Environmental Stochasticity

Abstract

AbstractEnvironmental stochasticity is unpredictable spatiotemporal fluctuation in environmental conditions, and it is one of the main sources of fluctuation in ecological processes. The term is often used in the ecology and evolution literature. Unpredictability is defined as a lack of ability to predict the future state precisely so that only its distribution can be known. Environment is typically defined as any set of physical, chemical and biological conditions that organisms experience, such as temperature, nutrient availability and the abundance of predators. Environmental stochasticity influences how population abundance fluctuates and affects the fate (e.g. persistence or extinction) of populations. In an evolutionary time scale, environmental stochasticity also affects life history strategy of organisms. Incorporating environmental stochasticity into analysis requires some care. Stochastic equations sometimes do not have explicit solutions so that simulations are required, and statistical analysis must separate other confounding factors such as stochastic sampling errors and demographic stochasticity.Key ConceptsEnvironmental stochasticity is unpredictable spatiotemporal fluctuations in environmental conditions.Correlation between time‐lagged environmental states is one statistical measure of its predictability.Environmental stochasticity is reflected in the fluctuations in ecological processes and affects their fate (e.g. extinction or persistence of populations).Observed population dynamics consist of fluctuation due to environmental stochasticity, but it is often confounded with other factors such as sampling errors and demographic stochasticity.All of the fluctuations in ecological data are often erroneously attributed to environmental stochasticity alone.Evolutionary dynamics are also affected by environmental stochasticity.Autoregressive moving average (ARMA) models are an example of statistical models incorporating environmental stochasticity.Using stochastic differential and difference equation models, mechanistic ecological models incorporating environmental stochasticity can be built.