Contact among animals is crucial for various ecological processes, including social behaviors, disease transmission, and predator-prey interactions. However, the distribution of contact events across time and space is heterogeneous, influenced by environmental factors and biological purposes. Previous studies have assumed that areas with abundant resources and preferred habitats attract more individuals and, therefore, lead to more contact. To examine the accuracy of this assumption, we used a use-available framework to identify landscape factors influencing contact locations. Our study focused on two wild pig populations in Florida and Texas, USA. We employed a contact-resource selection function (RSF) model, where contact locations were defined as used points and locations without contact as available points. By comparing the contact RSF with a population-level RSF, we assessed the factors driving both habitat selection and contact. We found that the landscape predictors (e.g., wetland, linear features, and food resources) played different roles in habitat selection and contact processes for wild pigs in both study areas. This indicates that pigs interacted with their landscapes differently when choosing habitats compared to when they encountered other individuals. Consequently, relying solely on the spatial overlap of individual or population-level RSF models may lead to a misleading understanding of contact-related ecology. Our findings challenge prevailing assumptions about contact and introduce innovative approaches to better understand the ecological drivers of spatially explicit contact. By accurately predicting the spatial distribution of contact events, we can enhance our understanding of ecological processes and their spatial dynamics.
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.
The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal, and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable framework that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible framework “Movement-driven modeling of spatio-temporal infection risk” (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.
Pigs (Sus scrofa) may be important surveillance targets for risk assessment and risk-based control planning against emerging zoonoses. Pigs have high-contact rates with humans and other animals, transmit similar pathogens as humans including CoVs, and serve as reservoirs and intermediate hosts for notable human pandemics. Wild and domestic pigs both interface with humans and each other but have unique ecologies that demand different surveillance strategies. Three fundamental questions shape any surveillance program: where, when, and how can surveillance be conducted to optimize the surveillance objective? Using theory of mechanisms of zoonotic spillover and data on risk factors, we propose a framework for determining where surveillance might begin initially to maximize a detection in each host species at their interface. We illustrate the utility of the framework using data from the United States. We then discuss variables to consider in refining when and how to conduct surveillance. Recent advances in accounting for opportunistic sampling designs and in translating serology samples into infection times provide promising directions for extracting spatio-temporal estimates of disease risk from typical surveillance data. Such robust estimates of population-level disease risk allow surveillance plans to be updated in space and time based on new information (adaptive surveillance) thus optimizing allocation of surveillance resources to maximize the quality of risk assessment insight.