A spatial mark–resight model augmented with telemetry data

Abstract

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.

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

“…In addition, as with other mark-resight models, detection rates of marked individuals and unmarked individuals are assumed to be the same (k 0 ). This allows for the model for marked individuals to be integrated with the model for unmarked individuals with a shared parameter for the expected encounter rate at activity centers (k 0 ), and is expected to improve estimation of w u in the unmarked model component by providing more specific information on detection from the marked model component (Chandler and Royle 2013;Sollmann et al 2013b;Royle et al 2013a). In both model components, we accounted for non-operational occasions (i.e.…”

“…Camera trap locations were selected to maximize detection of carnivores, including siting cameras along roads, in optimal habitat, and micrositing on sections of roads where lion sign was detected when possible. Background habitat generated from a 2012 USGS land use -land cover map of NKNP (Tappan 2012) marked individuals with counts of unmarked individuals (White and Shenk 2001;McClintock et al 2009;Chandler and Royle 2013;Sollmann et al 2013b;Rich et al 2014). This joint-model approach often allows for better precision compared to models that only consider detection of unmarked individuals because it includes information on detection rates of known individuals and shares parameters between the marked and unmarked populations Royle 2013, Rich et al 2014).…”

Unifying population and landscape ecology with spatial capture–recapture

“…density of sampling in order to obtain spatial recaptures, and spatial extent of sampling to obtain a sufficient sample of unique individuals , chapter 10). Sampling design studies have been considered by several authors including Efford and Fewster (2013), Sollmann et al (2013) and Sun et al (2014), with general guidance suggesting a trap spacing of about 2s (s here of the half-normal encounter probability model) in order to provide the optimal sample of spatial recaptures and unique individuals for a given fixed population size. When the geographic area is large relative to the amount of sampling effort that can be expended, cluster designs have been shown to be efficient.…”

The relationship between abundance and genetic effective population size in elasmobranchs: an example from the globally threatened zebra shark Stegostoma fasciatum within its protected range

“…For many wildlife populations, obtaining indicators of abundance is a challenging yet essential requirement for conservation planning and monitoring (Sollman et al 2013). Genetic estimates of contemporary N e have great potential to address this problem, particularly for rare and threatened species.…”