Life history traits and functional processes generate multiple pathways to ecological stability

Changing climate and reorganized species interactions modify community responses to climate variability

While an array of ecological mechanisms has been shown to stabilize natural community dynamics, how the effectiveness of these mechanisms-including both their direction (stabilizing vs. destabilizing) and strength-shifts under a changing climate remains unknown. Using a 35-y dataset (1985 to 2019) from a desert stream in central Arizona (USA), we found that as annual mean air temperature rose 1°C and annual mean precipitation reduced by 40% over the last two decades, macroinvertebrate communities experienced dramatic changes, from relatively stable states during the first 15 y of this study to wildly fluctuating states highly sensitive to climate variability in the last 10 y. Asynchronous species responses to climatic variability, the primary mechanism historically undergirding community stability, greatly weakened. The emerging climate regime-specifically, concurrent warming and prolonged multiyear drought-resulted in community-wide synchronous responses and reduced taxa richness. Diversity loss and new establishment of competitors reorganized species interactions. Unlike manipulative experiments that often suggest stabilizing roles of species interactions, we found that reorganized species interactions switched from stabilizing to destabilizing influences, further amplifying community fluctuations. Our study provides evidence of climate change-induced modifications of mechanisms underpinning long-term community stability, resulting in an overall destabilizing effect.

Fitting stochastic predator-prey models using both population density and kill rate data

Most mechanistic predator-prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator-prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data - including in small amounts - can make the estimates much more precise. Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species. These results may extend to other biotic interactions than predation, for which similar models combining interaction rates and population counts could be developed.

Temperature alters the shape of predator-prey cycles through effects on underlying mechanisms

Background

Predicting the effects of climate warming on the dynamics of ecological systems requires understanding how temperature influences birth rates, death rates and the strength of species interactions. The temperature dependance of these processes—which are the underlying mechanisms of ecological dynamics—is often thought to be exponential or unimodal, generally supported by short-term experiments. However, ecological dynamics unfold over many generations. Our goal was to empirically document shifts in predator–prey cycles over the full range of temperatures that can possibly support a predator–prey system and then to uncover the effect of temperature on the underlying mechanisms driving those changes.

Methods

We measured the population dynamics of the Didinium-Paramecium predator–prey system across a wide range of temperatures to reveal systematic changes in the dynamics of the system. We then used ordinary differential equation fitting to estimate parameters of a model describing the dynamics, and used these estimates to assess the long-term temperature dependance of all the underlying mechanisms.

Results

We found that predator–prey cycles shrank in state space from colder to hotter temperatures and that both cycle period and amplitude varied with temperature. Model parameters showed mostly unimodal responses to temperature, with one parameter (predator mortality) increasing monotonically with temperature and one parameter (predator conversion efficiency) invariant with temperature. Our results indicate that temperature can have profound, systematic effects on ecological dynamics, and these can arise through diverse and simultaneous changes in multiple underlying mechanisms. Predicting the effects of temperature on ecological dynamics may require additional investigation into how the underlying drivers of population dynamics respond to temperature beyond a short-term, acute response.