Ecology, 94(7), 2013, pp. 1456–1463 Ó 2013 by the Ecological Society of America Practical guidance on characterizing availability in resource selection functions under a use–availability design JOSEPH M. NORTHRUP, 1,5 MEVIN B. HOOTEN, 1,2,3 CHARLES R. ANDERSON,JR., 4 AND GEORGE WITTEMYER 1 1 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 3 Colorado State University, Department of Statistics, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 4 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Abstract. Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use– availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use–availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness. Key words: autocorrelation; GPS radio telemetry; resource selection function, RSF; spatial point process; species distribution model; use–availability data; wildlife. INTRODUCTION Habitat selection is a behavioral process by which animals choose the most suitable locations in order to maximize fitness (Fretwell and Lucas 1969). Under- standing the selection process can provide insight into population regulation, species interactions, and preda- tor–prey dynamics (Morris 2003) and thus is fundamen- tal to animal ecology. With advancements in global positioning systems (GPS), radio telemetry, and geo- graphic information systems (GIS), the data required to examine habitat selection patterns of free-ranging animals are increasingly available, spurring a prolifera- tion of recent studies on this topic. The most common method for examining habitat selection patterns from GPS radio collar data is the resource selection function (RSF, see Table 1 [Manly et al. 2002, Johnson et al. 2006]). Resource selection functions typically are fit in a use–availability frame- work, whereby environmental covariates (e.g., elevation) at the locations where the animal was present (the used locations) are contrasted with covariates at random locations taken from an area deemed to be available for selection (the availability sample [Manly et al. 2002, Johnson et al. 2006]). Such methods are inherently based on models for spatial point processes (as are many species distribution models; e.g., Warton and Shepherd [2010]), however logistic regression, which asymptoti- cally approximates a point process model (Johnson et al. 2006, Aarts et al. 2012), typically is used to estimate coefficients (but see Baddeley and Turner [2000], Lele and Keim [2006], Johnson et al. [2008], and Aarts et al. [2012] for alternate approaches). Logistic regression allows researchers to easily obtain inference on selection or avoidance of covariates and to generate maps for use in subsequent analysis (Boyce and McDonald 1999). Such methods have been used to examine numerous ecological processes and address important management questions, including the interplay between habitat and dispersal (Shafer et al. 2012), the presence of ecological traps (Northrup et al. 2012), and functional responses in wildlife interactions with anthropogenic development Manuscript received 2 October 2012; revised 7 March 2013; accepted 12 March 2013. Corresponding Editor: B. D. Inouye. 5 E-mail: joe.northrup@colostate.edu 1456 R eports