LAUSR

Abstract Animal movement, spanning all time and space scales in nature, is constrained by the individual's available energy to spend, creating a strong link between physiology and observed movement and distribution patterns. To progress, movement ecology needs an explicit focus on common mechanisms, such as energetics, linking behaviour to fitness consequences across scales, but simplified by process-based approaches, such as individual-based models (IBMs). We review the animal movement literature, from fine-scale patch foraging to large-scale geographic migration, focussing on IBMs incorporating individual energetics (hereafter termed eIBMs). The literature shows IBMs in movement ecology are mainly defined by the following four categories nested across different space and time scales: (1) fine-scale displacement, i.e. foraging and local habitat selection by animals under different resource availabilities, including (2) cognitive processes, such as risk perception, memory and learning, (3) home range occupancy and dispersal potential, and (4) migration and biogeographic distribution. Amongst eIBMs, a common issue emerges: individual traits like energetics are sometimes species- and problem-specific, leading to divergence in the way energetics are incorporated and aggregated in the models. Further, movement becomes more difficult to predict with increasing time and space scales and behaviour and biological complexity. Using this individual-level approach, we show 1) it is most effective in explaining fine-scale movement (foraging and competition) where the links between the animal and their habitat are immediate and absolute compared to coarser time and space scales, 2) coarser time and space scales present further challenges for the animal that require more careful interpretation of movement drivers, and 3) common, but more complex behaviours across scales, such as risk perception and memory, are better explained with richer ecological data that are well-integrated into movement models. We propose formulating individual energetics in a modelling framework as a next-gen extension to address the challenges of modelling movement across different scales, species, and constraints.