Accurate and complete data on fire locations and burned areas are needed to quantify trends and patterns of fire occurrence, characterize drivers of fire, project future fire pattern behavior, and help with assessments of fire effects on natural and social systems. The Landsat Collection 2 Level-3 Burned Area science product is designed to identify burned areas across all ecosystems (for example, forests, shrublands, and grasslands) for Landsat 4–9 data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223083","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Burned Area science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3083, 2 p., https://doi.org/10.3133/fs20223083.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139624","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":418240,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3083/coverthb2.jpg"},{"id":410296,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418243,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3083/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":418241,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3083/fs20223083.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3038"}],"edition":"Version 1.0: December 13, 2022; Version 1.1: June 20, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product provides raster data that represent surface water inundation per pixel in Landsat 4–9 imagery. The Collection 2 Dynamic Surface Water Extent science product contains six acquisition-based raster products relating to surface water. Surface water extent is modulated by weather and climate, stream network hydrology, and geological processes such as isostatic rebound. Land use, ecosystem and service management, and overall water management also are affected by changes in surface water extent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223084","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3084, 2 p., https://doi.org/10.3133/fs20223084.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139625","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":410308,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418247,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3084/coverthb2.jpg"},{"id":418249,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3084/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":418248,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3084/fs20223084.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3084"}],"edition":"Version 1.0: December 13, 2022; Version 1.1: June 21, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Many organizations in the Delaware River Basin (DRB) monitor surface-water quality for regulatory, scientific, and decision-making purposes. In support of these purposes, over 260,000 water-quality records provided by 8 different organizations were compiled, screened, and used to generate water-quality trends in the DRB. These trends, for periods of record that end in 2018, were generated for 124 sites and up to 16 constituents using 2 trend methods: the Seasonal Kendall Test and the Weighted Regressions on Time, Discharge, and Season model. Seasonal Kendall Tests were performed on all water-quality records to detect monotonic trends in concentration over the period of record and for as many as four additional trend periods (1978–2018, 1998–2018, 2003–18, and 2008–18). The Weighted Regressions on Time, Discharge, and Season model was applied to water-quality records that passed more stringent screening criteria and was used to detect monontonic and nonmonotonic trends, account for variations in streamflow, and estimate annual concentrations. These two trend methods produced different trend directions less than 1 percent of the time, illustrating general agreement between the methods despite the different approaches and data input requirements. Overall, the changes in concentration for salinity constituents (specific conductance and total dissolved solids), chloride, and sodium were increases; those increases were some of the largest changes observed in the basin, and they occurred at faster rates over time. Total dissolved solids concentration trends at 4 of the 60 sites increased from below to above the level of concern threshold (a secondary drinking water threshold) over the period of record, indicating potentially meaningful degradation in water quality. Nutrient constituent (ammonia, nitrate, orthophosphate, total nitrogen, and total phosphorus) concentrations tended to decrease over the period of record, although fewer sites had significant trends and the changes in concentration were smaller compared to the salinity constituents. Total nitrogen and total phosphorus were the only nutrient constituents to have decreasing concentration trends that crossed from above to below the level of concern threshold, U.S. Environmental Protection Agency (EPA) ecoregional nutrient criteria, (EPA, undated c). This finding indicates water-quality improvement at sites with these trends (nine sites with total nitrogen trends and one site with a total phosphorus trend), although many sites were still in exceedance of the level of concern. Trends for total suspended solids and some major ions (calcium, magnesium, potassium) were largely nonsignificant or variable between sites, with no prevalent patterns across the DRB; however, sulfate concentrations decreased at most sites. Cumulative land-surface change within each watershed had a strong positive relation with changes in water-quality concentrations for the salinity constituents and most major ions, but not for the other constituents, indicating that land-surface changes are related to the sources and transport of these constituents. Investigating long-term trends (a decade or longer) in water quality can help the DRB water management community quantify the success of management practices and identify potential threats to water availability.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225097","programNote":"Water Availability and Use Science Program, National Water Quality Program","usgsCitation":"Shoda, M.E., and Murphy, J.C., 2022, Water-quality trends in the Delaware River Basin calculated using multisource data and two methods for trend periods ending in 2018: U.S. Geological Survey Scientific Investigations Report 2022–5097, 60 p., https://doi.org/10.3133/sir20225097.","productDescription":"Report v, 60 p.; 2 Data Releases","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122487","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":503561,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113940.htm","linkFileType":{"id":5,"text":"html"}},{"id":433058,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233014","text":"USGS Fact Sheet 2023-3014","linkFileType":{"id":5,"text":"html"}},{"id":410213,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5097/images/"},{"id":410212,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5097/sir20225097.XML"},{"id":410211,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PX8LZO","text":"USGS data release","linkHelpText":"Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin"},{"id":410209,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225097/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5097"},{"id":410208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5097/sir20225097.pdf","text":"Report","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5097"},{"id":410207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5097/coverthb.jpg"},{"id":410210,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KMWNJ5","text":"USGS data release","linkHelpText":"Water-quality trends for rivers and streams in the Delaware River Basin using Weighted Regressions on Time, Discharge, and Season (WRTDS) models, Seasonal Kendall Trend (SKT) tests, and multisource data, water years 1978–2018"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.68904449140135,\n 38.5435194562952\n ],\n [\n -75.2278145853991,\n 38.47477347574127\n ],\n [\n -75.03014462568353,\n 38.95461543352394\n ],\n [\n -74.87640132368297,\n 39.566811726970116\n ],\n [\n -74.28339144453736,\n 40.70854805354401\n ],\n [\n -74.1735748002507,\n 41.56866114554592\n ],\n [\n -73.77823488082065,\n 42.41747384003048\n ],\n [\n -74.21750145796516,\n 42.64406475670245\n ],\n [\n -74.96425463911187,\n 42.44989429480822\n ],\n [\n -75.44744787397136,\n 42.043433704426945\n ],\n [\n -75.88671445111639,\n 41.486448364247764\n ],\n [\n -76.19420105511806,\n 40.79174109891525\n ],\n [\n -76.23812771283252,\n 40.37473571760768\n ],\n [\n -76.01849442425974,\n 39.702128532888\n ],\n [\n -75.75493447797302,\n 39.14224249538836\n ],\n [\n -75.68904449140135,\n 38.5435194562952\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Program Coordinator
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U.S. Geological Survey
Geologic maps are the fundamental building blocks of surface and subsurface three-dimensional geologic framework models of the Earth’s crust. However, as the production and availability of geologic map databases continues to increase, inconsistent data models and the lack of synthesized, national geologic map data at scales appropriate for informed decision making negatively affect the functional integration of geologic map data with other national datasets. The Geologic Map Schema (GeMS) is the publication and archive database standard for geologic map data funded by the U.S. Geological Survey National Cooperative Geologic Mapping Program, and standardizes the organization and content of a single map database. However, synthesizing multiple databases into a seamless geologic map database creates a different set of challenges and database needs than GeMS was designed to accommodate. The Seamless Integrated Geologic Mapping (SIGMa) extension is designed to expand the capabilities of GeMS by enabling integration of map-based geoscience data. In particular, the SIGMa extension enables capturing a diverse and ever-changing set of map units, produced by many contributors operating independently, and by incremental and noncontiguous assembly and publication. Feature-level metadata fields allow data sources and digital compilation methods to be attributed separately and a relational structure is designed to support the link between data sources and features attributed with multiple data sources. Instead of paragraph-style map-unit descriptions that can be highly inconsistent, SIGMa parses fundamental map-unit attributes, including material, genetic process, and age, into thematically specific fields. The SIGMa extension uses a hierarchical map-unit organization to facilitate a dynamic and evolving, formation-level stratigraphic framework. The hierarchy is developed around geologic provinces that represent temporally restricted geologic events, processes, and settings. Geologic provinces can include magmatic events, depositional settings associated with tectonic processes or stable continental margins, and processes that are actively shaping the modern landscape. A geologic province hierarchy places map units into a geologic context at subregional to continental scales and provides the flexibility to support incremental assembly of the stratigraphy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225115","usgsCitation":"Turner, K.J., Workman, J.B., Colgan, J.P., Gilmer, A.K., Berry, M.E., Johnstone, S.A., Warrell, K.F., Dechesne, M., VanSistine, D.P., Thompson, R.A., Hudson, A.M., Zellman, K.L., Sweetkind, D., and Ruleman, C.A., 2022, The Seamless Integrated Geologic Mapping (SIGMa) extension to the Geologic Map Schema (GeMS): U.S. Geological Survey Scientific Investigations Report 2022–5115, 33 p., https://doi.org/10.3133/sir20225115.","productDescription":"vii, 33 p.","onlineOnly":"Y","ipdsId":"IP-125234","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":410653,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225115/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5115"},{"id":410248,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5115/sir20225115.xml"},{"id":410247,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5115/images"},{"id":410246,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5115/sir20225115.pdf","text":"Report","size":"3.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5115"},{"id":410245,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5115/coverthb.jpg"}],"contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
Digital flood-inundation maps for a 2.4-mile reach of the Schoharie Creek in North Blenheim, New York, were created by the U.S. Geological Survey (USGS) in cooperation with the New York Power Authority. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://fim.wim.usgs.gov/fim/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Schoharie Creek near North Blenheim, N.Y. (station number 01350212). Near-real-time stage at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/. Flood profiles were computed for the stream reach by means of a two-dimensional implicit finite-volume hydraulic model. The model was calibrated by using the active (as of April 2021) stage-discharge ratings at the USGS streamgages on the Schoharie Creek near North Blenheim (station number 01350212) and at North Blenheim (station number 01350180) and documented high-water marks in the study reach from the floods of August 28, 2011; January 19, 1996; and April 4, 1987.
The hydraulic model was used to compute 13 water-surface profiles for flood stages at 1-foot intervals referenced to the datum at the streamgage on the Schoharie Creek near North Blenheim, N.Y. (01350212). These profiles range from 14 feet, or near bankfull, to 26 feet, which is the highest whole-foot increment on the stage-discharge rating for the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.52-foot vertical accuracy and 3.3-foot [1-meter] horizontal resolution) to delineate the area flooded at each stage. Flood inundation maps, along with near-real-time stage data from USGS streamgages, can provide emergency management personnel and residents with information critical for flood-response activities, such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225119","collaboration":"Prepared in cooperation with the New York Power Authority","usgsCitation":"Nystrom, E.A., 2022, Flood-inundation maps for Schoharie Creek in North Blenheim, New York: U.S. Geological Survey Scientific Investigations Report 2022–5119, 14 p., https://doi.org/10.3133/sir20225119.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122520","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":503571,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113939.htm","linkFileType":{"id":5,"text":"html"}},{"id":410179,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225119/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5119"},{"id":410180,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92YVB9V","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Geospatial dataset for flood inundation maps of Schoharie Creek in North Blenheim, New York"},{"id":410177,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5119/coverthb.jpg"},{"id":410182,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5119/images/"},{"id":410181,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5119/sir20225119.XML"},{"id":410178,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5119/sir20225119.pdf","text":"Report","size":"49.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5119"}],"country":"United States","state":"New York","city":"North Blenheim","otherGeospatial":"Schoharie Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.43435753120507,\n 42.481471549691946\n ],\n [\n -74.46929205403138,\n 42.481471549691946\n ],\n [\n -74.46929205403138,\n 42.457988603472074\n ],\n [\n -74.43435753120507,\n 42.457988603472074\n ],\n [\n -74.43435753120507,\n 42.481471549691946\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Islands support unique plants, animals, and human societies found nowhere else on the Earth. Local and global stressors threaten the persistence of island ecosystems, with invasive species being among the most damaging, yet solvable, stressors. While the threat of invasive terrestrial mammals on island flora and fauna is well recognized, recent studies have begun to illustrate their extended and destructive impacts on adjacent marine environments. Eradication of invasive mammals and restoration of native biota are promising tools to address both island and ocean management goals. The magnitude of the marine benefits of island restoration, however, is unlikely to be consistent across the globe. We propose a list of six environmental characteristics most likely to affect the strength of land–sea linkages: precipitation, elevation, vegetation cover, soil hydrology, oceanographic productivity, and wave energy. Global databases allow for the calculation of comparable metrics describing each environmental character across islands. Such metrics can be used today to evaluate relative potential for coupled land–sea conservation efforts and, with sustained investment in monitoring on land and sea, can be used in the future to refine science-based planning tools for integrated land–sea management. As conservation practitioners work to address the effects of climate change, ocean stressors, and biodiversity crises, it is essential that we maximize returns from our management investments. Linking efforts on land, including eradication of island invasive mammals, with marine restoration and protection should offer multiplied benefits to achieve concurrent global conservation goals.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2122354119","usgsCitation":"Sandin, S.A., Becker, P.A., Becker, C., Brown, K., Erazo, N.G., Figuerola, C., Fisher, R., Friedlander, A., Fukami, T., Graham, N.A., Gruner, D.S., Holmes, N.D., Holthuijzen, W.A., Jones, H.P., Rios, M., Samaniego, A., Sechrest, W., Semmens, B.X., Thornton, H.E., Thurber, R.V., Wails, C., Wolf, C.A., and Zgliczynski, B.J., 2022, Harnessing island–ocean connections to maximize marine benefits of island conservation: PNAS, v. 119, no. 51, e2122354119, 9 p., https://doi.org/10.1073/pnas.2122354119.","productDescription":"e2122354119, 9 p.","ipdsId":"IP-144780","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":445677,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9907155","text":"Publisher Index Page"},{"id":412678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"51","noUsgsAuthors":false,"publicationDate":"2022-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Sandin, Stuart A.","contributorId":301995,"corporation":false,"usgs":false,"family":"Sandin","given":"Stuart","email":"","middleInitial":"A.","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Becker, Penny A.","contributorId":173445,"corporation":false,"usgs":false,"family":"Becker","given":"Penny","email":"","middleInitial":"A.","affiliations":[{"id":27230,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":863264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Becker, Ceiba","contributorId":301997,"corporation":false,"usgs":false,"family":"Becker","given":"Ceiba","email":"","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Kate","contributorId":301999,"corporation":false,"usgs":false,"family":"Brown","given":"Kate","email":"","affiliations":[{"id":65382,"text":"Global Island Partnership, New Zealand","active":true,"usgs":false}],"preferred":false,"id":863266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Erazo, Natalia G.","contributorId":302000,"corporation":false,"usgs":false,"family":"Erazo","given":"Natalia","email":"","middleInitial":"G.","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863267,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Figuerola, Cielo","contributorId":302001,"corporation":false,"usgs":false,"family":"Figuerola","given":"Cielo","email":"","affiliations":[{"id":26976,"text":"Island Conservation, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":863268,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863269,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Friedlander, Alan M.","contributorId":302003,"corporation":false,"usgs":false,"family":"Friedlander","given":"Alan M.","affiliations":[{"id":65384,"text":"National Geographic Society, Washington DC","active":true,"usgs":false}],"preferred":false,"id":863270,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fukami, Tadashi","contributorId":195506,"corporation":false,"usgs":false,"family":"Fukami","given":"Tadashi","email":"","affiliations":[],"preferred":false,"id":863271,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Graham, Nicholas A. 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J.","affiliations":[{"id":65385,"text":"Lancaster Environment Centre, Lancaster University, UK","active":true,"usgs":false}],"preferred":false,"id":863272,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gruner, Daniel S.","contributorId":195507,"corporation":false,"usgs":false,"family":"Gruner","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":863273,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Holmes, Nick D.","contributorId":172091,"corporation":false,"usgs":false,"family":"Holmes","given":"Nick","email":"","middleInitial":"D.","affiliations":[{"id":26976,"text":"Island Conservation, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":863274,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Holthuijzen, Wieteke A.","contributorId":302006,"corporation":false,"usgs":false,"family":"Holthuijzen","given":"Wieteke","email":"","middleInitial":"A.","affiliations":[{"id":65387,"text":"University of Tennessee - Knoxville, TN","active":true,"usgs":false}],"preferred":false,"id":863275,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Jones, Holly P.","contributorId":270944,"corporation":false,"usgs":false,"family":"Jones","given":"Holly","email":"","middleInitial":"P.","affiliations":[{"id":56228,"text":"Northern Illinois University, Department of Biological Sciences; 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By splitting the core with a diamond saw into working and archive halves, the saw cuttings constitute a “channel” sample, the best subsample from which to obtain an average mineralogical and geochemical composition of a core. We apply this procedure to sampling core of the lower oceanic crust in the Hess Deep obtained during Expedition 345 of the Integrated Ocean Drilling Program (now International Ocean Discovery Program).
Our results show that particles produced by sawing range from sand to clay sizes. Sand- and silt-sized cuttings can be sampled with a spatula, whereas clay-sized particles remained in suspension after 12 h and could be collected only by settling, aided by centrifuge. X-ray diffraction (XRD) analysis and Rietveld refinement show that phyllosilicates were fractionated into the clay-sized fraction. Thus, collection of both the sedimented fraction and the clay-sized suspended fraction (commonly > 15 wt % of the total) is necessary to capture the whole sample. The strong positive correlation between the recovered sample mass (in grams) and length of core cut demonstrates that this sampling protocol was uniform and systematic, with almost 1.4 g sediment produced per centimeter of core cut. We show that major-element concentrations of our channel samples compare favorably with the compositions of billet-sized samples analyzed aboard the JOIDES Resolution, but the results show that individual billet analyses are rarely representative of the whole core recovered. A final test of the validity of our methods comes from the strong positive correlation between the loss on ignition (LOI) values of our channel samples and the H2O contents calculated from the modal mineralogy obtained by X-ray diffraction and Rietveld refinement. This sampling procedure shows that grain-sized fractionation modifies both mineralogical and chemical compositions; nevertheless, this channel sampling method is a reliable method of obtaining representative samples of bulk cores. With the ever-increasing precision offered by modern analytical instrumentation, this sampling protocol allows the accuracy of the analytical results to keep pace.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/sd-31-71-2022","usgsCitation":"Wintsch, R.P., Meyer, R., Bish, D., Deasy, R.T., Nozaka, T., and Johnson, C., 2022, A channel sampling strategy for measurement of mineral modal and chemical composition of drill cores: Application to lower oceanic crustal rocks from IODP Expedition 345 to the Hess Deep rift: Scientific Drilling, v. 31, p. 71-84, https://doi.org/10.5194/sd-31-71-2022.","productDescription":"14 p.","startPage":"71","endPage":"84","ipdsId":"IP-133634","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":445680,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/sd-31-71-2022","text":"Publisher Index Page"},{"id":410280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Hess Deep Rift, Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.57372796106893,\n 3.330136493398328\n ],\n [\n -111.57372796106893,\n -1.0981582918846584\n ],\n [\n -101.19437373696735,\n -1.0981582918846584\n ],\n [\n -101.19437373696735,\n 3.330136493398328\n ],\n [\n -111.57372796106893,\n 3.330136493398328\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationDate":"2022-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Wintsch, Robert P.","contributorId":192913,"corporation":false,"usgs":false,"family":"Wintsch","given":"Robert","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":858574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Romain","contributorId":148991,"corporation":false,"usgs":false,"family":"Meyer","given":"Romain","email":"","affiliations":[{"id":17609,"text":"Deutsche GeoForchungsZentrum Potsdam","active":true,"usgs":false}],"preferred":false,"id":858575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bish, David","contributorId":291943,"corporation":false,"usgs":false,"family":"Bish","given":"David","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":858576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Deasy, Ryan T. 0000-0002-7530-803X","orcid":"https://orcid.org/0000-0002-7530-803X","contributorId":299762,"corporation":false,"usgs":true,"family":"Deasy","given":"Ryan","middleInitial":"T.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":858577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nozaka, Toshio","contributorId":299763,"corporation":false,"usgs":false,"family":"Nozaka","given":"Toshio","email":"","affiliations":[{"id":64944,"text":"Okayama University","active":true,"usgs":false}],"preferred":false,"id":858578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Carley","contributorId":299764,"corporation":false,"usgs":false,"family":"Johnson","given":"Carley","email":"","affiliations":[{"id":64945,"text":"Marathon","active":true,"usgs":false}],"preferred":false,"id":858579,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70239015,"text":"70239015 - 2022 - Evaluating the sensitivity of multi-dimensional model predictions of salmon habitat to the source of remotely sensed river bathymetry","interactions":[],"lastModifiedDate":"2022-12-21T12:40:22.808277","indexId":"70239015","displayToPublicDate":"2022-12-12T06:37:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the sensitivity of multi-dimensional model predictions of salmon habitat to the source of remotely sensed river bathymetry","docAbstract":"Multi-dimensional numerical models are fundamental tools for investigating biophysical processes in aquatic ecosystems. Remote sensing techniques increase the feasibility of applying such models at riverscape scales, but tests of model performance on large rivers have been limited. We evaluated the potential to develop two-dimensional (2D) and three-dimensional (3D) hydrodynamic models for a 1.6-km reach of a large gravel-bed river using three sources of remotely sensed river bathymetry. We estimated depth from hyperspectral image data acquired from conventional and uncrewed aircraft and multispectral satellite imagery. Our results indicated that modeled water depth errors were similar between 2D and 3D models, with depth residuals that were comparable to the uncertainty associated with the bathymetry used as input. We found good agreement between measured and modeled depth-averaged velocities generated by 2D and 3D models, while 3D models provided superior predictions of near-bed velocities. We found that optimal model performance occurred for lower flow resistance values than previously reported in the literature, possibly as a consequence of the high-resolution bathymetry used as model input. Model predictions of winter-run Chinook salmon (Oncorhynchus tshawytscha) spawning and rearing habitat were not sensitive to the source of bathymetric information, but bioenergetic predictions related to adult holding costs were influenced by the input bathymetry. Our results suggest that hyperspectral imagery acquired from piloted and/or uncrewed aircraft can be used to map the bathymetry of clear-flowing, relatively shallow large rivers with sufficient accuracy to support multi-dimensional flow model development; models developed from multispectral satellite imagery had more limited predictive capability.
Interspecific interactions among walleye Sander vitreus, lake whitefish Coregonus clupeaformis, and yellow perch Perca flavescens in Green Bay could influence the population status of each species, but potential trophic interactions are poorly understood. Our objectives were to determine if diet assemblages for each species and diet overlap among species varied spatially and temporally within Green Bay. Adult walleye (≥ 381 mm total length (TL); N = 981), lake whitefish (≥ 432 mm TL; N = 1507), and yellow perch (≥ 150 mm TL; N = 1174) were collected during May-October of 2018 and 2019 from multiple locations in southern and northern Green Bay. Diet assemblages of all three species varied between zones but walleye diets were more temporally variable (among months within zones and between years) than diets of lake whitefish or yellow perch. Lake whitefish represented a seasonally important prey item for walleye in southern Green Bay, composing 10% and 41% of walleye diets by weight in May and June, respectively. Yellow perch generally composed < 15% of walleye diets by weight but were consumed at a broader spatiotemporal scale than lake whitefish. Diet overlap between walleye and both lake whitefish and yellow perch was generally weak or moderate, whereas diet overlap between whitefish and perch was generally strong. Our assessment of adult trophic interactions suggests that changes in the population status of one species could influence fisheries for all three, and we identify additional research questions to address potential population-level effects of these trophic interactions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.09.005","usgsCitation":"Koeniga, L., Dembkowski, D., Hansen, S., Tsehaye, I., Tammie J. Paoli, Zorn, T., and Isermann, D.A., 2022, Diet composition and overlap for adult walleye, lake whitefish, and yellow perch in Green Bay, Lake Michigan: Journal of Great Lakes Research, v. 48, no. 6, p. 1681-1695, https://doi.org/10.1016/j.jglr.2022.09.005.","productDescription":"15 p.","startPage":"1681","endPage":"1695","ipdsId":"IP-140407","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480917,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay, Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.25433685919877,\n 45.59617874752695\n ],\n [\n -87.9984005897609,\n 44.918805118493594\n ],\n [\n -87.98617785518738,\n 44.57675244172867\n ],\n [\n -87.3651194846989,\n 44.78875465437872\n ],\n [\n -86.56980682225971,\n 45.71326930572798\n ],\n [\n -87.03874882070055,\n 45.85231391585981\n ],\n [\n -87.25433685919877,\n 45.59617874752695\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Koeniga, Lucas D.","contributorId":348678,"corporation":false,"usgs":false,"family":"Koeniga","given":"Lucas D.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923697,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel J.","contributorId":348681,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923698,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Scott P.","contributorId":348684,"corporation":false,"usgs":false,"family":"Hansen","given":"Scott P.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923699,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tsehaye, Iyob","contributorId":348687,"corporation":false,"usgs":false,"family":"Tsehaye","given":"Iyob","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923700,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tammie J. Paoli","contributorId":348689,"corporation":false,"usgs":false,"family":"Tammie J. Paoli","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923701,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zorn, Troy G.","contributorId":348692,"corporation":false,"usgs":false,"family":"Zorn","given":"Troy G.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923702,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923703,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239422,"text":"70239422 - 2022 - Lithologic and geochemical observations of the lower Eagle Ford from the USGS GC-2 core in the East Texas Basin","interactions":[],"lastModifiedDate":"2026-03-18T15:49:31.332606","indexId":"70239422","displayToPublicDate":"2022-12-10T10:45:14","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Lithologic and geochemical observations of the lower Eagle Ford from the USGS GC-2 core in the East Texas Basin","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"38th Annual GCSSEPM Foundation Perkins-Rosen Research Conference and Core Workshop 2022: The Cenomanian-Turonian stratigraphic interval across the Americas","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Curran Associates","usgsCitation":"Flaum, J.A., Paxton, S.T., Birdwell, J.E., and French, K.L., 2022, Lithologic and geochemical observations of the lower Eagle Ford from the USGS GC-2 core in the East Texas Basin, in 38th Annual GCSSEPM Foundation Perkins-Rosen Research Conference and Core Workshop 2022: The Cenomanian-Turonian stratigraphic interval across the Americas, v. 38, p. 93-99.","productDescription":"7 p.","startPage":"93","endPage":"99","ipdsId":"IP-146329","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":501256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Flaum, Jason A. 0000-0003-1251-1142","orcid":"https://orcid.org/0000-0003-1251-1142","contributorId":300809,"corporation":false,"usgs":true,"family":"Flaum","given":"Jason","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":861535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paxton, Stanley T. 0000-0002-9098-1740 spaxton@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-1740","contributorId":739,"corporation":false,"usgs":true,"family":"Paxton","given":"Stanley","email":"spaxton@usgs.gov","middleInitial":"T.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":861536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":861537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"French, Katherine L. 0000-0002-0153-8035","orcid":"https://orcid.org/0000-0002-0153-8035","contributorId":205462,"corporation":false,"usgs":true,"family":"French","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":false,"id":861538,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70242930,"text":"70242930 - 2022 - Quantifying aspects of rangeland health at watershed scales in Colorado using remotely sensed data products","interactions":[],"lastModifiedDate":"2023-04-24T12:03:26.112971","indexId":"70242930","displayToPublicDate":"2022-12-09T07:00:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying aspects of rangeland health at watershed scales in Colorado using remotely sensed data products","docAbstract":"Allegheny woodrats Neotoma magister are an imperiled small mammal species most associated with emergent rock habitats in the central Appalachian Mountains and the Ohio River Valley. The monitoring of populations and their spatiotemporal distributions typically has relied on labor-intensive livetrapping. The use of remote-detecting cameras holds promise for being an equally or more effective method to determine species presence, although trap-based captures permit the estimation of other parameters (e.g., survival, population size, site fidelity). In 2017, 2018, and 2020 we compared standard livetrapping with paired cameras for determining site occupancy of Allegheny woodrats in the central Appalachian Mountains of western Virginia. We further examined the influence of baited vs. unbaited cameras at several sites of confirmed occupancy in 2019. We observed that the detection probability using cameras was approximately 1.7 times that of live traps. Also, detection probability at baited camera traps was 1.3–2.0 times that of unbaited camera traps. Estimates of occupancy ranged from 0.44 to 0.49. Our findings suggest that the use of baited remote-detecting cameras provides a more effective method than livetrapping for detecting Allegheny woodrats. Our study provides a framework for the development of a large-scale, long-term monitoring protocol of Allegheny woodrats with the goals of identifying changes in the distribution of the species and quantifying local extinction and colonization rates at emergent rock outcrops and caves throughout the species' known distribution.
","language":"English","publisher":"Allen Press","doi":"10.3996/jfwm-21-037","usgsCitation":"Thorne, E., Powers, K., Reynolds, R., Beckner, M., Ellis, K., and Ford, W., 2022, Comparison of two detection methods of a declining rodent, the Allegheny woodrat, in Virginia: Journal of Fish and Wildlife Management, v. 13, no. 2, p. 396-406, https://doi.org/10.3996/jfwm-21-037.","productDescription":"11 p.","startPage":"396","endPage":"406","ipdsId":"IP-128487","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467138,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-037","text":"Publisher Index Page"},{"id":466627,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"western Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.57409261674525,\n 36.66461181034798\n ],\n [\n -80.81091916110393,\n 36.533855981387575\n ],\n [\n -78.85974224585911,\n 36.5841620972601\n ],\n [\n -78.65794697364551,\n 36.66461181034798\n ],\n [\n -78.65794697364551,\n 38.46405975952615\n ],\n [\n -79.66268668892582,\n 38.48697812141013\n ],\n [\n -80.35661920699134,\n 37.64631193624106\n ],\n [\n -82.02034087287542,\n 37.53617858823333\n ],\n [\n -83.57409261674525,\n 36.66461181034798\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Thorne, Emily D.","contributorId":348807,"corporation":false,"usgs":false,"family":"Thorne","given":"Emily D.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":923790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Powers, Karen E.","contributorId":348808,"corporation":false,"usgs":false,"family":"Powers","given":"Karen E.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":923791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reynolds, Richard J.","contributorId":348811,"corporation":false,"usgs":false,"family":"Reynolds","given":"Richard J.","affiliations":[{"id":56188,"text":"Virginia Department of Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":923794,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beckner, Makayla E.","contributorId":348809,"corporation":false,"usgs":false,"family":"Beckner","given":"Makayla E.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":923792,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellis, Karissa A.","contributorId":348810,"corporation":false,"usgs":false,"family":"Ellis","given":"Karissa A.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":923793,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ford, W. 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The continued expansion of chronic wasting disease (CWD), a fatal neurodegenerative disease affecting wild and farmed cervids, poses a direct and indirect threat to state and federal government agency operations and cervid related economic activity. However, the scale of this disease’s direct economic costs is largely unknown. I synthesized existing publicly available data and stakeholder-provided data to estimate CWD’s costs within the continental United States. Federal government agencies collectively spent over $284.1 million on CWD-related efforts between 2000 and 2021, with $203.6 million of this total being spent by the U.S. Department of Agriculture’s Animal and Plant Health Inspection Service. In fiscal year 2020, state natural resources agencies and state agriculture/animal health agencies spent over $25.5 million and $2.9 million, respectively, on CWD-related work. Natural resources agencies in states with known CWD cases spent over 8 times as much on CWD as agencies from states with no known cases. The farmed cervid industry spent at least $307,950 on CWD sampling in 2020, though a lack of available data prevented a complete assessment of costs to this industry. Based on limited data, CWD’s economic effects on the hunting industry (i.e., outfitters and guides, companies leasing land to cervid hunters), may be negligible at this time. Overall, however, the realized economic costs of CWD appear considerable, and it is likely that the number of stakeholders financially affected by this disease and regulations meant to stem its spread will continue to grow. By understanding the current economic impacts of CWD, we are better positioned to assess the costs and benefits of investments in management and research and to understand the magnitude of this disease’s broader societal impacts.
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Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1088","displayTitle":"Assessment of Vulnerabilities and Opportunities to Restore Marsh Sediment Supply at Nisqually River Delta, West-Central Washington","title":"Assessment of vulnerabilities and opportunities to restore marsh sediment supply at Nisqually River Delta, west-central Washington","docAbstract":"A cascading set of hazards to coastal environments is intimately tied to sediment transport and includes the flooding and erosion of shorelines and habitats that support communities, industry, infrastructure, and ecosystem functions (for example, habitats critical to fisheries). This report summarizes modeling and measurement data used to evaluate the sediment budget of the Nisqually River Delta, the vulnerability of the largest estuary restoration project in Puget Sound at the Billy Frank Jr. Nisqually National Wildlife Refuge, and the role of coastal hydrodynamics and potential restoration alternatives for recovering sediment delivery to its marshes. The 2009 Brown’s Farm Restoration achieved many goals toward recovering salmon habitat, but the understanding of the delta and restoration area sediment budgets remain poorly quantified. Specifically, quantitative estimates of the amount of sediment delivered to the delta and restored marsh areas, which had subsided in response to historical diking and draining for grazing, were identified as important information needs. Forecasts of potential outcomes of proposed adaptive distributary channel restoration actions were also prioritized to inform potential solutions. These estimates can be used to evaluate whether sufficient sediment is available for marsh recovery downstream from Alder Lake, which traps about 90 percent the Nisqually River sediment load that could reach the delta. Additionally, quantitative sediment information was identified to help prioritize opportunities to recover and maintain the area marshes and guide ecosystem restoration investments across the delta to reduce the vulnerability of the system to drowning under projected sea level rise.
A coupled, numerical hydrodynamic-sediment transport model and measurements of the sediment load just upstream from the delta were used to evaluate the (1) availability of sediment for marsh recovery, (2) sediment transport dynamics across the estuary, and (3) potential outcomes of distributary reconnection alternatives under existing and projected conditions of streamflow and sea level. Modeling and measurements indicated that the volume of fluvial sediment load reaching and accumulating in the restoration area ranges from 7 to 32 percent and identified that restoration alternatives could recover about an additional 10–12 percent under current and projected sea-level rise by the year 2100. At these rates of sediment delivery, 85–200+ years may be necessary to fill for marsh vegetation development and maintenance. The model also reveals the sensitivity of sediment transport and accumulation to sediment properties, hydrodynamics, and wave conditions. The low sediment accumulation results in large part because of the role of waves in directing sediment transport offshore and challenges of restoring geomorphic processes suited to maintaining habitat structure where opportunity exists or least conflicts with land use. The findings therefore have implications for siting, phasing, and implementing strategies to route and retain sediment. This study shows that opportunities to recover sediment higher in the tidal prism, where a greater hydraulic gradient and gravity could promote progradation and greater sediment retention, may be more effective than alternatives lower in the tidal prism implemented to date and assessed in this study. Furthermore, the modeling indicates that distributary channel restoration also may provide additional benefits to society by reducing flood stage, and therefore, flood hazards surrounding the delta.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221088","collaboration":"Prepared in cooperation with Nisqually Indian Tribe, U.S. Fish and Wildlife Service, Billy Frank Jr. Nisqually National Wildlife Refuge, and Washington Department of Fish and Wildlife Estuary and Salmon Restoration Program","usgsCitation":"Grossman, E.E., Crosby, S.C., Stevens, A.W., Nowacki, D.J., vanAredonk, N.R., and Curran, C.A., 2022, Assessment of vulnerabilities and opportunities to restore marsh sediment supply at Nisqually River Delta, west-central Washington: U.S. Geological Survey Open-File Report 2022–1088, 50 p., https://doi.org/10.3133/ofr20221088.","productDescription":"Report: ix, 50 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-121432","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":410185,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GF0SG7","text":"USGS data release","description":"USGS data release","linkHelpText":"Stage, water velocity and water quality data collected in the Lower Nisqually River, McAllister Creek and tidal channels of the Nisqually River Delta, Thurston County, Washington, February 11, 2016 to September 18, 2017 (ver. 1.1, December, 2019)"},{"id":410186,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95N6CIT","text":"USGS data release","description":"USGS data release","linkHelpText":"Topobathymetric Model of Puget Sound, Washington, 1887 to 2017"},{"id":410184,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1088/ofr20221088.pdf","text":"Report","size":"32.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1088"},{"id":410183,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1088/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.12450821055306,\n 47.12862354087443\n ],\n [\n -123.12450821055306,\n 45.666890715537136\n ],\n [\n -121.49348505325844,\n 45.666890715537136\n ],\n [\n -121.49348505325844,\n 47.12862354087443\n ],\n [\n -123.12450821055306,\n 47.12862354087443\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Dynamic natural processes govern snow distribution in mountainous environments throughout the world. Interactions between these different processes create spatially variable patterns of snow depth across a landscape. Variations in accumulation and redistribution occur at a variety of spatial scales, which are well established for moderate mountain terrain. However, spatial patterns of snow depth variability in steep, complex mountain terrain have not been fully explored due to insufficient spatial resolutions of snow depth measurement. Recent advances in uncrewed aerial systems (UASs) and structure from motion (SfM) photogrammetry provide an opportunity to map spatially continuous snow depths at high resolutions in these environments. Using UASs and SfM photogrammetry, we produced 11 snow depth maps at a steep couloir site in the Bridger Range of Montana, USA, during the 2019–2020 winter. We quantified the spatial scales of snow depth variability in this complex mountain terrain at a variety of resolutions over 2 orders of magnitude (0.02 to 20 m) and time steps (4 to 58 d) using variogram analysis in a high-performance computing environment. We found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability within complex mountain terrain and that snow depths are autocorrelated within horizontal distances of 15 m at our study site. The results of this research have the potential to reduce uncertainty currently associated with snowpack and snow water resource analysis by documenting and quantifying snow depth variability and snowpack evolution on relatively inaccessible slopes in complex terrain at high spatial and temporal resolutions.
","language":"English","publisher":"Copernicus Journals","doi":"10.5194/tc-16-4907-2022","usgsCitation":"Miller, Z., Peitzsch, E.H., Sproles, E.A., Birkeland, K.W., and Palomaki, R.T., 2022, Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain: The Cryosphere, v. 16, no. 12, p. 4907-4930, https://doi.org/10.5194/tc-16-4907-2022.","productDescription":"24 p.","startPage":"4907","endPage":"4930","ipdsId":"IP-139965","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":445693,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-16-4907-2022","text":"Publisher Index Page"},{"id":435598,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YCIA1R","text":"USGS data release","linkHelpText":"2020 winter timeseries of UAS derived digital surface models (DSMs) from the Hourglass study site, Bridger Mountains, Montana, USA"},{"id":410274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Bridger Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.94,\n 45.84\n ],\n [\n -110.94,\n 45.830\n ],\n [\n -110.93,\n 45.83\n ],\n [\n -110.93,\n 45.84\n ],\n [\n -110.94,\n 45.84\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":858561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":858562,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sproles, Eric A. 0000-0003-1245-1653","orcid":"https://orcid.org/0000-0003-1245-1653","contributorId":299760,"corporation":false,"usgs":false,"family":"Sproles","given":"Eric","email":"","middleInitial":"A.","affiliations":[{"id":64943,"text":"Montana State University Earth Sciences Department","active":true,"usgs":false}],"preferred":false,"id":858563,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birkeland, Karl W.","contributorId":209943,"corporation":false,"usgs":false,"family":"Birkeland","given":"Karl","email":"","middleInitial":"W.","affiliations":[{"id":38033,"text":"U.S.D.A. Forest Service National Avalanche Center, Bozeman, Montana, USA","active":true,"usgs":false}],"preferred":false,"id":858564,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Palomaki, Ross T. 0000-0002-3304-9914","orcid":"https://orcid.org/0000-0002-3304-9914","contributorId":299761,"corporation":false,"usgs":false,"family":"Palomaki","given":"Ross","email":"","middleInitial":"T.","affiliations":[{"id":64943,"text":"Montana State University Earth Sciences Department","active":true,"usgs":false}],"preferred":false,"id":858565,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243187,"text":"70243187 - 2022 - Quantifying permanent uplift due to lithosphere-hotspot interaction","interactions":[],"lastModifiedDate":"2023-05-03T11:51:00.692258","indexId":"70243187","displayToPublicDate":"2022-12-08T06:48:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying permanent uplift due to lithosphere-hotspot interaction","docAbstract":"Vertical motions that accompany the passage of the lithosphere over a mantle hotspot can shed light on the nature of the hotspot and its effect on the lithosphere. However, quantifying the temporal vertical and spatial extent, is challenging due to the paucity of evidence in the geological record. Here, we utilize dense seismic and well data covering the intersection of the Great Meteor Hotspot (GMH) track with the U.S. Atlantic continental margin to constrain the surface expression of the hotspot passage under the lithosphere. The continuous sedimentary record of the eastern North American margin during its passage over the hotspot allows determination of the timing, magnitude, width and rate of denudation. We find that a ∼300 km wide region was denuded by up to 850 m between ∼97 and 86 Ma, ∼10 m.y. after the passage of the GMH. Stratigraphic relationships suggest a decaying rock uplift rate with time and no subsequent sagging. The broad, long-lasting, and delayed uplift was modeled as a surface manifestation of either sub-lithospheric mantle depletion, permanently eroded base of the continental lithosphere, or intrusions of depleted magma. We consider sub-lithospheric depletion to be the most likely cause, based on seismic imaging results.
Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.
Atlantic salmon (Salmo salar) return to rivers in spring for an energetically costly upstream migration for spawning. These fish are often delayed in the lower river below dams, subjecting them to warmer waters than occur in upstream sections of river, that may increase metabolic costs. We sought to quantify the energetic cost of dam-mediated delays in migrating adults in the Penobscot and Kennebec rivers, ME. We radio-tagged fish at the lower most dams, released them downstream (18 and 14 km), and tracked their movements back upstream. We used a Distell Fish Fatmeter as a noninvasive measurement of full-body energy at tagging and then again after re-ascending the fish-way at the dams. We found that adults (n = 99) experienced average delays of 16–23 days at dams, losing 11%–22% of initial fat reserves. Using linear regressions, we showed thermal experience as a strong predictor of fat loss. Delay time was also a contributing factor. Extensive delays at dams expose migrating Atlantic salmon to warmer temperatures and increase the depletion rate of energy reserves required for spawning and post-spawn survival.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2022-0008","usgsCitation":"Rubenstein, S., Peterson, E., Christman, P., and Zydlewski, J.D., 2022, Adult Atlantic salmon (Salmo salar) delayed below dams rapidly deplete energy stores: Canadian Journal of Fisheries and Aquatic Sciences, v. 80, no. 1, p. 170-182, https://doi.org/10.1139/cjfas-2022-0008.","productDescription":"13 p.","startPage":"170","endPage":"182","ipdsId":"IP-137191","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Lockwood Dam, Milford Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.36478332126005,\n 44.873642207371006\n ],\n [\n -70.36478332126005,\n 44.06312690380926\n ],\n [\n -67.77061776241788,\n 44.06312690380926\n ],\n [\n -67.77061776241788,\n 44.873642207371006\n ],\n [\n -70.36478332126005,\n 44.873642207371006\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"80","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rubenstein, Sarah R.","contributorId":349051,"corporation":false,"usgs":false,"family":"Rubenstein","given":"Sarah R.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":923956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, Erin","contributorId":349052,"corporation":false,"usgs":false,"family":"Peterson","given":"Erin","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":923957,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christman, Paul","contributorId":349053,"corporation":false,"usgs":false,"family":"Christman","given":"Paul","affiliations":[{"id":68617,"text":"Maine Department of Marine Resources","active":true,"usgs":false}],"preferred":false,"id":923958,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":923959,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238761,"text":"sir20225110 - 2022 - Water quality of sand and gravel aquifers in McHenry County, Illinois, 2020 and comparisons to conditions in 2010","interactions":[],"lastModifiedDate":"2026-04-28T14:25:05.24925","indexId":"sir20225110","displayToPublicDate":"2022-12-07T14:27:26","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5110","displayTitle":"Water Quality of Sand and Gravel Aquifers in McHenry County, Illinois, 2020 and Comparisons to Conditions in 2010","title":"Water quality of sand and gravel aquifers in McHenry County, Illinois, 2020 and comparisons to conditions in 2010","docAbstract":"McHenry County, Illinois, obtains most of its drinking water from shallow sand and gravel aquifers (groundwater). To evaluate this groundwater resource, the U.S. Geological Survey, in cooperation with McHenry County, Illinois, collected water-quality samples from 41 of 42 monitoring wells in the McHenry County Groundwater Monitoring Network and 4 monitoring wells from the U.S. Geological Survey National Water-Quality Assessment Project. Additionally, a subset of 12 monitoring wells was sampled and analyzed for pharmaceuticals and wastewater indicator compounds (WICs), collectively referred to as “contaminants of emerging concern” (CECs). Results from this 2020 study were compared to the 2010 results to assess changes in groundwater quality. Statistical analyses and chloride-bromide ratio analyses also were completed to assess changes in water quality.
Health-based benchmarks were exceeded for arsenic (about 24 percent; 11 of 45 monitoring wells), sodium (40 percent, 18 of 45), and manganese (about 2 percent, 1 of 45). Aesthetically based benchmarks were exceeded for dissolved solids (about 29 percent, 13 of 45), chloride (about 4 percent, 2 of 45), iron (about 87 percent, 39 of 45), and manganese (about 29 percent, 13 of 45). CECs were detected at low or estimated concentrations in 8 of the 12 (about 67 percent) monitoring wells analyzed.
In addition to sampling the groundwater monitoring wells, three surface-water-quality monitoring sites also were sampled and analyzed for pharmaceuticals and WICs to provide a preliminary assessment of the presence of CECs in the surface waters. CECs were detected in all three of the surface-water-quality monitoring samples collected, and WICs were more prevalent and more frequently detected than pharmaceutical compounds. These results provided a cursory understanding of the presence of CECs in surface waters and do not constitute a robust analysis of sources, seasonality, range of concentrations, persistence, or effects.
The 2020 groundwater-quality results had measurements of field properties, and concentrations of major ions, trace metals, and nutrients that were consistent with 2010 results with statistically significant increases for calcium, magnesium, and silica, and decreases for aluminum, ammonia, arsenic, barium, bromide, calcium, molybdenum, phosphate, specific conductance, sulfate, and dissolved solids. Increases generally were detected in the intermediate and deep parts of the sand and gravel aquifer, and decreases were detected in the shallow parts of the sand and gravel aquifer. The mixed distribution of increases and decreases among the various constituents and aquifer-depth groups could be reflecting dissolution and mobility of some of the redox sensitive constituents and dilution of some constituents in the shallow aquifer depths. These changes may be attributed to a combination of stable population of the past decade (2010–20), land-use management practices, and the recent wet years of 2017 through 2019 causing a dilution of the major ions in the shallow parts of the aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225110","collaboration":"Prepared in cooperation with McHenry County, Illinois","usgsCitation":"Gahala, A.M., Gruhn, L.R., Murphy, J.C., and Matson, L.A., 2022, Water quality of sand and gravel aquifers in McHenry County, Illinois, 2020 and comparisons to conditions in 2010: U.S. Geological Survey Scientific Investigations Report 2022–5110, 53 p., https://doi.org/10.3133/sir20225110.","productDescription":"Report: viii, 53 p.; 2 Data Releases; Dataset","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-137120","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":503567,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113938.htm","linkFileType":{"id":5,"text":"html"}},{"id":435599,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W1TPNF","text":"USGS data release","linkHelpText":"Reconnaissance of Per- and Polyfluoroalkyl Substances (PFAS) in Selected Groundwater and Surface Water Sites in McHenry County, Illinois, 2020"},{"id":410163,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":410162,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5110/images"},{"id":410161,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5110/sir20225110.XML"},{"id":410160,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5110/sir20225110.pdf","text":"Report","size":"4.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5110"},{"id":410159,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5110/coverthb.jpg"},{"id":410218,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225110/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":410164,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RBXV53","text":"USGS data release","linkHelpText":"Quality-assurance and quality-control data for discrete water-quality samples collected in McHenry County, Illinois, 2020"}],"country":"United States","state":"Illinois","county":"McHenry County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-88.3016,42.4979],[-88.1971,42.4981],[-88.1979,42.4562],[-88.1974,42.4167],[-88.1966,42.3286],[-88.1994,42.2432],[-88.1992,42.1555],[-88.2382,42.155],[-88.3539,42.1547],[-88.4703,42.1552],[-88.5891,42.1556],[-88.7061,42.1564],[-88.7057,42.2418],[-88.7041,42.329],[-88.705,42.4167],[-88.7059,42.4972],[-88.6737,42.4977],[-88.6288,42.4985],[-88.5047,42.4981],[-88.4099,42.4977],[-88.3016,42.4979]]]},\"properties\":{\"name\":\"McHenry\",\"state\":\"IL\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The ecological role of large thecosome pteropods in the pelagic ecosystem of the northern Gulf of Mexico (GoM) may be substantial, both in the food web and biogeochemical cycling. We analyzed species abundances, vertical and horizontal distributions of large species with calcareous shells (those collected in 3-mm mesh nets). Pteropod samples were collected following the 2010 Deepwater Horizon oil (DWH) spill by two midwater sampling programs: the Offshore Nekton Sampling and Analysis Program (ONSAP 2011) and the Deep Pelagic Nekton Dynamics of the Gulf of Mexico (DEEPEND 2015) projects. All samples were collected using a 10-m2 Multiple Opening/Closing Net and Environmental Sensing System (MOC10) midwater trawl, with 3-mm mesh. This gear sampled five discrete depths between 0–1500 m. Over 13,000 pteropod specimens were examined, with 25 species identified. Clio pyramidata Linnaeus 1767 was the most abundant species during both collection periods. Five genera (Diacria, Clio, Styliola, Cuvierina, Cavolinia) demonstrated diel vertical migration from the mesoto epipelagic zone.
","language":"English","publisher":"American Malacological Union","doi":"10.4003/006.039.0102","usgsCitation":"Shedler, S., Seibel, B., Vecchione, M., Griffin, D.W., and Judkins, H., 2022, Abundance and distribution of large thecosome pteropods in the northern Gulf of Mexico: American Malacological Bulletin, v. 39, no. 1, p. 1-11, https://doi.org/10.4003/006.039.0102.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-122312","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467141,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/54648","text":"External Repository"},{"id":410704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92,\n 30\n ],\n [\n -92,\n 26\n ],\n [\n -85.5,\n 26\n ],\n [\n -85.5,\n 30\n ],\n [\n -92,\n 30\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shedler, Sarah","contributorId":218584,"corporation":false,"usgs":false,"family":"Shedler","given":"Sarah","email":"","affiliations":[],"preferred":false,"id":859317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seibel, Brad","contributorId":300042,"corporation":false,"usgs":false,"family":"Seibel","given":"Brad","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":859318,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vecchione, Michael","contributorId":300043,"corporation":false,"usgs":false,"family":"Vecchione","given":"Michael","email":"","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":859319,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":859320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Judkins, Heather","contributorId":300044,"corporation":false,"usgs":false,"family":"Judkins","given":"Heather","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":859321,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238769,"text":"70238769 - 2022 - Physical controls on the hydrology of perennially ice-covered lakes, Taylor Valley, Antarctica (1996-2013)","interactions":[],"lastModifiedDate":"2022-12-15T16:05:28.641938","indexId":"70238769","displayToPublicDate":"2022-12-07T06:43:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7357,"text":"JGR Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Physical controls on the hydrology of perennially ice-covered lakes, Taylor Valley, Antarctica (1996-2013)","docAbstract":"The McMurdo Dry Valleys, Antarctica, are a polar desert populated with numerous closed-watershed, perennially ice-covered lakes primarily fed by glacial melt. Lake levels have varied by as much as 8 m since 1972 and are currently rising after a decade of decreasing. Precipitation falls as snow, so lake hydrology is dominated by energy available to melt glacier ice and to sublimate lake ice. To understand the energy and hydrologic controls on lake level changes and to explain the variability between neighboring lakes, only a few kilometers apart, we model the hydrology for the three largest lakes in Taylor Valley. We apply a physically based hydrological model that includes a surface energy balance model to estimate glacial melt and lake sublimation to constrain mass fluxes to and from the lakes. Results show that lake levels are very sensitive to small changes in glacier albedo, air temperature, and wind speed. We were able to balance the hydrologic budget in two watersheds using meltwater inflow and sublimation loss from the ice-covered lake alone. A third watershed, closest to the coast, required additional inflow beyond model uncertainties. We hypothesize a shallow groundwater system within the active layer, fed by dispersed snow patches, contributes 23% of the inflow to this watershed. The lakes are out of equilibrium with the current climate. If the climate of our study period (1996-2013) persists into the future, the lakes will reach equilibrium starting in 2300, with levels 2-17 m higher, depending on the lake, relative to the 2020 level.
No abstract available.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyac100","usgsCitation":"Giese, J.C., Agee, M., Campbell, C.G., Hatch, A., Mitsdarfer, C., Poincon, A., Russell, R.C., Chamberlain-Irwin, H.N., Kastner, M., Franklin, D., and Klaver, R.W., 2022, Book review: Restoring the balance: What wolves tell us about our relationship with nature: Journal of Mammalogy, v. 104, no. 2, p. 425-427, https://doi.org/10.1093/jmammal/gyac100.","productDescription":"3 p.","startPage":"425","endPage":"427","ipdsId":"IP-142336","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":504188,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyac100","text":"Publisher Index Page"},{"id":503960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Isle 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N.","contributorId":371058,"corporation":false,"usgs":false,"family":"Chamberlain-Irwin","given":"Heather","middleInitial":"N.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":960935,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kastner, Martin","contributorId":293508,"corporation":false,"usgs":false,"family":"Kastner","given":"Martin","email":"","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":960936,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Franklin, Derek","contributorId":371083,"corporation":false,"usgs":false,"family":"Franklin","given":"Derek","affiliations":[],"preferred":false,"id":960984,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Klaver, Robert W. 0000-0002-3263-9701 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Pondosa fault zone: A distributed dextral-normal-oblique fault system in northeastern California, USA","docAbstract":"The tectonic domains of Basin and Range extension, Cascadia subduction zone contraction, and Walker Lane dextral transtension converge in the Mushroom Rock region of northeastern California, USA. We combined analysis of high-resolution topographic data, bedrock mapping, 40Ar/39Ar geochronology, low-temperature thermochronology, and existing geologic and fault mapping to characterize an extensive dextral-normal-oblique fault system called the Pondosa fault zone. This fault zone extends north-northwest from the Pit River east of Soldier Mountain, California, into moderately high-relief volcanic topography as far north as the Bartle (California) townsite with normal and dextral offset apparent in geomorphology and fault exposures. New and existing 40Ar/39Ar and radiocarbon dating of offset lava flows provides ages of 12.4 ka to 9.6 Ma for late Cenozoic stratigraphic units. Scarp morphology and geomorphic expression indicate that the fault system was active in the late Pleistocene. The Pondosa fault zone may represent a dextral-oblique accommodation zone between north-south–oriented Basin and Range extensional fault systems and/or part of the Sierra Nevada–Oregon Coast block microplate boundary.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02450.1","usgsCitation":"Jobe, J.A., Briggs, R.W., Gold, R.D., DeLong, S.B., Hille, M., Delano, J., Johnstone, S., Pickering, A., Phillips, R., and Calvert, A.T., 2022, The Pondosa fault zone: A distributed dextral-normal-oblique fault system in northeastern California, USA: Geosphere, v. 19, no. 1, p. 179-205, https://doi.org/10.1130/GES02450.1.","productDescription":"27 p.","startPage":"179","endPage":"205","ipdsId":"IP-137700","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492497,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02450.1","text":"Publisher Index Page"},{"id":492284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"eastern California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.27831636230019,\n 41.12301709043055\n ],\n [\n -122.27831636230019,\n 39.963081252129996\n ],\n [\n -120.53795346715106,\n 39.963081252129996\n ],\n [\n -120.53795346715106,\n 41.12301709043055\n ],\n [\n -122.27831636230019,\n 41.12301709043055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson Jobe, Jessica A. 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Thompson Jobe","given":"Jessica","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science 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Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":943097,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70246612,"text":"70246612 - 2022 - Divergent Serpentoviruses in free-ranging invasive pythons and native colubrids in southern Florida, United States","interactions":[],"lastModifiedDate":"2024-02-28T16:47:09.562656","indexId":"70246612","displayToPublicDate":"2022-12-06T06:50:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3700,"text":"Viruses","active":true,"publicationSubtype":{"id":10}},"title":"Divergent Serpentoviruses in free-ranging invasive pythons and native colubrids in southern Florida, United States","docAbstract":"The Holocene reefs off southeast Florida provide unique insights into the biogeographical and ecological response of western Atlantic coral reefs to past climate change that can be used to evaluate future climate impacts. However, previous studies have focused on millennial-scale change during the stable mid-Holocene, making it difficult to make inferences about the impact of shorter-term variability that is relevant to modern climate warming. Using uranium-series dating of newly discovered subfossil coral rubble deposits, we establish a new high-resolution record of coral community development off southeast Florida during a period of variable climate in the late Holocene. Our results indicate that coral communities dominated by reef-building Acropora palmata and Orbicella spp. persisted in the nearshore environments off southeast Florida ~75 km north of their primary historical ranges between ~3500 and 1800 years before present. This timing coincides with regional warming at the northern extent of the Atlantic Warm Pool, suggesting a likely link between regional oceanographic climate and the expansion of cold-sensitive reef-building coral communities to the high-latitude reefs off southeast Florida. These findings not only extend the record of coral-reef development in southeast Florida into the late Holocene, but they also have important implications for future range expansions of reef-building coral communities in response to modern climate change.