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When mechanism matters: Bayesian forecasting using models of ecological diffusion.

Trevor J Hefley1, Mevin B Hooten2, Robin E Russell3

  • 1Department of Statistics, Kansas State University, 205 Dickens Hall, 1116 Mid-Campus Drive North, Manhattan, KS, 66506, USA.

Ecology Letters
|April 4, 2017
PubMed
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This summary is machine-generated.

Ecological diffusion models, enhanced by hierarchical Bayesian methods, accurately forecast disease spread in white-tailed deer populations. This approach offers superior insights compared to traditional regression methods.

Area of Science:

  • Ecology
  • Epidemiology
  • Statistical Modeling

Background:

  • Ecological diffusion theory explains spatio-temporal processes like disease spread.
  • Hierarchical Bayesian modeling offers a robust framework for statistical inference and probabilistic forecasting.
  • Chronic Wasting Disease (CWD) in white-tailed deer presents a significant ecological and management challenge.

Purpose of the Study:

  • To implement hierarchical Bayesian models of ecological diffusion for understanding and forecasting CWD spread.
  • To compare the statistical inference and forecasting accuracy of mechanistic diffusion models against phenomenological regression methods.
  • To demonstrate the application of these models to large, spatio-temporally dense datasets.

Main Methods:

  • Developed and applied hierarchical Bayesian models based on ecological diffusion principles.
Keywords:
Agent-based modelBayesian analysisboosted regression treesdispersalgeneralised additive modelinvasionpartial differential equationpredictionspatial confounding

Related Experiment Videos

  • Utilized mechanistic ecological models within a Bayesian inference framework.
  • Compared model performance against commonly used phenomenological regression-based methods for spatial occurrence data analysis.

    • Main Results:

    • The hierarchical Bayesian model provided accurate forecasts for CWD prevalence and geographic spread.
    • The mechanistic statistical model yielded significant ecological insights.
    • The ecological diffusion model identified and addressed a type of collinearity often overlooked in spatial analyses.
    • The mechanistic model outperformed regression-based methods in forecasting accuracy.

    Conclusions:

    • Hierarchical Bayesian modeling of ecological diffusion provides a powerful and accurate approach for forecasting wildlife disease dynamics.
    • This mechanistic modeling framework offers deeper ecological understanding than traditional statistical methods.
    • The approach is suitable for analyzing large, complex spatio-temporal ecological datasets.