Spatial capture-recapture with random thinning for unidentified encounters.

José Jiménez,Ben C Augustine,Daniel W Linden,Richard B Chandler,J Andrew Royle

doi:10.1002/ece3.7091

Abstract

Spatial capture–recapture (SCR) models have increasingly been used as a basis for combining capture–recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark–resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of “marked” and “unmarked” individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites.Here we describe a “random thinning” SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE.We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low‐density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear (Ursus arctos) density in Oriental Cantabrian Mountains (North Spain).Our model can improve density estimation for noninvasive sampling studies for low‐density populations with low rates of individual identification, by making use of available data that might otherwise be discarded.

Highlights

The estimation of population size using capture–recapture models is a standard approach in wildlife research and provides a rigorous quantitative method for informing species conservation and management (Williams et al, 2002)
With the hypothesis that the use of all data could improve the precision of the estimates, we describe an Spatial capture–recapture (SCR) model that combines identified and unidentified samples from a single class of individuals using modified methods from Chandler & Royle (2013)
We explored each case to assess the accuracy and precision of density estimates for the random thinning SCR model compared to a standard SCR model

Summary

| INTRODUCTION

The estimation of population size using capture–recapture models is a standard approach in wildlife research and provides a rigorous quantitative method for informing species conservation and management (Williams et al, 2002). The identification rate is rarely reported (Johansson et al, 2020), but Ngoprasert et al (2012) described 2% of raw images that were identifiable in Asiatic black bears (Ursus thibetanus) and sun bears (Helarctos malayanus); Molina et al (2017) identified 2.3% in Andean bear (Tremarctos ornatus), and Somers et al (2018) identified 54% in leopard (Panthera pardus) This loss of information can be translated into lower precision in the estimates. | 1189 the identified encounter histories alone via simulation and apply it to a large-scale noninvasive genetic sampling effort of brown bear (Ursus arctos) across a 2,624 km region of the Eastern Cantabrian Mountains, North Spain, where only 60% of the DNA samples provided an individual identity. We illustrate how noninvasive sampling studies can maximize the information used to provide population inferences from capture–recapture designs despite difficulties in determining individual identity

| METHODS

Findings

| DISCUSSION