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Deriving spatially explicit direct and indirect interaction networks from animal movement data
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  • Anni Yang,
  • Mark Wilber,
  • Kezia Manlove,
  • Ryan Miller,
  • Raoul Boughton,
  • James Beasley,
  • Joseph Northrup,
  • Kurt Vercauteren,
  • George Wittemyer,
  • Kim Pepin
Anni Yang
University of Oklahoma

Corresponding Author:[email protected]

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Mark Wilber
University of California Santa Barbara
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Kezia Manlove
Utah State University
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Ryan Miller
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Raoul Boughton
Range Cattle Research and Education Center
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James Beasley
University of Georgia Warnell School of Forestry and Natural Resources
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Joseph Northrup
Ontario Ministry of Natural Resources and Forestry
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Kurt Vercauteren
USDA-APHIS National Wildlife Research Center
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George Wittemyer
Colorado State University
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Kim Pepin
USDA National Wildlife Research Center
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Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions – individuals occurring at the same location, but at different times– while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease-relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host Chronic Wasting Disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30-minute intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered the majority of true interactions. Our method leverages advances in movement ecology to quantify fine-scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer-resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.
10 Jan 2023Submitted to Ecology and Evolution
10 Jan 2023Submission Checks Completed
10 Jan 2023Assigned to Editor
10 Jan 2023Review(s) Completed, Editorial Evaluation Pending
11 Jan 2023Editorial Decision: Accept