Deriving spatially explicit direct and indirect interaction networks from animal movement data

Authors

Anni Yang, University of OklahomaFollow
Mark Q. Wilber, University of Tennessee
Kezia R. Manlove, Utah State University
Ryan S. Miller, USDA APHIS NWRC, Fort Collins
Raoul Boughton, Archbold Biological Station, Buck Island Ranch, Florida
James Beasley, University of Georgia
Joseph Northrup, Ontario Ministry of Natural Resources and Forestry
Kurt C. Vercauteren, USDA APHIS NWRC, Fort Collins
George Wittemyer
Kim Pepin, USDA APHIS NWRC, Fort CollinsFollow

Document Type

Article

Date of this Version

2023

Citation

Yang, A., Wilber, M. Q., Manlove, K. R., Miller, R. S., Boughton, R., Beasley, J., Northrup, J., VerCauteren, K. C., Wittemyer, G., & Pepin, K. (2023). Deriving spatially explicit direct and indirect interaction networks from animal movement data. Ecology and Evolution, 13, e9774. https://doi.org/10.1002/ece3.9774

Comments

U.S. government work

Abstract

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-min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered 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.