Ecography ECOG-02034 Heino, J. and de Mendoza, G. 2016. Predictability of stream distributions is dependent on niche position, but not on biological traits or taxonomic relatedness of species. – Ecography doi: 10.1111/ ecog.02034

Supplementary material Appendix 1

Predictability of stream insect distributions is dependent on niche position, but not on

biological traits or taxonomic relatedness of species

Jani Heino & Guillermo de Mendoza

Ecography

Table A1 of the 36 species analysed in this study.

Table A2 Amount of deviance of binomial species distribution models, with information used for each species with regard to biological and ecological traits.

Table A3 Statistical significance of univariate analyses among independent variables.

Table A4 Explanatory variables in the binomial models for each species.

Table A5 Comparative analyses of the species’ adjusted D2 values from previous binomial

GLMs, as explained by Gaussian, quasi-Poisson, and negative binomial GLMs, with reduced models following AIC values and ANOVA tests.

Figure A1 Boundaries of the three drainage basins studied.

Figure A2 Relationships between the adjusted D2 from species models and niche position, niche breadth, and site occupancy for all models considered (i.e. E+M+B models, compared to ENV, MEM, and BAS models).

Figure A3 Comparison of niche positions and adjusted-D2 values of single-species binomial models between Ephemeroptera, , Coleoptera, and Trichoptera.

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Table A1. Taxonomy of the 36 stream insect species (class Insecta) analysed in this study, following de Jong et al. (2014).

Order Family Genus Species Ephemeroptera Baetidae Baetis B. muticus (Linnaeus 1758) Ephemeroptera Baetidae Baetis B. niger (Linnaeus 1761) Ephemeroptera Baetidae Baetis B. rhodani (Pictet 1843) Ephemeroptera Ameletidae Ameletus A. inopinatus Eaton 1887 Ephemeroptera Heptageniidae Heptagenia H. dalecarlica Bengtsson 1912 Ephemeroptera Leptophlebiidae Habrophlebia H. lauta Eaton 1884 Ephemeroptera Leptophlebiidae Leptophlebia L. marginata (Linnaeus 1767) Ephemeroptera Ephemerellidae Ephemerella E. aroni Eaton 1908 Plecoptera Diura D. bicaudata (Linnaeus 1758) Plecoptera Perlodidae Diura D. nanseni (Kempny 1900) Plecoptera Perlodidae Isoperla I. difformis (Klapalek 1909) Plecoptera Perlodidae Isoperla I. grammatica (Poda 1761) Plecoptera Chloroperlidae Siphonoperla S. burmeisteri (Pictet 1841) Plecoptera Nemouridae Amphinemura A. borealis (Morton 1894) Plecoptera Nemouridae Amphinemura A. sulcicollis (Stephens 1836) Plecoptera Nemouridae Protonemura P. intricata (Ris 1902) Plecoptera Nemouridae Protonemura P. meyeri (Pictet 1841) Plecoptera Capnopsis C. schilleri (Rostock 1892) Plecoptera Leuctridae Leuctra L. digitata Kempny 1899 Plecoptera Leuctridae Leuctra L. hippopus Kempny 1899 Plecoptera Leuctridae Leuctra L. nigra (Olivier 1811) Coleoptera Hydraenidae Hydraena H. gracilis Germar 1824 Coleoptera Elmis E. aenea (Muller 1806) Coleoptera Elmidae Limnius L. volckmari (Panzer 1793) Coleoptera Elmidae Oulimnius O. tuberculatus (Muller 1806) Megaloptera Sialidae Sialis S. fuliginosa Pictet 1836 Trichoptera Rhyacophilidae Rhyacophila R. nubila Zetterstedt 1840 Trichoptera Rhyacophilidae Rhyacophila R. obliterata McLachlan 1863 Trichoptera Plectrocnemia P. conspersa (Curtis 1834) Trichoptera Polycentropodidae Polycentropus P. flavomaculatus (Pictet 1834) Trichoptera Hydropsychidae Hydropsyche H. angustipennis (Curtis 1834) Trichoptera Hydropsychidae Hydropsyche H. saxonica McLachlan 1884 Trichoptera Brachycentridae Micrasema M. gelidum McLachlan 1876 Trichoptera Limnephilidae Potamophylax P. cingulatus (Stephens 1837) Trichoptera Goeridae Silo S. pallipes (Fabricius 1871) Trichoptera Sericostomatidae Sericostoma S. personatum (Kirby & Spence 1826)

Reference cited: de Jong, Y. et al. 2014. Fauna Europaea: all species on the web. – Data Journal 2: e4034.

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Table A2. Amount of deviance of binomial species distribution models that can be attributed to ecological variables (i.e. adjusted D2 values after Gaussian GLMs). Presence-absence models are binomial GLMs built with either environmental (E models), spatial (M models), or basin variables (B models), and with all variables combined (E+M+B models). For taxonomic (TAX) and trait (TRA) vectors, the scores from a Principal Coordinates Analysis (PCoA) are shown. Site occupancy (No_sites), niche position (NP), niche breadth (NB), and the original trait data are also shown for each species. FFG, functional feeding group; HTG, habit trait group.

Adjusted D2 from GLM Taxonomic vectors from PCoA Trait vectors from PCoA Niche measures Original trait data Species E+M+B M B E PCO1TAX PCO2TAX PCO3TAX PCO1TRA PCO2TRA PCO3TRA No_sites NP NB Body_size Dispersal FFG HTG Baetis muticus 0.355 0.037 0.150 0.361 -16.822 39.034 12.365 -0.301 -0.283 0.127 29 0.990 1.060 1-2_cm low Scra Swim Baetis niger 0.050 0.000 0.050 0.000 -16.822 39.034 12.365 -0.301 -0.283 0.127 32 0.780 1.850 1-2_cm low Scra Swim Baetis rhodani 0.254 0.117 0.054 0.076 -16.822 39.034 12.365 -0.301 -0.283 0.127 48 0.340 2.260 1-2_cm low Scra Swim Ameletus inopinatus 0.181 0.088 0.000 0.066 -13.902 29.901 7.537 -0.301 -0.283 0.127 35 0.280 1.310 1-2_cm low Scra Swim Heptagenia dalecarlica 0.189 0.052 0.109 0.153 -13.902 29.901 7.537 -0.045 -0.223 0.074 16 2.710 0.980 1-2_cm low Scra Clin Habrophlebia lauta 0.550 0.000 0.536 0.422 -14.700 32.260 8.585 -0.301 -0.283 0.127 12 4.680 0.830 1-2_cm low Scra Swim Leptophlebia marginata 0.378 0.228 0.203 0.094 -14.700 32.260 8.585 -0.301 -0.156 0.149 15 2.890 2.330 1-2_cm low Gath Swim Ephemerella aroni 0.064 0.000 0.000 0.060 -13.902 29.901 7.537 0.129 -0.064 -0.335 27 0.790 3.720 0.5-1_cm low Gath Clin Diura bicaudata 0.000 0.000 0.000 0.000 31.804 -4.026 3.809 0.360 -0.039 -0.089 7 1.060 1.270 2-4_cm low Pre Clin Diura nanseni 0.329 0.224 0.169 0.125 31.804 -4.026 3.809 0.360 -0.039 -0.089 11 2.360 0.760 2-4_cm low Pre Clin Isoperla difformis 0.459 0.071 0.338 0.282 31.804 -4.026 3.809 0.070 -0.091 0.105 22 1.780 1.550 1-2_cm low Pre Clin Isoperla grammatica 0.754 0.083 0.612 0.599 31.804 -4.026 3.809 0.070 -0.091 0.105 14 6.290 1.440 1-2_cm low Pre Clin Siphonoperla burmeisteri 0.365 0.000 0.000 0.367 25.593 -2.967 2.136 0.070 -0.091 0.105 6 5.610 4.130 1-2_cm low Pre Clin Amphinemura borealis 0.431 0.098 0.042 0.439 31.804 -4.026 3.809 -0.283 0.338 -0.364 11 2.380 2.160 0.5-1_cm low Shre Spra Amphinemura sulcollis 0.457 0.116 0.326 0.189 31.804 -4.026 3.809 -0.283 0.338 -0.364 22 1.170 1.360 0.5-1_cm low Shre Spra Protonemura intricata 0.257 0.075 0.031 0.086 31.804 -4.026 3.809 -0.368 0.207 0.004 25 0.820 0.700 1-2_cm low Shre Spra Protonemura meyeri 0.837 0.000 0.816 0.584 31.804 -4.026 3.809 -0.368 0.207 0.004 18 3.100 0.700 1-2_cm low Shre Spra Capnopsis schilleri 0.609 0.423 0.192 0.539 25.593 -2.967 2.136 -0.368 0.207 0.004 6 7.750 3.640 1-2_cm low Shre Spra Leuctra digitata 0.484 0.053 0.152 0.399 30.969 -3.873 3.504 -0.368 0.207 0.004 19 2.060 2.460 1-2_cm low Shre Spra Leuctra hippopus 0.800 0.743 0.366 0.282 30.969 -3.873 3.504 -0.368 0.207 0.004 14 4.440 1.870 1-2_cm low Shre Spra Leuctra nigra 0.297 0.000 0.156 0.228 30.969 -3.873 3.504 -0.368 0.207 0.004 9 5.010 2.640 1-2_cm low Shre Spra Hydraena gracilis 0.325 0.000 0.202 0.252 -6.986 3.954 -36.969 0.109 -0.308 -0.171 22 1.910 1.300 0.25-0.5_cm low Scra Clin Elmis aenea 0.443 0.060 0.188 0.345 -7.836 4.632 -48.903 0.109 -0.308 -0.171 38 0.830 1.740 0.25-0.5_cm low Scra Clin Oulimnius tuberculatus 0.548 0.384 0.166 0.272 -7.836 4.632 -48.903 0.109 -0.308 -0.171 12 2.630 1.210 0.25-0.5_cm low Scra Clin Limnius volckmari 0.600 0.286 0.121 0.343 -7.836 4.632 -48.903 0.109 -0.308 -0.171 8 5.410 0.950 0.25-0.5_cm low Scra Clin Sialis fuliginosa 0.669 0.295 0.178 0.294 -5.054 2.370 -6.747 0.224 0.019 -0.051 8 3.510 2.210 2-4_cm low Pre Burr Rhyacophila nubila 0.276 0.088 0.099 0.078 -25.188 -25.620 7.544 0.360 -0.039 -0.089 32 0.720 1.650 2-4_cm low Pre Clin Rhyacophila obliterata 0.604 0.315 0.362 0.242 -25.188 -25.620 7.544 0.360 -0.039 -0.089 25 1.930 0.850 2-4_cm low Pre Clin Plectrocnemia conspersa 0.426 0.150 0.083 0.197 -24.321 -24.407 6.915 0.635 0.160 0.172 20 2.380 1.050 2-4_cm high Pre Clin Polycentropus flavomaculatus 0.796 0.405 0.380 0.651 -24.321 -24.407 6.915 0.285 0.090 0.436 7 6.560 2.060 1-2_cm high Pre Clin Hydropsyche angustipennis 0.699 0.632 0.380 0.533 -25.188 -25.620 7.544 0.575 0.185 0.133 7 7.540 0.760 2-4_cm high Fil Clin Hydropsyche saxonica 0.400 0.093 0.134 0.248 -25.188 -25.620 7.544 0.575 0.185 0.133 8 3.700 0.680 2-4_cm high Fil Clin Micrasema gelidum 0.396 0.000 0.107 0.378 -23.002 -22.623 6.072 0.030 0.168 -0.344 25 2.530 1.720 0.5-1_cm low Shre Clin Potamophylax cingulatus 0.179 0.064 0.068 0.062 -23.002 -22.623 6.072 0.386 0.444 -0.004 23 1.100 1.160 2-4_cm high Shre Clin Silo pallipes 0.393 0.173 0.288 0.000 -23.002 -22.623 6.072 -0.045 -0.223 0.074 9 2.900 1.640 1-2_cm low Scra Clin Sericostoma personatum 0.373 0.075 0.169 0.288 -23.002 -22.623 6.072 -0.260 0.573 0.355 11 3.700 0.850 1-2_cm low Shre Spra 3

Table A3. Statistical significance (P-values) of univariate analyses among independent variables (i.e. site occupancy, niche characteristics, species trait and taxonomic variables). Because this includes continuous and categorical variables, and among the latter the number of categories also varies, statistical tests may refer to the significance of the Spearman correlation coefficient, to Kruskal-Wallis tests, to Mann-Whitney tests, or to Fisher’s exact tests on a contingency table. Overall, this implies that P-values are not always strictly comparable, yet still indicate to what extent two variables can be considered as correlated. Significant P-values (i.e. P < 0.05) are highlighted in boldface. Sites, site occupancy; NP, niche position; NB, niche breadth; DP, dispersal potential; BS, body size; FFG, functional feeding group; HTG, habit trait group; TRA, trait vector; TAX, taxonomic vector. Vectors are multivariate axes of a Principal Coordinates Analysis (PCoA) combining either trait or taxonomic variables.

Sites NP NB DP BS FFG HTG TRA-1 TRA-2 TRA-3 TAX-1 TAX-2 TAX-3 Sites - NP <0.001 - NB 0.983 0.974 - DP 0.184 0.170 0.104 - BS 0.664 0.625 0.088 0.033 - FFG 0.139 0.269 0.066 0.020 <0.001 - HTG 0.092 0.237 0.557 0.267 0.010 <0.001 - TRA-1 0.372 0.965 0.086 <0.001 <0.001 <0.001 <0.001 - TRA-2 0.128 0.179 0.916 0.081 0.004 <0.001 <0.001 0.588 - TRA-3 0.385 0.114 0.458 0.013 <0.001 0.308 0.067 0.622 0.873 - TAX-1 0.403 0.968 0.358 0.002 0.238 0.081 0.031 0.009 0.457 0.014 - TAX-2 0.122 0.184 0.048 0.002 0.006 <0.001 0.002 0.002 <0.001 0.955 0.123 - TAX-3 0.033 0.098 0.256 0.122 0.013 0.149 0.001 0.704 0.647 <0.001 <0.001 0.944 -

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Table A4. The explanatory variables in the binomial models for each species.

Species Full model Baetis muticus MEM.8 + Basin + macrophytes + stream width + conductivity Baetis niger Basin Baetis rhodani MEM.4 + MEM.8 + Basin + velocity Ameletus inopinatus MEM.2 + Basin + stream width Heptagenia dalecarlica MEM.8 + Basin + macrophytes Habrophlebia lauta Basin + pH + Boulder Leptophlebia marginata MEM.3 + MEM.12 + Basin + macrophytes Ephemerella aroni Basin + velocity Diura bicaudata Basin Diura nanseni MEM.4 + MEM.5 + Basin + Gravel + macrophytes Isoperla difformis MEM.20 + Basin + conductivity + macrophytes + velocity Isoperla grammatica MEM.7 + Basin + pH Siphonoperla burmeisteri Basin + stream width + Gravel + conductivity Amphinemura borealis MEM.8 + Basin + streamwidth + pH Amphinemura sulcollis MEM.6 + MEM.16 + Basin + conductivity Protonemura intricata MEM.20 + MEM.19 + Basin + Sand + pH Protonemura meyeri Basin + macrophytes + conductivity + Sand + velocity Capnopsis schilleri MEM.11 + Basin + streamwidth + conductivity + Sand Leuctra digitata MEM.2 + Basin + conductivity + Sand + meandepth Leuctra hippopus MEM.19 + MEM.14 + MEM.13 + Basin + conductivity + Cobble Leuctra nigra Basin + macrophytes + meandepth Hydraena gracilis Basin + velocity + macrophytes + streamwidth Elmis aenea MEM.20 + Basin + meandepth + macrophytes + Boulder Oulimnius tuberculatus MEM.5 + MEM.7 + MEM.11 + MEM.1 + Basin + Boulder + meandepth Limnius volckmari MEM.5 + MEM.8 + Basin + Gravel + meandepth Sialis fuliginosa MEM.9 + MEM.7 + MEM.1 + Basin + meandepth + streamwidth Rhyacophila nubila MEM.19 + MEM.4 + Basin + streamwidth Rhyacophila obliterata MEM.4 + MEM.9 + MEM.3 + MEM.2 + Basin + macrophytes + shading Plectrocnemia conspersa MEM.2 + Basin + macrophytes + meandepth + shading Polycentropus flavomaculatus MEM.7 + Basin + meandepth + macrophytes + Boulder Hydropsyche angustipennis MEM.8 + MEM.12 + MEM.11 + MEM.9 + Basin + pH + macrophytes Hydropsyche saxonica MEM.5 + Basin + Cobble + velocity Micrasema gelidum Basin + macrophytes + pH Potamophylax cingulatus MEM.10 + Basin + meandepth Silo pallipes MEM.2 + Basin Sericostoma personatum MEM.5 + Basin + Gravel + Sand

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Table A5 Comparative analyses of the species’ adjusted D2 values from previous binomial GLMs (i.e. E, environmental models; M, spatial models; B, basin models; E+M+B, combined models), as explained by Gaussian (link = “identity”), quasi-Poisson (link = “log”), and negative binomial (link = “logit”), generalised linear models (GLMs), with niche characteristics, site occupancy, and trait and taxonomic vectors. PCO = principal coordinate, TRA = trait, TAX = taxonomy. PCOs thus describe the first three trait or taxonomic vectors. In the reduced model, variables are chosen following forward selection according to AIC values and ANOVA tests (i.e. Chi-square tests on a contingency table comparing two models at a time, specifically testing the significance of adding one variable to a previous, simpler model). AIC values could not be used in quasi-Poisson GLMs. The order in which the variables are added to the reduced models, is indicated by numbers in brackets. In negative binomial GLMs explaining the D2 of B models, the selection of the second and third variables is supported by ANOVA only. Significant P-values for variables in the full model (i.e. P < 0.05) are highlighted in boldface.

E models:

Response Gaussian GLM Quasi-Poisson GLM Negative binomial GLM D2 of E models Estimate SE t P Reduced Estimate SE t P Reduced Estimate SE t P Reduced Intercept -0.099 0.107 -0.920 0.366 -2.509 0.514 -4.879 <0.001 -3.125 0.751 -4.160 <0.001 Site occupancy 0.008 0.003 2.348 0.027 0.025 0.017 1.461 0.156 0.046 0.024 1.917 0.066 Niche Position 0.092 0.018 5.099 <0.001 AIC/ANOVA (1) 0.283 0.073 3.896 <0.001 ANOVA (1) 0.518 0.132 3.934 <0.001 AIC/ANOVA (1) Niche Breadth -0.033 0.028 -1.173 0.251 -0.134 0.121 -1.109 0.277 -0.202 0.183 -1.103 0.280 TRA-PCO1 -0.023 0.104 -0.223 0.826 -0.274 0.523 -0.525 0.604 -0.239 0.709 -0.337 0.738 TRA-PCO2 0.113 0.152 0.742 0.465 0.382 0.780 0.490 0.628 0.854 1.026 0.833 0.413 TRA-PCO3 -0.060 0.156 -0.384 0.704 -0.037 0.709 -0.052 0.959 -0.460 0.966 -0.476 0.638 TAX-PCO1 0.001 0.001 1.070 0.294 0.004 0.005 0.765 0.451 0.006 0.008 0.817 0.421 TAX-PCO2 0.000 0.002 -0.027 0.979 -0.003 0.011 -0.321 0.751 0.000 0.013 0.027 0.979 TAX-PCO3 -0.002 0.002 -0.906 0.373 -0.008 0.008 -1.053 0.302 -0.012 0.010 -1.160 0.256

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M models:

Response Gaussian GLM Quasi-Poisson GLM Negative binomial GLM D2 of M models Estimate SE t P Reduced Estimate SE t P Reduced Estimate SE t P Reduced Intercept -0.024 0.147 -0.166 0.870 -2.705 0.981 -2.756 0.011 -2.757 1.178 -2.340 0.0272 Site occupancy 0.003 0.005 0.535 0.597 0.002 0.034 0.054 0.956 0.006 0.040 0.149 0.883 Niche Position 0.055 0.025 2.232 0.035 AIC/ANOVA (1) 0.260 0.132 1.976 0.059 ANOVA (1) 0.335 0.181 1.848 0.076 AIC/ANOVA (1) Niche Breadth -0.020 0.038 -0.518 0.609 -0.104 0.237 -0.439 0.664 -0.148 0.296 -0.499 0.622 TRA-PCO1 0.036 0.142 0.255 0.801 -0.111 0.920 -0.121 0.905 -0.088 1.109 -0.079 0.937 TRA-PCO2 0.060 0.208 0.287 0.777 0.139 1.347 0.103 0.919 0.149 1.601 0.093 0.927 TRA-PCO3 -0.048 0.213 -0.224 0.825 -0.233 1.322 -0.176 0.862 -0.271 1.640 -0.165 0.870 TAX-PCO1 -0.001 0.002 -0.333 0.742 -0.003 0.010 -0.283 0.780 -0.003 0.012 -0.275 0.786 TAX-PCO2 0.000 0.003 -0.076 0.940 -0.009 0.019 -0.488 0.630 -0.010 0.022 -0.437 0.666 TAX-PCO3 -0.001 0.002 -0.449 0.657 -0.009 0.014 -0.609 0.548 -0.010 0.017 -0.596 0.556

B models:

Response Gaussian GLM Quasi-Poisson GLM Negative binomial GLM D2 of B models Estimate SE t P Reduced Estimate SE t P Reduced Estimate SE t P Reduced Intercept 0.069 0.126 0.551 0.586 -2.052 0.563 -3.646 0.001 -2.075 0.759 -2.732 0.011 Site occupancy 0.006 0.004 1.491 0.148 0.023 0.019 1.208 0.238 0.035 0.026 1.353 0.188 Niche Position 0.070 0.021 3.276 0.003 AIC/ANOVA (1) 0.292 0.080 3.656 0.001 ANOVA (1) 0.408 0.124 3.296 0.003 AIC/ANOVA (1) Niche Breadth -0.111 0.033 -3.379 0.002 AIC/ANOVA (2) -0.648 0.156 -4.169 <0.001 ANOVA (2) -0.838 0.221 -3.784 <0.001 ANOVA (2) TRA-PCO1 -0.151 0.122 -1.246 0.224 -1.466 0.538 -2.725 0.011 ANOVA (3) -1.681 0.747 -2.251 0.033 ANOVA (3) TRA-PCO2 -0.233 0.178 -1.308 0.202 -1.705 0.744 -2.292 0.030 -2.105 1.071 -1.966 0.060 TRA-PCO3 -0.102 0.182 -0.558 0.581 0.002 0.828 0.003 0.998 -0.010 1.074 -0.010 0.992 TAX-PCO1 0.002 0.001 1.357 0.187 0.008 0.005 1.531 0.138 0.011 0.008 1.446 0.160 TAX-PCO2 -0.003 0.002 -1.136 0.266 -0.026 0.010 -2.519 0.018 -0.031 0.014 -2.190 0.038 TAX-PCO3 0.002 0.002 1.191 0.244 0.009 0.009 1.030 0.312 0.009 0.011 0.838 0.410

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E+M+B models:

Response Gaussian GLM Quasi-Poisson GLM Negative binomial GLM D2 of E+M+B models Estimate SE t P Reduced Estimate SE t P Reduced Estimate SE t P Reduced Intercept 0.123 0.130 0.950 0.351 -1.460 0.355 -4.109 <0.001 -1.914 0.672 -2.848 0.008 Site occupancy 0.007 0.004 1.763 0.090 0.014 0.012 1.190 0.245 0.038 0.021 1.782 0.086 Niche Position 0.094 0.022 4.296 <0.001 AIC/ANOVA (1) 0.190 0.052 3.617 0.001 ANOVA (1) 0.492 0.137 3.591 0.001 AIC/ANOVA (1) Niche Breadth -0.066 0.034 -1.958 0.061 -0.174 0.091 -1.915 0.067 -0.316 0.175 -1.808 0.082 TRA-PCO1 -0.141 0.125 -1.126 0.271 -0.530 0.347 -1.528 0.138 -0.732 0.597 -1.225 0.231 TRA-PCO2 -0.075 0.183 -0.408 0.687 -0.338 0.499 -0.678 0.504 -0.275 0.835 -0.329 0.745 TRA-PCO3 -0.066 0.188 -0.354 0.726 -0.019 0.501 -0.038 0.970 -0.451 0.905 -0.499 0.622 TAX-PCO1 0.001 0.001 0.781 0.442 0.003 0.004 0.734 0.469 0.004 0.007 0.561 0.579 TAX-PCO2 -0.003 0.002 -1.426 0.166 -0.012 0.007 -1.826 0.079 -0.016 0.011 -1.467 0.154 TAX-PCO3 -0.001 0.002 -0.720 0.478 -0.004 0.005 -0.844 0.406 -0.009 0.010 -0.904 0.374

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Figure A1 Boundaries of the three drainage basins, including Tenojoki (T), Koutajoki (K) and Iijoki (I) in Finland. Note that the Tenojoki basin extends to northern Norway and drains into the Arctic Sea, and the Koutajoki basin drains into to the White Sea in Russia. Figure credit: Annika Vilmi.

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Figure A2 Scatterplots and significance of Spearman correlation coefficients showing the relationships between the adjusted D2 from species models and niche position (NP, from the OMI analysis), niche breadth (NB, from the OMI analysis) and site occupancy (sites), for all models considered (i.e. E+M+B models as shown in Fig. 6 of the main manuscript, compared to ENV, MEM, and BAS models). Asterisks indicate statistical significance: * P < 0.05, ** P < 0.01, *** P < 0.001.

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Figure A3. Comparisons of niche positions (top) and adjusted-D2 values of single-species binomial models (bottom) between Ephemeroptera (Eph), Plecoptera (Plec), Coleoptera (Col), and Trichoptera (Trich). P-values refer to Kruskal-Wallis tests comparing all groups simultaneously.

10 (P ~ 0.203)

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6

4 Niche position Niche

2

0 Eph Plec Col Trich (a) (b) (ab) (ab)

1.0 E models (P ~ 0.223) 1.0 M models (P ~ 0.307) 1.0 B models (P ~ 0.545) 1.0 E+M+B models (P ~ 0.083)

0.8 0.8 0.8 0.8 value

2 0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4

0.2 0.2 0.2 0.2 Adjusted-D

0.0 0.0 0.0 0.0 Eph Plec Col Trich Eph Plec Col Trich Eph Plec Col Trich Eph Plec Col Trich (a) (b) (ab) (b)

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