Headwater streams account for more than half of the streams in the United States by length. The substantial occurrence and susceptibility to change of headwater streams makes regular updating of related maps vital to the accuracy of associated analysis and display. Here we present work testing new methods of completely automated remote headwater stream identification using metrics derived from channel Digital Elevation Model (DEM) cross-sections. A jump in standard deviation of curvature (sK) is found to correlate with the presence of stream heads. Field and remotely validated stream and channel initiation points from 4 diverse study areas in North Carolina as well as a simulated surface are used to test the sK findings. The sK value within individual catchments equal to 0.5*Tukey's upper inner fence is found to be a reliable threshold for identifying the upslope extent of channels in varied landscapes.
Mount Baker is the prominent andesitic stratocone that forms the youngest volcanic center in the Mount Baker volcanic field. Its heavily glaciated cone, rising to 3,286 meters, is an international landmark, dominating the skyline of Vancouver, British Columbia, even though the volcano is located 25 kilometers south of the international border. Mount Baker caught the attention of scientists and the public alike in 1975–76 during a period of increased steaming, thermal output, and near-vent lithic tephra falls. Although a magmatic eruption did not ensue, it awoke the populace to the possibility of renewed volcanic activity in the Cascade Range (the first since the 1914–17 eruptions of Lassen Peak, Calif.)—a possibility fulfilled just five short years later with the 1980 eruption of Mount St. Helens in southwest Washington. The 1980 Mount St. Helens eruption, with its dramatic edifice collapse, extraordinary pyroclastic density current, and catastrophic lahars, invigorated the scientific community into studying these then little-known processes. It also highlighted the need to better understand eruptive histories at other Cascade Range volcanoes in order to prepare for future eruptions.
The 1975 unrest also spawned one of the earliest volcano hazard assessments in the Cascade Range, which recognized the rich history of postglacial events at Mount Baker and identified the risk posed by volcanic mudflows, or lahars. The focus of this study is to more fully describe the late-glacial to present surficial geology, to better constrain the timing of events (including 19th-century floods), and to dovetail this history with Hildreth and others’ (2003) bedrock study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1865","usgsCitation":"Scott, K.M., Tucker, D.S., Riedel, J.L., Gardner, C.A., and McGeehin, J.P., 2020, Latest Pleistocene to present geology of Mount Baker Volcano, northern Cascade Range, Washington: U.S. Geological Survey Professional Paper 1865,\n170 p., https://doi.org/10.3133/pp1865.","productDescription":"xi, 170 p.","onlineOnly":"Y","ipdsId":"IP-068471","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":377020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1865/coverthb.jpg"},{"id":377021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1865/pp1865.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1865"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Baker","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.92214965820311,\n 48.70455661164196\n ],\n [\n -121.72233581542967,\n 48.70455661164196\n ],\n [\n -121.72233581542967,\n 48.84302835299516\n ],\n [\n -121.92214965820311,\n 48.84302835299516\n ],\n [\n -121.92214965820311,\n 48.70455661164196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of 2019, the network consisted of 120 wells and piezometers. (A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers screened at different depths.) The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels from the 120 wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at those 120 sites through water year 2019 (October 1, 2018, through September 30, 2019). Water levels that were collected from those discontinued wells in previous water years were published in previous USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1129","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Beman, J.E., 2020, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2019: U.S. Geological Survey Data Series 1129, 40 p., https://doi.org/10.3133/ds1129.","productDescription":"iii, 40 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-120239","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":377000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1129/coverthb.jpg"},{"id":377001,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1129/ds1129.pdf","text":"Report","size":"5.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1129"}],"country":"United States","state":"New Mexico","city":"Albuquerque","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.611083984375,\n 33.797408767572485\n ],\n [\n -105.941162109375,\n 33.797408767572485\n ],\n [\n -105.941162109375,\n 36.06686213257888\n ],\n [\n -107.611083984375,\n 36.06686213257888\n ],\n [\n -107.611083984375,\n 33.797408767572485\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Simple biometric data of fish aid fishery management tasks such as monitoring the structure of fish populations and regulating recreational harvest. While these data are foundational to fishery research and management, the collection of length and weight data through physical handling of the fish is challenging as it is time consuming for personnel and can be stressful for the fish. Recent advances in imaging technology and machine learning now offer alternatives for capturing biometric data. To investigate the potential of deep convolutional neural networks to predict biometric data, several regressors were trained and evaluated on data stemming from the FishL™ Recognition System and manual measurements of length, girth, and weight. The dataset consisted of 694 fish from 22 different species common to Laurentian Great Lakes. Even with such a diverse dataset and variety of presentations by the fish, the regressors proved to be robust and achieved competitive mean percent errors in the range of 5.5 to 7.6% for length and girth on an evaluation dataset. Potential applications of this work could increase the efficiency and accuracy of routine survey work by fishery professionals and provide a means for longer‐term automated collection of fish biometric data.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6618","usgsCitation":"Bravata, N., Kelly, D., Eickholt, J., Bryan, J., Miehls, S.M., and Zielinski, D., 2020, Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish: Ecology and Evolution, v. 10, no. 17, p. 9313-9325, https://doi.org/10.1002/ece3.6618.","productDescription":"13 p.","startPage":"9313","endPage":"9325","ipdsId":"IP-114453","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":455761,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.6618","text":"Publisher Index Page"},{"id":436838,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90BIDOL","text":"USGS data release","linkHelpText":"Image and biometric data for fish from Great Lakes tributaries collected during spring 2019"},{"id":436837,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90BIDOL","text":"USGS data release","linkHelpText":"Image and biometric data for fish from Great Lakes tributaries collected during spring 2019"},{"id":381446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Michigan, Ohio","otherGeospatial":"Black Mallard River, Cheboygan River, Illinois River, Little Manistee River, Menominee River, Muskegon River, Ocqueoc River, Sandusky River, Tittabawassee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.428955078125,\n 40.588928169693745\n ],\n [\n -82.529296875,\n 40.588928169693745\n ],\n [\n -82.529296875,\n 46.210249600187225\n ],\n [\n -88.428955078125,\n 46.210249600187225\n ],\n [\n -88.428955078125,\n 40.588928169693745\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"17","noUsgsAuthors":false,"publicationDate":"2020-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Bravata, Nicholas","contributorId":245794,"corporation":false,"usgs":false,"family":"Bravata","given":"Nicholas","email":"","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":807042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelly, Dylan","contributorId":245795,"corporation":false,"usgs":false,"family":"Kelly","given":"Dylan","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":807043,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eickholt, Jesse","contributorId":245796,"corporation":false,"usgs":false,"family":"Eickholt","given":"Jesse","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":807044,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bryan, Janine","contributorId":245797,"corporation":false,"usgs":false,"family":"Bryan","given":"Janine","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":807045,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807046,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zielinski, Daniel","contributorId":245798,"corporation":false,"usgs":false,"family":"Zielinski","given":"Daniel","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":807047,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211586,"text":"ofr20201053 - 2020 - Adjusted geomagnetic data—Theoretical basis and validation","interactions":[],"lastModifiedDate":"2020-08-04T20:32:20.375465","indexId":"ofr20201053","displayToPublicDate":"2020-08-04T12:30:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1053","displayTitle":"Adjusted Geomagnetic Data—Theoretical Basis and Validation","title":"Adjusted geomagnetic data—Theoretical basis and validation","docAbstract":"Adjusted geomagnetic data are magnetometer measurements with provisional correction factors applied such that vector quantities are oriented in a local Cartesian frame in which the X axis points north, the Y axis points east, and the Z axis points down. These correction factors are determined from so-called absolute measurements, which are “ground truth” observations made in the field using specialized magnetometers and survey equipment that are (nearly) colocated with the automated and continuously running magnetic measurement instrumentation. Correction factors can be substantial, up to hundreds of nanoTeslas, depending on the geologic and geomagnetic characteristics of the observatory site. They also tend to evolve over time because of instrument response instability and changing site characteristics. Historically, correction factors were determined offline, up to 1 year or more post-measurement, and applied to raw measurements to produce “Definitive” data for scientific analysis. Growing demand for corrected real-time geomagnetic data to better support space weather operations motivated development of an “Adjusted” geomagnetic data product. Modern computational tools, and some notable practical concerns, dictated a transition to affine transformations in lieu of more traditional baseline corrections, as well as a calibration parameter estimation algorithm that is more robust and statistically optimal, and therefore better suited for automated and unsupervised execution. A theoretical basis for this algorithm is presented, along with a demonstration and validation based on a comparison of results obtained with traditional techniques. Discrepancies between Definitive corrected data and near real-time Adjusted data obtained using affine transformations are minimal, generally much less than 5 nanoTeslas per vector component, and less than 1 nanoTesla for the total field magnitude, which satisfies International Real-Time Magnetic Observatory Network (INTERMAGNET) standards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201053","usgsCitation":"Rigler, E.J., and Claycomb, A.E., 2020, Adjusted geomagnetic data—Theoretical basis and validation: U.S. Geological Survey Open-File Report 2020–1053, 19 p., https://doi.org/10.3133/ofr20201053.","productDescription":"iv, 19 p.","onlineOnly":"Y","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":376988,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1053/coverthb.jpg"},{"id":376989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1053/ofr20201053.pdf","text":"Report","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1053"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
Methane hydrate was synthesized from pure water ice and flash frozen seawater, with varying amounts of sand or silt added. Electrical conductivity was determined by impedance spectroscopy, using equivalent circuit modeling to separate the effects of electrodes and to gain insight into conduction mechanisms. Silt and sand increase the conductivity of pure hydrate, we infer by contaminant NaCl contributing to conduction in hydrate, to values in agreement with resistivities observed in well logs through hydrate The addition of silt and sand lowers the conductivity of hydrate synthesized from seawater by an amount consistent with Archie's Law. All samples were characterized using cryogenic scanning electron microscopy and energy dispersive spectroscopy, which show good connectivity of salt and brine phases. Electrical conductivity measurements of pure hydrate and hydrate mixed with silt during pressure‐induced dissociation supports previous conclusions that sediment increases dissociation rate.
There is a growing body of tools available for science support for determining the fate and behavior of industrial and agricultural chemicals that are rapidly injected (“spilled”) into aquatic environments. A 2-day roundtable-style workshop was held by the U.S. Geological Survey (USGS) in Middleton, Wisconsin, in December 2017 to describe and explore existing Federal science support for spill fate and behavior tools used for inland spills, ongoing and new fate and behavior studies, and science gaps in planning and response tools as part of the USGS Midcontinent Region’s efforts to include spill response as part of its strategic plans. A total of 28 attendees representing a variety of Federal, State, and regional entities presented on programs and tools used in various aspects of spill response. Most programs and tools discussed were for spills in riverine environments but tools and applications for spills in lakes, on land surfaces, in urban storm sewer networks, and groundwater also were discussed. A primary workshop focus was to facilitate communication and increase potential for future collaboration among agencies for inland spill science support. The role and need for more USGS science support within the inland spill community was discussed. Enhanced communication is needed within the USGS and the U.S. Department of the Interior science programs, as well as within and among other agencies that do emergency planning and response. A main conclusion of the workshop was that there are untapped resources of the USGS outlined in the agency’s science strategy that could strengthen science support for fate and behavior tools in inland areas, especially in the Upper Mississippi River, Ohio River, and Great Lakes Basins where large freshwater resources overlap with dense corridors of oil and hazardous substances, with transportation networks, and with large populations centers.
Fate and behavior tools are being developed quickly for inland spill response by multiple Federal agencies in partnership with local and regional entities. Applicability of these tools ranges from planning and preparedness, to the early stages of spill response for protection of human life and property, and to the application of monitoring and models to assess the long-term consequences of spills. Key findings from the workshop, with an emphasis on potential further development of USGS science support, include the following:
•The national and regional response to spills occurs within an established system that must be respected by all parties involved in spill response. The USGS’s role is to support spill responders who are physically working at a spill scene, deploying booms and using other efforts to contain and recover spilled materials.
•The USGS has tools that have been used throughout spill response operations, from early response to recovery and restoration. Developing a more formal role for the USGS to participate in science support for inland spills on a consistent basis is a desired outcome. This will require the USGS to improve internal and external communication and would be best accomplished by assigning one or more coordinator positions within the agency to plan and oversee USGS spill-response efforts. More involvement of the USGS on National and Regional Response Teams, especially in the realm of the Science and Technology Subcommittees, will gofar in increasing external communication and integration of fate and behavior tools.
•Rapid response to spills requires modeling and mapping of plumes and associated time-of-travel estimation for a range of stream sizes across the United States. Many existing models use USGS streamgage data and the USGS National Hydrography Dataset. Nearly all existing models would benefit from updated linkages to USGS StreamStats and its soon-to-be released time-of-travel estimates,real-time velocity, stream morphology, and slope data. Integrating USGS tools with those from other agencies could be done to better serve the larger spill response community.
• A problem is that existing models to rapidly predict plume extent, as well as more followup/longer-term fate and transport models, can be unknown or unavailable to spill responders. Thus, creating and strengthening linkages among USGS scientists skilled at using these tools is needed to support spill response with the on-scene responders.
• Research for inland spill fate and behavior done outside of an immediate spill response can assist with spill planning and preparedness by (1) revealing sites likely to experience spills in the future (high-risk sites) and (2) understanding how a spilled substance might behave under a range of environmental conditions. However, USGS research on this topic has been scarce and subject to funding availability. Examples include the 2010 Line 6B Spill release into the Kalamazoo River in Michigan, where the USGS provided science support for a variety of fate and behavior tools for stream and impoundment environments. A long-term research site in Bemidji, Minnesota, provides important insights into transformations and longevity of spilled oil in groundwater and groundwater-surface water interactions.
• Linking stream models to other components of this inland environment, including groundwater, overland flow, and karst, is needed. Stream network data can be linked to underground conduits such as storm sewers and karst groundwater systems. Stream models can also be linked with geospatial data such as that contained in U.S. Environmental Protection Agency’s
interactive mapping tools.
• The USGS is uniquely qualified to collect water-quality data during spills in the United States because of its many geographically dispersed water science centers, its knowledge and preparedness for flood measurement and documentation, and its cadre of skilled water-quality employees. Rapid-deployment gages, used for floods, could also be used for spills if they included spill-specific sensors. Coordinated expertise at USGS water and environmental science centers can be used for monitoring spill effects and for assessing risk to water quality and ecological communities.
• Scientists at the USGS have proven capable of providing science coordination and technical assistance within the Incident Command Structure at the request of the lead on-scene coordinator. This external coordination, as well as internal communication within USGS Water, Hazards, and Ecosystems Mission Areas, could be improved by establishing and naming a USGS spills coordinator. Scott Morlock, Jo Ellen Hinck, and Faith Fitzpatrick are currently (2017) serving in informal coordination roles in addition to their traditional duties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201063","usgsCitation":"Sullivan, D.J., and Fitzpatrick, F.A., 2020, Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support: U.S. Geological Survey Open-File Report 2020–1063, 22 p., https://doi.org/10.3133/ofr20201063.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-111089","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1063/ofr20201063.pdf","text":"Report","size":"8.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1063"},{"id":376919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1063/coverthb.jpg"}],"contact":"Director, Upper Midwest Water Science Center
U.S Geological Survey
8505 Research Way
Middleton, WI 53562
Aquatic insects exhibit complex life cycles that include egg, larval, adult, and, in some instances, pupal stages. Disturbances at any of these life stages can affect overall population dynamics. Yet, efforts to understand the effects of disturbances, such as hydrologic alterations, overwhelmingly focus on the larval life stage of aquatic insects. We evaluated the potential for load-following flows associated with hydroelectric power production to act as a population bottleneck for aquatic insects via reductions in the availability and temporal persistence of optimal oviposition habitats. Specifically, we quantified the oviposition habitat selectivity of Baetis spp. (Baetidae), Brachycentrus occidentalis (Brachycentridae), Chironomidae (Diptera), and Hydropsyche occidentalis (Hydropsychidae) downstream of Flaming Gorge Dam, Utah, USA. We found that all taxa except H. occidentalis preferentially laid eggs on large emergent substrates located along the river edge. Peak discharge associated with load-following flows substantially reduced the number of emergent substrates available for oviposition, and daily low flows exposed eggs in these habitats to desiccation and drying. When subjected to experimental drying, both Baetis and H. occidentalis eggs experienced nearly 100% mortality after 2 h, whereas most B. occidentalis remained viable after 8 h. Our paired field and experimental results are consistent with the hypothesis that load-following flows from hydroelectric dams produce a population bottleneck for aquatic insects by short circuiting recruitment processes. Environmental flows that seek to improve the health of tailwater aquatic insect populations would benefit from consideration of habitat requirements for all life stages of aquatic insects.
","language":"English","publisher":"University of Chicago Press Journals","doi":"10.1086/710237","usgsCitation":"Miller, S.W., Schroer, M., Fleri, J.R., and Kennedy, T.A., 2020, Macroinvertebrate oviposition habitat selectivity and egg-mass desiccation tolerances: Implications for population dynamics in large regulated rivers: Freshwater Science, v. 39, no. 3, p. 584-599, https://doi.org/10.1086/710237.","productDescription":"16 p.","startPage":"584","endPage":"599","onlineOnly":"N","ipdsId":"IP-112469","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455768,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/710237","text":"Publisher Index Page"},{"id":377107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Green River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.52407836914062,\n 40.84706035607122\n ],\n [\n -109.10659790039062,\n 40.84706035607122\n ],\n [\n -109.10659790039062,\n 40.93841495689795\n ],\n [\n -109.52407836914062,\n 40.93841495689795\n ],\n [\n -109.52407836914062,\n 40.84706035607122\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Scott W.","contributorId":237002,"corporation":false,"usgs":false,"family":"Miller","given":"Scott","email":"","middleInitial":"W.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":794962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schroer, Matt","contributorId":237003,"corporation":false,"usgs":false,"family":"Schroer","given":"Matt","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":794963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleri, Jesse R.","contributorId":237004,"corporation":false,"usgs":false,"family":"Fleri","given":"Jesse","email":"","middleInitial":"R.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":794964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Theodore A. 0000-0003-3477-3629 tkennedy@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":167537,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","email":"tkennedy@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":794965,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209146,"text":"fs20203020 - 2020 - Water resources of Evangeline Parish, Louisiana","interactions":[],"lastModifiedDate":"2020-08-04T20:20:14.724487","indexId":"fs20203020","displayToPublicDate":"2020-08-04T09:02:52","publicationYear":"2020","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":"2020-3020","displayTitle":"Water Resources of Evangeline Parish, Louisiana","title":"Water resources of Evangeline Parish, Louisiana","docAbstract":"Information concerning the availability, use, and quality of water in Evangeline Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 282.66 million gallons per day (Mgal/d) of water were withdrawn in Evangeline Parish, including about 122.05 Mgal/d from groundwater sources and 160.61 Mgal/d from surface-water sources. Withdrawals for agricultural use, composed of aquaculture, general irrigation, livestock, and rice irrigation, accounted for 45 percent (126.86 Mgal/d) of the total water withdrawn. Withdrawals for power-generation use accounted for about 52 percent (146.33 Mgal/d) of the total water withdrawn. Other categories of use included public supply, industry, and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 1980.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203020","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Murphy, C.J., and White, V.E., 2020, Water resources of Evangeline Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3020, 6 p., https://doi.org/10.3133/fs20203020.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103346","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":376884,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":376882,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3020/coverthb.jpg"},{"id":376883,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3020/fs20203020.pdf","text":"Report","size":"1.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3020"}],"country":"United States","state":"Louisiana","county":"Evangeline Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.2809,30.9653],[-92.2811,30.9365],[-92.2381,30.8924],[-92.2377,30.8486],[-92.2132,30.8487],[-92.2127,30.7948],[-92.2079,30.7889],[-92.2073,30.7848],[-92.1977,30.7798],[-92.1918,30.7785],[-92.187,30.7758],[-92.1816,30.7694],[-92.1774,30.7685],[-92.1694,30.7677],[-92.1729,30.6758],[-92.175,30.6762],[-92.1797,30.6661],[-92.1861,30.667],[-92.1887,30.6647],[-92.1945,30.6596],[-92.2009,30.6564],[-92.2008,30.6477],[-92.205,30.639],[-92.2034,30.6372],[-92.2055,30.6353],[-92.2044,30.6331],[-92.206,30.6299],[-92.2038,30.6257],[-92.2064,30.6216],[-92.2107,30.6198],[-92.2117,30.6129],[-92.2113,30.569],[-92.2622,30.5682],[-92.263,30.5385],[-92.2795,30.5388],[-92.4148,30.5405],[-92.4227,30.5386],[-92.4285,30.5363],[-92.4397,30.5362],[-92.4508,30.532],[-92.4592,30.5246],[-92.4622,30.5163],[-92.4659,30.5108],[-92.4637,30.5008],[-92.4657,30.4967],[-92.471,30.4939],[-92.4805,30.4924],[-92.4874,30.4878],[-92.4942,30.4818],[-92.6304,30.4827],[-92.6305,30.4859],[-92.6284,30.4896],[-92.6237,30.4929],[-92.6232,30.4974],[-92.618,30.5021],[-92.6165,30.5067],[-92.6176,30.5135],[-92.6246,30.5185],[-92.6241,30.5208],[-92.6162,30.5259],[-92.6051,30.531],[-92.6,30.5434],[-92.5958,30.5457],[-92.5948,30.5517],[-92.5932,30.5554],[-92.588,30.5559],[-92.5859,30.5618],[-92.5844,30.5683],[-92.5871,30.5719],[-92.5903,30.5732],[-92.593,30.5796],[-92.5979,30.5832],[-92.5986,30.8726],[-92.5989,30.8945],[-92.5658,30.8948],[-92.5605,30.899],[-92.5537,30.9031],[-92.5484,30.9032],[-92.5451,30.9009],[-92.5366,30.8978],[-92.5275,30.8997],[-92.5253,30.8943],[-92.5142,30.8953],[-92.4961,30.9037],[-92.484,30.9138],[-92.4798,30.9226],[-92.4861,30.9536],[-92.484,30.9559],[-92.4728,30.9587],[-92.4664,30.9574],[-92.4568,30.9589],[-92.451,30.9626],[-92.4404,30.9686],[-92.4271,30.9733],[-92.4154,30.9788],[-92.4123,30.9853],[-92.405,30.994],[-92.3948,30.9968],[-92.3869,31.0033],[-92.3784,31.0029],[-92.3746,30.9974],[-92.3676,30.9916],[-92.3606,30.9925],[-92.3601,30.9898],[-92.3611,30.988],[-92.3579,30.9848],[-92.3419,30.9817],[-92.3408,30.9794],[-92.3418,30.9758],[-92.345,30.9739],[-92.3439,30.9703],[-92.3316,30.9736],[-92.3252,30.9704],[-92.331,30.9635],[-92.3171,30.9636],[-92.315,30.9655],[-92.2809,30.9653]]]},\"properties\":{\"name\":\"Evangeline\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Avoyelles Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 70 million gallons per day (Mgal/d) of water were withdrawn in Avoyelles Parish, including about 59.27 Mgal/d from groundwater sources and 10.95 Mgal/d from surface-water sources. Withdrawals for agricultural use—composed of aquaculture, general irrigation, livestock, and rice irrigation—accounted for 93 percent (65.59 Mgal/d) of the total water withdrawn. Other categories of use included public supply and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2014.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203011","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2020, Water resources of Avoyelles Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3011, 6 p., https://doi.org/10.3133/fs20203011.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-102165","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":376881,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Passive acoustic monitoring using autonomous recording units (ARUs) is a fast-growing area of wildlife research especially for rare, cryptic species that vocalize. Northern Spotted Owl (Strix occidentalis caurina) populations have been monitored since the mid-1980s using mark–recapture methods. To evaluate an alternative survey method, we used ARUs to detect calls of Northern Spotted Owls and Barred Owls (S. varia), a congener that has expanded its range into the Pacific Northwest and threatens Northern Spotted Owl persistence. We set ARUs at 30 500-ha hexagons (150 ARU stations) with recent Northern Spotted Owl activity and high Barred Owl density within Northern Spotted Owl demographic study areas in Oregon and Washington, and set ARUs to record continuously each night from March to July, 2017. We reviewed spectrograms (visual representations of sound) and tagged target vocalizations to extract calls from ~160,000 hr of recordings. Even in a study area with low occupancy rates on historical territories (Washington’s Olympic Peninsula), the probability of detecting a Northern Spotted Owl when it was present in a hexagon exceeded 0.95 after 3 weeks of recording. Environmental noise, mainly from rain, wind, and streams, decreased detection probabilities for both species over all study areas. Using demographic information about known Northern Spotted Owls, we found that weekly detection probabilities of Northern Spotted Owls were higher when ARUs were closer to known nests and activity centers and when owls were paired, suggesting passive acoustic data alone could help locate Northern Spotted Owl pairs on the landscape. These results demonstrate that ARUs can effectively detect Northern Spotted Owls when they are present, even in a landscape with high Barred Owl density, thereby facilitating the use of passive, occupancy-based study designs to monitor Northern Spotted Owl populations.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duaa017","usgsCitation":"Duchac, L.S., Lesmeister, D., Dugger, K.M., Ruff, Z.J., and Davis, R.J., 2020, Passive acoustic monitoring effectively detects Northern Spotted Owls and Barred Owls over a range of forest conditions: Condor, v. 122, no. 3, duaa017, 22 p., https://doi.org/10.1093/condor/duaa017.","productDescription":"duaa017, 22 p.","ipdsId":"IP-113895","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455771,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duaa017","text":"Publisher Index Page"},{"id":396168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Klamath Mountains, Olympic Peninsula, Oregon Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.51904296875,\n 47.24194882163242\n ],\n [\n -122.838134765625,\n 47.24194882163242\n ],\n [\n -122.838134765625,\n 48.23199134320962\n ],\n [\n -124.51904296875,\n 48.23199134320962\n ],\n [\n -124.51904296875,\n 47.24194882163242\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.12353515624999,\n 43.731414013769\n ],\n [\n -123.321533203125,\n 43.731414013769\n ],\n [\n -123.321533203125,\n 44.6061127451739\n ],\n [\n -124.12353515624999,\n 44.6061127451739\n ],\n [\n -124.12353515624999,\n 43.731414013769\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.64013671874999,\n 42.415346114253616\n ],\n [\n -122.64038085937499,\n 42.415346114253616\n ],\n [\n -122.64038085937499,\n 43.004647127794435\n ],\n [\n -123.64013671874999,\n 43.004647127794435\n ],\n [\n -123.64013671874999,\n 42.415346114253616\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Duchac, Leila S.","contributorId":279674,"corporation":false,"usgs":false,"family":"Duchac","given":"Leila","email":"","middleInitial":"S.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lesmeister, Damon B.","contributorId":279675,"corporation":false,"usgs":false,"family":"Lesmeister","given":"Damon B.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dugger, Katie M. 0000-0002-4148-246X","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":36037,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":517,"text":"Oregon Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":835344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruff, Zachary J.","contributorId":279676,"corporation":false,"usgs":false,"family":"Ruff","given":"Zachary","email":"","middleInitial":"J.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Raymond J.","contributorId":279677,"corporation":false,"usgs":false,"family":"Davis","given":"Raymond","email":"","middleInitial":"J.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835348,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211424,"text":"ofr20201073 - 2020 - Ecological forecasting—21st century science for 21st century management","interactions":[],"lastModifiedDate":"2024-03-04T18:30:12.945694","indexId":"ofr20201073","displayToPublicDate":"2020-08-04T07:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1073","displayTitle":"Ecological Forecasting—21st Century Science for 21st Century Management","title":"Ecological forecasting—21st century science for 21st century management","docAbstract":"Natural resource managers are coping with rapid changes in both environmental conditions and ecosystems. Enabled by recent advances in data collection and assimilation, short-term ecological forecasting may be a powerful tool to help resource managers anticipate impending near-term changes in ecosystem conditions or dynamics. Managers may use the information in forecasts to minimize the adverse effects of ecological stressors and optimize the effectiveness of management actions. To explore the potential for ecological forecasting to enhance natural resource management, the U.S. Geological Survey (USGS) convened a workshop titled \"Building Capacity for Applied Short-Term Ecological Forecasting\" on May 29—31, 2019, with participants from several Federal agencies, including the Bureau of Land Management, the U.S. Fish and Wildlife Service, the National Park Service, and the National Oceanic and Atmospheric Administration as well as all mission areas within the USGS.
Participants broadly agreed that short-term ecological forecasting—on the order of days to years into the future—has tremendous potential to improve the quality and timeliness of information available to guide resource management decisions. Participants considered how ecological forecasting could directly affect their agency missions and specified numerous critical tools for addressing natural resource management concerns in the 21st century that could be enhanced by ecological forecasting. Given this breadth of possible applications for forecast products, participants developed a repeatable framework for evaluating potential value of a forecast product for enhancing resource management. Applying that process to a large list of forecast ideas that were developed in a brainstorming session, participants identified a small set of promising forecast products that illustrate the value of ecological forecasting for informing resource management. Workshop outcomes also include insights about important likely obstacles and next steps. In particular, reliable production and delivery of operational ecological forecasts will require a sustained commitment by research agencies, in partnership with resource management agencies, to maintain and improve forecasting tools and capabilities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201073","usgsCitation":"Bradford, J.B., Weltzin, J.F., McCormick, M., Baron, J., Bowen, Z., Bristol, S., Carlisle, D., Crimmins, T., Cross, P., DeVivo, J., Dietze, M., Freeman, M., Goldberg, J., Hooten, M., Hsu, L., Jenni, K., Keisman, J., Kennen, J., Lee, K., Lesmes, D., Loftin, K., Miller, B.W., Murdoch, P., Newman, J., Prentice, K.L., Rangwala, I., Read, J., Sieracki, J., Sofaer, H., Thur, S., Toevs, G., Werner, F., White, C.L., White, T., and Wiltermuth, M., 2020, Ecological forecasting—21st century science for 21st century management: U.S. Geological Survey Open-File Report 2020–1073, 54 p., https://doi.org/10.3133/ofr20201073.","productDescription":"vii, 54 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114740","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":376787,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1073/ofr20201073.pdf","text":"Report","size":"598 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1073"},{"id":376786,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1073/coverthb.jpg"}],"contact":"Director, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
The accurate mapping of streams and their streamflow conditions in terms of presence or absence of surface water is important to both understanding physical, chemical, and biological processes in streams and to managing land, water, and ecological resources. This document describes a field form, FLOwPER (FLOw PERmanence), available within a mobile application (app), for standardized data collection of the presence or absence of surface flow in streams. The FLOwPER Database is a publicly available geodataset that can be used for research and management applications. This document provides instructions on how to (1) access and download the FLOwPER field form within the mobile app service, (2) use and complete a FLOwPER field form, and (3) view and download data from the FLOwPER Database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201075","collaboration":"Prepared in cooperation with the United States Forest Service and the Bureau of Land Management","usgsCitation":"Jaeger, K.L., Burnett, J., Heaston, E.D., Wondzell, S.M., Chelgren, N., Dunham, J.B., Johnson, S., and Brown, M., 2020, FLOwPER user guide—For collection of FLOw PERmanence field observations: U.S. Geological Survey Open-File Report 2020–1075, 40 p., https://doi.org/10.3133/ofr20201075.","productDescription":"Report: vi, 40 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-118616","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":436839,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13WFKYW","text":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence field observations, Jan 2021 - Dec 2021"},{"id":407336,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5edea67582ce7e579c6e5845","text":"USGS data release","description":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence Field Observations"},{"id":376985,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1075/coverthb.jpg"},{"id":377862,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075_appendix01.pdf","text":"Appendix 1","size":"507 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075 Appendix 1"},{"id":376986,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The Midcontinent Rift System (MRS) of North America is one of the world’s largest continental rifts and has an age of 1.1 Ga (giga-annum). The MRS hosts a diverse suite of magmatic and hydrothermal mineral deposits in the Lake Superior region where rift rocks are exposed at or near the surface. As part of the construction of a database summarizing information on mineral deposits in the MRS, data from regional mineral deposits were downloaded from the U.S. Geological Survey (USGS) Mineral Resources Data System (MRDS), the USGS Mineral Deposit Database (USMIN), and the Ontario Ministry of Energy, Northern Development and Mines Mineral Deposit Inventory (MDI). Deposits related to MRS rocks or mineralizing events were identified and compiled into a database to develop a space/time classification for MRS-related mineral deposits. Information from MRDS, USMIN, and MDI records and from the extensive literature describing MRS mineral deposits was used to classify each entry by deposit type, host rock age and type, and estimated mineralization age. Most deposits were readily classified because of unique mineralogy, location, or well-constrained host rock. These deposits were then put into a tectonic evolutionary framework for the MRS, which showed that many deposits formed within discrete spatial and temporal stages of rift evolution.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201069","usgsCitation":"Woodruff, L.G., Schulz, K.J., Dicken, C.L., and Nicholson, S.W., 2020, Mineral resource database for deposits related to the Mesoproterozoic Midcontinent Rift System, United States and Canada: U.S. Geological Survey Open-File Report 2020–1069, 20 p., https://doi.org/10.3133/ofr20201069.","productDescription":"Report: vi, 20 p.; 2 Tables","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113694","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":436840,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HTATKY","text":"USGS data release","linkHelpText":"Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376912,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069_table1.csv","text":"Table 1","size":"171 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376911,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069_table1.xlsx","text":"Table 1","size":"124 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376909,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1069/coverthb.jpg"},{"id":376910,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1069"}],"country":"United States, Canada","otherGeospatial":"Mesoproterozoic Midcontinent Rift System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.2216796875,\n 40.245991504199026\n ],\n [\n -81.8701171875,\n 50.792047064406866\n ],\n [\n -96.6357421875,\n 51.23440735163459\n ],\n [\n -96.1083984375,\n 43.45291889355465\n ],\n [\n -97.5146484375,\n 43.739352079154706\n ],\n [\n -97.55859375,\n 41.541477666790286\n ],\n [\n -99.931640625,\n 41.376808565702355\n ],\n [\n -100.1513671875,\n 37.16031654673677\n ],\n [\n -94.7021484375,\n 37.09023980307208\n ],\n [\n -94.833984375,\n 39.53793974517628\n ],\n [\n -82.2216796875,\n 40.245991504199026\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Coordinator, Mineral Resources Program
U.S. Geological Survey
913 National Center
Reston, VA 20192
Impacts of land use, specifically soil disturbance, are linked to reductions of soil organic carbon (SOC) stocks. Correspondingly, ecosystem restoration is promoted to sequester SOC to mitigate anthropogenic greenhouse gas emissions, which are exacerbating global climate change. Restored wetlands have relatively high potential to sequester carbon compared to other ecosystems, but SOC accumulation rates are variable, which leads to high uncertainty in sequestration rates. To assess soil properties and carbon sequestration rates of freshwater mineral soil wetlands, we analyzed an extensive database of SOC concentrations from the Prairie Pothole Region (549 wetlands over 160,000 km2), which is considered one of the largest wetland ecosystems in North America. We demonstrate that SOC of wetland catchments varies among inner, transition, toe slope, and upland landscape positions (LSPs), as well as among land uses and soil depth segments. Soil organic carbon concentrations were greatest in the inner portion of the catchment (66 Mg ha−1) and progressively decrease towards the upland LSP (43 Mg ha−1). We also conducted a regional extrapolation based on LSP- and land-use-specific SOC stocks, and estimated that wetland and upland areas of PPR wetland catchments contain 141 and 178 Tg of SOC in the upper 15 cm of the soil profile, respectively. Regressing SOC by restoration age (years restored) showed that sequestration rates, which differ by LSP and depth, ranged from 0.35 to 1.10 Mg ha−1 year−1. Using these SOC sequestration rates, along with data from natural and cropland reference sites, we estimated that it takes 20 to 64 years for SOC levels of restored wetlands to return to natural reference conditions, depending on LSP and depth segment. Accounting for LSP reduces uncertainty and should refine future assessments of the greenhouse gas mitigation potential from wetland restoration.
Globally accelerating frequency and extent of wildfire threatens the persistence of specialist wildlife species through direct loss of habitat and indirect facilitation of exotic invasive species. Habitat specialists may be especially prone to rapidly changing environmental conditions because their ability to adapt lags behind the rate of habitat alteration. As a result, these populations may become increasingly susceptible to ecological traps by returning to suboptimal breeding habitats that were dramatically altered by disturbance. We demonstrate a multistage modeling approach that integrates habitat selection and survival during the key nesting life‐stage of a bird species of high conservation concern, the greater sage‐grouse (Centrocercus urophasianus; hereafter, sage‐grouse). We applied these spatially explicit models to a spatiotemporally robust dataset of sage‐grouse nest locations and fates across wildfire‐altered sagebrush ecosystems of the Great Basin ecoregion, western United States. Female sage‐grouse exhibited intricate habitat selection patterns that varied across regional gradients of ecological productivity among sagebrush communities, but often selected nest sites that disproportionately resulted in nest failure. For example, 23% of nests occurred in wildfire‐affected habitats characterized by reduced sagebrush cover and greater composition of invasive annual grasses. We found survival of nests was negatively associated with wildfire‐affected areas, but positively associated with higher elevations with increased ruggedness and overall shrub cover. Strong site fidelity likely drove sage‐grouse to continue nesting in habitats degraded by wildfire. Hence, increasing frequency and extent of wildfire may contribute disproportionately to reduced reproductive success by creating ecological traps that act as population sinks. Identifying such habitat mismatches between selection and survival facilitates deeper understanding of the mechanisms driving reduced geographic niche space and population decline at broad spatiotemporal scales, while guiding management actions to areas that would be most beneficial to the species.
The St. Marys River connects Lake Superior to Lake Huron, comprising the international border between Michigan, United States, and Ontario, Canada. This Great Lakes connecting channel naturally encompasses various habitats including lakes, wetlands, islands, tributaries, side channels, and main channels. The St. Marys River Rapids are shallow rock areas with high flow velocities (>1 m/s) in the upper river adjacent to the navigation locks and electric power generating stations, while the Little Rapids are shallow, recently restored rocky areas with lower velocities located about 7 km downstream. The St. Marys River Rapids provide important spawning habitat for several native and introduced fishes, but spawning by lake sturgeon (Acipenser fulvescens) was not previously documented. We sampled for lake sturgeon eggs and larvae in both locations during June and July 2018–2019 using weekly benthic egg mat lifts and overnight D-frame larval fish drift nets. Viable lake sturgeon eggs (11 in 2018, 45 in 2019) were collected in the tailrace of a hydroelectric power facility adjacent to the St. Marys River Rapids. Larval lake sturgeon (21 in 2018, 1 in 2019) were collected in the same area as the eggs. Neither lake sturgeon eggs nor larvae were collected at Little Rapids in either year. Our results are the first documentation of successful lake sturgeon spawning and larval drift in the upper St. Marys River. While our observations showed spawning in a human-made tailrace area, the fate of larvae produced here is unknown and warrants further research.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.07.005","usgsCitation":"Roseman, E., Adams, E.J., DeBruyne, R.L., Gostiaux, J., Harrington, H., Kapuscinski, K., Moerke, A., and Olds, C., 2020, Lake sturgeon (Acipenser fulvescens) spawn in the St. Marys River Rapids, Michigan: Journal of Great Lakes Research, v. 46, no. 5, p. 1479-1484, https://doi.org/10.1016/j.jglr.2020.07.005.","productDescription":"6 p.","startPage":"1479","endPage":"1484","ipdsId":"IP-115133","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":436842,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VJMIPO","text":"USGS data release","linkHelpText":"Fish eggs collected in the St. Clair, Detroit, and St. Marys rivers, 2005-2018"},{"id":382461,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"St. Marys River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.49722290039062,\n 46.44069599413034\n ],\n [\n -84.1827392578125,\n 46.44069599413034\n ],\n [\n -84.1827392578125,\n 46.54091587805394\n ],\n [\n -84.49722290039062,\n 46.54091587805394\n ],\n [\n -84.49722290039062,\n 46.44069599413034\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":808630,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Eric John 0000-0001-9695-9483","orcid":"https://orcid.org/0000-0001-9695-9483","contributorId":248219,"corporation":false,"usgs":true,"family":"Adams","given":"Eric","email":"","middleInitial":"John","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":808631,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeBruyne, Robin L. 0000-0002-9232-7937 rdebruyne@usgs.gov","orcid":"https://orcid.org/0000-0002-9232-7937","contributorId":4936,"corporation":false,"usgs":true,"family":"DeBruyne","given":"Robin","email":"rdebruyne@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":808632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gostiaux, J.","contributorId":248221,"corporation":false,"usgs":false,"family":"Gostiaux","given":"J.","affiliations":[{"id":6983,"text":"Michigan DNR","active":true,"usgs":false}],"preferred":false,"id":808633,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harrington, H.","contributorId":248222,"corporation":false,"usgs":false,"family":"Harrington","given":"H.","email":"","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":808634,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kapuscinski, K.","contributorId":247567,"corporation":false,"usgs":false,"family":"Kapuscinski","given":"K.","email":"","affiliations":[{"id":49581,"text":"Lake Superior State Univ.","active":true,"usgs":false}],"preferred":false,"id":808635,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moerke, A.","contributorId":247569,"corporation":false,"usgs":false,"family":"Moerke","given":"A.","affiliations":[{"id":49581,"text":"Lake Superior State Univ.","active":true,"usgs":false}],"preferred":false,"id":808636,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Olds, C.","contributorId":248227,"corporation":false,"usgs":false,"family":"Olds","given":"C.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":808637,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70212762,"text":"70212762 - 2020 - Procedures for developing multi-period response spectra at non-conterminous United States sites","interactions":[],"lastModifiedDate":"2021-01-22T18:10:01.008346","indexId":"70212762","displayToPublicDate":"2020-08-01T11:57:53","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesNumber":"P-2078","title":"Procedures for developing multi-period response spectra at non-conterminous United States sites","docAbstract":"This study complements proposals to the Provisions Update Committee of the Building Seismic Safety Council that would incorporate multi-period response spectra (MPRS) in the 2020 edition of the NEHRP Recommended Seismic Provisions for New Buildings and Other Structures (2020 NEHRP Provisions) and related proposals to the ASCE 7-22 Seismic Subcommittee of the American Society of Civil Engineers for incorporation of MPRS in ASCE Standard, ASCE/SEI 7-22, Minimum Design Loads and Associated Criteria for Buildings and Other Structures (ASCE 7-22). Ultimately, the intent is that the proposed MPRS and related design requirements of ASCE 7-22 would be adopted, by reference, as part of the 2024 International Building Code.
The technical basis and associated methods herein enable the U.S. Geological Survey (USGS) to develop MPRS for sites in non-conterminous U.S. regions for which seismic hazard analyses have not yet been updated by the USGS to fully define all 22 periods and eight site classes of interest in the MPRS related proposals for the 2020 NEHRP Provisions and ASCE 7-22. These regions include Alaska, Hawaii, Guam and the Northern Mariana Islands, Puerto Rico and the U.S. Virgin Islands, and American Samoa.
The methods developed can be used to derive MPRS using only the three currently available ground motion parameters SS, S1, and TL for all nonconterminous United States regions of interest. The methods include models that characterize generic shapes of Risk-Targeted Maximum Considered Earthquake (MCER) ground motions as a function of these three parameters. For deriving MPRS that represent probabilistic MCER ground motions, models are based on statistical analyses of large sample sets of probabilistic MCER response spectra for Western United States (WUS) and Cascadia sites in California, Oregon, Washington (including Puget Sound), Idaho, and Nevada. For deriving MPRS that represent deterministic MCER ground motions, models are based on sets of deterministic MCER response spectra calculated using WUS shallow crustal ground motion models for earthquake magnitudes and shaking levels typical of sites governed by deterministic
MCER ground motions.
Temporal and spatial distribution of niclosamide in the water column and sediment were evaluated after the application of granular Bayluscide in six lentic sea lamprey (Petromyzon marinus) larval assessment plots. Water and sediment were collected 0.25, 1, 3, 5, and 7 hours after application and were analyzed for niclosamide, the active ingredient in granular Bayluscide. Water samples were collected from five heights in the water column (1 cm, 13 cm, 26 cm, 1/2 water column, and water surface) at five locations inside and four locations 10 m outside of each assessment plot. Sediment was collected from 18 locations within each plot. Niclosamide water concentrations inside and outside of the plots did not vary by depth but did vary between plots and by time. Niclosamide water concentrations also varied by sampler location outside of the plots. Following granular Bayluscide applications the mean niclosamide concentration in water for all levels, within the plots, decreased from 0.12 mgL-1 (SD = 0.12 mgL-1) at 15 minutes to 0.061 mgL-1 (SD = 0.040 mgL-1) at hour 1. The mean niclosamide concentration in the top 4 cm of sediment was 2.9 mgkg-1 (SD = 2.4 mgkg-1) 15 minutes after application and was 1.3 mgkg-1 (SD = 1.8 mgkg-1) at hour 7. Concentrations in all sediment samples ranged from < 0.001 to 30.730 mgkg-1 and varied between the six plots. Niclosamide concentrations measured in sediment samples were more than 1 order of magnitude greater than in the water and varied spatially by over 4 orders of magnitude.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Bernardy, J., Kaye, C., Schloesser, N., and Schueller, J., 2020, Distribution of niclosamide following granular Bayer applications in lentic environments: Project Completion Report, 30 p.","productDescription":"30 p.","ipdsId":"IP-107424","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":399095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377968,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/"}],"country":"United States","state":"Michigan, Wisconsin","county":"Mackinac County, Marinette County","otherGeospatial":"Hog Island Creek, Lake Michigan, Peshtigo Harbor","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.66952514648438,\n 44.96832008904543\n ],\n [\n -87.64514923095703,\n 44.96832008904543\n ],\n [\n -87.64514923095703,\n 44.98568481264677\n ],\n [\n -87.66952514648438,\n 44.98568481264677\n ],\n [\n -87.66952514648438,\n 44.96832008904543\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.28724074363708,\n 46.07139217240364\n ],\n [\n -85.28464436531067,\n 46.07139217240364\n ],\n [\n -85.28464436531067,\n 46.07282125858186\n ],\n [\n -85.28724074363708,\n 46.07282125858186\n ],\n [\n -85.28724074363708,\n 46.07139217240364\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bernardy, Jeffry 0000-0001-7443-1995","orcid":"https://orcid.org/0000-0001-7443-1995","contributorId":213528,"corporation":false,"usgs":true,"family":"Bernardy","given":"Jeffry","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":797469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaye, Cheryl","contributorId":167292,"corporation":false,"usgs":false,"family":"Kaye","given":"Cheryl","affiliations":[{"id":6599,"text":"U.S. Fish and Wildlife Service, Marquette Biological Station","active":true,"usgs":false}],"preferred":false,"id":797470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schloesser, Nicholas 0000-0002-3815-5302","orcid":"https://orcid.org/0000-0002-3815-5302","contributorId":237025,"corporation":false,"usgs":true,"family":"Schloesser","given":"Nicholas","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":797471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schueller, Justin R. 0000-0002-7102-3889","orcid":"https://orcid.org/0000-0002-7102-3889","contributorId":213527,"corporation":false,"usgs":true,"family":"Schueller","given":"Justin","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":797472,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228560,"text":"70228560 - 2020 - Spatiotemporal variation in occurrence and co-occurrence of pesticides, hormones, and other organic contaminants in rivers in the Chesapeake Bay Watershed, United States","interactions":[],"lastModifiedDate":"2022-02-15T12:22:36.066283","indexId":"70228560","displayToPublicDate":"2020-08-01T09:59:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal variation in occurrence and co-occurrence of pesticides, hormones, and other organic contaminants in rivers in the Chesapeake Bay Watershed, United States","docAbstract":"Investigating the spatiotemporal dynamics of contaminants in surface water is crucial to better understand how introduced chemicals are interacting with and potentially influencing aquatic organisms and environments. Within the Chesapeake Bay Watershed, USA, there are concerns about the potential role of contaminant exposure on fish health. Evidence suggests that exposure to contaminants in surface water is causing immunosuppression and intersex in freshwater fish species. Despite these concerns, there is a paucity of information regarding the complex dynamics of contaminant occurrence and co-occurrence in surface water across both space and time. To address these concerns, we applied a Bayesian hierarchical joint-contaminant model to describe the occurrence and co-occurrence patterns of 28 contaminants and total estrogenicity across six river sites and over three years. We found that seasonal occurrence patterns varied by contaminant, with the highest occurrence probabilities during the spring and summer months. Additionally, we found that the proportion of agricultural landcover in the immediate catchment, as well as stream discharge, did not have a significant effect on the occurrence probabilities of most compounds. Four pesticides (atrazine, metolachlor, fipronil and simazine) co-occurred across sites after accounting for environmental covariates. These results provide baseline information on the contaminant occurrence patterns of several classes of compounds within the Chesapeake Bay Watershed. Understanding the spatiotemporal dynamics of contaminants in surface water is the first step in investigating the effects of contaminant exposure on fisheries and aquatic environments.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.138765","usgsCitation":"McClure, C.M., Smalling, K., Blazer, V.S., Sperry, A., Schall, M.K., Kolpin, D., Phillips, P.J., Hladik, M.L., and Wagner, T., 2020, Spatiotemporal variation in occurrence and co-occurrence of pesticides, hormones, and other organic contaminants in rivers in the Chesapeake Bay Watershed, United States: Science of the Total Environment, v. 728, p. 1-13, https://doi.org/10.1016/j.scitotenv.2020.138765.","productDescription":"138765, 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-117478","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":365,"text":"Leetown Science 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snapping turtles in southwest Louisiana","interactions":[],"lastModifiedDate":"2021-03-05T21:31:13.265943","indexId":"70213082","displayToPublicDate":"2020-08-01T09:37:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"A trapping survey targeting head-started alligator snapping turtles in southwest Louisiana","docAbstract":"The alligator snapping turtle Macrochelys temminckii is the largest freshwater turtle in North America and is sought after as a food source, primarily in Louisiana. Decades of intensive commercial harvest of alligator snapping turtles has been implicated in population declines. The Louisiana Department of Wildlife and Fisheries initiated a head-start program for alligator snapping turtles and released 53 head-started juveniles at seven sites along an approximately 5.7-km stretch of Bundick Creek in southwest Louisiana between November 2015 and October 2016. Before release, all alligator snapping turtles were measured, weighed, and marked with both an internal passive integrated transponder tag and a numbered external tag. In 2018, the U.S. Geological Survey initiated a turtle trapping survey at those seven release sites targeting the head-started alligator snapping turtles. In one week of trapping effort at each site, we recorded 69 turtle captures comprising seven species, including 15 alligator snapping turtles (representing 12 individuals). Of those 12 individuals, 8 were head-started juveniles and 4 were native to the creek. An additional head-started juvenile alligator snapping turtle was captured by a landowner during our trapping and measurements were taken before release. A minimum of 17% of head-started alligator snapping turtles survived since release, and most captured head-started individuals were trapped near their release site and exhibited growth consistent with other studies, indicating acclimatization to their new environment. Three head-started alligator snapping turtles had their external tags entangled in the net mesh, and two of these turtles drowned. An additional two head-started individuals lost their external tags in the natural environment prior to their capture in this study. The use of external tags was discontinued by the Louisiana Department of Wildlife and Fisheries based on our findings, as they were detrimental to the health of head-started turtles.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-20-009","usgsCitation":"Glorioso, B., Muse, L.J., Hillard, C.J., Maldonado, B.R., Streeter, J., Battaglia, C.D., and Waddle, J.H., 2020, A trapping survey targeting head-started alligator snapping turtles in southwest Louisiana: Journal of Fish and Wildlife Management, v. 11, no. 2, p. 572-582, https://doi.org/10.3996/JFWM-20-009.","productDescription":"11 p.","startPage":"572","endPage":"582","ipdsId":"IP-108013","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455791,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-009","text":"Publisher Index Page"},{"id":436844,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G9BR1D","text":"USGS data release","linkHelpText":"Data from a turtle trapping effort at a release site of head-started alligator snapping turtles, 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Hardin 0000-0003-1940-2133 waddleh@usgs.gov","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":138953,"corporation":false,"usgs":true,"family":"Waddle","given":"J.","email":"waddleh@usgs.gov","middleInitial":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":798183,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236141,"text":"70236141 - 2020 - Dual clumped isotope thermometry resolves kinetic biases in carbonate formation temperatures","interactions":[],"lastModifiedDate":"2022-08-30T14:10:36.94438","indexId":"70236141","displayToPublicDate":"2020-08-01T09:07:39","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Dual clumped isotope thermometry resolves kinetic biases in carbonate formation temperatures","docAbstract":"Surface temperature is a fundamental parameter of Earth’s climate. Its evolution through time is commonly reconstructed using the oxygen isotope and the clumped isotope compositions of carbonate archives. However, reaction kinetics involved in the precipitation of carbonates can introduce inaccuracies in the derived temperatures. Here, we show that dual clumped isotope analyses, i.e., simultaneous Δ47 and Δ48 measurements on the single carbonate phase, can identify the origin and quantify the extent of these kinetic biases. Our results verify theoretical predictions and evidence that the isotopic disequilibrium commonly observed in speleothems and scleractinian coral skeletons is inherited from the dissolved inorganic carbon pool of their parent solutions. Further, we show that dual clumped isotope thermometry can achieve reliable palaeotemperature reconstructions, devoid of kinetic bias. Analysis of a belemnite rostrum implies that it precipitated near isotopic equilibrium and confirms the warmer-than-present temperatures during the Early Cretaceous at southern high latitudes.
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