The National Energy Technology Laboratory, the Japan Oil, Gas and Metals National Corporation, and the U.S. Geological Survey are leading an effort to conduct an extended gas hydrate production test in northern Alaska. The proposed production test required the drilling of an initial stratigraphic test well (STW) to confirm the geologic conditions of the proposed test site. This well was completed in January 2019 in cooperation with the Prudhoe Bay Unit Working Interest Owners. The Prudhoe Bay Unit Hydrate-01 STW was spudded on 10-December-2018. Downhole data acquisition was completed on 25-December-2018, and the rig was released on 01-January-2019. The Hydrate-01 STW was drilled in two sections, including the surface hole that was drilled to a depth of 2248 ft measured depth (MD) (685 m MD) and cased, and the production hole section that was drilled to a depth of 3558 ft MD (1084 m MD) and also cased. A thermally chilled mineral-oil-based mud was used in the main (production) hole section of the well to maintain wellbore stability and quality of the wellbore acquired data. The primary wellbore data were acquired using logging-while-drilling tools. A sidewall pressure core system was also deployed to gather grain size and other data needed for the design of the future production test wells. In addition to confirming the geologic conditions at the test site, the Hydrate-01 STW was designed to serve as a monitoring well during future field operations. Therefore, two sets of fiber-optic cables, each including a bundled distributed acoustic sensor (DAS) and a distributed temperature sensor (DTS), were clamped to the outside of the production casing and cemented in place. In March 2019, the project team acquired three-dimensional (3D) DAS vertical seismic profiling data in the Hydrate-01 STW. Temperature surveys were also acquired with the DTS as deployed in the Hydrate-01 STW during the completion of the well and nearly continuously since March-2019.
Understanding interactions among biogeochemical cycles is increasingly important as anthropogenic alterations of global climate and of carbon (C), nitrogen (N), and phosphorus (P) cycles interactively affect the Earth system. Ecosystem processes in the dryland biome, which makes up over 40% of Earth's terrestrial surface, are often distinctively sensitive to small changes in resource availability, likely because levels of many resources are low. However, data also suggest that simultaneous changes in the availability of multiple resources may be necessary to affect a response in these low-resource systems, offering an opportunity to test patterns and controls of co-limitation, serial limitation, and individual limitation in soil environments. While drylands may play a governing role in key aspects of Earth's C cycle, and while an improved understanding of resource limitation could substantially improve our forecasts of dryland responses to change, our understanding of interacting controls on soil C cycle processes remains notably poor in these dry systems. Here, we address multiple fundamental hypotheses of resource controls over ecosystem function to test how water, C, N, and P regulate soil C cycling individually and interactively in a dryland ecosystem on the Colorado Plateau. Using a series of laboratory incubations, we found that, while water, C, and N limited C cycling through serial limitation, water alone resulted in an extremely small respiratory response from target organisms, whereas water + C resulted in a dramatic increase in soil C cycling, suggesting a degree of functional co-limitation. Nitrogen additions alone resulted in no changes to soil C cycling, but when N was added in concert with water and C, N greatly increased soil C cycling rates relative to additions of water and C without N. Phosphorus additions had no effect on the C cycle either alone or synergistically. These patterns were consistent with the stoichiometry of the system and interactions among resources were surprising in ways that inform our understanding of critical theories in ecology, such as the Transient Maxima Hypothesis, supporting the suggestion that multiple resource limitation explains pulse-dynamic C cycling in drylands better than water limitation alone.
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University","active":true,"usgs":false}],"preferred":false,"id":845240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":845241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, Colin 0000-0002-4539-7780 ctucker@usgs.gov","orcid":"https://orcid.org/0000-0002-4539-7780","contributorId":167487,"corporation":false,"usgs":true,"family":"Tucker","given":"Colin","email":"ctucker@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":845242,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262533,"text":"70262533 - 2022 - Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains","interactions":[],"lastModifiedDate":"2025-01-23T17:03:28.007798","indexId":"70262533","displayToPublicDate":"2022-03-01T10:58:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains","docAbstract":"In the central Appalachians of Virginia and West Virginia, the Virginia northern flying squirrel (Glaucomys sabrinus fuscus; VNFS) is a subspecies of northern flying squirrel generally associated with red spruce (Picea rubens)-dominated forests at high elevations. Listed as endangered by the
U.S. Fish and Wildlife Service from 1985 to 2013, the VNFS currently is the subject of a 10-year post-delisting assessment. Still considered a state-listed species in Virginia and a species of greatest conservation need in West Virginia, the VNFS serves as a focal target for red spruce restoration activities in the High Allegheny Region (HAR) of the two states. Owing to the cryptic nature of VNFS and its low detection probability in live-capture surveys, managers in the region rely on habitat models to assess probable presence. Using long-term nest-box, live-trapping, and radio-telemetry data matched with updated high elevation forest-type coverage data for the region, we created a new VNFS resource selection function and spatial coverage map. Inputting red spruce cover, increasing elevation, and decreasing landform index (increasing site shelteredness) composed the best model explaining VNFS occurrence. The calculated amount of low-quality habitat was congruent with previous modeling efforts; however, inclusion of more VNFS occurrence records in the current effort indicated that previous efforts substantially underestimated the amount (>400%) of extant high quality VNFS habitat. We estimate the HAR to contain approximately 197,952 ha with ≥0.50 predicted probability of occurrence of VNFS. In addition to potentially improving current and future VNFS live-capture surveys, with this model managers may better target forests for red spruce restoration to increase high elevation forest ecological integrity and to improve habitat patch connectedness for VNFS.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Ford, W., Diggins, C., De La Cruz, J., and Silvis, A., 2022, Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 9, p. 168-175.","productDescription":"8 p.","startPage":"168","endPage":"175","ipdsId":"IP-129565","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia, West Virginia","otherGeospatial":"High Allegheny Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.54094480023322,\n 36.94509861616578\n ],\n [\n -79.74649202772338,\n 36.97475579082641\n ],\n [\n -77.97606039422519,\n 38.33504579423493\n ],\n [\n -77.55782664323384,\n 39.373334368570056\n ],\n [\n -78.88106642468261,\n 39.62337985209078\n ],\n [\n -80.73280550358555,\n 38.17478880030487\n ],\n [\n -80.94924239314835,\n 37.25410993243544\n ],\n [\n -80.54094480023322,\n 36.94509861616578\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":924488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diggins, Corinne A.","contributorId":270602,"corporation":false,"usgs":false,"family":"Diggins","given":"Corinne A.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":924489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De La Cruz, Jesse L.","contributorId":349583,"corporation":false,"usgs":false,"family":"De La Cruz","given":"Jesse L.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":924490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":924491,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231751,"text":"70231751 - 2022 - Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions","interactions":[],"lastModifiedDate":"2022-05-25T15:50:59.036384","indexId":"70231751","displayToPublicDate":"2022-03-01T10:50:05","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions","docAbstract":"Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized weight-of-evidence and logistic regression methods to estimate resource favorability, but these analyses relied upon some expert decisions. While expert decisions can add confidence to aspects of the modeling process by ensuring only reasonable models are employed, expert decisions also introduce human bias into assessments. This bias presents a source of error that may affect the performance of the models and resulting resource estimates. Our study aims to reduce expert input through robust data-driven analyses and better-suited data science techniques, with the goals of saving time, reducing bias, and improving predictive ability. We present six favorability maps for geothermal resources in the western United States created using two strategies applied to three modern machine learning algorithms (logistic regression, support-vector machines, and XGBoost). To provide a direct comparison to previous assessments, we use the same input data as the 2008 U.S. Geological Survey (USGS) conventional moderate- to high-temperature geothermal resource assessment. The six new favorability maps required far less expert decision-making, but broadly agree with the previous assessment. Despite the fact that the 2008 assessment results employed linear methods, the non-linear machine learning algorithms (i.e., support-vector machines and XGBoost) produced greater agreement with the previous assessment than the linear machine learning algorithm (i.e., logistic regression). It is not surprising that geothermal systems depend on non-linear combinations of features, and we postulate that the expert decisions during the 2008 assessment accounted for system non-linearities. Substantial challenges to applying machine learning algorithms to predict geothermal resource favorability include severe class imbalance (i.e., there are very few known geothermal systems compared to the large area considered), and while there are known geothermal systems (i.e., positive labels), all other sites have an unknown status (i.e., they are unlabeled), instead of receiving a negative label (i.e., the known/proven absence of a geothermal resource). We address both challenges through a custom undersampling strategy that can be used with any algorithm and then evaluated using F1 scores.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 47th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"47th Stanford Geothermal Workshop","conferenceDate":"Feb 7-9, 2022","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Mordensky, S.P., Lipor, J., DeAngelo, J., Burns, E., and Lindsey, C.R., 2022, Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions, in Proceedings, 47th workshop on geothermal reservoir engineering, Stanford, CA, Feb 7-9, 2022, 18 p.","productDescription":"18 p.","ipdsId":"IP-135047","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":401053,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":401039,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=35430"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.25976562499999,\n 31.728167146023935\n ],\n [\n -103.0078125,\n 31.952162238024975\n ],\n [\n -102.74414062499999,\n 36.80928470205937\n ],\n [\n -103.623046875,\n 41.902277040963696\n ],\n [\n -104.150390625,\n 49.15296965617042\n ],\n [\n -123.134765625,\n 48.980216985374994\n ],\n [\n -123.662109375,\n 48.22467264956519\n ],\n [\n -125.24414062499999,\n 48.22467264956519\n ],\n [\n -124.892578125,\n 42.68243539838623\n ],\n [\n -124.71679687499999,\n 39.774769485295465\n ],\n [\n -120.76171875,\n 34.45221847282654\n ],\n [\n -117.50976562499999,\n 32.54681317351514\n ],\n [\n -114.78515624999999,\n 32.76880048488168\n ],\n [\n -110.830078125,\n 31.42866311735861\n ],\n [\n -107.9296875,\n 31.57853542647338\n ],\n [\n -106.25976562499999,\n 31.728167146023935\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843643,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":843644,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843646,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lindsey, Cary Ruth 0000-0001-5693-9664","orcid":"https://orcid.org/0000-0001-5693-9664","contributorId":292016,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843647,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256683,"text":"70256683 - 2022 - Interannual colony exchange among breeding Eastern Brown Pelicans","interactions":[],"lastModifiedDate":"2024-08-30T15:49:16.568962","indexId":"70256683","displayToPublicDate":"2022-03-01T10:40:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2284,"text":"Journal of Field Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Interannual colony exchange among breeding Eastern Brown Pelicans","docAbstract":"Colonial seabirds often display high rates of interannual site fidelity to breeding locations, especially as adults. Species using more nearshore or coastal systems, however, may display comparatively less fidelity than highly pelagic species. We used long-term GPS tracking data to assess the frequency of interannual colony switching in Eastern Brown Pelicans (Pelecanus occidentalis carolinensis) in the South Atlantic Bight, a species about which adult site fidelity is poorly understood. By using recursive behavioral patterns to identify nest locations, we report 36% of reproductive attempts occurred at colonies that were different from the colony used in a prior year. Colony switching was especially prevalent in female pelicans, and distances between switched colonies ranged from 56 to 592 km. We suggest that long-term telemetry may be a reliable tool for assessing interannual breeding site fidelity, especially when compared to more traditional band resighting efforts.
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While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world’s rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals.
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These data are available at 1-degree grids along the U.S. coastline and downscaled specifically at NOAA tide-gauge locations. Estimates of flood exposure are assessed using contemporary U.S. coastal flood-severity thresholds for current conditions (e.g., sea levels and infrastructure footprint) and for the next 30 years (out to year 2050), assuming no additional risk reduction measures are enacted.
This effort builds upon the 2017 Task Force report (Sweet et al., 2017). In particular, the set of global mean sea level rise scenarios from that report are updated and downscaled with output directly from the United Nations Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6; IPCC, 2021a), through the efforts of the NASA Sea Level Change Team; updates include adjustments to the temporal trajectories and exceedance probabilities of these scenarios based upon end-of-century global temperatures. As with the 2017 report, these global mean sea level rise scenarios are regionalized for the U.S. coastline. In addition, methodology supporting the U.S. Department of Defense Regional Sea Level (DRSL) database (Hall et al., 2016) is adapted for the extreme water level dataset newly developed for this report.
This report will be a key technical input for the Fifth National Climate Assessment (NCA5). These data and information are being incorporated into current and planned agency tools and services, such as NOAA’s Sea Level Rise Viewer and Inundation Dashboard, NASA’s Sea Level Change Portal, and others. Although the intent of this report is not to provide authoritative guidance or design specifications for a specific project, it is intended to help inform Federal agencies, state and local governments, and stakeholders in coastal communities about current and future sea level rise to help contextualize its effects for decision-making purposes.
","language":"English","publisher":"National Oceanic and Atmospheric Administration","usgsCitation":"Sweet, W., Hamlington, B., Kopp, R.E., Weaver, C., Barnard, P.L., Bekaert, D., Brooks, W., Craghan, M., Dusek, G., Frederikse, T., Garner, G., Genz, A.S., Krasting, J.P., Larour, E., Marcy, D., Marra, J.J., Obeysekera, J., Osler, M., Pendleton, M., Roman, D., Schmied, L., Veatch, W., White, K., and Zuzak, C., 2022, Global and regional sea level rise scenarios for the United States: Technical Report NOS.01, xiv, 95 p.","productDescription":"xiv, 95 p.","ipdsId":"IP-135630","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":396604,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396585,"rank":1,"type":{"id":15,"text":"Index 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Administration","active":true,"usgs":false}],"preferred":false,"id":836745,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Marra, John J.","contributorId":215335,"corporation":false,"usgs":false,"family":"Marra","given":"John","email":"","middleInitial":"J.","affiliations":[{"id":39223,"text":"NOAA/NESDIS/National Centers for Environmental Information","active":true,"usgs":false}],"preferred":false,"id":836746,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Obeysekera, Jayantha 0000-0002-9261-1268","orcid":"https://orcid.org/0000-0002-9261-1268","contributorId":223708,"corporation":false,"usgs":false,"family":"Obeysekera","given":"Jayantha","affiliations":[{"id":40755,"text":"South Florida WMD West Palm Beach, FL","active":true,"usgs":false}],"preferred":false,"id":836747,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Osler, 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Risk Mgmt Directorate","active":true,"usgs":false}],"preferred":false,"id":836754,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70237201,"text":"70237201 - 2022 - USGS invasive carp database management and integration support","interactions":[],"lastModifiedDate":"2022-10-04T14:55:20.0136","indexId":"70237201","displayToPublicDate":"2022-03-01T09:47:50","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"USGS invasive carp database management and integration support","docAbstract":"Bigheaded carps (Bighead Carp and Silver Carp) tracking, monitoring, and contracted removal will continue throughout the Illinois River and Upper Mississippi River as part of an adaptive management effort to mitigate, control, and contain bigheaded carps. Other fish will also be tracked to maintain a holistic view of the transmitter distribution in the Upper Illinois River Waterway. To facilitate these actions, a need to compile and analyze invasive carp-related data from all agencies exists. Invasive carp-related data include all data sources that could inform the MRWG objectives or projects. These data, often in disparate formats, must be integrated into a common format that allows all agencies the opportunity to assess invasive carp monitoring, control, and removal efforts. Ensuring the interoperability of these datasets allows for their use in various analyzes and modeling efforts. Implementing an interoperable data management framework provides the mechanisms for end users to find and use integrated data. Integrating data for use in modeling and analysis furthers the partnership’s collective understanding of bigheaded carp life history, distribution, and movement and can be used to facilitate adaptive management actions (e.g., directing monitoring, sampling, and removal efforts, assessing invasive carp abundance to support modeling efforts, informing deployment of control actions, etc.). An effective data management strategy will streamline the data update process, providing all agencies with timely data and analyses in support of informed decision-making processes.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2021 Invasive carp interim summary report","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Invasive Carp Regional Coordinating Committee","usgsCitation":"Harrison, T.J., Brey, M.K., and Stone, J., 2022, USGS invasive carp database management and integration support, 4 p.","productDescription":"4 p.","startPage":"109","endPage":"112","ipdsId":"IP-139244","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":407860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":407845,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Illinois River, upper Mississippi River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.14306640625,\n 36.421282443649496\n ],\n [\n -88.11035156249999,\n 40.6306300839918\n ],\n [\n -87.73681640625,\n 41.95131994679697\n ],\n [\n -88.681640625,\n 43.644025847699496\n ],\n [\n -89.12109375,\n 46.118941506107056\n ],\n [\n -91.0986328125,\n 46.51351558059737\n ],\n [\n -93.1640625,\n 46.483264729155586\n ],\n [\n -93.80126953124999,\n 47.754097979680026\n ],\n [\n -94.833984375,\n 48.06339653776211\n ],\n [\n -96.3720703125,\n 46.73986059969267\n ],\n [\n -96.26220703125,\n 44.809121700077355\n ],\n [\n -94.8779296875,\n 42.21224516288584\n ],\n [\n -92.548828125,\n 39.57182223734374\n ],\n [\n -91.1865234375,\n 37.37015718405753\n ],\n [\n -90.10986328125,\n 36.98500309285596\n ],\n [\n -90.28564453124999,\n 36.63316209558658\n ],\n [\n -89.80224609374999,\n 36.12012758978146\n ],\n [\n -89.14306640625,\n 36.421282443649496\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":853617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":853659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stone, Jayme 0000-0002-0512-3072","orcid":"https://orcid.org/0000-0002-0512-3072","contributorId":251712,"corporation":false,"usgs":false,"family":"Stone","given":"Jayme","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":853618,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70264283,"text":"70264283 - 2022 - Wind River subbasin restoration: Annual Report of U.S. Geological Survey activities January 2020 through December 2020","interactions":[],"lastModifiedDate":"2025-03-10T15:02:41.714948","indexId":"70264283","displayToPublicDate":"2022-03-01T09:41:36","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Wind River subbasin restoration: Annual Report of U.S. Geological Survey activities January 2020 through December 2020","docAbstract":"We sampled juvenile wild Steelhead Trout Oncorhynchus mykiss in headwater streams of the Wind River, WA, to characterize population attributes and investigate life-history metrics, particularly migratory patterns, and early life-stage survival. We used passive integrated transponder (PIT) tagging and a series of instream PIT-tag interrogation systems (PTISs) to track juveniles and adults. The Wind River subbasin is considered a wild Steelhead refuge by Washington Department of Fish and Wildlife (WDFW). No hatchery Steelhead Trout have been released in the Wind River subbasin since 1997, and hatchery adults are estimated at less than one percent of spawners in most years. Over twenty years of Steelhead Trout status and trend monitoring and research in the subbasin is contributing to understanding of population response to numerous restoration actions in the subbasin, including removal of Hemlock Dam from Trout Creek in 2009, which had an outdated adult ladder and contributed to increased water temperatures reducing performance of juvenile Steelhead Trout.
Data from our study, and companion work by Washington Department of Fish and Wildlife, are contributing to Bonneville Power Administration’s (BPA) Research, Monitoring, and Evaluation (RM&E) Program Strategy of Fish Population Status Monitoring (https://www.cbfish.org/ProgramStrategy.mvc/Index). Specifically, this work addresses the substrategies of 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and Uncertainties Research regarding differing life histories of a wild Steelhead Trout population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives.
During summer and fall 2020, we PIT-tagged 1,415 Steelhead parr (age-0 and age-1) in the Trout Creek and upper Wind River watersheds. Recaptures and detections of PIT-tagged Steelhead Trout parr happened through repeat headwater sampling, smolt trap operations, and instream PTISs and Columbia River PIT-tag detection infrastructure. Throughout the year, we maintained a series of six instream PTISs to monitor movement of tagged Steelhead Trout parr, smolts, and adults, providing data to population assessments, and life-cycle research and modeling.
Detection data from PIT-tagged adult Steelhead Trout at PTISs allow assessment of adult escapement to tributary watersheds within the Wind River subbasin. Adult Steelhead Trout detection efficiency estimates at our primary PTIS in Trout Creek have been greater than 92 percent during eight of the past nine years and have exceeded 90% at our primary PTIS in the Wind River the past three years. Adult escapement estimates to tributary watersheds are helping evaluate the efficacy of the 2009 removal of Hemlock Dam from rkm 2.0 of Trout Creek. The dam had potential negative effects on Steelhead Trout populations in Trout Creek due to hydrologic impairment, increased temperatures, and adult passage issues. Hemlock Dam was laddered for adult passage, but not to modern standards, which likely resulted in avoidance by some adult Steelhead Trout.
We continue to improve our PTISs in the Wind River subbasin. The improvements in siting and addition of grid power to the upper Wind River PTIS (site code WRU, rkm 27.6) during 2016 and 2017, and the addition of the Mine Reach site (site code MIN, rkm 36.0) have much improved PIT-tagged fish monitoring in the upper Wind River watershed. The paired PTIS design in the upper Wind River watershed (sites WRU and MIN), matches that in the Trout Creek watershed (sites TRC and TC4) and will allow comparisons of Steelhead Trout population metrics between the two watersheds as response to Hemlock Dam removal continues and future restoration efforts occur in Trout Creek. We installed two new PTISs during 2020. Both were installed downstream of our primary interrogation sites on Trout Creek and in the mainstem Wind River. We hope the two new sites will provide interrogations information that will allow us to better estimate detection efficiencies of downstream moving juvenile Steelhead Trout at the primary interrogation sites. The additional interrogations will be particularly important for those fish tagged with 9-mm PIT tags as less information from downstream locations is available from them. These sites and other status and trend data will allow evaluation of further planned restoration within the watershed, particularly that proposed for the headwaters of Trout Creek.
Detections at the instream PTISs have demonstrated trends of age-0 and age-1 parr emigration from natal areas during summer and fall, in addition to the expected movement of parr and smolts in spring. We have estimated that from 15 to 51% of parr tagged as age-0 fish in headwater areas make downstream migrations at age 1 for additional rearing during both spring and fall. We have estimated that up to 27% of Steelhead Trout parr, tagged as age-1 fish, make downstream migrations during fall. These findings raise questions about where parr most successfully rear and whether migrations are density or habitat quality driven. Broader monitoring programs would give a more comprehensive understanding of juvenile Steelhead Trout production and rearing and productivity contribution.
Repeat sampling at consistent locations in the subbasin has enabled assessment of juvenile Steelhead Trout growth patterns. Growth rates (relative change in weight) of age-0 PITtagged parr during summer were similar across the subbasin but lower for age-1 parr in the Trout Creek watershed than the upper Wind River watershed. Yearly relative growth for parr tagged at age-0 is similar across the subbasin.
Non-native Brook Trout Salvelinus confluentus are present in the subbasin, chiefly the Trout Creek watershed, and repeat sampling has allowed us to index their prevalence. Mean percent-of-catch that is Brook Trout, at four sample sites in Trout Creek, has declined from the period 1998 – 2003 to the period 2011 – 2020. Percent-of-catch and number of Brook Trout at the Trout Creek sites from 2011 through 2020 declined, though both metrics increased in 2018.
Evaluation and planning of restoration efforts are critical to ensure efficient use of resources. Assessing Steelhead Trout life history variation in the Wind River subbasin will inform research and tracking of many populations and help inform habitat restoration and water allocation planning. Movement of Steelhead Trout parr from natal areas to other rearing areas raises questions regarding juvenile abundance, origin, and habitat use within watersheds. Improved PTISs and focused PIT tagging of age-0 and age-1 Steelhead Trout parr allow investigation of such questions. Increasingly detailed viable salmonid population information, such as that provided by PIT-tagging and instream PTIS networks like those in the Wind River can provide data to inform fisheries policy and management and understand life-history strategies and limiting factors. Such efforts also provide assessment of long-term effects of habitat restoration actions such as the removal of Hemlock Dam on Trout Creek, and the proposed Stage-0 restoration effort for upper Trout Creek, which would be a large-scale effort to reset sections of stream within their floodplain, restoring connectivity and interaction with surrounding landscape.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Jezorek, I., 2022, Wind River subbasin restoration: Annual Report of U.S. Geological Survey activities January 2020 through December 2020, 71 p.","productDescription":"71 p.","ipdsId":"IP-137356","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":483143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":483119,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P190880","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Wind River subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.1275,\n 46\n ],\n [\n -122.1275,\n 45.75\n ],\n [\n -121.8,\n 45.75\n ],\n [\n -121.8,\n 46\n ],\n [\n -122.1275,\n 46\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian 0000-0002-3842-3485","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":217811,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930257,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70229699,"text":"70229699 - 2022 - Strength and memory of precipitation's control over streamflow across the conterminous United States","interactions":[],"lastModifiedDate":"2022-03-15T14:19:39.099966","indexId":"70229699","displayToPublicDate":"2022-03-01T09:13:07","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":"Strength and memory of precipitation's control over streamflow across the conterminous United States","docAbstract":"How precipitation (P) is translated into streamflow (Q) and over what timescales (i.e., “memory”) is difficult to predict without calibration of site-specific models or using geochemical approaches, posing barriers to prediction in ungauged basins or advancement of general theories. Here, we used a data-driven approach to identify regional patterns and exogenous controls on P–Q interactions. We applied an information flow analysis, which quantifies uncertainty reduction, to a daily time series of P and Q from 671 watersheds across the conterminous United States. We first demonstrated that information transfer from P to Q primarily reflects the quickflow component of water-budgets, based on a watershed model. Readily quantifiable information flows show a functional relationship with model parameters, suggesting utility for model calibration. Second, applied to real watersheds, P–Q information flows exhibit seasonally varying behavior within regions in a manner consistent with dominant runoff generation mechanisms. However, the timing and the magnitude of information flows also reflect considerable subregional heterogeneity, likely attributable to differences in watershed size, baseflow contributions, and variation in aerial coverage of preferential flow paths. A regression analysis showed that a combination of climate and watershed characteristics are predictive of P–Q information flows. Though information flows cannot, in most cases, uniquely determine dominant runoff mechanisms, they provide a means to quantify the heterogeneous outcomes of those mechanisms within regions, thereby serving as a benchmarking tool for models developed at the regional scale. Last, information flows characterize regionally specific ways in which catchment connectivity changes from the wet to dry season.
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The conventional strategy for deciphering gas origins has been to collect gas and other related samples in the field then pack, ship, store, and later analyze these discrete samples in a laboratory using instruments that require specialized training and dedicated operators. Two critical drawbacks to the conventional strategy are that: 1) measurements made and analyzed after completing the field program often highlight where data should have been collected; 2) discrete sampling limits the spatial or temporal measurement density, increasing the likelihood of missing small-scale natural features.","language":"English","publisher":"U.S. Department of Energy National Energy Technology Laboratory","usgsCitation":"Pohlman, J., Casso, M., Boze, L., and Bergeron, E., 2022, USGS tools perform gas source analysis in the field: Fire in the Ice, v. 22, no. 1, p. 11-14.","productDescription":"4 p.","startPage":"11","endPage":"14","ipdsId":"IP-137796","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":405117,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":405115,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.netl.doe.gov/fire-in-the-ice","linkFileType":{"id":5,"text":"html"}}],"volume":"22","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pohlman, John 0000-0002-3563-4586","orcid":"https://orcid.org/0000-0002-3563-4586","contributorId":220804,"corporation":false,"usgs":true,"family":"Pohlman","given":"John","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":848887,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casso, Michael 0000-0002-6990-9090 mcasso@usgs.gov","orcid":"https://orcid.org/0000-0002-6990-9090","contributorId":2904,"corporation":false,"usgs":true,"family":"Casso","given":"Michael","email":"mcasso@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boze, Lee-Gray 0000-0003-1853-7888 lboze@usgs.gov","orcid":"https://orcid.org/0000-0003-1853-7888","contributorId":220996,"corporation":false,"usgs":true,"family":"Boze","given":"Lee-Gray","email":"lboze@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848889,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergeron, Emile 0000-0002-6157-2171","orcid":"https://orcid.org/0000-0002-6157-2171","contributorId":294785,"corporation":false,"usgs":true,"family":"Bergeron","given":"Emile","email":"","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848890,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70241168,"text":"70241168 - 2022 - Real-time telemetry and multi-state modeling","interactions":[],"lastModifiedDate":"2023-03-14T13:46:09.843378","indexId":"70241168","displayToPublicDate":"2022-03-01T08:39:37","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Real-time telemetry and multi-state modeling","docAbstract":"This project will result in real-time data and other invasive carp movement information to inform realtime management decisions and refine the SEICarP model. 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Population abundance (local density) or growth (λ) are the most sensitive metrics to change. We discuss the value in implementing spatially explicit capture-recapture (SCR), the current gold standard for density estimation, and open population SCR (OPSCR) to monitor changes in density over time. We provide guidance for designing studies to provide estimates with sufficient power to detect changes. Because of the wide availability of camera traps and interest in their use, we consider six density estimation methods and their extensions developed for use with camera traps, with specific consideration of assumptions and applications for monitoring Asian bears. We conducted a power analysis to calculate the precision in estimates needed to detect changes in populations with reference to IUCN Red List criteria. We performed a systematic review of empirical studies implementing camera trap abundance estimation methods and considered sample sizes, effort, and model assumptions required to achieve adequate precision for population monitoring. We found SCR and OPSCR, reliant on “marked” individuals, are currently the only methods with enough power to reliably detect even moderate to major (20–80%) declines. Camera trap methods with unmarked individuals rarely achieved precision sufficient to detect even large declines (80–90%), although with some exceptions (e.g., situations with moderate population densities, large number of sampling sites, or inclusion of ancillary local telemetry data. We describe additional estimation options including line transects, direct observations, monitoring age-specific survival and reproductive rates, and hybrid/integrated methodologies that may have potential to work for some Asian bear populations. We conclude monitoring changes in abundance or density is possible for most Asian bear populations but will require collaboration among researchers over broad spatial extents and extensive financial investment to overcome biological and logistical constraints. We strongly encourage practitioners to consider study design and sampling effort required to meet objectives by conducting simulations, power analyses, and assumption checks prior to implementing monitoring efforts, and reporting standardized dispersion measures such as coefficients of variation to allow for assessment of precision. Our guidance is relevant to other low-density and wide-ranging species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02058","usgsCitation":"Morin, D., Boulanger, J., Bischof, R., Lee, D., Ngoprasert, D., Fuller, A.K., McLellan, B., Steinmetz, R., Sharma, S., Garshelis, D., Gopalaswamy, A.M., Nawaz, M.A., and Karanth, U., 2022, Comparison of methods for estimating density and population trends for low-density Asian bears: Global Ecology and Conservation, e02058, 21 p., https://doi.org/10.1016/j.gecco.2022.e02058.","productDescription":"e02058, 21 p.","ipdsId":"IP-135458","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467198,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02058","text":"Publisher Index Page"},{"id":466442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Morin, Dana J.","contributorId":348354,"corporation":false,"usgs":false,"family":"Morin","given":"Dana J.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":923389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boulanger, John","contributorId":348355,"corporation":false,"usgs":false,"family":"Boulanger","given":"John","affiliations":[{"id":83347,"text":"Integrated Ecological Research","active":true,"usgs":false}],"preferred":false,"id":923390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bischof, Richard","contributorId":348356,"corporation":false,"usgs":false,"family":"Bischof","given":"Richard","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":923391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, David C.","contributorId":348357,"corporation":false,"usgs":false,"family":"Lee","given":"David C.","affiliations":[{"id":83348,"text":"University of South Wales","active":true,"usgs":false}],"preferred":false,"id":923392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ngoprasert, Dusit","contributorId":348358,"corporation":false,"usgs":false,"family":"Ngoprasert","given":"Dusit","affiliations":[{"id":83349,"text":"Program, King Mongkut’s University of Technology Thonburi","active":true,"usgs":false}],"preferred":false,"id":923393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923388,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McLellan, Bruce","contributorId":348359,"corporation":false,"usgs":false,"family":"McLellan","given":"Bruce","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":923394,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Steinmetz, Robert","contributorId":348360,"corporation":false,"usgs":false,"family":"Steinmetz","given":"Robert","affiliations":[{"id":83350,"text":"WWF Thailand","active":true,"usgs":false}],"preferred":false,"id":923395,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sharma, Sandeep","contributorId":348362,"corporation":false,"usgs":false,"family":"Sharma","given":"Sandeep","affiliations":[{"id":62105,"text":"University of Goettingen","active":true,"usgs":false}],"preferred":false,"id":923396,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Garshelis, Dave","contributorId":348363,"corporation":false,"usgs":false,"family":"Garshelis","given":"Dave","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":923397,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gopalaswamy, Arjun M.","contributorId":199394,"corporation":false,"usgs":false,"family":"Gopalaswamy","given":"Arjun","email":"","middleInitial":"M.","affiliations":[{"id":20302,"text":"Univeristy of Oxford","active":true,"usgs":false},{"id":35775,"text":"Indian Statistical Institute, Bangalore, India","active":true,"usgs":false}],"preferred":false,"id":923608,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Nawaz, Muhammad Ali","contributorId":300024,"corporation":false,"usgs":false,"family":"Nawaz","given":"Muhammad","email":"","middleInitial":"Ali","affiliations":[{"id":65003,"text":"Department of Biological and Environmental Sciences, Qatar University","active":true,"usgs":false}],"preferred":false,"id":923609,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Karanth, Ullas","contributorId":348577,"corporation":false,"usgs":false,"family":"Karanth","given":"Ullas","affiliations":[],"preferred":false,"id":923610,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70229694,"text":"70229694 - 2022 - Drivers of flight performance of California Condors (Gymnogyps californianus)","interactions":[],"lastModifiedDate":"2022-03-15T14:33:09.650195","indexId":"70229694","displayToPublicDate":"2022-02-28T09:28:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Drivers of flight performance of California Condors (Gymnogyps californianus)","title":"Drivers of flight performance of California Condors (Gymnogyps californianus)","docAbstract":"Flight behavior of soaring birds depends on a complex array of physiological, social, demographic, and environmental factors. California Condors (Gymnogyps californianus) rely on thermal and orographic updrafts to subsidize extended bouts of soaring flight, and their soaring flight performance is expected to vary in response to environmental variation and, potentially, with experience. We collected 6298 flight tracks described by high-frequency GPS telemetry data from five birds ranging in age from 1 to 19 yr old and followed over 32 d in summer 2016. Using these data, we tested the hypothesis that climb rate, an indicator of flight performance, would be related to the topographic and meteorological variables the bird experienced, and also to its age. Climb rate was greater when condors were flying in faster winds and during environmental conditions that were conducive to updraft development. However, we found no effect of age on climb rate. Although many of these relationships were expected based on flight theory, the lack of an effect of age was unexpected. Our work expands understanding of the relationship condors have with the environment, and it also suggests the potential for as-yet unexplored complexity to this relationship. As such, this study provides insight into avian flight behavior and, because flight performance influences bird behavior and exposure to anthropogenic risk, it has potential consequences for development of conservation management plans.
","language":"English","publisher":"Raptor Research Foundation","doi":"10.3356/JRR-20-94","usgsCitation":"Bonner, S.R., Poessel, S.A., Brandt, J.C., Astell, M.T., Belthoff, J.R., and Katzner, T.E., 2022, Drivers of flight performance of California Condors (Gymnogyps californianus): Journal of Raptor Research, v. 56, no. 1, p. 17-27, https://doi.org/10.3356/JRR-20-94.","productDescription":"11 p.","startPage":"17","endPage":"27","ipdsId":"IP-120507","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448665,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr-20-94","text":"Publisher Index Page"},{"id":397109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Bitter Creek and Hopper Mountain National Wildlife Refuges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.5,\n 34\n ],\n [\n -118.4,\n 34\n ],\n [\n -118.4,\n 36\n ],\n [\n -119.5,\n 36\n ],\n [\n -119.5,\n 34\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bonner, Sophie R.","contributorId":288473,"corporation":false,"usgs":false,"family":"Bonner","given":"Sophie","email":"","middleInitial":"R.","affiliations":[{"id":61767,"text":"Department of Geography, University of Texas at Austin, Austin, TX","active":true,"usgs":false}],"preferred":false,"id":837974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":837975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, Joseph C.","contributorId":288474,"corporation":false,"usgs":false,"family":"Brandt","given":"Joseph","email":"","middleInitial":"C.","affiliations":[{"id":61768,"text":"U.S. Fish and Wildlife Service, Hopper Mountain National Wildlife Refuge Complex, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":837976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Astell, Molly T.","contributorId":288475,"corporation":false,"usgs":false,"family":"Astell","given":"Molly","email":"","middleInitial":"T.","affiliations":[{"id":61768,"text":"U.S. Fish and Wildlife Service, Hopper Mountain National Wildlife Refuge Complex, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":837977,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belthoff, James R. 0000-0002-6051-2353","orcid":"https://orcid.org/0000-0002-6051-2353","contributorId":190592,"corporation":false,"usgs":false,"family":"Belthoff","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":837978,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191909,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd","email":"tkatzner@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":838016,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230066,"text":"70230066 - 2022 - Synthetic aperture radar volcanic flow maps (SAR VFMs): A simple method for rapid identification and mapping of volcanic mass flows","interactions":[],"lastModifiedDate":"2022-03-28T13:33:05.673737","indexId":"70230066","displayToPublicDate":"2022-02-28T08:30:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Synthetic aperture radar volcanic flow maps (SAR VFMs): A simple method for rapid identification and mapping of volcanic mass flows","docAbstract":"Volcanic mass flows, including lava, pyroclastic density currents, and lahars, account for the bulk of fatalities and infrastructure damage caused by volcanic eruptions. Mapping these flows soon after their emplacement is vital to understanding their impact and to forecasting the likely behavior of potential future flows. Synthetic aperture radar (SAR) can provide useful information about surface properties and changes regardless of environmental conditions or time of day, but no individual SAR product can unambiguously detect and map surface mass flows in all conditions. Combining SAR products, however, can capitalize on the strengths and compensate for the weaknesses of individual data types. SAR volcanic flow maps (SAR VFMs) merge cross-polarized amplitude imagery from two different dates with interferometric coherence spanning those dates. The combination of amplitude change with coherence provides a means of detecting volcanic mass flows regardless of surface conditions, and data collected by satellite provide the spatial coverage needed to detect changes over broad areas. Application to eruptions of Kīlauea (Hawaiʻi), Nyiragongo (Democratic Republic of Congo), Sinabung (Indonesia), and Fuego (Guatemala) demonstrate the value of SAR VFMs for monitoring hazardous volcanic activity, and the importance of acquiring cross-polarized satellite SAR imagery for volcano applications. The ever-growing number of public and private satellite SAR missions will provide for improved temporal resolution in SAR VFMs in the future, and the technique may be suitable for automated analysis that is capable of timely identification of changes due to volcanic activity, even in areas that are otherwise unmonitored.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01539-7","usgsCitation":"Poland, M., 2022, Synthetic aperture radar volcanic flow maps (SAR VFMs): A simple method for rapid identification and mapping of volcanic mass flows: Bulletin of Volcanology, v. 84, no. 3, 32, 11 p., https://doi.org/10.1007/s00445-022-01539-7.","productDescription":"32, 11 p.","ipdsId":"IP-132741","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":397696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.511474609375,\n 19.21780295966795\n ],\n [\n -154.92095947265622,\n 19.21780295966795\n ],\n [\n -154.92095947265622,\n 19.6\n ],\n [\n -155.511474609375,\n 19.6\n ],\n [\n -155.511474609375,\n 19.21780295966795\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Poland, Michael 0000-0001-5240-6123","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":49920,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":838941,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70241859,"text":"70241859 - 2022 - Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions","interactions":[],"lastModifiedDate":"2023-03-29T12:11:48.331001","indexId":"70241859","displayToPublicDate":"2022-02-28T07:08:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2344,"text":"Journal of Hydrometeorology","active":true,"publicationSubtype":{"id":10}},"title":"Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions","docAbstract":"High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments, including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-m resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for snow water equivalent, recharge, and runoff.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-21-0045.1","usgsCitation":"Stern, M.A., Flint, L.E., Flint, A.L., Boynton, R.M., Stewart, J.A., Wright, J.W., and Thorne, J.H., 2022, Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions: Journal of Hydrometeorology, v. 23, no. 3, p. 293-308, https://doi.org/10.1175/JHM-D-21-0045.1.","productDescription":"16 p.","startPage":"293","endPage":"308","ipdsId":"IP-127192","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":448670,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jhm-d-21-0045.1","text":"Publisher Index Page"},{"id":414886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":868014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867968,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boynton, Ryan M 0000-0002-3952-2573","orcid":"https://orcid.org/0000-0002-3952-2573","contributorId":303743,"corporation":false,"usgs":false,"family":"Boynton","given":"Ryan","email":"","middleInitial":"M","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":867969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stewart, Joseph A E","contributorId":247751,"corporation":false,"usgs":false,"family":"Stewart","given":"Joseph","email":"","middleInitial":"A E","affiliations":[{"id":49638,"text":"USGS WERC & UC Davis","active":true,"usgs":false}],"preferred":false,"id":867970,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wright, Jessica W","contributorId":303744,"corporation":false,"usgs":false,"family":"Wright","given":"Jessica","email":"","middleInitial":"W","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":867971,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thorne, James H.","contributorId":139144,"corporation":false,"usgs":false,"family":"Thorne","given":"James","email":"","middleInitial":"H.","affiliations":[{"id":12659,"text":"U C Davis","active":true,"usgs":false}],"preferred":false,"id":867972,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229414,"text":"70229414 - 2022 - State of stress in areas of active unconventional oil and gas development in North America","interactions":[],"lastModifiedDate":"2022-03-07T11:58:56.628602","indexId":"70229414","displayToPublicDate":"2022-02-28T05:54:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"State of stress in areas of active unconventional oil and gas development in North America","docAbstract":"In this paper, we present comprehensive data on stress orientation and relative magnitude in areas throughout North America where unconventional oil and gas are currently being developed. We find excellent agreement between maximum horizontal principal stress (SHmax) orientations over a wide range of depths, using multiple methods. In all basins studied, we observed coherent stress fields that in some cases vary systematically from one part of a basin to another. In the Appalachian Basin in the eastern United States, SHmax is oriented northeast–southwest to east-northeast–west-southwest and the style of faulting is compressive, transitioning from reverse faulting in eastern Pennsylvania and New York to principally strike-slip faulting in western Pennsylvania, Ohio, and West Virginia. In the midcontinent, central Oklahoma is characterized by an approximately east–west SHmax direction and strike-slip faulting. The Fort Worth Basin in northeastern Texas is characterized by normal–strike-slip faulting and a north-northeast–south-southwest SHmax direction. In the Midland subbasin of western Texas, SHmax is consistently approximately east–west and normal–strike-slip faulting is observed. Farther west, the Delaware subbasin of western Texas and southeastern New Mexico is characterized by normal faulting and SHmax rotates ∼150° clockwise from north to south. Marked changes in SHmax direction also occur across the Raton Basin of southern Colorado and northern New Mexico, the Denver-Julesburg Basin in northern Colorado, and the Uinta Basin in northeastern Utah, likely associated with their location near the margins of extensional provinces. The new data sets we present help improve operational efficiency by constraining absolute stress magnitudes and the ideal azimuth to drill horizontal wells (i.e., perpendicular to the local SHmax orientation) and make it possible to predict which fractures and faults are likely to be activated during hydraulic stimulation.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/08102120151","usgsCitation":"Lundstern, J., and Zoback, M., 2022, State of stress in areas of active unconventional oil and gas development in North America: AAPG Bulletin, v. 106, no. 2, p. 355-385, https://doi.org/10.1306/08102120151.","productDescription":"31 p.","startPage":"355","endPage":"385","ipdsId":"IP-120371","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":435943,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90LS6QF","text":"USGS data release","linkHelpText":"Maximum horizontal stress orientation and relative stress magnitude (faulting regime) data throughout North America"},{"id":396771,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lundstern, Jens-Erik 0000-0003-0000-8013","orcid":"https://orcid.org/0000-0003-0000-8013","contributorId":264189,"corporation":false,"usgs":true,"family":"Lundstern","given":"Jens-Erik","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":837336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zoback, Mark D. 0000-0002-8851-2099","orcid":"https://orcid.org/0000-0002-8851-2099","contributorId":288082,"corporation":false,"usgs":false,"family":"Zoback","given":"Mark D.","affiliations":[{"id":61706,"text":"Stanford University Department of Geophysics","active":true,"usgs":false}],"preferred":false,"id":837337,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230854,"text":"70230854 - 2022 - Isotopic discrimination of natural and anthropogenic perchlorate sources in groundwater in a semi-arid region of northeastern Oregon (USA)","interactions":[],"lastModifiedDate":"2022-04-27T11:48:51.54009","indexId":"70230854","displayToPublicDate":"2022-02-26T06:46:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Isotopic discrimination of natural and anthropogenic perchlorate sources in groundwater in a semi-arid region of northeastern Oregon (USA)","docAbstract":"Perchlorate (ClO4−) has synthetic and natural sources. Synthetic ClO4− is released to the environment from its use as an oxidant in military and aerospace applications, and from its presence in a variety of common commercial products, such as safety flares, chlorate herbicides, and fireworks. Natural sources of ClO4− in the environment include imported nitrate fertilizers derived from salt deposits in the Atacama Desert of Chile and indigenous natural ClO4− that accumulates in unsaturated soils and groundwaters in other arid and semi-arid environments, largely from atmospheric deposition. The stable isotope ratios of chlorine (37Cl/35Cl) and oxygen (18O/16O, 17O/16O) and the isotopic abundance of radioactive 36Cl in ClO4− can be used to discriminate these different sources. Perchlorate was previously detected at relatively high concentrations (3.8–34.7 μg/L) in groundwater from many wells in the Boardman-Umatilla area near the Columbia River in northeastern Oregon, which is a semi-arid, highly agricultural, heavily irrigated area that includes several past and current military installations. Eight representative groundwater wells were sampled throughout this region and isotopic characteristics of ClO4− collected from each well were measured along with other chemical and isotopic parameters including tritium and other groundwater age indicators. Isotopic data indicate that indigenous natural ClO4− was present in groundwater from all sampled wells and was the predominant source in five of the wells. Synthetic ClO4− was present in the three remaining wells with natural ClO4−, and a minor fraction of Atacama-fertilizer-derived ClO4− was indicated in one of the wells. Data from this study expand the geographic area of the USA in which indigenous natural ClO4− has been detected to include the semi-arid northwest. This study also illustrates the role of irrigation recharge as a mechanism for producing relatively high concentrations of indigenous natural ClO4− in groundwater by flushing accumulated salts from the unsaturated zone.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2022.105232","usgsCitation":"Hatzinger, P.B., Bohlke, J., Jackson, W., Gu, B., Mroczkowski, S.J., and Sturchio, N.C., 2022, Isotopic discrimination of natural and anthropogenic perchlorate sources in groundwater in a semi-arid region of northeastern Oregon (USA): Applied Geochemistry, v. 139, 105232, 11 p., https://doi.org/10.1016/j.apgeochem.2022.105232.","productDescription":"105232, 11 p.","ipdsId":"IP-130304","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448682,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1879959","text":"Publisher Index Page"},{"id":399737,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C28","displayTitle":"An Apparent Dip Calculator for Spreadsheets","title":"An apparent dip calculator for spreadsheets","docAbstract":"This report and spreadsheet calculator contain Microsoft Excel-based equations that are useful in structural geology to calculate plunge or apparent dip when measuring lineations on a plane. The spreadsheet allows users to measure the trend or the plunge of a lineation and calculate the corresponding unknown value of trend or plunge. The spreadsheet provides the user with two options:
Option 1: Calculates the plunge of a lineation from the measured strike and dip of a plane and the measured trend of a lineation.
Option 2: Calculates two potential trends of a lineation from the measured strike and dip of a plane and a measured plunge of a lineation. The user can decide which trend is appropriate for their data.
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U.S. Geological Survey
12201 Sunrise Valley Drive, MS 926A
Reston, VA 20192
We conducted a macroscale study of 2210 shallow lakes (mean depth ≤ 3 m or a maximum depth ≤ 5 m) in the Upper Midwestern and Northeastern USA. We asked the following: What are the patterns and drivers of shallow lake total phosphorus (TP), chlorophyll a (CHLa), and TP–CHLa relationships at the macroscale, how do these differ from those for 4360 non-shallow lakes, and do results differ by hydrologic connectivity class? Spatial patterns and Bayesian hierarchical models indicated that shallow lakes had higher TP and CHLa than non-shallow lakes, connected shallow lakes were more productive than unconnected shallow lakes, and there was regional variation in these patterns. Important predictors of TP and CHLa included lake-specific watershed:lake area ratio, forested land use/cover, and baseflow; unconnected lakes were more difficult to predict than connected lakes; and region-specific predictors were mostly unimportant. Shallow lake TP–CHLa relationships were less steep than for non-shallow lakes and these relationships varied regionally. Our results, combined with the facts that only 23% of lakes in the study extent have depth data and that shallow and unconnected lakes are undersampled, have important implications for estimates of lake contributions to global cycles that are based mainly on large (and deeper) lakes.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-022-04811-1","usgsCitation":"Spence Cheruvelil, K., Webster, K., King, K., Poisson, A., and Wagner, T., 2022, Taking a macroscale perspective to improve understanding of shallow lake total phosphorus and chlorophyll a: Hydrobiologia, v. 849, p. 3663-3677, https://doi.org/10.1007/s10750-022-04811-1.","productDescription":"15 p.","startPage":"3663","endPage":"3677","ipdsId":"IP-130204","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":465993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Illinois, Indiana, Iowa, Maine, Massachusetts, Michigan, Minnesota, Missouri, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, 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\"}}]}","volume":"849","noUsgsAuthors":false,"publicationDate":"2022-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Spence Cheruvelil, Kendra","contributorId":348079,"corporation":false,"usgs":false,"family":"Spence Cheruvelil","given":"Kendra","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webster, Katherine","contributorId":348080,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"King, Katelyn","contributorId":348081,"corporation":false,"usgs":false,"family":"King","given":"Katelyn","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922925,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poisson, Autumn C.","contributorId":348082,"corporation":false,"usgs":false,"family":"Poisson","given":"Autumn C.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922926,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922922,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229101,"text":"70229101 - 2022 - Lessons learned from 20 y of monitoring suburban development with distributed stormwater management in Clarksburg, Maryland, USA","interactions":[],"lastModifiedDate":"2022-09-15T14:05:49.817277","indexId":"70229101","displayToPublicDate":"2022-02-25T06:18:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Lessons learned from 20 y of monitoring suburban development with distributed stormwater management in Clarksburg, Maryland, USA","docAbstract":"Urban development is a well-known stressor for stream ecosystems, presenting a challenge to managers tasked with mitigating its effects. For the past 20 y, streamflow, water quality, geomorphology, and benthic communities were monitored in 5 watersheds in Montgomery County, Maryland, USA. This study presents a synthesis of multiple studies of monitoring efforts in the study area and new analysis of more recent monitoring data to document the primary lessons learned from monitoring. The monitored watersheds include a forested control, an urban control with centralized stormwater management, and 3 suburban treatment watersheds featuring low-impact development and a high density of infiltration-focused stormwater facilities distributed across the watershed. Treatment watersheds were monitored before development, during construction, and after development. Monitoring was initiated to inform adaptive management of stormwater and impervious cover limits within the study area, with a focus on the impacts of distributed stormwater management. Results from our synthesis indicate that distributed stormwater management is advantageous compared with centralized stormwater management in numerous ways. Hydrologic benefits were greater with distributed stormwater infrastructure, demonstrating the ability to mitigate runoff volumes and peak flows and, for small storms, replicate predevelopment conditions. Baseflow temporarily increased during the construction phase in the treatment watersheds. Water-quality benefits were mixed, with declines in baseflow nitrate concentrations but limited changes to nitrate export and increases in specific conductance after development. Substantial topographic changes occurred during construction in the treatment watersheds, including changes within the riparian zone, despite riparian buffer protections. Ecological monitoring indicated that even though index of biotic integrity scores rebounded in some cases, sensitive benthic macroinvertebrate families did not fully recover in the treatment watersheds. Lessons learned from this synthesis highlight the importance of tracking multiple indicators of stream health and considering past land use and that more stormwater facilities distributed across the watershed is beneficial but cannot mitigate the effects of all urban stressors on aquatic ecosystems.
Smear slide petrography has been a standard technique during scientific ocean drilling expeditions to characterize sediment composition and classify sediment types, but presentation of these percent estimates to track downcore trends in sediment composition has become less frequent over the past 2 decades. We compare semi-quantitative smear slide composition estimates to physical property (natural gamma radiation, NGR) and solid-phase geochemical (calcium carbonate, CaCO3 %) measurements from a range of marine depositional environments in the northern Indian Ocean (Bay of Bengal, Andaman Sea, Ninetyeast Ridge) collected during International Ocean Discovery Program (IODP) Expedition 353. We show that presenting smear slide estimates as percentages, rather than abundance categories, reveals similar downcore variation in composition to the more quantitative core analyses. Overall downcore trends in total calcareous components from smear slides (foraminifers + nannofossils + shell fragments + authigenic carbonate) follow similar downcore trends to samples measured by CaCO3 coulometry. Total lithogenic components (clay + mica + quartz + feldspars + lithic grains + vitric grains + glauconite + heavy minerals + iron oxides) and clay from smear slides track reasonably well with NGR measurements. Comparison of site averages of absolute percentages of total calcium carbonate from coulometry and total calcareous components from smear slide observations reveals an overestimation in carbonate percentages in smear slides (likely due in part to underestimation of the clay fraction), especially in sediments rich in smectite clays. Differences in sediment color between sites and settling of clay particles during slide preparation may contribute to this discrepancy. Although smear slide estimates range in accuracy depending on the training of the operator, we suggest that sedimentologists describing cores obtained during scientific drilling can use the percent estimates of sedimentary components in smear slides to identify trends and cyclicity in marine sediment records.
","language":"English","publisher":"Copernicus","doi":"10.5194/sd-30-59-2022","usgsCitation":"Phillips, S.C., and Littler, K., 2022, Comparison of sediment composition by smear slides to quantitative shipboard data: A case study on the utility of smear slide percent estimates, IODP Expedition 353, northern Indian Ocean: Scientific Drilling, v. 30, p. 59-74, https://doi.org/10.5194/sd-30-59-2022.","productDescription":"16 p.","startPage":"59","endPage":"74","ipdsId":"IP-129904","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448688,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/sd-30-59-2022","text":"Publisher Index Page"},{"id":396535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Northern Indian Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 65.126953125,\n 0\n ],\n [\n 100.01953125,\n 0\n ],\n [\n 100.01953125,\n 24.126701958681668\n ],\n [\n 65.126953125,\n 24.126701958681668\n ],\n [\n 65.126953125,\n 0\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","noUsgsAuthors":false,"publicationDate":"2022-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Phillips, Stephen C. 0000-0003-0858-4701","orcid":"https://orcid.org/0000-0003-0858-4701","contributorId":268177,"corporation":false,"usgs":true,"family":"Phillips","given":"Stephen","email":"","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":836486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Littler, Kate","contributorId":287090,"corporation":false,"usgs":false,"family":"Littler","given":"Kate","email":"","affiliations":[{"id":17840,"text":"University of Exeter","active":true,"usgs":false}],"preferred":false,"id":836487,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228825,"text":"fs20223006 - 2022 - Illinois and Landsat","interactions":[],"lastModifiedDate":"2023-01-21T15:55:11.063065","indexId":"fs20223006","displayToPublicDate":"2022-02-24T10:34:09","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3006","displayTitle":"Illinois and Landsat","title":"Illinois and Landsat","docAbstract":"Illinois is home to more than 12 million residents, including those living in Chicago, the third-largest city in the United States. Yet farmland claims about 75 percent of the largely flat terrain in Illinois. Tallgrass prairie once covered “The Prairie State,” and some remnants remain, but corn and soybeans are a far more common sight now. Adding variety to the landscape, beaches line the State’s Lake Michigan shoreline in the northeast, and more than 80,000 miles of rivers and streams flow along and through the State, including the central cities of Springfield and Peoria. Forests fill several million acres, mostly in the west and the rolling hills of the south.
Urban, agricultural, and forested areas each have environmental characteristics that are noticeable to those who live within them and to those who study the Earth’s surface from space. Landsat satellite data can reveal not only the current condition of these areas, but also when and where they have changed. A better knowledge of Illinois’ past helps its residents better prepare for the future.
Here are just a few examples of how Landsat benefits Illinois.
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Hawksbill sea turtles Eretmochelys imbricata inhabiting the Hawaiian Islands are extremely rare and listed as endangered under the US Endangered Species Act. The paucity of data on basic hawksbill ecology continues to hinder effective management of the species. We analyzed stranding data collected between 1984 and 2018 to gain insights into the distribution, demography, and conservation challenges facing hawksbills in Hawai‘i. In doing so, we present a comprehensive description of the population across developmental stages and rank threats that may be impeding their successful recovery. Over the >30 yr data set, we recorded a total of only 111 juvenile and adult hawksbill stranding events. Interactions with nearshore recreational fishing gear were documented for a large proportion (48.6%) of stranding events in the Hawaiian Islands, identifying this as the primary management challenge for the species. Stranding events were biased towards females (female to male sex ratio of 4.8:1.0), which may be indicative of the population as a whole. Even though the majority of hawksbills nest on the islands of Hawai‘i Moloka‘i, and Maui, the greatest number of juvenile to adult strandings was found to be on the island of Oahu (n = 47). Temporal distribution of the majority of adult hawksbill strandings (72.2%) occurred during a 4 mo period between June and September. We discuss these and other findings that help identify future research and conservation efforts to mitigate anthropogenic threats in Hawai‘i for this enigmatic population.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01167","usgsCitation":"Brunson, S., Gaos, A., Kelly, I., van Houtan, K., Swimmer, Y., Hargrove, S., Balazs, G., Work, T.M., and Jones, T., 2022, Three decades of stranding data reveal insights into endangered hawksbill sea turtles in Hawai‘i: Endangered Species Research, v. 47, p. 109-118, https://doi.org/10.3354/esr01167.","productDescription":"10 p.","startPage":"109","endPage":"118","ipdsId":"IP-134533","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":448691,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01167","text":"Publisher Index Page"},{"id":397393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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