1. Energetics are a key driver of animal decision-making, as survival
depends on the balance between foraging benefits and movement costs.
This fundamental perspective is often missing from habitat selection
studies, which mainly describe correlations between space use and
environmental features, rather than the mechanisms behind these
correlations. To address this gap, we present a new modelling framework,
the energy selection function (ESF), to assess how moving animals choose
habitat based on energetic considerations.
2. The ESF considers that the likelihood of an animal selecting a
movement step depends directly on the corresponding energetic gains and
costs. The parameters of the ESF measure selection for energetic gains
and against energetic costs; when estimated jointly, these provide
inferences about foraging and movement strategies. The ESF can be
implemented easily with standard conditional logistic regression
software, allowing for fast inference. We outline a workflow, from
data-gathering to statistical analysis, and use a case study of polar
bears (Ursus maritimus) as an illustrative example.
3. We show how defining gains and costs at the scale of the movement
step allows us to include detailed information about resource
distribution, landscape resistance, and movement patterns. We
demonstrate this in the polar bear case study, in which the results show
how cost-minimization may arise in species that inhabit environments
with an unpredictable distribution of energetic gains.
4. The ESF combines the energetic consequences of both movement and
resource selection, thus incorporating a key aspect of evolutionary
behaviour into habitat selection analysis. Because of its close links to
existing habitat selection models, the ESF is widely applicable to any
study system where energetic gains and costs can be derived, and has
immense potential for methodological extensions.
The Ornithodolite as a tool to quantify animal space use and habitat selection: a case study with birds diving in tidal waters
