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Katherine C. Kendall

Publications and source records attributed to Katherine C. Kendall.

At least 19 recordsLinked to original sources
USGS2018

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.

Montana↗
USGS2016

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.

Northern Continental Divide Ecosystem↗
USGS2015

Density, distribution, and genetic structure of grizzly bears in the Cabinet-Yaak Ecosystem

The conservation status of the 2 threatened grizzly bear ( Ursus arctos ) populations in the Cabinet-Yaak Ecosystem (CYE) of northern Montana and Idaho had remained unchanged since designation in 1975; however, the current demographic status of these populations was uncertain. No rigorous data on population density and distribution or analysis of recent population genetic structure were available to measure the effectiveness of conservation efforts. We used genetic detection data from hair corral, bear rub, and opportunistic sampling in traditional and spatial capture–recapture models to generate estimates of abundance and density of grizzly bears in the CYE. We calculated mean bear residency on our sampling grid from telemetry data using Huggins and Pledger models to estimate the average number of bears present and to correct our superpopulation estimates for lack of geographic closure. Estimated grizzly bear abundance (all sex and age classes) in the CYE in 2012 was 48–50 bears, approximately half the population recovery goal. Grizzly bear density in the CYE (4.3–4.5 grizzly bears/1,000 km 2 ) was among the lowest of interior North American populations. The sizes of the Cabinet ( n = 22–24) and Yaak ( n = 18–22) populations were similar. Spatial models produced similar estimates of abundance and density with comparable precision without requiring radio-telemetry data to address assumptions of geographic closure. The 2 populations in the CYE were demographically and reproductively isolated from each other and the Cabinet population was highly inbred. With parentage analysis, we documented natural migrants to the Cabinet and Yaak populations by bears born to parents in the Selkirk and Northern Continental Divide populations. These events supported data from other sources suggesting that the expansion of neighboring populations may eventually help sustain the CYE populations. However, the small size, isolation, and inbreeding documented by this study demonstrate the need for comprehensive management designed to support CYE population growth and increased connectivity and gene flow with other populations.

Idaho, Montana↗
USGS2014

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.

Landscape Ecology↗
USGS2013

Black bear density in Glacier National Park, Montana

We report the first abundance and density estimates for American black bears ( Ursus americanus ) in Glacier National Park (NP),Montana, USA.We used data from 2 independent and concurrent noninvasive genetic sampling methods—hair traps and bear rubs—collected during 2004 to generate individual black bear encounter histories for use in closed population mark–recapture models. We improved the precision of our abundance estimate by using noninvasive genetic detection events to develop individual-level covariates of sampling effort within the full and one-half mean maximum distance moved (MMDM) from each bear’s estimated activity center to explain capture probability heterogeneity and inform our estimate of the effective sampling area.Models including the one-halfMMDMcovariate received overwhelming Akaike’s Information Criterion support suggesting that buffering our study area by this distance would be more appropriate than no buffer or the full MMDM buffer for estimating the effectively sampled area and thereby density. Our modelaveraged super-population abundance estimate was 603 (95% CI¼522–684) black bears for Glacier NP. Our black bear density estimate (11.4 bears/100 km2, 95% CI¼9.9–13.0) was consistent with published estimates for populations that are sympatric with grizzly bears (U. arctos) and without access to spawning salmonids. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Montana↗
USGS2012

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.

PLoS ONE↗
USGS2011

Guidelines for collecting and maintaining archives for genetic monitoring

Rapid advances in molecular genetic techniques and the statistical analysis of genetic data have revolutionized the way that populations of animals, plants and microorganisms can be monitored. Genetic monitoring is the practice of using molecular genetic markers to track changes in the abundance, diversity or distribution of populations, species or ecosystems over time, and to follow adaptive and non-adaptive genetic responses to changing external conditions. In recent years, genetic monitoring has become a valuable tool in conservation management of biological diversity and ecological analysis, helping to illuminate and define cryptic and poorly understood species and populations. Many of the detected biodiversity declines, changes in distribution and hybridization events have helped to drive changes in policy and management. Because a time series of samples is necessary to detect trends of change in genetic diversity and species composition, archiving is a critical component of genetic monitoring. Here we discuss the collection, development, maintenance, and use of archives for genetic monitoring. This includes an overview of the genetic markers that facilitate effective monitoring, describes how tissue and DNA can be stored, and provides guidelines for proper practice.

Conservation Genetics Resources↗
USGS2011

Linking landscape characteristics to local grizzly bear abundance using multiple detection methods in a hierarchical model

Few studies link habitat to grizzly bear Ursus arctos abundance and these have not accounted for the variation in detection or spatial autocorrelation. We collected and genotyped bear hair in and around Glacier National Park in northwestern Montana during the summer of 2000. We developed a hierarchical Markov chain Monte Carlo model that extends the existing occupancy and count models by accounting for (1) spatially explicit variables that we hypothesized might influence abundance; (2) separate sub-models of detection probability for two distinct sampling methods (hair traps and rub trees) targeting different segments of the population; (3) covariates to explain variation in each sub-model of detection; (4) a conditional autoregressive term to account for spatial autocorrelation; (5) weights to identify most important variables. Road density and per cent mesic habitat best explained variation in female grizzly bear abundance; spatial autocorrelation was not supported. More female bears were predicted in places with lower road density and with more mesic habitat. Detection rates of females increased with rub tree sampling effort. Road density best explained variation in male grizzly bear abundance and spatial autocorrelation was supported. More male bears were predicted in areas of low road density. Detection rates of males increased with rub tree and hair trap sampling effort and decreased over the sampling period. We provide a new method to (1) incorporate multiple detection methods into hierarchical models of abundance; (2) determine whether spatial autocorrelation should be included in final models. Our results suggest that the influence of landscape variables is consistent between habitat selection and abundance in this system.

Animal Conservation↗
USGS2010

Genetic analysis of individual origins supports isolation of grizzly bears in the Greater Yellowstone Ecosystem

The Greater Yellowstone Ecosystem (GYE) supports the southernmost of the 2 largest remaining grizzly bear ( Ursus arctos ) populations in the contiguous United States. Since the mid-1980s, this population has increased in numbers and expanded in range. However, concerns for its long-term genetic health remain because of its presumed continued isolation. To test the power of genetic methods for detecting immigrants, we generated 16-locus microsatellite genotypes for 424 individual grizzly bears sampled in the GYE during 1983–2007. Genotyping success was high (90%) and varied by sample type, with poorest success (40%) for hair collected from mortalities found ≥1 day after death. Years of storage did not affect genotyping success. Observed heterozygosity was 0.60, with a mean of 5.2 alleles/marker. We used factorial correspondence analysis (Program GENETIX) and Bayesian clustering (Program STRUCTURE) to compare 424 GYE genotypes with 601 existing genotypes from grizzly bears sampled in the Northern Continental Divide Ecosystem (NCDE) ( F ST = 0.096 between GYE and NCDE). These methods correctly classified all sampled individuals to their population of origin, providing no evidence of natural movement between the GYE and NCDE. Analysis of 500 simulated first-generation crosses suggested that over 95% of such bears would also be detectable using our 16-locus data set. Our approach provides a practical method for detecting immigration in the GYE grizzly population. We discuss estimates for the proportion of the GYE population sampled and prospects for natural immigration into the GYE.

Idaho, Montana, Wyoming↗
USGS2002

Rocky road in the Rockies: Challenges to biodiversity

To people worldwide, the Rocky Mountains of the United States and Canada represent a last bastion of nature in its purest and rawest form-unspoiled forests teeming with elk and deer stalked by mountain lions and grizzly bears; bald eagles nesting near lakes and rivers; fat, feisty native trout in rushing mountain streams; and dazzling arrays of wildflowers in lush meadows. In fact, the total biodiversity of the Rocky Mountains is considerable, with relatively high diversity in birds, mammals, butterflies, reptiles, and conifers (Ricketts et al. 1999) and with geographic variation in the flora and fauna of alpine, forest, foothill, and adjacent shortgrass prairie and shrub communities over more than 20 degrees of latitude and more than 10' of longitude. Although the biodiversity of most North American regions has declined because of anthropogenic influences, the perception remains that the biodiversity of the Rocky Mountains is intact. This view exists in part because the Rocky Mountains are remote from urban centers, in part because so much of the land comprises protected areas such as national parks and wilderness areas, and in part because of wishful thinking-that nothing bad could happen to the biodiversity that is so much a part of the history, national self-image, legends, nature films, and movies of the United States and Canada. Despite modern technology and the homogenization and globalization of their cities and towns, at heart North Americans still regard their land as the New World, with pristine nature and untamed landscapes epitomized by the Rockies. The reality is that the biodiversity of the Rocky Mountains has not been free of anthropogenic influences since the West was settled in the 1800s, and in fact it was altered by Native Americans for centuries prior to settlement. A number of escalating problems and consequences of management choices are currently changing Rocky Mountain ecological communities at a dizzying pace. In Order to maintain some degree of natural ecosystem processes and preserve natural biodiversity in light of these challenges, Americans and Canadians are faced with the need for intensive, hands-on management of both ecosystems and selected plant and animal populations. In this chapter, we first discuss the primary issues regarding the biodiversity of the Rocky Mountains, including the Rocky Mountain portions of Arizona, Colorado, Idaho, Montana, New Mexico, Utah, Wyoming, British Columbia, and Alberta. Next, we survey groups of organisms to examine their status and special problems. Finally, we touch on major challenges to biodiversity that loom in the near future. Given that entire books may be written on these issues, the discussion is brief and general, but with case histories for more detailed examples.

Book chapter↗
USGS2001

Whitebark pine, grizzly bears, and red squirrels

Appropriately enough, much of this book is devoted to discussing management challenges and techniques. However, the impetus for action—the desire to save whitebark pine ( Pinus albicaulis ) - necessarily arises from the extent to which we cherish it for its beauty and its connections with other things that we value. Whitebark pine is at the hub of a fascinating web of relationships. It is the stuff of great stories (cf. Quammen 1994). One of the more interesting of these stories pertains to the dependence of certain grizzly bear ( Ursus arctos horribilis ) populations on its seeds, and the role that red squirrels ( Tamiasciurus hudsonicus ) play as an agent of transfer between tree and bear.

Book chapter↗
USGS1994

Chromatographic (TLC) differentiation of grizzly bear and black bear scats

While past work concluded that thin-layer chromatography (TLC) was inadequate for the separation of grizzly ( Ursus arctos horribilis ) and black bear ( U. americanus ) scats, our study found differences adequate for species separation. A key was constructed using 19 of 40 data points recorded on each( N )=356 profiles of 178) know-species scat. Accuracy was best for late summer scats (94%). Methods for specimen preparation, analysis, and reading the TLC profiles are discussed. Factors involved in scat variation were tested.

Bears: Their Biology and Management↗
USGS1994

Workshop proceedings: research and management in whitebark pine ecosystems

The purpose of this workshop is to exchange information on on-going and soon-to-be-initiated whitebark pine research and management projects. By doing so we hope to encourage future work on this valuable species. We also hope to promote the use of consistent methods for evaluation and investigation of whitebark pine, and to provide avenues of collaboration. Speakers will present information on a variety of topics related to whitebark pine management and research. Featured presentation topics include anthropomorphic utilization of whitepark pine forests, whitebark pine natural regeneration, blister rust and the decline of whitebark pine, blister rust resistance studies, ecological mapping of the species, restoration and management projects, and survey/monitoring techniques. Information gained from these presentations may hopefully be used in the planning of future projects for the conservation of whitebark pine.

Book↗