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

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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., \(> 10\)). 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.

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Acknowledgements

We thank Mason Fidino and one anonymous reviewer for insightful comments that improved the manuscript.

Funding

This work was supported by National Science Foundation (NSF) Grants DBI-1954406, DMS-1916395, and DEB-2213565.

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Authors and Affiliations

  1. Department of Integrative Biology, Michigan State University, East Lansing, MI, USA

    Jeffrey W. Doser & Elise F. Zipkin

  2. Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI, USA

    Jeffrey W. Doser, Andrew O. Finley & Elise F. Zipkin

  3. Department of Forestry, Michigan State University, East Lansing, MI, USA

    Andrew O. Finley

  4. Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA

    Andrew O. Finley

  5. Science Division, National Audubon Society, New York, NY, USA

    Sarah P. Saunders

  6. Swiss Ornithological Institute, Sempach, Switzerland

    Marc Kéry

  7. Northeast Temperate Inventory and Monitoring Network, National Park Service, Woodstock, VT, USA

    Aaron S. Weed

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  1. Jeffrey W. Doser
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Correspondence to Jeffrey W. Doser.

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Data Availability Statement

All data and code used in the manuscript are available at https://doi.org/10.5281/zenodo.10159508 (Doser et al. 2023)

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Doser, J.W., Finley, A.O., Saunders, S.P. et al. Modeling Complex Species-Environment Relationships Through Spatially-Varying Coefficient Occupancy Models. JABES (2024). https://doi.org/10.1007/s13253-023-00595-6

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