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

Abstract

Abstract1.Context and purposeThere has been increasing interest in simulating stochastic movement paths from step selection models. Hitherto, models used to simulate trajectories have not included temporally dynamic co-efficients on both the movement and external selection processes, despite animals having temporally dynamic behaviour over daily or seasonal timescales.2.Approach and methodsHere, we focused on simulating stochastic trajectories from step selection functions (SSFs) that include temporal dynamics using harmonic terms, focusing on dynamic behaviour on a daily timescale. The models also incorporated home ranging behaviour through a decaying memory process, which was also interacted with the harmonic terms. We simulated trajectories of individual animals and assessed how they compared to observed data through animal-movement-informed summary statistics. We applied our methods to GPS-tracked water buffalo (Bubalus bubalis), which are an invasive species in Northern Australia’s tropical savannas.3.Main resultsThe temporally dynamic models allowed for more informative interpretation of animal behaviour, particularly when quadratic terms were included in the models, allowing for insights about buffalo behaviour. The simulations generated from the temporally dynamic models reproduced the movement and habitat selection behaviour that we observed in the GPS data, particularly crepuscular movement behaviour and selecting for high canopy cover and vegetation during the middle of the day for thermoregulation. When assessed over longer time-scales, models both with and without daily temporal dynamics generated simulations that performed similarly according to the summary statistics, and had geographical space use of similar area and monthly overlap to that the observed data.4.Conclusions and wider implicationsWe recommend fitting temporally dynamic SSFs when generating simulated trajectories to most closely represent the animal’s dynamic behaviour. We considered daily behavioural dynamics here, although any timescale of interest can be incorporated, with the potential for interacting multi scale temporal dynamics. Including temporal dynamics in SSFs for the purpose of simulating new data can address a wide range of ecological and behavioural questions and provide valuable information for conservation management, particularly for species with clear daily or seasonal behaviour patterns. We provide code to replicate all analyses presented.