Using multiple data sources provides density estimates for endangered Florida panther

Abstract

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.

Potential for camera-traps and spatial mark-resight models to improve monitoring of the critically endangered West African lion (Panthera leo)

“…Bayesian implementations of SCR models, such as the SMR model, provide a flexible platform to generate unbiased estimates of density comparable across different study sites and over time. The modeling approach allows for joint estimation with shared parameters, so survey types and can be alternated across sites and in different years depending on specific research objectives and costs constraints (Gopalaswamy et al 2012;Sollmann et al 2013c). For example, if it was desirable to collect DNA from fecal samples or hair snares, a method identified as a promising direction in African carnivore monitoring (Thorn et al 2010), the SCR structure can be used with fecal genotype mark-recapture data or incorporated as an additional survey type into the larger camera survey study design (Kéry et al 2011;Sollmann et al 2011;Gopalaswamy et al 2012;Sollmann et al 2013c).…”

From distance sampling to spatial capture–recapture

“…This method is likely to be very useful for many camera trap datasets in which a fraction of a population of individuals that are otherwise not individually identifiable, can be marked. Sollmann et al (2013a) and Sollmann et al (2013b) extended this method to accommodate a tagged sample with telemetry data.…”

Reply to Efford on ‘Integrating resource selection information with spatial capture–recapture’

“…In this case, the two data sources share a single parameter (a 1 ), and both models are consistent with a bivariate Gaussian model of space usage. Therefore, telemetry data can be used to provide direct information about the scale parameter of the SCR encounter probability model (Sollmann et al 2013). More generally, the choice of any model of encounter probability can be regarded as being proportional to the intensity function of some Poisson point process; therefore, a stationary and isotropic model of home range is implied (Royle et al 2014, sec.…”