Evaluation of spatial variation in chronic wasting disease risk with Bayesian Poisson log-Gaussian model

Area of Science:

• Wildlife Ecology
• Epidemiology
• Conservation Biology

Background:

• Chronic wasting disease (CWD) is a fatal neurodegenerative prion disease affecting cervids.
• The disease has expanded geographically across North America, impacting wildlife populations and hunting.
• Understanding spatial CWD risk in Kansas is crucial for conservation and management.

Purpose of the Study:

• To explore the spatial variation in CWD risk within Kansas.
• To investigate the influence of habitat-level covariates on CWD distribution.
• To provide data-driven insights for CWD management strategies in the state.

Main Methods:

• Utilized surveillance data from Kansas spanning 2005-2023.
• Employed a Poisson log-Gaussian model within a Bayesian framework.
• Compared models with and without habitat covariates to assess their predictive power.

Main Results:

• Habitat-level covariates were significant predictors of CWD presence, with Model 2 outperforming Model 1.
• Higher CWD risk was identified in northwestern and southcentral Kansas.
• Spatial analysis revealed a gradual increase in risk from west to east, without a global smoothing effect.

Conclusions:

• The Poisson log-Gaussian model effectively assesses wildlife disease surveillance data.
• Spatial patterns and habitat associations are key to understanding and managing CWD in Kansas.
• Findings have direct relevance for targeted CWD management and conservation efforts.