A resource selection function is a model of the likelihood that an available spatial unit will be used by an animal, given its resource value. But how do we appropriately define availability? Step-selection analysis deals with this problem at the scale of the observed positional data, by matching each used step (connecting two consecutive observed positions of the animal) with a set of available steps randomly sampled from a distribution of observed steps or their characteristics. Here we present a simple extension to this approach, termed integrated step-selection analysis (iSSA), which relaxes the implicit assumption that observed movement attributes (i.e. velocities and their temporal autocorrelations) are independent of resource selection. Instead, iSSA relies on simultaneously estimating movement and resource-selection parameters, thus allowing simple likelihood-based inference of resource selection within a mechanistic movement model. We provide theoretical underpinning of iSSA, as well as practical guidelines to its implementation. Using computer simulations, we evaluate the inferential and predictive capacity of iSSA compared to currently used methods. Our work demonstrates the utility of iSSA as a general, flexible and user-friendly approach for both evaluating a variety of ecological hypotheses, and predicting future ecological patterns.
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