gcb12015-sup-0001-TableS1-S4
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Table S1. Results cyanobacterial literature review documenting the association between the most important driver of cyanobacterial biomass/dominance and lake mixis. Studies were identified from ISI web of science using the keywords “stability”, “cyanobacteria”, “temperature”, “phosphorus”, “nutrients” “eutrophication” and/or “mixing”, and subsequent cross referencing. Only studies comparing both the effects of nutrient concentrations and temperature (either direct or indirect) for polymictic, weakly stratified and/or dimictic lakes were kept. “§” indicates conclusion drawn qualitatively from study author(s). “‡” indicates qualitative conclusions summarized in Steinberg and Hartmann (1988). For all other studies, quantitative analyses were provided (no symbol). Author Findenegg Year 1965 Mixis Dimictic Strongest predictor Lake name Alpine lakes Region Austria Overbeck 1968 Dimictic Water column stability‡ Plusse Germany Zimmermann 1969 Dimictic Water column stability‡ Mauensee Switzerland Edmondson 1970 Dimictic Water column stability‡ Washington USA stability‡ Alpine lakes Austria Edeberg Germany Findenegg 1973 Dimictic Meffert 1975 Dimictic Water column stability‡ Water column Stability, CO2 and light § intensity Steinberg 1980 Dimictic Water column stability‡ Ammersee Germany Steinberg & Bucksteeg Faafeng & Nielssen 1980 Dimictic Water column stability‡ Fischkaltersee Germany 1981 Dimictic Water column stability‡ GjersjØn Estonia Steinberg, Wöhlecke & Hämmerle Konopka 1981 Dimictic Water column stability‡ Walchensee Germany 1982 Dimictic Water column stability‡ Crooked USA Hartmann 1985 Dimictic Water column stability‡ Germany Sommers Dokulil & Skolaut 1985 1986 Dimictic Dimictic Water column stability stability‡ Schliersee; Wesslinger Constance Mondsee McQueen & Lean Bürgi et al. 1987 1988 Dimictic Dimictic stability‡ St-George Lucerne Canada Switzerland Soranno 1996 Dimictic Mendota USA Salmaso 2003 Dimictic Italy Salmaso 2005 Dimictic Lugano; Iseo Garda Becker et al. 2010 Dimictic Sau Reservoir Spain Zhang & Prepas 1996 1997 Berger 1975 Polymictic P and C loads§ Poltz & Job 1981 Polymictic Light availability‡ Hamm & Kucklentz 1986 Polymictic TN: TP ratio‡ Jenkins Lofty KleinerGlubigsee; Melangsee; Petersdorfer See Drontermeer; Veluwemeer; Eemmeer; Gooimeer Zwischenahner Meer Rothauer Canada Rücker et al. Weakly stratified Weakly stratified Kusel-Fetzmann & Spatzierer Rücker et al. 1987 Polymictic Light availability‡ Neusiedler Austria 1997 Polymictic Nutrient limitation§ Germany Zhang & Prepas 1996 Polymictic TP Burger et al. Wagner & Adrian 2008 2009 Polymictic Polymictic Internal P loading TP Lebbiner See; Langer See; Wolziger See Baptiste; Crooked Rotorua Müggelsee Water column Temperature Water column Weather and physical variables Winter climate and spring mixing Winter climate and spring mixing Temperature and water column stability Temperature Light limitation§ Germany Austria Italy Germany Netherlands Germany Germany Canada New Zealand Germany References: Becker V, Caputo L, Ordóñez J, Marcé R, Armengol J, Crossetti LO, Huszar VLM (2010) Driving factors of the phytoplankton functional groups in a deep Mediterranean reservoir. Water Research, 44, 3345-3354. Berger C (1975) Occurrence of Oscillatoria agardhii Gom. in some shallow eutrophic lakes. Verhandlungen der Internationalen Vereinigung für Theoretisch und Angewandte Limnologie, 19, 2689-2697. Burger DF, Hamilton DP, Pilditch CA (2008) Modelling the relative importance of internal and external nutrient loads on water column nutrient concentrations and phytoplankton biomass in a shallow polymictic lake. Ecological Modelling, 211, 411-423. Bürgi HR, Ambühl H, Bührer H, Szabó E (1988) Wie reagiert das Seenplankton auf die Phosphor-Entlastung. Mitteilungen der Eidgenosischen Anstalt für Wasserversorgung, Abnwassereinigung und Gewässerschutz, 24, 2-10. Dokulil M, Skolaut C (1986) Succession of phytoplankton in a deep stratifying lake: Mondsee, Austria. Hydrobiologia, 138, 9-24. Edmondson WT (1970) Phosphorus, nitrogen and algae in Lake Washington after diversion of sewage. Science, 169, 190-192. Faafeng BA, Nilssen JP (1981) A twenty-year study of eutrophication in a deep, softwater lake. Verhandlungen der Internationalen Vereinigung für Theo retische und Angewandte Limnologie, 21, 412-424. Findenegg I (1965) Limnologische Unterschiede zwischen den österreichischen und ostschweizerischen Alpenseen und ihre Auswirkung auf das Phytoplankton. Vierteljahresschrift der Naturforschenden Gesellschaft in Zürich, 110, 289-300. Findenegg I (1973) Vorkommen und biologisches Verhalten der Blaualge Oscillatoria rubescens DC in den österreichischen Alpenseen. Carinthia II, 163, 317-330. Hamm A, Kueklentz V (1986) Abwasser-Nachreinigung und Nährstoffelimination durch einen bewachsenen Bodenfilter-Auswirkungen auf die Trophielage eines Kleinsees (Rothauer See/Bayerischer Wald). Bayerische Landesanstalt für Wasserforschung, Munich, Report No. 2, 1-102. Hartmann H (1985) Das Phytoplankton dreier oberbayerischer Kleinseen unter dem Einfluss verschiedener Therapiemassnahmen mit Berücksichtigung physikalischchemischer Parameter. Diploma Thesis, Ludwig-Maximilians-University, Munich. Konopka A (1982) Buoyancy regulation and vertical migration by Oscillatoria rubescens in Crooked Lake, Indiana. British Phycological Journal, 17, 427-442. Kusel-Fetzman E, Spatzierer G (1987) Einflussfaktoren für das Blaualgenwachstum im Neusiedler See-Ergebnisse der Biotests 1985/1986. Wissenschaftliche Arbeiten aus dem Burgenland, Sonderband, 77, 261-300. McOueen DJ, Lean DRS (1987) Influence of water temperature and nitrogen to phosphorus ratios on the dominance of blue-green algae in Lake St George, Ontario. Canadian Journal of Fisheries and Aquatic Sciences, 44, 598-604. Meffert ME (1975) Analysis of the population dynamics of Oscillatoria redekei Van Goor in Lake Edeberg. Verhandlungen der Internationalen Vereinigung für Theoretische und Angewandte Limnologie, 19, 2682-2688. Overbeck J (1968) Prinzipielles zum Vorkommen der Bakterien im See. Mitteilungen der Internationalen Vereinigung für Theoretische und Angewandte Limnotogie, 14, 134-144. Poltz J, Job E (1981) Limnologische Untersuchungen am Zwischenahner Meer und seinen Zuflüssen. Mitteilungen des Niedersächsischen Wasseruntersuchung samt, Hildesheim, 6, 156p. Rücker J, Wiedner C, Zippel P (1997) Factors controlling the dominance of Planktothrix agardhii and Limnothrix redekei in eutrophic shallow lakes. Hydrobiologia, 342/343, 107–115. Salmaso N (2003) Life strategies, dominance patterns, and mechanisms promoting species coexistence in phytoplankton communities along complex environm ental gradients. Hydrobiologia, 502, 13–36. Salmaso N (2005) Effects of climatic fluctuations and vertical mixing on the interannual trophic variability of Lake Garda, Italy. Limnology and Oceanography, 50, 553565. Sommers U (1985) Seasonal Succession of Phytoplankton in Lake Constance. BioScience, 35, 351-357. Soranno PA, Hubler SL, Carpenter SR, Lathrop RC (1996) Phosphorus loads to surface waters: A simple model to account for spatial pattern of land use. Ecological Applications, 6, 865-878. Steinberg C (1980) Ausmass und Auswirkungen von Nährstoffanreicherungen auf das Phytoplankton eines subalpinen Sees. Gewässer und Abwässer, 66/67, 175-187. Steinberg C, Bucksteeg K (1980) Versuch der therapie eines polytrophen Kleinsees mit Aluminiumchlorid. Vom Wasser, 55, 47-61. Steinberg C, Wöhlecke C, Hämmerle E (1981) Die Belastung des Walchensees und ihre Auswirkung auf den Seezustand. Vom Wasser, 57, 37-57. Steinberg C, Hartmann HM (1988) Planktonic bloom-forming Cyanobacteria and the eutrophication of lakes and rivers. Freshwater Biology, 20, 279-287. Wagner C, Adrian R (2009) Cyanobacteria dominance: Quantifying the effects of climate change. Limnology and Oceanography, 54, 2460–2468. Zhang Y, Prepas EE (1996) Regulation of the dominance of planktonic diatoms and cyanobacteria in four eutrophic hardwater lakes by nutrients, water column stability, and temperature. Canadian Journal of Fisheries and Aquatic Sciences, 53, 621–633. Zimmermann U (1969) Ökologische und physiologische Untersuchungen an der planktischen Blaualge Oscillatoria rubescens DC unter besonderer Berücksichtigung von Licht und Temperatur. Schweizerische Zeitschrift für Hydrologie, 31, 1-59. Table S2. Relationship between seasonality (day of the year; DOY) and our focal set of environmental variables (see Table 5 for basin-specific covariate transformations), where, tdf = total degrees of freedom, Dev Exp = Deviance explained, n = sample size, and BIC = Schwarz’s Bayesian Criterion. To evaluate nonlinearity, we applied AMs where the degree of nonlinearity is indicated by the effective degrees of freedom (edf). An edf of 1 represents a linear model and an edf > 1 represents a nonlinear model. Relationships that were significant (p<0.05) are highlighted in bold font. For significant relationships, the deviance of models with and without smoothers were compared using the Chi-square test to determine whether nonlinear models provided the most parsimonious fit. Lake basin CB ~ s(DOY) (mixing regime) Baptiste South R2-adj = 0.59 (dimictic) p < 0.0001 edf = 3.1 tdf = 4.1 Dev exp = 7% n = 31 BIC = 148 Ethel R2-adj = 0.29 (dimictic) p = 0.0001 edf = 2.6 tdf = 3.6 Dev exp = 32% n = 60 BIC = 245 Baptiste North R2 -adj = 0.68 (polymictic) p < 0.0001 edf = 3.9 tdf = 4.9 Dev exp = 72% n = 31 BIC = 148 Nakamun R2-adj = 0.32 (polymictic) p = 0.002 edf = 2.0 tdf = 3.0 Dev exp = 36% n = 34 BIC = 171 Wabamun R2-adj = 0.46 (polymictic) p < 0.0001 edf = 2.9 tdf = 3.9 Dev exp = 47% n = 100 BIC = 381 TP ~ s(DOY) TN ~ s(DOY) R2-adj = 0.38 p = 0.007 edf = 4.0 tdf = 5.0 Dev exp = 47% n = 31 BIC = -35 R2-adj = 0.34 p < 0.001 edf = 5.5 tdf = 6.5 Dev exp = 40% n = 60 BIC =343 R2-adj = 0.54 p < 0.001 edf = 3.8 tdf = 4.8 Dev exp = 60% n = 31 BIC = -67 R2-adj = 0.26 p = 0.038 edf = 3.4 tdf = 4.4 Dev exp = 33 n = 34 BIC = 320 R2-adj = 0.07 p = 0.061 edf = 2.4 tdf = 3.4 Dev exp = 9% n = 100 BIC = 695 R2-adj = 0.05 p = 0.209 edf = 1.9 tdf = 2.9 Dev exp = 15% n = 31 BIC = -40 R2-adj = 0.26 p < 0.001 edf = 2.7 tdf = 3.7 Dev exp = 30% n = 60 BIC = 672 R2-adj = 0.45 p = 0.001 edf = 3.4 tdf = 4.4 Dev exp = 50% n = 31 BIC = 436 R2-adj = 0.23 p = 0.037 edf = 3.0 tdf = 4.0 Dev exp = 30% n = 34 BIC = -52 R2-adj = 0.27 p < 0.0001 edf = 5.9 tdf = 6.9 Dev exp = 31% n = 100 BIC = 1272 Temperature ~ s(DOY) R2-adj = 0.92 p < 0.0001 edf = 5.0 tdf = 6.0 Dev exp = 94% n = 31 BIC = -126 R2-adj = 0.85 p < 0.0001 edf = 3.8 tdf = 4.8 Dev exp = 86% n = 60 BIC = 22 R2-adj = 0.91 p < 0.0001 edf = 4.3 tdf = 5.3 Dev exp = 92% n = 31 BIC = 126 R2-adj = 0.89 p < 0.0001 edf = 3.8 tdf = 4.8 Dev exp = 90% n = 34 BIC = 12 R2-adj = 0.86 p < 0.0001 edf = 4.3 tdf = 5.3 Dev exp = 87% n = 100 BIC = 407 SSI ~ s(DOY) R2-adj = 0.94 p < 0.0001 edf = 4.3 tdf = 5.3 Dev exp = 95% n = 31 BIC = 209 R2-adj = 0.83 p < 0.0001 edf = 3.5 tdf = 4.5 Dev exp = 84% n = 60 BIC = 448 R2-adj = 0.83 p < 0.0001 edf = 4.7 tdf = 5.7 Dev exp = 85% n = 31 BIC = 219 R2-adj = 0.35 p = 0.003 edf = 2.7 tdf = 3.7 Dev exp = 41% n = 34 BIC = 217 R2-adj = 0.31 p < 0.0001 edf = 4.7 tdf = 5.7 Dev exp = 35% n = 100 BIC = 288 Table S3. Autocorrelation function (acf) for our focal set of variables. The acf provide the estimates of the temporal autocorrelation by lagged time steps. Only lags with autocorrelation coefficients greater than or equal to r = 0.35 are presented. Lake Baptiste South (dimictic) Ethel (dimictic) Baptiste North (polymictic) acf(CB) lag-1: r = 0.38 lag-3: r = -0.39 lag-1: r = 0.52 — Nakamun (polymictic) — Wabamun (polymictic) — acf(TP) acf(TN) — — lag-2: r = -0.48 lag-2: r = -0.50 — — lag-3: r = -0.53 lag-4: r = -0.36 lag-6: r = 0.41 lag-7: r = 0.37 lag-3: r = -0.48 lag-6: r = 0.37 lag-7: r = 0.36 — — lag-2: r = -0.49 lag-2: r = -0.58 lag-1: r = 0.62 — lag-1: r = 0.6 — acf(Temperature) lag-1: r = 0.39 lag-3: r = -0.66 lag-4: r = -0.57 lag-6: r = 0.40 lag-7: r = 0.65 lag-10: r = -0.47 lag-13: r = 0.37 lag-14: r = 0.43 lag-1: r = 0.35 lag-3: r =-0.39 acf(SSI) lag-2: r = -0.40 lag-6: r = 0.44 lag-10: r = -0.40 — Table S4. Linear cyanobacteria models for basin-specific and across-basin datasets. For the basin-specific models, a random effect for year was evaluated (transformation: see Table 5). For the across-basin models, random effects for year within study basin and basin only were evaluated. To be concise, models with only random intercept structures are not shown. Regime Dimictic Basin Baptiste S (LM) Baptiste S (LMM) Ethel (LM) Ethel (LMM) Polymictic Baptiste N (LM) Baptiste N (LMM) Nakamun (LM) Nakamun (LM) Wabamun (LM) Wabamun (LMM) CB ~ TP R2 = 0.0 = -1.1 p = 0.812 df = 29 BIC = 171 R2 = 0.06 = -2.0 p = 0.670 -intercept = 4.7 -slope = 0.3 -residual = 3.3 Corr (Intr) = -1 df = 23 BIC = 169 R2 = 0.0 = -0.03 p = 0.598 df = 58 BIC = 271 R2 = 0.20 = -0.1 p = 0.335 -intercept = 0.8 -slope = 0.0 -residual = 2.0 Corr (Intr) = 0 df = 49 BIC = 285 R2 = 0.36 = 0.08 p < 0.001 df = 29 BIC = 163 R2 = 0.53 = 0.08 p < 0.001 -intercept = 1.3 -slope = 0.0 -residual = 2.7 Corr (Intr) = 0 df = 23 BIC = 178 R2 = 0.25 = 0.06 p = 0.002 df = 32 BIC = 172 R2 = 0.30 = 0.06 p = 0.007 -intercept = 1.4 -slope = 0.0 -residual = 2.7 Corr (Intr) = -1 df = 28 BIC = 188 R2 = 0.03 = 0.06 p = 0.041 df = 98 BIC = 431 R2 = 0.42 = 0.10 p < 0.001 -intercept = 2.6 -slope = 0.0 -residual = 1.6 Corr (Intr) = -1 df = 79 BIC = 433 CB ~ TN R2 = 0.40 = 18.9 p < 0.0001 df = 29 BIC = 155 R2 = 0.57 = 17.5 p = 0.009 -intercept = 32.7 -slope = 010.4 -residual = 2.3 Corr (Intr) = -1 df = 23 BIC = 157 R2 = 0.0 = 0.0 p = 0.933 df = 58 BIC = 271 R2 = 0.18 = 0.0 p = 0.715 -intercept = 1.2 -slope = 0.0 -residual = 2.0 Corr (Intr) = -1 df = 49 BIC = 289 R2 = 0.72 = 32.6 p < 0.0001 df = 29 BIC = 139 R2 = 0.80 = 33.4 p < 0.0001 -intercept = 12.1 -slope = 3.5 -residual = 1.8 Corr (Intr) = -1 df = 23 BIC = 143 R2 = 0.40 = 19.0 p < 0.0001 df = 32 BIC = 164 R2 = 0.42 = 18.9 p < 0.0001 -intercept = 3.9 -slope = 1.2 -residual = 2.4 Corr (Intr) = -1 df = 28 BIC = 170 R2 = 0.13 = 0.005 p = 0.0001 df = 98 BIC = 421 R2 = 0.49 = 0.005 p < 0.001 -intercept = 2.6 -slope = 0.0 -residual = 1.6 Corr (Intr) = -1 df = 79 BIC = 435 CB ~ Temperature R2 = 0.27 = 20.9 p = 0.002 df = 29 BIC = 161 R2 = 0.48 = 21.4 p = 0.004 -intercept = 12.9 -slope = 9.9 -residual = 2.6 Corr (Intr) = -1 df = 23 BIC = 163 R2 = 0.0 = -0.11 p = 0.801 df = 58 BIC = 271 R2 = 0.17 = -0.04 p = 0.925 -intercept = 1.7 -slope = 0.25 -residual = 2.0 Corr (Intr) = -1 df = 49 BIC = 282 R2 = 0.21 = 0.36 p = 0.009 df = 29 BIC = 171 R2 = 0.38 = 0.37 p = 0.006 -intercept = 1.3 -slope = 3.9 -residual = 3.1 Corr (Intr) = 0 df = 23 BIC = 181 R2 = 0.10 = 1.6 p = 0.038 df = 32 BIC = 178 -R2 = 0.42 = 1.7 p = 0.036 -intercept = 2.2 -slope = 1.0 -residual = 2.5 Corr (Intr) = -1 df = 28 BIC = 182 R2 = 0.26 = 0.23 p < 0.001 df = 98 BIC = 405 R2 = 0.54 = 0.22 p < 0.0001 -intercept = 1.1 -slope = 0.0 -residual = 1.5 Corr (Intr) = -0.6 df = 79 BIC = 414 CB ~ SSI R2 = 0.36 = 0.09 p < 0.001 df = 29 BIC = 157 R2 = 0.60 = 0.09 p = 0.001 -intercept = 1.2 -slope = 0.0 -residual = 2.3 Corr (Intr) = -1 df = 23 BIC = 169 R2 = 0.0 = 0.0 p = 0.988 df = 58 BIC = 271 R2 = 0.18 = 0.0 p = 0.766 -intercept = 1.9 -slope = 0.0 -residual = 2.0 Corr (Intr) = -1 df = 49 BIC = 292 R2 = 0.01 = 0.05 p = 0.238 df = 29 BIC = 177 R2 = 0.17 = 0.05 p = 0.217 -intercept = 1.1 -slope = 0.0 -residual = 3.5 Corr (Intr) = 0 df = 23 BIC = 190 R2 = 0.02 = 0.11 p = 0.198 df = 32 BIC = 181 R2 = 0.31 = 0.12 p = 0.185 -intercept = 0.57 -slope = 0.1 -residual = 2.7 Corr (Intr) = -1 df = 28 BIC = 191 R2 = 0.0 = 0.20 p = 0.284 df = 98 BIC = 435 R2 = 0.26 = 0.09 p = 0.602 -intercept = 0.54 -slope = 0.12 -residual = 1.8 Corr (Intr) = 0.9 df = 79 BIC = 446 Table S4 (continued). Linear cyanobacteria models for basin-specific and across-basin datasets. For the basin-specific models, a random effect for year was evaluated (transformation: see Table 5). For the across-basin models, random effects for year within study basin and basin only were evaluated. To be concise, models with only random intercept structures are not shown. Basin All basins (LM) All basins (LMM random effect: basin) All basins (LMM random effect: year/basin) CB0.25 ~ log10(TP) R2 = 0.20 = 5.4 p < 0.0001 df = 254 BIC = 1215 R2 = 0.26 = 5.9 p = 0.008 df = 250 -intercept = 8.4 -slope = 4.4 -residual = 2.4 Corr (Intr) = -1 BIC = 1226 R2 = 0.37 = 5.7 p = 0.015 df = 206 random - basin: -intercept = 8.7 -slope = 4.6 Corr (Intr) = -1 random - year/basin: -intercept = 4.1 -slope = 2.4 -residual = 2.3 Corr (Intr) = -1 BIC = 1237 CB0.25 ~ log10(TN) R2 = 0.28 = 9.5 p < 0.0001 df = 254 BIC = 1189 R2 = 0.44 = 15.7 p = 0.002 df = 250 -intercept = 32.3 -slope = 10.6 -residual = 2.1 Corr (Intr) = -1 BIC = 1165 R2 = 0.55 = 16.3 p = 0.002 df = 206 random - basin: -intercept = 32.6 -slope = 10.7 Corr (Intr) = -1 random - year/basin: -intercept = 8.9 -slope = 3.0 -residual = 2.0 Corr (Intr) = -1 BIC = 1175 CB0.25 ~ sqrt(Temp) R2 = 0.10 = 1.4 p < 0.0001 df = 254 BIC = 1244 R2 = 0.31 = 1.8 p = 0.001 df = 250 -intercept = 2.8 -slope = 1.1 -residual = 2.4 Corr (Intr) = -1 BIC = 1214 R2 = 0.47 = 1.8 p < 0.001 df = 206 random - basin: -intercept = 2.7 -slope = 1.0 Corr (Intr) = -1 random - year/basin: -intercept = 0.7 -slope = 0.4 -residual = 2.2 Corr (Intr) = -1 BIC = 1217 CB0.25 ~ SSI R2 = 0.0 = 0.0 p = 0.087 df = 254 BIC = 1268 R2 = 0.23 = 0.001 p = 0.019 df = 250 -intercept = 0.6 -slope = 0.0 -residual = 2.5 Corr (Intr) = 1 BIC = 1252 R2 = 0.39 = 0.001 p = 0.0128 df = 206 random - basin: -intercept = 0.7 -slope = 0.0 Corr (Intr) = 1 random - year/basin: -intercept = 0.8 -slope = 0.0 -residual = 2.3 Corr (Intr) = 0.3 BIC = 1260
