Using a Novel Framework of Animal Space-Use Behaviors Reveals a Gradient of Responses to Human Modification

Spatial behavior, including home-ranging behaviors, habitat selection, and movement, can be extremely informative in estimating how animals respond to landscape heterogeneity. Responses in these spatial behaviors to factors such as human modification and resources on the landscape can highlight a species’ spatial strategy to maximize fitness and minimize risk. These strategies can vary on spatial, temporal, and individual scales, and the combination of behaviors on these scales can lead to very different strategies among species. Harnessing the variation present at these scales, we developed a framework for predicting how species may respond to changes in their environments on a gradient ranging from generic, where a species exhibits broad-stroke spatial responses to their environment, to nuanced, in which a species uses a combination of temporal and spatial strategies paired with functional responses in selection behaviors. Using 46 GPS-tracked bobcats and coyotes inhabiting a landscape encompassing a range of human modification, we evaluated where each species falls along the generic-to-nuanced gradient. Bobcats and coyotes studied occupied opposite ends of this gradient, using different strategies in response to human modification in their home ranges, with bobcats broadly expanding their home range with increases in human modification and clearly selecting for or avoiding features on the landscape with temporal consistency. Meanwhile, coyotes did not expand their home ranges with human modification, but instead displayed temporal and spatial adjustments in their functional responses to human modification. These differences in response to habitat, resources, and risk between the two species highlighted the variation in spatial behaviors animals can use to exist in anthropogenic environments influenced by interspecific variation in behavioral plasticity. Categorizing animal spatial behavior based on the generic-to-nuanced gradient can help in predicting how a species will respond to future change based on their current spatial behavior.