## Introduction

Spatial capture–recapture (SCR) models are relatively new methods for inference about population density from capture–recapture data using auxiliary information about individual capture locations (Efford 2004; Borchers & Efford 2008; Royle & Young 2008). SCR models posit that *N* individuals are located within a region denoted . Each individual has a home range or activity area within which movement occurs during some well-defined time interval, and the center of the animal's activity has Cartesian coordinates for individuals *i* = 1,…,*N*. The population is sampled using *J* traps with coordinates for *j* = 1,…,*J*, and encounter probability is expressed as a function of the distance between trap location (), and individual activity or home range center (). While SCR models are a relatively recent innovation, their use is already becoming widespread (Efford *et al*. 2009; Gardner *et al*. 2010a, b; Kéry *et al*. 2011; Gopalaswamy *et al*. 2012; Foster & Harmsen 2012) because they resolve critical problems with ordinary non-spatial methods such as ill-defined area sampled and heterogeneity in encounter probability due to the juxtaposition of individuals with traps (Borchers 2012).

Despite the increasing popularity of SCR models, every application of them has been based on encounter probability models, such as the bivariate normal distribution, that imply symmetric and stationary (invariant to translation) models for home range. While such simple models might be necessitated in practice by sparse data, home range size and shape are often not well represented by stationary distributions because animals select resources that are unevenly distributed in space. Therefore, more complex models are needed to relate the capture process with the way in which individuals utilize space.

In this paper we extend SCR capture probability models to accommodate models of space usage or resource selection, by extending them to include one or more explicit landscape covariates, which the investigator believes might affect how individual animals use space within their home ranges [this is what Johnson (1980) called *third-order* selection]. We do this in a way that is entirely consistent with the manner in which parameters of classical resource selection functions (RSFs; Manly *et al*. 2002) or utilization distributions (UD; Worton 2012; Fieberg & Kochanny 2005; Fieberg 2007) are estimated from animal telemetry data. In fact, we argue that SCR models and RSF/UD models estimated from telemetry are based on the same basic underlying model of space usage. The important distinctions between SCR and RSF studies are that (i) resource selection studies do not result in estimates of population density because models for telemetry data do not allow for modeling of the encounter process (i.e., the sample size of individuals is *fixed*); and (ii) in SCR studies, encounter of individuals is imperfect (i.e., ‘*p* < 1’) whereas, with RSF data obtained by telemetry, encounter is perfect. With respect to the latter point, we can think of the RSF and SCR studies as being exactly equivalent either if we have a dense array of trapping devices, or if our telemetry apparatus samples time or space imperfectly. Then, observed use by individuals of trap locations can be modeled precisely as thinned telemetry data. Thus, the key concept of our paper is that, to integrate SCR and RSF data, we formulate both models in a manner which makes them consistent with respect to some underlying space utilization process. Telemetry data produce direct observations of space usage, and SCR data result from a thinning of such data.

The modeling framework we develop here simultaneously resolves three important problems: (i) it generalizes all existing capture probability models for SCR data to accommodate realistic patterns of space usage that result in asymmetric and irregular home ranges; (ii) it allows estimation of RSF parameters directly from SCR data, i.e., *absent* telemetry data; and (iii) it provides the basis for integrating telemetry data directly into SCR models to improve estimates of model parameters, including density. Our model greatly expands the applied relevance of SCR methods for conservation and management, and for addressing applied and theoretical questions related to animal space usage and resource selection.

In the following section we provide an introduction to ordinary SCR models, followed by a brief discussion of topics in resource selection models necessary to integrate the two types of models. A formal development of the combined likelihood for SCR and telemetry data is given in Section “The combined RSF/SCR likelihood”, followed by an application of the new model to a study of black bears in southwestern New York, and a brief simulation study to evaluate properties of the parameter estimators based on the combined data. We conclude with some general discussion, and directions for future work.