Joint Spatial Modeling Bridges the Gap Between Disparate Disease Surveillance and Population Monitoring Efforts Informing Conservation of At-risk Bat Species

Christian Stratton (), Kathryn M. Irvine, Katharine M. Banner, Emily S. Almberg, Dan Bachen and Kristina Smucker
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Christian Stratton: Montana State University
Kathryn M. Irvine: U.S. Geological Survey, Northern Rocky Mountain Science Center
Katharine M. Banner: Montana State University
Emily S. Almberg: Montana Department of Fish, Wildlife, and Parks
Dan Bachen: Montana State Library
Kristina Smucker: Montana Department of Fish, Wildlife, and Parks

Journal of Agricultural, Biological and Environmental Statistics, 2025, vol. 30, issue 1, No 6, 120-145

Abstract: Abstract White-Nose Syndrome (WNS) is a wildlife disease that has decimated hibernating bats since its introduction in North America in 2006. As the disease spreads westward, assessing the potentially differential impact of the disease on western bat species is an urgent conservation need. The statistical challenge is that the disease surveillance and species response monitoring data are not co-located, available at different spatial resolutions, non-Gaussian, and subject to observation error requiring a novel extension to spatially misaligned regression models for analysis. Previous work motivated by epidemiology applications has proposed two-step approaches that overcome the spatial misalignment while intentionally preventing the human health outcome from informing estimation of exposure. In our application, the impacted animals contribute to spreading the fungus that causes WNS, motivating development of a joint framework that exploits the known biological relationship. We introduce a Bayesian, joint spatial modeling framework that provides inferences about the impact of WNS on measures of relative bat activity and accounts for the uncertainty in estimation of WNS presence at non-surveyed locations. Our simulations demonstrate that the joint model produced more precise estimates of disease occurrence and unbiased estimates of the association between disease presence and the count response relative to competing two-step approaches. Our statistical framework provides a solution that leverages disparate monitoring activities and informs species conservation across large landscapes. Stan code and documentation are provided to facilitate access and adaptation for other wildlife disease applications.

Keywords: Bayesian modeling; Data integration; Hierarchical modeling; Population monitoring; Spatial modeling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13253-023-00593-8

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