Coexistence in the face of uncertainty

TL;DR

This paper reviews recent mathematical results on species coexistence under demographic and environmental stochasticity, highlighting how stochastic models predict persistence, extinction probabilities, and the influence of habitat size and environmental fluctuations.

Contribution

It synthesizes new theoretical insights into stochastic persistence and coexistence, extending classical models to include demographic and environmental randomness.

Findings

QSDs concentrate on deterministic attractors for large habitats

Extinction probability decreases exponentially with habitat size

Conditions for stochastic persistence involve Lyapunov exponents

Abstract

Over the past century, nonlinear difference and differential equations have been used to understand conditions for species coexistence. However, these models fail to account for random fluctuations due to demographic and environmental stochasticity which are experienced by all populations. I review some recent mathematical results about persistence and coexistence for models accounting for each of these forms of stochasticity. Demographic stochasticity stems from populations and communities consisting of a finite number of interacting individuals, and often are represented by Markovian models with a countable number of states. For closed populations in a bounded world, extinction occurs in finite time but may be preceded by long-term transients. Quasi-stationary distributions (QSDs) of these Makov models characterize this meta-stable behavior. For sufficiently large "habitat sizes", QSDs are shown to concentrate on the positive attractors of deterministic models. Moreover, the probability extinction decreases exponentially with habitat size. Alternatively, environmental stochasticity stems from fluctuations in environmental conditions which influence survival, growth, and reproduction. Stochastic difference equations can be used to model the effects of environmental stochasticity on population and community dynamics. For these models, stochastic persistence corresponds to empirical measures placing arbitrarily little weight on arbitrarily low population densities. Sufficient and necessary conditions for stochastic persistence are reviewed. These conditions involve weighted combinations of Lyapunov exponents corresponding to "average" per-capita growth rates of rare species. The results are illustrated with Bay checkerspot butterflies, coupled sink populations, the storage effect, and stochastic rock-paper-scissor communities. Open problems and conjectures are presented.