Regularized Latent Trajectory Models for Spatio-temporal Population Dynamics

Xinyi Lu (), Yoichiro Kanno, George P. Valentine, Matt A. Kulp and Mevin B. Hooten
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Xinyi Lu: Utah State University
Yoichiro Kanno: Colorado State University
George P. Valentine: Colorado State University
Matt A. Kulp: U.S. National Park Service
Mevin B. Hooten: The University of Texas at Austin

Journal of Agricultural, Biological and Environmental Statistics, 2025, vol. 30, issue 3, No 5, 683-699

Abstract: Abstract Climate change impacts ecosystems variably in space and time. Landscape features may confer resistance against environmental stressors, whose intensity and frequency also depend on local weather patterns. Characterizing spatio-temporal variation in population responses to these stressors improves our understanding of what constitutes climate change refugia. We developed a Bayesian hierarchical framework that allowed us to differentiate population responses to seasonal weather patterns depending on their “sensitive” or “resilient” states. The framework inferred these sensitivity states based on latent trajectories delineating dynamic state probabilities. The latent trajectories are composed of linear initial conditions, functional regression models, and additive random effects representing ecological mechanisms such as topological buffering and effects of legacy weather conditions. Further, we developed a Bayesian regularization strategy that promoted temporal coherence in the inferred states. We demonstrated our hierarchical framework and regularization strategy using simulated examples and a case study of native brook trout (Salvelinus fontinalis) count data from the Great Smoky Mountains National Park, southeastern USA. Our study provided insights into ecological processes influencing brook trout sensitivity. Our framework can also be applied to other species and ecosystems to facilitate management and conservation.

Keywords: Bayesian hierarchical model; State-space model; Functional analysis; Climate change refugia; Brook charr (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13253-024-00616-y

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