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When mechanism matters: Bayesian forecasting using models of ecological diffusion

April 24, 2017

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

Publication Year 2017
Title When mechanism matters: Bayesian forecasting using models of ecological diffusion
DOI 10.1111/ele.12763
Authors Trevor J. Hefley, Mevin Hooten, Robin E. Russell, Daniel P. Walsh, James A. Powell
Publication Type Article
Publication Subtype Journal Article
Series Title Ecology Letters
Index ID 70187129
Record Source USGS Publications Warehouse
USGS Organization Coop Res Unit Seattle; National Wildlife Health Center