The pitfalls of ignoring behaviour when quantifying habitat selection

AIM: Habitat selection is a behavioural mechanism by which animals attempt to maximize their inclusive fitness while balancing competing demands, such as finding food and rearing offspring while avoiding predation, in a heterogeneous and changing environment. Different habitat characteristics may be associated with each of these demands, implying that habitat selection varies depending on the behavioural motivations of the animal. Here, we investigate behaviour‐specific habitat selection in African elephants and discuss its implications for distribution modelling and conservation. LOCATION: Northern Botswana, Africa, case study. METHODS: We use Bayesian state‐space models to characterize location time series data of elephants into two behavioural states (encamped and exploratory). We then develop habitat selection models for each behavioural state and contrast them to models based on data pooled among behaviours. RESULTS: Spatial predictions of habitat use were often markedly different among the models. Behaviour‐specific and pooled habitat selection models differed in model structure, the magnitude of model coefficients and the form of the selection curve (linear or quadratic). Selection was typically strongest in the behaviour‐specific models, although this varied according to behavioural state and habitat covariate. MAIN CONCLUSIONS: Ignoring behavioural states often had important consequences for quantifying habitat selection. Quantifying selection irrespective of behaviour (among all behaviours) can obscure important species–habitat relationships, thereby risking weak or incorrect inferences. Behaviour‐specific habitat selection provides greater insight into the process of habitat selection and can improve predictive habitat selection estimates. As some behaviours are more relevant to specific conservation objectives than others, focusing on behaviour‐specific selection could improve how habitats are prioritized for conservation or management.