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USGS2023

Effects of noise from oil and gas development on ungulates and small mammals—A science synthesis to inform National Environmental Policy Act analyses

The U.S. Geological Survey is working with Federal land management agencies to develop a series of science syntheses to support environmental effects analyses that agencies conduct to comply with the National Environmental Policy Act (NEPA). This report synthesizes science information about the potential effects of noise from oil and gas development on North American ungulates and small mammals, including rodents and leporids. We conducted a structured search of published scientific literature to find information about noise levels produced during oil and gas development, methods for analyzing sound propagation, the effects of noise on ungulates and small mammals, and measures to reduce noise emissions. We organized the sections of this synthesis to align with standard elements of NEPA analyses. We found that oil and gas development is a common source of human-caused noise on public lands and includes noise sources such as heavy construction and drilling machinery, long-term production machinery, truck traffic, and aircraft. Common techniques for predicting potential noise include field data collection using a sound level meter, inference from previously published data, and sound propagation modeling. A substantial body of research shows that human-caused noise can affect wildlife health and behavior, with variation in sensitivity to noise among species. Studies have shown consistent, detectable effects of noise on ungulates, but the amount of literature on ungulates is very small, and additional research could improve our understanding of differences in effects among species, seasons, and individual indicators of fitness. Several species of small mammals are dependent on audible signals for predator detection and communication, and noise has been shown to affect their vigilance and foraging behavior. However, other studies have documented no effects to rodents in noisy areas, and the effects of noise on small mammals may differ by species and study system. Techniques suggested in the literature for reducing noise emissions include sound barriers, seasonal and daily timing restrictions, traffic control measures, and siting infrastructure to take advantage of natural sound barriers. Public land managers can use this report by incorporating it by reference in NEPA documentation, as supplemental information, or as a general reference for literature about the effects of noise from oil and gas development on ungulates and small mammals.

Scientific Investigations Report↗
USGS2001

Formation and transport of the sulfonic acid metabolites of alachlor and metolachlor in soil

Alachlor and metolachlor are dechlorinated and transformed into their corresponding ethane sulfonic acid (ESA) metabolites in soil. In a field-disappearance study, it was shown that alachlor ESA was formed at a faster rate and at concentrations 2−4 times higher than metolachlor ESA, conforming with the observed longer disappearance half-life of metolachlor (15.5 d) in the field as compared to alachlor (8 d). Runoff data also showed higher concentrations of alachlor ESA as compared to metolachlor ESA, even though they were applied at the same levels. Data from soil cores showed transport of the ESA compounds in soil to as far down as 75−90 cm below the surface, at concentrations ranging from less than 0.5 μg/L to about 50 μg/L. In contrast, no parent herbicide was detected at these depths. This observation correlates with the higher log K oc values for alachlor (3.33) and metolachlor (3.01) relative to their corresponding ESA metabolites, alachlor ESA (2.26), and metolachlor ESA (2.29).

Environmental Science & Technology↗
USGS2022

Value of information and decision pathways: Concepts and case studies

Information used in decision making arises from the structuring of observations and data. The collection, dissemination, and use of information has monetary and non-monetary costs (e.g., competition for attention) and necessitates trade-offs. Understanding the benefits of having information (i.e., the value of information, VOI), including resulting societal outcomes, is useful to information producers/funders and decision makers. Using theory, use cases, and hypotheticals, we describe how information (e.g., geospatial information) is valued and incorporated in decisions and actions related to managing natural resources, environments, and the impacts of natural and anthropogenic hazards. We discuss the nature of information and how it relates to models (conceptual, mental, scientific), beliefs, knowledge, and economic analyses. VOI approaches and behavioral factors that potentially affect information use and value are summarized. Framing of information and VOI through data to decision pathways (DDPs) at first simplifies understanding, then illustrates the benefits of information, and the human and societal challenges encountered in valuing and using it. We present approaches to overcome these challenges. Our transdisciplinary analysis concludes with a summary of critical issues affecting DDPs and VOI, and suggestions for improving both economic analyses and the actionability and use of information.

Frontiers in Environmental Science (Environmental ↗
USGS1998

Sexual difference in polychlorinated biphenyl accumulation rates of walleye (Stizostedion vitreum)

Adult male walleye (Stizostedion vitreum) exhibited significantly higher polychlorinated biphenyl (PCB) concentrations than similarly aged female walleye from Saginaw Bay (Lake Huron). To explain this difference, we tested the following three hypotheses: (i) females showed a considerably greater reduction in PCB concentration immediately following spawning than males, (ii) females grew at a faster rate and therefore exhibited lower PCB concentrations than males, and (iii) males spent more time in the Saginaw River system than females, and therefore received a greater exposure to PCBs. The first hypothesis was tested by comparing PCB concentration in gonadal tissue with whole-body concentration, the second hypothesis was tested via bioenergetics modeling, and we used mark-recapture data from the Saginaw Bay walleye fishery to address the third hypothesis. The only plausible explanation for the observed difference in PCB accumulation rate was that males spent substantially more time in the highly contaminated Saginaw River system than females, and therefore were exposed to greater environmental concentrations of PCBs. Based on the results of our study, we strongly recommend a stratified random sampling design for monitoring PCB concentration in Saginaw Bay walleye, with fixed numbers of females and males sampled each year.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2023

Combining local, landscape, and regional geographies to assess plant community vulnerability to invasion impact

Invasive species science has focused heavily on the invasive agent. However, management to protect native species also requires a proactive approach focused on resident communities and the features affecting their vulnerability to invasion impacts. Vulnerability is likely the result of factors acting across spatial scales, from local to regional, and it is the combined effects of these factors that will determine the magnitude of vulnerability. Here, we introduce an analytical framework that quantifies the scale-dependent impact of biological invasions on native richness from the shape of the native species–area relationship (SAR). We leveraged newly available, biogeographically extensive vegetation data from the U.S. National Ecological Observatory Network to assess plant community vulnerability to invasion impact as a function of factors acting across scales. We analyzed more than 1000 SARs widely distributed across the USA along environmental gradients and under different levels of non-native plant cover. Decreases in native richness were consistently associated with non-native species cover, but native richness was compromised only at relatively high levels of non-native cover. After accounting for variation in baseline ecosystem diversity, net primary productivity, and human modification, ecoregions that were colder and wetter were most vulnerable to losses of native plant species at the local level, while warmer and wetter areas were most susceptible at the landscape level. We also document how the combined effects of cross-scale factors result in a heterogeneous spatial pattern of vulnerability. This pattern could not be predicted by analyses at any single scale, underscoring the importance of accounting for factors acting across scales. Simultaneously assessing differences in vulnerability between distinct plant communities at local, landscape, and regional scales provided outputs that can be used to inform policy and management aimed at reducing vulnerability to the impact of plant invasions.

Ecological Applications↗
USGS2011

A multi-year comparison of IPCI scores for prairie pothole wetlands: implications of temporal and spatial variation

In the prairie pothole region of North America, development of Indices of Biotic Integrity (IBIs) to detect anthropogenic impacts on wetlands has been hampered by naturally dynamic inter-annual climate fluctuations. Of multiple efforts to develop IBIs for prairie pothole wetlands, only one, the Index of Plant Community Integrity (IPCI), has reported success. We evaluated the IPCI and its ability to distinguish between natural and anthropogenic variation using plant community data collected from 16 wetlands over a 4-year-period. We found that under constant anthropogenic influence, IPCI metric scores and condition ratings varied annually in response to environmental variation driven primarily by natural climate variation. Artificially forcing wetlands that occur along continuous hydrologic gradients into a limited number of discrete classes (e.g., temporary, seasonal, and semi-permanent) further confounded the utility of IPCI metrics. Because IPCI scores vary significantly due to natural climate dynamics as well as human impacts, methodology must be developed that adequately partitions natural and anthropogenically induced variation along continuous hydrologic gradients. Until such methodology is developed, the use of the IPCI to evaluate prairie pothole wetlands creates potential for misdirected corrective or regulatory actions, impairment of natural wetland functional processes, and erosion of public confidence in the wetland sciences.

Wetlands↗
USGS2021

Machine learning models of arsenic in private wells throughout the conterminous United States as a tool for exposure assessment in human health studies

Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.

Environmental Science and Technology↗
USGS2002

Variations in suspended sediment and associated trace element concentrations in selected riverine cross sections

Detailed sampling and subsequent analyses of riverine suspended sediment obtained from six rivers in the United States indicate substantial differences in suspended sediment concentrations and possibly in some associated trace elements, depending on whether depth- and width-integrated, point, or pumping samples are used. In addition, the data from time-series, depth-integrated sampling indicate that there can be substantial short-term (on the order of 20-30 min) spatial and/or temporal variations in suspended-sediment concentrations. Despite this, major element concentrations are remarkably stable both spatially and temporally. Trace element concentrations are generally stable; however, some spatial and temporal variations may occur.

Environmental Science and Technology↗
USGS2014

Bacterial pathogen gene abundance and relation to recreational water quality at seven Great Lakes beaches

Quantitative assessment of bacterial pathogens, their geographic variability, and distribution in various matrices at Great Lakes beaches are limited. Quantitative PCR (qPCR) was used to test for genes from E. coli O157:H7 ( eae O157 ), shiga-toxin producing E. coli ( stx2 ), Campylobacter jejuni ( mapA ), Shigella spp. ( ipaH ), and a Salmonella enterica -specific ( SE ) DNA sequence at seven Great Lakes beaches, in algae, water, and sediment. Overall, detection frequencies were mapA > stx2 > ipaH > SE > eae O157 . Results were highly variable among beaches and matrices; some correlations with environmental conditions were observed for mapA , stx2 , and ipaH detections. Beach seasonal mean mapA abundance in water was correlated with beach seasonal mean log 10 E. coli concentration. At one beach, stx2 gene abundance was positively correlated with concurrent daily E. coli concentrations. Concentration distributions for stx2 , ipaH , and mapA within algae, sediment, and water were statistically different (Non-Detect and Data Analysis in R). Assuming 10, 50, or 100% of gene copies represented viable and presumably infective cells, a quantitative microbial risk assessment tool developed by Michigan State University indicated a moderate probability of illness for Campylobacter jejuni at the study beaches, especially where recreational water quality criteria were exceeded. Pathogen gene quantification may be useful for beach water quality management.

Environmental Science & Technology↗
USGS2009

Mercury cycling in stream ecosystems. 3. Trophic dynamics and methylmercury bioaccumulation

Trophic dynamics (community composition and feeding relationships) have been identified as important drivers of methylmercury (MeHg) bioaccumulation in lakes, reservoirs, and marine ecosystems. The relative importance of trophic dynamics and geochemical controls on MeHg bioaccumulation in streams, however, remains poorly characterized. MeHg bioaccumulation was evaluated in eight stream ecosystems across the United States (Oregon, Wisconsin, and Florida) spanning large ranges in climate, landscape characteristics, atmospheric Hg deposition, and stream chemistry. Across all geographic regions and all streams, concentrations of total Hg (THg) in top predator fish and forage fish, and MeHg in invertebrates, were strongly positively correlated to concentrations of filtered THg (FTHg), filtered MeHg (FMeHg), and dissolved organic carbon (DOC); to DOC complexity (as measured by specific ultraviolet absorbance); and to percent wetland in the stream basins. Correlations were strongest for nonurban streams. Although regressions of log[Hg] versus δ 15 N indicate that Hg in biota increased significantly with increasing trophic position within seven of eight individual streams, Hg concentrations in top predator fish (including cutthroat, rainbow, and brown trout; green sunfish; and largemouth bass) were not strongly influenced by differences in relative trophic position. Slopes of log[Hg] versus δ 15 N, an indicator of the efficiency of trophic enrichment, ranged from 0.14 to 0.27 for all streams. These data suggest that, across the large ranges in FTHg (0.14−14.2 ng L −1 ), FMeHg (0.023−1.03 ng L −1 ), and DOC (0.50−61.0 mg L −1 ) found in this study, Hg contamination in top predator fish in streams likely is dominated by the amount of MeHg available for uptake at the base of the food web rather than by differences in the trophic position of top predator fish.

Environmental Science & Technology↗
USGS2011

BioData: A national aquatic bioassessment database

BioData is a U.S. Geological Survey (USGS) web-enabled database that for the first time provides for the capture, curation, integration, and delivery of bioassessment data collected by local, regional, and national USGS projects. BioData offers field biologists advanced capabilities for entering, editing, and reviewing the macroinvertebrate, algae, fish, and supporting habitat data from rivers and streams. It offers data archival and curation capabilities that protect and maintain data for the long term. BioData provides the Federal, State, and local governments, as well as the scientific community, resource managers, the private sector, and the public with easy access to tens of thousands of samples collected nationwide from thousands of stream and river sites. BioData also provides the USGS with centralized data storage for delivering data to other systems and applications through automated web services. BioData allows users to combine data sets of known quality from different projects in various locations over time. It provides a nationally aggregated database for users to leverage data from many independent projects that, until now, was not feasible at this scale. For example, from 1991 to 2011, the USGS Idaho Water Science Center collected more than 816 bioassessment samples from 63 sites for the National Water Quality Assessment (NAWQA) Program and more than 477 samples from 39 sites for a cooperative USGS and State of Idaho Statewide Water Quality Network (fig. 1). Using BioData, 20 years of samples collected for both of these projects can be combined for analysis. BioData delivers all of the data using current taxonomic nomenclature, thus relieving users of the difficult and time-consuming task of harmonizing taxonomy among samples collected during different time periods. Fish data are reported using the Integrated Taxonomic Information Service (ITIS) Taxonomic Serial Numbers (TSN's). A simple web-data input interface and self-guided, public data-retrieval web site provides access to bioassessment data. BioData currently accepts data collected using two national protocols: (1) NAWQA and (2) U.S. Environmental Protection Agency (USEPA) National Rivers and Streams Assessment (NRSA). Additional collection protocols are planned for future versions.

Fact Sheet↗
USGS2002

Measuring streamflow in Virginia (2002 revision)

The U.S. Geological Survey (USGS), U.S. Department of the Interior, is the Nation's largest Earth-science information agency. Among its many responsibilities, such as map making and providing information on earthquakes and other natural hazards, the USGS provides information on the Nation's water resources. The USGS has collected and analyzed hydrologic (water-related) information for more than 100 years. In 1889, the first streamflow-gaging station (a site where regular observations of streamflow data are collected) operated in the United States by the USGS was established on the Rio Grande near Embudo, New Mexico. As the need for streamflow data increased, the USGS's streamflow-gaging program has grown to include more than 7,000 continuous-record streamflow-gaging stations. More than 90 percent of these stations are operated with at least partial support from State, local, or other Federal agencies In Virginia, the Department of Environmental Quality (DEQ) is a major cooperator in the streamflow-gaging program, which consists of 161 continuous-record streamflow-gaging stations located throughout the State (fig. 1). The USGS and DEQ cooperate to publish the annual USGS State data report, 'Water Resources Data-Virginia'; this two-volume publication includes streamflow data collected at the 161 streamflow-gaging stations, chemical data collected at 19 streamflow-gaging stations, and ground-water data collected from more than 250 wells located in Virginia.

Virginia↗
USGS1999

Measuring streamflow in Virginia (1999 revision)

The U.S. Geological Survey (USGS), U.S. Department of the Interior, is the Nation's largest Earth-science information agency. Among its many responsibilities, such as map making and providing information on earthquakes and other natural hazards, the USGS provides information on the Nation's water resources. The USGS has collected and analyzed hydrologic (water-related) information for more than 100 years. In 1889, the first streamflow-gaging station (a site where regular observations of streamflow data are collected) operated in the United States by the USGS was established on the Rio Grande near Embudo, New Mexico. As the need for streamflow data increased, the USGS's streamflow-gaging program has grown to include more than 7,000 continuous-record streamflow-gaging stations. More than 90 percent of these stations are operated with at least partial support from State, local, and other Federal agencies. In Virginia, the Department of Environmental Quality (DEQ) is a major cooperator in the streamflow-gaging program, which consists of 152 continuous-record streamflow-gaging stations located throughout the State (fig. 1). The USGS and DEQ cooperate to publish the annual USGS State data report, "Water Resources Data-Virginia;" this two-volume publication includes streamflow data collected at the 152 streamflow-gaging stations, chemical data collected at more than 24 streamflow-gaging stations, and ground-water data collected from more than 338 wells.

Virginia↗
USGS2013

Data management challenges in species distribution modeling

An important component in the fields of ecology and conservation biology is understanding the environmental conditions and geographic areas that are suitable for a given species to inhabit. A common tool in determining such areas is species distribution modeling which uses computer algorithms to determine the spatial distribution of organisms. Most commonly the correlative relationships between the organism and environmental variables are the primary consideration. The data requirements for this type of modeling consist of known presence and possibly absence locations of the species as well as the values of environmental or climatic covariates thought to define the species habitat suitability at these locations. These covariate data are generally extracted from remotely sensed imagery, interpolated/gridded historical climate data, or downscaled climate model output. Traditionally, ecologists and biologists have constructed species distribution models using workflows and data that reside primarily on their local workstations or networks. This workflow is becoming challenging as scientists increasingly try to use these modeling techniques to inform management decisions under different climate change scenarios. This challenge stems from the fact that remote sensing products, gridded historical climate, and downscaled climate models are not only increasing in spatial and temporal resolution but proliferating as well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis accounting for various sources of uncertainty. The scientists fitting these models generally do not have the background in computer science required to take advantage of recent advances in web-service based data acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the field of modeling are in need of a tractable platform that abstracts the inherent computational complexity required to incorporate the burgeoning field of coupled climate and ecological response modeling. In this paper we describe the computational challenges in species distribution modeling and solutions using scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a package within VisTrails, an open-source scientific workflow system.

Bulletin of the Technical Committee on Data Engine↗
USGS2008

A method for assigning species into groups based on generalized Mahalanobis distance between habitat model coefficients

Habitat association models are commonly developed for individual animal species using generalized linear modeling methods such as logistic regression. We considered the issue of grouping species based on their habitat use so that management decisions can be based on sets of species rather than individual species. This research was motivated by a study of western landbirds in northern Idaho forests. The method we examined was to separately fit models to each species and to use a generalized Mahalanobis distance between coefficient vectors to create a distance matrix among species. Clustering methods were used to group species from the distance matrix, and multidimensional scaling methods were used to visualize the relations among species groups. Methods were also discussed for evaluating the sensitivity of the conclusions because of outliers or influential data points. We illustrate these methods with data from the landbird study conducted in northern Idaho. Simulation results are presented to compare the success of this method to alternative methods using Euclidean distance between coefficient vectors and to methods that do not use habitat association models. These simulations demonstrate that our Mahalanobis-distance- based method was nearly always better than Euclidean-distance-based methods or methods not based on habitat association models. The methods used to develop candidate species groups are easily explained to other scientists and resource managers since they mainly rely on classical multivariate statistical methods. ?? 2008 Springer Science+Business Media, LLC.

Environmental and Ecological Statistics↗
USGS2003

Multichannel analysis of surface wave method with the autojuggie

The shear (S)-wave velocity of near-surface materials and its effect on seismic-wave propagation are of fundamental interest in many engineering, environmental, and groundwater studies. The multichannel analysis of surface wave (MASW) method provides a robust, efficient, and accurate tool to observe near-surface S-wave velocity. A recently developed device used to place large numbers of closely spaced geophones simultaneously and automatically (the 'autojuggie') is shown here to be applicable to the collection of MASW data. In order to demonstrate the use of the autojuggie in the MASW method, we compared high-frequency surface-wave data acquired from conventionally planted geophones (control line) to data collected in parallel with the automatically planted geophones attached to steel bars (test line). The results demonstrate that the autojuggie can be applied in the MASW method. Implementation of the autojuggie in very shallow MASW surveys could drastically reduce the time required and costs incurred in such surveys. ?? 2003 Elsevier Science Ltd. All rights reserved.

Soil Dynamics and Earthquake Engineering↗
USGS2023

The bee lab

Introduction The U.S. Geological Survey (USGS) Bee Lab is a collaborative interagency joint venture and international leader for bee (Hymenoptera: Apoidea) identification, survey design, quantification of bee and plant interrelations, and development and maintenance of occurrence databases. Each of these objectives supports native bee conservation by providing critical data and tools for the United States and other countries. The Bee Lab is part of the USGS Eastern Ecological Science Center (EESC) and located in Laurel, Maryland, at the U.S. Fish and Wildlife Service (USFWS) Patuxent Research Refuge. The laboratory houses scientists from the EESC, USGS’s Cooperative Fish and Wildlife Research Units, and the USFWS to develop identification tools and survey design support for State, Federal, Tribal, and nongovernment organization partners. In addition to the development of identification tools, important objectives include developing keys for native and nonnative bee species and making those tools accessible to partners and the public. Among the most visible and reused products produced during the development of the tools are the detailed photographs of the bees themselves. Accurate bee identification allows for better monitoring of bee species and examination of environmental factors that may influence their populations.

Fact Sheet↗
USGS2003

USGS Alaska Tissue Archival Projects: An update on FY02 activities

The banking of environmental specimens under cryogenic conditions for future retrospective analysis has been recognized for many years as an important part of environmental monitoring programs. Since 1987, the Alaska Marine Mammal Tissue Archival Project (AMMTAP) has been collecting tissue samples from marine mammals for archival in the National Biomonitoring Specimen Bank (NBSB) at the National Institute of Standards and Technology (NIST) in Gaithersburg, Maryland, USA. The USGS, Alaska Biological Science Center (ABSC), the NOAA Fisheries, Office of Protected Resources (NMFS), and the NIST conduct this partnership project, which began under the Mineral Management Service (MMS) Outer Continental Shelf Environmental Assessment Program. MMS remains the primary client agency for the AMMTAP providing programmatic guidance and review. The purpose of the project is to collect tissue samples from Alaska marine mammals and to store these specimens under the best conditions so that they can be analyzed for environmental contaminants and other constituents. A substantial part of the sample collection is from Arctic species and, since most of the animals sampled are from Alaska Native subsistence harvests, the project relies on cooperation and collaboration with several Alaska Native organizations and local governmental agencies. Although a substantial amount of recent research has been conducted on contaminants in Alaskan marine mammals, few data exists on colonial seabirds nesting in Alaska. Like marine mammals, seabirds are an important group of upper trophic level marine organisms with a potential for accumulating lipophilic contaminants and are identified by MMS as species of interest for monitoring activities. More than 95% of the seabirds breeding in the continental United States nest at colonies in the Bering and Chukchi seas and Gulf of Alaska (see USFWS 1992). Realizing the value of colonial seabirds in environmental monitoring and the lack of recent data from Alaskan seabird colonies, the U.S. Fish and Wildlife Service Alaska Maritime National Wildlife Refuge (USFWS-AMNWR), the U.S. Geological Survey Biological Resources Division (USGS-BRD), and the National Institute of Standards and Technology (NIST) initiated the Seabird Tissue Archival and Monitoring Project (STAMP) in 1998. The project was designed as a 100-year-long program to monitor long-term trends in environmental quality by collecting eggs at nesting colonies using standardized protocols, banking the egg contents under conditions that ensure chemical stability during long-term (decadal) storage, and analyzing subsamples of the stored material to establish baseline levels for persistent bioaccumulative contaminants (e.g., chlorinated pesticides, PCBs, mercury).

Conference Paper↗