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