Behavioral states in space and time: understanding landscape use by an invasive mammal

Abstract

Animal movement models can be used to understand species behavior and assist with implementation of management activities. We explored behavioral states of an invasive wild pig (Sus scrofa) population that recently colonized central Michigan, USA, 2014–2018. To quantify environmental factors related to wild pig movement ecology and spatio‐temporal landscape use, we predicted wild pig behavioral states relative to land cover type, landscape structure (i.e., edge and patch cohesion), and weather conditions. We used global positioning system (GPS)‐collars and monitored 8 wild pigs from 2014–2018. We fit local convex hulls and calculated movement metrics revealing 3 wild pig behavioral states (resting, exploratory, and relocating) and constructed a 3‐level model to predict behavioral state probabilities relative to biotic and abiotic conditions. Probabilities of exploratory and resting behaviors were higher nearer to riparian and open herbaceous cover types (oftentimes emergent marsh), indicating that these cover types provided security cover during activity and bedding. Hard mast cover types had a strong positive association with relocating behaviors. More cohesive patches of agriculture and shrub cover types were associated with higher probabilities of exploratory behaviors, while resting was more likely in continuous patches of agriculture (mostly mid‐summer corn). The probability of exploratory behaviors increased exponentially with warming ambient temperature. Our results may be used by managers to develop control strategies conducive to landscape and environmental conditions where the likelihood of encountering wild pigs is highest or targeting wild pigs when in a behavioral state most vulnerable to a particular removal technique.

Improving the precision and accuracy of wildlife monitoring with multi‐constellation, multi‐frequency GNSS collars

“…Nevertheless, habitat‐biased locations and imprecise movement paths, also affected by the fix interval, may become unsuitable for ecological analyses. The recent scientific literature contains numerous examples of how GPS collar data are used in wildlife monitoring for habitat modeling (Laguna et al 2021), and identifying animal movement and landscape use (Wittemyer et al 2019, Gray et al 2022), migratory strategies (Peterson et al 2022), predation sites (Irvine et al 2022), birth sites (Dion et al 2019, Marchand et al 2021, Hooven et al 2022), and capture locations (Hampton et al 2020, Bengsen et al 2021), or to define social behavior (Davis et al 2018, Wielgus et al 2021). Improvements to GNSS technology allow for more reliable data collection from collars; thus, new ecological questions can be investigated.…”