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The ability of animals to adapt to their changing environment will depend in part on shifts in their ranging patterns, but when and why individuals choose to move requires detailed understanding of their decision‐making processes. We develop a simple decision‐making model accounting for resource availability in habitually used ranges. We suggest that disparities between model predictions and animal tracking data indicate additional factors influencing movement decisions, which may be identified given detailed system‐specific knowledge. The model was evaluated using movement data from satellite‐tracked elephants (Loxodonta africana) inhabiting the Amboseli basin in Kenya, moving from savannah areas with low quality but constant resource availability, to areas with temporally constrained higher nutrient availability. Overall, the model fits the data well: There was a good correlation between predicted and observed locations for the combined data from all elephants, but variation between individuals in how well the model fits. For those elephants where model predictions were less successful, additional factors likely to affect movement decisions, including reproduction, anthropogenic threats, memory and perception are suggested. This protocol for building and testing decision‐making models should contribute to success in attempts to preserve sufficient space for large herbivores in their increasingly human‐dominated ecosystems. [ABSTRACT FROM AUTHOR]

Highlights • Energy-budget model of how individual elephants respond to changes in forage. • Population size and structure emerge from model outputs. • Predicts changes in population dynamics induced by climate and land-use change. • Can be adapted for other mammalian herbivores in grassland ecosystems. Abstract Strategies for the conservation and management of many wild species requires an improved understanding of how population dynamics respond to changes in environmental conditions, including key drivers such as food availability. The development of mechanistic predictive models, in which the underlying processes of a system are modelled, enables a robust understanding of these demographic responses to dynamic environmental conditions. We present an individual-based energy budget model for a mega-herbivore, the African elephant (Loxodonta africana), which relates remotely measured changes in food availability to vital demographic rates of birth and mortality. Elephants require large spaces over which to roam in search of seasonal food, and thus are vulnerable to environmental changes which limit space use or alter food availability. The model is constructed using principles of physiological ecology; uncertain parameter values are calibrated using approximate Bayesian computation. The resulting model fits observed population dynamics data well. The model has critical value in being able to project elephant population size under future environmental conditions and is applicable to other mammalian herbivores with appropriate parameterisation. [ABSTRACT FROM AUTHOR]

Global ecosystem change presents a major challenge to biodiversity conservation, which must identify and prioritize the most critical threats to species persistence given limited available funding. Mechanistic models enable robust predictions under future conditions and can consider multiple stressors in combination. Here we use an individual‐based model (IBM) to predict elephant population size in Amboseli, southern Kenya, under environmental scenarios incorporating climate change and anthropogenic habitat loss. The IBM uses projected food availability as a key driver of elephant population dynamics and relates variation in food availability to changes in vital demographic rates through an energy budget. Habitat loss, rather than climate change, represents the most significant threat to the persistence of the Amboseli elephant population in the 21st century and highlights the importance of collaborations and agreements that preserve space for Amboseli elephants to ensure the population remains resilient to environmental stochasticity. [ABSTRACT FROM AUTHOR]