When mechanism matters: Bayesian forecasting using models of ecological diffusion

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...

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Bibliographic Details
Published in:Ecology letters 2017-05, Vol.20 (5), p.640-650
Main Authors: Hefley, Trevor J., Hooten, Mevin B., Russell, Robin E., Walsh, Daniel P., Powell, James A., Adler, Frederick
Format: Article
Language:English
Subjects:
Agent‐based model
Animals
Bayes Theorem
Bayesian analysis
boosted regression trees
Chronic wasting disease
Collinearity
Data processing
Deer
Diffusion
Dispersal
Ecological models
Ecological monitoring
Female
Forecasting
generalised additive model
invasion
Male
Mathematical models
Models, Theoretical
partial differential equation
prediction
Prevalence
Probabilistic inference
Regression analysis
Spatial analysis
spatial confounding
Spatial distribution
Statistical analysis
Statistical inference
Transmissible spongiform encephalopathy
Wasting Disease, Chronic - epidemiology
Wasting Disease, Chronic - etiology
Wisconsin - epidemiology
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Summary: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.
ISSN:1461-023X
1461-0248
DOI:10.1111/ele.12763