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We present satellite telemetry data for a subadult brown bear (Ursus arctos) in Serbia, documenting movements and activity for 273 days (Apr 2007 to Jan 2008). Average of daily movements was 4.29 (±2.99 SD) km. The longest daily movement was recorded in June (15.62 km), while the largest home range was recorded in May–June (1,060.9 km2). Total 95% minimum convex polygon home range was 4,366.5 km2, which is one of the largest home ranges recorded for a brown bear in Europe. During the monitoring period the bear moved throughout the western part of Serbia, and made movements into Bosnia and Herzegovina, highlighting the necessity of international coordination in the conservation of bears in the region. We propose an increase in brown bear research, and continued monitoring and management efforts at a national level.
Activity sensors are often included in wildlife transmitters and can provide information on the behavior and activity patterns of animals remotely. However, interpreting activity-sensor data relative to animal behavior can be difficult if animals cannot be continuously observed. In this study, we examined the performance of a mercury tip-switch and a tri-axial accelerometer housed in collars to determine whether sensor data can be accurately classified as resting and active behaviors and whether data are comparable for the 2 sensor types. Five captive bears (3 polar [Ursus maritimus] and 2 brown [U. arctos horribilis]) were fitted with a collar specially designed to internally house the sensors. The bears’ behaviors were recorded, classified, and then compared with sensor readings. A separate tri-axial accelerometer that sampled continuously at a higher frequency and provided raw acceleration values from 3 axes was also mounted on the collar to compare with the lower resolution sensors. Both accelerometers more accurately identified resting and active behaviors at time intervals ranging from 1 minute to 1 hour (≥91.1% accuracy) compared with the mercury tip-switch (range = 75.5–86.3%). However, mercury tip-switch accuracy improved when sampled at longer intervals (e.g., 30–60 min). Data from the lower resolution accelerometer, but not the mercury tip-switch, accurately predicted the percentage of time spent resting during an hour. Although the number of bears available for this study was small, our results suggest that these activity sensors can remotely identify resting versus active behaviors across most time intervals. We recommend that investigators consider both study objectives and the variation in accuracy of classifying resting and active behaviors reported here when determining sampling interval.
Recreational hunting is the tool most commonly used to manage American black bear (Ursus americanus) populations in North America. However, bear populations can be sensitive to overharvest, particularly of mature females that can directly affect population growth. Managers need a thorough understanding of the factors affecting harvest vulnerability when using hunting as a primary management strategy. Here, we coupled Global Positioning System spatial data from female black bears and human hunters in western Maryland, USA, from 2005 to 2007, in order to model bear harvest vulnerability. Specifically, we developed maximum entropy (Maxent) predictive occurrence models for bears and for bear hunters and evaluated the influence of 7 environmental variables on their distributions. We then assessed predicted distribution maps for probability of co-occurrence to identify areas of high and low harvest vulnerability. Slope and land ownership (i.e., private–public) were the 2 most important variables determining female bear distributions, whereas land ownership and cover type were the most important variables influencing hunter distributions. We classified roughly 12% and 16% of the study area as being of high relative use for bears and bear hunters, respectively. Only 5.4% of the study area was considered to have high harvest vulnerability (i.e., high probability of co-occurrence). Areas with high bear relative use but low hunter use (i.e., low harvest vulnerability) comprised 0.9% of the study area. We were most interested in areas of high and low harvest vulnerability to enable resource managers to adjust hunting regulations that meet harvest goals.
The Central Georgia Population (CGP) is the least abundant and most geographically isolated American black bear (Ursus americanus) population in Georgia, USA. We used DNA-based spatially explicit capture–recapture techniques to estimate density and abundance of bears in the CGP. We sampled bear hair over 2 8-week periods during the summers of 2012 and 2013 and recorded capture histories of individual bears identified via microsatellite genotyping. Population density for females was 0.123 bears/km2 (SE = 0.018) and 0.152 bears/km2 (SE = 0.024) in 2012 and 2013, respectively. Male bear density was 0.109 bears/km2 (SE = 0.015) to 0.088 bears/km2 (SE = 0.013) during the same years. Derived estimates of abundance of female bears was 125.4 (SE = 18.3) in 2012 and 154.9 (SE = 24.3) in 2013. Male bear abundance was 111.3 (SE = 15.2) and 89.8 (SE = 12.9) for 2012 and 2013, respectively. Based on these estimates and the isolated nature of the CGP, we recommend continued monitoring of demographic parameters and a conservative approach to determining annual harvest rates.
Following functional extirpation in Missouri, American black bear (Ursus americanus) populations in this state have been increasing in recent years through recolonization from re-established populations in northern Arkansas. To increase our understanding of resource selection by recolonizing black bears in the Ozark Highlands of the United States, we attached Global Positioning System (GPS) transmitters to 54 black bears during May–August 2010–2013, and used location data based on biological seasons. We constructed models with anthropogenic (distance to nearest development, distance to nearest road), biological (sex, age class, season), and environmental (distance to nearest water, land cover) categories. We used infinitely weighted logistic regression to approximate the inhomogeneous Poisson point process model for presence-only (i.e., GPS locations) data to fit models. We used Bayesian Information Criterion and found that the best-performing model in the set of 81 models included all independent variables except sex and all combinations of 2-way interactions except those between biological covariates. Forested areas generally were more strongly selected than non-forested areas and bears generally selected areas distant from roads and other human development. However, selection for areas proximate to roads in the composite cover type (e.g., shrub–scrub, woody wetlands) occurred, where roads may have been used as travel corridors in unsuitable cover during the breeding season (ad) or dispersal (subad), or alternatively as a potential barrier, depending on road type and traffic volume. Use of apparent lower quality non-forested areas by bears suggests that the current level of human development in southern Missouri is unlikely to halt their recolonization.
Brown bears (Ursus arctos) inhabit much of the northern hemisphere, including portions of North America, Europe, and Asia. Whereas northern populations generally are healthy, their distribution becomes fragmented and conservation status more tenuous in their southern range. Many fragmented populations across southern Asia are poorly understood, and abundance and distribution data are minimal. One such population contains the Gobi bear, a brown bear surviving in the Great Gobi Strictly Protected Area of southwestern Mongolia. The number of bears in this area was assumed to be low, without data-based abundance estimates. Whereas bears frequent 3 oases complexes, it was not known to what extent bears moved or bred among these complexes, which span approximately 300 km. As part of a larger science-based conservation effort, we conducted a DNA-based mark–recapture population survey in 2009 to estimate abundance, inter-oases movements of individual bears and geneflow, and genetic variability. We placed barb-wire hair-collection sites surrounding 13 supplemental feeders at most water sources within the 3 oases complexes: Atas–Inges, Shar Khuls, and Tsagaan Bogd. During 5 sessions throughout spring and summer, we collected 600 bear hair samples and genotyped 205 samples at 12 variable microsatellite loci (from 24). We identified 21 individual bears (14 M and 7 F) 48 times and developed a mark–recapture population estimate of 22 bears (95% CI = 21–29). Estimates of mean detection probability were 0.27 (SE = 0.09, CI = 0.13–0.49) and 0.51 (SE = 0.063, CI = 0.39–0.64) for female and male bears, respectively. One female and 4 males were sampled at 2 oases complexes and 3 males were sampled at all 3 oases complexes. The genetic variability (heterozygosity) was low compared with other brown bear populations. We suggest this population is isolated from other bear populations and is likely critically endangered with fewer than 40 individuals.
Sampling bias can lead to erroneous interpretations of spatial genetic structure that can subsequently impact conservation efforts and management decisions. Genetic sampling of rare and elusive species can be challenging and, by necessity, samples are often collected opportunistically from multiple sources that could differ in spatial dispersion and timing of collection. Here we quantified the effects of timing of sample collection and sampling methods on spatial and temporal variability in sample dispersion and measures of American black bear (Ursus americanus) local spatial genetic structure. Hair (N = 890) and tissue (N = 1,017) samples were collected from Michigan's Northern Lower Peninsula (USA) during the summer using non-invasive (hair snares) and autumn harvest methods during 2003, 2005, and 2009. Point pattern analyses of sample dispersion revealed that sample density did not differ significantly between seasons or among years. Measures of spatial genetic structure (i.e., spatial autocorrelation) revealed significant positive genetic spatial structuring at distances of 0–10 km when samples were analyzed separately by year, season, and when temporally distinct samples were grouped. Local genetic spatial autocorrelation analyses revealed spatial genetic patterns over the entire study area were consistent across seasons and years. Spatial genetic structure is indicative of the extent of dispersal and gene flow, which is crucial for developing management plans for harvested species. Collectively, our data show that non-invasive and harvest collection methods similarly capture spatial genetic heterogeneity at the scale and spatial extent appropriate for management.