Grizzly bears (brown bears; Ursus arctos) are imperiled in the southern extent of their range worldwide. The threatened population in northwestern Montana, USA, has been managed for recovery since 1975; yet, no rigorous data were available to monitor program success. We used data from a large noninvasive genetic sampling effort conducted in 2004 and 33 years of physical captures to assess abundance, distribution, and genetic health of this population. We combined data from our 3 sampling methods (hair trap, bear rub, and physical capture) to construct individual bear encounter histories for use in Huggins—Pledger closed mark—recapture models. Our population estimate, Ň = 765 (95% CI = 715–831) was more than double the existing estimate derived from sightings of females with young. Based on our results, the estimated known, human‐caused mortality rate in 2004 was 4.6% (95% CI = 4.2–4.9%), slightly above the 4% considered sustainable; however, the high proportion of female mortalities raises concern. We used location data from telemetry, confirmed sightings, and genetic sampling to estimate occupied habitat. We found that grizzly bears occupied 33,480 km2 in the Northern Continental Divide Ecosystem (NCDE) during 1994–2007, including 10,340 km2 beyond the Recovery Zone. We used factorial correspondence analysis to identify potential barriers to gene flow within this population. Our results suggested that genetic interchange recently increased in areas with low gene flow in the past; however, we also detected evidence of incipient fragmentation across the major transportation corridor in this ecosystem. Our results suggest that the NCDE population is faring better than previously thought, and they highlight the need for a more rigorous monitoring program.

BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

: We present the first rigorous estimate of grizzly bear (Ursus arctos) population density and distribution in and around Glacier National Park (GNP), Montana, USA. We used genetic analysis to identify individual bears from hair samples collected via 2 concurrent sampling methods: 1) systematically distributed, baited, barbed‐wire hair traps and 2) unbaited bear rub trees found along trails. We used Huggins closed mixture models in Program MARK to estimate total population size and developed a method to account for heterogeneity caused by unequal access to rub trees. We corrected our estimate for lack of geographic closure using a new method that utilizes information from radiocollared bears and the distribution of bears captured with DNA sampling. Adjusted for closure, the average number of grizzly bears in our study area was 240.7 (95% CI = 202–303) in 1998 and 240.6 (95% CI = 205–304) in 2000. Average grizzly bear density was 30 bears/1,000 km2, with 2.4 times more bears detected per hair trap inside than outside GNP. We provide baseline information important for managing one of the few remaining populations of grizzlies in the contiguous United States.

Over a 3-year period, we assessed 2 sampling designs for estimating grizzly bear (Ursus arctos) population size using DNA capture-mark-recapture methods on a population of bears that included radiomarked individuals. We compared a large-scale design (with 8 × 8-km grid cells and sites moved for 4 sessions) and a small-scale design (5 × 5-km grid cells with sites not moved for 5 sessions) for closure violation, capture-probability variation, and estimate precision. We used joint telemetry/capture-mark-recapture ( JTMR) analysis and traditional closure tests to analyze the capture-mark-recapture data with each design. A simulation study compared the performance of each design for robustness to heterogeneity bias caused by reduced capture probabilities of cubs. Our results suggested that the 5 × 5-km grid cell design was more precise and more robust to potential sample biases, but the risk of closure violation due to smaller overall grid size was greater. No design exhibited complete closure as estimated by JTMR. The results of simulation studies suggested that CAPTURE heterogeneity models are relatively robust to probable forms of capture-probability variation when capture probabilities are >0.2. Only the 5 × 5-km designs exhibited this capture-probability level, suggesting that this design is preferred to ensure estimator robustness when population size is <100. The power of the CAPTURE model selection routine to detect capture probability variation was low regardless of sampling design used. Our study illustrated the trade-off between intensive sampling to ensure robustness and adequate precision of estimators while being extensive enough to avoid closure violation bias. JOURNAL OF WILDLIFE MANAGEMENT 68(3):457-469

A fundamental challenge to estimating population size with mark-recapture methods is heterogeneous capture probabilities and subsequent bias of population estimates. Confronting this problem usually requires substantial sampling effort that can be difficult to achieve for some species, such as carnivores. We developed a methodology that uses two data sources to deal with heterogeneity and applied this to DNA mark-recapture data from grizzly bears (Ursus arctos). We improved population estimates by incorporating additional DNA "captures" of grizzly bears obtained by collecting hair from unbaited bear rub trees concurrently with baited, grid-based, hair snag sampling. We consider a Lincoln-Petersen estimator with hair snag captures as the initial session and rub tree captures as the recapture session and develop an estimator in program MARK that treats hair snag and rub tree samples as successive sessions. Using empirical data from a large-scale project in the greater Glacier National Park, Montana, USA, area and simulation modeling we evaluate these methods and compare the results to hair-snag-only estimates. Empirical results indicate that, compared with hair-snag-only data, the joint hair-snag-rub-tree methods produce similar but more precise estimates if capture and recapture rates are reasonably high for both methods. Simulation results suggest that estimators are potentially affected by correlation of capture probabilities between sample types in the presence of heterogeneity. Overall, closed population Huggins-Pledger estimators showed the highest precision and were most robust to sparse data, heterogeneity, and capture probability correlation among sampling types. Results also indicate that these estimators can be used when a segment of the population has zero capture probability for one of the methods. We propose that this general methodology may be useful for other species in which mark-recapture data are available from multiple sources.