Katherine C. Kendall

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.

Why replication is important in landscape genetics: American black bear in the Rocky Mountains.

We investigated how landscape features influence gene flow of black bears by testing the relative support for 36 alternative landscape resistance hypotheses, including isolation by distance (IBD) in each of 12 study areas in the north central U.S. Rocky Mountains. The study areas all contained the same basic elements, but differed in extent of forest fragmentation, altitude, variation in elevation and road coverage. In all but one of the study areas, isolation by landscape resistance was more supported than IBD suggesting gene flow is likely influenced by elevation, forest cover, and roads. However, the landscape features influencing gene flow varied among study areas. Using subsets of loci usually gave models with the very similar landscape features influencing gene flow as with all loci, suggesting the landscape features influencing gene flow were correctly identified. To test if the cause of the variability of supported landscape features in study areas resulted from landscape differences among study areas, we conducted a limiting factor analysis. We found that features were supported in landscape models only when the features were highly variable. This is perhaps not surprising but suggests an important cautionary note – that if landscape features are not found to influence gene flow, researchers should not automatically conclude that the features are unimportant to the species’ movement and gene flow. Failure to investigate multiple study areas that have a range of variability in landscape features could cause misleading inferences about which landscape features generally limit gene flow. This could lead to potentially erroneous identification of corridors and barriers if models are transferred between areas with different landscape characteristics.

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.

An evaluation of long-term preservation methods for brown bear (Ursus arctos) faecal DNA samples

Relatively few large-scale faecal DNA studieshave been initiated due to difficulties inamplifying low quality and quantity DNAtemplate. To improve brown bear faecal DNA PCRamplification success rates and to determinepost collection sample longevity, fivepreservation methods were evaluated: 90%ethanol, DETs buffer, silica-dried, oven-driedstored at room temperature, and oven-driedstored at −20 °C. Preservationeffectiveness was evaluated for 50 faecalsamples by PCR amplification of a mitochondrialDNA (mtDNA) locus (∼146 bp) and a nuclear DNA(nDNA) locus (∼200 bp) at time points of oneweek, one month, three months and six months. Preservation method and storage timesignificantly impacted mtDNA and nDNAamplification success rates. For mtDNA, allpreservation methods had ≥ 75% success atone week, but storage time had a significantimpact on the effectiveness of the silicapreservation method. Ethanol preserved sampleshad the highest success rates for both mtDNA(86.5%) and nDNA (84%). Nuclear DNAamplification success rates ranged from 26–88%, and storage time had a significant impacton all methods but ethanol. Preservationmethod and storage time should be importantconsiderations for researchers planningprojects utilizing faecal DNA. We recommendpreservation of faecal samples in 90% ethanolwhen feasible, although when collecting inremote field conditions or for both DNA andhormone assays a dry collection method may beadvantageous.

Power of Sign Surveys to Monitor Population Trends

The urgent need for an effective monitoring scheme for grizzly bear (Ursus arctos) populations led us to investigate the effort required to detect changes in populations of low-density dispersed animals, using sign (mainly scats and tracks) they leave on trails. We surveyed trails in Glacier National Park for bear tracks and scats during five consecutive years. Using these data, we modeled the occurrence of bear sign on trails, then estimated the power of various sampling schemes. Specifically, we explored the power of bear sign surveys to detect a 20% decline in sign occurrence. Realistic sampling schemes appear feasible if the density of sign is high enough, and we provide guidelines for designs with adequate replication to monitor long-term trends of dispersed populations using sign occurrences on trails.

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.

The influence of diet on faecal DNA amplification and sex identification in brown bears (Ursus arctos).

To evaluate the influence of diet on faecal DNA amplification, 11 captive brown bears (Ursus arctos) were placed on six restricted diets: grass (Trifolium spp., Haplopappus hirtus and Poa pratensis), alfalfa (Lupinus spp.), carrots (Daucus spp.), white‐tailed deer (Odocoileus virginianus), blueberries (Vaccinium spp.) and salmon (Salmo spp.). DNA was extracted from 50 faecal samples of each restricted diet, and amplification of brown bear DNA was attempted for a mitochondrial DNA (mtDNA) locus and nuclear DNA (nDNA) locus. For mtDNA, no significant differences were observed in amplification success rates across diets. For nDNA, amplification success rates for salmon diet extracts were significantly lower than all other diet extracts (P < 0.001). To evaluate the accuracy of faecal DNA sex identification when female carnivores consume male mammalian prey, female bears were fed male white‐tailed deer. Four of 10 extracts amplified, and all extracts were incorrectly scored as male due to amplification of X and Y‐chromosome fragments. The potential biases highlighted in this study have broad implications for researchers using faecal DNA for individual and sex identification, and should be evaluated in other species.

A simulation test of the effectiveness of several methods for error-checking non-invasive genetic data

Non-invasive genetic sampling (NGS) is becoming a popular tool for population estimation. However, multiple NGS studies have demonstrated that polymerase chain reaction (PCR) genotyping errors can bias demographic estimates. These errors can be detected by comprehensive data filters such as the multiple-tubes approach, but this approach is expensive and time consuming as it requires three to eight PCR replicates per locus. Thus, researchers have attempted to correct PCR errors in NGS datasets using non-comprehensive error checking methods, but these approaches have not been evaluated for reliability. We simulated NGS studies with and without PCR error and ‘filtered’ datasets using non-comprehensive approaches derived from published studies and calculated mark–recapture estimates using CAPTURE. In the absence of data-filtering, simulated error resulted in serious inflations in CAPTURE estimates; some estimates exceeded N by200%. When data filters were used, CAPTURE estimate reliability varied with per-locus error (Eμ). At Eμ=0.01, CAPTURE estimates from filtered data displayed <5% deviance from error-free estimates. When Eμ was 0.05 or 0.09, some CAPTURE estimates from filtered data displayed biases in excess of 10%. Biases were positive at high sampling intensities; negative biases were observed at low sampling intensities. We caution researchers against using non-comprehensive data filters in NGS studies, unless they can achieve baseline per-locus error rates below 0.05 and, ideally, near 0.01. However, we suggest that data filters can be combined with careful technique and thoughtful NGS study design to yield accurate demographic information.

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.

Estimating landscape resistance to dispersal

Dispersal is an inherently spatial process that can be affected by habitat conditions in sites encountered by dispersers. Understanding landscape resistance to dispersal is important in connectivity studies and reserve design, but most existing methods use resistance functions with cost parameters that are subjectively chosen by the investigator. We develop an analytic approach allowing for direct estimation of resistance parameters that folds least cost path methods typically used in simulation approaches into a formal statistical model of dispersal distributions. The core of our model is a frequency distribution of dispersal distances expressed as least cost distance rather than Euclidean distance, and which includes terms for feature-specific costs to dispersal and sex (or other traits) of the disperser. The model requires only origin and settlement locations for multiple individuals, such as might be obtained from mark–recapture studies or parentage analyses, and maps of the relevant habitat features. To evaluate whether the model can estimate parameters correctly, we fit our model to data from simulated dispersers in three kinds of landscapes (in which resistance of environmental variables was categorical, continuous with a patchy configuration, or continuous in a trend pattern). We found maximum likelihood estimators of resistance and individual trait parameters to be approximately unbiased with moderate sample sizes. We applied the model to a small grizzly bear dataset to demonstrate how this approach could be used when the primary interest is in the prediction of costs and found that estimates were consistent with expectations based on bear ecology. Our method has important practical applications for testing hypotheses about dispersal ecology and can be used to inform connectivity planning efforts, via the resistance estimates and confidence intervals, which can be used to create a data-driven resistance surface.