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.

Ocelots (Leopardus pardalis) are listed as least concern on the International Union for Conservation of Nature (IUCN) Red list of Threatened Species, yet we lack knowledge on basic demographic parameters across much of the ocelot's geographic range, including population density. We used camera-trapping methodology and spatially explicit capture-recapture (SECR) models with sex-specific detection function parameters to estimate ocelot densities across 7 field sites over 1 to 12 years (from data collected during [2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015] in Belize, Central America. Ocelot densities in the broadleaf rainforest sites ranged between 7.2 and 22.7 ocelots/100 km 2 , whereas density in the pine (Pinus spp.) forest site was 0.9 ocelots/100 km 2 . Applying an inverse-variance weighted average over all years for each broadleaf site increased precision and resulted in average density ranging from 8.5 to 13.0 ocelots/100 km 2 . Males often had larger movement parameter estimates and higher detection probabilities at their activity centers than females. In most years, the sex ratio was not significantly different from 50:50, but the pooled sex ratio estimated using an inverse weighted average over all years indicated a female bias in 1 site, and a male bias in another. We did not detect any population trends as density estimates remained relatively constant over time; however, the power to detect such trends was generally low. Our SECR density estimates were lower but more precise than previous estimates and indicated population stability for ocelots in Belize. Ó

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 discard some 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. * Corresponding Author 1 2 AUGUSTINE ET AL.1. Introduction. The inferential goal of capture-recapture studies is to estimate population density, D, or abundance, N , in the presence of imperfect detection. Individuals are either naturally or manually marked and subjected to repeated capture attempts in order to estimate their capture probability and thus D or N . Generally, capture-recapture models for wildlife species regard the individual identity of each capture event as known; however in practice, the identities of individuals for some capture events can be ambiguous or erroneous. In live-capture studies, tags can be lost. In camera trapping studies, researchers often obtain partial identity samples -left-only and right-only photographs that cannot be deterministically linked. In genetic capture-recapture studies, partial genotypes and allelic dropout can lead to partial identification or misidentification, respectively. Statistical models have been developed to address the problem of imperfect identification in live capture using double tagging (e.g. Wimmer et al., 2013) and in camera trap and genetic capture-recapture studies by regarding the complete identification of partial or potentially erroneous samples as latent and specifying models for both the capture-recapture process and the imperfect observation process conditional on the capture (e.g. McClintock et al., 2013;Bonner and Holmberg, 2013;Wright et al., 2009). However, relatively little attention has been paid to one of the most important determinants of sample identity-the spatial location where it was collected. The identity of ambiguous samples should more likely match the identity of other samples collected closer together in space than those collecte...

We used open population, spatial capture–recapture (SCR) models to estimate sex‐specific density, survival, per capita recruitment, and population growth rate of ocelots (Leopardus pardalis) at five sites in Belize with up to 12 yr of data per site. Open population SCR models enabled us to separate survival and recruitment from migration using an ecologically realistic, spatially explicit movement model. Yearly survival probability across 4 broadleaf forest sites was estimated at 0.73–0.84 for males and 0.81–0.87 for females, with no clear indication of sex differences. Yearly per capita recruitment was estimated across four broadleaf forest sites at 0.06–0.08 recruits/N for males and 0.09–0.12 recruits/N for females, again with no clear indication of sex differences. At a pine forest site with a population comprised largely of males, survival and recruitment estimates were similar to the broadleaf sites. Population densities in the broadleaf forest sites ranged from 6.5 to 14.7 ocelots/100 km2, and 0.9–2.5 ocelots/100 km2 in the pine forest site, with strong evidence of a female‐biased sex ratio in the broadleaf sites and a male‐biased sex ratio in the pine forest site. We also found strong evidence that female within‐year space use at the broadleaf sites was smaller than that of males, and that within‐year space use at the pine forest site was larger than that at broadleaf sites. Between‐year home‐range relocation at broadleaf sites was of a similar spatial scale as within‐year space use, consistent with philopatry. We found evidence of a small population decline (posterior probability > 0.9) at two of four broadleaf sites; however, given the level of uncertainty about decline magnitudes, we suggest continued monitoring of these sites to increase site‐years and gain further precision on population growth rate estimates. Estimating demographic parameters at large spatial and temporal scales is important for establishing reliable baseline estimates for future comparison and for understanding changes in population dynamics. Long‐term data sets like those we collected are of particular importance for long‐lived species living at low densities and large spatial scales, where not many individuals are exposed to capture in any one year.