Factors associated with black bear density and implications for management

Abstract

Wildlife density estimates are important to accurately formulate population management objectives and understand the relationship between habitat characteristics and a species’ abundance. Despite advances in density and abundance estimation methods, management of common game species continues to be challenged by a lack of reliable population estimates. In Washington, USA, statewide American black bear (Ursus americanus) abundance estimates are predicated on density estimates derived from research in the 1970s and are hypothesized to be a function of precipitation and vegetation, with higher densities in western Washington. To evaluate current black bear density and landscape relationships in Washington, we conducted a 4‐year capture‐recapture study in 2 areas of the North Cascade Mountains using 2 detection methods, non‐invasive DNA collection and physical capture and deployment of global positioning system (GPS) collars. We integrated GPS telemetry from collared bears with spatial capture‐recapture (SCR) data and created a SCR‐resource selection model to estimate density as a function of spatial covariates and test the hypothesis that density is higher in areas with greater vegetative food resources. We captured and collared 118 bears 132 times and collected 7,863 hair samples at hair traps where we identified 537 bears from 1,237 detections via DNA. The most‐supported model in the western North Cascades depicted a negative relationship between black bear density and an index of human development. We estimated bear density at 20.1 bears/100 km2, but density varied from 13.5/100 km2 to 27.8 bears/100 km2 depending on degree of human development. The model best supported by the data in the eastern North Cascades estimated an average density of 19.2 bears/100 km2, which was positively correlated with primary productivity, with resulting density estimates ranging from 7.1/100 km2 to 33.6 bears/100 km2. The hypothesis that greater precipitation and associated vegetative production in western Washington supports greater bear density compared to eastern Washington was not supported by our data. In western Washington, empirically derived average density estimates (including cubs) were nearly 50% lower than managers expected prior to our research. In eastern Washington average black bear density was predominantly as expected, but localized areas of high primary productivity supported greater than anticipated bear densities. Our findings underscore the importance that black bear density is not likely uniform and management risk may be increased if an average density is applied at too large a scale. Disparities between expected and empirically derived bear density illustrate the need for more rigorous monitoring to understand processes that affect population numbers throughout the jurisdiction, and suggest that management plans may need to be reevaluated to determine if current harvest strategies are achieving population objectives. © 2019 The Wildlife Society.

Black Bears Select Large Woody Structures for Dens in Southeast Alaska

“…The existence of used locations amongst the sample of available points (i.e., contamination) has been cited as a cause for bias in model coefficient estimates (Keating and Cherry 2004). Johnson et al (2006), however, reported that contamination rates must be extreme to affect coefficient estimates, and the density of black bears in Southeast Alaska and adjacent regions (0.13–1.51/km 2 ; Peacock et al 2011, Welfelt et al 2019) suggest low levels of contamination.…”

“…The existence of used locations amongst the sample of available points (i.e., contamination) has been cited as a cause for bias in model coefficient estimates (Keating and Cherry 2004). Johnson et al (2006), however, reported that contamination rates must be extreme to affect coefficient estimates, and the density of black bears in Southeast Alaska and adjacent regions (0.13-1.51/km 2 ; Peacock et al 2011, Welfelt et al 2019) suggest low levels of contamination. We developed a pool of candidate models for each of the first 4 black bear den selection models and included in each candidate model a target covariate of interest (Table 1) we were interested in relating to the relative probability of den presence.…”

Noninvasive Genetic Assessment Is an Effective Wildlife Research Tool When Compared with Other Approaches

“…For example, genetic analysis is not suitable to provide information on body weight [71], age [83], or behaviour [84]. Therefore, for some research goals, it would be most optimal to use a different approach, or a combination of noninvasive genetic sampling and another method, preferably also noninvasive [10,[84][85][86].…”

Density-habitat relationships of white-tailed deer (Odocoileus virginianus) in Finland

“…SCR can estimate the effect of certain habitat types on density and encounter probability, even if individuals are not directly encountered in that habitat, by predicting the locations of individual home range centers in the vicinity of the sample units. One main application of SCR has been research on large carnivores (Proffitt et al 2015, Sun et al 2017, Stetz et al 2019, Welfelt et al 2019), but non-invasive DNA sampling with SCR has also been used to study the relationship of mule deer (Odocoileus hemionus) population density and habitat structure (Brazeal et al 2017).…”