Geology topics

Daniel J. Thompson

Publications and source records attributed to Daniel J. Thompson.

7 recordsLinked to original sources

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↗

USGS2018

Grizzly bear depredation on grazing allotments in the Yellowstone ecosystem

Grizzly bear (Ursus arctos) conflicts with humans, including livestock depredation on public land grazing allotments, have increased during the last several decades within the Greater Yellowstone Ecosystem (GYE) in the western United States as the grizzly bear population has grown in number and occupied range. Minimizing conflicts and improving conservation efficacy requires information on the relationships between livestock depredations, allotment management, grizzly bear habitat conditions, and their interactions. We used generalized linear mixed models to evaluate spatio-temporal relationships between grizzly bear depredation of livestock and the characteristics of 316 United States Department of Agriculture Forest Service and National Park Service grazing allotments in the GYE during 1992–2014. We evaluated relationships at 2 spatial extents, representing daily and annual grizzly bear activity areas. During the study period, more grazing allotments became occupied by grizzly bears and most livestock depredations were associated with these areas of population expansion. Number of livestock (beta = 1.15 +/- 0.19 [SE]) and grizzly bear density index (beta = 1.13 +/- 0.10) had the greatest effects on the number of livestock depredation events relative to other allotment attributes. Estimated number of depredation events increased by approximately 20% when cow-calf pairs increased by 100 pairs and grizzly bear density index increased by 1 bear/196 km2 (the average annual home-range size of a female grizzly bear in the GYE). Additionally, grazing allotment size was positively related to the number of depredation events (beta = 0.56 +/- 0.16), whereas the presence of bull cattle or horses was associated with an approximately 50% reduction in depredations (beta = -0.71 +/- 0.37). Livestock depredation events were greater for allotments with lower road density (beta = -0.89 +/- 0.28), less rugged terrain (beta = -0.57 +/- 0.25), higher vegetative primary productivity (beta = 0.33 +/- 0.16), and more whitebark pine coverage (beta = 0.30 +/- 0.15). Relationships between depredations and grizzly bear habitat conditions varied across spatial extents. As the grizzly bear population continues to expand, natural resource managers and livestock producers could focus efforts on allotments with a higher density of grizzly bears, fewer roads, and quality grizzly bear habitat, including higher vegetative productivity, when developing cooperative management plans and preventative measures to reduce the likelihood of depredation. The perspectives gained from our analysis provide context for long-term, landscape-level planning to accommodate livestock production on public lands while meeting conservation goals for grizzly bears.

Idaho, Montana, Wyoming↗

USGS2016

Detecting grizzly bear use of ungulate carcasses using global positioning system telemetry and activity data

Global positioning system (GPS) wildlife collars have revolutionized wildlife research. Studies of predation by free-ranging carnivores have particularly benefited from the application of location clustering algorithms to determine when and where predation events occur. These studies have changed our understanding of large carnivore behavior, but the gains have concentrated on obligate carnivores. Facultative carnivores, such as grizzly/brown bears ( Ursus arctos ), exhibit a variety of behaviors that can lead to the formation of GPS clusters. We combined clustering techniques with field site investigations of grizzly bear GPS locations ( n = 732 site investigations; 2004–2011) to produce 174 GPS clusters where documented behavior was partitioned into five classes (large-biomass carcass, small-biomass carcass, old carcass, non-carcass activity, and resting). We used multinomial logistic regression to predict the probability of clusters belonging to each class. Two cross-validation methods—leaving out individual clusters, or leaving out individual bears—showed that correct prediction of bear visitation to large-biomass carcasses was 78–88%, whereas the false-positive rate was 18–24%. As a case study, we applied our predictive model to a GPS data set of 266 bear-years in the Greater Yellowstone Ecosystem (2002–2011) and examined trends in carcass visitation during fall hyperphagia (September–October). We identified 1997 spatial GPS clusters, of which 347 were predicted to be large-biomass carcasses. We used the clustered data to develop a carcass visitation index, which varied annually, but more than doubled during the study period. Our study demonstrates the effectiveness and utility of identifying GPS clusters associated with carcass visitation by a facultative carnivore.

Greater Yellowstone Ecosystem↗

USGS2015

Density dependence, whitebark pine, and vital rates of grizzly bears

Understanding factors influencing changes in population trajectory is important for effective wildlife management, particularly for populations of conservation concern. Annual population growth of the grizzly bear ( Ursus arctos ) population in the Greater Yellowstone Ecosystem, USA has slowed from 4.2–7.6% during 1983–2001 to 0.3–2.2% during 2002–2011. Substantial changes in availability of a key food source and bear population density have occurred. Whitebark pine ( Pinus albicaulis ), the seeds of which are a valuable but variable fall food for grizzly bears, has experienced substantial mortality primarily due to a mountain pine beetle ( Dendroctonus ponderosae ) outbreak that started in the early 2000s. Positive growth rates of grizzly bears have resulted in populations reaching high densities in some areas and have contributed to continued range expansion. We tested research hypotheses to examine if changes in vital rates detected during the past decade were more associated with whitebark pine decline or, alternatively, increasing grizzly bear density. We focused our assessment on known-fate data to estimate survival of cubs-of-the-year (cubs), yearlings, and independent bears (≥2 yrs), and reproductive transition of females from having no offspring to having cubs. We used spatially and temporally explicit indices for grizzly bear density and whitebark pine mortality as individual covariates. Models indicated moderate support for an increase in survival of independent male bears over 1983–2012, whereas independent female survival did not change. Cub survival, yearling survival, and reproductive transition from no offspring to cubs all changed during the 30-year study period, with lower rates evident during the last 10–15 years. Cub survival and reproductive transition were negatively associated with an index of grizzly bear density, indicating greater declines where bear densities were higher. Our analyses did not support a similar relationship for the index of whitebark pine mortality. The results of our study support the interpretation that slowing of population growth during the last decade was associated more with increasing grizzly bear density than the decline in whitebark pine. Grizzly bear density and its potential effect on vital rates and population trajectory warrant consideration for management of the grizzly bear population in the Greater Yellowstone Ecosystem.

Idaho, Montana, Wyoming↗

USGS2014

Whitebark pine, population density, and home-range size of grizzly bears in the greater Yellowstone ecosystem

Changes in life history traits of species can be an important indicator of potential factors influencing populations. For grizzly bears ( Ursus arctos ) in the Greater Yellowstone Ecosystem (GYE), recent decline of whitebark pine (WBP; Pinus albicaulis ), an important fall food resource, has been paired with a slowing of population growth following two decades of robust population increase. These observations have raised questions whether resource decline or density-dependent processes may be associated with changes in population growth. Distinguishing these effects based on changes in demographic rates can be difficult. However, unlike the parallel demographic responses expected from both decreasing food availability and increasing population density, we hypothesized opposing behavioral responses of grizzly bears with regard to changes in home-range size. We used the dynamic changes in food resources and population density of grizzly bears as a natural experiment to examine hypotheses regarding these potentially competing influences on grizzly bear home-range size. We found that home-range size did not increase during the period of whitebark pine decline and was not related to proportion of whitebark pine in home ranges. However, female home-range size was negatively associated with an index of population density. Our data indicate that home-range size of grizzly bears in the GYE is not associated with availability of WBP, and, for female grizzly bears, increasing population density may constrain home-range size.

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↗