Spatial Capture-Recapture for Categorically Marked Populations with An Application to Genetic Capture-Recapture

Abstract

Recently introduced unmarked spatial capture-recapture (SCR), spatial mark-resight (SMR), and 2-flank spatial partial identity models (SPIM) 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. These models 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 with home range sizes smaller than the extent of the trapping array because a latent identity 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 determined by two key ecological concepts, population density and home range size. The number of individuals that could have produced a latent or partial identity sample increases as density and home range size increase because more individual home ranges will overlap any given trap. We show this uncertainty can be quantified using a metric describing the expected magnitude of uncertainty in individual identity for any given population density and home range size, the Identity Diversity Index (IDI). We then show that the performance of latent and partial identity SCR models varies as a function of this index and produces imprecise and biased estimates in many high IDI scenarios when data are sparse. We then extend the unmarked SCR model to incorporate 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, 2 . CC-BY-NC-ND4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/265678 doi: bioRxiv preprint first posted online Feb. 15, 2018;where we reproduce the full data set estimates with only slightly less precision using fewer loci than necessary for confident individual identification. The categorical SPIM offers an alternative to using probability of identity criteria for classifying genotypes as unique, shifting the "shadow effect", where more than one individual in the population has the same genotype, from a source of bias to a source of uncertainty. We discuss the difficulties th...

Incorporating citizen science data in spatially explicit integrated population models

“…Our simulated population parameters highlight the demographic information contained in opportunistic PA data when the population density is low and constant over time, and we expect our IM to be more beneficial in such conditions. With low population densities, resolving individual identities is easier because there are fewer potential combinations of different individuals that can generate the PA data (Augustine et al 2019), and there is a stronger relationship between the PA data and variation in abundance (Linden et al 2017). We also showcased the ability of opportunistically collected PA data to inform parameter estimation when SCR data are extremely lacking (i.e., "first" scenario), when population density was constant over time.…”

Integrating multiple genetic detection methods to estimate population density of social and territorial carnivores

“…Both Mexican gray wolves and red wolves are annually radiomonitored and attempts to estimate their population densities and abundances have either failed because of sparse detection data or resulted in imprecise and possibly biased estimates (Adams et al 2003, Piaggio et al 2016, Seamster et al 2016, Hinton et al 2017. Spatial partial identity models that probabilistically link spatial detections of partial individuality to improve precision of spatial capture-recapture density estimates (Augustine et al 2018a) have been recently extended to incorporate partial genotypes from noninvasive genetic detection data (Augustine et al 2018b). Spatial partial identity models that probabilistically link spatial detections of partial individuality to improve precision of spatial capture-recapture density estimates (Augustine et al 2018a) have been recently extended to incorporate partial genotypes from noninvasive genetic detection data (Augustine et al 2018b).…”

“…Employing noninvasive genetic sampling while simultaneously collecting telemetry data could have considerable promise for improving demographic estimates of those and other carnivores that exhibit high degrees of sociality and territoriality. Spatial partial identity models that probabilistically link spatial detections of partial individuality to improve precision of spatial capture-recapture density estimates (Augustine et al 2018a) have been recently extended to incorporate partial genotypes from noninvasive genetic detection data (Augustine et al 2018b). We obtained partial genotypes (≥1 <9 amplified loci) for 16 scat and 21 hair samples, data that could be incorporated to improve parameter estimate precision if those genotypes are reliable or if appropriate error processes are accounted for (e.g., genotyping error; Wright et al 2009).…”

Spatial Mark-Resight for Categorically Marked Populations with an Application to Genetic Capture-Recapture

“…One of the most robust and well-developed class of models for estimating density, mark-recapture, and its extension to spatial mark-recapture (SCR), require the determination of the individual identities of all captured individuals; however, many species are not reliably identifiable using noninvasive methods. SCR models specifically, have been extended to accommodate fully unmarked populations , but unfortunately, the unmarked SCR model produces biased and imprecise density estimates in many scenarios typically encountered when using noninvasive methods Augustine et al, 2018). The SCR modeling framework does; however, offer several ways to improve density estimates for unmarked populations including the introduction of marked individuals Sollmann et al, 2013), informative priors related to home range size Ramsey et al, 2015), telemetry data Whittington et al, 2016), and/or partial identity information (e.g., categorical marks such as individual sex; Augustine et al, 2018).…”

“…SCR models specifically, have been extended to accommodate fully unmarked populations , but unfortunately, the unmarked SCR model produces biased and imprecise density estimates in many scenarios typically encountered when using noninvasive methods Augustine et al, 2018). The SCR modeling framework does; however, offer several ways to improve density estimates for unmarked populations including the introduction of marked individuals Sollmann et al, 2013), informative priors related to home range size Ramsey et al, 2015), telemetry data Whittington et al, 2016), and/or partial identity information (e.g., categorical marks such as individual sex; Augustine et al, 2018). SCR models utilizing only partial individual identities have only recently been introduced and have not yet been extended to accommodate marked individuals or individual-linked telemetry data.…”