Integrated modeling to estimate population size and composition of mule deer

Abstract

Estimating population size, age composition, and sex ratio of mule deer (Odocoileus hemionus) is important to conservation and managed hunting of this species in the western United States. Increasingly, wildlife agencies are estimating abundance of deer using fecal DNA (fDNA), especially in forested habitats where aerial surveys are not feasible. These same data can be used to estimate overall sex ratio but require additional data on age structure to quantify adult‐ and fawn‐specific sex ratios, which are expected to differ substantially. We demonstrate an integrated modeling approach to estimating population sizes of adult females, adult males, and fawns from 3 sources of data: fDNA, camera stations, and global positioning system (GPS) telemetry. We conducted the study on an 11,500‐km2 forested region in northern California, USA, corresponding to 3 hunt management zones. Within a Bayesian framework, we used spatial capture–recapture (SCR) modeling of fDNA samples and prior information on home range sizes from telemetry to estimate sex‐specific densities, and N‐mixture modeling of camera detections to separate adult and fawn densities. We estimated 29,317 adult females (90% CI = 24,550–34,592), 10,845 adult males (90% CI = 7,778–14,858), and 19,587 fawns (90% CI = 15,340–24,430) within the study area. The inclusion of telemetry increased precision of our results, and cameras provided comparable estimates of density when we calibrated them on the SCR results. Based on these results, we recommend a monitoring program of fDNA transects repeated once every 5 years, camera stations repeated at half of transects every year, and telemetry data from 1 deer for every 2 transects on average. We estimated an average annual cost of $1,316 (U.S.) per transect to sustain this endeavor. The integration of cameras with fDNA to combine age structure data with sex‐specific abundance data represents a novel and significant step forward in the capacity to estimate deer population parameters. © 2018 The Authors. Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.

Estimating Deer Populations Using Camera Traps and Natural Marks

“…Our density estimates of approximately 8 deer/km 2 are higher than those from a study approximately 300 km to the north of our study site which found approximately 5.0 (95% CI = 2.3-7.8) deer/km 2 ; however, the 95% confidence and credible intervals overlap (Brazeal et al 2017). Our density estimates and credible intervals are higher than the estimate of 5.2 deer/km 2 (90% CRI = 4.4-6.1) reported by Furnas et al (2018) in a study approximately 450 km to the north of our study site. The lower density estimates reported in these studies may be due to the more mountainous and heavily forested terrain that may not provide the same forage resources and support the same densities of deer as the milder coastal environment where we conducted this study.…”

“…The n jk records of unmarked deer are assigned to M hypothetical individuals, where M is chosen larger than the expected population size to not truncate the estimate of the number of unmarked individuals in the population. We used a value of 450 because this population density was more than twice that reported in other recent studies in California (Furnas et al 2018). The model then estimates a latent individual covariate (z i ), which is 1 if the animal is part of the population, and 0 otherwise, using a Bernoulli distribution,…”

“…Other approaches such as N-mixture models that explicitly incorporate detection probability require other assumptions (e.g., individuals can only be counted at 1 site [Royle 2004, Barker et al 2017 or that animals are not attracted to detectors [Rowcliffe et al 2008]). Fecal DNA spatial capture-recapture methods (Brazeal et al 2017, Furnas et al 2018 have been paired with additional information about home range size and sex ratio to estimate populations, but this method also requires that individuals are only detected at 1 site during a sampling occasion and require DNA processing.…”

Genetic characterization of kit foxes at their northern range extent and monitoring recommendations

“…First, the quantitative increase in numbers of loci available translates to a qualitative shift in how best to address the misclassification problem. Rather than assessing the minimum number of loci to minimize one of these errors (falsely lumping 2 individuals) under very stringent conditions of zero genotyping error (e.g., Taberlet et al , Waits and Leberg , McKelvey and Schwartz ), use of sufficiently large numbers of loci can be used to simultaneously minimize both types of misclassification errors, while explicitly incorporating a known and acceptable level of genotyping error (e.g., 2–3%; Lounsberry et al , Furnas et al ). The present study utilized 3 multiplex reactions, averaging approximately 6 markers each (including the sex marker), such that genotyping in duplicate required only 12 μL of extracted DNA and 6 total reactions.…”

“…Ideally, estimation of abundance (i.e., census population size, N c ) should be based on a systematic survey design, which enables application of standard spatially agnostic capture–recapture estimators (e.g., Miller et al , Lounsberry et al ) and more powerful spatial mark–recapture methods (e.g., Brazeal et al , Furnas et al , Lonsinger et al ). Our present study was based on a relatively small sample size; therefore, we used a simple approach found to be especially useful for small populations of rare, wide‐ranging carnivores, based directly on the distribution of recaptures, as implemented in Program Capwire (Miller et al ).…”