thumbnail

When mechanism matters: Bayesian forecasting using models of ecological diffusion

Ecology Letters

By:
, ORCID iD , ORCID iD , ORCID iD , and
https://doi.org/10.1111/ele.12763

Links

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.

Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
When mechanism matters: Bayesian forecasting using models of ecological diffusion
Series title:
Ecology Letters
DOI:
10.1111/ele.12763
Volume:
20
Issue:
5
Year Published:
2017
Language:
English
Publisher:
Wiley
Contributing office(s):
Coop Res Unit Seattle, National Wildlife Health Center
Description:
11 p.
First page:
640
Last page:
650