 0.2135 0.0721 -0.5784 -0.0845 0.5634 -0.1090 96 LAli -0.5617 -0.3206 0.2104 0.0812 -0.6908 -0.6406 0.2866 -0.3727 97 LAR1 1.2840 0.9481 1.4318 0.6788 1.6426 2.1511 3.9893 -1.0271 98 LCSL1 -0.3481 0.0380 0.1240 0.0882 -0.4943 0.0740 0.7797 0.4483 99 LCSU1 -0.3190 -0.1032 0.1651 0.0083 -0.4647 -0.2522 0.8338 0.1122 100 LDE1 -0.3972 -0.1312 0.1009 0.1502 -0.5018 -0.2500 0.3256 0.2910 101 LDE2 -0.5722 -0.2294 0.0309 -0.0474 102 LENY1 0.0026 -0.3578 0.2090 -0.1499 -0.0252 -0.8327 0.6281 -0.6155 103 LGR1 -0.5116 0.3669 -0.0391 0.2071 -0.5429 0.9657 -0.4091 0.3221 106 LLDU1 0.1968 -0.1088 0.3549 -0.0342 0.1935 -0.2929 1.2900 -0.3542 107 LLGH1 -0.4230 0.2795 0.0525 0.1576 -0.4984 0.7086 0.1727 0.4025 108 LOD1 -0.4564 -0.2491 0.2684 0.1334 -0.5722 -0.5036 0.5688 -0.2793 109 LOWT1 -0.4049 0.0315 0.1817 0.0695 -0.5601 0.0754 0.8338 0.3078 1 i 0 MABE1 0.4320 0.6860 0.0860 -0.0204 0.7559 1.6974 -0.5043 -1.1041 111 MACA1 -0.3587 0.2356 0.0031 0.1547 -0.4120 0.5982 0.0350 0.3999 112 MANN1 1.2868 0.2600 -0.0445 -0.0691 1.6005 0.4606 0.7085 0.5176 113 MINN1 -0.2684 0.7714 -0.5659 0.1837 -0.3181 1.7205 -0.7518 2.1448 114 MOAN1 0.1193 0.6873 0.0324 -0.0207 0.2346 1.5969 0.0875 -0.1530 115 MUCK1 0.1793 0.5478 -0.0148 0.1322 0.4209 1.3852 -0.6944 -0.4508 116 NAGA1 -0.6216 -0.0058 -0.0927 0.0543 -0.8179 0.0066 0.0979 0.6962 117 OCHl1 0.7002 0.4071 0.2224 -0.0490 1.0536 1.0125 -0.0266 -1.4011 118 PARC1 1.0438 -0.0143 0.4303 -0.4552 1.2079 -0.2340 1.9178 -1.1270 119 PENR1 -0.4384 0.1118 0.0118 0.0485 -0.5215 0.3201 0.0015 0.1102 120 RIEC1 -0.1921 0.2407 0.0200 0.1028 -0.1701 0.6173 -0.0981 0.0706 121 RLGH1 -0.6077 0.1102 0.1155 0.1826 -0.7534 0.3262 0.3230 0.3775 122 RLGH2 -0.6984 0.1351 -0.0786 0.1213 123 RONA1 1 .4558 0.2734 0.4664 0.0993 125 S101 0.0600 -0.5312 -0.2806 -0.1015 0.1902 -1.1108 -1.3009 -0.4745 126 S11 0.8223 -0.6709 -0.3789 -0.1850 1.1665 -1.4471 -1.4891 -0.3254 127 S111 0.5131 0.7182 -0.4354 -0.2479 0.8936 1.7773 -1.8247 -0.8881 128 S121 -0.2128 -0.6206 -0.0985 -0.2185 -0.2214 -1.3529 -0.6577 -0.8069 129 S131 -0.0324 0.1558 -0.1132 -0.1633 0.1218 0.5040 -1.0014 -1.0673 130 S141 -0.1486 -0.4699 -0.2083 -0.1224 -0.0580 -0.9410 -1.2831 -0.7979 131 S151 1.6496 -1.4784 -1.0020 0.8583 2.1129 -3.3329 -1.9708 4.4456 132 S161 0.3770 -0.2596 -0.2320 -0.2409 0.6247 -0.4882 -1.2533 -1.1299 133 S171 1.0401 0.3339 -0.0981 -0.7379 1.2769 0.6174 0.4494 -1.2025 134 S181 0.5886 0.1419 0.0646 -0.4657 0.7887 0.3061 0.1521 -1.4535 135 S191 0.1201 0.6652 -0.1762 -0.0156 0.4237 1.7252 -1.5134 -1.0002 136 S201 -0.4162 -0.1397 -0.1799 -0.0488 -0.4169 -0.1857 -0.9944 -0.4566 137 S21 3.2779 -1.2273 -1.2559 0.6987 4.2564 -2.7824 -2.5659 4.7998 139 S221 0.2929 1.6497 -0.5734 0.1067 0.3747 3.6315 -0.1663 2.4726 140 S241 -0.0728 -0.1874 -0.1574 -0.2674 0.0336 -0.3168 -1.0576 -1.1412 141 S251 0.8286 -0.6308 -0.3033 -0.2405 1.0640 -1.4707 -0.6515 0.1554 142 S261 1.5459 -0.1347 -0.3595 -0.1706 2.2845 -0.0933 -2.2978 -1.4738 143 S271 -0.3854 0.3472 -0.5051 -0.0211 -0.1823 1.0669 -2.6607 -0.6701 144 S281 -0.5060 0.1184 -0.3063 0.0091 -0.5049 0.4265 -1.3086 -0.0141 145 S291 -0.4490 0.0631 -0.2079 -0.0868 -0.5038 0.2316 -0.7028 -0.0224 146 S301 -0.3156 -0.0727 -0.2321 -0.1110 -0.2352 0.0038 -1.4078 -0.8056 147 S31 0.0824 -0.5670 -0.1177 -0.2275 0.1797 -1.2249 -0.7761 -0.9447 148 S311 0.2538 0.2265 -0.1919 -0.2759 0.4604 0.5918 -0.9577 -1.0020 Table 8 Continued. Sample Sample 157 sample data-set 138 sample data-set number code Axis 1 Axis 2 Axis 3 Axis 4 Axis 1 Axis 2 Axis 3 Axis 4 153 S81 -0.1479 -0.8408 -0.0382 0.0528 -0.1857 -1.8751 -0.2424 0.0810 154 S91 1.1299 0.2711 -0.1630 -0.7587 1.7083 0.7573 -1.6814 -3.1043 155 SCOATT1 -0.3755 0.3596 -0.0865 0.2226 ·0.5194 0.7938 0.4549 1.3799 156 SKAK1 0.3259 0.3789 0.0808 -0.1798 157 SKAK2 0.0050 -0.0997 0.2179 -0.2361 158 SKE1 0.0390 0.5764 -0.2547 0.0923 0.1611 1.3764 -0.7114 0.6028 159 SKE2 -0.4544 0.2054 -0.0890 0.0599 160 SKOMAKV1 -0.8156 0.0209 -0.0362 0.1696 161 STRO1 -0.1199 1.0655 -0.6244 0.0735 -0.0371 2.4521 -1.3273 1.5831 162 TANN1 -0.2140 0.3753 -0.0190 0.1488 -0.2871 0.8465 0.4483 0.8230 164 TECW1 0.3373 -0.1344 0.3597 -0.2683 0.3648 -0.3805 1.2905 -0.9536 165 TINK1 -0.4116 -0.4615 0.1378 -0.0693 -0.4956 -0.9809 0.0326 -0.7185 166 TROO1 -0.2796 0.4546 -0.1120 0.1439 -0.3391 1.0435 0.0758 0.8940 167 UAl1 -0.1524 -0.0393 0.1369 0.0263 -0.2627 -0.1440 0.8966 0.3189 168 UIS1 -0.1121 -0.4680 0.1587 0.1328 -0.0979 -0.9902 0.0980 -0.4431 169 URR1 0.5517 0.1436 -0.0936 -0.2605 170 VAL1 -0.6271 0.1465 0.0727 0.2162 -0.7729 0.4121 0.2243 0.5361 172 WHIN1 1.1587 -0.1114 -0.2475 0.2437 173 WHIN2 0.4714 -0.6510 0.0739 -0.0485 174 WHIT1 1.8959 -0.0859 0.5932 0.5425 175 WOOD1 1.6483 -0.0054 0.1100 -0.0318 2.0838 -0.1200 0.8519 0.1595 176 WOOD2 1.0748 -0.4202 0.0223 -0.0802 177 YBl1 -0.2904 0.1841 0.1036 0.0306 -0.4034 0.4051 0.6824 0.3178 178 YGAD1 -0.2852 -0.2484 0.1382 0.0182 -0.3894 -0.5575 0.5169 -0.0319 In order to incorporate DOC into the analysis, the data-set was further restricted to the 138 samples with DOC data. The correlation biplot of an analysis of the correlation matrix is shown in Figure 8. The summary statistics are shown in Table 9 while the variable loadings and sample scores for this analysis are listed in Table 8. Table 9 Eigenvalues and cumulative variance accounted for in a PCA of the 138 sample x 10 chemical variable data-set Eigenvalue Cumulative variance represented Axis 1 0.574 57.4% Axis 2 0.176 75.0% Axis 3 0.106 85.6% Axis 4 0.050 90.6% As with the PCA of the 157 sample data-set, the PCA of the 138 sample data-set represents an efficient summary of the data with 75% of the variance of the data-set accounted for by the first two axes (Figure 8). The dominant trends appear to be the same as the 157 lake analysis, namely high to low pH, calcium, etc. Samples with high DOC values lie mid-way along axis 1. The inclusion of a limited range of catchment characteristics albeit run as passive variables, suggests that conifer-forested catchments tend to have high DOC values, and that deep, high-altitude lakes that have unforested catchments have low DOC values (Figure 8). The most acid lakes show no strong correlations with any catchment characteristics. NUMERICAL ANALYSIS OF THE DIATOM DATA Basic statistics A total of 277 diatom taxa was recorded in the 178 samples within the SW AP training set with a relative abundance of greater than 1 % in at least one sample. A total of 210 taxa was present in fewer than 25% of all samples. Appendix B lists full names, authorities, codes for all taxa, and environmental optima estimated by weighted averaging regression for selected taxa. The principal patterns of floristic variation in these data have been explored using detrended correspondence analysis (DCA), whereas the patterns of floristic variation in the data that can be explained statistically by the measured environmental variables are detected by means of canonical correspondence analysis (CCA). Detrended correspondence analysis DCA (Hill and Gauch 1980) is one of the most powerful and numerically robust ordination or indirect gradient analytical techniques currently available (Jongman et al. 1987). It provides a good approximation to the problem of ordinating samples and species in two or more dimensions when the underlying model of species responses to their environment is unimodal, when the data contain many zero values for samples at which a species is absent, and when the number of species is large (ter Braak and Prentice 1988). In the DCA presented here (Table 10, Figures 9 and 10), detrending was by segments with nonlinear rescaling of axes. Rare species were downweighted (ter Braak 1987). Not surprisingly with 277 taxa and many zero values in the data, the DCA axes do not capture a large proportion of the cumulative variance of the species data (Table 10). Despite the low (14.6%) of the variance or "inertia" of the species data explained by two dimensions, the ordination plot is still infonnative. Low percentages explained commonly result from noisy data containing many zero values. Table 10 Eigenvalues and cumulative variance accounted for in a DCA of the 178 diatom samples Eigenvalue Cumulative variance represented Axis 1 0.598 9.3% Axis 2 0.338 14.6% Axis 3 0.256 18.6% Axis 4 0.187 21.5% It is not possible statistically to relate these axes to the measured environmental variables by, for example, regression because only one variable (pH) is available for all 178 samples (Table 5). Preliminary interpretation of the DCA axes is therefore attempted solely on the basis of the known ecological preferences of the major diatom taxa. Axis 1 appears to be partly a pH axis with alkaline species and sites positioned on the right-hand side of the plot, e.g. Aulacoseira ambigua (Au002a on Figure 10), Aulacoseira granulata var. angustissima (Au003d), Cyclostephanus dubius (CcOOla), Hagasjon (S26 on Figure 9), Vagsjoarna (S25), and Glasfryn (GLAS), and acid species such as Tabellaria binalis (Ta003a), Semiorbis hemicyclus (SeO0la), Eunotia bactriana (Eu046c), Aulacoseira tethera (Au023a), and Surirella biseriata var. biseriata (Su004a) and acid lakes, e.g. Llyn y Bi (YBI), Ljosvatn (LJOSV), and Scoat Tarn (SCOAT) positioned to the left. No interpretation is presented for axis 2 or later axes. Interpretation of the diatom data in relation to the available environmental ,-- I I 7 "° I u:, N ◄ ~ ln ◄ ◄ ◄ (f) ◄ 0 !;;: -, -' 0 ◄ u I L_ ◄ (f) -◄ 0 ro >-~ L~ I N I lr) ~ 0 ~ N I I z SlXl? VJO . Figure 9 Detrended correspondence analysis (DCA) plot of the 178 diatom samples on axes 1 and 2. See Table 2 for site codes. Only selected samples are labelled. ~ r.l.l -~ ?< co < u Q AA .. .. "' N o• 0 :, <( ,. . . . . . ... . • .. ~-0 0 ~ . tl "' 0 0 ::, <( . .. . . • ;E. • 0 a "' (/) . . . . . . . . . . . . . . .. .. . . .. 'I, . . . . ... . . . . . • e • e . . "' "' N 0 ::, . <( 0 N I 0 N I < u 0 Figure 10 Detrended correspondence analysis (DCA) plot of the diatom taxa in the 178 samples on DCA axes 1 and 2. See Appendix B for taxon codes. Only selected taxa are labelled. data is more readily attempted by means of the direct gradient technique of canonical corres1xmdence analysis. Canonical correspondence analysis Like DCA, canonical correspondence analysis (ter Braak 1986) derives a set of ordination axis scores for species and for samples. For the first axis, species scores and sample scores are chosen to maximise the correlation between them. For subsequent axes, the sample and species scores are also maximally correlated but are chosen to be uncorrelated to species and sample scores on previous axes. In CCA the sample scores are also constrained to be simple linear combinations of the available environmental variables. A CCA ordination diagram can thus include not only the sample and site scores but also vectors representing the different environmental variables. In such a diagram, the vector for an environmental variable points in the direction of maximum variation of that variable across the diagram, and its length is proportional to its importance. Environmental variables with long vectors are more strongly correlated with the ordination axes than those with short vectors, and are more closely related to the patterns of biological variation displayed in the diagram (ter Braak 1986, 1987). A CCA diagram thus simultaneously displays the major patterns of variation in the diatom assemblages, as far as these can be related to the available environmental variables and the major patterns in the weighted averages of the individual species in relation to the environmental variables. CCA assumes a common unimodal response model for all species in relation to the measured environmental gradients. The same 138 samples that were used for in the PCA of the chemical data were used here. The CCA presented is similar to that shown in Birks et al. (1990b) except that here the environmental data were not subjected to logarithmic transfonnation, all 138 samples were included, and physical variables such as maximum depth and altitude and catchment attributes such as forest status and forest type were included. In both analyses rare species were downweighted. The results using untransfonned environmental data are very similar to those obtained with logarithmically transfonned data. The CCA results are shown in Figures 11 and 12. The CCA plot can be interpreted in a broadly similar manner to that of the PCA biplot with small angles between those environmental variables that are highly correlated, and with variables with the highest variance and hence often being the most important having the longest arrows. Tables 11 and 12 present a summary of the CCA statistics while Table 13 presents the full species and site scores and environmental variable loadings. The CCA of the 138 data-set captures the variance in the species abundance-environmental relationship well with over 46% of the variance of the species weighted averages being accounted for by the first two axes. Table 11 Summary statistics of the environmental variables included in the CCA of the 138 diatom-environmental data-set Weighted Standard Variance mean deviation inflation factor pH 5.5598 .7050 3.7455 Conductivity 43.6492 26.2270 46.0034 DOC 3.8761 3.2846 3.0576 Ca 101.5286 107.7411 27.4217 Mg 64.9368 49.5752 15.5114 K 9.9082 6.9200 3.4507 S0 4 105.1257 64.1189 15.6147 Cl 182.5068 141.3208 22.2756 Alkalinity 46.2302 98.4989 20.0193 Total Al 125.3340 94.9714 2.5304 Altitude 352.1492 262.1623 2.3075 Unforested .3984 .4896 46.5526 Forested .5938 .4911 55.5266 Conifer F. .57(1) .4949 12.5739 Deciduous F. .0228 .1494 .0000 Maximum depth 15.7617 11.2323 1.4453 CCA axes 1 2 3 4 Eigenvalues .483 .248 .179 .141 Species-environment correlations .932 .847 .783 .788 Cumulative percentage variance of species data 7.8 11.8 14.6 17.0 of species-environment relationship 30.4 46.1 57.4 66.7 (F. = forest) The CCA results (Tables 11 and 12, Figures 11 and 12) clearly show that axis 1 is strongly related to pH (r = 0.84), along with conductivity (0.58), calcium (0.78), and alkalinity (0.69), and negatively correlated with altitude (-0.45), maximum depth (-0.35), and total aluminium (-0.21). Thus high pH lakes with associated high alkalinity, calcium, potassium, magnesium, and conductivity contrast with low pH lakes of low alkalinity, calcium, potassium, magnesium, and conductivity. The low pH lakes tend to be at high altitudes, to be deep, to have unforested catchments,and to have high total aluminium. Acid lakes such as Loch Enoch (ENO on Figure 11), Hovvatn (4.1), and Ovre Brandsvatn ~ tlJ 1""4 ~ t'lj < ;) ;) 4 ,----·-·· ... I ··~ 88.2 , i 3 .. ......j ,. ,. ,. ,. .. ,. Alt 2 I ... \ j r ! ,.,. \ Max dep ,. ,. A ,. A 1 ,. ! ,. ,. pH ---I Unfor 111 ,. ,. A ,. ,. ,. ,. A ,. ,. ,. ,. 0 ,. ,. ,. Ca ... ,. ,,. FERN 7 ... ,. I ENO ,. ,. I I "' ,. I 4.1,. ,. ,. ,. ,. J I>,. "" ,. ,. " "' S2 -1 Tot Al t. ... "' ,. ,. " ... ,. ... ,. ... ,. ,. ... ,. ... " DOC .. " .. ... .. ,. ... ... ,. A S26 ,. -2 -2 -1 0 1 2 3 4 CCA . 1 axis Figure 11 Canonical correspondence analysis (CCA) plot of the 138 samples on CCA axes 1 and 2 in relation to the 16 environmental variables included in the analysis. Continuous environmental variables are shown as vectors from the plot origin, nominal variables are plotted as centroids and shown as solid squares. Only selected samples are labelled. See Table 2 for site codes. Abbreviations follow the caption for Figure 7. M fll -~ Iii< Cd < C) C) 4 Pi139a . 3 Su002a Pi055a L . . Alt 2 . . .. . . . . . pH 1 St010a Unfor 111 . . . . ... • 8 . . . Alkalinity : . · ... . 0 Eu048a0 . Ca . . . Ta003a •• " • • • • ·. . : . .... Fr008a .: . , . . . . . . .... "' Mg Au003b . "' . . . . .. . . Cc001a .. . -1 . . Cl Tot Al .. Fr9991 . Eu031a DOC -2 -2 -1 0 1 2 3 CCA axis 1 Figure 12 Canonical correspondence analysis (CCA) plot of all diatom taxa included in the CCA of the 138 samples on CCA axes 1 and 2 in relation to the 16 environmental variables included in the analysis. The diagram is constructed in the same way as Figure II. Only selected taxa are labelled. See Appendix B for taxon codes. Abbreviations follow the caption for Figure 7. 4 Table 12 Weighted correlations between ordination axes and the environmental variables in the CCA of the 138 sample data-set. The species-environment correlations are 0.932, 0.847, 0.783 and 0.788, respectively. Species axis 1 1.00 Species axis 2 0.00 1.00 Species axis 3 -0.02 0.02 1.00 Species axis 4 -0.06 -0.03 -0.00 1.00 Env. axis 1 0.93 0.00 0.00 0.00 1.00 Env. axis 2 0.00 0.85 0.00 0.00 0.00 1.00 Env. axis 3 0.00 0.00 0.78 0.00 0.00 0.00 1.00 Env. axis 4 0.00 0.00 0.00 0.79 0.00 0.00 0.00 1.00 pH 0.84 0.31 -0.09 -0.02 0.90 0.36 -0.11 -0.02 1.00 Conductivity 0.58 -0.20 0.08 0.25 0.62 -0.23 0.10 0.32 0.37 DOC 0.45 -0.36 0.37 -0.30 0.48 -0.43 0.48 -0.38 0.22 Ca 0.78 0.03 0.29 0.07 0.84 0.03 0.38 0.09 0.70 Mg 0.66 -0.12 0.11 0.27 0.71 -0.14 0.14 0.34 0.49 K 0.61 -0.09 0.31 0.25 0.66 -0.10 0.40 0.31 0.50 SO4 0.56 -0.19 0.14 0.00 0.60 -0.23 0.18 0.00 0.33 Cl 0.32 -0.20 -0.17 0.35 0.34 -0.24 -0.22 0.45 0.17 Alkalinity 0.69 0.11 0.28 0.27 0.74 0.13 0.36 0.34 0.67 Total Al -0.21 -0.25 0.19 0.09 -0.23 -0.30 0.24 0.12 -0.44 Altitude -0.45 0.61 0.11 -0.09 -0.49 0.72 0.14 -0.11 -0.15 Unforested -0.32 0.21 -0.14 0.21 -0.35 0.25 -0.17 0.26 -0.14 Forested 0.34 -0.20 0.12 -0.22 0.37 -0.23 0.15 -0.28 0.17 Conifer F. 0.33 -0.19 0.16 -0.26 0.35 -0.22 0.21 -0.33 0.14 Deciduous F. 0.05 -0.02 -0.16 0.13 0.05 -0.02 -0.21 0.17 0.09 Max. depth -0.35 0.47 0.06 -0.17 -0.37 0.56 0.08 -0.21 -0.17 1.00 0.40 0.70 0.93 0.74 0.80 0.86 0.62 0.18 -0.58 -0.28 0.25 0.25 0.00 -0.42 Spec Spec Spec Spec Env. Env. Env. Env. pH Cond. axis 1 axis 2 axis 3 axis 4 axis 1 axis 2 axis 3 axis 4 DOC 1.00 Ca 0.49 1.00 Mg 0.41 0.71 1.00 K 0.47 0.76 0.76 1.00 SO4 0.47 0.66 0.83 0.64 1.00 Cl 0.12 0.32 0.79 0.52 0.56 1.00 Alkalinity 0.31 0.91 0.61 0.70 0.41 0.31 1.00 Total Al 0.30 -0.o? 0.11 0.06 0.29 0.14 -0.14 1.00 Altitude -0.46 -0.36 -0.54 -0.44 -0.57 -0.50 -0.23 -0.25 1.00 Unforested -0.50 -0.32 -0.29 -0.23 -0.45 -0.06 -0.21 -0.35 0.50 1.00 Forested 0.51 0.33 0.28 0.23 0.43 0.04 0.22 0.28 -0.47 -0.98 Conifer F. 0.54 0.32 0.28 0.24 0.42 0.04 0.22 0.31 -0.43 -0.94 Deciduous F. -0.12 0.02 -0.01 -0.05 0.02 0.02 0.00 -0.11 -0.14 -0.12 Max. depth -0.28 -0.29 -0.38 -0.33 -0.36 ·0.35 -0.23 ·0.19 0.38 0.04 DOC Ca Mg K SO4 Cl Alk. Tot. Al All. Unforest Conifer F. 0.95 1.00 Deciduous F. 0.13 -0.18 1.00 Max. depth -0.02 -0.01 -0.04 Forested Conif. Decid. (Env. = environmental, max.= maximum, F. = forest) Table 13 Scores for samples and diatom taxa and environmental variable loadings for the CCA of the 138 sample data-set. Sample codes follow Table 4, species codes follows Appendix B. Axis 1 Axis 2 Axis 3 Axis4 Axis 1 Axis 2 Axis 3 Axis 4 Environmental variable loadings Sample scores pH 0.9034 0.3620 -0.1136 ·0.0220 6.21 -1.2255 -0.0560 0.9862 0.0859 Conductivity 0.6216 -0.2325 0.1041 0.3228 65.21 0.4235 1.5593 -1.5094 -2.6249 DOC 0.4845 -0.4289 0.4753 -0.3758 80.11 -1.0089 0.0326 0.2277 0.3841 Ca 0.8375 0.0343 0.3754 0.0941 81.21 -1.1516 -1.3351 1.0141 0.0595 Mg 0.7076 -0.1440 0.1429 0.3446 82.11 · 1.1343 -0.3871 1.0484 -0.3473 K 0.65~9 -0.1027 0.3995 0.3137 83.11 -1.5118 2.9970 1.2716 ·0.0912 SO4 0.6029 ·0.2271 0.1779 0.0020 86.11 -0.9211 -0.4700 0.6921 -0.6041 Cl 0.3437 -0.2398 ·0.2229 0.4452 87.21 ·0.8612 0.4848 0.5990 -1.0980 Alkalinity 0.7441 0.1305 0.3579 0.3401 88.21 -1.5960 4.2539 1.8824 -0.8744 Total Al -0.2286 -0.2970 0.2373 0.1187 89.11 -0.0140 2.4163 -0.5482 -1.2441 Altitude -0.4868 0.7165 0.1412 -0.1146 9.11 -1.2212 ·0.4524 1.2701 -0.2514 Unforesied -0.3466 0.2499 -0.1735 0.2639 ACH1 -0.4668 -1.0056 -0.3973 -0.2629 Forested 0.3684 -0.2327 0.1492 -0.2762 ARR1 0.5146 -0.0067 -1.2303 1.0377 Conifer F. 0.3498 -0.2239 0.2105 -0.3255 ARTH1 2.0141 0.3096 1.0324 1.6511 Deciduous F. 0.0523 -0.0234 -0.2071 0.1705 BARE1 1 .6819 0.8016 -0.9070 0.1328 Max. depth -0.3713 0.5600 0.0784 -0.2114 BARL1 i.0772 0.0491 -0.9091 0.8466 BODG1 1.3430 1.5162 -2.1564 -2.5975 Sample scores BODL1 -0.3949 -0.5468 -0.6654 0.2639 BUGE1 -0.5194 -1.3916 -0.0129 0.2552 1.21 -1.1471 -0.5617 0.6353 0.3855 BYCH1 1.6687 0.6397 -1.3262 1.7077 10.21 0.1685 0.1936 -0.6498 -0.6172 CFYN1 -0.4280 -1.2173 -0.6664 -0.1417 111.11 0.1561 1.1350 -0.7459 -1.1987 CHN1 -0.2034 -0.4965 -0.8927 0.6296 113.21 0.1728 0.5094 -0.8235 -0.7099 CLON1 2.2385 0.8853 0.5155 2.6972 115.11 -0.4269 -0.3134 0.2246 -1.2361 CLYD1 0.5683 -0.3670 -1.4655 0.1425 12.11 -1.1415 1.8089 1.0289 -0.5172 CON1 -0.1584 -0.6425 -0.8268 -0.6600 15.11 -0.6783 0.0564 -0.0816 -0.2813 COR1 -1.5236 2.9395 1.1582 0.7675 17.21 -0.5435 -0.2769 0.1103 -0.8524 CWBY1 -0.8200 -0.5668 -0.4101 0.8955 18.11 -0.6896 2.5975 0.5552 -0.2183 DIWA1 0.0203 -0.1791 -1.0860 0.3516 19.21 -0.3891 2.4497 -0.1441 -2.2105 0011 -0.1735 -0.0830 -0.8156 0.8699 2.11 -0.7273 0.2427 0.1554 -0.2671 DOON1 0.3936 -0.3670 0.2425 0.6098 20.11 0.2888 2.8203 -0.1651 -1.4961 DUH1 -1.2242 1.0827 0.4939 1.0607 21.11 -0.2000 -0.0794 -0.3185 -0.4726 DUL1 -0.5851 -0.5873 -0.2583 0.4362 3.11 -1.1254 -1.1334 0.9413 0.1623 ENO1 -1.2261 -0.5873 0.5281 0.4753 34.11 -1.4422 2.7048 1.5552 -0.4943 EUN1 -1.4776 2.3548 0.9548 2.0384 4.11 -1.2384 -0.6001 1.8519 -0.2491 FERN1 2.5696 0.0915 4.5015 3.8387 42.11 -1.0566 0.6691 0.4842 -0.1637 FHl1 -1.1347 0.0719 -0.1302 2.1341 44.21 1.0556 2.5510 0.1396 -1.2088 FINL1 0.3854 -0.3361 :0.7594 0.4544 49.11 -0.7809 1.3701 0.2307 -0.4350 FLE1 -0.8873 -1.4799 -0.1210 0.2352 58.11 -1.1816 0.8461 0.8887 -0.2904 GARN1 1.1352 1.5006 -2.4793 -3.0029 59.11 -1.3152 3.9592 1.8019 -0.9078 GEIR1 1.2322 0.5953 -2.5188 0.9907 Table 13 Continued. Axis 1 Axis 2 Axis 3 Axis 4 Axis 1 Axis 2 Axis 3 Axis 4 Sample scores Sample scores GLAS1 S191 0.1544 -1.2776 0.9721 -2.0814 GLFR1 2.2882 0.0246 1.2022 3.6458 S201 -0.3589 -0.8067 -0.0120 -0.6478 GLYN1 -1.0532 -0.2031 0.0000 0.6694 S21 3.2049 -0.6306 7.2128 1.7697 GOD1 0.4605 -1.0976 -1.2519 -0.8773 S221 -1.4422 -1.0326 1.4979 0.8276 GREENT1 -0.8819 -0.4223 0.0419 0.6327 S241 0.5333 0.0324 -0.0425 -1.3076 GWYN1 1.4296 0.7857 -2.0587 2.2169 S251 1.9078 1.0080 -0.2704 -1.8208 GYN1 -0.1273 -1.4752 -0. 7044 -0.7741 S261 3.3389 -1.8518 8.6923 -5.3154 HARR1 -0.3355 -0.2233 -0.7051 0.4806 S271 -0.7847 -1.2494 0.6840 -0.5911 HOWl1 1.3018 1.2509 -2. 1252 -1.3549 S281 -0.8558 -1.4173 0.6950 -0.2853 INVA1 1.7270 0.4914 0.3179 2.0218 S291 -0.1962 -0.7128 0.6257 -0.2689 IRD1 0. 1282 -0.2612 -0.9386 -0.3910 S301 -0.0349 -0.6498 0.0919 -0.9483 LAG1 -1.0781 -1.7110 -0.2040 0.7220 S31 2.2321 0.6759 1.9231 0.1556 LAl1 -0.3487 -0.5441 -0.5827 -0.2191 S311 0.0043 -0.5514 0.0070 -0.9578 LAR1 -0.5441 -1 .0894 -0.5702 0.4182 S41 0.6489 -1.6628 1.4248 -2.8840 LCSL1 -0.9772 0.3340 -0.0239 1.9584 S51 2.6145 -0.5579 4.2589 -0.4855 LCSLU1 -1.0640 1.0108 -0.2573 3.4904 S61 0.6436 -0.9340 -0.3674 -1.2659 LDE1 -0.0451 -0.0807 -1.0411 0.3703 S71 1.4897 0.3028 0.6374 -2.7576 LENY1 0.8174 0.1820 -1.6711 0.2654 S81 1.0228 0.1024 1.4228 -1.0042 LGR1 -1.0373 -1.1337 0.0488 0.7541 S91 1.3904 -0.7665 1.4638 -1.9251 LLDU1 0.9098 -0.3335 -1.2841 0.9025 SCOATT1 -1.4821 1.9646 1.1985 1.1321 LLGH1 -0.6266 -1.1489 -0.3428 0.1284 SKE1 0.3369 -0.8150 -0.8077 -0.6101 LOD1 -0.6671 -1.0323 -0.4304 0.4063 STRO1 -0.4286 -0.4988 -0.4410 0.2891 LOWT1 -0.9183 -0.5482 -0.2469 1.2544 TANN1 -1.2181 -1.1639 0.6951 0.9731 MABE1 0.6508 -0.9521 0.0222 -0.3539 TECW1 0.3855 -0.7076 -1.5762 0.1959 MACAi -0.2307 -0.7373 -0.7929 -0.0048 TINK1 0.3329 -0.6169 -1.4092 -0.4088 MANN1 1.7710 0.8694 -1.3416 3.4906 TROO1 -0.7679 -0.5100 -0.1374 0.4022 MINN1 0.3192 -0.2579 -1.0182 0.2420 UAl1 -1.0791 1.8543 0.1875 0.9741 MOAN1 0.9592 -1.4696 0.3610 -0.5984 UIS1 0.8414 0.1170 -1.0813 1.2269 MUCK1 0.2229 -0.6128 -0.4098 0.3991 VAL1 -1.0127 -1.0700 0.1326 0.4675 NAGA1 -1.2566 1.8234 0.7883 1.6440 WOOD1 2.2146 0.3460 1.6022 3.2482 OCHl1 0.8494 -0.7185 -0.1555 0.2797 YBl1 -1.4369 -1.6170 0.6667 1.9031 PARC1 0.9579 0.3835 -2.2672 0.5901 YGAD1 0.0760 -0.7692 -1.3832 -0.0943 PENR1 -0.8996 -1.5666 0.0912 0.3248 RIEC1 0.2671 -0.2951 -0.4266 0.4924 Species scores RLGH1 -0.9942 -1.3425 0.0509 0.4663 S101 1.1769 0.2943 -0.3208 0.0906 AC001A 1.6899 -0.1795 1.5185 1.5867 S11 2.1663 -0.4443 2.7228 -0.7423 AC002A 1.3914 0.3388 -0.1467 1.1984 S111 0.3536 -0.7962 0.2809 -1.2691 AC004A 0.789 0.0113 -0.9852 -0.2666 S121 1.3345 1.1996 -0.8224 -2.501 i AC013A 0.9944 0.3003 -0.4687 0.5173 S131 0.1261 -1.4548 0.6285 -1.5302 AC014A -0.7764 -0.8351 -0.147 0.0952 S141 1.0874 0.4129 0.6541 -1.5422 AC0148 -0.6728 0.2492 -0.0227 0.4389 S151 1.9793 1.3815 0.8897 1.2983 AC014C -0.8404 1.3334 0.3423 -0.1842 S161 2.1517 -0.5117 3.2695 -0.8750 AC017A -0.4366 0.9487 -0.2655 -0.2601 S171 0.7911 0.3159 -0.2482 -1.9843 AC018A 0.8966 0.3299 0.7267 -0.5418 S181 0.5154 -1.3095 -0.0136 -2.0656 AC019A -0.0461 1.8739 -0.1859 -0.675 Table 13 Continued. Axis 1 Axis 2 Axis 3 Axis 4 Axis 1 Axis 2 Axis 3 Axis 4 Species scores Species scores AC022A -0.8934 1.0497 0.2627 0.1962 AU9983 1.4653 -0.0343 1.0601 0.1300 AC025A 1.0026 0.0521 0.1306 -0.2750 AU9984 0.4651 -0.7769 0.9543 -1.2948 AC028A -0.0873 0.4214 -0.5534 0.5572 AU9987 0.4662 -0.5886 0.6692 -0.5432 AC029A -0.1956 1.2400 0.0517 -0.0506 AU9988 -0.6335 -0.2560 0.0643 0.3239 AC030A 0.7973 -0.1837 -0.1701 0.8590 BR001A 0.4828 0.1031 -0.5508 -0.0190 AC034A 0.1173 1.3782 -0.2199 -0.2014 BR003A -0.7787 -0.4764 0.5013 0.1473 AC035A 1.1584 -0.3236 0.3864 -0.6356 BR004A -0.1307 -0.5056 0.0891 -0.3544 AC039A 0.8483 1.1260 0.1301 -0.5480 BR005A 1.4196 0.5004 0.9003 0.2126 AC042A 0.4621 0.1834 -0.7294 0.1896 BR006A -0.2876 -0.1699 -0.0470 0.0521 AC044A -0.7153 1.5517 0.3013 -0.4542 BR9997 0.0815 -0.6336 0.1409 -0.2974 AC046A 0.2070 -0.2669 -0.2009 -0.1041 CA018A 1.2971 0.5499 0.5456 0.0118 AC048A -0.2426 0.5060 -0.4113 0.2841 CC001A 3.5205 -0.6385 5.7894 2.5756 AC9964 i.0190 0.1866 -1.6164 0.2052 CM004A 1.0637 0.3120 -0.1595 0.5018 AC9965 -0.5441 1.8883 -0.0912 0.2523 CM010A -0.3728 -0.2709 -0.2461 0.1346 AC9968 -0.8644 0.8802 0.2628 0.7370 CM013A 1.5485 0.2330 ·0.2750 0.3461 AC9969 0.6456 0.1851 -i.2039 0.0431 CM014A -0.6358 0.1504 -0.2602 0.8818 AC9975 -0.7954 -0.0413 -0.0414 0.4196 CM015A 0.9905 1.3333 0.5498 0.0402 AC9996 0.4068 0.2414 -0.9895 0.2815 CM015B 0.1895 0.8767 -0.5612 0.4271 AM001A 0.2296 1.4951 -0.5444 -0.6865 CM017A -0.6678 0.3027 0.1392 -0.0302 AM001B 2.0718 0.5413 0.2810 1.9810 CM020A 0.1751 0.1159 -0.3104 0.1169 AM0010 1.2727 0.0521 0.3781 0.3339 CM031A 0.6091 0.3567 0.1409 0.4928 AS001A i .9559 0.1141 1.2662 0.9593 CM031C -0.7942 0.8607 0.5024 -0.2224 AS003A -0.3820 -0.2260 0.1476 0.0699 CM038A 1.7701 0.8351 -0.4588 -0.9251 AT001A -0.2538 -0.9039 0.6460 -0.6040 CM048A 0.2781 -0.1382 -0.3317 -0.1613 AU001C 0.2428 -0.4280 0.8089 -0.7820 CM050A 1.9456 1.3046 2.5753 0.8923 AU002A 2.0185 -0.6951 2.3578 -1.5464 CM051A 0.7304 0.2687 0.1119 -0.2407 AU003B 3.4368 -0.6324 5.5688 2.3286 CM052A 1.1110 0.4021 0.1356 0.2407 AU003D 2.8253 -0.6595 4.2602 1.4549 CM101B 1.5050 0.5467 1.7817 0.1523 AU004A -0.1396 0.3659 0.6744 -0.8001 CM9989 -0.2803 -0.9832 0.6341 -0.3240 AU0048 0.6408 -0.1681 0.2916 -0.5637 CM9995 -0.6088 -0.6766 ·0.6281 0.6764 AU004C 1.0122 -0.6119 0.0806 -0.7998 CO001A 2.0440 0.5675 1.5978 2.2770 AU004D -0.3524 -0.4759 -0.4553 0.3050 C0001B 1.8583 -0.1782 0.8582 2.5966 AU005A ·0.9096 1.4167 0.5598 -0.5133 CY001A 1.7893 0.0776 0.8165 -0.0609 AU005B 1.2562 -0.7694 0.9576 -1.4014 CY002A 2.0824 0.4612 2.6205 2.7430 AU005D 0.8397 -0.9422 1.0091 -1.3301 CY003A 2.1145 0.4705 2.9871 2.8811 AU005E -0.9348 0.6500 0.5300 0.0533 CY004A 1.1053 -0.4011 0.6230 -0.7413 AU005J 0.5691 -0.3965 -1.4348 0.6434 CY007A 1.4767 0.7214 0.9250 -0.4488 AU005L 2.1700 -0.6461 0.5880 -1.4765 CY010A 1.3850 0.4074 -0.4482 -0.8744 AU009A 0.9369 ·0.8632 -0.1322 -0.2217 CY9991 0.6017 0.5226 -0.4854 -0.6902 AU009B 0.1715 -0.9776 1.2412 -1.1978 DE001A 1.4473 0.4164 -0.3033 0.5709 AU010A -0.1366 -0.7201 0.0342 -0.2616 DT002A -0.0771 0.7017 -0.7876 -0.1078 AU010B -0.2670 -0.5542 0.3032 -0.0429 DT003A 1.1811 0.2373 -0.3030 1.7729 AU014A 0.4788 -0.6774 -0.0070 -0.8122 DT004B 1.9895 0.4393 2.4925 2.6469 AU022A 3.2431 -0.6231 5.3677 2.1436 EU002A -0.0600 -0.1388 -0.0377 -0.0259 AU023A -1.1026 1.0837 0.2047 1.3545 EU002B ·0.1970 -0.1557 -0.0692 0.0781 Table 13 Continued. Axis 1 Axis 2 Axis 3 Axis 4 Axis 1 Axis 2 Axis 3 Axis 4 Species scores Species scores EU002D -0.0867 -0.5482 -0.2298 0.0210 FR006A 1.2592 0.5120 0.7383 -0.2438 EU002E 0.1141 -0.4925 -0.3146 0.2006 FR007A 1.1568 0.3497 0.3446 1.0980 EU002K -0.2968 -0.4065 -0.0457 0.0636 FR008A 3.3606 -0.3173 4.9776 2.9053 EU003A -0.3385 0.1925 0.5987 0.2130 FR009F 0.4035 0.5858 -1.0782 0.2434 EU004A -0.5271 0.1042 0.1646 -0.1855 FR010A 0.1059 -0.5736 0.2689 0.0103 EU009A -0.5928 0.1885 0.0626 0.0118 FR011A 0.7202 -0.1348 0.2391 -1.0642 EU009C -0.2451 -0.9517 0.6651 -0.3686 FR015A 0.3354 -0.6579 1.0746 -0.9732 EU011A -0.3908 -0.6246 -0.2106 0.1964 FR018A 1.7816 -0.3498 0.9768 0.7719 EU013A 0.0956 0.1737 -0.3700 0.3136 FR9991 -0.7581 -1.0974 0.5035 -0.0288 EU014A -1.0547 -0.4261 0.7125 0.0615 FU002A -0.3516 -0.2884 0.2166 -0.1877 EU015A -0.6335 -0.0508 -0.1342 0.5919 FU002B -0.3009 -0.2927 0.0823 -0.2021 EU016A -0.3102 -0.4595 0.0187 -0.1364 FU002F -0.1804 -0.5761 -0.0184 -0.1407 EU017A 0.2507 0.0443 -0.3995 -0.1606 GO003A 0.4344 ·0.2829 -0.2046 0.1926 EU019A -0.1941 -0.5327 0.1597 -0.2360 GO004A 0.3255 -0.0642 -0.4147 0.2290 EU020A -0.5081 0.0356 0.4325 -0.6128 GO006C 0.9164 -0.1068 0.1295 0.1122 EU021A -0.4224 1.4526 0.0905 -0.4890 GO013A 0.9262 0.0531 0.1584 1.1142 EU022A -0.4442 -0.7451 -0.8003 0.1016 GO023A 1.3064 0.4697 0.4130 1.5054 EU025A -0.7041 -0.5174 0.3829 0.3562 GO025B 1.4884 0.1364 -0.1399 0.3359 EU027A -1.0008 0.6698 0.4752 -0.2126 GO025F 2.1683 0.5641 1 .4358 2.9968 EU028A -0.7668 -0.2033 0.7263 -0.4168 GY005A 2.1432 -0.6588 2.8632 1.1976 EU028B -0.9582 -0.5036 0.5717 -0.1383 HN001A 0.6079 0.0490 -0.1574 0.9380 EU031A 0.0822 -1.3041 1.2257 -i.0257 ME019A 0.3717 -0.5344 0.2359 -0.0290 EU034A -0.3601 -0.8009 0.8198 -0.5964 NA002A 0.3793 -0.4572 0.1487 -0.3683 EU039A -0.7099 0.4672 0.1177 -0.0696 NA003A 0.9933 0.4016 1.0317 0.3748 EU040A -0.3633 -0.5010 0.0734 0.0537 NA003B 0.6915 0.0290 0.2571 -0.0399 EU046C -0.7876 -0.1582 0.2058 0.0038 NA005A 0.5923 0.4918 0.1966 -0.3140 EU047A -0.4962 -0.3850 -0.0425 0.1131 NA005B 1.4538 -0.1194 0.4481 0.1047 EU048A -0.6280 -0.3058 0.3789 -0.1044 NA006A -0.2562 -0.0458 0.0012 -0.1402 EU049A 0.1568 -0.4634 0.0272 0.0802 NA006B -0.4642 ·0.2859 -0.0431 0.7012 EU049B 0.0357 -0.7171 0.4150 -0.3418 NA007A 1.4543 0.0694 0.1552 1.4593 EU051A -0.3995 -0.3453 -0.0462 -0.0689 NA008A 1.8411 0.0471 1.0247 1.4825 EU051 B -0.0663 -0.2831 0.1815 0.0282 NA013A 0.8272 0.3576 0.0654 0.4566 EU056A -0.2508 -0.1896 -0.0086 0.3938 NA014A 1.0751 0.1401 0.5760 0.1504 EU057A -0.3611 0.0101 -0.4935 0.5540 NA015A -0.2445 0.2417 0.0518 -0.4004 EU058A -1.0916 -0.4690 0.2542 0.1260 NA016A 0.5924 -0.6264 0.1131 -0.4906 EU9961 -0.4984 -0.4568 -0.4172 0.4457 NA032A -0.0222 0.3806 -0.3645 -0.3483 EU9962 0.0062 -0.5069 -1.i 328 0.0293 NA033A -0.3186 -0.1398 -0.5263 0.5557 EU9965 -0.7097 -0.7981 -0.1753 0.1021 NA037A 0.0780 -0.0876 -0.6700 0.3761 EU9969 -0.7059 -0.3684 -0.1813 0.6706 NA038A -0.0392 -0.7703 0.8130 -0.5966 FR001A 1.1103 ·0.0821 0.2666 0.6491 NA042A 0.1649 1.4311 0.0367 -0.7125 FR0018 -0.0020 1.8173 -0.2127 -1.0213 NA043A 0.2037 2.4077 -0.0389 -0.9120 FR002A 2.1966 -0.5489 2.6001 -0.4854 NA044A -1.1233 1.5436 0.5569 -0.1500 FR002C 0.9102 -0.4566 0.3945 -0.0793 NA045A 0.4480 -0.4734 -0.0875 0.2308 FR005A 0.2446 0.2192 0.1928 1.0149 NA046A 0.1526 -0.0190 -0.9131 0.5302 FR005D 0.3249 -0.4105 -0.2088 -0.2903 NA048A -0.6533 -0.2098 0.1917 0.0226 Table 13 Continued. Axis 1 Axis 2 Axis 3 Axis 4 Axis 1 Axis 2 Axis 3 Axis 4 Species scores Species scores NA063A 1.7726 0.5586 0.7394 2.5096 OP001A 1.7273 -0.3946 1.7120 -1.2102 NA068A 0.5721 ·0.7508 0.0454 0.0131 PE002A -0.3250 ·0.0590 -0.2476 0.1355 NA084A 0.1647 2.4581 -0.2824 -1.2409 PI005A -0.0214 -0.7035 0.5575 -0.1703 NA086A 1.3144 0.0432 0.0488 1.5890 PI007A 0.0135 -0.4462 0.0205 -0.0739 NA099A -0.3307 -0.3958 -0.3238 0.5438 PI011A -0.2679 0.2199 0.1632 -0.1407 NA101A 1.5591 0.7624 2.7641 0.9421 PI014A -0.0721 1.4322 -0.2448 -0.6244 NA102A 0.9071 1.1808 0.5036 0.2663 PI015A 0.3488 -0.0142 0.0993 ·0.1741 NA112D 0.1379 2.0673 0.0546 -0.9077 PI016A 0.0894 1.2693 -0.3503 -0.5264 NA113A 0.2868 1.1740 0.2575 ·1.0593 PI018A -0.3811 0.2447 0.3234 ·0.1105 NA114A 0.1647 2.4581 -0.2824 -1.2409 PI018B -1.1096 1.4139 0.6403 -0.2014 NA115A -0.4493 -0.8688 -0.4627 0.4594 PI022B -0.4720 -0.1788 -0.1012 0.0612 NA129A -0.1786 2.4249 0.0343 -0.8883 PI023A -0.0808 -0.5824 -0.1682 0.3830 NA133A 0.1186 0.2004 -0.7875 0.3057 PI055A 0.2775 2.7291 0.0128 -0.5398 NA135A -0.4686 -0.1237 -0.1459 0.9722 PI056A -0.0605 -0.5861 0.9291 -1.0021 NA140A -0.6777 ·0.2944 -0.1395 0.6951 Pl139A -1.3046 3.6570 0.9759 -1.2352 NA149A 0.1797 0.5963 -0.7161 -0.0130 Pl164A -0.4804 0.8943 0.0464 -0.5723 NA151A 0.3135 2.0253 0.2249 ·0.6206 RH006B 0.1646 1.9319 0.1377 -0.5807 NA156A -0.4718 -0.4372 -0.1554 0.1721 SA001A 0.0462 0.2461 ·0.0839 -0.4066 NA158A -0.5952 -0. 7841 0.2172 0.0977 SA001B 0.3036 0.2504 -0.2850 -0.2457 NA160A -0.7917 1.4718 0.2943 ·0.2962 SA006A 0.4996 0.5214 0.1635 0.3099 NA167A -0.7538 -0.0627 0.4082 -0.1466 SA042A -0.6356 0.4317 0.2113 ·0.5206 NA170A -0.2571 2.3630 -0.0496 -0.5952 SE001A -0.8347 -0.4466 0.5961 0.0681 NA9904 1.2586 -0.7176 0.9303 -1.7391 SP002A 0.0164 -0.4808 0.2002 -0.4488 NA9919 0.6526 -0.2838 0.6052 -0.4190 ST004A 2.3952 0.4667 0.7932 2.4064 NA9955 0.6438 -0.0700 -0.7378 -0.0138 ST010A 2.2691 0.9322 -0.0762 0.8913 NA9963 0.0714 -0.3313 -0.8740 0.2590 SU002A -0.8256 3.0145 1.1845 -0.9072 NA9964 -0.2876 -0.3691 -0.3745 0.2331 SU004A -0.7229 0.8787 0.0616 0.6032 NE003A -0.1637 0.0333 -0.1667 -0.1041 SU005A -0.1439 -0.2227 0.5034 -0.5068 NE003B -0.5156 -0.0650 0.3166 -0.3255 SU006A -0.6193 -0.0464 0.2716 -0.0799 NE003C 0.5523 -0.5526 0.5049 -1.2488 SY002A 0.8466 0.1270 0.3422 -0.2710 NE004A -0.5806 0.2897 0.2517 -0.1128 SY003A 1.5476 -0.1047 0.7676 -0.1523 NE012A -0.6081 0.8839 -0.1527 0.2482 SY004A 1.4098 0.1741 1.5038 -0.0385 NE020A -0.9238 2.4475 0.5267 -0.8178 SY009A 1.0141 -0.1305 0.1470 -1.1588 NE023A -0.5330 1.6142 0.0895 -0.4764 SY010A 0.8277 -0.0310 0.0631 1.0540 NI002A 0.8847 0.8737 0.1900 0.4981 SY013A 2.0547 0.1031 1.5071 1.6104 NI005A 0.1481 -0.2851 -0.4731 0.0012 SY043A 0.2572 0.4764 0.1217 -1.2400 NI008A 0.6191 0.9456 -0.2453 -0.1595 TA001A --0.1330 ·0.0427 -0.1354 -0.0701 Ni009A 0.9314 0.7083 0.4151 -0.0832 TA002A 0.5643 -0.2020 -0.0689 0.8093 NI009B 0.1734 1.5078 --0.2178 -0.8509 TA003A -0.9081 -0.4661 0.3138 0.3646 N1017A 0.7381 -0.4811 0.4191 -1.2318 TA004A -0.7676 -0.5835 0.0316 0.1539 Nl021A 0.5666 0.1211 0.2759 -0.9816 TA9996 1.0004 -0.1313 0.4291 0.2097 NI026A 0.2981 --0.1201 -0.2399 0.8647 NI027A 0.2840 2.1940 --0.3789 -1.0883 Nl152A -0.2825 1.5753 0.0713 -0.4663 Nl9984 2.0570 1.4420 3.9939 1.4340 (88.2) are located on the left of axis 1 and alkaline sites such as Edasjon (S2), Loch Fem (FERN), and Hagasjon (S26) are located on the right-hand side of axis 1. Acid-tolerant diatoms such as Navicula krasskei (Na044a on Figure 12), Aulacoseira tethera (Au023a), Tabellaria binalis (Ta003a), and Eunotia naegelii (Eu048a) occur with negative scores on the left side of CCA axis 1, whereas alkaline-tolerant diatoms such as Cyclostephanus dubius (CcOOla), Aulacoseira granulata var. angustissima (Au003b), Fragilaria crotonensis (Fr008a), and Stephanodiscus parvus (St010a) are positioned on the right-hand side with positive scores. Axis 2 is more difficult to interpret. Altitude (r = 0.61), maximum depth (0.47), total aluminium (-0.25), DOC (-0.36), and pH (0.31) are the only variables with large correlations with axis 2. The length of the total Al arrow is, however, short. Forward selection and associated Monte Carlo unrestricted permutation tests (99 permutations) of the significance of the environmental variables (ter Braak 1990) suggest that all environmental variables except conductivity, calcium, and alkalinity make significant (p < 0.05) contributions to explaining the underlying variation in the diatom assemblages. Axis 3 is correlated with DOC (r = 0.37), calcium (0.29), potassium (0.31), and alkalinity (0.28), whereas axis 4 is positively associated with chloride (0.35), conductivity (0.25), magnesium (0.27), potassium (0.25), and alkalinity (0.27) and negatively associated with coniferous forest (-0.26) and DOC (-0.30). No biological interpretations of these axes are presented here. RECONSTRUCTION OF LAKE SURFACE-WATER CHEMISTRY FROM DIATOM ASSEMBLAGES The principal reason for developing the SW AP diatom-lake chemistry data-set was to provide a basis for the quantitative reconstruction of lake pH and other chemical variables from fossil diatom assemblages. Diatoms have long been known to be sensitive ecological indicators of certain lake chemical variables. This feature has been extensively exploited in the last decade to reconstruct pH from fossil assemblages (e.g. Battarbee 1984), and a range of numerical procedures has been developed for quantitative inference of pH. Such reconstructions, in practice, consist of two mathematical stages. First, the responses of modem diatoms to contemporary lake chemistry are modelled by regression. This involves a modem training set of diatom assemblages ("responses") from surficial sediment samples with associated lake chemical data ("predictors"). Second, these modelled responses are used to infer past chemistry from the composition of fossil assemblages by means of calibration. Nearly all the numerical procedures used are variants of the basic multiple linear regression model and usually involve grouping diatoms into ecological or chemical categories. Within SW AP we have used the two procedures developed by ter Braak and van Dam (1989), namely maximum likelihood (ML) and weighted averaging (WA) regression and calibration because they are more sound theoretically and perform better than other, more widely used "ad hoc" reconstruction procedures (e.g. Battarbee 1984). Full details of the ML and WA procedures used in SWAP are given by Birks et al. (1990a). In a comparison of the two procedures using the SW AP training set and pH, the computationally simple but heuristic approach of WA gave superior results in terms of a lower root-mean-square error of prediction (RMSE) in cross-validation than the computationally demanding but formal statistical approach of ML Gaussian logit regression and ML calibration (Birks et al. 1990a; see also ter Braak and van Dam, 1989). For this reason, we used simple WA regression and calibration to infer separately pH, total Al, and DOC using the SW AP training set (Birks et al. 1990a, 1990b) implemented by the computer programs WACALIB 2.1 (Line and Birks 1990) and 3.0 written by J.M. Line in conjunction with C.J.F. ter Braak and H.J.B. Birks. In any large heterogeneous data-set such as the SW AP set, it is inevitable that some samples are "rogues" or atypical observations, for example with unusual diatom assemblages poorly related to chemistry or with poor or unreliable chemical data. It was therefore necessary to screen the data numerically to detect potential "rogues". The procedures used for data-screening are described by Birks et al. (1990a). Using these procedures, the 178-sample pH set was reduced to a training set of 167 samples, the 157-sample total Al set was reduced to a training set of 126 samples, and the 138-sample DOC set was reduced to a training set of 123 samples. Details of the three training sets after data screening are given in Table 14, along with lists of the samples deleted. Simple WA without tolerance downweighting was used as it consistently gives lower RMSE as estimated by bootstrapping (Birks et al. 1990a). An inverse deshrinking regression was used to deshrink the inferred DOC and total Al values because it minimises RMSE in the training set. Classical regression was used to deshrink the inferred pH values because it takes inferred values further away from the mean and in these data the mean lies in the pH interval where lake pH is most variable, and because in acidification studies we require our reconstructions to be most precise at the lower end of the pH range in the training set (Birks et al. 1990a). Root-mean-square of the error (x; - X;) was calculated for each training set, where xi is the observed value of chemical variable x in sample i (Table 15). The correlation (r) between '.X; and x; was also calculated (Table 15). Plots of xi and of (X: - x) against X; for pH, DOC, and total Al are shown in Figure 13. As the apparent RMSE is invariably an underestimate when based solely on training sets (ter Braak and van Dam, 1989), bootstrapping was used to estimate a more realistic RMSE of prediction for each training set (Table 15). This is partitioned into two components, sil, that part of the prediction error due to estimation error in the taxon parameter used in WA, namely its WA optimum, and s2, that part of the error due to imperfections in the calibration function, even if the optima are known without error. Diatom assemblages vary even among lakes with the same chemistry or, conversely, because lakes with the same diatom assemblage may differ chemically. Component s2 also contains any model specification errors (see Birks et al. 1990a). The s;1 component is small compared with the s2 component (Table 15). The ratio of sjs 2 is 0.2j (pH), 0.27 (total Al), and 0.26 (DOC). The training sets are thus adequate to yield reliable estimates of taxon optima by WA regression required for WA calibration. As noted by Birks et al. (1990b) there is, however, some trend in the differences (xi x) plotted against X; for DOC and Al (Figure 13). Inferences are too high at low observed total Al or DOC and too low at high total Al or DOC, probably because of functional interactions between pH, Al, and DOC. Such interactions are not accounted for in WA reconstructions of individual variables. Multivariate calibration procedures that permit inference of more than one environmental variable simultaneously are required to allow for confounding and interacting environmental variables. The pH, total Al, and DOC optima and tolerances of the diatom taxa in the training sets, as estimated by WA regression are tabulated in Appendix B, along with the number of occurrences of each taxon in the training sets. These optima plus the regression equations listed in Table 16 provide the basis for reconstructing pH, total Al, and DOC for any fossil diatom assemblage with identical taxonomy to the SW AP training sets. Table 14 Details of the three SW AP training sets before and after data screening (sample numbers follow Table 4) Training set Number of Number of Range Mean Median Standard samples samples deviation prior to after screening screening pH 178 167 4.23-7.25 5.56 5.27 0.77 Al (µg r1) 157 126 13.0-256.3 103.2 104.3 60.04 DOC(mg r 1) 138 123 0.12-11.60 3.17 2.50 2.29 Training set samples before screening Training set pH Al DOC Samples All samples 1, 2, 4-7, 9-11, 13-15, 17-18, 20, 22-29, 32, 35-55, 56-63, 65-82, 84-87, 90-103, 106-123, 125-137, 139-162, 164-170, 172-178. 1, 2, 4-7, 9-11, 13-15, 17, 18, 20, 22-25, 27-29, 32, 36-49, 51, 52, 54, 56-63, 65-82, 84-86, 90, 92, 93, 95-100, 102, 103, 103-121, 125-137, 139-155, 158, 161, 162, 164-168, 170, 175, 177, 178. Training set samples deleted after data screening Training set pH Al DOC Samples 4, 27, 72, 79, 90, 91, 114, 124, 150, 157, 177 1,2,26,27, 32,45,53,57, 74, 86,90,96, 108,109,110,112,113,114, 115, 127, 135, 139, 143, 151, 156, 158, 159, 161, 166, 172, 177. 72,110,115,117,127,135,137,142,143,146,148,150,151,152,154. Table 15 Apparent root-mean-square error of prediction (RMSE), correlation (r) between observed and inferred chemistry and RMSE estimated by bootstrapping (RMSEb0,,J and its error components ( s11, s.J for the three screened SW AP training sets. Training set Apparem r RMSE RMSE RMSE RMSE boot Sil Sz pH 0.297 0.933 0.320 0.072 0.312 Al (µg 1-1) 37.820 0.777 49.663 12.985 47.935 DOC (mg 1·1) 1.251 0.837 1.580 0.403 1.527 :a a I :a "" _,;; (a) 8 ,-------,-----,----.----, 7 :a I 6 '.i;; 5 ,. I /~ .... '. , ., 4 '---------'-----~--~---~ 4 5 6 7 8 Oooervoo pH 1.0 0.5 .. .. .. -0.0 . . .. , ... .. . . ,• . .. .,.. ... -0.5 -1.0 4 5 6 8 Oooervoo pH :: ~,---,- ,-i 81 § 6 I 4 • .. I . •:. . .;~.: . . ' .. ~ ~ " ~ [ .. ~.\.';· .. .. " : ~,,,•,. I I --! ---'------L-____J 0 2 4 6 8 10 12 Oooervoo 000 (mg/1) 3 2 , i .. "" .. ~ ·:.. ... "' § .. . ~.... ..· ., : ~ ~ : " I "• .: is the WA estimate of the optimum of taxon k; x is the chemical variable to be reconstracted; X; is the inferred value of the chemical variable x for the sample i (k = 1, ... m diatom tax.a). DIATOM INDICATOR TAXA Within a training set such as the SW AP set, some taxa may be sensitive to particular chemical variables, whereas others may be insensitive. Tax.a that are sensitive, that have narrow ecological amplitudes ("tolerances") for that variable, and that are frequent may be good "indicator" taxa for particular chemical components. The approach we have adopted in drawing up lists of potential indicator taxa for pH, DOC, and total Al is as follows. (1) Do separate canonical correspondence analyses of the three screened training sets (Table 14) with pH, Al, and DOC as the sole environmental variables for the three individual sets. This provides a direct gradient analysis of the tax.a along the gradient of the single chemical variable of interest. (2) Examine the fraction of the variance of a taxon fitted by this gradient (ter Braak 1990), namely CCA axis 1. For a tax.on to be considered as a potential indicator, we require that 10% or more of a taxon's variance be explained by the chemical variable of interest. Using this criterion (A), there are 31 potential indicators for pH, 8 for Al, and 13 for DOC. The mathematical basis for estimating the fraction of the variance of a taxon fitted or the·taxon's relative contribution or the contribution of dimensions to the inertia of the species is given by ter Braak (1990). (3) Estimate the taxon's tolerance, as estimated by weighted averaging, for the chemical variables of interest, with each tolerance adjusted for the effective number of occurrences of the taxon, N2 (ter Braak, 1990). The WA tolerances are divided by (1 - l/N2t where N2 is analogous to Hill's (1973) N2 diversity measure. Select taxa as possible indicators that fulfil criterion A and that have tolerances less than the mean tolerance of all taxa in the training set (criterion B). Note that these tolerances are slightly different from the simple WA tolerances listed in Appendix B which are not adjusted for N2. (4) Consider the number of occurrences of each taxon that fulfils criteria A and B, and tabulate the taxa with 10 or more occurrences (criterion C). These taxa lists (Table 17) are ordered by number of occurrences. For pH 30 taxa fulfil the three criteria but only the 10 most frequent taxa are listed. For Al only 8 taxa fulfil the criteria and for DOC only 7 taxa qualify. Scatter plots of some of these indicator taxa in relation to pH, Al, and DOC for the total available data, rather than the screened data, are shown in Figure 14. Taxa that appear to be "indicator species" have a star in the top of their relevant scatter plots. Table 17 List of indicator taxa for pH, DOC, and total Al that fulfil the criteria of having 10% or more of their variance explained by the chemical variable concerned and of having a tolerance (t) that is less than the mean tolerance for all taxa in the training sets. a = WA optimum, n = number of occurrences. For pH only the 10 most frequent taxa are listed. pH 11 t u Frustulia rhomboides var. saxonica 154 0.61 5.17 Eunotia incisa 145 0.47 5.06 Brachysira brebissonii 143 0.61 5.30 Brachysira vitrea 140 0.68 5.94 Eunotia exigua 121 0.46 5.06 Achnanthes minutissima 118 0.55 6.34 Eunotia naegelii 111 0.41 4.95 Frustulia rhomboides 109 0.55 5.12 Cymbella hebridica 104 0.49 5.09 Cyclotella kuetzingiana agg. 101 0.62 6.29 Dissolved Organic Carbon n t 0 Achnanthes marginulata 89 1.22 1.25 Gomphonema acuminatum var. coronatum 31 2.47 4.57 Cyclotella comta 21 2.20 6.12 Aulacoseira distans var. tenella 20 2.91 9.93 Achnanthes pusilla 20 2.23 5.90 Aulacoseira subarctica agg. 18 1.91 6.40 Navicula indifferens 16 2.50 5.44 Total Aluminium 11 t 0 Achnanthes minutissima 94 51.34 70.76 Frustulia rhomboides 77 54.88 133.36 N avicula hoefleri 56 59.46 140.20 Stauroneis anceps f. gracilis 45 52.21 67.87 Brachysira serians 39 57.31 158.51 Stenopterobia sigmatella 38 57.74 156.33 T abellaria binalis 41 52.03 171.31 Semiorbis hemicyclus 24 45.70 177.20 r· I I I I Achnanthes pusilla Aul a cosei ra distans var. tenella Aul a cosei ra [subarctica agg.] Brachysira vitrea Brachysira serians Brachysira brebissonii pH * Dissolved Organic Carbon ,. /· . .:, .: '. ..";,t •• ·. ,:::._G.:::; ,.,.:,:.,..:~ .<::.tlJ-- •• • ·.'M;...,-~ ... ,..i ........ _ * . ~... . .. .* * . . ,...,_ . -··-·- .. ~· .... ---:: .. ·. :· ~· . ·. * . . .. . ,• ;.". ~,~~~~:/·~\•:.: .::.) . . ·~·-_ .. \• Jl_:;~-f~:.~ .#=.,. ef. =·~ * • •• : •• --~ ':':_. :· ••• : • 0 ,-"::);r. :;::· }_~;/:.:.' .. .... :- .~ }i.:{/ ..::... :·· ... Total Aluminium . .. ,· .:.·. . ~,~:·.:.-: ..... . .... .,, I.,., • ,: . .... :· ... ·: .... : / .. :~~~~;-:.~-: .... _:_. : * I • I l i . I *! Figure 14 Scatter plots of selected diatom taxa showing their relative abundance (vertical axis scaledjrom zero to maximum abundance) in relation to pH, dissolved organic carbon, and total Al in the SWAP training sets/or pH (n = 178), DOC (n= 138), and total Al (n= 157). * indicate those taxa that satisfy the criteria/or potential indicator taxa (see Table 17) when the screened training data-sets are used. i ! Cymbella hebridica : :·:. ·_ ·::{. _: .. ·_~ ·.-··: pH * . ····.:· .. ;_,: ·- ... .. ' Cyclotella com ta I . ,·.. . .. --1--~•'.'• .... ' ! '---Cyclotella kuetzingiana i--I ! Eunotia Eunotia incisa Eunotia naegelii Frustulia rhomboides Frustulia rhomboides var saxonica . '• ~ .{(·~.;;~·.,.~,:: .. ::: . ~-~"--'•• * . .. .. : . . , -::·':; ..... ... ,,. .. .. ~ :'. ·:1 ·.·-.: - •::-....... "' .':W.2-:",,' .. •,. * . ••. . ::. ;.. ~i~-> >·,·.;. ... : .::__,,, * . / .. ':•···. •• :! ~~-~-:t/.' ~_:,_:', .. ~-~:,-:'---· ... ..... ):, • $ • ':·•· <<;>. :~: f::..;~:-·:..., · .. Figure 14 Continued. Dissolved Organic Carbon "': t. ';•.:-~: ~: .. ·:• .. .: .:,.: .. ,·_:'. ·.: .· .... ' * Total Aluminium ·-·~ i . I . : . ' . . ' . ....._ __ . ,.._; ___ . ·---~ :,'.,•;.;-;, .. :;. : . : ,~'\,("o,l,•,._.v,.\..,:::,. ••• •' • ' ·'.,·.· .. . . ~ ... :., . d.{:/.::\\~: :, \. . , ... ii;~~:;;:.~ ..... .... . .k~~:~ ~-.: :. ,. ~. ··:.,.: ,:-:/\\,-:= . =: '. ~ ............ , ;.· :· . ·: . .. ii./}:/.:·: :% ;. :· .. . ~~iit:' ...... _:_ . •• • t ,1•·, .... ,_. ~~;~~~-.: :_: .... ~ . =··: .-::--:::: · ... !i:~::0./.\ ... :/ l ' I * pH ~--·· .·• Navicula hoefleri ' -, Dissolved Organic Carbon T -----1-----····· ·-··-··•--1---Total Aluminium i ~-I ·. :-.. _-.·~: ..... ·.·.·.:... •_:·.·_ . _·_____ -: ' .•..•. : > . . · I Stau rone is a·-·n_c_e_p--f-s _ -_-_-_-_·... _-_-·---1--..1:.._·_·--~~-• ~_·._:_: :: .... ·_· _'·-·---+--~-'_··=_· " -~::C~ • I I var. anceps • ·.· · l L--·--··--·-··-··---+-_____ ,. ~~-:.·_.: : _ _.. _ -4-... _·:·:.-.::_:·_~ :_;· ~ -_·.. --1--i~_,· ._;.: ~ . : .·:·.~- . i • -- * Semiorbis I 1i hemicyclus Stenopterobia sigmatella Tabellaria binalis Tabellaria quadriseptata Tabellaria flocculosa agg. Figure 14 Continued. ··.·~· ... · ---··· . . ~ . . • •\" .. ·!' 15 •• ~ .... ~/ ............. . . . ~·.::,:,.::. ......... ··-•· .... .·•.•-.,t••·· . .. ,u ....... . ": ; ; =••:....:~~,~• -, ---- •• ~lr~..: b$O,>O •• o Oo •, . . ............. ~· -i: .. • &.., ~ .. • .......... . . ' -..:..~::..· .. .:. ...... . .... ~::....:.:.:. .... ,. __ • . ...ti.d~· .. :::..., . .··· .. . . ~~t.:...:~-:.: .. .:i:~· .. ,.. * * I i CONCLUSIONS The SW AP set of modem diatom assemblages and associated environmental variables has, to date, been used almost exclusively as a lake-water chemical calibration training set. The 178-sample pH set, reduced to a training set of 167 samples after data-screening, provided the pH reconstructions for all the lakes studied in SW AP palaeolimnological sub-project (Birks et al. 1990a, Battarbee 1990, Atkinson and Haworth 1990, Renberg 1990, Birks et al. 1990c, Renberg et al. 1990a, Anderson and Korsman 1990, Kreiser et al. 1990, Berge et al. 1990, Renberg et al. 1990b, Jones et al. 1990, Renberg and Battarbee 1990). The 157-sample total Al training set (screened to 126 samples) and the 138-sample DOC set (screened to 123 samples) were used to reconstruct total Al and DOC at Round Loch of Glenhead (Birks et al. 1990b). The SW AP data-set contains an enonnous amount of modem but largely unexplored biological information at the individual taxon, samples, and lake scales, in addition to the basic diatom assemblage/lake chemistry information that has been exploited in the SW AP palaeolimnological investigations. 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(1982) Treatment of potable water containing humus by electroanalytical addition of aluminium. PhD Thesis, University of Washington, Department of Civil Engineering, Seattle, USA Williams, D.M., Hartley, B., Ross, R., Munro, M.A.R., Juggins, S. and Battarbee, R.W. (1988) A Coded Checklist of British Diatoms. ENSIS publishing, London, 74pp. Appendix A THE SW AP TAXONOMIC GUIDE - REVISED 1989 The diatom taxa discussed by participants at the 1987, 1988, and 1989 SWAP diatom workshops are listed below in the following format: a) Taxon and authority. b) Taxonomic decisions, agreements, or alterations. c) Ecological comments. a) Achnanthes altaica (Poretsky) Cleve-Euler b) Supposedly synonymous with A. recurvata Hust. although both names appear to be valid on the BM checklist. A small non-curved form of this taxon A. [altaica var. minor] has been identified by RJF and is described in the guide circulated at the SW AP workshop, July 1988. a) Achnanthes hyperborea Grun. b) Synonymous with A. frigida. a) Achnanthes gibberula Grun. b) Synonymous with A. biasolettiana a) Achnanthes pusilla Grun. b) Synonymous with A. linearis var. pusilla Grun. a) Achnanthes austriaca var. helvetica b) Two new forms of this taxon were discussed at the July 1988 workshop; f. alpina (formerly elongata) and f. minor. They are described in Flower and Jones (1989) but will not be used generally until their distribution in the surface sediment data-set has been assessed. a) Achnanthes microcephala Kiitz. b) Following the results of the 1988 slide counts it was decided not to differentiate between A. minutissima and A. microcephala in the central data-set although individual analysts could split the two taxa at the local level if they wished. a) Achnanthes marginulata Grun. b) Counts of this species have possibly included the diatom described as A. [marginulata f. major] by VJJ at the SWAP July 1988 workshop. a) Achnanthes [scotica] Jones & Flower b) This small Achnanthes has probably been included in counts of A. marginulata in the past. Since it has been found by RJF and VJJ to occur independently of A. marginulata in cores, it was agreed to re-examine surface sediment slides to determine its distribution in modern material. Taxonomy follows Flower and Jones (1989). a) Achnanthes marginulata Grun. b) Synonymous with A. [sp. 5] Loch Daine (VJJ). a) Amphora ova/is var. affinis Kiltz. b) Synonymous with Amphora ova/is var. libyca. a) Asterionella ralfsii W. Smith b) Problems arise in distinguishing this taxon from A. formosa, particularly when only broken valves are present. A category A. ralfsii!formosa has been created for use when such uncertainty exists. c) A ralfsii appears to prefer coloured waters and is therefore often associated with basins receiving artificial drainage from peat. a) Aulacoseira distans (Ehr.) Simonsen b) Synonym Melosira distans. All transfers from Melosira to Aulacoseira follow Hartley (1986). Refer to Camburn and Kingston (1986) for a description of the taxon. a) Aulacoseira lirata var. alpigena (Grun.) Haworth b) Synonym Melosira distans var. alpigena, Aulacoseira distans var. alpigena and Aulacoseira [sp. 4]. a) Aulacoseira [cf distans var. distans] SW AP 1989 b) Synonymous with A. distans var. distans (Ehrenb.) Simonsen 1979. a) Aulacoseira distans var. nivalis b) Synonym Melosira distans var. nivalis, but has not yet been put into Aulacoseira in the BM checklist. Taxonomy follows Camburn and Kingston (1986). Bears some similarity to A distans var. africana although the two are separate taxa. a) Aulacoseira [subborealislsubarctica] b) This taxon has a coarsely punctate valve face and large spines. In girdle view the slightly spiral striae consist of two rows of punctae. The reference site for this tax on is Botungen. a) Aulacoseira distans var. tenella (Nygaard) Ross b) Synonym Melosira distans var. tenella. Taxonomy follows Camburn and Kingston (1986). a) Aulacoseira lirata (Ehr.) Ross b) Synonym Melosira Urata. Taxonomy follows Camburn and Kingston (1986). The valve view maybe confused with that of A. perglabra var. floriniae. a) Aulacoseira lirata var. lacustris (Grun.) Ross b) Synonym Melosira lirata var. lacustris. Taxonomy follows Camburn and Kingston (1986). a) Aulacoseira lirata f. biseriata b) Synonym Melosira Urata f. biseriata. To be transferred to A. distans var. Urata f. biseriata. a) Aulacoseira perglabra b) Synonym Melosira perglabra. Taxonomy follows Camburn and Kingston (1986). This taxon is recorded as A. lirata var. perglabra on the BM checklist but will be transferred to AuLacoseira perglabra. a) Aulacoseira perglabra var. floriniae b) Synonym Melosira perglabra var.floriniae. Taxonomy follows Camburn and Kingston (1986). Valve view may be confused with that of A. lirata if one is not familiar with both taxa. This taxon does not appear on the BM checklist but should be added shortly. a) Aulacoseira [subarctica agg.] b) This includes Aulacoseira subarctica var. subborealis, a description of which is in Haworth (1988). SEM and LM photographs of M. italica var. subborealis Nygaard can be found in Engelmark et al. (1976), p. 151, fig. 4. a) Aulacoseira [var.5 PIRLA] b) This unknown taxon from the surface sediments of Llyn Tecwyn closely resembles the PIRLA Aulacoseira distans var.5 PIRLA (1984-1986) Iconograph plate 51, fig. 5. It has a punctate valve face with a suggestion of curvature and prominent spines. The sulcus is poorly developed and the valve measures 9-12 µm in diameter. In girdle view the parallel striae are 20 in 10 µm. The valves are 5-6 µm deep. This diatom is similar to the A. distans var. laevissima found by EYH. a) Berkeleya rutilans (Trentepohl ex Roth.) Grun. 1868 b) Synonymous with Amphipleura rutilans (Trentepohl ex Roth.) Oeve 1894. a) Brachysira brebissonii Ross b) Synonym Anomoeoneis brachysira (Breb.) Grun. It was decided not to use f. thermalis since the two taxa cannot be consistently separated. a) Brachysira [serians small] b) This small form has been found by IR and NJA in some Scandinavian lakes. Although diatomists may separate this form in their counts, it was agreed not to include it in the database. a) Brachysira follis (Ehr.) Ross b) Synonym Anomoeoneis follis. a) Brachysira serians (Breb. ex Kiltz.) Round & Mann b) Synonym Anomoeoneis serians. a) Brachysira serians var. modesta b) This small variety of B. serians follows Cleve-Euler (1951-1955) described in vol. III p. 197, but is not separated from B. brebissonii in the data-set. a) Brachysira styriaca (Grun.) Ross b) Synonym Anomoeoneis styriaca. a) Brachysira vitrea (Grun.) Ross b) Synonym Anomoeoneis vitrea. a) Brachysira vitrea var. lanceolata b) Synonym Anomoeoneis vitrea var. lanceolata. It was agreed to distinguish this taxon from the nominate at the local level but not in the data-set. a) Brachysira [sp.1 Round Loch] b) It was agreed to include this unpublished Brachysira in the data-set. a) Caloneis ventricosa (Ehrenb.) Meister 1912 b) Synonymous with Caloneis silicula (Ehrenb. (Cleve 1894)). a) Cyclotella comensis Grun. b) This is a separate taxon from C. kuetzingiana although it is probable that the two taxa have been confused in the past. a) Cyclotella [kuetzingiana agg.] b) There are no agreed criteria within the group for separating C. kuetzingiana from its varieties. Therefore, this name should be used for both the nominate and its varieties in the data-set. a) Cyclotella kuetzingiana Thwaites b) Only to be used at the local level (see above). a) Cyclotella kuetzingiana var. planetophora Fricke b) Only to be used at the local level (see above). a) Cyclotella kuetzingiana var. radiosa Fricke b) Only to be used at the local level (see above). a) Cyclotella comta (Ehrenb.) Kiltz. b) Synonymous with C. radiosa HAkansson but has not been revised in the BM checklist. a) Cyclotella glomerata Bachm. b) Synonym C. stelligera var. glomerata Haworth. See Haworth and Hurley (1987). Included in the BM checklist as C. glomerata. a) Cymbella aequalis W. Smith b) This taxon may have been confused with C. angustata in the past although it is clear only C. aequalis is present in the SW AP slides. a) Cymbella gawnannii Meister b) There is some taxonomic confusion due to the similarity with C. perpusilla. Taxonomy follows the description in Krammer and Lange-Bertalot (1986). The C. amphicephala in Hustedt (1930) is synonymous with C. gaumannii. a) Cymbella lunata W. Smith b) Synonym C. gracilis. For taxonomy see Patrick and Reimer (1966). a) Cymbella minuta Hilse ex Rabh. b) Synonym C. ventricosa Kiltz. (the smaller, finer forms). For taxonomy see Krammer and Lange-Bertalot (1986). See also C. silesiaca. a) Cymbella perpusilla A. Cleve b) Follows the description in Krammer and Lange-Bertalot (1986). C. bipartita Mayer is synonymous with C. perpusilla, and was used to describe the oval forms. See discussion on C. gaumannii above. a) Cymbella scotica var. naviculacea (Grun. ex Cleve) R. Ross 1947. b) Synonymous with C. naviculacea Grun. ex Cleve 1881. a) Cymbella silesiaca Bleisch in Rabenhorst (1864). b) This taxon includes the larger, coarser fonns of C. ventricosa. See Krammer and Lange-Bertalot (1986). a) Cymbella elginensis Krammer b) This is synonymous with C. turgida Greg. This should not be confused with C. turgida sensu Cleve (1894) or C. turgida sensu Hustedt (1930) pro parte, which are now in C. mesiana Cholnoky. See Krammer and Lange-Bertalot (1986) for details. a) Diatoma tenue var. elongatum Lyngb. 1819 b) Synonymous with D. elongatum (Lyngb.) Ag. 1824 a) Eunotia arcus Ehr. b) No apparent taxonomic problems. a) Eunotia bactriana Ehr. b) No taxonomic problems. a) Eunotia curvata (Kiltz.) Lagerst. b) Synonym E. lunaris. Used for diatoms of the E. curvata!naegelii group with width> 3 µm and striae count < 20 in 1 0µm. a) Eunotia curvata var. subarcuata (Naegeli) Woodhead & Tweed b) This name was probably used for the shorter forms of E. naegelii in the past but should now be used with care for the short forms of E. curvata, applying the criteria described above. Do not use the Hustedt description of this taxon. a) Eunotia denticulata (Breb.) Rabh. b) This name is to be applied to the "classic" E. denticulata as described in Hustedt (1930 -1966), vol II, p. 293 figs. a-i. a) Eunotia denticulata!lapponica b) This combination has been formed to accommodate the broader fonn of E. denticulata with the robust apices characteristic of E. lapponica. a) Eunotia exigua (Breb.) Rabh. b) This name has been applied to diatoms showing a number of morphological variations. Refer to Hustedt (1930-1966) vol. II p. 287 fig. 751 a-r as a starting point, being aware that Petersen has removed E. paludosa and E. steineckii from Hustedt's concept of E. exigua. See Flower and Kreiser (1988). a) Eunotia flexuosa Kiitz. b) Two forms of this taxon, fine and coarse have been noted. a) Eunotia incisa W. Smith ex Greg. b) Synonymous with the diatom commonly called E. veneris a) Eunotia major (W. Smith) Rabh. b) Synonym E. monodon var. major a) E. minutissima Cleve-Euler b) For description see Cleve-Euler (1955) and SWAP taxonomy update (Flower and Kreiser 1988). It is possible that this taxon has been confused with small, fine forms of E. naegelii in the past. a) Eunotia [sp.13 minutissima] b) Used to describe a small Eunotia similar to E. minutissima but finer (see Flower and Kreiser 1988). a) Eunotia naegelii Migula b) Synonym E. alpina sensu Hustedt in Schmidt Atlas vol H plate 291 figs 7 & 8. This name to be used for diatoms in the E. curvatalnaegelii group with width <3 µm and striae >20 in 10 µm. It is probable that shorter specimens of this taxon have been assigned to E. curvata var. subarcuata in the past. a) Eunotia nodosa Ehr. b) Synonym E. formica. a) Eunotia nymanniana Grun b) Synonym E. exigua var. compacta. Description in Cleve-Euler (1951-1953), vol. II, fig. 445 a &d. a) Eunotia pectinalis (Kiltz.) Rabh. b) No taxonomic problems. a) Eunotia pectinalis var. minor (Kiltz.) Rabh. b) See SWAP taxonomy update (Flower and Kreiser 1988). a) Eunotia pectinalis var. ventricosa Grun. b) Synonym E. pectinalis var. ventralis. a) Eunotia praerupta-nana Berg b) It was agreed to keep this taxon. a) Eunotia rhomboidea Hust. b) It was agreed to keep this taxon although its morphology can come close to E. incisa. a) Eunotia serra Ehr. b) Synonym E. robusta. a) Eunotia serra var. diadema (Ehr.) Patr. b) Synonym E. robusta var. diadema. This taxon also includes E. robusta var. tetraodon. a) Eunotia tenella (Grun.) Hust. b) See SWAP taxonomy update (Flower and Kreiser 1988). a) Eunotia tibia var. bidens (W. Smith) Cleve-Euler b) Synonym E. major var. bidens and E. monodon var. bidens. a) Eunotia tridentula Ehr. b) Synonym E. polydentula and E. tridentula var. perminuta (see BM list). a) Eunotia vanheurckii Patrick b) Synonym E.faba. a) Eunotia vanheurckii var. intermedia b) Synonym E. Jaba var. intermedia. See SWAP taxonomy update (Flower and Kreiser 1988). a) Eunotia exgracilis A. Berg ex Cleve-Euler 1953 b) Synonymous with E. [PIRLA sp.8] PIRLA (1984-1986). a) Fragilaria [if oldenburgiana] b) This taxon does not correspond to Hustedt's type material from Bremerhaven, (Simonsen 1987, vol. 3 plate 680 figs. 1-5) although it is agreed the F. cf oldenburgiana in the PIRLA material is the same as that found in Europe. Reference: PIRLA (1984-1986) Iconograph plate 20 figs. 61-62 & plate 38 fig. 107. a) Fragilaria heidenii 0str. 1910 b) Synonymous with F. inflata (Heiden) Hust. 1931. a) Fragilaria intermedia Grun. a) Fragilaria vaucheriae (Kiltz.) Boye Petersen b) Petersen put these two taxa together but EYH separates them using striae count, TEM structure, and whether or not the cells form colonies. It was agreed to separate them if possible but more information is needed on their differences. a) Fragilaria virescens var. exigua Grun. b) This name is applied within PIRLA to the taxon known as F. virescens in Europe (PIRLA ( 1984-1986) lconograph plate 25 figs. 105-112) since the taxonomy of F. virescens is uncertain. It was decided to adopt the above name for the taxon currently called F. virescens until the type material of F. virescens has been examined. a) Frustulia [rhomboides agg.] b) It was agreed to use this classification for central areas or ends which can not be confidently assigned to the nominate or one of the varieties. a) Frustulia rhomboides (Ehr) De Toni b) This is split from the variety saxonica by its size (generally >60µm) and striae density (<30 in lOµm) but see unpublished notes by RJF. It differs from the variety viridula in that it has a straight raphe, symmetrical central area, and the striae continue around the apices of the valve. A good reference is Krammer and Lange-Bertalot (1986), fig. 95, nos. 2 & 3. (No. 1 is ambiguous). See also Haworth et al. (1988). a) Frustulia rhomboides var. viridula (Breb. ex Kiltz.) Oeve b) This taxon can be separated from the nominate by its curved raphe, asymmetrical central area, and gap in the striae at the apices. It is also usually larger than the nominate. See Krammer and Lange-Bertalot (1986), fig. 96 nos. 1-3 and also Haworth et al. (1988). a) Frustulia rhomboides var. saxonica (Rab) De Toni b) Can be distinguished from the nominate by its smaller size (<60 µm) and finer striae (>30 in 10 µm). a) Gomphonema olivaceum (Homemann) P. Dawson ex R. Ross in Hartley (1986) b) Synonymous with G. olivaceum (Homemann) Breb. 1838. 'iA a) Gomphonema vibrio var. intricatum (Kutz.) R.Ross in Hartley (1986) b) Synonymous with G. intricatum Kutz. 1844. a) Gomphonema vibrio var. pumilum (Grun.) R. Ross in Hartley (1986) b) Synonymous with G. intricatum var. pumilum Grun. 1880 a) Melosira arentii (Kolbe) Nagumo & Kobayasi b) Synonym Cyclotella arentii. a) Navicula angusta Grun. b) Synonyms: N. cari var. angusta and N. lobeliae. a) Navicula bremensis Hust b) The N. bremensis seen in the SWAP material does not exactly fit the description in Hustedt (1930-1966), although it corresponds with the N. bremensis in the PIRLA (1984-1986) Iconograph, plate 1 figs. 3-4. It was decided to continue using the name for the time being. a) Navicula capitata var. hungarica (Grun.) R. Ross 1947 b) Synonymous with N. hungarica Grun. 1860. a) Navicula cocconeifonnis Gregory b) No taxonomic problem. a) Navicula contenta var. parallela Petersen b) Synonymous with the Navicula sp. at 37 cm in the Tveita core. Reference: Hustedt (1930-1966) vol. III p. 210 a) Navicula cumbriensis Haworth b) Synonymous with N. madumensis var. 1 (UCL) and N. subtilissima var. 2 (I. Renberg). See Haworth (1988). Note that N. cumbriensis is strictly an invalid name because it is preempted by Koboyasi and Nagumo's description of this taxon as N. parasubtilissima (E.Y. Haworth, personal communication). For the purposes of SW AP, this taxon is still informally termed N. cumbriensis. a) Navicula [cumbriensis small] b) This is a small variety of the above but is not N. cumbriensis var. minor as identified by EYH. It was agreed to make the distinction between this and the nominate at the local level but to amalgamate them in the data-set. a) Navicula detenta Hust. 1943 b) Synonymous with N. dicephala Ehrenb. 1838. a) Navicula cryptocephala var. exilis (Kutz.) Grun. b) Synonymous with N. exilis Kutz. 1844 a) Navicula festiva Krasske b) No taxonomic problem. a) Navicula gysingensis Foged b) Previously called N. cf. arvensis in the Oresjon SW AP slides. Reference: PIRLA (1984-1986) iconograph, plate 41 figs. 23-24. a) Navicula hoefleri Cholnoky b) Not to be confused with N. [hoefleri sensu Ross & Sims]. Reference: Hustedt (1930-1966) voL III, p. 97. a) Navicula [hoefleri sensu Ross & Sims] b) Synonymous with N. simsii and N. subtilissima var 4, PIRLA (1984-1986) iconograph, plate 42 figs. 75-77. a) Navicula leptostriata Jorgensen b) Synonym N. heimansii. a) Navicula madumensis Jorgensen b) Synonymous with N. subtilissima var.I (L Renberg). a) Navicula mediocris Krasske b) No taxonomic problem. a) Navicula minuscula var. muralis (Grun.) Lange-Bertalot. b) Synonymous with N. muralis Grun. a) Navicula radiosa var. tenella (Breb.) Grun. b) This includes the concept of N. cari sensu Germain, as used at UCL. a) Navicula subtilissima Cleve b) Reference: Hustedt (1930-1966) vol. Ill, p. 89 a) Navicula tenuicephala Hust. b) The SW AP concept of this taxon conforms with that found in Simonsen (1987) vol. 3 plate 457, figs. 11-16. There is some variability in the completeness of the stauros and some specimens have an asymmetrical stauros. There is possibly another taxon with a symmetrical central area. a) Neidium affine (Ehr.) Cleve b) No taxonomic problem. a) Neidium glaberrimum (0strup) Ross b) Synonym N. alpinum a) Nitzschia perminuta (Grun.) M. Perag. b) The N. perminuta in the SW AP material is synonymous with Nitzschia sp.1 PIRLA (1984-1986) iconograph, (plate 44, fig. 123). However, the taxonomy of this species remains unclear. In the BM checklist N. perminuta has been put into N.jrustulum yet the SW AP N. perminuta is clearly not related to N. frustulum. The above name will be used in the data-set but further work is required. a) Nitzschia pusilla Grun. 1862 b) Synonymous with N. kuetzinghiana Hilse 1863. a) Pinnularia abaujensis var. abaujensis (Pant.) R. Ross b) Synonymous with P. gibba (Ehrenb.) Ehrenb. 1843. a) Pinnularia biceps Greg. b) Synonym P. interrupta. It was decided not to separate the varieties of P. biceps. a) Pinnularia irrorata (Grun.) Hust. b) Decision made not to synonymise P. irrorata with P. appendiculata although Krammer and Lange-Bertalot (1986) do so. The concept of P. irrorata is represented in plate 193 and figs. 21-26. P. sylvatica may need to be included in this taxon but more research is required. a) Pinnularia subcapitata var. hilseana (Janisch) 0. Mull. b) Synonym P. hilseana a) Pinnularia rupestris Hantzsch b) Synonym Pinnularia viridis var. rupestris (Hantzsch) Oeve. illustrations can be found in Merilainen (1969), p. 96, fig. 171 and in Renberg (1976), p. 151 plate 5 fig. 12. a) Semiorbis hemicyclus (Ehr.) Patr. b) Synonym Amphicampa hemicyclus. a) Stenopterobia sigmatella (Greg.) Ross b) Synonym Stenopterobia intermedia. a) Surirella delicatissima Lewis b) It was agreed to split fine and coarse forms at the local level, but not in the data-set. a) Tabellaria binalis (Ehr.) Grun. b) For the purposes of the data-set, this name will include both the elliptic and panduric forms although the two varieties will probably be split at the local level. a) Tabellaria fenestrata (Lyngbye) Kiltz. b) This may have been confused in the past with the longer forms of T. flocculosa. More work is required on morphology and colony structure. a) Tabellaria flocculosa var. flocculosa (Roth.) Kiltz. b) This name is to be applied to those diatoms confonning to Koppen's strain IV, i.e. length <39 µm and central inflation distinctly wider than those at the apices. a) Tabellaria fflocculosa agg.] b) For the purposes of the data-set this category is to be applied to all specimens of T. flocculosa which do not fit into the above description. This will include Koppen's strain III and the twisted fonn HIP plus var. linearis. Analysts may differentiate between these fonns at the local level if they wish. Abbreviations AK Annette Kreiser EYH Elizabeth Haworth IR Ingemar Renberg NJA John Anderson RJF Roger Flower UCL University College London VJJ Vivienne Jones Appendix B List of diatoms in the SWAP training set and their optima for pH, DOC (mg i 1), and Total Al (Jig i 1) estimated by weighted averaging (WA) regression. (0 = WA optimum, T = WA tolerance, N = number of occurrences in total data-set or the different screened training sets, Max= maximum abundance in the total data-set.) Taxon Taxon name and authority Total data-set pH DOC Al code N Max 0 T N 0 T N 0 T N AC046A Achnanthes altaica (Poretzky) A. Cleve-Euler 195:3 93 5.9 5.7 0.6 88 3.2 2.5 69 98.3 67.1 70 AC014A Achnanthes austriaca Hust 1922 7 3.4 4.9 0.4 7 3.5 L3 7 120.2 36.8 6 AC9965 Achnanthes austriaca var. alpina Uaine (VJJ) 1988 3 6.3 5.8 0.3 3 0.4 0.1 3 103.3 45.3 3 AC014C Achnanthes austriaca var. helvetica Hust. 1933 94 24.5 5.4 0.4 88 1.5 1.5 73 84.9 47.l 66 AC014B Achnanthes austriaca var. minor L. Grannoch (RJF) 1986 30 5.5 5.1 0.4 29 2.6 2.1 25 124.0 45.6 25 AC9996 Achnanthes cf. levanderi 21 11.2 6.1 0.4 19 1.4 1.3 17 32.7 25.6 15 AC042A Achnanthes detha 41 21.7 6.1 0.5 38 1.8 2.2 33 41.3 49.5 33 AC039A Achnanthes didyma Hust. 1933 7 LO 6.3 0.6 6 3.8 3.3 4 72.9 60.7 6 AC025A Achnanthes flexella (Kutz.) Brun 1880 23 1.3 6.3 0.6 21 5.1 2.0 13 77.8 35.8 15 AC017A Achnanthes kryophila J.B. Petersen 1924 11 3.2 6.0 0.7 10 1.5 0.4 7 63.2 40.6 4 ACOOlA Achnanthes lanceolata (Breb. ex Kutz.) Grun. 1880 13 0.9 6.4 0.7 11 4.0 1.8 7 87.6 55.9 11 AC018A Achnanthes laterostrata Hust. 1933 11 1.4 6.2 0.7 9 4.5 3.4 6 43.2 27.9 8 AC044A Achnanthes levanderi Hust. 1933 36 12.5 5.6 0.6 34 1.5 1.2 27 79.2 57.1 27 AC002A Achnanthes linearis ry./. Sm.) Grun. in Cleve & Grun. 1880 31 4.6 6.4 0.5 26 5.0 2.0 19 102.0 49.8 22 AC022A Achnanthes marginulata Grun. in Cleve & Grun. 1880 122 46.9 5.2 0.4 118 1.3 1.2 89 124.2 64.9 89 AC9968 Achnanthes rnarginulata form major Uaine (VJJ) 1988 20 40.5 5.2 0.3 20 0.7 0.5 20 138.4 76.5 16 ACOBA Achnanthes rninutissima Kutz. 1833 127 46.0 6.3 0.5 118 3.4 2.2 92 70.8 50.7 94 AC9964 Achnanthes minutissima var. scotica (Carter) RJF 1988 7 1.3 6.2 0.6 6 1.9 0.6 4 49.3 15.8 5 AC019A Achnanthes nodosa A. Cleve-Euler 1900 17 5.6 6.2 0.4 17 1.9 1.2 14 29.2 15.4 12 AC004A Achnanthes pseudoswazi J.R. Carter 1963 31 3.4 6.1 0.5 28 2.9 1.3 18 61.4 38.3 22 AC035A Achnanthes pusilla Grun. in Cleve & Grun. 1880 35 2.0 6.2 0.6 31 5.9 2.1 20 99.7 54.8 24 AC028A Achnanthes saxonica Krasske in Hust. 1933 11 0.9 5.7 0.5 11 1.8 1.3 10 83.9 68.7 11 AC048A Achnanthes scotica Jones & Flower 28 5.3 5.6 0.5 27 1.0 0.8 25 70.7 49.6 23 AC029A Achnanthes sublaevis Hust. 1936 30 8.0 5.8 0.6 26 2.1 1.6 20 57.5 36.2 22 AC034A Achnanthes suchlandtii Hust. 1933 12 1.7 6.3 0.4 10 l.5 1.9 7 48.7 48.l 10 AC030A Achnanthes umara J.R. Carter 1970 11 1.2 6.1 0.5 10 3.3 1.3 5 96.6 69.2 8 AC9975 Achnanthes [altaica var. minor] L. Grannoch (RJF) 1988 31 20.7 4.9 0.3 31 2.6 2.2 28 160.3 67.7 24 AC9969 Achnanthes [scotica/rnarginulata] Groningen (RJF) 1988 20 3.0 6.1 0.5 19 1.9 0.9 16 53.4 42.1 18 ATOOlA Actinella punctata Lewis 13 1.1 5.1 0.3 13 5.3 1.4 8 133.3 30.4 9 AMOOlA Amphora ovalis (Kutz.) Kutz. 1844 4 1.4 6.2 0.5 3 2.8 1.1 4 50.5 38.7 3 AMOOlD Amphora ovalis var. affinis (Kutz.) Van Heurck 1885 16 1.8 6.4 0.5 13 4.8 2.4 12 62.5 51.9 16 Taxon Taxon name and authority Total data-set pH DOC Al code N Max 0 T N 0 T N 0 T N AMOOlB Amphora ovalis var. pediculus (Kutz.) Van Heurck 1885 11 1.3 6.8 0.2 11 4.9 1.9 4 108.2 62.1 9 ASOOlA Asterionella forrnosa Hassall 1850 30 18.5 6.7 0.4 27 6.2 1.3 15 107.8 45.9 23 AS003A Asterionella ralfsii W. Sm. 1856 11 25.5 4.9 0.4 11 3.6 0.7 7 125.0 27.9 8 AU002A Aulacoseira ambigua (Grun. in Van Heurck) Simonsen 1979 13 67.3 6.5 0.4 12 7.6 2.0 8 114.2 41.9 12 AU005A Aulacoseira distans (Ehrenb.) Simonsen 1979 31 11.9 5.4 0.6 30 2.0 1.5 20 104.6 57.4 19 AU005L Aulacoseira distans var. humilis (A. Cleve-Euler) R. Ross in Hartley 1986 2 3.8 6.8 0.1 2 9.1 1 69.4 24.7 2 AU005J Aulacoseira distans var. laevissima (Grun.) Haworth 2 9.2 6.0 0.1 2 1.7 0.0 2 27.6 11.1 2 AU005E Aulacoseira distans var. nivalis 50 9.6 5.0 0.5 49 2.0 1.6 38 132.8 64.0 39 AU005B Aulacoseira distans var. nivaloides Camburn 1987 6 1.1 6.1 0.7 5 9.1 0.9 3 100.1 41.9 4 AU005D Aulacoseira distans var. tenella (Nygaard) R. Ross in Hartley 1986 30 31.3 5.8 0.6 28 9.9 2.3 20 146.5 30.1 22 AU003D Aulacoseira granulata (Ehrenb.) Simonsen 1979 3 2.4 7.1 0.2 2 64.6 32.3 3 AU003B Aulacoseira granulata var. angustissima (0. Mull.) Simonsen 1979 2 2.4 7.0 0.4 2 5.4 1 76.7 4.9 2 AU009A Aulacoseira islandica (0. Mull.) Simonsen 1979 2 2.7 6.5 0.2 2 137.5 18.9 2 AU009B Aulacoseira islandica subsp. helvetica (0. Mull.) Simonsen 1979 2 1.8 5.4 0.2 2 9.3 1.4 2 144.9 16.1 2 AUOOlC Aulacoseira italica var. valida (Grun. in Van Hei.rrck) Simonsen 1979 6 2.4 5.6 0.6 6 6.7 3.1 5 107.7 50.4 5 AU004A Aulacoseira lirata (Ehrenb.) R. Ross in Hartley 1986 52 14 .. 9 5.2 0.7 51 3.6 2.5 33 101.8 63.6 36 AU004C Aulacoseira lirata form biseriata 9 2.0 5.7 0.8 9 6.5 2.7 6 85.1 28.7 7 AU004D Aulacoseira lirata var. alpigena (Grun.) Haworth 10 9.4 5.3 0.4 9 2.5 1.5 10 88.8 28.3 8 AU004B Aulacoseira lirata var. lacustris (Grun. in Van Hei.rrck) R. Ross 1986 21 3.4 6.0 0.7 21 4.7 3.6 16 78.7 50.9 15 AU014A Aulacoseira nygaardii Camburn 21 3.7 5.7 0.5 21 5.1 2.6 16 100.1 42.7 19 AUOlOA Aulacoseira perglabra 52 5.2 5.2 0.6 49 4.1 2.3 41 113.3 39.8 35 AUOlOB Aulacoseira perglabra var. floriniae 45 10.4 5.2 0.4 42 3.6 2.4 33 113.5 36.4 33 AU022A Aulacoseira subborealis SWAP 1989 4 12.0 6.9 0.6 4 7.2 1.2 3 81.0 15.7 4 AU023A Aulacoseira tethera Haworth nov. sp. 1989 1 5.3 5.0 1 0.5 1 223.0 1 AU9988 Aulacoseira [ cf. dis tans distans] SW AP 1989 13 2.8 5.0 0.3 13 3.1 1.9 9 134.4 34.9 10 AU9984 Aulacoseira [lirata alpigcna (small)] SWAP Sweden (IR & NJA) 1989 10 6.8 5.6 0.5 10 8.8 1.8 7 140.2 31.3 10 AU9987 Aulacoseira [lirata alpigena/distans girdle view] SW AP 1989 13 ~-8 5.6 0.4 12 6.2 4.3 9 121.9 50.3 11 AU9983 Aulacoseira [subarctica agg.] SW AP 1989 33 19.l 6.4 0.6 29 6.4 1.8 18 114.7 48.9 29 BR006A Brachysira brebissonii R. Ross in Hartley 1986 149 9.2 5.3 0.6 143 3.0 2.1 110 112.1 57.l 103 BR003A Brachysira serians (Breb. ex Kutz.) Round & Mann 1981 65 14.5 4.8 0.4 64 2.9 1.7 42 158.5 55.2 39 BR004A Brachysira styriaca (Grun. in Van Heurck) R. Ross 1986 29 1.6 5.2 0.6 29 3.9 1.5 19 115.0 49.1 15 BROOlA Brachysira vitrea (Grun.) R. Ross in Hartley 1986 149 48.3 5.9 0.7 140 3.0 2.1 105 74.8 50.3 102 BR005A Brachysira zellensis (Grun.) Round & Mann 1981 2 3.1 6.7 0.1 2 7.3 1 102.3 15.4 2 BR9997 Brachysira [sp. 1] Round L. Glenhead (VJJ) 1985 18 9.9 5.4 0.5 18 5.0 2.3 14 105.4 29.0 11 Taxon Taxon name and authority Total data-set pH DOC Al code N Max 0 T N 0 T N 0 T N CA018A Caloneis tenuis Gregory (Krammer) 1985 3 1.3 6.6 0.0 3 6.6 1.4 3 88.7 32.7 3 COOOlA Cocconeis placentula Ehrenb. 1838 13 3.3 6.7 0.3 12 5.1 LO 5 137.5 36.5 10 cooorn Cocconeis placentula var. euglypta (Ehrenb.) Grun. 1884 2 10.l 6.9 1 5.7 0.1 2 28.6 18.3 2 CCOOIA Cyclostephanus dubius (Fricke in A. Schmidt) Round 1982 2 10.5 7.2 1 69.9 13.7 2 CY013A Cyclotella antiqua W. Sm. 1853 1 1.8 4.5 1 CY0lOA Cyclotella comensis Grun. in Van Heurck 1882 32 41.5 6.7 0.3 29 4.9 1.5 21 66.0 43.3 25 CY0OlA Cyclotella comta (Ehrenb.) Kutz. 1849 40 15.4 6.7 0.4 39 6.1 2.1 21 85.6 43.8 31 CY007A Cyclotella glomerata Bachm. 1911 21 19.5 6.7 0.4 20 6.0 1.8 12 88.4 62.5 17 CY9991 Cyclotel!a kuetzingiana agg. 106 72.3 6.3 0.6 101 3.2 2.1 77 81.1 53.1 82 CY003A Cyclotella meneghiniana Kutz. 1844 2 5.2 6.9 l 6.6 1 172.8 2.8 2 CY002A Cyclotella pseudostelligera Hust. 1939 11 26.1 6.9 0.2 10 5.8 0.7 5 128.3 55.0 9 CY004A Cyclotella stelligera (Cleve & Grun. in Cleve) Van Heurck 1882 25 7.9 6.2 0.5 24 7.0 2.3 13 91.9 43.4 19 CM014A Cymbella aequalis W. Sm. ex Grev. 1855 51 20.6 5.1 0.3 48 1.2 1.1 40 166.5 72.7 38 CM015A Cymbella cesatii (Rabenh.) Grun. in A. Sclunidt 1881 24 4.5 6.4 0.5 23 4.1 2.2 16 43.4 23.3 16 CM015B Cymbelia cesatii var. capitata Krieger 1933 2 2.7 6.3 0.1 2 0.5 1 16.3 1 CM038A Cymbella delicatula Kutz. 1849 8 1.5 6.3 1.0 8 5.0 1.4 4 58.6 13.9 4 CM052A Cymbella descripta (Hust.) Krammer & Lange-Bertalot 1985 46 6.0 6.4 0.6 44 4.2 2.3 30 65.9 44.6 31 CM051A Cymbella elginensis Krammer 1981 11 1.1 6.2 0.6 11 4.6 2.4 8 66.6 46.5 9 CM020A Cymbella gaeumannii Meister 1934 19 2.6 5.9 0.5 19 2.5 2.5 16 97.0 61.8 13 CM017A Cymbella hebridica (Grun. ex Cleve) Cleve 1894 109 6.6 5.1 0.5 104 2.3 1.8 78 116.2 54.6 69 CM013A Cymbella helvetica Kutz. 1844 8 1.4 6.7 0.3 8 3.8 2.4 4 68.7 44.9 6 CM048A Cymbella lunata W. Sm. in Grev. 1855 124 7.3 5.7 0.6 118 3.2 2.0 92 84.2 56.2 91 CM004A Cymbella rnicrocephala Grun. in Van Heurck 1880 58 8.8 6.3 0.7 53 3.6 2.4 44 52.0 36.1 45 CM031A Cymbella rninuta Hilse ex Rabenh. 1862 52 4.0 6.1 0.7 46 3.6 2.3 37 89.6 55.5 39 CM031C Cymbella minuta var. silesiaca (Bleisch ex Rabenh.) Reimer 1975 6 2.5 5.3 0.3 5 1.8 1.4 6 68.2 31.0 4 CM0lOA Cymbella perpusilla A. Cleve 1895 127 19.4 5.2 0.5 120 2.7 1.7 91 107.7 63.7 88 CM101B Cymbella scotica W. Sm. 1853 7 1.4 6.7 0.6 7 6.6 1.3 7 61.6 32.6 7 CM050A Cymbella subaequalis Grun. in Van Heurck 1880 2 1.2 7.1 0.2 2 5.9 2.5 2 37.5 0.9 2 CM9989 Cymbella [cf. deliculata] SWAP-IR 1988 6 1.3 5.1 0.4 6 4.9 1.4 4 136.2 23.0 4 CM9995 Cymbella [PIRLA sp. 1] PIRLA 1985 4 10.0 5.0 0.3 3 1.6 1.2 4 62.l 1.6 2 DEOOlA Denticula tenuis Kutz. 1844 14 4.3 6.8 0.4 13 3.9 2.5 7 66.4 39.2 9 DT002A Diatoma hyemale (Roth) Heib. 1863 8 1.5 5.8 0.4 8 1.5 0.7 7 43.0 28.1 7 DT004B Diatoma tenue var. elongatum Lyngb. 1819 15 7.7 6.5 0.5 12 5.5 1.4 6 159.2 30.0 11 DT003A Diatoma vulgare Bory 1824 5 2.8 6.3 0.3 5 3.6 1.1 4 52.7 50.l 3 EU013A Eunotia arcus Ehrenb. 1837 47 1.4 5.8 0.7 44 2.4 1.6 26 104.5 61.3 32 Taxon Taxon Name Total Dataset pH DOC Al Code N Max 0 T N 0 T N 0 T N EU014A Eunotia bactriana Ehrenb. 1854 42 7.6 4.7 0.3 42 3.0 1.5 26 154.6 57.7 26 EU022A Eunotia bigibba Kutz. 1849 4 0.4 5.3 0.4 4 2.5 0.2 2 93.0 EU049A Eunotia curvata (Kutz.) Lagcrst. 1884 60 4.9 5.5 0.6 57 3.9 1.9 40 101.9 49.5 42 EU049B Eunotia curvata var. subarcuata (Naegeli ex Kutz.) Woodhead & Tweed 1954 34 1.8 5.3 0.5 33 3.6 2.7 21 136.6 39.9 24 EU015A Eunotia denticulata (Breb. ex Kutz.) Rabenh. 1864 70 9.4 5.1 0.4 69 1.7 1.2 55 143.6 71.6 50 EU9969 Eunotia denticulata/lapponica 1987 5 2.9 5.0 0.1 5 0.7 0.3 5 130.2 56.7 4 EU016A Eunotia diodon Ehrenb. 1837 20 1.2 5.3 0.5 18 3.9 2.0 14 127.7 43.4 12 EU057A Eunotia exgracilis A. Berg ex A. Cleve-Euler 1953 38 5.6 5.3 0.5 35 1.3 0.9 27 96.7 47.1 25 EU009A Eunotia exigua (Breb. ex Kutz.) Rabenh. 1864 126 11.0 5.1 0.5 121 2.4 1.6 93 114.2 54.3 85 EU009C Eunotia exigua var. tridentula 10 1.3 5.1 0.4 10 5.3 2.2 4 154.5 26.7 6 EU025A Eunotia fallax A. Cleve 1895 25 1.9 5.1 0.8 24 2.9 2.0 15 129.3 61.4 14 EU017A Eunotia flexuosa Kutz. 1849 48 1.4 5.7 0.6 48 3.1 2.2 35 97.0 64.0 33 EU019A Eunotia iatriaensis Foged 1970 35 1.4 5.3 0.6 33 3.6 2.4 22 109.5 41.9 24 EU047A Eunotia incisa W. Sm. ex Greg. 1854 156 28.0 5.1 0.5 145 3.0 1.7 107 119.9 53.4 109 EU020A Eunotia meisteri Hust. 1930 54 6.6 5.2 0.5 48 3.7 1.4 34 115.0 49.7 32 EU028A Eunotia rnicrocephala Krasske ex Hust. 1932 26 3.1 4.9 0.4 25 3.7 2.0 16 138.1 46.7 16 EU028B Eunotia rnicrocephala var. tridentata (A. Mayer) Hust. 6 1.4 4.7 0.3 6 2.7 2.1 5 117.7 49.7 5 EU056A Eunotia minutissima A. Cleve-Euler 1934 44 2.6 5.3 0.5 43 2.2 1.7 35 117.1 64.2 34 EU048A Eunotia naegelii Migula 1907 115 10.9 5.0 0.4 111 2.9 2.0 83 130.2 52.4 78 EU040A Eunotia paludosa Grun. 1862 38 1.3 5.1 0.6 38 3.4 2.0 29 118.1 52.4 31 EU034A Eunotia parallela Ehrenb. 1843 9 2.0 5.1 0.4 9 5.6 1.7 6 150.6 45.3 7 EU002A Eunotia pectinalis (O.F. Mull.) Rabenh. i864 79 3.2 5.5 0.7 74 3.2 2.2 57 99.4 57.6 57 EU002B Eunotia pectinalis var. minor (Kutz.) Rabenh. 1864 134 5.2 5.4 0.6 128 3.4 1.8 94 126.7 60.5 95 EU002E Eunotia pectinalis var. minor form impressa (Ehr.) Hust. 50 1.3 5.5 0.6 48 3.8 1.9 33 115.l 54.5 33 EU002D Eunotia pectinalis var. undulata (Ralfs) Rabenh. 1864 7 1.1 5.4 0.4 7 2.4 0.5 6 68.9 45.2 6 EU002K Eunotia pectinalis var. ventricosa Grun. in Van Heurck 1881 58 2.7 5.2 0.6 56 3.3 2.1 45 107.5 51.5 45 EU046C Eunotia perpusilla Grun. in Van Heurck 1881 4 2.2 4.7 0.2 4 3.0 0.8 2 193.7 8.8 2 EU003A Eunotia praerupta Ehrenb. 1843 22 3.7 5.4 0.7 20 2.1 2.4 14 64.6 46.5 15 EU0llA Eunotia rhomboidea Hust. 1950 92 7.0 5.1 0.5 86 3.2 1.9 64 112.3 50.2 61 EU058A Eunotia schwabei Krasske 9 1.7 4.7 0.2 9 2.8 1.9 8 111.2 44.5 7 EU031A Eunotia septentrionalis Ostr. 1898 7 1.2 5.5 0.6 7 8.8 1 113.0 1 EU021A Eunotia sudetica 0. Mull. 1898 23 2.5 5.6 0.5 23 2.2 1.9 17 70.0 51.9 14 EU004A Eunotia tenella (Grun. in Van Heurck) A. Cleve 1895 104 6.9 5.2 0.5 98 3.0 1.6 76 114.4 53.5 69 EU027A Eunotia trinacria Krasske 1929 24 6.2 4.8 0.4 22 1.9 1.8 16 84.2 52.4 14 EU039A Eunotia triodon Ehrenb. 1837 25 2.4 5.1 0.6 24 2.2 1.3 18 146.3 61.4 15 Taxon Taxon Name Total Dataset pH DOC Al Code N Max 0 T N 0 T N 0 T N EU051A Eunoria vanheurckii Patr. 1958 78 4.0 5.1 0.6 74 3.2 1.5 58 122.5 52.7 54 EU051B Eunoria vanheurckii var. intermedia (Krasske) Cleve 40 5.1 5.4 0.5 40 3.5 2.8 30 156.8 42.6 25 EU9962 Eunoria [exigua var. 2 (kummer.)] Cranmer Pond (RJF) 1988 5 1.4 5.5 0.5 5 2.3 0.3 4 74.9 30.l 4 EU9965 Eunoria [sp. 10 (mini.ma)] L. Grannoch (RJF) 1988 25 4.7 4.9 0.4 24 3.6 1.6 22 136.6 50.6 15 EU9961 Eunoria [vanheurckii var. l] Round L. Glenhead (RJF) 1988 39 2.9 5.2 0.5 38 2.4 1.3 31 118.4 58.7 28 FR006A Fragilaria brevistriata Grun. in Van Heurck 1885 30 9.5 6.5 0.4 28 4.4 3.4 16 58.5 44.0 27 FR009F Fragilaria capucina var. lanceolata Grun. in Van Heurck 1881 1 2.4 6.2 1 0.5 l 13.0 1 FROI0A Fragilaria constricta Ehrenb. 1843 21 3.3 5.4 0.4 19 5.3 2.8 14 134.4 47.8 14 FR002A Fragilaria construens (Ehrenb.) Grun. 1862 24 8.0 6.6 0.6 21 5.9 2.9 13 96.8 41.8 20 FR002C Fragilaria construens var. venter (Ehrenb.) Grun. in Van Heurck 1881 54 27.6 6.2 0.7 49 5.4 2.1 33 104.2 56.l 44 FR008A Fragilaria crotonensis Kitton 1869 6 1.5 7.0 0.2 6 6.1 0.4 2 89.5 28.7 5 FR018A Fragilaria elliptica Schum. 1867 35 34.3 6.6 0.5 30 6.2 2.0 21 64.9 51.0 29 FR0llA Fragilaria lapponica Grun. in Van H eurck 1881 4 2.2 6.0 0.4 3 2.9 3.6 2 81.7 65.9 3 FR015A Fragilaria lata Renberg 23 9.2 5.5 0.5 23 9.1 3.8 11 164.l 23.2 11 FROOlA Fragilaria pinnata Ehrenb. 1843 43 10.4 6.3 0.6 37 4.0 2.3 23 80.8 64.4 32 FROOlB Fragilaria pinnata var. lancettula (Schum.) Hust. in A. Schmidt 1913 2 1.6 6.3 0.7 2 2.3 1.1 2 43.2 35.2 2 FR007A Fragilaria vaucheriae (Kutz.) J.B. Petersen 1938 65 9.0 6.3 0.6 57 4.4 2.5 40 98.0 54.9 48 FR005A Fragilaria virescens Ralfs 1843 8 0.5 5.6 0.8 7 2.7 1.8 7 117.8 71.7 6 FR005D Fragilaria virescens var. exigua Grun. in Van Heurck 1881 122 51.1 5.7 0.5 113 3.4 2.5 87 79.2 48.7 92 FR9991 Fragilaria [cf. oldenburgiana PIRLA pl 20, 61-2] PIRLA 1987 45 27.9 4.7 0.3 45 4.7 2.0 29 187.6 54.0 27 FU002A Frustulia rhomboides (Ehrenb.) De Toni 1891 114 11.8 5.1 0.5 109 3.4 2.3 82 133.4 54.2 77 FU002B Frustulia rhomboides var. saxonica (Rabenh.) De Toni 1891 162 32.5 5.2 0.6 154 3.5 2.0 116 116.6 56.2 113 FU002F Frustulia rhomboides var. viridula (Breb. ex Kutz.) Cleve 1894 99 15.1 5.3 0.5 95 4.0 2.3 71 120.0 66.0 68 G0006C Gomphonema acuminatum var. coronatum (Ehrenb.) W. Sm. 1853 41 1.0 6.1 0.6 38 4.6 2.4 31 103.3 53.l 31 G0003A Gomphonema angustatum (Kutz.) Rabenh. 1864 34 3.6 5.8 0.6 31 3.2 1.7 26 82.4 57.1 24 G0004A Gomphonema gracile Ehrenb. 1838 67 4.2 5.8 0.7 62 3.1 1.9 50 85.1 49.3 51 G0013A Gomphonema parvulum (Kutz.) Kutz. 1849 23 4.2 6.2 0.6 20 4.3 2.0 15 128.l 61.9 19 G0023A Gomphone.ma truncatum Ehrenb. 1832 6 1.3 6.7 0.5 6 4.5 0.8 4 130.9 4.1 4 G0025B Gomphone.ma vibrio var. intricatum (Kutz.) R. Ross in Hartley 1986 31 2.7 6.6 0.5 27 5.3 1.9 15 104.1 51.5 20 G0025F Gomphonema vibrio var. pumilum (Grun. in Van Heurck) R. Ross 1986 2 1.8 6.6 0.5 2 5.8 1 138.3 1 GY005A Gyrosigma acuminatum (Kutz.) Rabenh. 1853 2 1.3 7.2 1 38.3 13.9 2 HNOOlA Hannaea arcus (Ehrenb.) Patr. in Patr. & Reimer 1966 10 1.2 6.0 0.6 9 3.8 1.0 6 108.2 51.8 6 ME019A Melosira arentii (Kolbe) Nagumo & Kobayasi 1977 15 0.8 5.6 0.6 15 5.4 2.8 10 138.7 38.8 8 NA113A Navicula acceptata Hust. 1950 10 3.2 6.2 0.7 10 2.7 2.0 7 54.0 51.8 8 NA037A Navicula angusta Grun. 1860 59 5.6 5.6 0.6 56 2.6 1.6 48 77.0 51.6 42 Taxon Taxon Name Total Dataset pH DOC Al Code N M!!.X 0 T N 0 T N 0 T N NA038A Navicula arvensis Hust. 15 1.1 5.2 0.6 14 4.8 2.3 9 127.4 50.2 10 NA084A Navicula atomus (Kutz.) Grun. 1860 1 1.3 6.6 1 1.8 1 26.4 1 NA099A Navicula bremensis Hust. 1957 33 2.6 5.0 0.5 31 2.0 1.7 23 119.5 53.9 19 NA045A Navicula bryophila J.B. Petersen 1928 25 2.4 5.6 0.6 25 4.5 2.1 18 126.1 63.7 16 NA032A Navicula cocconeifonnis Greg. ex Greville 1855 75 3.1 5.7 0.6 72 2.6 1.7 57 81.1 47.1 57 NA046A Navicula contenta Grun. in Van Heurck 1885 12 0.9 5.8 0.7 12 2.1 2.0 12 83.2 69.5 11 NA007A Navicula cryptocephala Kutz. 1844 17 1.8 6.6 0.4 15 4.4 2.1 10 108.3 83.2 13 NA158A Navicula cumbriensis Haworth 1987 42 4.7 4.9 0.3 41 3.3 1.6 32 125.0 44.7 31 NA115A Navicula difficillima Hust. 1950 11 2.2 5.0 0.3 8 4.3 1.5 8 84.9 32.9 8 NA149A Navicula digitulus Hust. 1943 17 2.2 6.0 0.6 17 1.6 1.2 15 37.5 27.9 14 NA151A N avicula gysingensis Fo ged 1952 3 1.4 6.4 0.5 3 2.0 2.0 2 27.4 25.2 2 NA015A Navicula hassiaca Krasske 1925 15 1.6 5.4 0.7 15 1.9 1.3 7 98.4 52.6 10 NA167A Navicula hoefleri Sensu Ross et Sims 88 11.1 4.9 0.4 84 3.1 1.6 65 140.2 58.3 56 NA068A Navicula impexa Hust. 1961 24 4.7 5.8 0.6 21 4.1 2.5 14 144.9 63.2 18 NA016A Navicula indifferens Hust. 1942 27 1.8 5.9 0.7 25 5.4 2.3 16 143.0 71.5 19 NA101A Navicula jaagii Meister 1934 5 1.4 6.9 0.6 4 7.1 0.5 2 57.8 50.2 2 NA002A Navicula jaernefeltii Hust. 1942 22 1.4 5.5 0.7 22 4.5 1.2 12 120.0 47.3 15 NA044A Navicula krasskei Hust. 1930 29 13.9 5.3 0.4 28 1.1 0.7 25 94.0 55.5 23 NA102A Navicula laevissima Kutz. 1844 6 7.3 6.6 0.4 6 3.6 1.2 4 22.9 22.4 4 NA156A Navicula leptostriata Jorgensen 1948 102 18.7 5.1 0.4 97 2.5 1.7 76 121.7 49.2 68 NA140A Navicula madumensis E.G. Jorg. 1948 42 6.6 4.9 0.4 40 1.8 1.4 30 148.8 60.9 24 NA006A Navicula mcdiocris Krasske 1932 110 2.3 5.4 0.6 105 2.8 2.0 75 104.8 52.8 74 NA006B Navicula mediocris var. atomus Hust. 14 5.7 5.2 0.5 14 1.5 0.7 13 80.l 24.0 14 NA042A Naviculn minima Grun. in Van Heurck 1880 39 8.7 6.1 0.5 33 2.6 2.0 25 61.0 48.4 25 NA112D Navicula minuscula var. muralis (Grun. in Van Heurck) Lange-Beralot 1981 7 3.3 6.4 0.3 7 2.0 1.8 5 44.2 44.9 7 NA013A Navicula pseudoscutiformis Hust. 1930 36 2.5 6.1 0.6 31 3.5 2.2 22 62.4 50.4 26 NA014A Navicula pupula Kutz. 1844 52 4.0 6.3 0.6 47 4.4 2.3 33 71.9 57 .6 39 NA003A Navicula radiosa Kutz. 1844 62 5.4 6.2 0.8 55 4.9 2.2 36 72.l 57.5 39 NA003B Navicula radiosa var. tenella (Breb, ex Kutz.) Grun.ex Van Heurck 1885 71 8.4 6.l 0.7 66 3.7 2.6 50 64.7 42.l 55 NA008A Navicula rhyncocephala Kutz. 1844 21 2.5 6.6 0.4 17 5.9 1.5 12 99.6 62.3 17 NA133A Navicula schassmannii Hust. 1937 13 4.9 5.9 0.6 13 1.7 2.0 12 40.7 36.3 12 NA129A Navicula seminuloides Hust. 1937 9 12.3 6.2 0.5 9 1.5 0.3 7 33.3 19.9 9 NA005A Navicula seminulum Grun. 1860 41 2.4 6.1 0.6 38 3.4 2.7 25 88.1 59.0 34 NA005B Navicula seminulum var. intcrmedia Hust. 13 1.2 6.4 0.6 12 7.1 1.6 9 128.5 36.5 10 NA048A Navicula soehrensis Krasske 1923 13 24.5 5.1 0.2 13 3.4 2.3 8 230.6 50.3 9 Taxon Taxon Name Total Dataset pH DOC Al Code N Max 0 T N 0 T N 0 T N NA170A Navicula strenzkii Hust. 3 4.6 6.1 0.4 3 1.1 0.2 3 22.1 1.6 3 NA043A Navicula subatomoides Hust. ex Patr. 1945 5 1.4 6.5 0.4 5 2.1 1.2 5 41.9 47.2 5 NA160A Navicula submolesta Hust. 1949 13 1.4 5.5 0.5 13 1.3 0.8 11 74.l 33.5 12 NA114A Navicula subrotundata Hust. 1945 2 1.3 6.5 0.2 2 1.8 1 26.4 1 NA033A Navicula subtilissima Cleve 1891 30 2.2 5.2 0.6 28 1.5 0.8 19 116.9 66.0 17 NA086A Navicula tantula Hust. 1943 9 5.5 6.3 0.4 7 4.2 1.8 8 42.8 29.9 7 NA135A Navicula tenuicephala Hust. 1942 23 32.5 5.3 0.2 23 0.9 1.0 12 87.0 33.3 15 NA063A Navicula trivialis Lange-Bertalot 1980 5 2.9 6.6 0.1 5 4.8 0.9 2 96.4 30.7 3 NA9964 Navicula [cf. spirata] L. Hir (SF) 1986 16 1.3 5.3 0.6 15 2.4 1.6 13 80.6 36.3 13 NA9955 Navicula [cf. vitiosa] L. Hir (SF) 1986 16 2.3 6.1 0.6 14 3.3 2.2 10 116.2 75.2 11 NA9919 Navicula [cumbriensis (small)] Gulspettvatn (IR) 1988 11 2.7 6.0 0.7 11 6.5 1.1 7 114.7 37.6 9 NA9963 Navicula [sp. 1] L. Hir (SF) 1986 29 7.1 5.6 0.5 27 2.0 1.2 21 62.2 30.0 21 NA9904 Navicula [sp. a Merilainen 1969: fig. 143] SWAP Sweden (IR & NJA) 1989 10 1.6 6.1 0.5 9 7.2 1.6 5 105.7 44.2 8 NE003A Neidium affine (Ehrenb.) Pfitz. 1871 47 2.2 5.3 0.6 45 3.5 1.8 35 119.4 76.7 34 NE003C Neidium affine var. amphirhynchus (Ehrenb.) Cleve 1894 17 2.6 5.7 0.5 17 4.9 2.5 11 120.4 33.6 11 NE003B Neidium affine var. longiceps (Greg.) Cleve 1896 20 2.4 5.0 0.4 18 3.1 2.3 15 152.7 83.0 15 NE004A Neidium bisulcatum (Lagerst.) Cleve 1894 58 2.6 5.1 0.6 57 2.7 1.7 45 128.1 54.7 44 NE012A Neidium g!aberrimum (Ostr.) R. Ross in Hartley 1986 6 1.9 5.5 0.4 6 0.8 1.3 5 83.9 52.8 5 NE020A Neidium hercynicum A. Mayer 1917 5 2.1 5.5 0.4 4 1.3 0.8 5 64.4 46.1 5 NE023A Neidium inconspicuum Hust. 1921 8 1.1 5.7 0.7 7 1.4 0.9 8 58.3 43.9 6 Nl021A Nitzschia acula Hantzsch ex Cleve & Grun. 1880 14 1.7 5.9 0.6 14 5.1 2.3 10 90.0 48.5 10 Nl002A Nitzschia fonticola Grun. in Van Heurck 1881 49 4.9 6.3 0.6 43 3.6 2.3 36 59.4 45.4 39 Nl008A Nitzschia frustulum (Kutz.) Grun. in Cleve & Grun. 1880 23 1.4 6.4 0.4 19 3.1 2.1 18 72.9 54.0 20 NI017A Nitzschia gracilis Hantzsch 1860 30 2.2 5.9 0.5 28 5.1 2.0 17 107.0 42.4 20 Nl027A Nitzschia microcephala Grun. in Cleve & Grun. 1880 7 8.5 6.5 0.2 7 2.2 1.0 4 35.5 30.2 7 NI009A Nitzschia palea (Kutz.) W. Sm. 1856 34 2.3 6.2 0.6 30 4.3 2.3 22 79.2 50.4 24 Nl009B Nitzschia pal ea var. tenuirostris Grun. in Van Heurck 1881 9 2.8 6.2 0.4 9 3.2 1.6 9 64.5 44.7 8 Nl005A Nitzschia perminuta (Grun. in Van Heurck) M. Perag. 1903 73 5.6 5.7 0.5 71 2.8 1.9 55 68.1 37.5 55 Nll52A Nitzschia pusilla Grun. 1862 7 2.7 5.8 0.6 6 2.0 1.3 7 40.9 36.4 6 Nl026A Nitzschia romana Grun. in Van Heurck 1881 12 14.9 6.3 0.5 11 3.8 1.6 7 152.3 21.7 4 Nl9984 Nitzschia [cf. gracilis] SW AP Sweden (IR & NJA) 1989 1 3.5 7.3 1 7.3 1 38.0 1 OPOOlA Opephora martyi Herib. 1902 6 1.8 6.5 0.5 6 2.2 1.9 3 110.1 40.4 6 PE002A Peronia fibula (Breb. ex Kutz.) R. Ross 1956 135 9.7 5.3 0.5 128 2.3 1.6 96 112.7 59.6 91 PI015A Pinnularia abaujensis (Pant.) R. Ross in Hartley 1986 69 3.7 5.6 0.8 67 3.4 1.9 44 91.6 57.7 49 PI014A Pinnularia appendiculata (Ag.) Cleve 1896 9 2.8 5.8 0.6 8 2.6 1.3 8 76.5 62.5 71 Taxon Taxon Name· Total Dataset pH DOC Al Code N Max 0 T N 0 T N 0 T N PI055A Pinnularia balfouriana Gnm. ex Cleve 1896 1 1.4 6.6 1 1.4 1 20.0 1 PI018A Pinnularia biceps Greg. 1856 95 4.7 5.2 0.6 93 2.7 2.2 66 122.9 60.0 65 PI018B Pinnularia biceps form petersenii R. Ross 1947 9 2.0 5.3 0.5 9 1.7 1.5 8 99.5 69.1 8 PI016A Pinnularia divergentissima (Grun.in Van Heurck) Cleve 1896 9 1.6 5.9 0.5 7 2.6 1.9 7 44.1 31.9 4 PI023A Pinnularia irrorata (Grun. in Van Heurck) Hust. 1939 47 3.0 5.4 0.7 43 4.4 1.7 28 136.8 69.3 32 PI005A Pinnularia major (Kutz.) W. Sm. 1853 22 2.2 5.3 0.5 21 4.7 2.2 14 134.3 55.9 16 PIOllA Pinnularia microstauron (Ehrenb.) Cleve 1891 81 3.7 5.4 0.6 80 2.8 2.4 59 100.3 49.5 56 PI139A Pinnularia obscura Krasske 1932 1 1.2 5.5 I 0.7 1 37.9 1 PI056A Pinnularia rupestris Hantzsch in Rabenh. 1861 21 3.4 4.9 0.5 21 4.7 2.2 8 120.8 20.7 9 PI022B Pinnularia subcapitata var. hilseana (Janisch ex Rabenh.) 0. Mull. 1898 73 9.4 5.0 0.5 69 3.1 2.1 54 115.4 53.9 50 Pl164A Pinnularia termitina (Ehrenb.) Patr. in Patr. & Reimer 1966 8 3.4 5.5 0.7 8 2.2 1.1 7 74.0 45.1 6 PI007A Pinnularia viridis (Nitzsch) Ehrenb. 1843 41 1.4 5.5 0.7 37 4.0 2.5 30 107.7 63.5 30 RH006B Rhopalodia operculata (Ag.) Hakansson 1979 2 1.4 6.3 0.7 2 2.3 1.7 2 50.1 59.7 2 SEOOlA Semiorbis hemicyclus (Ehrenb.) Patr. in Patr. & Reimer 1966 42 24.8 4.8 0.3 42 3.4 1.4 25 177.2 43.1 24 SAOOIA Stauroneis anceps Ehrenb. 1843 51 2.1 5.6 0.6 47 3.0 2.0 34 82.5 56.3 36 SAOOIB Stauroneis anceps form gracilis Rabenh. 1864 62 1.8 5.9 0.6 58 2.6 1.8 44 67.9 51.3 45 SA042A Stauroneis gracillima Hust. 1943 16 1.3 5.1 0.5 14 3.1 1.5 16 118.0 63.1 14 SA006A Stauroneis phoenicenteron (Nitzsch) Ehrenb. 1943 27 1.8 5.9 0.8 24 3.6 2.1 18 100.6 85.0 18 SP002A Stenopterobia sigrnatella (Greg.) R. Ross in Hartley 1986 56 2.8 5.3 0.7 53 3.8 2.3 37 156.3 56.0 38 ST004A Stephanodiscus minutula (Kutz.) Round 5 2.1 6.9 0.2 4 6.6 1.5 2 108.0 40.2 5 ST0lOA Stephanodiscus parvus Stoermer & Hakansson 1984 1 8.6 7.1 1 4.6 I 133.3 1 SU004A Surirella biseriata Breb. & Oodey, 1835 21 4.0 5.3 0.4 21 0.8 0.7 20 125.1 48.7 18 SU006A Surirella delicatissima Lewis 1864 84 8.2 5.0 0.4 81 2.7 2.1 62 129.9 63.2 60 SU005A Surire!la linearis W. Sm. 1853 40 3.6 5.3 0.6 39 4.2 2.2 29 131.9 50.6 28 SU002A Surirella ovata Kutz. 1844 2 1.0 5.5 1 0.7 1 37.2 1.7 2 SY003A Synedra acus Kutz. 1844 27 3.5 6.4 0.5 24 6.0 1.8 16 89.1 52.9 23 SY043A Synedra famelica Kutz. 1844 2 2.8 5.9 0.3 2 4.8 0.3 2 107.6 1 SY0lOA Synedra minuscula Gnm. in Van Heurck 1881 29 3.6 6.0 0.6 26 3.7 1.5 23 117.3 70.8 23 SY009A Synedra nana Meister 1912 5 2.7 6.2 0.7 5 5.8 0.5 4 80.8 62.6 3 SY004A Synedra parasitica (Yv. Sm.) Hust. 1930 9 1.4 6.7 0.4 8 4.2 1.9 4 51.6 40.0 9 SY002A Synedra rumpens Kutz. 1844 20 2.6 6.4 0.6 18 4.5 1.8 12 80.6 30.0 14 SY013A Synedra tenera W. Sm. 1856 26 4.3 6.6 0.7 24 6.0 1.3 12 113.5 43.3 17 TA003A Tabellaria binalis (Ehrenb.) Grun. in Van Heurck 1881 63 51.5 4.7 0.3 60 2.6 1.7 43 171.3 50.5 41 TA002A Tabellaria fenestrata (Lyngb.) Kutz. 1844 18 1.0 5.9 0.7 18 4.0 2.1 15 110.6 54.9 13 TAOOlA Tabellaria flocculosa (Roth) Kutz. 1844 171 28.3 5.4 0.6 161 3.4 1.9 118 106.0 51.l 122 Taxon Code Taxon Name TA9996 Tabellaria flocculosa agg. TA004A Tabellaria quadriseptata Knudson 1952 Total Dataset N Max 44 24.5 89 47.8 0 T N 6.1 0.7 43 4.9 0.3 87 DOC 0 T N 5.6 2.2 28 2.7 1.4 66 0 Al T 108.7 41 N 119.3 45.0 58

lake

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lake

Abstract

Extracted data

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