Wild pigs (Sus scrofa) are a highly destructive invasive species across the globe, making them subject to intensive management including lethal control. Robust estimates of population abundance are needed to assess and optimize the effectiveness of control efforts. Genetic capture mark-recapture (GCMR) offers considerable promise for monitoring invasive wild pigs. However, obtaining robust estimates from GCMR can be difficult due to low quality DNA, particularly in moist, hot environments that promote fast DNA degradation. To examine if GCMR is feasible for estimating wild pig abundance, we collected pig fecal samples in three sites (bottomland hardwoods, mixed forest, and upland pine) at the Savannah River Site, South Carolina, USA. Amplification success across nine microsatellite loci varied across habitats with bottomland hardwoods having the lowest success (18%) compared to the mixed forest (56%) and upland pine (65%). Resultant abundance and density estimates were relatively similar between field-based methods and GCMR in the bottomland hardwoods and upland habitats, but estimates for the bottomland hardwoods had larger confidence intervals. Tests with additional extraction methods in the bottomland hardwoods found low amplification, with a combination of Nucleospin soil kits and Zymo clean-up kits performing the best. While our study found reasonable estimates of density across three habitats, environmental conditions have a powerful influence on amplification success and the corresponding number of recaptures in wild pig GCMR, particularly in the bottomland hardwoods where flooding was frequent during sampling. Successful GCMR studies should, therefore, consider both sampling intensity and laboratory costs when designing studies because dryer habitats have higher amplification success but lower pig densities.

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.

The importance of social and spatial structuring of wildlife populations for disease spread, though widely recognized, is still poorly understood in many host-pathogen systems. In particular, system specific kin relationships among hosts can create contact heterogeneities and differential disease transmission rates. Here, we investigate how distance-dependent infection risk is influenced by genetic relatedness in a novel wild boar ( Sus scrofa) - African swine fever (ASF) system. We hypothesized that the infection risk would correlate positively with proximity and relatedness to ASF-infected individuals but expected those relationships to weaken with distance between individuals due to decay in contact rates and genetic similarity. ASF infection risk was shaped by the number of infected animals throughout the zone of potential contact (0-10 km) but not beyond it. This effect was the strongest at close distances (0-2 km) and weakened further on (2-10 km), consistent with decreasing probability of contact. Overall, there was a positive association between genetic relatedness to infectees and infection risk within the contact zone but this effect varied in space. In the high-contact zone (0-2 km), infection risk was not influenced by relatedness when controlled for the number of ASF-positive animals. However, infections were more frequent among close relatives indicating that familial relationships could have played a role in ASF transmission. In the medium-contact zone (2-5 km), infection risk and frequency of paired infections were associated with relatedness. Relatedness did not predict infection risk in low- and no-contact zones (5-10 and >10 km, respectively). Together, our results indicate that the number of nearby infected individuals overrides the effect of relatedness in shaping ASF transmission rates which nevertheless can be higher among close relatives. Highly localized transmission highlights the possibility to control the disease if containment measures are employed quickly and efficiently.

Environment drives the host movements that shape pathogen transmission through three mediating processes: host density, host mobility, and contact. These processes combine with pathogen life-history to give rise to an “epidemiological landscape” that determines spatial patterns of pathogen transmission. Yet despite its central role in transmission, strategies for predicting the epidemiological landscape from real-world data remain limited. Here, we develop the epidemiological landscape as an interface between movement ecology and spatial epidemiology. We propose a movement-pathogen pace-of-life heuristic for prioritizing the landscape’s central processes by positing that spatial dynamics for fast pace-of-life pathogens are best-approximated by the spatial ecology of host contacts; spatial dynamics for slower pace-of-life pathogens are best approximated by host densities; and spatial dynamics for pathogens with environmental reservoirs reflect a convolution of those densities with the spatial configuration of environmental reservoir sites. We then identify mechanisms that underpin the epidemiological landscape and match each mechanism to emerging tools from movement ecology. Finally, we outline workflows for describing the epidemiological landscape and using it to predict subsequent patterns of pathogen transmission. Our framework links transmission to environmental context, providing a scaffold for mechanistically understanding how environmental context can generate and shift existing patterns in spatial epidemiology.

A document by Kim Pepin. Click on the document to view its contents.

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.