Geology topics

Kevin L. Frey

Publications and source records attributed to Kevin L. Frey.

6 recordsLinked to original sources

USGS2023

Enhancements to population monitoring of Yellowstone grizzly bears

In the Greater Yellowstone Ecosystem, counts of female grizzly bears ( Ursus arctos ) with cubs-of-the-year (females with cubs) from systematic aerial surveys and opportunistic ground sightings are combined with demographic data to derive annual population estimates. We addressed 2 limitations to the monitoring approach. As part of a rule set, a conservative distance of > 30 km currently is used as a threshold to assign sightings to unique females with cubs, resulting in underestimation bias. Using telemetry locations of females with cubs collected during 1997–2019, we created 1,000 data sets for each of 5 levels of simulated number of females with cubs, simulated sightings by selecting among these locations, and evaluated the classification performance of alternative distance criteria (12–30 km). Under all scenarios, 12–16-km criteria maximized classification performance and minimized estimation bias; the 16-km criterion was optimal for current conditions and sampling efforts. Our second objective was to test generalized additive models (GAMs) as a flexible trend analysis technique. We simulated 1,000 time series for each of 10 scenarios (10, 15, and 20% decline over periods of 5, 10, and 15 yrs, plus stability), applied GAMs, and assessed metrics associated with the posterior distribution of the instantaneous rate of change. We detected declines among > 99.6% of replicates under the 15 and 20% decline scenarios and in 84.7–94.7% of replicates under the 10% decline scenario. From decline onset to first detection, periods ranged from 3.7 (20% decline over 5 yrs) to 11.1 (10% decline over 15 yrs), with 3.9–8.8 years mean duration of detection events. The GAM approach allows detection of directional changes in population trend, including early warning metrics, and stabilization after such changes. Retrospective application of the 16-km distance criterion and GAMs resulted in higher population estimates and growth rates than are reported using current methods.

Wyoming↗

USGS2021

A reassessment of Chao2 estimates for population monitoring of grizzly bears in the Greater Yellowstone Ecosystem

The Yellowstone Ecosystem Subcommittee (YES) asked the Interagency Grizzly Bear Study Team (IGBST) to re-assess a technique used in annual population estimation and trend monitoring of grizzly bears in the Greater Yellowstone Ecosystem (GYE). This technique is referred to as the Chao2 approach and estimates the number of females with cubs-of-the-year (hereafter, females with cubs) and, in association with other demographic data, is used by the IGBST to produce annual population estimates. Females with cubs are an easily recognizable population segment, and trends for this reproductive segment of the population are assumed to be representative of trend for the entire population. The overarching objective of the analyses presented in this report was to provide a more accurate representation of the GYE grizzly bear population using the current methodologies in place. Specifically, we addressed two limitations of the current Chao2 approach: 1) underestimation bias associated with a distance criterion used to differentiate annual sightings of females with cubs into unique individuals and 2) limitations of the model-averaging approach to effectively distinguish among potential future population trajectories (decline, stability, and growth). The first issue addressed in this report is the underestimation bias associated with the rule set that Knight et al. (1995) developed to differentiate sightings of females with cubs into unique individuals (i.e., unique family groups). The rule set was originally designed to be conservative by reducing the risk of identifying more females with cubs than actually existed, primarily through use of a distance criterion of 30 km to separate sightings of unique females. This approach resulted in an underestimation bias, and previous research demonstrated that this bias increases with increasing number of females with cubs. Using location data from radio-marked females with cubs, we evaluated alternative distance criteria by simulating scenarios with varying numbers of true females with cubs and sightings. Findings from these analyses demonstrate that bias in estimates of females with cubs can be substantially reduced by changing the 30-km distance criterion in the rule set to 16 km, which produced relatively unbiased estimates. Findings also indicate, however, the importance of adaptability with regard to the distance criteria because of the complex relationships and biases among the various parameters involved in estimation of unique females with cubs. The total number of annual sightings and the true number of females with cubs play particularly important roles. Whereas these analyses remind us that there is no perfect approach to estimating the number of females with cubs from sightings under various scenarios, they provide us with new tools to determine when and how to adapt the monitoring program. The second issue we were tasked to investigate was the potential for improvement of the technique referred to as model-averaging, which serves to smooth relatively high variation in annual estimates. This technique was chosen by YES as the basis for monitoring the Yellowstone grizzly bear population, as described in the 2016 Conservation Strategy. This choice was made in part because the technique has been well documented and population estimates derived from counts of females with cubs are conservative. Using simulations of population trends, we demonstrate why the model-averaging technique currently used cannot distinguish between plausible future trend scenarios. As a suitable alternative to model averaging, we propose the use of generalized additive models (GAMs). Using a suite of simulated trend dynamics relevant to management, we demonstrate GAM performance for tracking trends in females with cubs within the context of the annual monitoring program. We demonstrate the ability to not only document directional changes in population trend but also patterns of stabilization or resiliency after such changes. Furthermore, the proposed monitoring framework provides objective measures useful for early detection of directional changes in trend. The new framework is flexible, allowing retrospective analysis of Chao2-based estimates and future applications to time series of other population metrics, such as vital rates. The aforementioned updates provide us with new tools to determine when and how to adapt the monitoring program. Within the context of current monitoring protocols and effort, and considering the full suite of simulations presented in this report and previous studies, the IGBST plans to incorporate the following changes to the population monitoring protocol: 1) modify the distance criterion, starting with 16 km under current sampling conditions and 2) revise the population monitoring framework using GAMs as the basis for smoothing of annual estimates and detecting trends and changes in trend. Implementation of the 16-km distance criterion combined with use of GAM techniques would affect some of the population metrics (e.g., annual population size and uncertainty, population trend, mortality rates) used to inform management responses. A primary consideration is that the 16-km distance criterion results in total population estimates derived from the Chao2 estimates that are greater than those we have reported in the past. This increase is due to a change in the implementation of the technique and more accurately represents the number of females with cubs in the GYE grizzly bear population. Additionally, interpretation of retrospective trend patterns may change due to the combination of a different distance criterion and enhanced trend monitoring based on the GAM approach we present here. Implementation will require relatively minor changes in the monitoring protocols described in Appendices B and C of the 2016 Conservation Strategy. Finally, we note that the IGBST has ongoing investigations into the merits of an Integrated Population Model (IPM), for which annual Chao2-based estimates are important input data. The IGBST plans to continue those investigations using the 16-km distance criterion to derive Chao2 estimates.

Idaho, Montana, Wyoming↗

USGS2014

Dietary breadth of grizzly bears in the Greater Yellowstone Ecosystem

Grizzly bears ( Ursus arctos ) in the Greater Yellowstone Ecosystem (GYE) are opportunistic omnivores that eat a great diversity of plant and animal species. Changes in climate may affect regional vegetation, hydrology, insects, and fire regimes, likely influencing the abundance, range, and elevational distribution of the plants and animals consumed by GYE grizzly bears. Determining the dietary breadth of grizzly bears is important to document future changes in food resources and how those changes may affect the nutritional ecology of grizzlies. However, no synthesis exists of all foods consumed by grizzly bears in the GYE. We conducted a review of available literature and compiled a list of species consumed by grizzly bears in the GYE. We documented >266 species within 200 genera from 4 kingdoms, including 175 plant, 37 invertebrate, 34 mammal, 7 fungi, 7 bird, 4 fish, 1 amphibian, and 1 algae species as well as 1 soil type consumed by grizzly bears. The average energy values of the ungulates (6.8 kcal/g), trout (Oncorhynchus spp., 6.1 kcal/g), and small mammals (4.5 kcal/g) eaten by grizzlies were higher than those of the plants (3.0 kcal/g) and invertebrates (2.7 kcal/g) they consumed. The most frequently detected diet items were graminoids, ants (Formicidae), whitebark pine seeds (Pinus albicaulis), clover (Trifolium spp.), and dandelion (Taraxacum spp.). The most consistently used foods on a temporal basis were graminoids, ants, whitebark pine seeds, clover, elk (Cervus elaphus), thistle (Cirsium spp.), and horsetail (Equisetum spp.). Historically, garbage was a significant diet item for grizzlies until refuse dumps were closed. Use of forbs increased after garbage was no longer readily available. The list of foods we compiled will help managers of grizzly bears and their habitat document future changes in grizzly bear food habits and how bears respond to changing food resources.

Idaho, Montana, Wyoming↗

USGS2013

Methods to estimate distribution and range extent of grizzly bears in the Greater Yellowstone Ecosystem

The distribution of the Greater Yellowstone Ecosystem grizzly bear ( Ursus arctos ) population has expanded into areas unoccupied since the early 20th century. Up-to-date information on the area and extent of this distribution is crucial for federal, state, and tribal wildlife and land managers to make informed decisions regarding grizzly bear management. The most recent estimate of grizzly bear distribution (2004) utilized fixed-kernel density estimators to describe distribution. This method was complex and computationally time consuming and excluded observations of unmarked bears. Our objective was to develop a technique to estimate grizzly bear distribution that would allow for the use of all verified grizzly bear location data, as well as provide the simplicity to be updated more frequently. We placed all verified grizzly bear locations from all sources from 1990 to 2004 and 1990 to 2010 onto a 3-km × 3-km grid and used zonal analysis and ordinary kriging to develop a predicted surface of grizzly bear distribution. We compared the area and extent of the 2004 kriging surface with the previous 2004 effort and evaluated changes in grizzly bear distribution from 2004 to 2010. The 2004 kriging surface was 2.4% smaller than the previous fixed-kernel estimate, but more closely represented the data. Grizzly bear distribution increased 38.3% from 2004 to 2010, with most expansion in the northern and southern regions of the range. This technique can be used to provide a current estimate of grizzly bear distribution for management and conservation applications.

Wildlife Society Bulletin↗

USGS2013

Response of Yellowstone grizzly bears to changes in food resources: A synthesis. Final report to the Interagency Grizzly Bear Committee and Yellowstone Ecosystem Subcommittee

The Yellowstone grizzly bear ( Ursus arctos ) was listed as a threatened species in 1975 (Federal Register 40 FR:31734-31736). Since listing, recovery efforts have focused on increasing population size, improving habitat security, managing bear mortalities, and reducing bear-human conflicts. The Interagency Grizzly Bear Committee (IGBC; partnership of federal and state agencies responsible for grizzly bear recovery in the lower 48 states) and its Yellowstone Ecosystem Subcommitte (YES; federal, state, county, and tribal partners charged with recovery of grizzly bears in the Greater Yelowston Ecosystem [GYE]) tasked the Interagency Grizzly Bear Study Team to provide information and further research relevant to three concerns arising from the 9th Circuit Court of Appeals November 2011 decision: 1) the ability of grizzly bears as omnivores to find alternative foods to whitebark pine seeds; 2) literature to support their conclusions; and 3) the non-intuitive biological reality that impacts can occur to individuals without causing the overall population to decline. Specifically, the IGBC and YES requested a comprehensive synthesis of the current state of knowledge regarding whitebark pinbe decline and individual and population-level responses of grizzly bears to changing food resources in the GYE. This research was particularly relevant to grizzly bear conservation given changes in the population trajectory observed during the last decade.

Idaho, Montana, Wyoming↗

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↗