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When mechanism matters: Bayesian forecasting using models of ecological diffusion
Author(s) -
Hefley Trevor J.,
Hooten Mevin B.,
Russell Robin E.,
Walsh Daniel P.,
Powell James A.
Publication year - 2017
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/ele.12763
Subject(s) - bayesian probability , bayesian inference , collinearity , ecology , bayesian hierarchical modeling , computer science , inference , statistical model , chronic wasting disease , odocoileus , hierarchical database model , statistical inference , machine learning , artificial intelligence , econometrics , data mining , statistics , mathematics , biology , disease , prion protein , medicine , pathology , scrapie
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.