Raccoon Density Estimation from Camera Traps for Raccoon Rabies Management

Authors

Amy J. Davis, United States Department of Agriculture, Animal and Plant Health Inspection Services, Wildlife Services, National Wildlife Research Center, Fort Collins, ColoradoFollow
Wesley C. Dixon, University of Georgia
Richard B. Chipman, United States Department of Agriculture, Animal and Plant Health Inspection Services, Wildlife Services, National Rabies Management Program, Concord, New HampshireFollow
Amy T. Gilbert, United States Department of Agriculture, Animal and Plant Health Inspection Services, Wildlife Services, National Wildlife Research Center, Fort Collins, ColoradoFollow
Jacob E. Hill, Michigan State University
James C. Beasley, University of Georgia
Olin E. Rhodes Jr., University of Georgia
Guha Dharmarajan, Krea University

ORCID IDs

Davis http://orcid.org/0000-0002-4962-9753

Gilbert http://orcid.org/0000-0002-8256-0081

Hill http://orcid.org/0000-0003-2761-8786

Document Type

Article

Date of this Version

2024

Citation

Journal of Wildlife Management (2024) 89(2): e22701

doi: 10.1002/jwmg.22701

Comments

United States government work

Abstract

Density estimation for unmarked animals is particularly challenging, yet density estimates are often necessary for effective wildlife management. Raccoons (Procyon lotor) are the primary terrestrial wildlife reservoir for Lyssavirus rabies within the United States. The raccoon rabies variant (RRVV) is actively managed at landscape scales using oral rabies vaccination (ORV) within the eastern United States. To effectively manage RRVV, it is important to know the density of raccoons to appropriately scale the density of ORV baits distributed on the landscape. We compared methods to estimate raccoon densities from camera‐trap data versus more intensive capture‐mark‐recapture (CMR) estimates across 2 land cover types (upland pine and bottomland hardwood) in the southeastern United States during 2019 and 2020. We evaluated the effect of alternative camera configurations and durations of camera trapping on density estimates and used an N‐mixture model to estimate raccoon densities, including covariates on abundance and detection. We further compared different methods of scaling camera‐based counts, with the maximum number of raccoons seen on any given image within a day best explaining density. Camera‐trap density estimates were moderately correlated with CMR estimates (r = 0.56). However, densities from camera‐trap data were more reliable when classifying category of density as an index used to inform management (83% correct when compared to CMR estimates), although the densities in our study fell into the 2 lowest density classes only. Using more cameras reduced bias and uncertainty around density estimates; however, if ≤ 6 camera traps were used at a site, a line transect approach proved less biased than a grid design. Camera trapping should be conducted for at least 3 weeks for more accurate estimates of raccoon population density in our study area (< 5% bias). We show that camera‐trap data can be used to assign raccoon densities to management‐relevant density index bins, but more studies are needed to ensure reliability across a greater range of environmental conditions and raccoon densities.