When mechanism matters: Bayesian forecasting using models of ecological diffusion
Authored by Trevor J Hefley, Mevin B Hooten, Robin E Russell, Daniel P Walsh, James A Powell
Date Published: 2017
DOI: 10.1111/ele.12763
Sponsors:
United States National Science Foundation (NSF)
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
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.
Tags
Agent-based model
regression
selection
Dispersal
Guide
invasion
Prediction
Population-dynamics
Spread
Partial-differential-equations
Generalized additive-models
Mule deer
Chronic
wasting disease
Bayesian analysis
Boosted regression trees
Generalised additive model
Partial differential
equation
Spatial confounding