Data from: Order matters: Autocorrelation of temperatures dictates extinction risk in populations with nonlinear thermal performance

Forecasting the risks caused by climate change often relies upon combining
species' thermal performance curves with expected statistical
distributions of experienced temperatures, without considering the order
in which those temperatures occur. Such averaging approaches may obscure
the disproportionate impacts that extreme events like heatwaves have on
fitness and survival. In this study, we instead incorporate thermal
performance curves with population dynamical modeling to elucidate the
relationship between the sequence of temperature events -- driven by
temporal autocorrelation -- and extinction risk. We show that the
permutation of temperatures determines the extent of risk; as thermal
regimes grow warmer, more variable, and more autocorrelated, the risk of
extinction grows non-linearly and is driven by interactions between the
thermal distribution and its temporal autocorrelation. Given that the
mean, variance, and autocorrelation of temperatures are changing in
nuanced ways across the globe, understanding these interactions is
paramount for forecasting risk. Using empirical data from a benchmarked
set of thermal performance curves, we demonstrate how extinction risk is
impacted by interacting changes to temperature's distribution and
autocorrelation level, while controlling for seasonal and diurnal cycling.
Our results and modeling approach offer new tools for testing the
robustness of thermal performance curves and emphasize the importance of
looking beyond temporally-blind metrics, like mean population size or
average thermal distributions, for forecasting impending extinction risks.