Modeling Complex Species-Environment Relationships Through Spatially-Varying Coefficient Occupancy Models

Jeffrey W. Doser (), Andrew O. Finley, Sarah P. Saunders, Marc Kéry, Aaron S. Weed and Elise F. Zipkin
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Jeffrey W. Doser: Michigan State University
Andrew O. Finley: Michigan State University
Sarah P. Saunders: National Audubon Society
Marc Kéry: Swiss Ornithological Institute
Aaron S. Weed: National Park Service
Elise F. Zipkin: Michigan State University

Journal of Agricultural, Biological and Environmental Statistics, 2025, vol. 30, issue 1, No 7, 146-171

Abstract: Abstract Occupancy models are frequently used by ecologists to quantify spatial variation in species distributions while accounting for observational biases in the collection of detection-nondetection data. However, the common assumption that a single set of regression coefficients can adequately explain species-environment relationships is often unrealistic, especially across large spatial domains. Here we develop single-species (i.e., univariate) and multi-species (i.e., multivariate) spatially-varying coefficient (SVC) occupancy models to account for spatially-varying species-environment relationships. We employ Nearest Neighbor Gaussian Processes and Pólya-Gamma data augmentation in a hierarchical Bayesian framework to yield computationally-efficient Gibbs samplers, which we implement in the spOccupancy R package. For multi-species models, we use spatial factor dimension reduction to efficiently model datasets with large numbers of species (e.g., $$> 10$$ > 10 ). The hierarchical Bayesian framework readily enables generation of posterior predictive maps of the SVCs, with fully propagated uncertainty. We apply our SVC models to quantify spatial variability in the relationships between maximum breeding season temperature and occurrence probability of 21 grassland bird species across the USA. Jointly modeling species generally outperformed single-species models, which all revealed substantial spatial variability in species occurrence relationships with maximum temperatures. Our models are particularly relevant for quantifying species-environment relationships using detection-nondetection data from large-scale monitoring programs, which are becoming increasingly prevalent for answering macroscale ecological questions regarding wildlife responses to global change.Supplementary material to this paper is provided online.

Keywords: Bayesian; Species distribution model; Wildlife; Monitoring; Nonstationarity (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13253-023-00595-6

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