Simulating animal space use from fitted integrated Step-Selection Functions (iSSF)

A standing challenge in the study of animal movement ecology is the capacity to predict where and when an individual animal might occur on the landscape, the so-called, utilisation distribution (UD). Under certain assumptions, the steady-state UD can be predicted from a fitted exponential habitat selection function. However, these assumptions are rarely met. Furthermore, there are many applications that require the estimation of transient dynamics rather than steady-state UDs (e.g. when modelling migration or dispersal). Thus, there is a clear need for computational tools capable of predicting UDs based on observed animal movement data. Integrated Step-Selection Analyses (iSSAs), which integrates movement of the animal into habitat selection analyses, are widely used to study habitat selection and movement of wild animals, and result in a fully parametrised individual-based model of animal movement, which we refer to as an integrated Step Selection Function (iSSF). An iSSF can be used to generate stochastic animal paths based on random draws from a series of Markovian redistribution kernels, each consisting of a selection-free, but possibly habitat-influenced, movement kernel and a movement-free selection function. The UD can be approximated by a sufficiently large set of such stochastic paths. Here, we present a set of functions in R to facilitate the simulation of animal space use from fitted iSSFs. Our goal is to provide a general purpose simulator that is easy to use and is part of an existing workflow for iSSAs (within the amt R package). We demonstrate through a series of applications how the simulator can be used to address a variety of questions in applied movement ecology. By providing functions in amt and coded examples, we hope to encourage ecologists using iSSFs to explore their predictions and model goodness-of-fit using simulations, and to further explore mechanistic approaches to modelling landscape connectivity.