This report addresses system characterization of the China-Brazil Earth Resources Satellite-4A (CBERS–4A) multispectral remote sensing satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
CBERS–4A is a joint Chinese-Brazilian medium-resolution satellite launched in December 2019 by the China National Space Agency/National Institute for Space Research (Brazil) on a Chang Zheng 4B rocket from the Taiyuan Satellite Launch Center for Earth resources monitoring. The CBERS–4A mission continues the CBERS mission that has been in continual operation since the launch of CBERS–1 in 1999.
The CBERS–4A satellite was designed and built by Academia Chinesa de Tecnologia Espacial/National Institute for Space Research and uses the Phoenix-Eye bus. CBERS–4A carries the multispectral camera and wide field imager sensors for medium-resolution land imaging and the wide swath panchromatic and multispectral camera sensor for high-resolution land imaging. This assessment focused on the multispectral camera sensor only. More information on CBERS sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at https://www.gov.br/pt-br/servicos/obter-imagens-de-sensoriamento-remoto-da-terra-geradas-pelo-satelite-cbers-04a.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that CBERS–4A provides an interior (band-to-band) geometric performance in the range of −0.02 to −0.16 pixel; an exterior geometric accuracy performance of −22.02 (−1.47 pixels) to −16.06 meters (−1.07 pixels); a radiometric accuracy performance of –0.006 to 0.925 (offset and slope); and a spatial performance for relative edge response in the range of 0.39 to 0.44, for full width at half maximum in the range of 2.38 to 2.56 pixels, and for a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.013.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030J","usgsCitation":"Vrabel, J.C., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., Park, S., and Cantrell, S., 2021, System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A), chap. J of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 35 p., https://doi.org/10.3133/ofr20211030J.","productDescription":"v, 35 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130782","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":388510,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/j/ofr20211030j.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030J"},{"id":388509,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/j/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Within the forests of Pictured Rocks National Lakeshore, biologists are trying to understand the effects beech bark disease has on wildlife species, especially species that need forest connectivity to thrive. This project used aerial imagery collected in 2005, shortly after beech bark disease infestation, and satellite imagery from 2018. The 2018 imagery represents present day conditions and was used to locate forest canopy gaps through object-based image analysis. Forest canopy gaps were identified using the multiresolution segmentation algorithm within Trimble’s eCognition software. A time change analysis was completed to understand how the forest canopy had changed from 2005 to 2018. The analysis showed areas that had maintained forest canopy, maintained a forest canopy gap, created a new canopy gap (closed forest canopy in 2005 but open canopy gap in 2018), or created new forest canopy (open canopy gap in 2005 but closed forest canopy in 2018). There were 9,127 acres of forest canopy lost, and 72.8 percent of that lost canopy occurred in a forest type where Fagus grandifolia Ehrh. (American beech) is a common tree species. The datasets developed through this project can enhance knowledge of where canopy gaps exist and help place focus on certain areas for wildlife studies. In addition, these datasets can be used in future studies to monitor the health of the forest and conduct additional change analyses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211069","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Sattler, S.R., 2021, Changes in forest connectivity from beech bark disease in Pictured Rocks National Lakeshore in the Upper Peninsula of Michigan: U.S. Geological Survey Open-File Report 2021–1069, 12 p., https://doi.org/10.3133/ofr20211069.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-124452","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388432,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1069/images"},{"id":388429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1069/coverthb.jpg"},{"id":388430,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1069/ofr20211069.pdf","text":"Report","size":"6.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1069"},{"id":388431,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EZEAYD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Effects of beech bark disease on forest connectivity in Pictured Rocks National Lakeshore from 2005 to 2018"}],"country":"United States","state":"Michigan","otherGeospatial":"Pictures Rocks National Lakeshore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.62307739257812,\n 46.42176587242696\n ],\n [\n -86.4349365234375,\n 46.45961954102543\n ],\n [\n -86.20147705078125,\n 46.57585481240773\n ],\n [\n -86.02706909179688,\n 46.619261036171515\n ],\n [\n -86.00509643554686,\n 46.669229446893404\n ],\n [\n -86.08612060546875,\n 46.66545985627255\n ],\n [\n -86.14105224609375,\n 46.677710064644344\n ],\n [\n -86.4459228515625,\n 46.557916007595786\n ],\n [\n -86.48712158203125,\n 46.55602736725248\n ],\n [\n -86.6217041015625,\n 46.44826620185314\n ],\n [\n -86.62307739257812,\n 46.42176587242696\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
Arctic barriers islands are highly dynamic features influenced by a variety of oceanographic, geologic, and environmental factors. Many Alaskan barrier islands and spits serve as habitat and protection for native species, as well as shelter the coast from waves and storms that cause flooding and degradation of coastal villages. This study summarizes changes to barrier morphology in time and space along the North Slope coast of Alaska between the United States-Canadian border and Cape Beaufort from 1947 to 2020. Changes considered in this study include number of barriers, area and perimeter, shoreline length, barrier sinuosity and width, presence and number of relict terminus features, presence and coverage of tundra vegetation, barrier orientation, and elevation metrics. Wave conditions are also summarized and related to changes in barrier morphology. The results in this report help to better predict future barrier evolution and prevalence along Alaska’s coast by increasing our understanding of Arctic barrier development, migration and degradation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211074","usgsCitation":"Hamilton, A.I., Gibbs, A.E., Erikson, L.H., and Engelstad, A.C., 2021, Assessment of barrier island morphological change in northern Alaska: U.S. Geological Survey Open-File Report 2021–1074, 28 p., https://doi.org/10.3133/ofr20211074.","productDescription":"Report: vi , 28 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-122308","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":388442,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90EQ1H7","linkHelpText":"Historical shorelines and morphological metrics for barrier islands and spits along the north coast of Alaska between Cape Beaufort and the U.S.-Canadian border, 1947 to 2019"},{"id":388441,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1074/ofr20211074.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388440,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1074/covrthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -160.83984375,\n 69.16255790810499\n ],\n [\n -141.240234375,\n 69.16255790810499\n ],\n [\n -141.240234375,\n 72.01972876525514\n ],\n [\n -160.83984375,\n 72.01972876525514\n ],\n [\n -160.83984375,\n 69.16255790810499\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060
The avian conservation community struggles to design and implement large scale, long-term coordinated bird monitoring programs within the northern Gulf of Mexico due to the complexity of the conservation enterprise in the region; this complexity arises from the diverse stakeholders, multiple jurisdictions, complex ecological processes, myriad habitats, and over 500 species of birds using the region for at least some part of their annual cycle. In addition, long-term monitoring over large spatial scales is difficult because of the need for monitoring data to both (1) evaluate management and restoration outcomes, and (2) provide reliable information about the status and trends of bird populations over time.
To address these challenges, the Gulf of Mexico Avian Monitoring Network developed a problem statement:
“How can a cost-effective monitoring strategy for the Gulf Coast bird community and ecosystem be developed that evaluates ongoing conservation activities and chronic and acute threats; maximizes learning; and is flexible and holistic enough to detect novel ecological threats and evaluate new and emerging conservation activities?”
A structured decision-making framework was then used to articulate and quantify stakeholder values related to the problem statement. One use of the stakeholder values was to develop a regional, strategic plan for bird monitoring, which is presented elsewhere. A formal and complete decision support tool for conservation investments in monitoring and research guided by the stakeholder values is presented in this report. The technical aspects of the stakeholder value model and a portfolio analysis that could be used to guide decision making when allocating resources for monitoring activities is described. Whereas the decision analysis presented here could be useful to any decision maker faced with difficult choices about resource allocation, it is designed for decision makers who request monitoring study proposals and then determine which combination of proposals to fund. The portfolio decision support tool is designed to help funding agencies and organizations identify resource allocation strategies to maximize stated objectives.
To begin the decision analysis, an objectives hierarchy and quantitative performance metrics from the values of the Gulf of Mexico bird conservation community were created by a panel of regional stakeholders. Each fundamental objective and sub-objective in the hierarchy is composed of several performance metrics. To test the decision support tool, the authors evaluated a combination of monitoring study proposals written for the region and simulated proposals. Each proposal was scored against the performance metrics and used multi-attribute utility theory to combine the multiple objectives into a measure of total monitoring benefit. The total monitoring benefit and costs of each proposal were then used in a constrained optimization routine to identify optimal monitoring portfolios, that is, a combination of activities that maximizes monitoring benefits while meeting cost and other constraints of interest to stakeholders. A graphical solution based on the concept of Pareto efficiency, which is useful in situations when cost constraints and exact budgets are not known, is also provided. Finally, an evaluation of the sensitivity of the decision-making framework to the weights assigned to objectives by stakeholders is included. This decision support tool allows decision makers to identify an optimal suite of monitoring proposals with a transparent portfolio analysis that includes user-defined constraints (such as costs).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201122","collaboration":"Prepared in Cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Fournier, A.M.V., Wilson, R.R., Lyons, J.E., Gleason, J.S., Adams, E.M., Barnhill, L.M., Brush, J.M., Cooper, R.J., DeMaso, S.J., Driscoll, M.J.L., Eaton, M.J., Frederick, P.C., Just, M.G., Seymour, M.A., Tirpak, J.M, and Woodrey, M.S., 2021, Structured decision making and optimal bird monitoring in the northern Gulf of Mexico: U.S. Geological Survey Open-File Report 2020–1122, 62 p., https://doi.org/10.3133/ofr20201122.","productDescription":"Report: ix, 62 p.; 6 Companion Files","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100582","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387878,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/sdm_tool_excel_version_2019_12_22.xlsm","text":"2. 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Gulf of Mexico Avian Monitoring Network Birds of Conservation Concern","size":"47.1 KB","linkHelpText":"- Zip file of tables in CSV format"},{"id":387873,"rank":12,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_database.docx","text":"6. R Code for Using Deepwater Horizon Project Tracker Database","size":"14.1 KB"},{"id":387882,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_proposals.docx","text":"3. R Code to Simulate Monitoring Proposals","size":"15.7 KB"},{"id":387881,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_projects-portfolios_csv.zip","text":"4. All Test Projects and Portfolios","size":"101 KB","linkHelpText":"- Zip file of tables in CSV format"},{"id":387877,"rank":11,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_matrix_csv.zip","text":"5. Matrix of Management Actions and Bird Species","size":"2.83 KB","linkHelpText":"- Zip file of tables in CSV format"},{"id":387880,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_projects-portfolios.xlsm","text":"4. All Test Projects and Portfolios","size":"1.28 MB"},{"id":387879,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/sdm_tool_excel_version_2019_12_22.zip","text":"2. Portfolio Analysis Spreadsheet","size":"5.35 KB","linkHelpText":"- Zip file of tables in CSV format"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.2509765625,\n 25.997549919572112\n ],\n [\n -91.43920898437499,\n 26.194876675795218\n ],\n [\n -87.703857421875,\n 26.175158990178133\n ],\n [\n -86.37451171875,\n 27.205785724383325\n ],\n [\n -85.4736328125,\n 26.204734267107604\n ],\n [\n -83.155517578125,\n 24.647017162630366\n ],\n [\n -81.71630859375,\n 24.347096633808512\n ],\n [\n -80.22216796875,\n 24.926294766395593\n ],\n [\n -79.881591796875,\n 26.115985925333536\n ],\n [\n -80.584716796875,\n 27.926474039865017\n ],\n [\n -81.221923828125,\n 27.858503954841247\n ],\n [\n -81.793212890625,\n 28.806173508854776\n ],\n [\n -82.957763671875,\n 30.344435586368462\n ],\n [\n -83.265380859375,\n 30.65681556429287\n ],\n [\n -84.957275390625,\n 30.751277776257812\n ],\n [\n -85.0341796875,\n 31.015278981711266\n ],\n [\n -87.51708984375,\n 30.987027960280326\n ],\n [\n -87.7587890625,\n 31.512995857454676\n ],\n [\n -88.29711914062499,\n 31.55981453201843\n ],\n [\n -88.450927734375,\n 30.996445897426373\n ],\n [\n -89.395751953125,\n 30.949346915468563\n ],\n [\n -89.97802734375,\n 30.826780904779774\n ],\n [\n -90.758056640625,\n 30.477082932837682\n ],\n [\n -91.92260742187499,\n 30.543338954230222\n ],\n [\n -94.10888671875,\n 30.344435586368462\n ],\n [\n -94.7900390625,\n 30.230594564932193\n ],\n [\n -95.69091796875,\n 29.735762444449076\n ],\n [\n -95.51513671875,\n 29.372601506681402\n ],\n [\n -95.9326171875,\n 29.19053283229458\n ],\n [\n -96.5478515625,\n 29.094577077511826\n ],\n [\n -97.6025390625,\n 28.488005204159457\n ],\n [\n -97.987060546875,\n 27.819644755099446\n ],\n [\n -98.009033203125,\n 27.176469131898898\n ],\n [\n -97.767333984375,\n 26.352497858154024\n ],\n [\n -97.2509765625,\n 25.997549919572112\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
We created this guide to introduce the user to the San Francisco Volcanic Field as a terrestrial analog site for planetary volcanic processes. For decades, the San Francisco Volcanic Field has been used to teach scientists to recognize the products of common types of volcanic eruptions and associated volcanic features. The volcanic processes and products observed in this volcanic field are like those observed on lunar and Martian surfaces. As a result, this region has been a favored location for training National Aeronautics and Space Administration astronauts and engineers since the Apollo missions.
Though the San Francisco Volcanic Field has more than 600 volcanic vents and flows, this guide will focus on S P Mountain (known locally as S P Crater, located ~30 miles north of Flagstaff, Arizona), one of the best preserved and most accessible of the volcanic cones and lava flows. S P Mountain presents both major types of basaltic eruptions—explosive and effusive—as well as some commonly associated tectonic landforms.
We assume that the user has a basic understanding of geologic concepts and terminology. For more specialized terminology, we include tables showing the classification scheme for lava compositions, styles of eruptions, and tephra sizes (tables 1, 2, and 3). If a further introduction or refresher in volcanological terminology is desired, we suggest reviewing such terms on the U.S. Geological Survey Volcano Science Center’s online glossary (https://volcanoes.usgs.gov/vsc/glossary/).
One term requires clarification at the start of this guide—the term cinder. The terms cinder and cinder cone are widely used to describe the material and edifice produced by lava fountains. However, the term comes from the mining and construction industries and has no clear or formal definition. The international committees in geology and volcanology have chosen the term tephra to be the general term to describe pyroclasts (material ejected through a volcanic explosion or from a volcanic vent). Therefore, in this guide, we use the term tephra rather than cinder.
This guide is outlined as follows:
Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Along the coast of the U.S. Gulf of Mexico, the eastern oyster (Crassostrea virginica) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach.
Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs.
Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions.
While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data.
Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management.
Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211063","usgsCitation":"La Peyre, M.K., Marshall, D.A., and Sable, S.E., 2021, Oyster model inventory: Identifying critical data and modeling approaches to support restoration of oyster reefs in coastal U.S. Gulf of Mexico waters: U.S. Geological Survey\nOpen-File Report 2021–1063, 40 p., https://doi.org/10.3133/ofr20211063.","productDescription":"Report: viii, 40p.; 3 Appendix Tables","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126014","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1063/images"},{"id":388041,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1063/coverthb.jpg"},{"id":388042,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063.pdf","text":"Report","size":"37.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1063"},{"id":388043,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.csv","text":"Table 1.1 (.csv)","size":"5.07 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388044,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.csv","text":"Table 2.1 (.csv)","size":"5.40 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388045,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.csv","text":"Table 3.1 (.csv)","size":"63.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"},{"id":388046,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.xlsx","text":"Table 1.1 (.xlsx)","size":"14.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388047,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.xlsx","text":"Table 2.1 (.xlsx)","size":"13.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388048,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.xlsx","text":"Table 3.1 (.xlsx)","size":"46.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.177734375,\n 27.527758206861886\n ],\n [\n -82.6171875,\n 29.267232865200878\n ],\n [\n -83.8916015625,\n 30.372875188118016\n ],\n [\n -85.078125,\n 30.259067203213018\n ],\n [\n -86.7919921875,\n 30.826780904779774\n ],\n [\n -88.681640625,\n 30.675715404167743\n ],\n [\n -90.263671875,\n 30.486550842588485\n ],\n [\n -90.3076171875,\n 29.649868677972304\n ],\n [\n -91.62597656249999,\n 30.14512718337613\n ],\n [\n -94.3505859375,\n 29.916852233070173\n ],\n [\n -96.45996093749999,\n 29.036960648558267\n ],\n [\n -97.470703125,\n 27.800209937418252\n ],\n [\n -97.734375,\n 26.78484736105119\n ],\n [\n -97.0751953125,\n 25.918526162075153\n ],\n [\n -96.767578125,\n 27.01998400798257\n ],\n [\n -96.0205078125,\n 28.34306490482549\n ],\n [\n -94.39453125,\n 29.075375179558346\n ],\n [\n -92.94433593749999,\n 29.34387539941801\n ],\n [\n -92.2412109375,\n 29.075375179558346\n ],\n [\n -90.9228515625,\n 28.806173508854776\n ],\n [\n -89.033203125,\n 28.613459424004414\n ],\n [\n -88.59374999999999,\n 29.267232865200878\n ],\n [\n -88.9013671875,\n 29.916852233070173\n ],\n [\n -87.4951171875,\n 29.80251790576445\n ],\n [\n -86.17675781249999,\n 29.99300228455108\n ],\n [\n -85.341796875,\n 29.19053283229458\n ],\n [\n -84.0673828125,\n 29.57345707301757\n ],\n [\n -83.4521484375,\n 28.998531814051795\n ],\n [\n -83.3642578125,\n 27.72243591897343\n ],\n [\n -82.4853515625,\n 26.667095801104814\n ],\n [\n -82.177734375,\n 27.527758206861886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Cooperative Fish and Wildlife Research Units
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Southern sea otters (Enhydra lutris nereis) have recovered slowly from their near extinction a century ago, and their continued recovery has been challenged by multiple natural and anthropogenic factors. Development of an integrated population model (IPM) for southern sea otters has been identified as a management priority, to help in evaluating the relative impacts of known threats and guide best management options for species recovery. An IPM represents an analytical modeling framework where various types of data relevant to animal health, population trends, and survival can be evaluated collectively to project future population dynamics under different resource management scenarios. Here, we describe the development of a spatially explicit IPM for southern sea otters that is fit by using Bayesian methods to multiple datasets including a time series of range-wide survey counts, estimated survival rates of tagged animals from telemetry-based population studies, and cause-of-death data from comprehensive necropsies of beach-cast carcasses. The core of the model is a stage-structured matrix, in which survival rates for a given life history stage, year, and location are computed as the outcome of multiple ‘competing risks,’ or hazards, allowing for spatiotemporal variation in each hazard, density-dependence, and stochasticity. The parameterized IPM was used to (1) examine how age and sex-specific hazards vary over space and time, (2) gain insights into density-dependent variation in specific hazards, (3) assess population-level effects of known mortality hazards in the past and in future projections, and (4) evaluate the relative benefits of various potential management actions to address these hazards.
Our results indicated that different types of hazards have variable impacts at different life history stages of sea otters; for example, shark-bite mortality had a strong impact on mortality of subadult females but relatively low impacts on aged adult female survival, whereas End Lactation Syndrome showed just the opposite age-based pattern. There also was spatial and temporal variation in exposure to different hazards; for example, shark-bite mortality generally was highest at the north and south ends of the sea otter range, End Lactation Syndrome and cardiac disease were highest in the center part of the range, and harmful algal bloom intoxication and protozoal infection mortalities were highest around Morro Bay. The relative impacts of hazards depended on population density; for example, shark-bite mortality had the greatest effect on male survival when population abundance was low, but as densities increased the impacts of cardiac disease (for aged adults) and acanthocephalan peritonitis (for subadults) exceeded the effects of shark-bite mortality. Sensitivity analyses showed that modifying certain hazard rates can have substantial impacts on future population growth; for example, if the shark-bite hazard rate were to decrease by 20 percent, projected abundance after 50 years is predicted to be 18-percent higher, on average, than under baseline conditions. We used the IPM to evaluate the possible impacts of a potential management action: the reintroduction of sea otters to currently unoccupied parts of their historical range. We found that there were large increases in expected growth potential associated with reintroduction programs to various locations to the north and south of the currently occupied range, although a reintroduction to San Francisco Bay was projected to have the greatest potential impacts on future population growth.
The IPM for southern sea otters presented here provides resource managers with a useful tool for evaluating the impacts of specific hazards, forecasting future population dynamics and range expansion, and evaluating alternative management scenarios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211076","programNote":"Wildlife Program","usgsCitation":"Tinker, M.T., Carswell, L.P., Tomoleoni, J.A., Hatfield, B.B., Harris, M.D., Miller, M.A., Moriarty, M.E., Johnson, C.K., Young, C., Henkel, L.A., Staedler, M.M., Miles, A.K., and Yee, J.L., 2021, An integrated population model for southern sea otters: U.S. Geological Survey Open-File Report 2021–1076, 50 p., https://doi.org/10.3133/ofr20211076.","productDescription":"vii, 50 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-126237","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":387937,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1076/images"},{"id":387936,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.xml"},{"id":387935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":387934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1076/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.23388671874999,\n 37.125286284966805\n ],\n [\n -121.59667968749999,\n 37.37015718405753\n ],\n [\n -121.55273437499999,\n 37.666429212090605\n ],\n [\n -122.1240234375,\n 38.61687046392973\n ],\n [\n -122.84912109375,\n 39.30029918615029\n ],\n [\n -123.37646484374999,\n 40.329795743702064\n ],\n [\n -123.37646484374999,\n 40.84706035607122\n ],\n [\n -123.3544921875,\n 41.705728515237524\n ],\n [\n -123.22265625000001,\n 42.00032514831621\n ],\n [\n -124.49707031249999,\n 42.01665183556825\n ],\n [\n -124.98046874999999,\n 40.94671366508002\n ],\n [\n -124.67285156250001,\n 39.90973623453719\n ],\n [\n -124.18945312500001,\n 38.92522904714054\n ],\n [\n -123.3544921875,\n 37.579412513438385\n ],\n [\n -122.9150390625,\n 37.23032838760387\n ],\n [\n -122.23388671874999,\n 37.125286284966805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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Aside from construction aggregate materials, the value of nonfuel mineral commodities that have been produced in North Dakota is small, although there is potential for the existence of several mineral resource deposit types which are not economically viable at this time. In this report, we present a mineral resource inventory of the State of North Dakota, developed by the U.S. Geological Survey at the request the Bureau of Land Management. To set the stage for that inventory, we briefly outline the long and complex geologic history of North Dakota that extends back more than 3 billion years. Using several existing databases, we summarize the distribution of known mineral commodities and the results of commodity exploration over time. Using all available data, we discuss the potential for economic occurrences of 13 commodities in North Dakota, including some listed as Critical Minerals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211057","collaboration":"Prepared in cooperation with Bureau of Land Management","usgsCitation":"Box, S.E., and Cossette, P.M., 2021, Mineral resource inventory of North Dakota: U.S. Geological Survey Open-File Report 2021–1057, 42 p., https://doi.org/10.3133/ofr20211057.","productDescription":"Report: vii, 42 p.; 4 Appendixes","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-116051","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":399509,"rank":12,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94BA7RO","text":"USGS data release","description":"USGS data release","linkHelpText":"Dataset for mineral resource inventory of North 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Dakota\",\"nation\":\"USA \"}}]}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Long Island National Wildlife Refuge Complex in New York. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of five marsh management units within the refuge complex and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge-complex scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to about $24,000, but that further expenditures may yield diminishing return on investment. Potential management actions in optimal portfolios at total costs less than $24,000 consistently included approaches for increasing drainage from the marsh surface within the marsh management units. The potential management benefits were derived from expected improvements in surface-water drainage and capacity for marsh elevation to keep pace with sea-level rise, and presumed increases in numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds. The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Long Island National Wildlife Refuge Complex that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211070","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., and Williams, M.R., 2021, Optimization of salt marsh management at the Long Island National Wildlife Refuge Complex, New York, through use of structured decision making (ver. 1.1, August 2021): U.S. Geological Survey Open-File Report 2021–1070, 34 p., https://doi.org/10.3133/ofr20211070.","productDescription":"Report: vi, 34 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126538","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387845,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1070/versionHist.txt","size":"640 B","linkFileType":{"id":2,"text":"txt"}},{"id":387151,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1070/ofr20211070.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1070"},{"id":387150,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1070/coverthb2.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0478515625,\n 40.576412521044425\n ],\n [\n -73.6138916015625,\n 40.54720023441049\n ],\n [\n -73.1854248046875,\n 40.60978237983301\n ],\n [\n -72.66357421875,\n 40.77638178482896\n ],\n [\n -72.015380859375,\n 40.96330795307353\n ],\n [\n -71.795654296875,\n 41.091772220976644\n ],\n [\n -72.2625732421875,\n 41.18278832811288\n ],\n [\n -72.7294921875,\n 41.02964338716638\n ],\n [\n -73.245849609375,\n 40.94256444133327\n ],\n [\n -73.4820556640625,\n 40.967455873296714\n ],\n [\n -73.707275390625,\n 40.8595252289932\n ],\n [\n -73.8775634765625,\n 40.79301881008675\n ],\n [\n -74.0203857421875,\n 40.693134153308065\n ],\n [\n -74.0478515625,\n 40.576412521044425\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: July 13, 2021; Version 1.1: August 11, 2021","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
This report addresses system characterization of Planet’s Dove-R and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 4-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Dove-R has an interior geometric performance in the range of −0.306 (−0.102 pixel) to 0.286 m (0.095 pixel) in easting and 0.090 (0.030 pixel) to 1.084 m (0.361 pixel) in northing in band-to-band registration, an exterior geometric performance of −5.10 m (−0.51 pixel) in easting and 3.30 m (0.33 pixel) in northing offset in comparison to Sentinel-2, a radiometric performance in the range of −0.023 to −0.008 in offset and 0.948 to 1.077 in slope, and a spatial performance in the range of 2.96 to 3.15 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.003.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030D","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2021, System characterization report on Planet’s Dove-R, chap. D of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 34 p., https://doi.org/10.3133/ofr20211030D.","productDescription":"v, 34 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-126678","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":387784,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/d/ofr20211030d.pdf","text":"Report","size":"3.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030D"},{"id":387783,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/d/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Arkansas bauxite district, which comprises about 275 square miles (710 square kilometers) of central Arkansas, produced an order of magnitude more bauxite and alumina than the other bauxite districts in the United States combined. Bauxite was mined in the region continuously from 1898 to 1982. These bauxites are laterite deposits, formed from intensive in-place weathering of the exposed surface of the Granite Mountain pluton, a Late Cretaceous batholith composed mainly of nepheline syenite and lesser amounts of syenite. Nepheline syenite was the aluminum source for the bauxite and clay deposits that blanket the pluton. The early Eocene continental sedimentary rocks that contain and overlie the bauxite deposits indicate that central Arkansas had a warm tropical environment during bauxite formation.
Bauxite ores are the principal sources of aluminum. Some of the global bauxite deposits have been found to contain co-occurring metals that have essential applications in modern technologies. For example, bauxite is the largest global source of gallium (Ga), used in semiconductors, which is recovered as a byproduct of processing bauxite to recover alumina. Other critical metal commodities within some bauxites that reportedly have potential for byproduct recovery include niobium (Nb), scandium (Sc), and rare earth elements (REEs). Currently (2021), the United States is wholly dependent on imports for its supplies of bauxite for processing to produce alumina. The United States is also dependent on foreign sources of gallium, niobium, and scandium, as well for most of its domestic requirements of REEs.
For these reasons, samples were collected from Arkansas bauxite deposits, associated clays, mill residue wastes (respectively referred to as red muds and black sands), and the parent nepheline syenite to determine their elemental content, with a particular focus on gallium, niobium, scandium, and REEs. Each sample was analyzed for 60 elements; these data and the methods used are published as a U.S. Geological Survey data release.
The results indicate that, of the critical metals in bauxites, gallium is a potential byproduct from the central Arkansas bauxite deposits. The highest gallium concentrations occur in the raw bauxite ore, with an average concentration of 76 parts per million (ppm). Gallium partitions with alumina (the product) rather than into mine waste residues. Results indicate an average niobium content of 662 ppm in the Arkansas bauxite ores. Niobium progressively increases in concentration from parent syenite (247 ppm) to clays (315 ppm) and further from bauxite (662 ppm) to processed residues (1,075 ppm). Low concentrations of scandium were found in all samples, averaging 10 ppm or less in the parent rock (syenite), bauxite, clays, and processing residues. Modest concentrations of the light and heavy REEs were found in samples of bauxite ores, bauxitic clays and interbedded clays, syenite, and the residues of ore. The highest REE values were found in processed residues, with average concentrations of 613 ppm total light REEs and 130 ppm total heavy REEs. These concentrations suggest that additional processing to recover REEs is unlikely to be economic in the foreseeable future.
Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225
Historically, the Mississippi Alluvial Valley (MAV) (Partners in Flight Bird Conservation Region #26) was predominantly bottomland hardwood forest, but natural vegetation has been cleared from about 80 percent of this ecoregion and converted primarily to agriculture. Because most bird species that are of conservation concern in this region are dependent on forested wetlands, bottomland hardwood forest is the habitat of greatest conservation concern in the MAV. Past conservation planning for forest-dwelling birds in this region has focused on habitat objectives with presumptions regarding bird population goals being met through habitat provision. To better define population objectives, we estimated current populations of silvicolous birds on the basis of detections during 10 years of North American Breeding Bird Surveys (BBS). For each species, we used their estimated population and historical (1966–2015) change in their relative abundance, as assessed from BBS data, to establish regional population goals. We used the variance associated with historical BBS trends to estimate the minimum forest area required to sustain greater than or equal to (≥) 25 breeding pairs, which we combined with predicted probability of occupancy to identify sustainable forested habitat. For 54 species, we used published empirical density estimates, as affected by forest management, to estimate the proportion of the population objective that could be provisioned within sustainable forest patches. The area of presumed population-sustaining habitat, under existing forest management, was sufficient to support the species’ population objective for 23 species. We estimated that the target populations of seven additional species (Black-and-white Warbler, Brown Thrasher, Cerulean Warbler, Eastern Towhee, Indigo Bunting, Wood Thrush, and Yellow-breasted Chat) could be supported by current forest area through widespread changes in forest management. Target populations of seven other species (American Robin, Barred Owl, Boat-tailed Grackle, Chipping Sparrow, Eastern Phoebe, Mississippi Kite, and Red-headed Woodpecker) were accommodated within the MAV when populations in both forest and nonforest habitats are considered. For the remaining 20 species, we estimated the population increase needed to achieve their population goals. For these species, we estimated the additional area of forest restoration required to achieve their population goal within sustainable forest patches or, alternatively, the additional area of occupied habitat required to support their population goal within both forest and nonforest habitat. An additional 700,000 hectares of sustainable forest habitat may be enough to attain the forest-dependent population goals for most bird species within the MAV.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201097","collaboration":"Prepared in cooperation with the Lower Mississippi Valley Joint Venture","usgsCitation":"Twedt, D.J., and Mini, A., 2021, Forest area to support landbird population goals for the Mississippi Alluvial Valley (ver. 1.1, August 2021): U.S. Geological Survey Open-File Report 2020–1097, 84 p., https://doi.org/10.3133/ofr20201097.","productDescription":"Report: vi, 75 p.; 2 Appendixes; Version History","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112336","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":436253,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YMSM8I","text":"USGS data release","linkHelpText":"Eastern Ecological Science CenterxLegacy Data ReleasesPatuxent Wildlife Research CenterPredicted Avian Species Occupancy, Area of Sustainable Forest Habitat, and Area of Occupied Habitat within the Mississippi Alluvial Valley Bird Conservation Region Predicted Avian Species Occupancy, Area of Sustainable Forest Habitat, and Area of Occupied Habitat within the Mississippi Alluvial Valley Bird Conservation Region"},{"id":436252,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AFKXXK","text":"USGS data release","linkHelpText":"Stop locations along Breeding Bird Survey routes in the Gulf Coastal Plains & Ozarks region"},{"id":381438,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1097/ofr20201097.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1097"},{"id":387552,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1097/versionHist.txt","size":"519 B","linkFileType":{"id":2,"text":"txt"}},{"id":381541,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://doi.org/10.5066/P9YMSM8I","text":"Appendixes 7, 8, and 9","linkHelpText":"- Predicted avian species occupancy"},{"id":381539,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://doi.org/10.5066/P9AFKXXK","text":"Appendixes 2 and 3","linkHelpText":"- Bird detections during North American Breeding Bird Surveys"},{"id":381437,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1097/coverthb3.jpg"}],"country":"United States","state":"Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Alluvial Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.76953125,\n 36.932330061503144\n ],\n [\n -89.80224609374999,\n 37.142803443716836\n ],\n [\n -90.06591796875,\n 37.055177106660814\n ],\n [\n -92.04345703125,\n 34.63320791137959\n ],\n [\n -91.91162109375,\n 32.47269502206151\n ],\n [\n -92.197265625,\n 30.41078179084589\n ],\n [\n -90.06591796875,\n 29.22889003019423\n ],\n [\n -89.4287109375,\n 30.012030680358613\n ],\n [\n -91.1865234375,\n 31.372399104880525\n ],\n [\n -90.63720703125,\n 32.565333160841035\n ],\n [\n -89.7802734375,\n 33.46810795527896\n ],\n [\n -89.7802734375,\n 34.615126683462194\n ],\n [\n -88.76953125,\n 36.932330061503144\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: August 2021; Version 1.0: February 2021","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
The Wyoming Landscape Conservation Initiative (WLCI) was established in 2007 as a collaborative interagency partnership to develop and implement science-based conservation actions. During the past 11 years, partners from U.S. Geological Survey (USGS), State and Federal land management agencies, universities, and the public have collaborated to implement a long-term (more than 10 years) science-based program that assesses and enhances the quality and quantity of wildlife habitats in the southwest Wyoming region while facilitating responsible development. The USGS WLCI Science Team completes scientific research and develops tools that inform and support WLCI partner planning, decision making, and on-the-ground management actions.
In fiscal year 2018, the USGS initiated 3 new projects and continued efforts on 21 ongoing science and web-development projects. The first new project was initiated to support Secretarial Order 3362 which calls on the USGS to assist Western States in mapping big-game migration corridors and developing new mapping tools. During 2018, the USGS hosted a workshop in Laramie, Wyoming, which included more than 70 State and Federal wildlife experts from Colorado, New Mexico, Texas, and Wyoming. Most of the mapping and migration tool curricula used in the workshop were derived from prior WLCI studies and mapping efforts of big-game migration movement in habitats undergoing large-scale energy development.
The second new project was in response for WLCI partners to better understand sedimentation and hydrogeomorphic processes in a cold-desert headwater and the third new project was designed to improve our approach for people to access, manage, and analyze WLCI data and WLCI resource information. The USGS published 18 products (including peer-reviewed journal articles, USGS series publications, and data releases) and provided more than a dozen professional oral and poster presentations at scientific meetings and numerous informal presentations to WLCI partners at meetings and workshops. This report summarizes the objectives and status of each project and highlights the USGS 2018 accomplishments and products.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211067","usgsCitation":"Anderson, P.J., Aldridge, C.L., Alexander, J.S., Assal, T.J., Aulenbach, S., Bowen, Z.H., Chalfoun, A.D., Chong, G.W., Copeland, H., Edmunds, D.R., Germaine, S., Graves, T., Heinrichs, J.A., Homer, C.G., Huber, C.C., Johnston, A., Kauffman, M.J., Manier, D.J., McShan, R.R., Eddy-Miller, C.A., Miller, K.A., Monroe, A.P., O’Donnell, M.S., Ortega, A., Walters, A.W., Wieferich, D., Wyckoff, T.B., and Zeigenfuss, L., 2020, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2018 annual report: U.S. Geological Survey Open-File Report 2021–1067, 33 p., https://doi.org/10.3133/ofr20211067.","productDescription":"ix, 33 p.","onlineOnly":"Y","ipdsId":"IP-117398","costCenters":[{"id":207,"text":"Core Research Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":387403,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1067/coverthb.jpg"},{"id":387404,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1067/ofr20211067.pdf","text":"Report","size":"6.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1067"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.060791015625,\n 43.11702412135048\n ],\n [\n -111.060791015625,\n 41.02964338716638\n ],\n [\n -105.677490234375,\n 41.0130657870063\n ],\n [\n -105.875244140625,\n 41.60722821271717\n ],\n [\n -105.83129882812499,\n 42.32606244456202\n ],\n [\n -106.10595703125,\n 42.50450285299051\n ],\n [\n -107.083740234375,\n 42.49640294093705\n ],\n [\n -107.940673828125,\n 42.52879629320373\n ],\n [\n -108.896484375,\n 42.49640294093705\n ],\n [\n -109.3798828125,\n 42.62587560259137\n ],\n [\n -109.75341796875,\n 42.956422511073335\n ],\n [\n -110.06103515625,\n 43.197167282501276\n ],\n [\n -111.060791015625,\n 43.11702412135048\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
This report addresses system characterization of Planet’s Dove Classic satellites and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 4-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Dove Classic has an interior geometric performance in the range of −0.218 (−0.073 pixel) to −0.037 m (−0.012 pixel) in easting and −0.167 (−0.056 pixel) to −0.111 m (−0.037 pixel) in northing in band-to-band registration, an exterior geometric error of −6.841 (−2.280 pixels) in easting and −6.235 m (−2.078 pixels) in northing offset in comparison to Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.057 to −0.010 in offset and 0.963 to 1.298 in slope, and a spatial performance in the range of 2.77 to 3.35 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.003 to 0.010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030C","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2021, System characterization report on Planet’s Dove Classic, chap. C of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030C.","productDescription":"v, 28 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-126677","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":387443,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/c/ofr20211030c.pdf","text":"Report","size":"31.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030C"},{"id":387442,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/c/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
A study was conducted during June–October 2020 to evaluate factors affecting the migration success of adult sockeye salmon (Oncorhynchus nerka) in the Yakima River, Washington. A total of 144 adult sockeye salmon were tagged and released during the study. Most fish (112 fish) were collected, tagged with passive integrated transponder (PIT), and released at the mouth of the Yakima River. The remaining fish were tagged with a radio transmitter and PIT tag: 13 fish were collected, tagged, and released at Prosser Dam; 13 fish were collected and tagged at Prosser Dam, transported downstream, and released at the mouth of the Yakima River; and 6 fish were collected, tagged, and released at the mouth of the Yakima River. Radio-tagged fish released at Prosser Dam initially moved upstream and spread out in the river reach between Prosser and Sunnyside Dams, but all fish stopped moving and several transmitters were recovered. Detection records and temperature data from recovered transmitters were the basis for inferring that avian predators consumed at least 6 of the 13 fish. Fifteen of the 19 radio-tagged sockeye salmon released at the mouth of the Yakima River moved upstream in the Columbia River and were detected at Johnson Island in the Hanford Reach, or at Priest Rapids Dam. Two of these fish, tagged on August 7, eventually moved back downstream and entered the Yakima River when water temperatures in the lower Yakima River were 16–18 degrees Celsius (°C). One fish moved upstream to Sunnyside Dam where its tag was later recovered. The other fish moved farther upstream and was detected at Prosser Dam, but eventually moved downstream and its tag was recovered near Benton City, Washington. None of the recovered tags were found near a carcass. More than one-half of the sockeye salmon that were collected, tagged, and released at the mouth of the Yakima River were subsequently detected, and the greatest proportion of fish from groups released during June, July, and August entered the Yakima River. This finding suggests that adult sockeye salmon are present at the mouth of the Yakima River throughout the summer. Detection records for tagged fish at monitoring sites located near cool water inputs in the lower Yakima River suggest that sockeye salmon do not spend a substantial amount of time at these locations. Fish count data at Prosser Dam fish ladders showed that sockeye salmon had a bi-modal pattern of upstream migration with peaks in late June/early July and September when water temperature in the lower Yakima River was 20 °C or less. Sixty-one percent of PIT-tagged sockeye salmon detected at Prosser Dam were eventually collected at the adult fish trapping facility at Roza Dam where fish are collected and transported upstream to Cle Elum Reservoir. These data, in conjunction with results from other studies, suggest that a substantial proportion of Yakima River sockeye salmon fail to arrive at Roza Dam. Additional research will be required to better understand factors affecting Yakima River sockeye salmon.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211075","collaboration":"Prepared in cooperation with Bureau of Reclamation, Yakama Nation Fisheries, and Washington Department of Fish and Wildlife","usgsCitation":"Kock, T.J., Hansen, A.C., Evans, S.D., Visser, R., Saluskin, B., Matala, A., and Hoffarth, P., 2021, Evaluation of factors affecting migration success of adult sockeye salmon (Oncorhynchus nerka) in the Yakima River, Washington, 2020: U.S. Geological Survey Open-File Report 2021–1075, 30 p., https://doi.org/10.3133/ofr20211075.","productDescription":"vi, 30 p.","onlineOnly":"Y","ipdsId":"IP-128700","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":387441,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1075/ofr20211075.pdf","text":"Report","size":"5.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1075"},{"id":387440,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1075/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.8770751953125,\n 45.96642454131025\n ],\n [\n -118.67431640625,\n 45.96642454131025\n ],\n [\n -118.67431640625,\n 46.916503267244835\n ],\n [\n -120.8770751953125,\n 46.916503267244835\n ],\n [\n -120.8770751953125,\n 45.96642454131025\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
More than 250 landslides were triggered across the eastern volcanic islands of Chuuk State in the Federated States of Micronesia by torrential rainfall from tropical storm Chata’an on July 2, 2002. Landslides triggered during nearly 20 inches of rainfall in less than 24 hours caused 43 fatalities and the destruction or damage of 231 structures, including homes, schools, community centers, and medical dispensaries. Landslides also buried roads, crops, and water supplies. The landslides ranged in volume from a few cubic meters to more than 1 million cubic meters. Most of the failures began as slumps and transformed into debris flows, some of which traveled several hundred meters across coastal flatlands into populated areas. A landslide-inventory map produced after the storm shows that the island of Tonoas had the largest area affected by landslides, although the islands of Weno, Fefan, Etten, Uman, Siis, Udot, Eot, and Fanapanges also had significant landslides. Based on observations since the storm, we estimate the continuing hazard from landslides triggered by Chata’an to be relatively low. However, tropical storms and typhoons similar to Chata’an frequently develop in Micronesia and are likely to affect the islands of Chuuk in the future.
To assess the landslide hazard from future tropical storms, we produced a hazard map that identifies landslide-source areas of high, moderate, and low hazard. This map can be used to identify relatively safe areas for relocating structures or establishing areas where people could gather for shelter in relative safety during future typhoons or tropical storms similar to Chata’an.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20041348","productDescription":"Report: 22 p.; 2 Plates: 35.71 x 40.02 inches","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387302,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2004/1348/ofr20041348_plate1_Revision.pdf","text":"Plate 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2004-1348 Plate 1","linkHelpText":"Landslide Inventory Map of Chuuk Islands Affected by Typhoon Chata'an"},{"id":182255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2004/1348/coverthb.jpg"},{"id":387301,"rank":2,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2004/1348/versionHist.txt","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2004-1348 version history"},{"id":387300,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2004/1348/ofr20041348_pamphlet_Revision.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2004-1348 Pamphlet"},{"id":387303,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2004/1348/ofr20041348_plate2_Revision.pdf","text":"Plate 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2004-1348 Plate 2","linkHelpText":"Debris-Flow Hazard Map of Chuuk Islands Affected by Typhoon Chata’an"},{"id":391875,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_69259.htm"}],"country":"Federated States of Micronesia","state":"Chuuk State","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 151.675,\n 7.2789\n ],\n [\n 151.9022,\n 7.2789\n ],\n [\n 151.9022,\n 7.4689\n ],\n [\n 151.675,\n 7.4689\n ],\n [\n 151.675,\n 7.2789\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: July 21, 2021","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225
Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The Coconino aquifer (C aquifer) is a regionally extensive multiple-aquifer system supplying water for municipal, agricultural, and industrial use in northeastern Arizona, northwestern New Mexico, and southeastern Utah. This report focuses on the C aquifer in the arid to semi-arid area between St. Johns, Ariz., and Flagstaff, Ariz., along the Interstate-40 corridor where an increase in groundwater withdrawals coupled with ongoing drought conditions increase the potential for substantial water-level decline within the aquifer.
The U.S. Geological Survey (USGS) C-aquifer Monitoring Program began in 2005 to establish baseline groundwater and surface-water conditions and to quantify physical and water-chemistry responses to pumping stresses and climate. This report presents data previously reported in Brown and Macy (2012) that extend back as far as the 1950s, along with new data collected from the USGS C-aquifer Monitoring Program since that publication, from water years 2012 to 2019.
Water levels in 17 wells are measured quarterly as part of the C-aquifer Monitoring Program, and five of those are continuously monitored at 15-minute intervals. Water levels in an additional 18 wells in the study area are measured periodically by the USGS or other agencies. The largest historical change in water level in the study area was a decrease of 81.20 feet in Lake Mary 1 Well near Flagstaff between 1962 and 2018. Changes in water levels were greatest around major pumping centers and in the eastern extent of the study area.
Surface-water water-quality parameters (pH, water temperature, specific conductance, and dissolved oxygen) and streamflow discharge measurements were collected and analyzed along perennial, groundwater-fed reaches of Clear Creek, Chevelon Creek, and the Little Colorado River during nine baseflow investigations of varying extent between 2005 and 2019. Both Clear Creek and Chevelon Creek gain in flow from the beginning of their perennial reaches to their outflow into the Little Colorado River. The Little Colorado River has relatively steady streamflow in the reach between where the two tributaries enter the river. Chevelon Creek showed an increase in median specific conductance during all baseflow investigations of nearly 4,000 microsiemens per centimeter (μS/cm) from near the headwaters to the confluence with the Little Colorado River; Clear Creek also showed an increase in median specific conductance of almost 5,000 μS/cm from headwaters to confluence. Water temperature, dissolved oxygen, and pH do not show substantial trends along the reaches of Clear Creek, Chevelon Creek, or the Little Colorado River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211051","collaboration":"Prepared in cooperation with the Navajo Nation and the City of Flagstaff","usgsCitation":"Jones, C.J.R., and Robinson, M.J., 2021, Groundwater and surface-water data from the C-aquifer monitoring program, Northeastern Arizona, 2012–2019: U.S. Geological Survey Open-File Report 2021–1051, 34 p., https://doi.org/10.3133/ofr20211051.","productDescription":"vi, 34 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-115787","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":387185,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20121196","text":"Open-File Report 2012-1196","linkHelpText":"- Groundwater, Surface-Water, and Water-Chemistry Data from C-aquifer Monitoring Program, Northeastern Arizona, 2005-11"},{"id":387177,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1051/covrthb.jpg"},{"id":387178,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1051/ofr20211051.pdf","text":"Report","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.829833984375,\n 34.27083595165\n ],\n [\n -109.149169921875,\n 34.27083595165\n ],\n [\n -109.149169921875,\n 36.146746777814364\n ],\n [\n -111.829833984375,\n 36.146746777814364\n ],\n [\n -111.829833984375,\n 34.27083595165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Data from a multi-year radio telemetry study were used to assess seasonal distribution patterns for two long-lived, federally endangered catostomids across substantially different water conditions in Clear Lake Reservoir, northern California. Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) suckers, two species endemic to the Klamath Basin, were implanted with radio transmitters in each of 3 years in an effort to expand our understanding of seasonal sucker movements within the reservoir and their migrations in spawning tributaries. Clear Lake Reservoir and its tributaries are part of a critical management unit within the Lost River Basin Recovery Unit for populations of Lost River and shortnose suckers. We documented residency and migratory behaviors and how behaviors were affected by lake surface elevations and water management practices.
Adult suckers were captured during autumn trammel net sampling in the west lobe of the reservoir and implanted with internal radio transmitters. A total of 163 suckers were radio-tagged (75 in 2014, 64 in 2015, and 24 in 2016); 27 more shortnose suckers were tagged than Lost River suckers to reflect the larger population of shortnose suckers in the reservoir. Sex ratios were approximately equal for each species. Aerial telemetry surveys were used to monitor radio-tagged fish from January 20 to December 2 each year and to document the upstream extent of spawning migrations in the tributaries. Surveys were scheduled more frequently during the spawning season (February–June) when suckers are known to move out of the reservoir and into spawning tributaries.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211061","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Banet, N.V., Hewitt, D.A., Dolan-Caret, A., and Harris, A.C., 2021, Spatial and temporal distribution of radio-tagged Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) suckers in Clear Lake Reservoir and associated spawning tributaries, Northern California, 2015–17: U.S. Geological Survey Open-File Report 2021–1061, 37 p., https://doi.org/10.3133/ofr20211061.","productDescription":"vi, 37 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120279","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":387167,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2021/1061/ofr20211061_landing.html","text":"Animated movements and migrations","description":"OFR 2021-1061 Animated movements and migrations."},{"id":387166,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1061/ofr20211061.pdf","text":"Report","size":"12.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1061"},{"id":387165,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1061/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Clear Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.25610351562499,\n 41.78616105896385\n ],\n [\n -121.03637695312499,\n 41.78616105896385\n ],\n [\n -121.03637695312499,\n 41.93548729665268\n ],\n [\n -121.25610351562499,\n 41.93548729665268\n ],\n [\n -121.25610351562499,\n 41.78616105896385\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Bedload and ancillary data were collected to calculate and compare the bedload trapping efficiencies of four types of pressure-difference bedload samplers as part of episodic, sediment-recirculating flume experiments at the St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, in January–March 2006. The bedload-sampler experiments, which were conceived, organized, and led by the U.S. Geological Survey’s Office of Surface Water, were part of a broader suite of experiments performed in the rectangular, concrete-lined, sediment-recirculating Main Channel Facility (“main channel flume”). Collectively referred to as “StreamLab06,” the experiments were conducted under the auspices of the National Center for Earth-Surface Dynamics, University of Minnesota.
Four pressure-difference-type bedload samplers—a standard Helley-Smith, US BLH-84, Elwha, and Toutle River-2—were deployed by using hand-held rods in the main flume in a series of trials during steady flows as part of the first two of seven phases of the StreamLab06 experiments. The Phase I flows were released over a sand bed. Gravel composed the bed during the Phase II flows. Bedload samples were collected during flows ranging from 2.0 cubic meters per second (near the incipient motion of bed material) to 5.5 cubic meters per second. A total of 2,030 bedload samples were collected—1,000 as part of 19 sand-bed trials, and 1,030 as part of 27 gravel-bed trials.
Bedload was captured in five contiguous weigh drums inside a slot spanning the full width of the main flume channel 8.5 meters downstream from the cross-section in which the bedload samplers were deployed. The contents of each drum were automatically weighed and recorded as a time series about every 1.1 seconds. Each drum automatically, independently, and episodically dumped its contents into the bottom of the slot upon the accumulation of a pre-determined mass of entrapped sediment, after which the drum continued to capture and weigh bedload. An auger at the bottom of the slot evacuated the accumulating sediment to a side-channel pump that piped the captured sediments upstream and discharged them back to the flume.
Bedload-transport rates were calculated from measurements of the masses of material trapped by the bedload samplers and from the data produced by the automated bedload capture-and-weigh system of the main channel flume. These data were used to compute at-a-point and mean bedload-transport rates for subsequent use in developing bedload-trapping efficiency (calibration) coefficients for each bedload sampler and for comparing the relative trapping efficiencies of the manually deployed bedload samplers. The data were collected to enable the use of several computational methods for deriving bedload-trapping coefficients.
Continuous ancillary data including stage, water discharge, and water temperature were automatically collected and stored. Flow depths were manually measured and recorded concurrent with each at-a-point bedload-sampler deployment. Other information obtained during parts of the experiments included longitudinal water-surface slope, bedload particle-size distributions, and suspended-sediment concentrations and percent sand analyzed from samples collected by depth integration with a US DH-48 isokinetic suspended-sediment sampler.
This report describes the types and availability of the bedload and ancillary data derived through the StreamLab06 experiments. The data are available from the St. Anthony Falls Laboratory and the U.S. Geological Survey through a data release. Also included are selected descriptive and historical information as well as the background, experimental design, experimental caveats, and other factors relevant to the production of the bedload-transport and ancillary data produced through Phases I and II of the StreamLab06 experiments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211064","usgsCitation":"Gray, J.R., Schwarz, G.E., Dean, D.J., Czuba, J.A., and Groten, J.T., 2021, Instruments, methods, rationale, and derived data used to quantify and compare the trapping efficiencies of four types of pressure-difference bedload samplers: U.S. Geological Survey Open-File Report 2021–1064, 61 p., https://doi.org/10.3133/ofr20211064.","productDescription":"Report: vii, 61 p.; Data Release","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098017","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386969,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1064/ofr20211064.pdf","text":"Report","size":"70.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1064"},{"id":386970,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1064/coverthb.jpg"},{"id":386971,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VBB2YF","text":"USGS data release","linkHelpText":"Data describing the trapping efficiency of four types of pressure-difference bedload samplers, St. Anthony Falls Laboratory, Minneapolis, Minnesota, 2006"}],"contact":"Chief, Analysis and Prediction Branch
Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 415
Reston, VA 20192
We simulated the concurrent rapid motion of landslides on an unstable slope at Barry Arm, Alaska. Movement of landslides into the adjacent fjord displaced fjord water and generated a tsunami, which propagated out of Barry Arm. Rather than assuming an initial sea surface height, velocity, and location for the tsunami, we generated the tsunami directly using a model capable of simulating the dynamics of both water and landslide material. The fjord below most of the landslide source area was occupied by the Barry Glacier until about 2012; therefore, our direct simulation of tsunami generation by landslide motion required new topographic and bathymetric data, which was collected in 2020. The topographic data also constrained landslide geometries and volumes. We considered four scenarios based on two landslide volumes and two landslide mobilities—a more mobile, contractive landslide and a less mobile, noncontractive landslide. The larger of the two volumes is 689 × 106 cubic meters (m3)—larger than the volume estimate in a previous study—and reflects the largest plausible volume given current observational data. The considered scenario that generated the largest wave heights resulted in forecast wave heights of over 200 meters (m) in the northern part of Barry Arm, adjacent to the landslide source area and runup on the opposite fjord wall in excess of 500 m. Simulated wave heights in excess of 5 m in southern Barry Arm and in Harriman Fjord occurred within 10–15 minutes (min) of landslide motion. The simulated tsunami reached Whittier, Alaska, approximately 20 min after initial rapid landslide motion, with peak heights of just over 2 m in Passage Fjord, 500 m offshore Whittier, occurring 26 min after initial rapid motion. Time of peak wave heights was consistent with previous modeling. Although results are preliminary and can be refined with additional observations and analyses, they provide a refined assessment of the upper bound of the hazard presented by the Barry Arm landslides. The results herein support the National Oceanic and Atmospheric Administration’s National Tsunami Warning Center mission to detect, forecast, and warn for tsunamis in Alaska.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211071","usgsCitation":"Barnhart, K.R., Jones, R.P., George, D.L., Coe, J.A., and Staley, D.M., 2021, Preliminary assessment of the wave generating potential from landslides at Barry Arm, Prince William Sound, Alaska: U.S. Geological Survey Open-File Report 2021–1071, 28 p., https://doi.org/10.3133/ofr20211071.","productDescription":"Report: v, 28 p.; Data Release","ipdsId":"IP-130004","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":386958,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XVJDNP","text":"USGS data release","linkHelpText":"Select model results from simulations of hypothetical rapid failures of landslides into Barry Arm Fjord, Prince William Sound, Alaska"},{"id":386957,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1071/ofr20211071.pdf","text":"Report","size":"13.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1071"},{"id":386956,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1071/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Barry Arm, Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.90869140625,\n 60.77659627851085\n ],\n [\n -147.95562744140625,\n 60.77659627851085\n ],\n [\n -147.95562744140625,\n 61.18165309177166\n ],\n [\n -148.90869140625,\n 61.18165309177166\n ],\n [\n -148.90869140625,\n 60.77659627851085\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Edwin B. Forsythe National Wildlife Refuge in New Jersey. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of 23 marsh management units within the refuge and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to about \\$980,000, but that further expenditures may yield diminishing return on investment. Potential management actions in optimal portfolios at total costs less than \\$980,000 included applying sediment to the marsh surface to increase elevation in five marsh management units, digging runnels on the marsh surface to improve drainage in five marsh management units, and breaching roads and berms to improve tidal flow in five marsh management units. The potential management benefits were derived from expected reduction in the duration of surface flooding, improved capacity for marsh elevation to keep pace with sea-level rise and increases in numbers of spiders (as an indicator of trophic health), tidal marsh obligate birds, and wintering American black ducks. The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Edwin B. Forsythe National Wildlife Refuge that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211037","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Castelli, P.M., and Rettig, V., 2021, Optimization of salt marsh management at the Edwin B. Forsythe National Wildlife Refuge, New Jersey, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1037, 41 p., https://doi.org/10.3133/ofr20211037.","productDescription":"vi, 41 p.","ipdsId":"IP-120822","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":386007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1037/coverthb.jpg"},{"id":386008,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1037/ofr20211037.pdf","text":"Report","size":"7.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1037"},{"id":386009,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1037/images"}],"country":"United States","state":"New Jersey","otherGeospatial":"Edwin B. Forsythe National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.41967010498045,\n 39.388182633584485\n ],\n [\n -74.36851501464844,\n 39.40967202224426\n ],\n [\n -74.36817169189453,\n 39.433011014927224\n ],\n [\n -74.33040618896484,\n 39.45395640766923\n ],\n [\n -74.31255340576172,\n 39.48125549646666\n ],\n [\n -74.3276596069336,\n 39.50059690888215\n ],\n [\n -74.4107437133789,\n 39.51807903374736\n ],\n [\n -74.43305969238281,\n 39.519138415094176\n ],\n [\n -74.4601821899414,\n 39.51198727745152\n ],\n [\n -74.4275665283203,\n 39.49397374330326\n ],\n [\n -74.45743560791016,\n 39.46959506012395\n ],\n [\n -74.44267272949219,\n 39.45766759232811\n ],\n [\n -74.41967010498045,\n 39.388182633584485\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
To investigate whether the cumulative years of treatment had an impact on vireo reproductive effort, we looked at the effects of the Treatment Index on reproductive parameters. Results from generalized linear models indicated that treatment did not have an effect on vireo nesting effort or the number of vireo fledglings per pair produced in 2020.
Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
From 2014 to 2017, the U.S. Geological Survey’s Florence Bascom Geoscience Center (FBGC) entered into an inter-agency agreement with the Federal Highway Administration’s Turner-Fairbank Highway Research Center (TFHRC) to assist in field site selection and auger drilling fieldwork. The TFHRC was developing a device to measure the erosional properties of clay-rich sediments to be used for in situ testing at locations of bridge pier construction. FBGC scientists selected 15 drilling locations at 14 different field sites across Northern Virginia and Southern Maryland for the investigation of near-surface sediment properties and the development and testing of the TFHRC’s in situ scour testing device (ISTD). This report provides information about the project and summarizes the data collected during fieldwork including sediment descriptions of the borehole cores and the methods used during fieldwork.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211038","collaboration":"Prepared in cooperation with the U.S. Department of Transportation Federal Highway Administration","usgsCitation":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2021, Borehole sampling of surficial sediments in Northern Virginia and Southern Maryland: U.S. Geological Survey Open-File Report 2021–1038, 27 p., https://doi.org/10.3133/ofr20211038.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","ipdsId":"IP-120037","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":386418,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1038/ofr20211038.pdf","text":"Report","size":"6.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1038"},{"id":386421,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A8G5LQ","text":"USGS Data Release","linkHelpText":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2021, Datasheets associated with borehole sampling of surficial sediments in Northern Virginia and Southern Maryland conducted by the U.S. Geological Survey for the Federal Highways Administration Turner-Fairbanks Research Center In Situ Scour Testing Device: U.S. Geological Survey data release"},{"id":386417,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1038/coverthb.jpg"}],"country":"United States","state":"Maryland, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.06884765624999,\n 37.77071473849609\n ],\n [\n -75.509033203125,\n 37.77071473849609\n ],\n [\n -75.509033203125,\n 39.257778150283364\n ],\n [\n -78.06884765624999,\n 39.257778150283364\n ],\n [\n -78.06884765624999,\n 37.77071473849609\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
Nonnative crayfish are an immediate and pervasive threat to aquatic environments and their biodiversity. Crayfish control can be achieved by physical methods, water chemistry modification, biological methods, biocidal application, and application of crayfish physiology modifiers. The purpose of this report is to identify suitable candidates for potential control of nonnative crayfish through a comprehensive literature review. This review focuses on control methods, specifically on the available data to support registration of a crayfish pesticide. The literature search resulted in 28,058 documents, which were searched to determine if they contained information on physical, chemical, biological, and (or) biocidal approaches to control crayfish. Pesticides directly toxic to crayfish in this literature review include: pyrethroids (natural pyrethrins and synthetic), fipronil, mirex, antimycin-A, and rotenone. Some chemicals, such as diflubenzuron and emamectin benzoate, alter crayfish physiology resulting in a lower pesticide dose needed to control crayfish. Environmental damage, application rate, exposure duration, nontarget effects, environmental persistence, and registration data gaps were used as criteria to define which pesticides are potentially selective to crayfish, along with which have the greatest amount of data to support registration by the U.S. Environmental Protection Agency.
Synthetic pyrethroids were identified as the most likely candidate to be developed into a crayfish pesticide. A type-2 synthetic pyrethroid, cyfluthrin, has the greatest potential for eradicating nonnative crayfish. Although other invertebrate species will be negatively affected at the concentrations required for crayfish control, compared with other pyrethroids and other potential control chemicals, cyfluthrin offers rapid ecosystem recovery due to being more selective, having fewer effects on native fish, and having a short aquatic persistence. Cyfluthrin also has few data gaps for U.S. Environmental Protection Agency registration purposes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211048","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Schueller, J.R., Smerud, J.R., Fredricks, K.T., and Putnam, J.G., 2021, Literature review for candidate chemical control agents for nonnative crayfish: U.S. Geological Survey Open-File Report 2021–1048, 32 p., https://doi.org/10.3133/ofr20211048.","productDescription":"vii, 32 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-115061","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":386879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1048/ofr20211048.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1048"},{"id":386878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1048/coverthb.jpg"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602