Simulating animal movement trajectories from temporally dynamic step selection functions

Animals display temporally dynamic behaviour over daily and seasonal timescales, which influences their movements, distribution and home ranging behaviour. Developing predictive models that include temporal dynamics on these processes allows for a more realistic and informative representation of an animal’s behaviour, which can also provide more accurate predictions for times that are relevant to conservation management actions. In recent years, animal movement models have been used to address conservation concerns such population distribution and invasion spread, as well as ecological and behavioural questions such as landscape connectivity, foraging efficiency and the nature of memory processes and social interactions. These models often take the form of step selection functions (SSFs), which are attractive due to their being relatively easy both to parameterise and to simulate from, but there has yet to be simulations from SSFs that include temporally dynamic processes of movement, habitat selection and memory. Here, we develop SSFs that incorporate temporal dynamics using harmonic terms to understand the drivers of space use patterns in water buffalo ( Bubalus bubalis ), which can be used to simulate trajectories. Water buffalo are an invasive species in Northern Australia’s tropical savannas, and their management depends on spatiotemporal predictions over multiple time-scales. We simulated trajectories using the estimated temporally dynamic SSF parameters, and compared them against the observed data using animal-movement informed summary statistics. The simulations generated from the temporally dynamic models replicated the crepuscular movement patterns of the buffalo, as well as the selection of high canopy cover and denser vegetation during the middle of the day for thermoregulation, which the static model could not. By integrating temporally dynamic processes into animal movement trajectories, we demonstrate an approach that can enhance conservation management strategies and deepen our understanding of ecological and behavioural patterns across various timescales. ### Competing Interest Statement The authors have declared no competing interest.