Using bear rub data and spatial capture-recapture models to estimate trend in a brown bear population.

Trends in population abundance can be challenging to quantify during range expansion and contraction, when there is spatial variation in trend, or the conservation area is large. We used genetic detection data from natural bear rubbing sites and spatial capture-recapture (SCR) modeling to estimate local density and population growth rates in a grizzly bear population in northwestern Montana, USA. We visited bear rubs to collect hair in 2004, 2009-2012 (3,579-4,802 rubs) and detected 249-355 individual bears each year. We estimated the finite annual population rate of change 2004-2012 was 1.043 (95% CI = 1.017-1.069). Population density shifted from being concentrated in the north in 2004 to a more even distribution across the ecosystem by 2012. Our genetic detection sampling approach coupled with SCR modeling allowed us to estimate spatially variable growth rates of an expanding grizzly bear population and provided insight into how those patterns developed. The ability of SCR to utilize unstructured data and produce spatially explicit maps that indicate where population change is occurring promises to facilitate the monitoring of difficult-to-study species across large spatial areas.

Demographic mechanisms underpinning genetic assimilation of remnant groups of a large carnivore.

Current range expansions of large terrestrial carnivores are occurring following human-induced range contraction. Contractions are often incomplete, leaving small remnant groups in refugia throughout the former range. Little is known about the underlying ecological and evolutionary processes that influence how remnant groups are affected during range expansion. We used data from a spatially explicit, long-term genetic sampling effort of grizzly bears (Ursus arctos) in the Northern Continental Divide Ecosystem (NCDE), USA, to identify the demographic processes underlying spatial and temporal patterns of genetic diversity. We conducted parentage analysis to evaluate how reproductive success and dispersal contribute to spatio-temporal patterns of genetic diversity in remnant groups of grizzly bears existing in the southwestern (SW), southeastern (SE) and east-central (EC) regions of the NCDE. A few reproductively dominant individuals and local inbreeding caused low genetic diversity in peripheral regions that may have persisted for multiple generations before eroding rapidly (approx. one generation) during population expansion. Our results highlight that individual-level genetic and reproductive dynamics play critical roles during genetic assimilation, and show that spatial patterns of genetic diversity on the leading edge of an expansion may result from historical demographic patterns that are highly ephemeral.

Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?

Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method.