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Prevailing theories about animal foraging behaviours and the food webs they occupy offer divergent predictions about whether seasonally limited food availability promotes dietary diversification or specialization. Emphasis on how animals compete for food predominates in work on the foraging ecology of large mammalian herbivores, whereas emphasis on how the diversity of available foods generally constrains dietary opportunity predominates work on entire food webs. Reconciling predictions about what promotes dietary diversification is challenging because species’ different body sizes and mobilities modulate how they seek and compete for resources—the mechanistic bases of common predictions may not pertain to all species equally. We evaluated predictions about five large-herbivore species that differ in body size and mobility in Yellowstone National Park using GPS tracking and dietary DNA. The data illuminated remarkably strong and significant correlations between body size and five key indicators of diet seasonality (R²= 0.71–0.80). Compared to smaller species, bison and elk showed muted diet seasonality and maintained access to more unique foods when winter conditions constrained food availability. Evidence from GPS collars revealed size-based differences in species’ seasonal movements and habitat-use patterns, suggesting that better accounting for the allometry of foraging behaviours may help reconcile disparate ideas about the ecological drivers of seasonal diet switching. 

Integrating diverse concepts from animal behavior, movement ecology, and machine learning, we develop an overview of the ecology of learning and animal movement. Learning-based movement is clearly relevant to ecological problems, but the subject is rooted firmly in psychology, including a distinct terminology. We contrast this psychological origin of learning with the task-oriented perspective on learning that has emerged from the field of machine learning. We review conceptual frameworks that characterize the role of learning in movement, discuss emerging trends, and summarize recent developments in the analysis of movement data. We also discuss the relative advantages of different modeling approaches for exploring the learning-movement interface. We explore in depth how individual and social modalities of learning can matter to the ecology of animal movement, and highlight how diverse kinds of field studies, ranging from translocation efforts to manipulative experiments, can provide critical insight into the learning process in animal movement.