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Forecasting Animal Distribution through Individual Habitat Selection: Insights for Population Inference and Transferable Predictions
  • +5
  • Veronica Winter,
  • Brian Smith,
  • Danielle Berger,
  • Ronan Hart,
  • John Huang,
  • Kezia Manlove,
  • Frances Buderman,
  • Tal Avgar
Veronica Winter
The Pennsylvania State University

Corresponding Author:vaw5154@psu.edu

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Brian Smith
Utah State University
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Danielle Berger
Utah State University
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Ronan Hart
Utah State University
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John Huang
Utah State University
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Kezia Manlove
Utah State University
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Frances Buderman
The Pennsylvania State University
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Tal Avgar
Utah State University
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Abstract

Species distribution and habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (Antilocapra americana), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional RSF models - variability in habitat selection - into a tool to improve understanding and predicting animal distribution across space and time.
13 Nov 2023Submitted to Ecography
14 Nov 2023Submission Checks Completed
14 Nov 2023Assigned to Editor
14 Nov 2023Review(s) Completed, Editorial Evaluation Pending
31 Jan 2024Editorial Decision: Revise Major
29 Feb 2024Assigned to Editor
29 Feb 2024Submission Checks Completed
29 Feb 2024Review(s) Completed, Editorial Evaluation Pending
07 May 2024Editorial Decision: Revise Minor
04 Jun 2024Assigned to Editor
04 Jun 2024Submission Checks Completed
04 Jun 2024Review(s) Completed, Editorial Evaluation Pending
10 Jun 2024Editorial Decision: Revise Minor
10 Jun 20243rd Revision Received
12 Jun 2024Editorial Decision: Accept