Statistical analyses
All statistical analyses were conducted in R v.4.1.2 (R Core Team, 2021). Comparisons of exponential and linear decay models showed that the former was superior to the latter in most cases (see Results). Thus, the following statistical analyses use λE as the dependent variable.
Inspection of the distribution of standard errors (SE) for estimates ofλE were used to exclude experiments that had poor model fit for the exponential decline models (Fig. S2). This was based on judgement, trading-off maintaining sample size while avoiding inclusion of potentially biased estimates. Using a threshold SE value of 0.01 enabled us to retain 78% of the experiments (n = 240 out of 307). The statistical analyses described below use estimates obtained below this threshold. Increasing the threshold SE value to 0.02 increased inclusion rate to 88% (n = 271) without resulting in qualitative changes in the results (Table S1). The bias in estimatedλE for experiments with SE > 0.02 and within the range 0.01-0.02 can be observed by comparingλE in experiments within different SE bins (Fig. S3).
Variation in λE among taxonomic classes of ectothermic animals was analysed using linear mixed-effect models, fitted using the function lme in the package nlme(Pinheiro et al., 2022). The full model contained the fixed effects of class, body mass, acclimation temperature, and slope of the estimated exponential decay function at tn , whereas species was included as a random effect.
The full model was compared to simplified ones based on AICc values using the function dredge from the package MuMIn (Barton 2020). For all analyses inspection of residual plots suggested that assumptions of their normality and homogeneity were satisfied.