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At least 1,063 records · Page 59Linked to original sources
USGS2013

Golden eagle population trends in the western United States: 1968-2010

In 2009, the United States Fish and Wildlife Service promulgated permit regulations for the unintentional lethal take (anthropogenic mortality) and disturbance of golden eagles (Aquila chrysaetos). Accurate population trend and size information for golden eagles are needed so agency biologists can make informed decisions when eagle take permits are requested. To address this need with available data, we used a log-linear hierarchical model to average data from a late-summer aerial-line-transect distance-sampling survey (WGES) of golden eagles in the United States portions of Bird Conservation Region (BCR) 9 (Great Basin), BCR 10 (Northern Rockies), BCR 16 (Southern Rockies/Colorado Plateau), and BCR 17 (Badlands and Prairies) from 2006 to 2010 with late-spring, early summer Breeding Bird Survey (BBS) data for the same BCRs and years to estimate summer golden eagle population size and trends in these BCRs. We used the ratio of the density estimates from the WGES to the BBS index to calculate a BCR-specific adjustment factor that scaled the BBS index (i.e., birds per route) to a density estimate. Our results indicated golden eagle populations were generally stable from 2006 to 2010 in the 4 BCRs, with an estimated average rate of population change of −0.41% (95% credible interval [CI]: −4.17% to 3.40%) per year. For the 4 BCRs and years, we estimated annual golden eagle population size to range from 28,220 (95% CI: 23,250–35,110) in 2007 to 26,490 (95% CI: 21,760–32,680) in 2008. We found a general correspondence in trends between WGES and BBS data for these 4 BCRs, which suggested BBS data were providing useful trend information. We used the overall adjustment factor calculated from the 4 BCRs and years to scale BBS golden eagle counts from 1968 to 2005 for the 4 BCRs and for 1968 to 2010 for the 8 other BCRs (without WGES data) to estimate golden eagle population size and trends across the western United States for the period 1968 to 2010. In general, we noted slightly declining trends in southern BCRs and slightly increasing trends in northern BCRs. However, we estimated the average rate of golden eagle population change across all 12 BCRs for the period 1968–2010 as +0.40% per year (95% CI = −0.27% to 1.00%), suggesting a stable population. We also estimated the average rate of population change for the period 1990–2010 was +0.5% per year (95% CI = −0.33% to 1.3%). Our annual estimates of population size for the most recent decade range from 31,370 (95% CI: 25,450–39,310) in 2004 to 33,460 (95% CI: 27,380–41,710) in 2007. Our results clarify that golden eagles are not declining widely in the western United States. © 2013 The Wildlife Society.

Arizona;California;Colorado;Idaho;Iowa;Kansas;Minn↗
USGS2013

Descriptive and geoenvironmental model for Co-Cu-Au deposits in metasedimentary rocks

Introduction This report is a revised model for a specific type of cobalt-copper-gold (Co-Cu-Au) deposit that will be evaluated in the next U.S. Geological Survey (USGS) assessment of undiscovered mineral resources in the United States (see Ferrero and others, 2012). Emphasis is on providing an up-to-date deposit model that includes both geologic and geoenvironmental aspects. The new model presented here supersedes previous USGS models by Earhart (1986) and Evans and others (1995), which are based solely on deposits in the Blackbird mining district of central Idaho. This report is a broader synthesis of information on 19 Co-Cu-Au deposits occurring in predominantly metasedimentary successions worldwide (table 1–1) that generally share common geologic, mineralogical, and geochemical features; preliminary summary versions were presented in Slack and others (2010) and Slack and others (2011), which are superseded by this report. As defined herein, the individual Co-Cu-Au deposits are located more than 500 meters from similar deposits and contain 0.1 percent or more by weight of Co in ore or mineralized rock; some deposits included in the database lack reported average Co grades, but they contain high Co concentrations, at least locally. Most of the deposits also have high As contents, present in Co arsenide and sulfarsenide minerals. Type examples of the Co-Cu-Au deposits are those in the Blackbird district, Skuterud in Norway, and Kouvervarra and Juomasuo in Finland. Some deposits in the database have low grades for Cu (for example, NICO in Canada) or Au (for example, Lemmonlampi in Finland), but these deposits are included because their geological, mineralogical, and alteration features are similar to those of the type examples. Several deposits included in the model are partly hosted by metavolcanic or metaigneous rocks (including granite), but regionally these deposits are within metasedimentary successions; no deposits are wholly within granite or other plutonic igneous intrusions. Despite having a lower average Co grade, the Mt. Cobalt deposit in Australia is included here because it has past Co production from higher-grade ore zones (Nisbet and others, 1983). The Black Pine deposit in the Idaho cobalt belt is included because it contains mineable Co- and Au-rich lenses within Cu-rich mineralized zones (Formation Metals, Inc., 2012). Six deposits that lack data for average Co grades are also included because each reportedly contains abundant Co (>0.1 weight percent Co), at least locally. Many of the deposits are noteworthy as possible resources of Ag, Bi, W, Ni, Y, REE, and (or) U. Detailed data on the deposits listed in table 1–1, including references, are available in appendix 1. Significantly, the grouping in this report of Co-Cu-Au deposits in metasedimentary rocks into a single model includes deposits that other workers have previously classified in different ways. For background information, a global overview of different types of Co deposits worldwide is given in Smith (2001). Additional geologically and compositionally similar deposits are known, but have average Co grades less than 0.1 percent. Most of these deposits contain cobalt-rich pyrite and lack appreciable amounts of distinct Co sulfide and (or) sulfarsenide minerals. Such deposits are not discussed in detail in the following sections, but these deposits may be relevant to the descriptive and genetic models presented below. Examples include the Scadding Au-Co-Cu deposit in Ontario, Canada; the Vähäjoki Co-Cu-Au deposit in Finland; the Tuolugou Co-Au deposit in Qinghai Province, China; the Lala Co-Cu-UREE deposit in Sichuan Province, China; the Guelb Moghrein Cu-Au-Co deposit in Mauritania; and the Great Australia Co-Cu, Greenmount Cu-Au-Co, and Monakoff Cu-Au-Co-UAg deposits in Queensland, Australia. Detailed information on these deposits is presented in appendix 2.

Scientific Investigations Report↗
USGS1977

Tactical approach for determining impact of energy development on wildlife in Wyoming: special report number 1

The U.S. Fish and Wildlife Service, within the Department of Interior, is responsible for providing national leadership in the management and protection of the nation's fish and wildlife resources, their habitat, and environment. Specifically, the Office of Biological Services obtains and assimilates biological and environmental data and identifies additional informational needs and means necessary to provide biological input into major natural resource decisions. The Wyoming Coal Coordinator for the U.S Fish and Wildlife Service is assigned the duty of becoming attuned to the ramifications of energy development within the State and its potential impact upon wildlife. In order for the Coal Coordinator to fulfill his obligations in becoming an "energy expert" for the Fish and Wildlife Service in Wyoming, it will be necessary for him to develop a means of obtaining an overview of the energy development situation. Similarly, in order for him to identify those area of critical concern to wildlife resources, it will be necessary that he determine or predict where energy development will occur, the nature of this development, and the cumulative effects of energy development upon wildlife. Likewise, in order to identify where wildlife data gaps occur and to determine future wildlife information needs, it will be necessary for him to compile and synthesize relevant existing data resources. It therefore becomes apparent that a need exists for him to gather and interpret pertinent information in a systematic way to meet these ends. The following is an account of the tactical number in which the Wyoming Coal Coordinator intends to approach the duties outlined above the rationale to be followed in meeting these needs. First, a general discussion on assessing impacts on wildlife is provided to form a basis for obtaining an overview. Secondly, a series of reports which will be generated to summarize existing information and to help identify potential problem areas is outlined. These reports will be further utilized to determine wildlife data gaps and future information needs related to wildlife and future energy development.

Wyoming↗
USGS2024

Detecting avalanche path ground cover and vegetation change across multiple scales through time using remote sensing tools

Large-magnitude avalanches often alter vegetation composition, avalanche path dimensions, and subsequent avalanche return periods. Understanding temporal changes in individual avalanche path trimlines, runout zones, and geomorphic characteristics helps forecasters, planners, and engineers estimate potential avalanche destructive size and impact on infrastructure or settlements in the runout zone. Understanding these changes on a large scale also provides information on post-cycle avalanche distribution. Here, we use remote sensing platforms and change detection techniques to examine vegetation change in avalanche paths in Montana and Colorado. In northwest Montana, we implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. In Colorado, we investigated changes in avalanche path vegetation characteristics prior to and after a widespread large-magnitude avalanche cycle. The highest frequency of avalanche return periods was broadly characterized by grassland and shrubland, but topography greatly influences vegetation classes and return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. We used optical sensors from satellite imagery to analyze changes in Normalized Difference Vegetation Index (NDVI) to calculate ground cover change over time. NDVI, a measure of near-infrared and red bands within the imagery, allowed us to distinguish between green vegetation (e.g., trees and shrubs) and non-vegetated ground cover (e.g., dead and downed trees, rocks, and dirt) within avalanche paths. For this study, we calculated changes in NDVI values by comparing imagery from 2018 to imagery from 2019 after a widespread large magnitude avalanche cycle occurred in March 2019 in Colorado, United States. We applied a filtering process to reduce error, classified NDVI change based on the value distribution, and then calculated area change of all areas within each avalanche path. We completed this process for 1633 avalanche paths throughout Colorado. We found that using NDVI difference values pre- and post-avalanche cycle allowed us to identify ground cover change in avalanche paths throughout Colorado. These changes span from a slight expansion of existing avalanche paths to substantial landscape disturbance. For example, a size D5 avalanche caused severe ground cover change in 18% of one single path near Aspen, Colorado. This suggests that large magnitude avalanches can redefine avalanche path dimensions and could impact subsequent avalanche size and frequency. Using NDVI from satellite imagery is a simple way to detect ground cover changes in avalanche paths on a large scale or in remote areas. In general, remote sensing products to detect and examine vegetation and ground cover change in avalanche paths can help inform avalanche distribution and benefit planning efforts.

Montana↗
USGS2023

Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA

Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system ( https://sitkalandslide.org/ , last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

Alaska↗
USGS2007

Factors affecting herbicide yields in the Chesapeake Bay watershed, June 1994

Median concentrations and instantaneous yields of alachlor, metolachlor, atrazine, cyanazine, and simazine were generally highest at sites in the Lower Susquehanna River Basin and in agricultural subbasins. Instantaneous herbicide yields are related to land use, hydrogeologic setting, streamflow yield, and agricultural row cropping practices. The significance of these relations may be affected by the interdependence of the factors. The percentage of basin area planted in corn is the most influential factor in the prediction of herbicide yield. Instantaneous yields of all five herbicides measured in June 1994 related poorly to averaged 199094 herbicide use. Annually averaged herbicide-use data are too general to use as a predictor for short-term herbicide yields. An evaluation of factors affecting herbicide yields could be refined with more-current land use and land cover information and a more accurate estimate of the percentage of basin area planted in corn. Factors related to herbicide yields can be used to predict herbicide yields in other basins within the Chesapeake Bay watershed and to develop an estimate of herbicide loads to Chesapeake Bay.Median concentrations and instantaneous yields of alachlor, metolachlor, atrazine, cyanazine, and simazine were generally highest at sites in the Lower Susquehanna River Basin and in agricultural subbasins. Instantaneous herbicide yields are related to land use, hydrogeologic setting, streamflow yield, and agricultural row cropping practices. The significance of these relations may be affected by the interdependence of the factors. The percentage of basin area planted in corn is the most influential factor in the prediction of herbicide yield. Instantaneous yields of all five herbicides measured in June 1994 related poorly to averaged 1990-94 herbicide use. Annually averaged herbicide-use data are too general to use as a predictor for short-term herbicide yields. An evaluation of factors affecting herbicide yields could be refined with more-current land use and land cover information and a more accurate estimate of the percentage of basin area planted in corn. Factors related to herbicide yields can be used to predict herbicide yields in other basins within the Chesapeake Bay watershed and to develop an estimate of herbicide loads to Chesapeake Bay.

Water Resources Bulletin↗
USGS1992

Resistance of young wolf pups to inclement weather

Based on information about young dog ( Canis familiaris ) pups, it was thought that wolf ( Canis lupus ) pups 0-2 weeks of age do not thermoregulate well. This problem, plus the relative immobility of young pups, was thought to explain why pups generally remain inside dens until about 3 weeks of age, and pups younger than 3-weeks-old were thought to be highly vulnerable to loss from exposure. this report details more information about the tolerance of young wolf pups to exposure.

Research Information Bulletin↗
USGS2002

Porphyry copper deposits of the world: Database, maps, and preliminary analysis

Mineral deposit models are important in exploration planning and quantitative resource assessments for two reasons: (1) grades and tonnages among deposit types are significantly different, and (2) many types occur in different geologic settings that can be identified from geologic maps. Mineral deposit models are the keystone in combining the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Far too few thoroughly explored mineral deposits are available in most local areas for reliable identification of the important geoscience variables or for robust estimation of undiscovered deposits—thus we need mineral-deposit models. Globally based deposit models, such as those presented here, allow recognition of important features because the global models demonstrate how common different features are. Well-designed and -constructed deposit models allow geologists to know from observed geologic environments the possible mineral deposit types that might exist, and allow economists to determine the possible economic viability of these resources in the region. Thus, mineral deposit models play the central role in transforming geoscience information to a form useful to policy makers. The foundation of mineral deposit models is information about known deposits—the purpose of this publication is to make this kind of information available in digital form for a group of porphyry copper deposits. This publication contains a computer file of information on porphyry copper deposits around the world. It also presents new grade and tonnage models for three subtypes of porphyry copper deposits, maps showing locations and general ages of these deposits, and a preliminary analysis with a number of figures summarizing many of the properties of these porphyry-style deposits. These summaries can be considered a new, quantified, form of most parts of descriptive models such as those in Cox and Singer (1986). The value of this information and analyses depends critically on the consistent manner of data gathering. For this reason, we first discuss the rules used in this compilation. Next the fields of the data file are considered. Finally, we discuss some of the things that can be done with the data.

Open-File Report↗
USGS1990

Hydrology and land use in Grand Traverse County, Michigan

Glacial deposits are the sole source of ground-water supplies in Grand Traverse County. These deposits range in thickness from 100 to 900 feet and consist of till, outwash, and materials of lacustrine and eolian origin. In some areas, the deposits fill buried valleys that are 500 feet deep. Sedimentary rocks of Paleozoic age, which underlie the glacial deposits, are mostly shale and are not used for water supply. Of the glacial deposits, outwash and lacustrine sand are the most productive aquifers. Most domestic wells obtain water from sand and gravel at depths ranging from 50 to 150 feet and yield at least 20 gallons per minute. Irrigation, municipal, and industrial wells capable of yielding 250 gallons per minute or more are generally greater than 150 feet deep. At places in the county where moranial deposits contain large amounts of interbedded silt and clay, wells are generally deeper and yields are much lower. Areal variations in the chemical and physical characteristics of ground and surface water are related to land use and chemical inputs to the hydrologic system. Information on fertilizer application, septic-tank discharges, animal wastes, and precipitation indicate that 40 percent of nitrogen input is from precipitation, 6 percent from septic tanks, 14 percent from animal wastes, and 40 percent from fertilizers. Streams and lakes generally have a calcium bicarbonate-type water. The dissolved-solids concentration of streams ranged from 116 to 380 milligrams per liter, and that of lakes, from 47 to 170 milligrams per liter. Water of streams is hard to very hard; water of lakes ranges from soft to hard. The maximum total nitrogen concentration found in streams was 4.4 milligrams per liter. Water of lakes have low nitrogen concentrations; the median nitrate concentration is less than 0.01 milligrams per liter. Pesticides (Parathion and Simazine) were detected in low concentrations at six stream sites; 2,4-D was detected in low concentrations in water of two lakes. Relationships between land use and the yield of dissolved and suspended substances could not be established for most stream basins. Calcium and bicarbonate are the principal dissolved substances in ground water. Dissolved-solids concentrations ranged from 70 to 700 milligrams per liter; the countywide mean concentration is 230 milligrams per liter. The mean nitrate concentration is 1.3 milligrams per liter; about 1.6 percent of the county's ground water has nitrate concentrations that exceed the U.S. Environmental Protection Agency's maximum drinking water level of 10 milligrams per liter. An effect of fertilizer applications on ground-water quality is evident in some parts of the county.

Michigan↗
USGS2025

Cancer risk and estimated lithium exposure in drinking groundwater in the US

Importance Lithium is a naturally occurring element in drinking water and is commonly used as a mood-stabilizing medication. Although clinical studies have reported associations between receiving lithium treatment and reduced cancer risk among patients with bipolar disorder, to our knowledge, the association between environmental lithium exposure and cancer risk has never been studied in the general population. Objectives To evaluate the association between exposure to lithium in drinking groundwater and cancer risk in the general population. Design, Setting, and Participants This cohort study included participants with electronic health record and residential address information but without cancer history at baseline from the All of Us Research Program between May 31, 2017, and June 30, 2022. Participants were followed up until February 15, 2023. Statistical analysis was performed from September 2023 through October 2024. Exposure Lithium concentration in groundwater, based on kriging interpolation of publicly available US Geological Survey data on lithium concentration for 4700 wells across the contiguous US between May 12, 1999, and November 6, 2018. Main Outcome and Measures The main outcome was cancer diagnosis or condition, obtained from electronic health records. Stratified Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and 95% CIs for risk of cancer overall and individual cancer types for increasing quintiles of the estimated lithium exposure in drinking groundwater, adjusting for socioeconomic, behavioral, and neighborhood-level variables. The analysis was further conducted in the western and eastern halves of the US and restricted to long-term residents living at their current address for at least 3 years. Results A total of 252 178 participants were included (median age, 52 years [IQR, 36-64 years]; 60.1% female). The median follow-up time was 3.6 years (IQR, 3.0-4.3 years), and 7573 incident cancer cases were identified. Higher estimated lithium exposure was consistently associated with reduced cancer risk. Compared with the first (lowest) quintile of lithium exposure, the HR for all cancers was 0.49 (95% CI, 0.31-0.78) for the fourth quintile and 0.29 (95% CI, 0.15-0.55) for the fifth quintile. These associations were found for all cancer types investigated in both females and males, among long-term residents, and in both western and eastern states. For example, for the fifth vs first quintile of lithium exposure for all cancers, the HR was 0.17 (95% CI, 0.07-0.42) in females and 0.13 (95% CI, 0.04-0.38) in males; for long-term residents, the HR was 0.32 (95% CI, 0.15-0.66) in females and 0.24 (95% CI, 0.11-0.52) in males; and the HR was 0.01 (95% CI, 0.00-0.09) in western states and 0.34 (95% CI, 0.21-0.57) in eastern states. Conclusions and Relevance In this cohort study of 252 178 participants, estimated lithium exposure in drinking groundwater was associated with reduced cancer risk. Given the sparse evidence and unknown mechanisms of this association, follow-up investigation is warranted.

contiguous United States↗
USGS2024

Assessing the attractiveness of native wildflower species to bees (Hymenoptera: Anthophila) in the southeastern United States

Habitat loss, agricultural intensification, pesticide use, disease and climate change have contributed to the decline of numerous insect groups. Recent government initiatives have recognized the importance of supplementary wildflower plantings to support native bee populations, yet little information exists on the attractiveness of recommended plant species to bees. With the use of network analysis and generalized linear mixed models, we evaluated the attractiveness of 18 wildflower species belonging to 6 families (Apiaceae, Apocynaceae, Asteraceae, Fabaceae, Lamiaceae and Verbenaceae) planted in a randomized block design by conducting timed targeted sweep netting surveys. We also evaluated the floral preferences of four bee species ( Apis mellifera , Halictus poeyi/ligatus , Bombus griseocollis and Bombus impatiens ) to the planted wildflower species with the use of GLMs. We found that Asclepias tuberosa, Gaillardia pulchella and Verbena hastata attracted the greatest abundance, richness and diversity of native bees compared to the other wildflower species. Collectively, Asclepias tuberosa, Gaillardia pulchella and Verbena hastata attracted 89% of all bee taxa in the study, which exemplifies the importance of including them in a wildflower mixture to attract native bees. While several wildflower species attracted low abundance, richness and diversity of native bees such as Monarda fistulosa, Symphyotrichum pilosum and Eryngium yuccifolium , they are still ecologically important for attracting uncommon or specialist bee species and should be included in seed mixes for this reason. Solution . Our results highlight that each species of native wildflower varies in the abundance, richness and diversity of native bees they attract; this could be considered in the decision-making of landowners and land managers to promote wildflower plantings to reflect the needs of certain groups of bees of conservation interest.

Alabama↗
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↗
USGS2011

Baseline ecological risk assessment of the Calcasieu Estuary, Louisiana: 3. An evaluation of the risks to benthic invertebrates associated with exposure to contaminated sediments

The sediments in the Calcasieu Estuary are contaminated with a wide variety of chemicals of potential concern (COPCs), including heavy metals, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, phthalates, chlorinated benzenes, and polychlorinated dibenzo-p-dioxins and dibenzofurans. The sources of these COPCs include both point and non-point source discharges. As part of a baseline ecological risk assessment, the risks to benthic invertebrates posed by exposure to sediment-associated COPCs were assessed using five lines of evidence, including whole-sediment chemistry, pore-water chemistry, whole-sediment toxicity, pore-water toxicity, and benthic invertebrate community structure. The results of this assessment indicated that exposure to whole sediments and/or pore water from the Calcasieu Estuary generally posed low risks to benthic invertebrate communities (i.e., risks were classified as low for 68% of the sampling locations investigated). However, incremental risks to benthic invertebrates (i.e., compared with those associated with exposure to conditions in reference areas) were indicated for 32% of the sampling locations within the estuary. Of the three areas of concern (AOCs) investigated, the risks to benthic invertebrates were highest in the Bayou d'Inde AOC; risks were generally lower in the Upper Calcasieu River AOC and Middle Calcasieu River AOC. The areas showing the highest risks to sediment-dwelling organisms were generally located in the vicinity of point source discharges of COPCs. These results provided risk managers with the information required to make decisions regarding the need for remedial actions at the site.

Louisiana↗
USGS2017

Comparing stream-specific to generalized temperature models to guide salmonid management in a changing climate

Global climate change is predicted to increase air and stream temperatures and alter thermal habitat suitability for growth and survival of coldwater fishes, including brook charr (Salvelinus fontinalis), brown trout (Salmo trutta), and rainbow trout (Oncorhynchus mykiss). In a changing climate, accurate stream temperature modeling is increasingly important for sustainable salmonid management throughout the world. However, finite resource availability (e.g. funding, personnel) drives a tradeoff between thermal model accuracy and efficiency (i.e. cost-effective applicability at management-relevant spatial extents). Using different projected climate change scenarios, we compared the accuracy and efficiency of stream-specific and generalized (i.e. region-specific) temperature models for coldwater salmonids within and outside the State of Michigan, USA, a region with long-term stream temperature data and productive coldwater fisheries. Projected stream temperature warming between 2016 and 2056 ranged from 0.1 to 3.8 °C in groundwater-dominated streams and 0.2–6.8 °C in surface-runoff dominated systems in the State of Michigan. Despite their generally lower accuracy in predicting exact stream temperatures, generalized models accurately projected salmonid thermal habitat suitability in 82% of groundwater-dominated streams, including those with brook charr (80% accuracy), brown trout (89% accuracy), and rainbow trout (75% accuracy). In contrast, generalized models predicted thermal habitat suitability in runoff-dominated streams with much lower accuracy (54%). These results suggest that, amidst climate change and constraints in resource availability, generalized models are appropriate to forecast thermal conditions in groundwater-dominated streams within and outside Michigan and inform regional-level salmonid management strategies that are practical for coldwater fisheries managers, policy makers, and the public. We recommend fisheries professionals reserve resource-intensive stream-specific models for runoff-dominated systems containing high-priority fisheries resources (e.g. trophy individuals, endangered species) that will be directly impacted by projected stream warming.

Michigan↗
USGS2016

Modeling abundance using multinomial N-mixture models

Multinomial N-mixture models are a generalization of the binomial N-mixture models described in Chapter 6 to allow for more complex and informative sampling protocols beyond simple counts. Many commonly used protocols such as multiple observer sampling, removal sampling, and capture-recapture produce a multivariate count frequency that has a multinomial distribution and for which multinomial N-mixture models can be developed. Such protocols typically result in more precise estimates than binomial mixture models because they provide direct information about parameters of the observation process. We demonstrate the analysis of these models in BUGS using several distinct formulations that afford great flexibility in the types of models that can be developed, and we demonstrate likelihood analysis using the unmarked package. Spatially stratified capture-recapture models are one class of models that fall into the multinomial N-mixture framework, and we discuss analysis of stratified versions of classical models such as model Mb, Mh and other classes of models that are only possible to describe within the multinomial N-mixture framework.

Book chapter↗
USGS2007

Growth rates of young-of-year shovelnose sturgeon in the Upper Missouri River

Information on growth during the larval and young-of-year life stages in natural river environments is generally lacking for most sturgeon species. In this study, methods for estimating ages and quantifying growth were developed for field-sampled larval and young-of-year shovelnose sturgeon Scaphirhynchus platorynchus in the upper Missouri River. First, growth was assessed by partitioning samples of young-of-year shovelnose sturgeon into cohorts, and regressing weekly increases in cohort mean length on sampling date. This method quantified relative growth because ages of the cohorts were unknown. Cohort increases in mean length among sampling dates were positively related (P < 0.05, r 2 > 0.59 for all cohorts) to sampling date, and yielded growth rate estimates of 0.80–2.95 mm day −1 (2003) and 0.44–2.28 mm day −1 (2004). Highest growth rates occurred in the largest (and earliest spawned) cohorts. Second, a method was developed to estimate cohort hatch dates, thus age on date of sampling could be determined. This method included quantification of post-hatch length increases as a function of water temperature (growth capacity; mm per thermal unit, mm TU −1 ), and summation of mean daily water temperatures to achieve the required number of thermal units that corresponded to post-hatch lengths of shovelnose sturgeon on sampling dates. For six of seven cohorts of shovelnose sturgeon analyzed, linear growth models ( r 2 ≥ 0.65, P < 0.0001) or Gompertz growth models ( r 2 ≥ 0.83, P < 0.0001) quantified length-at-age from hatch through 55 days post-hatch (98–100 mm). Comparisons of length-at-age derived from the growth models indicated that length-at-age was greater for the earlier-hatched cohorts than later-hatched cohorts. Estimated hatch dates for different cohorts were corroborated based on the dates that newly-hatched larval shovelnose sturgeon were sampled in the drift. These results provide the first quantification of growth dynamics for field-sampled age-0 shovelnose sturgeon in a natural river environment, and provide an accurate method for estimating age of wild-caught individuals. Methods of age determination used in this study have applications to sturgeons in other regions, but require additional testing and validation.

North Dakota↗
USGS2005

Appalachian coal assessment: Defining the coal systems of the Appalachian basin

The coal systems concept may be used to organize the geologic data for a relatively large, complex area, such as the Appalachian basin, in order to facilitate coal assessments in the area. The concept is especially valuable in subjective assessments of future coal production, which would require a detailed understanding of the coal geology and coal chemistry of the region. In addition, subjective assessments of future coal production would be enhanced by a geographical information system that contains the geologic and geochemical data commonly prepared for conventional coal assessments. Coal systems are generally defined as one or more coal beds or groups of coal beds that have had the same or similar genetic history from their inception as peat deposits, through their burial, diagenesis, and epigenesis to their ultimate preservation as lignite, bituminous coal, or anthracite. The central and northern parts of the Appalachian basin contain seven coal systems (Coal Systems A-G). These systems may be defined generally on the following criteria: (1) on the primary characteristics of their paleopeat deposits, (2) on the stratigraphic framework of the Paleozoic coal measures, (3) on the relative abundance of coal beds within the major stratigraphic groupings, (4) on the amount of sulfur related to the geologic and climatic conditions under which paleopeat deposits accumulated, and (5) on the rank of the coal (lignite to anthracite). ??2005 Geological Society of America.

Special Paper of the Geological Society of America↗
USGS2012

Sources and sinks of nitrogen and phosphorus to a deep, oligotrophic lake, Lake Crescent, Olympic National Park, Washington

Lake Crescent, in Olympic National Park in the northwest corner of Washington State is a deep-water lake renowned for its pristine water quality and oligotrophic nature. To examine the major sources and sinks of nutrients (as total nitrogen, total phosphorus, and dissolved nitrate), a study was conducted in the Lake Crescent watershed. The study involved measuring five major inflow streams, the Lyre River as the major outflow, recording weather and climatic data, coring lake bed sediment, and analyzing nutrient chemistry in several relevant media over 14 months. Water samples for total nitrogen, total phosphorous, and dissolved nitrate from the five inflow streams, the outlet Lyre River, and two stations in the lake were collected monthly from May 2006 through May 2007. Periodic samples of shallow water from temporary sampling wells were collected at numerous locations around the lake. Concentrations of nutrients detected in Lake Crescent and tributaries were then applied to the water budget estimates to arrive at monthly and annual loads from various environmental components within the watershed. Other sources, such as leaf litter, pollen, or automobile exhaust were estimated from annual values obtained from various literature sources. This information then was used to construct a nutrient budget for total nitrogen and total phosphorus. The nitrogen budget generally highlights vehicle traffic-diesel trucks in particular-along U.S. Highway 101 as a potential major anthropogenic source of nitrogen compounds in the lake. In contrast, contribution of nitrogen compounds from onsite septic systems appears to be relatively minor related to the other sources identified.

Washington↗