Trap Array Configuration Influences Estimates and Precision of Black Bear Density and Abundance

Clay M Wilton,Lori S Eggert,Jerrold L Belant,Emily E Puckett,Beth Gardner,Jeff Beringer,Christof M Aegerter

doi:10.1371/journal.pone.0111257

Abstract

Spatial capture-recapture (SCR) models have advanced our ability to estimate population density for wide ranging animals by explicitly incorporating individual movement. Though these models are more robust to various spatial sampling designs, few studies have empirically tested different large-scale trap configurations using SCR models. We investigated how extent of trap coverage and trap spacing affects precision and accuracy of SCR parameters, implementing models using the R package secr. We tested two trapping scenarios, one spatially extensive and one intensive, using black bear (Ursus americanus) DNA data from hair snare arrays in south-central Missouri, USA. We also examined the influence that adding a second, lower barbed-wire strand to snares had on quantity and spatial distribution of detections. We simulated trapping data to test bias in density estimates of each configuration under a range of density and detection parameter values. Field data showed that using multiple arrays with intensive snare coverage produced more detections of more individuals than extensive coverage. Consequently, density and detection parameters were more precise for the intensive design. Density was estimated as 1.7 bears per 100 km2 and was 5.5 times greater than that under extensive sampling. Abundance was 279 (95% CI = 193–406) bears in the 16,812 km2 study area. Excluding detections from the lower strand resulted in the loss of 35 detections, 14 unique bears, and the largest recorded movement between snares. All simulations showed low bias for density under both configurations. Results demonstrated that in low density populations with non-uniform distribution of population density, optimizing the tradeoff among snare spacing, coverage, and sample size is of critical importance to estimating parameters with high precision and accuracy. With limited resources, allocating available traps to multiple arrays with intensive trap spacing increased the amount of information needed to inform parameters with high precision.

Highlights

Knowledge of population size and spatial distribution is important for protection of threatened or endangered species [1,2,3], and management of harvested animal populations [4,5]
The probability of identity between siblings was 7.1861024; this level of PIDsib required samples to be genotyped at eight loci for inclusion in the study and required a genotyping rate of 61.5% for the extensive design and 53.3% for the intensive design to be included in the sample
224 unique hair samples were genotyped with an 83.8% genotyping rate

Summary

Introduction

Knowledge of population size and spatial distribution is important for protection of threatened or endangered species [1,2,3], and management of harvested animal populations [4,5]. Estimates of species’ abundance or density are useful as a baseline for developing protected areas [6,7], prioritizing conservation actions [8,9], and allocating harvest quotas [10]. Large mammals often persist at low densities over large areas, are not uniformly distributed, and have large home ranges [11,12,13]. These characteristics may undermine abundance estimation and hinder subsequent conservation efforts [14,15]. Capture-recapture methods are often used to estimate density and abundance of rare or elusive carnivores [2,15,16]. Trap configurations that do not adequately reflect population distributions and individual variation in space use may limit precise and accurate estimates of density and abundance [15,21]

Methods

Results

Discussion

Conclusion