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USGS2001

A data management life-cycle

Documented, reliable, and accessible data and information are essential building blocks supporting scientific research and applications that enhance society's knowledge base (fig. 1). The U.S. Geological Survey (USGS), a leading provider of science data, information, and knowledge, is uniquely positioned to integrate science and natural resource information to address societal needs. The USGS Central Energy Resources Team (USGS-CERT) provides critical information and knowledge on the quantity, quality, and distribution of the Nation's and the world's oil, gas, and coal resources. By using a life-cycle model, the USGS-CERT Data Management Project is developing an integrated data management system to (1) promote access to energy data and information, (2) increase data documentation, and (3) streamline product delivery to the public, scientists, and decision makers. The project incorporates web-based technology, data cataloging systems, data processing routines, and metadata documentation tools to improve data access, enhance data consistency, and increase office efficiency

Fact Sheet↗

USGS2025

The 3D Elevation Program—Supporting Utah’s economy

Introduction High-quality elevation data for Utah inform decision making to improve the State’s economy. Light detection and ranging (lidar) data are used to support infrastructure planning and management, assess natural resources, and improve resiliency to hazards. The expanding availability of current and more accurate lidar data helps to better support natural resource conservation, wildfire risk management, geologic hazard investigation and mitigation, flood risk management, water supply planning, and urban planning and development. Critical applications that meet the State’s management needs depend on lidar data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features. The 3D Elevation Program is managed by the U.S. Geological Survey in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Utah. The status of available and in-progress 3DEP baseline lidar data in Utah is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 ( https://www.usgs.gov/3dep/lidarspec ) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $8.70 million in new benefits annually to the State. The top 10 Utah business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.

Utah↗

USGS2014

Introduction to selected references on fossil fuels of the central and southern Appalachian basin

The Appalachian basin contains abundant coal and petroleum resources that have been studied and extracted for at least 150 years. In this volume, U.S. Geological Survey (USGS) scientists describe the geologic framework and geochemical character of the fossil-fuel resources of the central and southern Appalachian basin. Separate subchapters (some previously published) contain geologic cross sections; seismic profiles; burial history models; assessments of Carboniferous coalbed methane and Devonian shale gas; distribution information for oil, gas, and coal fields; data on the geochemistry of natural gas and oil; and the fossil-fuel production history of the basin. Although each chapter and subchapter includes references cited, many historical or other important references on Appalachian basin and global fossil-fuel science were omitted because they were not directly applicable to the chapters.

Appalachian basin↗

USGS2025

A case for assemblage-level conservation to address the biodiversity crisis

Traditional conservation efforts have centred on safeguarding individual species, but these strategies have limitations in a world where entire ecosystems are rapidly changing. Ecosystem conservation can maintain critical ecological functions, but often lacks the detail necessary for the effective conservation of threatened or endangered species. The conservation of such species is mandated by policies and remains a dominant focus of natural resource management. In this Perspective, we propose that assemblage-level conservation targeting groups of taxonomically related or functionally similar species can bridge the gap between species and ecosystems and help to address global biodiversity loss. This approach has previously been limited by data and methodological constraints, but the ongoing growth of biodiversity data, advances in ecological modelling and breakthroughs in computational power have now made effective assemblage-level conservation feasible. Community models provide insights at both the species level and the assemblage level while appropriately accounting for species variability in detection during sampling and uncertainty in biological inferences. Assemblage-level conservation can link both species-specific needs and broader ecological dynamics, ultimately enabling effective strategies for conserving threatened species, ecological communities and ecosystem functions.

Nature Reviews Biodiversity↗

USGS2021

Using fecal DNA and closed-capture models to estimate feral horse population size

Accurate population estimates provide the foundation for managing feral horses ( Equus caballus ferus ) across the western United States. Certain feral horse populations are protected by the Wild and Free-Roaming Horses and Burros Act of 1971 and managed by the Bureau of Land Management (BLM) or the United States Forest Service on designated herd management areas (HMAs) or wild horse territories, respectively. Horses are managed to achieve an appropriate management level (AML), which represents the number of horses determined by BLM to contribute to a thriving natural ecological balance and avoid deterioration of the range. To achieve AML for each HMA, BLM resource managers need accurate and precise population estimates. We tested the use of non-invasive fecal samples in a genetic capture-recapture framework to estimate population size in a closed horse population at the Little Book Cliffs HMA, Colorado, USA, with a known size of 153 individuals. We collected 1,957 samples over 3 independent sampling periods in 2014 and amplified them at 8 microsatellite loci. We applied mark-recapture models to determine population size using 954 samples that amplified at all 8 loci. We subsampled and reanalyzed our dataset to simulate different data collection protocols and evaluated effects on accuracy and precision of estimates using N-mixture modeling, full likelihood closed-capture modeling, and capwire single-occasion modeling that used data from all 3 sampling periods. Our model results were accurate and precise for analyses that used data from all 3 occasions; however, capwire single-occasion modeling was not accurate when we analyzed each sampling period separately. For all subsampling analysis scenarios, reducing sample size decreased precision, whether by reducing number of field staff, field days, or geographic areas surveyed on each period. Reducing spatial coverage of the survey area did not result in accurate population estimates and only marginally lowered the number of samples that would need to be collected to maintain accuracy. Because laboratory analysis contributes the greatest expense for this method ($80 U.S./sample), reducing fecal sample size is advantageous. Our results demonstrate that non-invasive sampling combined with good survey design and careful genetic and capture-recapture analyses can provide an alternative method to estimate the number of feral horses in a closed population. This method may be especially appropriate in situations where aerial inventories are not practical or accurate because of low sighting conditions. But the higher costs associated with laboratory sample analyses may reduce the method's feasibility compared to helicopter surveys.

Colorado↗

USGS2014

Threshold-dependent sample sizes for selenium assessment with stream fish tissue

Natural resource managers are developing assessments of selenium (Se) contamination in freshwater ecosystems based on fish tissue concentrations. We evaluated the effects of sample size (i.e., number of fish per site) on the probability of correctly detecting mean whole-body Se values above a range of potential management thresholds. We modeled Se concentrations as gamma distributions with shape and scale parameters fitting an empirical mean-to-variance relationship in data from southwestern West Virginia, USA (63 collections, 382 individuals). We used parametric bootstrapping techniques to calculate statistical power as the probability of detecting true mean concentrations up to 3 mg Se/kg above management thresholds ranging from 4 to 8 mg Se/kg. Sample sizes required to achieve 80% power varied as a function of management thresholds and Type I error tolerance (α). Higher thresholds required more samples than lower thresholds because populations were more heterogeneous at higher mean Se levels. For instance, to assess a management threshold of 4 mg Se/kg, a sample of eight fish could detect an increase of approximately 1 mg Se/kg with 80% power (given α = 0.05), but this sample size would be unable to detect such an increase from a management threshold of 8 mg Se/kg with more than a coin-flip probability. Increasing α decreased sample size requirements to detect above-threshold mean Se concentrations with 80% power. For instance, at an α-level of 0.05, an 8-fish sample could detect an increase of approximately 2 units above a threshold of 8 mg Se/kg with 80% power, but when α was relaxed to 0.2, this sample size was more sensitive to increasing mean Se concentrations, allowing detection of an increase of approximately 1.2 units with equivalent power. Combining individuals into 2- and 4-fish composite samples for laboratory analysis did not decrease power because the reduced number of laboratory samples was compensated for by increased precision of composites for estimating mean conditions. However, low sample sizes (<5 fish) did not achieve 80% power to detect near-threshold values (i.e., <1 mg Se/kg) under any scenario we evaluated. This analysis can assist the sampling design and interpretation of Se assessments from fish tissue by accounting for natural variation in stream fish populations.

Integrated Environmental Assessment and Management↗

USGS2021

Using social values in the prioritization of research: Quantitative examples and generalizations

Identifying critical uncertainties about ecological systems can help prioritize research efforts intended to inform management decisions. However, exclusively focusing on the ecological system neglects the objectives of natural resource managers and the associated social values tied to risks and rewards of actions. I demonstrate how to prioritize research efforts for a harvested population by applying expected value of perfect information (EVPI) to harvest decisions made with a density-independent matrix population model. Research priorities identified by EVPI diverge from priorities identified by matrix elasticity analyses that ignore social utility. Using a density-dependent harvest model, the value of information about the intrinsic productivity of a population is shown to be sensitive to the socially determined penalty for implementing a harvest rate that deviates from the goal because of imperfection in estimation. Synthesis and applications . The effect of including social values into harvest decision-making depends on the assumed population model, uncertainty in population vital rates, and the particular form of the utility function used to represent risk/reward of harvest. EVPI analyses that include perceived utility of different outcomes can be used by managers seeking to optimize monitoring and research spending. Collaboration between applied ecologists and social scientists that quantitatively measure peoples' values is needed in many structured decision-making processes.

Ecology and Evolution↗

USGS2016

Tree-ring-based estimates of long-term seasonal precipitation in the Souris River Region of Saskatchewan, North Dakota and Manitoba

Historically unprecedented flooding occurred in the Souris River Basin of Saskatchewan, North Dakota and Manitoba in 2011, during a longer term period of wet conditions in the basin. In order to develop a model of future flows, there is a need to evaluate effects of past multidecadal climate variability and/or possible climate change on precipitation. In this study, tree-ring chronologies and historical precipitation data in a four-degree buffer around the Souris River Basin were analyzed to develop regression models that can be used for predicting long-term variations of precipitation. To focus on longer term variability, 12-year moving average precipitation was modeled in five subregions (determined through cluster analysis of measures of precipitation) of the study area over three seasons (November–February, March–June and July–October). The models used multiresolution decomposition (an additive decomposition based on powers of two using a discrete wavelet transform) of tree-ring chronologies from Canada and the US and seasonal 12-year moving average precipitation based on Adjusted and Homogenized Canadian Climate Data and US Historical Climatology Network data. Results show that precipitation varies on long-term (multidecadal) time scales of 16, 32 and 64 years. Past extended pluvial and drought events, which can vary greatly with season and subregion, were highlighted by the models. Results suggest that the recent wet period may be a part of natural variability on a very long time scale.

Manitoba, North Dakota, Saskatchewan↗

USGS2015

Development of an assessment tool for agricultural best management practice implementation in the Great Lakes Restoration Initiative priority watersheds—Upper East River, tributary to Green Bay, Wisconsin

Introduction The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.

Wisconsin↗

USGS2015

Development of an assessment tool for agricultural best management practice implementation in the Great Lakes restoration initiative priority watersheds—Eagle Creek, tributary to Maumee River, Ohio

Ohio↗

USGS2015

Development of an assessment tool for agricultural best management practice Implementation in the Great Lakes Restoration Initiative priority watersheds—Alger Creek, tributary to Saginaw River, Michigan

The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.

Michigan↗

USGS2021

Tradeoffs in habitat value to maximize natural resource benefits from coastal restoration in a rapidly eroding wetland: Is monitoring land area sufficient?

Louisiana contains nearly 40% of estuarine herbaceous wetlands in the contiguous United States, supporting valuable ecosystem services and providing significant economic benefits to the state and the entire United States. However, coastal Louisiana is a hotspot for rapid land loss from factors including hurricanes, land use change, and high subsidence rates contributing to high relative sea-level rise. The Coastal Protection and Restoration Authority (CPRA) was established after major hurricanes in 2005 to coordinate coastal restoration in Louisiana and develop the Louisiana Coastal Master Plan. The LA Coastal Master Plan uses numerical modeling of multiple scenarios to select a suite of restoration projects based on maximum land area created and flood reduction (as proxies for ecosystem value). Using potential value to aquatic, terrestrial, and social resources, our work compared habitat value of shallow open water areas to emergent wetland. While potential resource benefits varied by emergent wetland salinity type and emergent wetland versus water, they were similar, suggesting that restoration planning based primarily on wetland land area may not achieve the maximum possible ecosystem benefits. After nearly 20 years of integrated restoration planning in coastal Louisiana, a reassessment of restoration planning decision drivers may be beneficial to ensure maximum benefits from coastal restoration. As a result of the Deepwater Horizon oil spill, settlement funds will be a major support to coastal restoration in Louisiana for many years. Assessing potential habitat value to multiple natural and social resources in Louisiana has potential to maximize synergy with large northern Gulf of Mexico restoration programs.

Louisiana↗

USGS1997

Digital shaded-relief image of Alaska

One of the most spectacular physiographic images of the conterminous United States, and the first to have been produced digitally, is that by Thelin and Pike ( USGS I-2206, 1991 ). The image is remarkable for its crispness of detail and for the natural appearance of the artificial land surface. Our goal has been to produce a shaded-relief image of Alaska that has the same look and feel as the Thelin and Pike image. The Alaskan image could have been produced at the same scale as its lower 48 counterpart (1:3,500,000). But by insetting the Aleutian Islands into the Gulf of Alaska, we were able to print the Alaska map at a larger scale (1:2,500,000) and about the same physical size as the Thelin and Pike image. Benefits of the 1:2,500,000 scale are (1) greater resolution of topographic features and (2) ease of reference to the U.S. Geological Survey (USGS) (1987) Alaska Map E and the statewide geologic map (Beikman, 1980), which are both 1:2,500,000 scale. Manually drawn, shaded-relief images of Alaska's land surface have long been available (for example, Department of the Interior, 1909; Raisz, 1948). The topography depicted on these early maps is mainly schematic. Maps showing topographic contours were first available for the entire State in 1953 (USGS, 1:250,000) (J.H. Wittmann, USGS, written commun., 1996). The Alaska Map E was initially released in 1954 in both planimetric (revised in 1973 and 1987) and shaded-relief versions (revised in 1973, 1987, and 1996); topography depicted on the shaded-relief version is based on the 1:250,000-scale USGS topographic maps. Alaska Map E was later modified to include hypsometric tinting by Raven Maps and Images (1989, revised 1993) as copyrighted versions. Other shaded-relief images were produced for The National Geographic Magazine (LaGorce, 1956; 1:3,000,000) or drawn by Harrison (1970; 1:7,500,000) for The National Atlas of the United States. Recently, the State of Alaska digitally produced a shaded-relief image of Alaska at 1:2,500,000 scale (Alaska Department of Natural Resources, 1994), using the 1,000-m digital elevation data set referred to below. An important difference between our image and these previous ones is the method of reproduction: like the Thelin and Pike (1991) image, our image is a composite of halftone images that yields sharp resolution and preserves contrast. Indeed, the first impression of many viewers is that the Alaskan image and the Thelin and Pike image are composites of satellite-generated photographs rather than an artificial rendering of a digital elevation model. A shaded-relief image represents landforms in a natural fashion; that is, a viewer perceives the image as a rendering of reality. Thus a shaded-relief image is intrinsically appealing, especially in areas of spectacular relief. In addition, even subtle physiographic features that reflect geologic structures or the type of bedrock are visible. To our knowledge, some of these Alaskan features have not been depicted before and so the image should provide earth scientists with a new "look" at fundamental geologic features of Alaska.

Alaska↗

USGS2022

Linear regression model documentation for computing water-quality constituent concentrations using continuous real-time water-quality data for the Republican River, Clay Center, Kansas, July 2018 through March 2021

The Republican River is the primary inflow to Milford Lake and drains areas of Kansas, Nebraska, and Colorado. Milford Lake has been listed as impaired and designated hypereutrophic by the Kansas Department of Health and Environment because of excessive nutrient loading. Milford Lake had confirmed harmful algal blooms every summer from 2011 through 2017 and in 2020 and 2021. In the lower Republican River drainage basin, the Regional Conservation Partnership Program, administered by the Natural Resources Conservation Service, provides reimbursement to agricultural producers that implement best management practices intended to decrease sediment and nutrient runoff and loading into Milford Lake. Sediment and nutrient loads could potentially be driving factors in the development of harmful algal blooms in the reservoir. Since July 2018, the U.S. Geological Survey, in cooperation with the Kansas Water Office, has collected continuous and discrete water-quality data at the Republican River at Clay Center, Kansas, streamgage (U.S. Geological Survey station 06856600), which is about 15 river miles upstream from Milford Lake. This report documents site-specific regression models for the computation of continuous concentrations of suspended sediment, total nitrogen, total phosphorus, and total carbon developed using continuous and discrete data collected from July 24, 2018, the date of continuous water-quality monitor installation, through March 31, 2021. The objective of this study is to characterize sediment and nutrient transport in the Milford Lake drainage basin before, during, and after best management practice implementation using the models described in this report. The explanatory variable turbidity explained a high amount (72–96 percent) of the variance in suspended-sediment, total nitrogen, total phosphorus, and total carbon concentrations. Statistical plots for the four selected models showed the desired normality and homoscedasticity in residuals, and model standard error ratios indicated that recomputing each selected model after removing a randomly selected 10 percent of the data did not substantially change model coefficients.

Kansas↗

USGS2011

Swallows as a sentinel species for contaminant exposure and effect studies

Tree swallows are an important model species to study the effects of contaminants in wild bird populations and have been used extensively in studies across North America. The advantages of swallows compared to other avian species are detailed. Three case histories are provided where swallows have been successfully used in Natural Resource Damage and Ecological Risk Assessments. The final two sections of this chapter are for individuals who want more in-depth information and include a summary of the chemical classes for which there are swallow data, including effect levels when known. Information provided in this section can be used to put exposure to most classes of contaminants into context with other sites across North America. Finally, commonly used endpoints, ranging from population-level down to cellular and genetic endpoints, are discussed including considerations and pitfalls, and when further work is needed to more fully understand the role of environmental and biological variation in interpreting these endpoints.

Book chapter↗

USGS2017

Cooperative Fish and Wildlife Research Units - A model partnership program

The U.S. Geological Survey (USGS) Cooperative Fish and Wildlife Research Units (CRU) program is a unique model of cooperative partnership among the USGS, other U.S. Department of the Interior and Federal agencies, universities, State fish and wildlife agencies, and the Wildlife Management Institute. These partnerships are maintained as one of the USGS’s strongest links to Federal and State land and natural resource management agencies. Established in 1935 to meet the need for trained professionals in the growing field of wildlife management, the program currently consists of 40 Cooperative Fish and Wildlife Research Units located on university campuses in 38 States and supports 119 research scientist positions when fully funded. The threefold mission of the CRU program is to (1) conduct scientific research for the management of fish, wildlife, and other natural resources; (2) provide technical assistance to natural resource managers in the application of scientific information to natural resource policy and management; and (3) train future natural resource professionals.

Fact Sheet↗

USGS2023

The 3D Elevation Program—Supporting Montana’s economy

Introduction Montana, America’s fourth largest State with an area of 147,040 square miles, is defined by its diverse terrain. The western two-fifths of the State falls within the Rocky Mountains and the eastern three-fifths is in the Great Plains. Because of its location along the Continental Divide, the rivers in Montana drain into either the Pacific Ocean or the Gulf of Mexico. Montana is often called the Treasure State due to its mineral wealth, which includes oil, gas, and coal, but the State’s primary economic activity is agriculture. Other economic activities include natural resources conservation, water supply and quality, infrastructure and construction management, flood risk management, and geologic resource assessment and hazard mitigation. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

Montana↗

USGS2016

Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood

Sea-level rise (SLR) poses a range of threats to natural and built environments1, 2, making assessments of SLR-induced hazards essential for informed decision making3. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30 × 30 m resolution predictions for more than 38,000 km2 of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.

Nature Climate Change↗