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.