Present address: Section of Ecology, Behavior and Evolution, University of California- San Diego, 9500 Gilman Drive # 0116, La Jolla, CA 92093-0116. E-mail: email@example.com
Environmental stability and lake zooplankton diversity – contrasting effects of chemical and thermal variability
Version of Record online: 21 JAN 2010
© 2010 Blackwell Publishing Ltd/CNRS
Volume 13, Issue 4, pages 453–463, April 2010
How to Cite
Shurin, J. B., Winder, M., Adrian, R., Keller, W., Matthews, B., Paterson, A. M., Paterson, M. J., Pinel-Alloul, B., Rusak, J. A. and Yan, N. D. (2010), Environmental stability and lake zooplankton diversity – contrasting effects of chemical and thermal variability. Ecology Letters, 13: 453–463. doi: 10.1111/j.1461-0248.2009.01438.x
- Issue online: 15 MAR 2010
- Version of Record online: 21 JAN 2010
- Editor, David Post Manuscript received 12 November 2009 First decision made 5 December 2009 Manuscript accepted 10 December 2009
Appendix S1 The relationship between number of zooplankton individuals counted per sample and the number of species identified in four ELA lakes. Numbers on the panels indicate the lake number. Richness was calculated by the method of Dodson (1992), which excludes littoral species and is therefore slightly different from the one we use. A mixed-effects model with only an intercept and a random term for “Lake” provided a superior fit to models with a fixed effect for the number of individuals with either the same (P = 0.0002) or different (P = 0.0042) slopes for each lake. Thus, lakes differ in species richness and the number of animals counted had no discernable effect on the estimate of species richness.
Appendix S2 (a) The correlation between average daily and annual zooplankton species richness. (b) The best-fit models for average annual richness on the four time scales, and for all variables on all time scales.
Appendix S3 The scaling between the mean and standard deviation for each of the ten limnological variables on a log-log scale. The top rows are the relationships between the annual mean and the standard deviation among monthly averages, the bottom rows are for the long-term mean and the standard deviation of annual averages. The parameter values shown are for the mixed-effects models with the following random effects: none, Lake, or Lake nested within Dataset. A likelihood ratio test was used to select among the three models with different random terms included. The parameter values shown are for the fixed effects of the log(mean(x)) on log(StDev(x)). A slope of 1 indicates that a multiplicative model is appropriate form of variance decomposition, while an additive model is appropriate for a slope of 0 (Chatfield 2004). The slopes were significantly greater than zero in every case except for the relationship between the long-term mean and annual standard deviation for pH and Surface Temperature. We therefore applied a multiplicative variance decomposition model for all of the variables in our dataset.
Appendix S4 Summary statistics on environmental variables. Each column indicates the mean and 95% bootstrapped confidence intervals for each variable. Units are given in the text.
Appendix S5 Comparison of the best subset models containing between one and 11 of the variables included in any of the final models for each time scale (in Table 2). The first and second order terms for interannual variability in TP were removed together from the model. The models are ranked from best (top) to worst (bottom) based on AIC (indicated in the first column). Each row indicates a model, and the shaded rectangles indicate the variables included in the model. The colour indicates the time scale as shown by the top row. The number of models containing a variable is an indication of its importance as a predictor of average daily zooplankton species richness after accounting for variance explained by all of the others.
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