ArticleEcology and evolution2023
Deriving spatially explicit direct and indirect interaction networks from animal movement data.
Article in Ecology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Intraspecific Contact Among White-Tailed Deer: A Literature Review and Chronic Wasting Disease Case Study.Ecology and evolution · 2026Review
- Trash Talking: Anthropogenic Resources Facilitate Raccoon Interactions in Urban Environments.Ecology and evolution · 2025Article
- Estimating Environmental Transmission Risk From Host Movement Data.Ecology and evolution · 2025Article
- Spatiotemporal Overlap of Mallards With Poultry Farms Is Associated With Greater Risk of Avian Influenza Wild Bird Spillover Events.Ecology and evolution · 2025Article
- Expanding National-Scale Wildlife Disease Surveillance Systems With Research Networks.Ecology and evolution · 2025Article
- Article
- Towards transient space-use dynamics: re-envisioning models of utilization distribution and their applications.Movement ecology · 2025Article
- How do non-independent host movements affect spatio-temporal disease dynamics? Partitioning the contributions of spatial overlap and correlated movements to transmission risk.Movement ecology · 2025Article
- A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.Movement ecology · 2024Article
- Host-, Environment-, or Human-Related Effects Drive Interspecies Interactions in an Animal Tuberculosis Multi-Host Community Depending on the Host and Season.Transboundary and emerging diseases · 2024Article
- Individual-level patterns of resource selection do not predict hotspots of contact.Movement ecology · 2023Article
- Deriving spatially explicit direct and indirect interaction networks from animal movement data.Ecology and evolution · 2023Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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 (
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