A new mark-recapture approach for abundance estimation of social species.

Jena R Hickey,Rahel Sollmann,Leszek Karczmarski

doi:10.1371/journal.pone.0208726

Jena R Hickey, Rahel Sollmann + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0208726

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Abstract

Accurate estimates of population abundance are a critical component of species conservation efforts in order to monitor the potential recovery of populations. Capture-mark-recapture (CMR) is a widely used approach to estimate population abundance, yet social species moving in groups violate the assumption of CMR approaches that all individuals in the population are detected independently. We developed a closed CMR model that addresses an important characteristic of group-living species–that individual-detection probability typically is conditional on group detection. Henceforth termed the Two-Step model, this approach first estimates group-detection probability and then–conditional on group detection–estimates individual-detection probability for individuals within detected groups. Overall abundance is estimated assuming that undetected groups have the same average group size as detected groups. We compared the performance of this Two-Step CMR model to a conventional (One-Step) closed CMR model that ignored group structure. We assessed model sensitivity to variation in both group- and individual-detection probability. Both models returned overall unbiased estimates of abundance, but the One-Step model returned deceptively narrow Bayesian confidence intervals (BCI) that failed to encompass the correct population abundance an average 52% of the time. Contrary, under the Two-Step model, CI coverage was on average 96%. Both models had similar root mean squared errors (RMSE), except for scenarios with low group detection probability, where the Two-Step model had much lower RMSE. For illustration with a real data set, we applied the Two-Step and regular model to non-invasive genetic capture-recapture data of mountain gorillas (Gorilla beringei beringei). As with simulations, abundance estimates under both models were similar, but the Two-Step model estimate had a wider confidence interval. Results support using the Two-Step model for species living in constant groups, particularly when group detection probability is low, to reduce risk of bias and adequately portray uncertainty in abundance estimates. Important sources of variation in detection need to be incorporated into the Two-Step model when applying it to field data.

Highlights

Robust estimates of species population abundance are necessary for understanding baseline conditions, gauging population trends, assessing the success of conservation actions, and developing management strategies
There is no CMR approach that explicitly describes the dependence of pi on group detection that is characteristic of many group-living species
Average bias in the posterior mean abundance estimates was consistently low to moderate across all combinations of pi and pg

Summary

Introduction

Robust estimates of species population abundance are necessary for understanding baseline conditions, gauging population trends, assessing the success of conservation actions, and developing management strategies. Closed population Capture-mark-recapture (CMR) is considered the gold standard to estimating wildlife abundance while accounting for our imperfect ability to detect all individuals in a population [1, 2, 3, 4, 5, 6]. Social species provide a particular challenge when applying CMR approaches because the nature of group living typically violates a primary assumption of CMR–that individuals are detected independently of each other–which introduces a special kind of capture heterogeneity. The distributions of individuals of group-living species are clumpy; in most cases, detection of the individual is dependent on first detecting the group [16] In such cases, individual-level detection probability pi = 0 for individuals belonging to undetected groups and pi>0 for individuals belonging to detected groups, thereby representing a special case of heterogeneity in detection probability among individuals in the population. Over time, biased abundance estimates coupled with falsely narrow confidence intervals (CIs) could lead to erroneous conclusions about population trends, whether they be false estimates of growth or decline [24], and misguided conservation actions

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