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

Abstract

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.

The Statistical Building Blocks of Animal Movement Simulations

“…[72, 73, 74, 75, 76] are examples. Others represent movement in terms of partial differential equations some random components that switch between gradient following search (advection plus random noise) and random search [77] or discrete approaches using integrated step-selection functions [78]. In addition, machine learning methods have been shown to outperform SDE models for predicting the next location of individuals in some systems [79].…”

“…[52, 53, 54, 55] are examples. Others represent movement in terms of partial differential equations some random components that switch between gradient following search (advection plus random noise) and random search [56] or discrete approaches using integrated step-selection functions [57].…”

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

“…Recently, SSFs have been gaining popularity for generating predictions, both for estimating utilisation distributions (UDs) of an individual (or set of individuals) in a given area (Signer et al, 2017; Potts, Börger, et al, 2022; Signer et al, 2023) and for predicting conservation-relevant measures such as connectivity and movement corridors (Osipova et al, 2019; Hooker et al, 2021; Whittington et al, 2022; Aiello et al, 2023; Hofmann et al, 2023; Sells et al, 2023). Using the estimated parameters of an SSF, several approaches have been developed to generate predictions, which include analytic and simulation-based approaches (Barnett & Moorcroft, 2008; Avgar et al, 2016; Signer et al, 2017; Potts & Schlägel, 2020; Potts, Börger, et al, 2022; Potts, Giunta, et al, 2022).…”

“…For a review of approaches to ‘scale-up’ from step selection functions to spatiotemporal patterns, see Potts and Börger (2023). A flexible and robust approach that allows for several prediction outcomes is to generate trajectories from the inferred parameters of the SSF via stochastic simulation (Avgar et al, 2013; Duchesne et al, 2015; Avgar et al, 2016; Signer et al, 2017; Potts & Börger, 2023; Signer et al, 2023).…”

“…Simulation-based models can include temporal dynamics on any scale, and they explicitly incorporate the animal’s movement dynamics. 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).…”