Spatial capture–recapture with partial identity: An application to camera traps

Abstract

Camera trapping surveys frequently capture individuals whose identity is only known from a single flank. The most widely used methods for incorporating these partial identity individuals into density analyses do not use all of the partial identity capture histories, reducing precision, and while not previously recognized, introducing bias. Here, we present the spatial partial identity model (SPIM), which uses the spatial location where partial identity samples are captured to probabilistically resolve their complete identities, allowing all partial identity samples to be used in the analysis. We show that the SPIM out-performs other analytical alternatives. We then apply the SPIM to an ocelot data set collected on a trapping array with double-camera stations and a bobcat data set collected on a trapping array with single-camera stations. The SPIM improves inference in both cases and in the ocelot example, individual sex determined from photographs is used to further resolve partial identities, one of which is resolved to near certainty. The SPIM opens the door for the investigation of trapping designs that deviate from the standard 2 camera design, the combination of other data types between which identities cannot be deterministically linked, and can be extended to the problem of partial genotypes.

Estimating density and detection of bobcats in fragmented midwestern landscapes using spatial capture–recapture data from camera traps

“…Fortunately, researchers have developed increasingly sophisticated camera survey designs and model advancements to improve precision of estimates, reduce sampling effort, and standardize camera survey methods (Kelly et al , Morin et al ). These include techniques for improving the identity of individuals from photos (e.g., modifying density, placement and orientation of cameras, minimizing observer bias, use of white flash cameras; Larrucea et al , Kelly et al , Mendoza et al ), more rigorous analyses of data from single‐side and hybrid camera station designs (McClintock et al , Augustine et al ), and accounting for a proportion of individuals that cannot be positively identified (Rich et al ). Collectively, these advancements should aid in developing more rigorous camera survey designs and increase confidence and utility of camera‐trapping for density estimation of small felids that may be more difficult to detect and positively identify than other larger and more recognizable species (e.g., jaguars [ Panthera onca ], leopards [ Panthera pardus ], and tigers) that are commonly used in camera‐trap research (Thornton and Pekins ).…”

Unifying population and landscape ecology with spatial capture–recapture

“…However, such methods have been developed in the context of classical capture-recapture methods which ignore the spatial information inherent in most animal population sampling studies. On the other hand, for most populations we should expect the spatial location of samples to be informative about the uncertain identity of those samples (Chandler and Royle 2013, Chandler and Clark 2014, Royle 2015, Augustine et al 2016. That is, all other things being equal, samples that are in close spatial proximity to one another should more likely be of the same individual than samples that are far apart.…”

Zooming in on mechanistic predator–prey ecology: Integrating camera traps with experimental methods to reveal the drivers of ecological interactions

“…Camera traps provide a non‐invasive approach for detecting and monitoring wildlife that has been made more accessible through continued improvements in camera quality and cost‐efficiency, and their use in addressing fundamental ecological questions is on the rise (Burton et al., 2015; Caravaggi et al., 2017; Frey, Fisher, Burton, & Volpe, 2017). Beyond monitoring, utilization of camera traps for observational research in predator–prey ecology has exploded in recent years (Figure 1), largely due to advances in statistical techniques, such as occupancy modelling and spatial capture–recapture analysis (Augustine et al., 2018; Chandler & Royle, 2013; MacKenzie et al., 2017; Royle, Chandler, Sun, & Fuller, 2013; Sollmann et al., 2013).…”