Biology subjects

Robey, A. J.

Publications and source records attributed to Robey, A. J..

3 recordsLinked to original sources

bioRxiv2025

Temporal autocorrelation increases temperature-driven extinction risk by clustering stressful conditions

Environments are becoming increasingly autocorrelated as global climate change progresses, leading to the intensification of deadly events like heatwaves and droughts. Theory shows that temporally-autocorrelated environments generate a higher risk of population extinction; however, little work has been done to incorporate temporal autocorrelation into thermal performance-based projections of extinction risk. Here, we pair stochastic simulation models of population dynamics with systematically generated temperature time series to determine when higher levels of autocorrelation generate greater extinction risk. We show that autocor-relation is a significant mediator of risk under stressful temperature regimes, with important ramifications for forecasting temperature-driven extinctions in ectothermic organisms. We validate our predictions with a factorial experiment in microcosms of the single-celled protist Paramecium caudatum. Taken together, these results provide the foundation for predicting which species and environments face the greatest risks under increasing autocorrelation.

ecology↗

bioRxiv2025

Forecasting Extinction Risk using Thermal Performance Curves and Population Dynamic Modeling

Thermal Performance Curves (TPCs) have become a popular tool for assessing the risks imposed by climate warming and variability on ectotherms. These assessments typically measure the match between an organism or populations TPC and the distribution of its current or future thermal environment as a proxy for extinction risk. However, extinction can occur even when the average thermal environment appears closely matched to a populations TPC because population dynamics can be very sensitive to thermal stress. Here, we develop a new metric for assessing extinction risk using a stochastic model of logistic growth as a foundation. We show that boundaries delimiting persistence and extinction regions of parameter space can be derived for the simple case where the intrinsic (Malthusian) growth rate r varies stochastically and that these boundaries continue to make reliable predictions when temperature T varies stochastically and the Malthusian growth rate is given by a thermal performance curve r (T). We accomplish this by combining theory with stochastic simulations of population dynamics and a laboratory experiment where populations of the single-celled protist Paramecium caudatum were cultured across different temporal means and variances of temperature. The measure of risk that we develop and validate is straightforward and easily applicable to any population for which the thermal performance of Malthusian fitness is known, allowing more rigorous identification of the risks imposed by warmer and more variable temperatures across the globe.

ecology↗

bioRxiv2024

Order matters: Autocorrelation of temperature 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 consideration for 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 among our three treatment variables. 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 the change in mean, variance, and autocorrelation, 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.

ecology↗