Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. camera traps, non‐invasive genetics) that have vastly improved our ability to collection spatially explicit observation data on individuals. However, the utility of SCR methods reaches far beyond simply convenience and data availability. SCR presents a formal statistical framework that can be used to test explicit hypotheses about core elements of population and landscape ecology, and has profound implications for how we study animal populations. In this software note, we describe the technical basis and analytical workflow of oSCR, an R package for analyzing spatial encounter history data using a multi‐session sex‐structured likelihood. The impetus for developing oSCR was to create an accessible and transparent analysis tool that allows users to conveniently and intuitively formulate statistical models that map directly to fundamental processes of interest in spatial population ecology (e.g. space use, resource selection, density and connectivity). We have placed an emphasis on creating a transparent and accessible code base that is coupled with a logical workflow that we hope stimulates active participation in further technical developments.

Harvest data are often used by wildlife managers when setting harvest regulations for species because the data are regularly collected and do not require implementation of logistically and financially challenging studies to obtain the data. However, when harvest data are not available because an area had not previously supported a harvest season, alternative approaches are required to help inform management decision making. When distribution or density data are required across large areas, occupancy modeling is a useful approach, and under certain conditions, can be used as a surrogate for density. We collaborated with the New York State Department of Environmental Conservation (NYSDEC) to conduct a camera trapping study across a 70,096-km 2 region of southern New York in areas that were currently open to fisher (Pekania [Martes] pennanti) harvest and those that had been closed to harvest for approximately 65 years. We used detection-nondetection data at 826 sites to model occupancy as a function of site-level landscape characteristics while accounting for sampling variation. Fisher occupancy was influenced positively by the proportion of conifer and mixed-wood forest within a 15-km 2 grid cell and negatively associated with road density and the proportion of agriculture. Model-averaged predictions indicated high occupancy probabilities (>0.90) when road densities were low (<1 km/km 2 ) and coniferous and mixed forest proportions were high (>0.50). Predicted occupancy ranged 0.41-0.67 in wildlife management units (WMUs) currently open to trapping, which could be used to guide a minimum occupancy threshold for opening new areas to trapping seasons. There were 5 WMUs that had been closed to trapping but had an average predicted occupancy of 0.52 (0.07 SE), and above the threshold of 0.41. These areas are currently under consideration by NYSDEC for opening a conservative harvest season. We demonstrate the use of occupancy modeling as an aid to management decision making when harvest-related data are unavailable and when budgetary constraints do not allow for capture-recapture studies to directly estimate density. Ó 2016 The Wildlife Society.

1. The challenges associated with monitoring low-density carnivores across large landscapes have limited the ability to implement and evaluate conservation and management strategies for such species. Non-invasive sampling techniques and advanced statistical approaches have alleviated some of these challenges and can even allow for spatially explicit estimates of density, one of the most valuable wildlife monitoring tools. 2. For some species, individual identification comes at no cost when unique attributes (e.g. pelage patterns) can be discerned with remote cameras, while other species require viable genetic material and expensive laboratory processing for individual assignment. Prohibitive costs may still force monitoring efforts to use species distribution or occupancy as a surrogate for density, which may not be appropriate under many conditions. 3. Here, we used a large-scale monitoring study of fisher Pekania pennanti to evaluate the effectiveness of occupancy as an approximation to density, particularly for informing harvest management decisions. We combined remote cameras with baited hair snares during 2013-2015 to sample across a 70 096-km 2 region of western New York, USA. We fit occupancy and Royle-Nichols models to species detection-non-detection data collected by cameras, and spatial capture-recapture (SCR) models to individual encounter data obtained by genotyped hair samples. Variation in the state variables within 15-km 2 grid cells was modelled as a function of landscape attributes known to influence fisher distribution. 4. We found a close relationship between grid cell estimates of fisher state variables from the models using detection-non-detection data and those from the SCR model, likely due to informative spatial covariates across a large landscape extent and a grid cell resolution that worked well with the movement ecology of the species. Fisher occupancy and density were both positively associated with the proportion of coniferous-mixed forest and negatively associated with road density. As a result, spatially explicit management recommendations for fisher were similar across models, though relative variation was dampened for the detection-non-detection data. 5. Synthesis and applications. Our work provides empirical evidence that models using detection-non-detection data can make similar inferences regarding relative spatial variation of the focal population to models using more expensive individual encounters when the selected spatial grain approximates or is marginally smaller than home range size. When occupancy alone is chosen as a cost-effective state variable for monitoring, simulation and sensitivity analyses should be used to understand how inferences from detection-non-detection data will be affected by aspects of study design and species ecology.

18 1. The challenges associated with monitoring low-density carnivores across large 19York, USA. We fit occupancy and Royle-Nichols models to species detection-34 nondetection data collected by cameras, and spatial capture-recapture models to 35 individual encounter data obtained by genotyped hair samples. 36 4. We found a close relationship between grid-cell estimates of fisher state variables from 37 the models using detection-nondetection data and those from the SCR model, likely due 38 to informative spatial covariates across a large landscape extent and a grid cell resolution 39 that worked well with the movement ecology of the species. Spatially-explicit 40 3 management recommendations for fisher were similar across models. We discuss design-41 based approaches to occupancy studies that can improve approximations to density. 42

14Estimating population size and resource selection functions (RSFs) are common approaches in 15 applied ecology for addressing wildlife conservation and management objectives. Traditionally 16 such approaches have been undertaken separately with different sources of data. Spatial capture-17 recapture (SCR) provides a framework for jointly estimating density and multi-scale resource 18 selection, and data integration techniques provide opportunities for improving inferences from 19 SCR models. Here we illustrate an application of integrated SCR-RSF modeling to a population 20 of American marten (Martes americana) in alpine forests of northern New England. Spatial 21 encounter data from camera traps were combined with telemetry locations from radio-collared 22 individuals to examine how density and space use varied with spatial environmental features. 23We compared multi-model inferences between the integrated SCR-RSF model with telemetry 24 and a standard SCR model with no telemetry. The integrated SCR-RSF model supported more 25 complex relationships with spatial variation in third-order resource selection (i.e., individual 26 space use), including selection for areas with shorter distances to mixed coniferous forest and 27 rugged terrain. Both models indicated increased second-order selection (i.e., density) for areas 28 close to mixed coniferous forest, while the integrated SCR-RSF model had a lower effect size 29 due to modulation from spatial variability in space use. Our application of the integrated SCR-30 RSF model illustrates the improved inferences from spatial encounter data that can be achieved 31 from integrating auxiliary telemetry data. Integrated modeling allows ecologists to join 32 empirical data to ecological theory using a robust quantitative framework to better address 33 conservation and management objectives. 34