Spatial capture-recapture for categorically marked populations with an application to genetic capture-recapture
Augustine, Ben C.
Royle, J. Andrew
Murphy, Sean M.
Chandler, Richard B.
Cox, John J.
Kelly, Marcella J.
MetadataShow full item record
Recently introduced unmarked spatial capture-recapture (SCR), spatial mark-resight (SMR), and 2-flank spatial partial identity models (SPIMs) extend the domain of SCR to populations or observation systems that do not always allow for individual identity to be determined with certainty. For example, some species do not have natural marks that can reliably produce individual identities from photographs, and some methods of observation produce partial identity samples as is the case with remote cameras that sometimes produce single-flank photographs. Unmarked SCR, SMR, and SPIM share the feature that they probabilistically resolve the uncertainty in individual identity using the spatial location where samples were collected. Spatial location is informative of individual identity in spatially structured populations because a sample is more likely to have been produced by an individual living near the trap where it was recorded than an individual living further away from the trap. Further, the level of information about individual identity that a spatial location contains is related to two key ecological concepts, population density and home range size, which we quantify using a proposed Identity Diversity Index (IDI). We show that latent and partial identity SCR models produce imprecise and biased density estimates in many high IDI scenarios when data are sparse. We then extend the unmarked SCR model to incorporate categorical, partially identifying covariates, which reduce the level of uncertainty in individual identity, increasing the reliability and precision of density estimates, and allowing reliable density estimation in scenarios with higher IDI values and with more sparse data. We illustrate the performance of this "categorical SPIM" via simulations and by applying it to a black bear data set using microsatellite loci as categorical covariates, where we reproduce the full data set estimates with only slightly less precision using fewer loci than necessary for confident individual identification. We then discuss how the categorical SPIM can be applied to other wildlife sampling scenarios such as remote camera surveys, where natural or researcher-applied partial marks can be observed in photographs. Finally, we discuss how the categorical SPIM can be added to SMR, 2-flank SPIM, or other latent identity SCR models.
Showing items related by title, author, creator and subject.
Leveraging Partial Identity Information in Spatial Capture-Recapture Studies with Applications to Remote Camera and Genetic Capture-Recapture Surveys Augustine, Ben C. (Virginia Tech, 2018-04-03)Noninvasive methods for monitoring wildlife species have revolutionized the way population parameters, such as population density and survival and recruitment rates, are estimated while accounting for imperfect detection ...
Use of PIT tags to assess individual heterogeneity of laboratory-reared juveniles of the endangered Cumberlandian combshell (Epioblasma brevidens) in a mark-recapture study Hua, Dan; Jiao, Yan; Neves, Richard; Jones, Jess W. (Wiley, 2015-03-01)The federally endangered Cumberlandian combshell (Epioblasma brevidens) was propagated and reared to taggable size (5-10 mm), and released to the Powell River, Tennessee, to augment a relict population. Methodology using ...
Estimating abundance, density, and occupancy of lion, leopard and serval in the Niokolo Koba National Park in Senegal Kane, Mamadou Daha (Virginia Tech, 2014-07-17)Carnivore are undoubtedly among the most threatened of the mammal species in Africa because of the low density at which they occur and their large home range requirements that do not match with human propensity to develop ...