Combining animal interactions and habitat selection into models of space use: a case study with white‐tailed deer

Abstract

Animals determine their daily movement trajectories in response to a network of ecological processes, including interactions with other organisms, their memories of previous events, and the changing environment. These combine to cause the emergent space use patterns observed over longer periods of time, such as a whole season. Understanding which processes cause these patterns to emerge, and how, requires a process‐based modelling approach. Individual‐based decisions can be described as a system of partial‐differential equations (PDEs) to produce a dynamic description of space use built from the underlying movement process. Here we combine PDE‐based models with step‐selection analysis to investigate the combined effects of three established ecological processes that partially shape movement and space use: 1) a heterogeneous environment; 2) the environmental markings of moving conspecifics; and 3) the memory of direct interactions with conspecifics. We apply this framework to a large GPS‐based dataset of white‐tailed deer Odocoileus virginianus in the southeastern US. We fit models at the population level to provide predictive models, then tailor these to fit individual deer. We specifically incorporate relationships between each possible pair of deer and define each animal's responses to their unique local environments using separate integrated step‐selection analyses. We show how individual movements and decisions yield emergent patterns in animal distributions, and we provide a full generalised description of the framework so that it may be applied to any species simultaneously responding to multiple potentially interacting stimuli (e.g. sociality, morphology, etc.). We found that the population of bucks had highly varied preferences for vegetation, but were shaping their space use in response to conspecific interactions, dependent on the individual relationships between two deer. We advocate for increased consideration of individual‐based movement rules as determinants of realized animal space use, and particularly how these affect emergent distributions of entire species.

Predicting fine-scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations

“…To highlight the influence of temporal dynamics we opted for a simple base model that has only movement, habitat selection and memory processes, although there are additional parameters that could be included to increase the realism of the simulated trajectories. We ignored the influence of conspecifics resulting in social dynamics, despite the fact that they are influential to animal movement (Potts, Börger, et al, 2022; Ellison et al, 2024). In part this was a decision made with respect to our data, as individuals from mostly separate clans were GPS-tagged, resulting in little observable influence between individuals.…”

“…Step selection functions (SSFs) are particularly advantageous as they can be used to simulate trajectories (Signer et al, 2017; Potts & Börger, 2023; Signer et al, 2023), are straightforward to parameterise, and can incorporate temporal dynamics (Ager et al, 2003; Forester et al, 2009; Tsalyuk et al, 2019; Richter et al, 2020; Klappstein et al, 2024). An SSF combines a movement and a external selection kernel, can take a range of forms (Munden et al, 2021; Klappstein et al, 2022; Beumer et al, 2023; Eisaguirre et al, 2024; Pohle et al, 2024), and can accommodate a wide range of covariates including habitat, linear features, distance-to-feature variables, proximity to other animals (Potts et al, 2022; Ellison et al, 2024) and representations of previous space use (Schlägel & Lewis, 2014; Oliveira-Santos et al, 2016; Rheault et al, 2021).…”