Abundance and density estimation of the American black bear population in central Georgia

Hooker, Michael J.; Laufenberg, Jared S.; Ashley, Annaliese K.; Sylvest, Joshua Thomas; Chamberlain, Michael J.

doi:10.2192/ursus-d-14-00025

Cited by 22 publications

(23 citation statements)

References 17 publications

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“…Black bear density was highest on the east coast in pocosin, which is characterized by high food production and cover, low human disturbance, and agricultural food resources mixed throughout (Tredick and Vaughan , Drewry et al ). Eastern populations inhabiting pine plantations were at densities comparable to New Mexico populations likely because pine plantations had limited food, insufficient cover, and fewer agricultural food resources as compared to pocosin (Tredick and Vaughan , Drewry et al , Hooker et al ). Locally, our estimates are similar to or lower than those in southern Colorado, USA, and similar to or higher than those in northern Colorado (Table ); however, estimates for southern Colorado fluctuated substantially within each study area and over multiple years.…”

Section: Discussionmentioning

confidence: 99%

“…Innovations in non‐invasive genetic sampling techniques (NGS; Woods et al ), coupled with robust statistical analyses such as spatially explicit capture–recapture (SECR; Efford ), have provided researchers with improved tools to estimate the abundance and density of carnivore populations from which harvest limits can be established. These tools have facilitated monitoring efforts and produced density estimates for black bear populations across much of their range (Stetz et al , Hooker et al , Sun et al ).…”

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confidence: 99%

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Density of American black bears in New Mexico

et al. 2018

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Considering advances in noninvasive genetic sampling and spatially explicit capture-recapture (SECR) models, the New Mexico Department of Game and Fish sought to update their density estimates for American black bear (Ursus americanus) populations in New Mexico, USA, to aide in setting sustainable harvest limits. We estimated black bear density in the Sangre de Cristo, Sandia, and Sacramento Mountains, New Mexico, 2012-2014. We collected hair samples from black bears using hair traps and bear rubs and used a sex marker and a suite of microsatellite loci to individually genotype hair samples. We then estimated density in a SECR framework using sex, elevation, land cover type, and time to model heterogeneity in detection probability and the spatial scale over which detection probability declines. We sampled the populations using 554 hair traps and 117 bear rubs and collected 4,083 hair samples. We identified 725 (367 male, 358 female) individuals. Our density estimates varied from 16.5 bears/100 km 2 (95% CI ¼ 11.6-23.5) in the southern Sacramento Mountains to 25.7 bears/100 km 2 (95% CI ¼ 13.2-50.1) in the Sandia Mountains. Overall, detection probability at the activity center (g0) was low across all study areas and ranged from 0.00001 to 0.02. The low values of g0 were primarily a result of half of all hair samples for which genotypes were attempted failing to produce a complete genotype. We speculate that the low success we had genotyping hair samples was due to exceedingly high levels of ultraviolet (UV) radiation that degraded the DNA in the hair. Despite sampling difficulties, we were able to produce density estimates with levels of precision comparable to those estimated for black bears elsewhere in the United States. Ó 2018 The Wildlife Society.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Density of American black bears in New Mexico

et al. 2018

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“…, Hooker et al. ). With lower detectability, it becomes more difficult to achieve adequate sample size for density estimation (Sun et al.…”

Section: Discussionmentioning

confidence: 99%

Spatially explicit abundance estimation of a rare habitat specialist: implications forSECRstudy design

Kristensen

Kovach

2018

Ecosphere

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Abstract. Estimating abundance is an essential component of monitoring and recovery of rare species, and spatially explicit capture-recapture (SECR) models provide the means for robust density estimation. Previous work has elucidated principles of SECR study design for large, generalist carnivores, but less attention has been paid to study design considerations for smaller species, with less extensive home ranges. Here, we integrated data from an intensive pilot study with simulation modeling to evaluate the influence of survey sampling intensity on precision and accuracy in SECR abundance estimation for a rare lagomorph that specializes on patchily distributed early-successional habitats. Doing so, we obtained the first mark-recapture density estimates for the New England cottontail (Sylvilagus transitionalis). Capture probability and density on the landscape both impacted the required intensity of the sampling design. The optimal study design for robust estimation also required a greater number of traps relative to home range size or spatial extent than those recommended in prior SECR studies. This divergence emphasizes that SECR study design considerations will differ among organisms with varying spatial extent and habitat use. Demonstrating the appropriate sampling design for a study system is important prior to embarking in a SECR study. Integrating pilot empirical data with simulations provides a powerful means for optimizing SECR study design and for facilitating applicability of SECR approaches to a wider array of organisms with varying habitat and space use. This methodology may be employed in planning a monitoring program that maximizes effectiveness while minimizing cost and effort, as part of the adaptive management approach to monitor and recover rare or endangered species.

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“…Once defined, these habitat features or geographical boundaries could be used in the habitat mask feature of secr . Detection parameters could be modeled with these covariates to exclude nonhabitat areas, improve density estimates, and reduce probable underestimation in future surveys (Efford and Fewster , Hooker et al ).…”

Section: Discussionmentioning

confidence: 99%

Applying spatially explicit capture–recapture models to estimate black bear density in South Carolina

et al. 2019

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Population density is an important component of wildlife management decisions, but can be difficult to estimate directly for an itinerant, wide-ranging species such as the American black bear (Ursus americanus). In South Carolina, USA, where there has been growth in black bear populations and bear-human-conflict reports during the past several decades, managers need robust estimates of population size to inform management strategies. We used maximum-likelihood capture-recapture models, using hair snares to collect DNA samples, to estimate density and abundance for a harvested population of black bear in northwestern South Carolina during 2013 to 2014. Models were tested in a spatially explicit framework using the secr package in Program R. Black bear density was estimated at 0.133 bears/km 2 (SE = 0.034) in 2013 and 0.179 bears/km 2 (SE = 0.043) in 2014. Black bear abundance in our study area was estimated to be 586 bears (SE = 95) in 2013 and 680 bears (SE = 128) in 2014, which are 2-3-fold lower than previous estimates. We suggest that these estimates be considered a baseline for state biologists to employ in the population's management and in developing future harvest-regulation strategies. Overall our study highlighted the potential for model choice to influence density estimates, and we concluded that spatially explicit models were appropriate for this study because geographic closure could not be assumed.

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Abundance and density estimation of the American black bear population in central Georgia

Cited by 22 publications

References 17 publications

Density of American black bears in New Mexico

Density of American black bears in New Mexico

Spatially explicit abundance estimation of a rare habitat specialist: implications forSECRstudy design

Applying spatially explicit capture–recapture models to estimate black bear density in South Carolina

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