Summary1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture-recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection. 2. In this paper we develop the first statistical framework for jointly modeling space usage, resource selection, and population density by integrating SCR data, such as from camera traps, mist-nets, or conventional catch traps, with resource selection data from telemetered individuals. We provide a framework for estimation based on marginal likelihood, wherein we estimate simultaneously the parameters of the SCR and RSF models. 3. Our method leads to increases in precision for estimating parameters of ordinary SCR models. Importantly, we also find that SCR models alone can estimate parameters of RSFs and, as such, SCR methods can be used as the sole source for studying space-usage; however, precision will be higher when telemetry data are available. 4. Finally, we find that SCR models using standard symmetric and stationary encounter probability models may not fully explain variation in encounter probability due to space usage, and therefore produce biased estimates of density when animal space usage is related to resource selection. Consequently, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture-recapture methods.

An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

Summary1. Movement is influenced by landscape structure, configuration and geometry, but measuring distance as perceived by animals poses technical and logistical challenges. Instead, movement is typically measured using Euclidean distance, irrespective of location or landscape structure, or is based on arbitrary cost surfaces. A recently proposed extension of spatial capture-recapture (SCR) models resolves this issue using spatial encounter histories of individuals to calculate least-cost paths (ecological distance: Ecology, 94, 2013, 287) thereby relaxing the Euclidean assumption. We evaluate the consequences of not accounting for movement heterogeneity when estimating abundance in highly structured landscapes, and demonstrate the value of this approach for estimating biologically realistic space-use patterns and landscape connectivity. 2. We simulated SCR data in a riparian habitat network, using the ecological distance model under a range of scenarios where space-use in and around the landscape was increasingly associated with water (i.e. increasingly less Euclidean). To assess the influence of miscalculating distance on estimates of population size, we compared the results from the ecological and Euclidean distance based models. We then demonstrate that the ecological distance model can be used to estimate home range geometry when space use is not symmetrical. Finally, we provide a method for calculating landscape connectivity based on modelled species-landscape interactions generated from capture-recapture data. 3. Using ecological distance always produced unbiased estimates of abundance. Explicitly modelling the strength of the species-landscape interaction provided a direct measure of landscape connectivity and better characterised true home range geometry. Abundance under the Euclidean distance model was increasingly (negatively) biased as space use was more strongly associated with water and, because home ranges are assumed to be symmetrical, produced poor characterisations of home range geometry and no information about landscape connectivity. 4. The ecological distance SCR model uses spatially indexed capture-recapture data to estimate how activity patterns are influenced by landscape structure. As well as reducing bias in estimates of abundance, this approach provides biologically realistic representations of home range geometry, and direct information about specieslandscape interactions. The incorporation of both structural (landscape) and functional (movement) components of connectivity provides a direct measure of species-specific landscape connectivity.

Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio-temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population state and structure. Spatial capture-recapture (SCR) represents an individual-based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and they have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g. camera trapping, non-invasive DNA sampling). We describe ways in which SCR methods stand to advance the study of animal population ecology.

Spatial capture–recapture (SCR) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, DNA sampling and other methods which produce spatially explicit individual encounter information. One of the core assumptions of SCR models is that individuals possess home ranges that are spatially stationary during the sampling period. For many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. In this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary SCR models when dispersal or transience is present in the population. Then, using both simulated and real data, we demonstrate that such models can easily be described in the BUGS language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture–recapture data. We find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. Thus, use of ordinary SCR models to make inferences about density is feasible, but interpretation of SCR model parameters in relation to movement should be avoided. Instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.

We investigated habitat selection and home‐range characteristics of American martens (Martes americana) that occupied home ranges with partially harvested stands characterized by basal area of trees <18 m2/ha and canopy closure <30%. During the leaf‐on season (1 May–31 Oct), martens selected second‐growth (80–140‐years‐old, >9‐m tree height) forest stands (deciduous, coniferous, and mixed coniferous‐deciduous) and mixed stands that were partially harvested (x̄ = 13 m2/ha residual basal area, >9‐m tree height), and they selected against forests regenerating after clearcutting (≤6‐m tree height, cuts ≤24‐years‐old). Marten home ranges included a greater proportion of partially harvested stands during the leaf‐on season (maximum = 73%) than during leaf‐off (1 Nov–30 Apr; maximum = 34%). Higher use of partially harvested stands during the leaf‐on season coincided with greater canopy closure, higher use of small mammals, and greater relative densities of small mammals. During the leaf‐off season, martens exhibited reduced relative selection for partially harvested and regenerating stands and increased selection for second‐growth forest types. Partially harvested and regenerating clearcut stands had canopy closure <30% and basal area of trees >9‐m tall of <13m2/ha; both were below published thresholds required by martens. Coincidentally, home‐range areas of martens increased during the leaf‐off season to include a greater proportion of second‐growth forest and less partially harvested forest. Further, martens with partial harvesting in their home ranges used areas almost twice as large during the leaf‐off season as martens with no partial harvesting. Snowshoe hares (Lepus americanus) were prevalent prey for martens during the leaf‐off season, and partially harvested stands had the lowest density of hares among all forest overstory types. Our findings suggest that the combination of insufficient basal area and overhead canopy closure, subnivean behavior of small mammals, increased reliance on hares, and reduced density of snowshoe hares relative to second‐growth forest types reduced habitat quality in partially harvested stands during the leaf‐off season. We suggest land managers retain basal areas >18 m2/ha and canopy closure >30% during winter to maximize use by martens in stands where partial harvesting is practiced.

We related winter habitat selection by Canada lynx (Lynx canadensis), relative abundance of snowshoe hares (Lepus americanus), and understory stem densities to evaluate whether lynx select stands with the greatest snowshoe hare densities or the greatest prey accessibility. Lynx (3 F, 3 M) selected tall (4.4‐7.3 m) regenerating clear‐cuts (11‐26 yr postharvest) and established partially harvested stands (11‐21 yr postharvest) and selected against short (3.4‐4.3 m) regenerating clear‐cuts, recent partially harvested stands (1‐10 yr), mature second‐growth stands (>40 yr), and roads and their edges (30 m on either side of roads). Lynx selected stands that provided intermediate to high hare density and intermediate cover for hares (i.e., prey access) but exhibited lower relative preference for stand types with highest hare densities where coniferous saplings exceeded 14,000 stems/ha.

Although interspecific competition plays a principal role in shaping species behaviour and demography, little is known about the population-level outcomes of competition between large carnivores, and the mechanisms that facilitate coexistence. We conducted a multilandscape analysis of two widely distributed, threatened large carnivore competitors to offer insight into coexistence strategies and assist with species-level conservation. We evaluated how interference competition affects occupancy, temporal activity and population density of a dominant competitor, the lion (Panthera leo), and its subordinate competitor, the leopard (Panthera pardus). We collected camera-trap data over 3 years in 10 study sites covering 5,070 km . We used multispecies occupancy modelling to assess spatial responses in varying environmental and prey conditions and competitor presence, and examined temporal overlap and the relationship between lion and leopard densities across sites and years. Results showed that both lion and leopard occupancy was independent of-rather than conditional on-their competitor's presence across all environmental covariates. Marginal occupancy probability for leopard was higher in areas with more bushy, "hideable" habitat, human (tourist) activity and topographic ruggedness, whereas lion occupancy decreased with increasing hideable habitat and increased with higher abundance of very large prey. Temporal overlap was high between carnivores, and there was no detectable relationship between species densities. Lions pose a threat to the survival of individual leopards, but they exerted no tractable influence on leopard spatial or temporal dynamics. Furthermore, lions did not appear to suppress leopard populations, suggesting that intraguild competitors can coexist in the same areas without population decline. Aligned conservation strategies that promote functioning ecosystems, rather than target individual species, are therefore advised to achieve cost- and space-effective conservation.