Land modified for human use alters matrix shape and composition and is a leading contributor to global biodiversity loss. It can also play a key role in facilitating range expansion and ecosystem invasion by anthrophilic species, as it can alter food abundance and distribution while also influencing predation risk; the relative roles of these processes are key to habitat selection theory. We researched these relative influences by examining human footprint, natural habitat, and predator occurrence on seasonal habitat selection by range-expanding boreal white-tailed deer (Odocoileus virginianus) in the oil sands of western Canada. We hypothesized that polygonal industrial features (e.g. cutblocks, well sites) drive deer distributions as sources of early seral forage, while linear features (e.g. roads, trails, and seismic lines) and habitat associated with predators are avoided by deer. We developed seasonal 2nd -order resource selection models from three years of deer GPS-telemetry data, a camera-trap-based model of predator occurrence, and landscape spatial data to weigh evidence for six competing hypotheses. Deer habitat selection was best explained by the combination of polygonal and linear features, intact deciduous forest, and wolf (Canis lupus) occurrence. Deer strongly selected for linear features such as roads and trails, despite a potential increased risk of wolf encounters. Linear features may attract deer by providing high density forage opportunity in heavily exploited landscapes, facilitating expansion into the boreal north.

Biologging data allow animal ecologists to directly measure species’ fine-scale spatiotemporal responses to environments, such as movement – critical for our understanding of biodiversity declines in the Anthropocene. Animal movement between resource patches is a behavioral expression of multiple ecological processes that affect individual fitness. Protected area (PA) networks are a tool used to conserve biodiversity by sustaining habitat patches across vast heterogeneous landscapes. However, our ability to design PA networks that conserve biodiversity relies on our accurate understanding of animal movement and functional connectivity; this understanding is rarely tested in real-world situations due to the large geographic expanse of most PA networks. Using a tractable PA network mesocosm, we employ cutting-edge biologging technology to analyze animal movement decisions in response to a highly heterogeneous landscape. We analyze these data to test, in a novel way, three common hypotheses about functional connectivity – structural corridors, least cost paths, and stepping stones. Consistently, animals moved along structurally self-similar corridors. In reference to the Aichi 2020 Biodiversity Targets, relying on species to “stepping stone” across habitat remnants may not achieve protected area network conservation objectives.

Animals moving through landscapes need to strike a balance between finding sufficient resources to grow and reproduce while minimizing encounters with predators. Because encounter rates are determined by the average distance over which directed motion persists, this trade-off should be apparent in individuals' movement. Using GPS data from 1,396 individuals across 62 species of terrestrial mammals, we show how predators maintained directed motion ~7 times longer than for similarly-sized prey, revealing how prey species must trade off search efficiency against predator encounter rates. Individual search strategies were also modulated by resource abundance, with prey species forced to risk higher predator encounter rates when resources were scarce. These findings highlight the interplay between encounter rates and resource availability in shaping broad patterns mammalian movement strategies.