1.Occupancy estimation and modelling based on detection-nondetection data provide an effective way of exploring change in a species' distribution across time and space in cases where the species is not always detected with certainty. Today, many monitoring programmes target multiple species, or life stages within a species, requiring the use of multiple detection methods. When multiple methods or devices are used at the same sample sites, animals can be detected by more than one method. 2. We develop occupancy models for multiple detection methods that permit simultaneous use of data from all methods for inference about method-specific detection probabilities. Moreover, the approach permits estimation of occupancy at two spatial scales: the larger scale corresponds to species' use of a sample unit, whereas the smaller scale corresponds to presence of the species at the local sample station or site. 3. We apply the models to data collected on two different vertebrate species: striped skunks Mephitis mephitis and red salamanders Pseudotriton ruber . For striped skunks, large-scale occupancy estimates were consistent between two sampling seasons. Small-scale occupancy probabilities were slightly lower in the late winter/spring when skunks tend to conserve energy, and movements are limited to males in search of females for breeding. There was strong evidence of method-specific detection probabilities for skunks. As anticipated, large-and small-scale occupancy areas completely overlapped for red salamanders. The analyses provided weak evidence of method-specific detection probabilities for this species. 4. Synthesis and applications. Increasingly, many studies are utilizing multiple detection methods at sampling locations. The modelling approach presented here makes efficient use of detections from multiple methods to estimate occupancy probabilities at two spatial scales and to compare detection probabilities associated with different detection methods. The models can be viewed as another variation of Pollock's robust design and may be applicable to a wide variety of scenarios where species occur in an area but are not always near the sampled locations. The estimation approach is likely to be especially useful in multispecies conservation programmes by providing efficient estimates using multiple detection devices and by providing device-specific detection probability estimates for use in survey design.

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Abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. Mark--resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark-recapture. However, density estimation using mark-resight is difficult because the area sampled must be explicitly defined, historically using ad hoc approaches. We developed a spatial mark--resight model for estimating population density that combines spatial resighting data and telemetry data. Incorporating telemetry data allows us to inform model parameters related to movement and individual location. Our model also allows <100% individual identification of marked individuals. We implemented the model in a Bayesian framework, using a custom-made Metropolis-within-Gibbs Markov chain Monte Carlo algorithm. As an example, we applied this model to a mark--resight study of raccoons (Procyon lotor) on South Core Banks, a barrier island in Cape Lookout National Seashore, North Carolina, USA. We estimated a population of 186.71 +/- 14.81 individuals, which translated to a density of 8.29 +/- 0.66 individuals/km2 (mean +/- SD). The model presented here will have widespread utility in future applications, especially for species that are not naturally marked.

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Large‐scale, multispecies monitoring programs are widely used to assess changes in wildlife populations but they often assume constant detectability when documenting species occurrence. This assumption is rarely met in practice because animal populations vary across time and space. As a result, detectability of a species can be influenced by a number of physical, biological, or anthropogenic factors (e.g., weather, seasonality, topography, biological rhythms, sampling methods). To evaluate some of these influences, we estimated site occupancy rates using species‐specific detection probabilities for meso‐ and large terrestrial mammal species on Cape Cod, Massachusetts, USA. We used model selection to assess the influence of different sampling methods and major environmental factors on our ability to detect individual species. Remote cameras detected the most species (9), followed by cubby boxes (7) and hair traps (4) over a 13‐month period. Estimated site occupancy rates were similar among sampling methods for most species when detection probabilities exceeded 0.15, but we question estimates obtained from methods with detection probabilities between 0.05 and 0.15, and we consider methods with lower probabilities unacceptable for occupancy estimation and inference. Estimated detection probabilities can be used to accommodate variation in sampling methods, which allows for comparison of monitoring programs using different protocols. Vegetation and seasonality produced species‐specific differences in detectability and occupancy, but differences were not consistent within or among species, which suggests that our results should be considered in the context of local habitat features and life history traits for the target species. We believe that site occupancy is a useful state variable and suggest that monitoring programs for mammals using occupancy data consider detectability prior to making inferences about species distributions or population change.

Sampling animal populations with camera traps has become increasingly popular over the past two decades, particularly for species that are cryptic, elusive, exist at low densities or range over large areas. The results have been widely used to estimate population size and density. We analyzed data from 13 camera trap surveys conducted at five sites across the Kaa-Iya landscape, Bolivian Chaco, for jaguar, puma, ocelot and lowland tapir. We compared two spatially explicit capture-recapture (SCR) software packages: secr, a likelihood-based approach, and SPACECAP, a Bayesian approach, both of which are implemented within the R environment and can be used to estimate animal density from photographic records of individual animals that simultaneously employ spatial information about the capture location relative to the sample location. As a non-spatial analysis, we used the program CAPTURE 2 to estimate abundance from the capturerecapture records of individuals identified through camera trap photos combined with an ad hoc estimation of the effective survey area to estimate density. SCR methods estimated jaguar population densities from 0.31 to 1.82 individuals per 100 km 2 across the Kaa-Iya sites; puma from 0.36 to 7.99; ocelot from 1.67 to 51.7; and tapir from 7.38 to 42.9. Density estimates using either secr or SPACECAP were generally lower than the estimates generated using the non-spatial method for all surveys and species; and density estimates using SPACECAP were generally lower than that using secr. We recommend using either secr or SPACECAP because the spatially explicit methods are not biased by an informal estimation of an effective survey area. Although SPACECAP and secr are less sensitive than non-spatial methods to the size of the grid used for sampling, we recommend grid sizes several times larger than the average home range (known or estimated) of the target species.

Summary1. To assess recovery of endangered species, reliable information on the size and density of the target population is required. In practice, however, this information has proved hard to acquire, especially for large carnivores that exist at low densities, are cryptic and range widely. Many large carnivore species such as the endangered Florida panther Puma concolor coryi lack clear visual features for individual identification; thus, using standard approaches for estimating population size, such as camera-trapping and capture-recapture modelling, has so far not been possible. 2. We developed a spatial capture-recapture model that requires only a portion of the individuals in the population to be identifiable, using data from two 9-month camera-trapping surveys conducted within the core range of panthers in southwestern Florida. Identity of three radio-collared individuals was known, and we incorporated their telemetry location data into the model to improve parameter estimates. 3. The resulting density estimates of 1Á51 (AE0Á81) and 1Á46 (AE0Á76) Florida panthers per 100 km 2 for each year are the first estimates for this endangered subspecies and are consistent with estimates for other puma subspecies. 4. A simulation study showed that estimates of density may exhibit some positive bias but coverage of the true values by 95% credible intervals was nominal. 5. Synthesis and applications. This approach provides a framework for monitoring the Florida panther -and other species without conspicuous markings -while fully accounting for imperfect detection and varying sampling effort, issues of fundamental importance in the monitoring of wildlife populations.

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