Energy Selection Functions: Modelling the Energetic Drivers of Animal Movement and Habitat Use

Abstract

Energetics are a key driver of animal decision-making, as survival depends on the balance between foraging benefits and movement costs. However, this fundamental perspective is often missing from habitat selection studies, which mainly describe correlations between observed space use and environmental features, rather than assessing the mechanisms behind these correlations. To address this gap, we present a new model, the energy selection function (ESF), to assess how moving animals choose habitat based on energetic considerations, thus incorporating a key aspect of evolutionary behaviour into habitat selection analysis. The ESF provides a way to test foraging and movement hypotheses, by evaluating selection for energetic gains and costs. In this thesis, we contrast the ESF to other habitat selection models, provide guidelines for defining energetic covariates, and demonstrate the model's utility with simulations and a case study of polar bears. Simulations indicate that the ESF can be fitted with low estimation errors, under a number of modelling choices and biological scenarios. Our case study shows how cost-minimization may arise in species that inhabit environments with an unpredictable distribution of energetic gains. Because of its close links to existing habitat selection models, the ESF is widely applicable to any study system where energetics can be derived, and has immense potential for methodological extensions.