Characterizing recolonization by a reintroduced bear population using genetic spatial capture-recapture

Abstract

Many large carnivores are recolonizing range as a result of improved management and conservation policy, habitat restoration, and reintroduction programs. American black bears (Ursus americanus) are projected to recolonize portions of the United States, but few studies have characterized or provided practical methods for monitoring this process. We used noninvasive hair sampling at 4 proximal study areas along the Kentucky-Virginia, USA, border during 2012-2013 to estimate demographics and population genetics, and investigate recolonization patterns of an American black bear population that was founded by 55 bears reintroduced to a fragmented mountainous landscape during the 1990s and subjected to harvest 6 years postreintroduction. Using spatially explicit capture-recapture (SECR) models, we estimated a density of 0.26 bear/ km 2 , or minimum abundance of 482 bears, distributed among 2 primary core areas previously identified by occupancy analysis: a southern and northern core area. The southern core area was established by a founder adult female that exhibited post-release dispersal, but moderate asymmetrical gene flow (Nm ¼ 6 bears) from the northern core area mitigated deleterious genetic consequences typical of such founder events. Effective number of breeders (N B ¼ 62 bears) was similar to the number of founders, suggesting that genetically, the population remains mostly the product of reintroduction. Despite limited connectivity with other populations in the region, genetic diversity (H E ¼ 0.78) was retained because of rapid population growth during the 16 years postreintroduction (l ¼ 1.14/year). This bear population exhibited demographic characteristics indicative of continued recolonization, including a significantly female-biased sex ratio (0.53M:1.00F) and female density decreasing with increasing distance from the reintroduction release areas in the northern core. Few bear detections at 2 peripheral study areas and results from SECR model detection function transformation suggested recolonization may continue to the southwest and northeast along prominent linear mountain ridges. Although the population has grown and is genetically stable, because of relatively low population density and recolonization direction, we suggest monitoring demographic vital rates to evaluate harvest sustainability and population viability. Our study demonstrates the utility of noninvasive genetic sampling in conjunction with SECR models to characterize and monitor recolonizing bear populations, which may also be useful for management of expanding populations of other large carnivores. Ó 2016 The Wildlife Society.

Non‐circular home ranges and the estimation of population density

“…A circular detection model based on will therefore predict more or fewer detected individuals depending on whether the major or minor axis of each home range is sampled preferentially (Murphy et al. ). Some positive bias remains even when elongated home ranges are oriented at random with respect to a linear array (Table ).…”

“…The anisotropic model of Murphy et al. () is effective when home ranges are oriented and elongated uniformly, and there is sufficient information from re‐detections in two dimensions to estimate the corresponding parameters.…”

“…Murphy et al. (; also B. Augustine, personal communication ) allowed for range elongation by basing the SECR detection model on Euclidean distances computed in a space transformed to make home ranges more nearly circular. Transformation is straightforward when elliptical ranges are all oriented in the same direction and elongated to the same degree.…”

“…The circular model was also compromised when ellipses aligned with the rows or columns of a regular grid. Murphy et al (2016;also B. Augustine, personal communication) allowed for range elongation by basing the SECR detection model on Euclidean distances computed in a space transformed to make home ranges more nearly circular.…”

Factors associated with black bear density and implications for management

“…Royle et al () recognized the inherent similarities between detection probability functions and spatial distribution of individuals using SCR and those of resource selection functions that are typically inferred by telemetry data, and developed methods to jointly model population density, space‐use, and resource selection. Consequently, SCR is progressively being used to describe spatial patterns including resource selection (Sollmann et al , Linden et al ), distribution (Morehouse and Boyce , Murphy et al ), dispersal (Ergon and Gardner , Schaub and Royle ), and connectivity (Fuller et al , Morin et al ).…”