A primary goal of monitoring wildlife populations is the estimation of population growth rate, λ. Two common methods by which biologists estimate λ are demographic studies of marked individuals, which tend to be expensive and labor-intensive, and estimators derived from time series of population indices. We compare grizzly bear (Ursus arctos) population growth rates in the Banff ecosystem (Alberta, Canada) from a published demographic study to estimates from concurrent monitoring of an index of population size, the number of females with cubs-of-the-year (F_cub). We estimated population trends by transforming the index into 2 population estimators (bias-corrected Chao and summation), and used each to estimate λ. The 95% confidence intervals of λ̂ from the 2 monitoring-based estimators overlapped the point estimate of the demographic study. Precision of the bias-corrected Chao estimator was very low (95% CI of λ  =  0.572–1.679); its application to the time-series used here is essentially fruitless. Precision of the summation estimator (95% CI of λ  =  0.847–1.137) and the demographic study (0.99–1.09) were higher, but the CI of the former at least could be artificially narrow. Because all estimates were close to 1.00, the long-term fate of this population may depend critically on subtle changes in growth rate and on environmental stochasticity. Given that long-term demographic studies are not feasible in this system, population monitoring may be a worthwhile way to assess population dynamics. However, given the low power of many monitoring techniques to detect trends and the low precision of the F_cub estimators in particular, long time-series and explicit measures to remove sampling variance should be employed to increase trend estimate precision.

Brown bears (Ursus arctos) in the Cantabrian Mountains of northwestern Spain occur in 2 small, isolated, and endangered populations: the western population (WP) and the smaller eastern population (EP) were studied from 1989 to 2004. We documented the number of unique females with cubs-of-the-year (F_CUB), the number of cubs per female, and the area occupied by F_CUB. The estimated number of F_CUB using the Chao mark–resight estimator was similar to a conservative number of F_CUB obtained using protocols to distinguish unique animals (N̂_Obs). In the WP, N̂_Obs increased during the period, whereas the trend suggested by the index in the EP did not differ from 1.0. The number of cubs per female was slightly higher in the WP (1.8) than in the EP (1.5). The area occupied by F_CUB initially decreased followed by a recovery in both populations. Nevertheless, the area occupied as of 1989–92 had not been completely re-colonized by 2001–04. The areas apparently abandoned by F_CUB were situated in the middle of the 2 populations, so the gap between them was wider in 2001–04 than in 1989–92. We conclude that brown bears in the Cantabrian Mountains are recovering, but the isolation of the 2 populations jeopardizes this recovery. Both populations are still endangered, especially the EP, for which we estimated only 0–3 breeding females/year. Conservation priorities include promoting recovery of range previously occupied by breeding females and increasing contact between the populations.

Counts of females with cubs-of-the-year (FWC) have been used as an index for monitoring brown bear (Ursus arctos) populations or estimating a minimum number of adult females in several small and medium-sized populations. Because discriminating among family groups is crucial to this procedure, we sought to improve criteria used to differentiate among FWC using spatial and temporal distances between sightings. We used telemetry data from 11 FWC from southern and central Europe and 15 FWC from Sweden to determine the likelihood that observations were of the same FWC based on the distance moved and elapsed time period. Euclidean distances traveled by each FWC were estimated daily. We then calculated straight-line distances traveled by each FWC using intervals of 1–180 days, or the maximum available. We obtained the maximum values (highest percentiles) of distances over time for each FWC. We considered 2 periods of bear activity: early spring, from first observations after denning until 30 June, and the remaining active season from 1 July until the onset of denning. Native FWC living in the boreal forest of Scandinavia moved farther than those living in the temperate forests of southern and central Europe. Differences among FWC in southern and central Europe may be related to habitat characteristics and to the origin (native or released) of the bears we studied. For example, based on the upper 95% prediction interval of the curve fitted of the 80 percentile in the early spring–June period, 2 observations 30 days apart are unlikely to be of the same individual if >13 km apart for FWC in the boreal forest, >15 km and >7 km, respectively, for released and native FWC in southern and central Europe. Our findings may be useful for biologists and managers to help differentiate FWC and thereby estimate the minimum number of family groups present, particularly in areas with low densities of FWC.

Grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem of the US Rocky Mountains have recently increased in numbers, but remain vulnerable due to isolation from other populations and predicted reductions in favored food resources. Harris et al. (2006) projected how this population might fare in the future under alternative survival rates, and in doing so estimated the rate of population growth, 1983–2002. We address issues that remain from that earlier work: (1) the degree of uncertainty surrounding our estimates of the rate of population change (λ); (2) the effect of correlation among demographic parameters on these estimates; and (3) how a future monitoring system using counts of females accompanied by cubs might usefully differentiate between short-term, expected, and inconsequential fluctuations versus a true change in system state. We used Monte Carlo re-sampling of beta distributions derived from the demographic parameters used by Harris et al. (2006) to derive distributions of λ during 1983–2002 given our sampling uncertainty. Approximate 95% confidence intervals were 0.972–1.096 (assuming females with unresolved fates died) and 1.008–1.115 (with unresolved females censored at last contact). We used well-supported models of Haroldson et al. (2006) and Schwartz et al. (2006a,b,c) to assess the strength of correlations among demographic processes and the effect of omitting them in projection models. Incorporating correlations among demographic parameters yielded point estimates of λ that were nearly identical to those from the earlier model that omitted correlations, but yielded wider confidence intervals surrounding λ. Finally, we suggest that fitting linear and quadratic curves to the trend suggested by the estimated number of females with cubs in the ecosystem, and using AIC_c model weights to infer population sizes and λ provides an objective means to monitoring approximate population trajectories in addition to demographic analysis.

Costs for genetic analysis of hair samples collected for individual identification of bears average approximately US$50 [2004] per sample. This can easily exceed budgetary allowances for large-scale studies or studies of high-density bear populations. We used 2 genetic datasets from 2 areas in the southeastern United States to explore how reducing costs of analysis by sub-sampling affected precision and accuracy of resulting population estimates. We used several sub-sampling scenarios to create subsets of the full datasets and compared summary statistics, population estimates, and precision of estimates generated from these subsets to estimates generated from the complete datasets. Our results suggested that bias and precision of estimates improved as the proportion of total samples used increased, and heterogeneity models (e.g., M_h[Chao]) were more robust to reduced sample sizes than other models (e.g., behavior models). We recommend that only high-quality samples (>5 hair follicles) be used when budgets are constrained, and efforts should be made to maximize capture and recapture rates in the field.

Little is known about the biology, status, or distribution of sloth bears (Melursus ursinus) in Sri Lanka. To facilitate sloth bear conservation, information is needed about where bears occur and what landscapes support their populations. We overlaid a 5- × 5-km grid on 1:50,000-scale land-use maps covering historic sloth bear range in Sri Lanka. In 2004, we documented current (2002–04) sloth bear presence or absence in each 25-km² cell by interviewing knowledgeable forest users. We sought as respondents hunters, wildlife and security personnel, and others with experience in their local forests as most likely to supply reliable information regarding the presence or absence of sloth bears. We also assessed respondents' perceptions and attitudes toward sloth bears. Sloth bear range occupied <17% of Sri Lanka's land area with approximately 40% contained within national parks and strict nature reserves where hunting is banned and human access regulated. Except for a few small, isolated areas, sloth bear range was largely contiguous. However, large portions of sloth bear range in the north and east of the island were unprotected. Prevalence of monsoon forest was the strongest positive predictor of sloth bear presence. Elevation, road density, and human population density were significant negative predictors. Perceptions that sloth bear populations had increased were common among almost half (49%) the respondents. Although 70% of respondents regarded sloth bears as a threat, 66% supported legal protection. This positive attitude toward protection may facilitate conservation efforts. The establishment of additional protected areas in the north and east of the island and strict regulation of human activity in protected areas may enhance sloth bear conservation.

There is very little published information on denning ecology of sloth bears (Melursus ursinus). We identified 109 day dens in Pendra and Marwahi ranges of North Bilaspur Forest Division. Except for 3 dens, all sloth bear day dens were found in hillocks made of boulders. Annual mean temperature outside and inside the dens in daytime was 39.0°C (n  =  104, SD  =  4.1) and 28.4°C (n  =  104, SD  =  3.9), respectively. Ninety-five of the 109 day dens were characterized by the presence of gusti (Ficus virens), pakri (Ficus tinctoria), and tendu (Diospyros melanoxylon) trees. On average, vegetation cover in a 10-m radius circle around day den centers was 14.8% (n  =  109, SD  =  8.3). To provide protection to sloth bear habitat, we recommend that the Forest Department should stop stone extraction and regulate grazing and removal of firewood and timber from hillocks that have day dens.

We present a database application designed to standardize the collection and entry of brown and black bear (Ursus arctos and U. americanus)–human interaction data, formalize data storage methods, and analyze patterns of bear–human interactions in Alaska's National Parks. The National Park Service Alaska Region Bear–Human Information Management System (BHIMS) facilitates the systematic collection of biologically relevant data, consolidates bear management information, helps identify management priorities, facilitates the development of science-based bear management plans, helps evaluate the effectiveness of management strategies, helps provide more effective bear safety messages, creates permanent digital copies of original data, establishes bear management institutional memory, and ultimately improves bear conservation and human safety. The BHIMS can be modified for use in other locales and has applicability to bear management across North America.

Managing interactions between humans and American black bears (Ursus americanus) has evolved from public feeding and viewing of garbage-habituated bears to nationwide bear education campaigns focused on removing food attractants. We conducted a self-administered survey to assess how wildlife agencies respond to human–bear conflict and identified techniques currently used to manage conflicts throughout US, Canada, and Mexico. Forty-eight agencies responded to the survey and answered questions about bear populations, levels of complaints, types of interactions, and agency responses. Most (75%) agencies surveyed relocated problem bears, but only 15% believed relocation was an effective tool. Half (50%) of the agencies always marked problem bears that were captured and released; 50% both monitored the results of relocated bears and maintained a database. Most (69%) agencies ranked garbage/food attractants the most common type of human–bear conflict. Our results suggest that management responses to human–black bear conflict can be strengthened by adopting protocols for marking, monitoring, and maintaining a database for all bears captured in association with conflict incidents; moving from reactive to proactive approaches for garbage management; and developing comprehensive bear education programs that strive to make education a more dynamic and interactive process. Despite the unique circumstances of local politics and laws, all agencies need to strive to develop systems to document and evaluate the effectiveness of their actions to prevent and manage conflict. By monitoring actions and results, agencies can design improvements and move forward in an adaptive management framework.

We developed and tested a system that alerts personnel when a radiocollared animal enters an area designated as off-limits. The remote alarm combines the monitoring capabilities of data loggers with a message transmitter that sends a voice message via 2-way radios when an animal enters a monitored area. We tested the remote alarm with food-conditioned American black bears (Ursus americanus) in Yosemite National Park by setting up 6 remote alarms in areas designated off-limits to bears (i.e., campgrounds and parking lots) and alternated nights when the message transmitters on the alarms were activated. We recorded the number of times a radiotagged bear entered an off-limits area, the number of times bear management detected a bear in areas off-limits, and the number of hazing events. Data loggers recorded 153 bear visits by 6 radiotagged bears, 59 with the alarm on and 94 with the alarm off. With the message transmitter activated, bear-managers found bears in areas off-limits 4 times more often than with the message transmitter off. Twelve hazing events occurred with the message transmitters active and 5 with them inactive. The number of bear visits/night to monitored areas was lower when message transmitters were active than when they were inactive, probably because bears entering areas off-limits were more likely to be detected and hazed with the message transmitter on. The remote alarm functioned well and aided park managers with their hazing program to reduce bear–human conflict.