Biology subjects

Vasseur, D. A.

Publications and source records attributed to Vasseur, D. A..

5 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↗

bioRxiv2024

Stabilization of fluctuating population dynamics via the evolution of dormancy

Dormancy is usually understood as a strategy for coping with extrinsically variable environments, but intrinsic population fluctuations also create conditions where dormancy is adaptive. By analyzing simple population models, we show that, very generally, population fluctuations favor the evolution of dormancy, but dormancy stabilizes population dynamics. This sets up a feedback loop that can enable the coexistence of alternative dormancy strategies. Over longer timescales, we show that evolution of dormancy to an evolutionary stable state can drive populations to the edge of stability, where dynamics are only weakly stabilized. We briefly consider how these conclusions are likely to apply in more complex community contexts. Our results suggest that chaos and high-amplitude population cycles are highly vulnerable to invasion and subsequent stabilization by dormancy, potentially explaining their rarity. At the same time, the propensity of ecological dynamics to fluctuate may be an underappreciated driver of the evolution of dormancy.

ecology↗

bioRxiv2019

Evolutionary rescue is determined by differential selection on demographic rates and density dependence.

Accelerated rates of climate change are expected to either lead to populations adapting and persisting, or suffering extinction. Traditionally ecological models make extinction predictions based on how environmental change alters the intrinsic growth rate (r). However, these often ignore potential for evolutionary rescue, or to avoid extinction via adaptive evolution. Moreover, the environment may impose selective pressure on specific demographic rates (birth and death) rather than directly on r (the difference between the birth and death rates). Therefore, when we consider the potential for evolutionary rescue, populations with the same r can have different abilities to persist amidst environmental change. We cant adequately understand evolutionary rescue without accounting for demography, and interactions between density dependence and environmental change. Using stochastic birth-death population models, we found evolutionary rescue more likely when environmental change alters birth rather than the death rate. Furthermore, species that evolve via density dependent selection are less vulnerable to extinction than species that undergo selection independent of population density. Resolving the key demographic factors affected by environmental change can lead to an understanding of how populations evolve to avoid extinction. By incorporating these considerations into our models we can better predict how species will respond to climate change.

ecology↗