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.

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.

Resource selection analysis (RSA) is a cornerstone approach for understanding animal distributions, yet there exists no rigorous quantification of sample sizes required to obtain reliable results. We provide closed-form mathematical expressions for both the number of animals and relocations per animal required for parameterising RSA to a given degree of precision. Required sample sizes depend on just two quantities: habitat selection strength and an index of landscape complexity, which we define rigorously. We validate our solutions using 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores, and herbivores from boreal, temperate, and tropical forests, montane woodlands, swamps, and tundra). Our results contradict conventional wisdom by showing that environmental effects on distributions can often be estimated with fewer animals and relocations than assumed, with far-reaching implications for ecologists, conservationists, and natural resource managers.