Jeffrey B. Stetz

Demography and Genetic Structure of a Recovering Grizzly Bear Population

Abstract Grizzly bears (brown bears; Ursus arctos) are imperiled in the southern extent of their range worldwide. The threatened population in northwestern Montana, USA, has been managed for recovery since 1975; yet, no rigorous data were available to monitor program success. We used data from a large noninvasive genetic sampling effort conducted in 2004 and 33 years of physical captures to assess abundance, distribution, and genetic health of this population. We combined data from our 3 sampling methods (hair trap, bear rub, and physical capture) to construct individual bear encounter histories for use in Huggins–Pledger closed mark–recapture models. Our population estimate, N̂ = 765 (95% CI = 715–831) was more than double the existing estimate derived from sightings of females with young. Based on our results, the estimated known, human-caused mortality rate in 2004 was 4.6% (95% CI = 4.2–4.9%), slightly above the 4% considered sustainable; however, the high proportion of female mortalities raises concern. We used location data from telemetry, confirmed sightings, and genetic sampling to estimate occupied habitat. We found that grizzly bears occupied 33,480 km2 in the Northern Continental Divide Ecosystem (NCDE) during 1994–2007, including 10,340 km2 beyond the Recovery Zone. We used factorial correspondence analysis to identify potential barriers to gene flow within this population. Our results suggested that genetic interchange recently increased in areas with low gene flow in the past; however, we also detected evidence of incipient fragmentation across the major transportation corridor in this ecosystem. Our results suggest that the NCDE population is faring better than previously thought, and they highlight the need for a more rigorous monitoring program.

Grizzly Bear Density in Glacier National Park, Montana

Abstract We present the first rigorous estimate of grizzly bear (Ursus arctos) population density and distribution in and around Glacier National Park (GNP), Montana, USA. We used genetic analysis to identify individual bears from hair samples collected via 2 concurrent sampling methods: 1) systematically distributed, baited, barbed-wire hair traps and 2) unbaited bear rub trees found along trails. We used Huggins closed mixture models in Program MARK to estimate total population size and developed a method to account for heterogeneity caused by unequal access to rub trees. We corrected our estimate for lack of geographic closure using a new method that utilizes information from radiocollared bears and the distribution of bears captured with DNA sampling. Adjusted for closure, the average number of grizzly bears in our study area was 240.7 (95% CI = 202–303) in 1998 and 240.6 (95% CI = 205–304) in 2000. Average grizzly bear density was 30 bears/1,000 km2, with 2.4 times more bears detected per hair trap inside than outside GNP. We provide baseline information important for managing one of the few remaining populations of grizzlies in the contiguous United States.

Neglect of Genetic Diversity in Implementation of the Convention on Biological Diversity

Genetic diversity is the foundation for all biological diversity; the persistence and evolutionary potential of species depend on it. World leaders have agreed on the conservation of genetic diversity as an explicit goal of the Convention on Biological Diversity (CBD). Nevertheless, actions to protect genetic diversity are largely lacking. With only months left to the 2010-biodiversity target, when the 191 parties to the CBD have agreed on achieving a significant reduction of the rate of biodiversity loss, gene-level diversity is still not being monitored, and indicators and thresholds that can be used to devise strategies to conserve this important component of biodiversity are missing. Immediate action is needed to ensure that genetic diversity is not neglected in conservation targets beyond 2010.

Estimating Grizzly and Black Bear Population Abundance and Trend in Banff National Park Using Noninvasive Genetic Sampling

We evaluated the potential of two noninvasive genetic sampling methods, hair traps and bear rub surveys, to estimate population abundance and trend of grizzly (Ursus arctos) and black bear (U. americanus) populations in Banff National Park, Alberta, Canada. Using Huggins closed population mark-recapture models, we obtained the first precise abundance estimates for grizzly bears ( = 73.5, 95% CI = 64–94 in 2006;  = 50.4, 95% CI = 49–59 in 2008) and black bears ( = 62.6, 95% CI = 51–89 in 2006;  = 81.8, 95% CI = 72–102 in 2008) in the Bow Valley. Hair traps had high detection rates for female grizzlies, and male and female black bears, but extremely low detection rates for male grizzlies. Conversely, bear rubs had high detection rates for male and female grizzlies, but low rates for black bears. We estimated realized population growth rates, lambda, for grizzly bear males ( = 0.93, 95% CI = 0.74–1.17) and females ( = 0.90, 95% CI = 0.67–1.20) using Pradel open population models with three years of bear rub data. Lambda estimates are supported by abundance estimates from combined hair trap/bear rub closed population models and are consistent with a system that is likely driven by high levels of human-caused mortality. Our results suggest that bear rub surveys would provide an efficient and powerful means to inventory and monitor grizzly bear populations in the Central Canadian Rocky Mountains.

Evaluation of Bear Rub Surveys to Monitor Grizzly Bear Population Trends

Abstract Wildlife managers need reliable estimates of population size, trend, and distribution to make informed decisions about how to recover at-risk populations, yet obtaining these estimates is costly and often imprecise. The grizzly bear (Ursus arctos) population in northwestern Montana, USA, has been managed for recovery since being listed under the United States Endangered Species Act in 1975, yet no rigorous data were available to evaluate the programs success. We used encounter data from 379 grizzly bears identified through bear rub surveys to parameterize a series of Pradel model simulations in Program MARK to assess the ability of noninvasive genetic sampling to estimate population growth rates. We evaluated model performance in terms of 1) power to detect gender-specific and population-wide declines in population abundance, 2) precision and relative bias of growth rate estimates, and 3) sampling effort required to achieve 80% power to detect a decline within 10 years. Simulations indicated that ecosystem-wide, annual bear rub surveys would exceed 80% power to detect a 3% annual decline within 6 years. Robust-design models with 2 simulated surveys per year provided precise and unbiased annual estimates of trend, abundance, and apparent survival. Designs incorporating one survey per year require less sampling effort but only yield trend and apparent survival estimates. Our results suggest that systematic, annual bear rub surveys may provide a viable complement or alternative to telemetry-based methods for monitoring trends in grizzly bear populations.

Evaluation of Noninvasive Genetic Sampling Methods for Cougars in Yellowstone National Park

ABSTRACT Conventional methods for monitoring cougar, Puma concolor, populations involve capture, tagging, and radio-collaring, but these methods are time-consuming, expensive, and logistically challenging. For difficult-to-study species such as cougars, noninvasive genetic sampling (NGS) may be a useful alternative. The ability to identify individuals from samples collected through NGS methods provides many opportunities for developing population-monitoring tools, but the utility of these survey methods is dependent upon collection of samples and accurate genotyping of those samples. In January 2003, we initiated a 3-yr evaluation of NGS methods for cougars using a radio-collared population in Yellowstone National Park (YNP), USA. Our goals were to: 1) determine which DNA collection method, hair snares or snow tracking, provided a better method for obtaining samples for genetic analysis, 2) evaluate reliability of the genetic data derived from hair samples collected in the field, and 3) evaluate the potential of NGS for demographic monitoring of cougar populations. Snow tracking yielded more hair samples and was more cost effective than snagging hair with rub pads. Samples collected from bed sites and natural hair snags (e.g., branch tips, thorn bushes) while snow tracking accurately identified and sexed 22 individuals (9 F, 13 M). The ratio of the count from snow tracking to the count from radio-telemetry was 15:24 in winter 2004,13:12 in 2005, and 22:29 for both years combined. Annual capture probabilities for obtaining DNA from snow tracking varied considerably between years for females (0.42 in 2004 and 0.88 in 2005) but were more consistent for males (0.77 in 2004 and 0.88 in 2005). Our results indicate that snow tracking can be an efficient, reliable NGS method for cougars in YNP and has potential for estimating demographic and genetic parameters of other carnivore populations in similar climates.

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.

Genetic Monitoring for Managers: A New Online Resource

Abstract Monitoring our natural resources will increasingly rely on genetic tools in order to understand and respond to invasive species, habitat degradation, fragmentation, disease, or climate-related changes. In recent years, the rapidly evolving field of genetic monitoring has seen explosive growth in sampling methods, genetic markers, and analytical approaches designed to estimate a wide range of parameters from connectivity to population growth rates. Some of these methods have taken root and now dominate particular aspects of population assessment and monitoring, whereas others have seen less success in real-world applications. To aid managers and researchers with limited genetics experience, we developed a web-based resource to help them identify which, if any, molecular genetic methods would be appropriate for population assessments or monitoring. The site was developed in cooperation with a team of experts in fields such as evolutionary biology, demographic parameter estimation, and exotic specie...

Using spatially‐explicit capture–recapture models to explain variation in seasonal density patterns of sympatric ursids

Understanding how environmental factors interact to determine the abundance and distribution of animals is a primary goal of ecology, and fundamental to the conservation of wildlife populations. Studies of these relationships, however, often assume static environmental conditions, and rarely consider effects of competition with ecologically similar species. In many parts of their shared ranges, grizzly bears Ursus arctos and American black bears U. americanus have nearly complete dietary overlap and share similar life history traits. We therefore tested the hypothesis that density patterns of both bear species would reflect seasonal variation in available resources, with areas of higher primary productivity supporting higher densities of both species. We also hypothesized that interspecific competition would influence seasonal density patterns. Specifically, we predicted that grizzly bear density would be locally reduced due to the ability of black bears to more efficiently exploit patchy food resources such as seasonally abundant fruits. To test our hypotheses, we used detections of 309 grizzly and 597 black bears from two independent genetic sampling methods in spatially‐explicit capture–recapture (SECR) models. Our results suggest grizzly bear density was lower in areas of high black bear density during spring and summer, although intraspecific densities were also important, particularly during the breeding season. Black bears had lower densities in areas of high grizzly bear density in spring; however, density of black bears in early and late summer was best explained by primary productivity. Our results are consistent with the hypothesis that smaller‐bodied, more abundant black bears may influence the density patterns of behaviorally‐dominant grizzly bears through exploitative competition. We also suggest that seasonal variation in resource availability be considered in efforts to relate environmental conditions to animal density.

Modeling multi-scale resource selection for bear rubs in northwestern Montana

Abstract Both black (Ursus americanus) and grizzly bears (U. arctos) are known to rub on trees and other objects, producing a network of repeatedly used and identifiable rub sites. In 2012, we used a resource selection function to evaluate hypothesized relationships between locations of 887 bear rubs in northwestern Montana, USA, and elevation, slope angle, density of open roads and distance from areas of heightened plant-productivity likely containing forage for bears. Slope and density of open roads were negatively correlated with rub presence. No other covariates were supported as explanatory variables. We also hypothesized that bear rubs would be more strongly associated with closed roads and developed trails than with game trails. The frequencies of bear rubs on 30 paired segments of developed tracks and game trails were not different. Our results suggest bear rubs may be associated with bear travel routes, and support their use as “random” sampling devices for non-invasive spatial capture–recapture population monitoring.