Predictive movement ecology using spatial simulations

Scott William Forrest , Mitchell A Cowan, David Denis Hofmann, Natasha Klappstein, Jonathan Potts, Théo Michelot, John Fieberg, Johannes Signer, James Pay, Charlotte R Patterson, Andrew Hoskins, Rachel Mawer, Luca Börger, Michael Bode, Dale Nimmo

The active movement of animals is fundamental to the structure and function of ecosystems globally. In recent years, reductions in the size and cost of animal tracking equipment have precipitated an explosion of interest in movement ecology, accelerating our understanding of the causes and consequences of animal movement. Until recently, movement ecology was largely a descriptive science. However, recent developments signal that the field is shifting toward a more predictive lens, facilitated by a surge in the availability of data and analytical tools. Here, we review recent advances in predictive movement ecology and clarify promising future directions for the field. We particularly focus on predictions generated with spatial simulations from step-selection functions, but cover topics relevant to other predictive approaches. We first provide an overview of key concepts in predictive movement ecology, before presenting a detailed summary of a predictive movement ecology workflow from study conceptualisation to the final presentation of outputs. At each step of the workflow, we summarise current and emerging methods and highlight key sources for technical details. We offer guidance for predictions made within the same domain as the collected data, as well as those projected into novel locations, times or landscape configurations. We distinguish between animal-centric predictions, which quantify prediction metrics specific to a single simulated animal, and landscape-centric predictions, which predict quantities that are tied to spatial locations. To accompany our review of concepts, applications, and methods for predicting animal movement, we include a detailed case-study of African wild dogs (Lycaon pictus) that steps through each decision-point in a predictive movement ecology study. We finish by outlining future directions for predictive movement ecology, emphasising the need for continued development to enhance the accuracy and ecological realism of simulations. Overall, we aim to summarise the progress of an emerging field, which will guide the next generation of predictive movement models and their application for conservation management and environmental decision-making under global change.