Publications

Abstract: 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 autocorrelation 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.

Publication timeline: Submitted Jul 2, 2025 - Returned for major revisions Aug 20, 2025 - Resubmitted Sep 22, 2025

Abstract: 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 population’s 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 population’s 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.

Publication timeline: Submitted Jul 23, 2025

Abstract: 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.

Publication timeline: Submitted Aug 28, 2024 - Returned for major revisions Jul 1, 2025 - Resubmitted Jul 17, 2025 - Returned for minor revisions Oct 30, 2025 - Resubmitted Nov 10, 2025 - Accepted Nov 25, 2025

Abstract: Deterministic chaos — unpredictable, non-random, long-term dynamics that are highly sensitive to initial conditions — has intrigued ecologists ever since models began to show its potential to arise in natural systems. While interest and belief in ecological chaos has fluctuated over the years, contextualizing our understanding of the phenomenon is crucial to recognizing when it may be important in today׳s rapidly changing ecosystems. In this chapter, we review what chaos looks like in ecological models, how and where chaos is detected empirically and experimentally, and why dependence on mechanisms like overcompensation limits the natural occurrence of chaos in ecology.

Abstract: To optimize strategies for curbing the transmission of airborne pathogens, the efficacy of three key controls—face masks, ventilation, and physical distancing—must be well understood. In this study, we used the Quadrature-based model of Respiratory Aerosol and Droplets to quantify the reduction in exposure to airborne pathogens from various combinations of controls. For each combination of controls, we simulated thousands of scenarios that represent the tremendous variability in factors governing airborne transmission and the efficacy of mitigation strategies. While the efficacy of any individual control was highly variable among scenarios, combining universal mask-wearing with distancing of 1m or more reduced the median exposure by more than 99% relative to a close, unmasked conversation, with further reductions if ventilation is also enhanced. The large reductions in exposure to airborne pathogens translated to large reductions in the risk of initial infection in a new host. These findings suggest that layering controls is highly effective for reducing transmission of airborne pathogens and will be critical for curbing outbreaks of novel viruses in the future.

Abstract: In temperate climates, the peak in infection rates of enveloped viruses during the winter is likely heightened by seasonal variation in relative humidity within indoor spaces. While these seasonal trends are established in influenza and human coronaviruses, the mechanisms driving this seasonality remain poorly understood. Relative humidity impacts the evaporation rate and equilibrium size of airborne particles, which in turn may impact particle removal rates and virion viability. However, the relative importance of these two processes is not known. Here we use the Quadrature-based model of Respiratory Aerosol and Droplets to explore whether the seasonal variation in enveloped viruses is driven by differences in particle removal rates or by differences in virion inactivation rates. Through a large ensemble of simulations, we found that dry indoor conditions typical of winter lead to slower virion inactivation than humid indoor conditions typical of summer; in poorly ventilated spaces, this reduction in inactivation rates increases the airborne concentration of active virions, but this effect was important to virion exposure only when the susceptible person was farther than 2 m downwind of the infectious person. On the other hand, the impact of relative humidity on particle settling velocity did not significantly affect the removal or travel distance of virus-laden particles, suggesting that relative humidity is more likely to affect seasonal transmission via inactivation rates than via particle removal.

Abstract: Some infectious diseases, such as influenza, tuberculosis, and SARS-CoV-2, may be transmitted when virus-laden particles expelled from an infectious person are inhaled by someone else, which is known as the airborne transmission route. These virus-laden particles are more concentrated in the expiratory jet of an infectious person than elsewhere in a well-mixed room, but this near-field enhancement in virion exposure has not been well quantified. Transmission of airborne viruses depends on factors that are inherently variable and, in many cases, poorly constrained, and quantifying this uncertainty requires large ensembles of model simulations that span the variability in input parameters. However, models that are well-suited to simulate the near-field evolution of respiratory particles are also computationally expensive, which limits the exploration of parametric uncertainty. In order to perform many simulations that span the wide variability in factors governing airborne transmission, we developed the Quadrature-based model of Respiratory Aerosol and Droplets (QuaRAD). QuaRAD is an efficient framework for simulating the evolution of virus-laden particles after they are expelled from an infectious person, their deposition to the nasal cavity of a susceptible person, and the subsequent risk of initial infection. We simulated 10,000 scenarios to quantify the risk of initial infection by a particular virus, SARS-CoV-2. The predicted risk of infection was highly variable among scenarios and, in each scenario, was strongly enhanced near the infectious individual. In more than 50% of scenarios, the physical distancing needed to avoid near-field enhancements in airborne transmission was beyond the recommended safe distance of two meters (six feet) if the infectious person is not wearing a mask, though this distance defining the near-field extent was also highly variable among scenarios; the variability in the near-field extent is explained predominantly by variability in expiration velocity. Our findings suggest that maintaining at least two meters of distance from an infectious person greatly reduces exposure to airborne virions; protections against airborne transmission, such as N95 respirators, should be available when distancing is not possible.