Environmental Stochasticity

Abstract

Abstract Environmental stochasticity refers to unpredictable spatiotemporal fluctuation in environmental conditions. The term is often used in the literature on ecology and evolution. Unpredictability is defined as an inability to predict the future state precisely such that only its distribution can be known. The environment is typically defined as any set of abiotic (e.g. temperature and nutrient availability) and biotic (e.g. predator, competitor and food) conditions that organisms experience. Environmental stochasticity influences how population abundance fluctuates and affects the fate (e.g. persistence or extinction) of populations. In an evolutionary timescale, environmental stochasticity also affects the life history strategy of organisms. Environmental stochasticity is included in population models using univariate difference equations, stochastic matrix population models, stochastic differential equations and partial differential equations. Ecological data are analysed to determine the effect of environmental stochasticity using methods such as spectral analysis, capture–recapture analysis, state‐space analysis, generalised linear models and multivariate statistical analyses. Key Concepts Environmental stochasticity is unpredictable spatiotemporal fluctuations in environmental conditions. Observed population dynamics consist of fluctuation due to environmental stochasticity, but it is often confounded with other factors such as observational errors, deterministic fluctuation and demographic stochasticity. Environmental stochasticity is reflected in the fluctuations in ecological processes and affects their fate (e.g. extinction or persistence of populations). Environmental stochasticity plays an important role in the evolution of life history strategies of organisms by affecting their fitness. Stochastic discrete‐time models, stochastic matrix population models, stochastic differential equation models and partial differential equation models are the four basic population models that include environmental stochasticity. Spectral analysis, state‐space model analysis, capture–recapture analysis, generalised linear models and multivariate statistical analysis are commonly used for separating the effects of environmental stochasticity in data.