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

Integrated step selection analysis: bridging the gap between resource selection and animal movement