{"pageNumber":"237","pageRowStart":"5900","pageSize":"25","recordCount":46677,"records":[{"id":70211568,"text":"ofr20201052 - 2020 - Calibration of the U.S. Geological Survey National Crustal Model","interactions":[],"lastModifiedDate":"2020-08-05T18:39:28.395394","indexId":"ofr20201052","displayToPublicDate":"2020-07-31T12:40:00","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":"2020-1052","displayTitle":"Calibration of the U.S. Geological Survey National Crustal Model","title":"Calibration of the U.S. Geological Survey National Crustal Model","docAbstract":"

The U.S. Geological Survey National Crustal Model (NCM) is being developed to include spatially varying estimates of site response in seismic hazard assessments. Primary outputs of the NCM are continuous velocity and density profiles from the Earth’s surface to the mantle transition zone at 410-kilometer (km) depth for each location on a 1-km grid across the conterminous United States. Datasets used to produce the NCM may have a resolution of better than 1 km near the Earth’s surface in some regions, but, with increasing depth, NCM resolution decreases to tens to hundreds of kilometers in the mantle. Basic subsurface information is provided by the NCM geologic framework, thermal model, and petrologic and mineral physics database. In this report, the velocities and densities that can be extracted from the NCM are calibrated through the development of a porosity model based on Biot-Gassmann theory and more than 2,000 compressional- and (or) shear-wave velocity profiles less than 10 km deep from across the conterminous United States and southwestern Canada.

Sediment and rock porosities are derived from shear-wave velocity and are found to depend on effective pressure, rock type, and age (for sedimentary and extrusive volcanic deposits). Porosity-effective pressure functions are then estimated for each rock type (and age for sedimentary and extrusive volcanic deposits). Unconsolidated sediments are found to have higher porosities than consolidated units, which have higher porosities than unweathered igneous units; young sedimentary units (for example, Quaternary age units) tend to have higher porosities than older sedimentary units (for example, pre-Cenozoic age units); porosity decreases with increasing effective pressure; and porosities can decrease quickly through the weathered layer of intrusive rocks.

Comparing two Los Angeles area velocity models and the U.S. Geological Survey Bay Area velocity model with the NCM, the NCM does a better job on average of reproducing observed shear-wave velocities below 1 km per second because it has less bias and uncertainty. Approaching and above 1 km per second, the NCM tends to underpredict observed shear-wave velocity. Whereas several factors could contribute to this, the primary factor is probably bias in the NCM geologic framework. For example, the NCM will predict lower velocities in places where the depth to bedrock and basement appear shallower in the measured velocity profiles than specified in the NCM geologic framework. With regard to observed compressional-wave velocity and density, the NCM has significantly less bias than California models for the former, especially below 2 km per second, and all models tend to overpredict density for densities less than about 2,200 kilograms per cubic meter.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201052","usgsCitation":"Boyd, O.S., 2020, Calibration of the U.S. Geological Survey National Crustal Model: U.S. Geological Survey Open-File Report 2020–1052, 23 p., https://doi.org/10.3133/ofr20201052.","productDescription":"Report: vi, 23 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-115717","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":436847,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NQ5LNU","text":"USGS data release","linkHelpText":"GeoPhys"},{"id":376928,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1052/ofr20201052.pdf","text":"Report","size":"6.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1052"},{"id":376929,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GO3CP8","text":"USGS data release","linkHelpText":"Calibration Coefficients for the U.S. Geological Survey National Crustal Model and Depth to Water Table"},{"id":376927,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1052/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n 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Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046

","tableOfContents":"","publishedDate":"2020-07-31","noUsgsAuthors":false,"publicationDate":"2020-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":794641,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70220279,"text":"70220279 - 2020 - Quarterly wildlife mortality report July 2020","interactions":[],"lastModifiedDate":"2023-10-13T13:41:12.547149","indexId":"70220279","displayToPublicDate":"2020-07-31T07:53:17","publicationYear":"2020","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":9359,"text":"Wildlife Disease Association Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"Quarterly wildlife mortality report July 2020","docAbstract":"The USGS National Wildlife Health Center (NWHC) Quarterly Mortality Report provides brief summaries of epizootic mortality and morbidity events by quarter. The write-ups, highlighting epizootic events and other wildlife disease topics of interest, are published in the Wildlife Disease Association quarterly newsletter. A link is provided in this WDA newsletter to the Wildlife Health Information Sharing Partnership event reporting system (WHISPers) so readers can view associated data.","language":"English","publisher":"Wildlife Disease Association","usgsCitation":"Richards, B.J., Ballmann, A., Bodenstein, B., Dusek, R.J., and Sleeman, J.M., 2020, Quarterly wildlife mortality report July 2020: Wildlife Disease Association Newsletter, p. 12-14.","productDescription":"3 p.","startPage":"12","endPage":"14","ipdsId":"IP-120249","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":385415,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385395,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.wildlifedisease.org/PersonifyEbusiness/Resources/Publications/Newsletter/Archive"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Richards, Bryan J. 0000-0001-9955-2523","orcid":"https://orcid.org/0000-0001-9955-2523","contributorId":219535,"corporation":false,"usgs":true,"family":"Richards","given":"Bryan","email":"","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":814996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ballmann, Anne 0000-0002-0380-056X aballmann@usgs.gov","orcid":"https://orcid.org/0000-0002-0380-056X","contributorId":140319,"corporation":false,"usgs":true,"family":"Ballmann","given":"Anne","email":"aballmann@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":814997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bodenstein, Barbara L. 0000-0001-7946-0103 bbodenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-7946-0103","contributorId":189820,"corporation":false,"usgs":true,"family":"Bodenstein","given":"Barbara","email":"bbodenstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":814998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dusek, Robert J. 0000-0001-6177-7479 rdusek@usgs.gov","orcid":"https://orcid.org/0000-0001-6177-7479","contributorId":174374,"corporation":false,"usgs":true,"family":"Dusek","given":"Robert","email":"rdusek@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":814999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815000,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211444,"text":"ofr20201082 - 2020 - seawaveQ—An R package providing a model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables, version 2.0.0","interactions":[],"lastModifiedDate":"2020-08-04T20:24:39.347599","indexId":"ofr20201082","displayToPublicDate":"2020-07-30T09:24:24","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":"2020-1082","displayTitle":"seawaveQ—An R Package Providing a Model and Utilities for Analyzing Trends in Chemical Concentrations in Streams with a Seasonal Wave (seawave) and Adjustment for Streamflow (Q) and Other Ancillary Variables, Version 2.0.0","title":"seawaveQ—An R package providing a model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables, version 2.0.0","docAbstract":"

The seawaveQ R package provides functionality and help to fit a parametric regression model, SEAWAVE-Q, to pesticide concentration data from stream-water samples to assess trends. The model incorporates the strong seasonality and high degree of censoring common in pesticide data, and users can incorporate numerous ancillary variables such as streamflow anomalies. The model is fitted to pesticide data using maximum likelihood methods for censored data and is robust in terms of pesticide, stream location, and degree of censoring of the concentration data. This R package standardizes this methodology for trend analysis, documents the code, and provides help and tutorial information.

In previous investigations, the SEAWAVE-Q model assumed a linear trend across the period analyzed. For short trend periods, this assumption of a linear trend is adequate. However, as the period of record analyzed becomes longer, the assumption of linearity is problematic because of changes in pesticide regulation and use, some of which can be abrupt. In this update to the model, a restricted cubic spline option was added for long trend periods. This option allows for more flexibility in the time component of the model. Bootstrap functionality is included to determine statistical significance. Model results with the new restricted cubic spline option are compared to the linear trend option for two pesticide-site combinations.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201082","collaboration":"National Water Quality Program","usgsCitation":"Ryberg, K.R., and York, B.C., 2020, seawaveQ—An R package providing a model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables, version 2.0.0: U.S. Geological Survey Open-File Report 2020–1082, 25 p., https://doi.org/10.3133/ofr20201082.","productDescription":"Report: vi, 25; 3 Appendixes","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101011","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":376796,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082_appendix_1.pdf","text":"Appendix 1.","size":"356 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082 Appendix 1","linkHelpText":"— Vignette for seawaveQ—An R Package Providing a Model and Utilities for Analyzing Trends in Chemical Concentrations in Streams with a Seasonal Wave (seawave) and Adjustment for Streamflow (Q) and Other Ancillary Variables"},{"id":376797,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082_appendix_2.pdf","text":"Appendix 2.","size":"228 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082 Appendix 2","linkHelpText":"— R Documentation"},{"id":376798,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082_appendix_4.pdf","text":"Appendix 4.","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082 Appendix 4","linkHelpText":"— Model Comparisons Using seawaveQ"},{"id":376794,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1082/coverthb.jpg"},{"id":376795,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082"}],"contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503

1608 Mountain View Road
Rapid City, SD

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-07-30","noUsgsAuthors":false,"publicationDate":"2020-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"York, Benjamin C. 0000-0002-3449-3574 byork@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-3574","contributorId":213613,"corporation":false,"usgs":true,"family":"York","given":"Benjamin","email":"byork@usgs.gov","middleInitial":"C.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794151,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211992,"text":"70211992 - 2020 - Implanted satellite transmitters affect sea duck movement patterns at short- and long-term time scales","interactions":[],"lastModifiedDate":"2020-09-23T15:55:41.727144","indexId":"70211992","displayToPublicDate":"2020-07-30T07:59:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1318,"text":"Condor","active":true,"publicationSubtype":{"id":10}},"title":"Implanted satellite transmitters affect sea duck movement patterns at short- and long-term time scales","docAbstract":"Studies of the effects of transmitters on wildlife often focus on survival. However, non-lethal behavioral changes resulting from radiomarking have the potential to affect inferences from telemetry data and may vary based on individual and environmental characteristics. We used a long-term, multi-species tracking study of sea ducks to assess behavioral patterns at multiple temporal scales following implantation of intracoelomic satellite transmitters. We applied state-space models to assess short-term behavioral patterns in individuals with implanted satellite transmitters, as well as comparing breeding site attendance and migratory phenology across multiple years after capture. In the short term, our results suggest an increase in dispersive behavior immediately following capture and transmitter implantation; however, behavior returned to seasonally-average patterns within approximately five days after release. Over multiple years, we found that breeding site attendance by both males and females was depressed during the first breeding season after radiomarking relative to subsequent years, with larger relative decreases in breeding site attendance among males than females. We also found that spring migration occurred later in the first year after radiomarking than in subsequent years. Across all behavioral effects, the severity of behavioral change often varied by species, sex, age, and capture season, suggesting heterogeneity in individual sensitivity. We conclude that, although individuals appear to adjust relatively quickly (i.e., within one week) to implanted satellite transmitters, changes in breeding phenology may occur over the longer term and should be considered when analyzing and reporting telemetry data.","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duaa029","usgsCitation":"Lamb, J.S., Paton, P.W., Osenkowski, J.E., Badzinski, S.S., Berlin, A., Bowman, T.D., Dwyer, C., Fara, L., Gilliland, S.G., Kenow, K.P., Lepage, C., Mallory, M.L., Olsen, G.H., Perry, M., Petrie, S.A., Savard, J.L., Savoy, L., Schummer, M.L., Spiegel, C.S., and McWilliams, S.R., 2020, Implanted satellite transmitters affect sea duck movement patterns at short- and long-term time scales: Condor, duaa029, 16 p., https://doi.org/10.1093/condor/duaa029.","productDescription":"duaa029, 16 p.","ipdsId":"IP-117761","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper 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aberlin@usgs.gov","orcid":"https://orcid.org/0000-0002-5275-3077","contributorId":168416,"corporation":false,"usgs":true,"family":"Berlin","given":"Alicia","email":"aberlin@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796124,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bowman, Timothy D.","contributorId":80779,"corporation":false,"usgs":false,"family":"Bowman","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":796125,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dwyer, Chris","contributorId":177908,"corporation":false,"usgs":false,"family":"Dwyer","given":"Chris","affiliations":[],"preferred":false,"id":796126,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fara, Luke J. 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L.","contributorId":101776,"corporation":false,"usgs":false,"family":"Savard","given":"Jean-Pierre","email":"","middleInitial":"L.","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":796135,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Savoy, Lucas","contributorId":171896,"corporation":false,"usgs":false,"family":"Savoy","given":"Lucas","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":796136,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Schummer, Michael L.","contributorId":176347,"corporation":false,"usgs":false,"family":"Schummer","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":796137,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Spiegel, Caleb 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Daily mean streamflow was estimated for all the nontidal parts of the Chesapeake Bay riverine system with the Unit Flows in Networks of Channels computer application using measured streamflow at the most downstream gage of selected rivers. The streamflows estimated by the Unit Flows in Networks of Channels computer application were aggregated at the 12-digit Hydrologic Unit Code level, after which base flow was estimated by two hydrograph-separation methods. Based on six sites selected for comparison, modeled streamflows are typically within an order of magnitude of measured streamflows, and monthly mean streamflows are in better agreement than daily streamflows. For the six selected sites, the base-flow values calculated by the two hydrograph-separation methods were compared. The monthly base-flow values also were in better agreement than the daily base-flow values. The modeled data were animated to better visualize spatial and temporal variability of streamflow and base-flow index.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205055","usgsCitation":"Buffington, P.C., and Capel, P.D., 2020, Estimating streamflow and base flow within the nontidal Chesapeake Bay riverine system: U.S. Geological Survey Scientific Investigations Report 2020–5055, 26 p., https://doi.org/10.3133/sir20205055.","productDescription":"Report: v, 26 p.; Figure Animations: Figures 15–18; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098068","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":376516,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation","linkHelpText":"— National Water Information System database"},{"id":376515,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P906K5GZ","text":"USGS data 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U.S. Geological Survey
National Water-Quality Assessment (NAWQA) Science Team
12201 Sunrise Valley Drive
Reston, VA 20192

https://water.usgs.gov/nawqa/

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-07-30","noUsgsAuthors":false,"publicationDate":"2020-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Buffington, Patrick C.","contributorId":229470,"corporation":false,"usgs":false,"family":"Buffington","given":"Patrick","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":793268,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793267,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218467,"text":"70218467 - 2020 - Nutrient removal and uptake by native planktonic and biofilm bacterial communities in an anaerobic aquifer","interactions":[],"lastModifiedDate":"2021-03-02T13:01:03.632408","indexId":"70218467","displayToPublicDate":"2020-07-29T10:42:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"title":"Nutrient removal and uptake by native planktonic and biofilm bacterial communities in an anaerobic aquifer","docAbstract":"

Managed aquifer recharge (MAR) offers a collection of water storage and storage options that have been used by resource managers to mitigate the reduced availability of fresh water. One of these technologies is aquifer storage and recovery (ASR), where surface water is treated then recharged into a storage zone within an existing aquifer for later recovery and discharge into a body of water. During the storage phase of ASR, nutrient concentrations in the recharge water have been shown to decrease due, presumably via the uptake by the native aquifer microbial community. In this study, the native microbial community in an anaerobic carbonate aquifer zone targeted for ASR storage was segregated into planktonic and biofilm communities then challenged with NO3-N, PO4-P, and acetate as dissolved organic carbon (DOC) to determine their respective removal and uptake rates. The planktonic community removed NO3-N at a rate of 0.059 mg L–1d–1, PO4-P at 5.73 × 10–8–1.03 × 10–7 mg L–1d–1 and DOC at 0.015–0.244 mg L–1d–1. The biofilm community was significantly more proficient, removing NO3-N at 0.116 mg L–1d–1 (1.6–9.0 μg m–2d–1), PO4-P at 4.20–5.91 × 10–5 mg L–1d–1 (2.47–9.88 ng m–2d–1) and DOC at 0.301–0.696 mg L–1d–1 (29.0–71.0 μg m–2d–1). Additionally, the PO4-P sorption rate onto the carbonate aquifer matrix ranged from 1.64 × 10–7 to 9.25 × 10–7 mg PO4-P m–2 day–1. These rates were applied to field data collected at an ASR facility in central Florida and from the same aquifer storage zone from which the biofilm communities were grown. With only 10% of the available surface area within the storage zone being colonized by biofilms, typical concentrations of NO3-N, PO4-P, and DOC in the recharged filtered surface waters would be reduced to below detection limits, and by 81.4 and 91.1%, respectively, during a 150 days storage period.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmicb.2020.01765","usgsCitation":"Lisle, J.T., 2020, Nutrient removal and uptake by native planktonic and biofilm bacterial communities in an anaerobic aquifer: Frontiers in Microbiology, v. 11, 1765, 13 p., https://doi.org/10.3389/fmicb.2020.01765.","productDescription":"1765, 13 p.","ipdsId":"IP-111177","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455831,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2020.01765","text":"Publisher Index Page"},{"id":436853,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EOM5RC","text":"USGS data release","linkHelpText":"Microbial Nutrient Cycling in the Upper Floridan Aquifer"},{"id":436852,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EOM5RC","text":"USGS data release","linkHelpText":"Microbial Nutrient Cycling in the Upper Floridan Aquifer"},{"id":383695,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Kissimmee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.87735652923584,\n 27.15044802232913\n ],\n [\n -80.86731433868408,\n 27.15044802232913\n ],\n [\n -80.86731433868408,\n 27.15772232531679\n ],\n [\n -80.87735652923584,\n 27.15772232531679\n ],\n [\n -80.87735652923584,\n 27.15044802232913\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2020-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Lisle, John T. 0000-0002-5447-2092 jlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-5447-2092","contributorId":2944,"corporation":false,"usgs":true,"family":"Lisle","given":"John","email":"jlisle@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811085,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70216060,"text":"70216060 - 2020 - Recording the aurora borealis (northern lights) at seismometers across Alaska","interactions":[],"lastModifiedDate":"2021-01-04T16:13:15.81959","indexId":"70216060","displayToPublicDate":"2020-07-29T07:05:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7344,"text":"Seismological Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Recording the aurora borealis (northern lights) at seismometers across Alaska","docAbstract":"

We examine three continuously recording data sets related to the aurora: all‐sky camera images, three‐component magnetometer data, and vertical‐component, broadband seismic data as part of the EarthScope project (2014 to present). Across Alaska there are six all‐sky cameras, 13 magnetometers, and >200\">>200>200 seismometers. The all‐sky images and magnetometers have the same objective, which is to monitor space weather and improve our understanding of auroral activity, including the influence on magnetic fields in the ground. These variations in the magnetic field are also visible on seismometers, to the extent that during an auroral event, the long‐period (40–800 s) waves recorded by a seismometer are magnetic field variations, not true ground motion. Although this is a problem—one that can be rectified with magnetic shielding at each seismometer site—it is also an opportunity because the present seismic array in Alaska is much broader than the coverage by magnetometers and all‐sky cameras. Here we focus on three aurora events and document a direct link between aurora images in the night sky and seismometer recordings on ground. Simultaneous recordings by magnetometers provide a critical link between the sky images and the seismometer recordings. We document qualitative correlations among sky, magnetic, and seismic data. The findings suggest that the signature of auroral activity is widespread across seismometers in Alaska, implying that the seismic array could be used to enhance the spatial resolution of the existing network of all‐sky cameras and magnetometers. Future efforts to improve the multisensor seismic stations in Alaska, for the purpose of monitoring seismic and auroral activity, should consider installation of all‐sky cameras, installation of magnetometers, and magnetic shielding of seismic sensors.

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C.","contributorId":244363,"corporation":false,"usgs":false,"family":"Tape","given":"C.","email":"","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":803895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":145576,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":803896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hampton, D.L.","contributorId":244364,"corporation":false,"usgs":false,"family":"Hampton","given":"D.L.","email":"","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":803897,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211634,"text":"70211634 - 2020 - Legacy effects of hydrologic alteration in playa wetland responses to droughts","interactions":[],"lastModifiedDate":"2020-12-29T21:21:31.795804","indexId":"70211634","displayToPublicDate":"2020-07-28T15:46:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Legacy effects of hydrologic alteration in playa wetland responses to droughts","docAbstract":"

Wetland conservation increasingly must account for climate change and legacies of previous land-use practices. Playa wetlands provide critical wildlife habitat, but may be impacted by intensifying droughts and previous hydrologic modifications. To inform playa restoration planning, we asked: (1) what are the trends in playa inundation? (2) what are the factors influencing inundation? (3) how is playa inundation affected by increasingly severe drought? (4) do certain playas provide hydrologic refugia during droughts, and (5) if so, how are refugia patterns related to historical modifications? Using remotely sensed surface-water data, we evaluated a 30-year time series (1985–2015) of inundation for 153 playas of the Great Basin, USA. Inundation likelihood and duration increased with wetter weather conditions and were greater in modified playas. Inundation probability was projected to decrease from 22% under average conditions to 11% under extreme drought, with respective annual inundation decreasing from 1.7 to 0.9 months. Only 4% of playas were inundated for at least 2 months in each of the 5 driest years, suggesting their potential as drought refugia. Refugial playas were larger and more likely to have been modified, possibly because previous land managers selected refugial playas for modification. These inundation patterns can inform efforts to restore wetland functions and to conserve playa habitats as climate conditions change.

","language":"English","publisher":"Springer","doi":"10.1007/s13157-020-01334-0","usgsCitation":"Russell, M.T., Cartwright, J.M., Collins, G.H., Long, R.A., and Eitel, J., 2020, Legacy effects of hydrologic alteration in playa wetland responses to droughts: Wetlands, v. 40, p. 2011-2024, https://doi.org/10.1007/s13157-020-01334-0.","productDescription":"14 p.","startPage":"2011","endPage":"2024","ipdsId":"IP-111867","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":455846,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-020-01334-0","text":"Publisher Index Page"},{"id":377103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada, Oregon","otherGeospatial":"Sheldon-Hart Mountain National Wildlife Refuge Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.96246337890624,\n 41.52091689636249\n ],\n [\n -118.69354248046875,\n 41.52091689636249\n ],\n [\n -118.69354248046875,\n 42.84777884235988\n ],\n [\n -119.96246337890624,\n 42.84777884235988\n ],\n [\n -119.96246337890624,\n 41.52091689636249\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Russell, Micah T.","contributorId":236988,"corporation":false,"usgs":false,"family":"Russell","given":"Micah","email":"","middleInitial":"T.","affiliations":[{"id":38118,"text":"Western Colorado University","active":true,"usgs":false}],"preferred":false,"id":794877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Gail H.","contributorId":59170,"corporation":false,"usgs":false,"family":"Collins","given":"Gail","email":"","middleInitial":"H.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":794879,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, Ryan A.","contributorId":236989,"corporation":false,"usgs":false,"family":"Long","given":"Ryan","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":794880,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eitel, Jan H.","contributorId":236991,"corporation":false,"usgs":false,"family":"Eitel","given":"Jan H.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":794881,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211358,"text":"70211358 - 2020 - Quantifying development to inform management of Mojave and Sonoran desert tortoise habitat in the American southwest","interactions":[],"lastModifiedDate":"2020-08-27T14:47:07.029758","indexId":"70211358","displayToPublicDate":"2020-07-28T13:40:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying development to inform management of Mojave and Sonoran desert tortoise habitat in the American southwest","docAbstract":"Two tortoise species native to the American southwest have experienced significant habitat loss from development and are vulnerable to ongoing threats associated with continued development. Mojave desert tortoises Gopherus agassizii are listed as threatened under the US Endangered Species Act, and Sonoran desert tortoises G. morafkai are protected in Arizona (USA) and Mexico. Substantial habitat for both species occurs on multiple-use public lands, where development associated with traditional and renewable energy production, recreation, and other activities is likely to continue. Our goal was to quantify development to inform and evaluate actions implemented to protect and manage desert tortoise habitat. We quantified a landscape-level index of development across the Mojave and Sonoran desert tortoise ranges using models of potential habitat for each species (152485 total observations). We used 13 years of Mojave desert tortoise monitoring data (4732 observations) to inform the levels and spatial scales at which tortoises may be affected by development. Most (66–70%) desert tortoise habitat has some development within 1 km. Development levels on desert tortoise habitat are lower inside versus outside areas protected by actions at national, state, and local levels, suggesting that protection efforts may be having the desired effects and providing a needed baseline for future effectiveness evaluations. Of the relatively undeveloped desert tortoise habitat, 43% (74030 km2) occurs outside of existing protections. These lands are managed by multiple federal, state, and local entities and private landowners, and may provide opportunities for future land acquisition or protection, including as mitigation for energy development on public lands.","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01045","usgsCitation":"Carter, S.K., Nussear, K., Esque, T., Leinwand, I.I., Masters, E.H., Inman, R.D., Carr, N.B., and Allison, L.J., 2020, Quantifying development to inform management of Mojave and Sonoran desert tortoise habitat in the American southwest: Endangered Species Research, v. 42, p. 167-184, https://doi.org/10.3354/esr01045.","productDescription":"18 p.","startPage":"167","endPage":"184","ipdsId":"IP-088248","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":455849,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01045","text":"Publisher Index Page"},{"id":377921,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada, Utah","otherGeospatial":"Mojave Desert, Sonoran Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.137451171875,\n 31.868227816180674\n ],\n [\n -110.863037109375,\n 34.134541681937364\n ],\n [\n -112.34619140625,\n 35.69299463209881\n ],\n [\n -113.785400390625,\n 36.85325222344018\n ],\n [\n -113.48876953125,\n 37.69251435532741\n ],\n [\n -115.68603515624999,\n 38.40194908237822\n ],\n [\n -119.10278320312499,\n 38.35027253825765\n ],\n [\n -119.871826171875,\n 38.20365531807149\n ],\n [\n -118.30078125,\n 35.639441068973944\n ],\n [\n -115.29052734375,\n 32.667124733120325\n ],\n [\n -112.181396484375,\n 31.737511125687828\n ],\n [\n 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tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":794013,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leinwand, Ian IF","contributorId":229704,"corporation":false,"usgs":false,"family":"Leinwand","given":"Ian","email":"","middleInitial":"IF","affiliations":[{"id":41706,"text":"Cherokee Services Group Inc.","active":true,"usgs":false}],"preferred":false,"id":794014,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Masters, Elroy H.","contributorId":229705,"corporation":false,"usgs":false,"family":"Masters","given":"Elroy","email":"","middleInitial":"H.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":794015,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Inman, Richard D. 0000-0002-1982-7791 rdinman@usgs.gov","orcid":"https://orcid.org/0000-0002-1982-7791","contributorId":187754,"corporation":false,"usgs":true,"family":"Inman","given":"Richard","email":"rdinman@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":794016,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carr, Natasha B. 0000-0002-4842-0632 carrn@usgs.gov","orcid":"https://orcid.org/0000-0002-4842-0632","contributorId":1918,"corporation":false,"usgs":true,"family":"Carr","given":"Natasha","email":"carrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":794017,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Allison, Linda J. 0000-0003-1983-901X","orcid":"https://orcid.org/0000-0003-1983-901X","contributorId":229706,"corporation":false,"usgs":false,"family":"Allison","given":"Linda","email":"","middleInitial":"J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":794018,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228377,"text":"70228377 - 2020 - Complex patterns of genetic and morphological differentiation in the Smallmouth Bass subspecies (Micropterus dolomieu dolomieu and M. d. velox) of the Central Interior Highlands","interactions":[],"lastModifiedDate":"2022-02-09T16:41:59.820113","indexId":"70228377","displayToPublicDate":"2020-07-28T10:31:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Complex patterns of genetic and morphological differentiation in the Smallmouth Bass subspecies (Micropterus dolomieu dolomieu and M. d. velox) of the Central Interior Highlands","title":"Complex patterns of genetic and morphological differentiation in the Smallmouth Bass subspecies (Micropterus dolomieu dolomieu and M. d. velox) of the Central Interior Highlands","docAbstract":"

Due to geologic processes and recent anthropogenic introductions, patterns of genetic and morphological diversity within the Smallmouth Bass (Micropterus dolomieu), which are endemic to the central and eastern United States (USA), are poorly understood. We assessed genetic and morphological differentiation between the widespread Northern Smallmouth Bass (M. d. dolomieu) and the more restricted Neosho Smallmouth Bass (M. d. velox) where their ranges meet in the Central Interior Highlands ecoregion (CIH). Data from 14 microsatellite loci were used to conduct STRUCTURE and principal components analyses to evaluate diversity across populations and screen for hybridization with sympatric Spotted Bass (M. punctulatus). We also tested for morphological differences using five morphometric traits and one meristic trait. We found support for three genetic clusters corresponding to previously described taxonomic variation; five clusters largely corresponding to river systems; and nine clusters representing hierarchical population structure within both ranges. We found evidence of a unique genetic cluster in tributaries of the White River within the Northern Smallmouth Bass range and admixture between the subspecies throughout the Neosho range. We also found evidence of morphological differentiation between subspecies; Neosho Smallmouth Bass exhibited larger head length than Northern Smallmouth Bass relative to total length, and there was a significant interaction of subspecies and orbital length, possibly indicating differential growth patterns between subspecies. Our results reveal multiple levels of divergence, suggesting the CIH harbors greater and more complex Smallmouth Bass diversity than previously thought.

","language":"English","publisher":"Springer","doi":"10.1007/s10592-020-01295-1","usgsCitation":"Gunn, J.C., Berkman, L.K., Koppelman, J.K., Taylor, A.T., Brewer, S.K., Long, J.M., and Eggert, L.S., 2020, Complex patterns of genetic and morphological differentiation in the Smallmouth Bass subspecies (Micropterus dolomieu dolomieu and M. d. velox) of the Central Interior Highlands: Conservation Genetics, v. 21, p. 891-904, https://doi.org/10.1007/s10592-020-01295-1.","productDescription":"14 p.","startPage":"891","endPage":"904","ipdsId":"IP-111223","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Illinois, Kansas, Mississippi, Missouri, Oklahoma, Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.7890625,\n 33.99802726234877\n ],\n [\n -88.41796875,\n 33.99802726234877\n ],\n [\n -88.41796875,\n 39.80853604144591\n ],\n [\n -98.7890625,\n 39.80853604144591\n ],\n [\n -98.7890625,\n 33.99802726234877\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationDate":"2020-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Gunn, Joe C.","contributorId":275348,"corporation":false,"usgs":false,"family":"Gunn","given":"Joe","email":"","middleInitial":"C.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":834024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berkman, Leah K.","contributorId":275349,"corporation":false,"usgs":false,"family":"Berkman","given":"Leah","email":"","middleInitial":"K.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":834025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koppelman, Jeff K.","contributorId":275350,"corporation":false,"usgs":false,"family":"Koppelman","given":"Jeff","email":"","middleInitial":"K.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":834026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, A. T.","contributorId":275351,"corporation":false,"usgs":false,"family":"Taylor","given":"A.","email":"","middleInitial":"T.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":834027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":834028,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834029,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eggert, Lori S.","contributorId":106325,"corporation":false,"usgs":false,"family":"Eggert","given":"Lori","email":"","middleInitial":"S.","affiliations":[{"id":13259,"text":"USDA Forest Service Northern Research Station","active":true,"usgs":false}],"preferred":false,"id":834030,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227091,"text":"70227091 - 2020 - Hypogeous, sequestrate fungi (genus Elaphomyces) found at small-mammal foraging sites in high-elevation conifer forests of West Virginia","interactions":[],"lastModifiedDate":"2021-12-29T15:11:29.100926","indexId":"70227091","displayToPublicDate":"2020-07-28T08:54:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Hypogeous, sequestrate fungi (genus Elaphomyces) found at small-mammal foraging sites in high-elevation conifer forests of West Virginia","title":"Hypogeous, sequestrate fungi (genus Elaphomyces) found at small-mammal foraging sites in high-elevation conifer forests of West Virginia","docAbstract":"Little is known about hypogeous, sequestrate (i.e., truffles) fungi in the eastern United States. Since the fruiting bodies of these fungi are part of the diet of multiple rodent species, filling data gaps is important to understanding more about truffle species distribution and habitat associations. During a microhabitat study on radio-collared Virginia Northern Flying Squirrels (Glaucomys sabrinus fuscus Miller) in 2013, we opportunistically sampled truffles at small mammal digs and scratches within our microhabitat plots. All sampling was conducted within known squirrel foraging home ranges. We found three Elaphomyces species: Elaphomyces macrosporus Castellano and Elliott, E. verruculosus Castellano, and E. americanum Castellano. Our observations of E. macroporus are the first from West Virginia. Herein, we describe the microhabitat associations for each fungal species. We suggest using small mammal digs and scratches as potential indicators to opportunistically gather more information on truffle species in coniferous forests of the eastern United States.","language":"English","publisher":"Humboldt Field Research Institute","doi":"10.1656/045.027.0305","usgsCitation":"Diggins, C., Castellano, M., and Ford, W., 2020, Hypogeous, sequestrate fungi (genus Elaphomyces) found at small-mammal foraging sites in high-elevation conifer forests of West Virginia: Northeastern Naturalist, v. 27, no. 3, p. N40-N47, https://doi.org/10.1656/045.027.0305.","productDescription":"8 p.","startPage":"N40","endPage":"N47","ipdsId":"IP-117955","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":455853,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/102440","text":"External Repository"},{"id":393584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","county":"Pocahontas County, Randolph County, Webster County","otherGeospatial":"Kumbrabow State Forest, Monogahela National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.54901123046875,\n 38.16209595668554\n ],\n [\n -79.92828369140625,\n 38.16209595668554\n ],\n [\n -79.92828369140625,\n 38.52668162061619\n ],\n [\n -80.54901123046875,\n 38.52668162061619\n ],\n [\n -80.54901123046875,\n 38.16209595668554\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Richardson, David ","contributorId":223903,"corporation":false,"usgs":false,"family":"Richardson","given":"David ","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":829651,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Diggins, Corinne A.","contributorId":270604,"corporation":false,"usgs":false,"family":"Diggins","given":"Corinne A.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":829609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Castellano, Michael A.","contributorId":270606,"corporation":false,"usgs":false,"family":"Castellano","given":"Michael A.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":829610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":829608,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211344,"text":"ds1128 - 2020 - Biotelemetry data for Golden Eagles (Aquila chrysaetos) captured in coastal southern California, February 2017–December 2019","interactions":[],"lastModifiedDate":"2020-07-28T22:10:09.934325","indexId":"ds1128","displayToPublicDate":"2020-07-28T07:45:24","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1128","displayTitle":"Biotelemetry Data for Golden Eagles (Aquila chrysaetos) Captured in Coastal Southern California, February 2017–December 2019","title":"Biotelemetry data for Golden Eagles (Aquila chrysaetos) captured in coastal southern California, February 2017–December 2019","docAbstract":"

Because of a lack of clarity about the status of golden eagles (Aquila chrysaetos) in coastal southern California, the U.S. Geological Survey, in collaboration with U.S. Fish and Wildlife Service, California Department of Fish and Wildlife, Bureau of Land Management, and San Diego Management and Monitoring Program, began a multi-year survey and tracking program of golden eagles to address questions regarding habitat use, movement behavior, nest occupancy, genetic population structure, and human impacts on eagles. Golden eagle trapping and tracking efforts began in September 2014. During trapping efforts from September 29, 2014, to February 23, 2017, 37 golden eagles were captured. During trapping efforts from February 24, 2017, to December 2, 2019, an additional 7 golden eagles (4 females and 3 males) were captured, and one previously captured female was recaptured in San Diego County. Biotelemetry data for 27 of the 44 golden eagles that were transmitting data from February 24, 2017, to December 2, 2019, are presented. These eagles ranged as far north as British Columbia, Canada, and as far south as Ciudad Insurgentes, Baja California, Mexico.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1128","collaboration":"Prepared in cooperation with the San Diego Association of Governments (SANDAG), U.S. Fish and Wildlife Service (USFWS), California Department of Fish and Wildlife (CDFW), Bureau of Land Management (BLM), and San Diego Management and Monitoring Program (SDMMP)","usgsCitation":"Tracey, J.A., Madden, M.C., Molden, J.C., Sebes, J.B., Bloom, P.H., and Fisher, R.N., 2020, Biotelemetry data for Golden Eagles (Aquila chrysaetos) captured in coastal southern California, February 2017–December 2019: U.S. Geological Survey Data Series 1128, 34 p., https://doi.org/ 10.3133/ ds1128.","productDescription":"vii, 34 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-117961","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":376715,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1128/covrthb.jpg"},{"id":376716,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1128/ds1128.pdf","text":"Report","size":"40 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.0810546875,\n 32.62087018318113\n ],\n [\n -115.81787109375,\n 32.62087018318113\n ],\n [\n -115.81787109375,\n 33.925129700072\n ],\n [\n -118.0810546875,\n 33.925129700072\n ],\n [\n -118.0810546875,\n 32.62087018318113\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-07-28","noUsgsAuthors":false,"publicationDate":"2020-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Tracey, Jeff A. 0000-0002-1619-1054 jatracey@usgs.gov","orcid":"https://orcid.org/0000-0002-1619-1054","contributorId":5780,"corporation":false,"usgs":true,"family":"Tracey","given":"Jeff","email":"jatracey@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":793939,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madden, Melanie C. 0000-0003-4147-7254 mmadden@usgs.gov","orcid":"https://orcid.org/0000-0003-4147-7254","contributorId":229684,"corporation":false,"usgs":true,"family":"Madden","given":"Melanie","email":"mmadden@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":793940,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Molden, James C. 0000-0002-3316-5288","orcid":"https://orcid.org/0000-0002-3316-5288","contributorId":231475,"corporation":false,"usgs":true,"family":"Molden","given":"James","email":"","middleInitial":"C.","affiliations":[],"preferred":true,"id":793941,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sebes, Jeremy B. 0000-0001-6829-2220 jsebes@usgs.gov","orcid":"https://orcid.org/0000-0001-6829-2220","contributorId":191910,"corporation":false,"usgs":true,"family":"Sebes","given":"Jeremy","email":"jsebes@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":true,"id":793942,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bloom, Peter H.","contributorId":191356,"corporation":false,"usgs":false,"family":"Bloom","given":"Peter","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":793943,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":793944,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211345,"text":"sir20205072 - 2020 - Quality of pesticide data for groundwater analyzed for the National Water-Quality Assessment Project, 2013–18","interactions":[],"lastModifiedDate":"2021-05-27T13:25:01.758364","indexId":"sir20205072","displayToPublicDate":"2020-07-27T15:40:18","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5072","displayTitle":"Quality of Pesticide Data for Groundwater Analyzed for the National Water-Quality Assessment Project, 2013–18","title":"Quality of pesticide data for groundwater analyzed for the National Water-Quality Assessment Project, 2013–18","docAbstract":"

The National Water-Quality Assessment (NAWQA) Project of the U.S. Geological Survey (USGS) submitted nearly 1,900 samples collected from groundwater sites across the United States in 2013–18 for analysis of 225 pesticide compounds (pesticides and pesticide degradates, hereafter referred to as “pesticides”) by USGS National Water Quality Laboratory schedule 2437 (S2437). For the associated NAWQA study of pesticide occurrence and concentration in groundwater, and for other studies using pesticide results determined by S2437, it is necessary to assess the ability of reported results to meet data-quality requirements that will allow study objectives to be achieved. This assessment of the quality of S2437 results reported in 2013–18 examined data from field and laboratory quality-control samples, along with third-party performance assessment samples, to estimate bias and variability and to identify their potential sources, with an emphasis on implications for the interpretation of pesticide data for groundwater. Results indicate that measurements produced by the S2437 method for most pesticides have bias and variability that would be considered acceptable for many interpretative studies, which could therefore use the results without qualification or censoring. However, the reported data for a subset of pesticides have the potential for unacceptable contamination bias, high or low recovery bias, or high variability as a consequence of method performance and (or) nonlaboratory factors that could preclude their use for certain common objectives or could necessitate adjustment or qualification to meet those objectives.

Based on data for laboratory blanks, censoring of some detections for a subset of pesticides reported by the laboratory in environmental samples might be necessary or desirable to avoid an unacceptably high likelihood of a false-positive result caused by laboratory contamination. The 90-percent upper confidence limit for the 95th percentile of laboratory blank concentration equals or exceeds the minimum reported groundwater concentration in at least 1 water year for 28 pesticides. During at least 1 water year, this upper confidence limit exceeds the maximum laboratory detection limit for 17 pesticides and exceeds the maximum laboratory reporting limit for 3 pesticides (ametryn, atrazine, and diazinon). The level of contamination indicated by this upper confidence limit should not substantially affect the suitability of reported environmental concentrations for any compound for comparison with corresponding human-health benchmarks.

Despite being subjected to the same laboratory processes as laboratory blanks, field blanks indicated little evidence of contamination bias. This observation could largely be the consequence of data-reporting practices, which utilize detections in laboratory blanks to censor results in associated field samples (including blanks and environmental samples) when relative concentrations indicate that a result could have a substantial contribution from laboratory contamination. Laboratory censoring appears likely to reduce the risk of false-positive results in environmental samples below the level that laboratory blank results alone would imply.

Whereas data available for third-party blind blank samples analyzed in 2018 indicate that only propoxur had any false-positive results, data for pesticides that were not spiked into blind spike samples analyzed in 2013–18 indicate that the false-positive rates for 31 pesticides exceeded 1 percent when considering only detections reported at concentrations greater than the maximum detection limit. Although about half of these pesticides lack substantial supporting evidence of contamination bias based on laboratory blank or field blank detections, indicating that spiking issues or degradation of parent compounds within the spiked samples might be a contributing factor to some false-positive results, these results indicate the need to closely examine detections reported for some pesticides in environmental samples analyzed during a similar period for possible contributions from contamination bias. Data for blind spike samples that were spiked at concentrations above the maximum reporting limit indicate that false-negative rates for eight pesticides exceed 10 percent; substantial low bias could affect results reported for these pesticides in environmental samples analyzed during a similar period.

Data for laboratory reagent spikes, which measure recovery of pesticides in blank water, show little evidence for unacceptable recovery bias for S2437 pesticides. However, field matrix spikes, which measure recovery of pesticides in environmental matrices, indicate that degradation and (or) matrix effects could result in moderate to substantial low bias for groundwater results for several pesticides. Low bias could cause some reported concentrations to be categorized as being below a benchmark when the actual concentration in groundwater is greater than the benchmark. Occurrence and concentrations in groundwater could be substantially underrepresented for six pesticides with benchmarks (1H-1,2,4-triazole, asulam, bifenthrin, cis-permethrin, fenbutatin oxide, and naled) that have median recoveries between zero and 50 percent in field matrix spikes. Two compounds (didealkylatrazine and 2-hydroxy-6-ethylamino-4-amino-s-triazine) have median recoveries near or greater than 150 percent in field matrix spikes, indicating a substantial high bias. Plots of data for all spike types show clear changes in the typical recovery with time for some pesticides, which would require further examination for evaluation of temporal trends in environmental concentrations.

Data for laboratory reagent spikes indicate that nearly all S2437 pesticides have acceptable variability resulting from random measurement error. Only two compounds (fenbutatin oxide and naled) have F-pseudosigma values greater than 30 percent for recovery, which implies the potential for relatively high variability in reported concentrations and could affect comparison of concentrations to benchmarks and determination of whether concentrations for samples collected at separate locations or times are truly different with a specified level of confidence. Data for third-party blind spike samples show relatively high variability for a greater number of pesticides, although these results likely reflect the influence of degradation and (or) differences in the magnitude and variability of concentrations used for blind spikes relative to laboratory reagent spikes. Detailed analysis of variability using field replicate data is possible for only 12 pesticides on S2437; low variability in analyte detection and concentration is indicated for most of these pesticides in groundwater.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205072","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Bexfield, L.M., Belitz, K., Sandstrom, M.W., Beaty, D., Medalie, L., Lindsey, B.D., and Nowell, L.H., 2020, Quality of pesticide data for groundwater analyzed for the National Water-Quality Assessment Project, 2013–18: U.S. Geological Survey Scientific Investigations Report 2020–5072, 35 p., https://doi.org/10.3133/sir20205072.","productDescription":"Report: vi, 35 p.; 11 Tables; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-111029","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":376742,"rank":17,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table11.xlsx","text":"Table 11","size":"34.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5072 Table 11","linkHelpText":"— Summary of results of data-quality assessment for schedule 2437 pesticide compounds based on all quality-control sample types, May 2013 through September 2018"},{"id":376741,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table08.csv","text":"Table 8","size":"22.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5072 Table 8","linkHelpText":"— Summary statistics for the recovery of schedule 2437 pesticide compounds in field matrix spikes, May 2013 through September 2018"},{"id":376740,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table08.xlsx","text":"Table 8","size":"64.7 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Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-07-27","noUsgsAuthors":false,"publicationDate":"2020-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Bexfield, Laura M. 0000-0002-1789-654X bexfield@usgs.gov","orcid":"https://orcid.org/0000-0002-1789-654X","contributorId":1273,"corporation":false,"usgs":true,"family":"Bexfield","given":"Laura","email":"bexfield@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":793947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beaty, Delicia 0000-0003-2044-2319 dbeaty@usgs.gov","orcid":"https://orcid.org/0000-0003-2044-2319","contributorId":3469,"corporation":false,"usgs":true,"family":"Beaty","given":"Delicia","email":"dbeaty@usgs.gov","affiliations":[],"preferred":true,"id":793948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Medalie, Laura 0000-0002-2440-2149 lmedalie@usgs.gov","orcid":"https://orcid.org/0000-0002-2440-2149","contributorId":3657,"corporation":false,"usgs":true,"family":"Medalie","given":"Laura","email":"lmedalie@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793949,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":793950,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nowell, Lisa H. 0000-0001-5417-7264 lhnowell@usgs.gov","orcid":"https://orcid.org/0000-0001-5417-7264","contributorId":490,"corporation":false,"usgs":true,"family":"Nowell","given":"Lisa","email":"lhnowell@usgs.gov","middleInitial":"H.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":793951,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211335,"text":"sir20205039 - 2020 - Assessing the influence of natural copper-nickel-bearing bedrocks of the Duluth Complex on water quality in Minnesota, 2013–15","interactions":[],"lastModifiedDate":"2020-07-28T14:27:47.098254","indexId":"sir20205039","displayToPublicDate":"2020-07-27T15:39:08","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5039","displayTitle":"Assessing the Influence of Natural Copper-Nickel-Bearing Bedrocks of the Duluth Complex on Water Quality in Minnesota, 2013–15","title":"Assessing the influence of natural copper-nickel-bearing bedrocks of the Duluth Complex on water quality in Minnesota, 2013–15","docAbstract":"

The U.S. Geological Survey, in cooperation with the University of Minnesota-Duluth Natural Resources Research Institute, completed an assessment of regional water quality in areas of potential base-metal mining in Minnesota. Bedrock, soil, streambed sediment, and surface-water samples were collected in three watersheds that cross the basal part of the Duluth Complex with different mineral-deposit settings: (1) copper-nickel-platinum group element mineralization (Filson Creek), (2) iron-titanium-oxide mineralization (headwaters of the St. Louis River), and (3) no identified mineralization (Keeley Creek). At least 10 bedrock, 30 soil (2 each from 15 sites), and as many as 13 streambed sediment samples were collected in each watershed and analyzed for 44 major and trace elements, total and inorganic carbon, and 10 loosely bound metals (when possible). Surface-water samples were collected at four to nine locations in each watershed three to four times per year for 2 years (total of 141 environmental samples). Surface-water samples were analyzed for 10 trace metals (total and dissolved concentrations), 8 trace elements, 8 major ions (dissolved concentrations), alkalinity, and total and dissolved organic carbon.

Metal and element concentrations in solid media varied by watershed, representing local geology. Copper-nickel sulfide mineralization in the Filson Creek watershed was evidenced in bedrock, soil, and streambed sediments. In the Keeley Creek watershed, silicate mineralogy of underlying bedrock contributed metals to streambed sediments. Thick glacial cover masked potential bedrock contributions to solid media in the St. Louis River watershed. Water-quality data indicate that waters in all three watersheds are dilute. Water quality is more similar between the Filson and Keeley Creek watersheds, compared to the St. Louis River watershed, because of the difference in glacial cover. Metal concentrations (copper and nickel, in particular) in surface-water samples follow similar patterns of concentrations in solid media, indicating the influence of bedrock on water quality in Filson and Keeley Creeks. Data from this study provide a baseline of metal concentrations and general water quality within an area of active mineral exploration.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205039","collaboration":"Prepared in cooperation with the University of Minnesota-Duluth Natural Resources Research Institute","usgsCitation":"Elliott, S.M., Jones, P.M., Woodruff, L.G., Jennings, C.E., Krall, A.L., and Morel, D.L., 2020, Assessing the influence of natural copper-nickel-bearing bedrocks of the Duluth Complex on water quality in Minnesota, 2013–15: U.S. Geological Survey Scientific Investigations Report 2020–5039, 51 p., https://doi.org/10.3133/sir20205039.","productDescription":"Report: x, 51 p.; 1 Table; 2 Appendices; Data Release","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-110010","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376695,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5039/sir20205039_appendix_tables.xlsx","text":"Appendix Tables 1.1 and 1.2","size":"207 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5039 Appendix Tables 1.1–1.2"},{"id":376694,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5039/sir20205039_tables5to7.xlsx","text":"Tables 5 to 7","size":"42.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5039 Tables 5–7"},{"id":376696,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VO251H","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geochemical characterization of solid media from three watersheds that transect the basal contact of the Duluth Complex, northeastern Minnesota"},{"id":376692,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5039/coverthb.jpg"},{"id":376693,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5039/sir20205039.pdf","text":"Report","size":"28.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5039"}],"country":"United States","state":"Minnesota","otherGeospatial":"Duluth Complex, Filson Creek, Keeley Creek, headwaters of the St. Louis River watersheds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.6806640625,\n 47.1075227853425\n ],\n [\n -92.076416015625,\n 47.12995075666307\n ],\n [\n -91.38427734374999,\n 47.1075227853425\n ],\n [\n -91.373291015625,\n 47.98256841921405\n ],\n [\n -92.691650390625,\n 47.98256841921405\n ],\n [\n -92.6806640625,\n 47.1075227853425\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112

","tableOfContents":"","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2020-07-27","noUsgsAuthors":false,"publicationDate":"2020-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Perry M. 0000-0002-6569-5144 pmjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6569-5144","contributorId":2231,"corporation":false,"usgs":true,"family":"Jones","given":"Perry","email":"pmjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodruff, Laurel G. 0000-0002-2514-9923 woodruff@usgs.gov","orcid":"https://orcid.org/0000-0002-2514-9923","contributorId":2224,"corporation":false,"usgs":true,"family":"Woodruff","given":"Laurel","email":"woodruff@usgs.gov","middleInitial":"G.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":793828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennings, Carrie E.","contributorId":229673,"corporation":false,"usgs":false,"family":"Jennings","given":"Carrie E.","affiliations":[],"preferred":false,"id":793829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krall, Aliesha L. 0000-0003-2521-5043 adiekoff@usgs.gov","orcid":"https://orcid.org/0000-0003-2521-5043","contributorId":176545,"corporation":false,"usgs":true,"family":"Krall","given":"Aliesha","email":"adiekoff@usgs.gov","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793830,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morel, Daniel L.","contributorId":175447,"corporation":false,"usgs":false,"family":"Morel","given":"Daniel","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":793831,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211352,"text":"fs20203026 - 2020 - Citizen science collaboration with the U.S. Geological Survey in Alaska","interactions":[],"lastModifiedDate":"2020-07-28T14:45:16.823492","indexId":"fs20203026","displayToPublicDate":"2020-07-27T12:42:59","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":"2020-3026","displayTitle":"Citizen Science Collaboration with the U.S. Geological Survey in Alaska","title":"Citizen science collaboration with the U.S. Geological Survey in Alaska","docAbstract":"

Citizen science is science undertaken by the public, usually in collaboration with professional scientific institutions. It encourages citizens to tackle real-world scientific problems and augments traditional science by expanding the coverage of data collection and by reducing costs of fieldwork in remote locations. Information collected by volunteers enables us all to gain a deeper understanding of how the natural world works and how it is changing. The U.S. Geological Survey is a leading Federal agency in fostering citizen-science collaborations, and the examples in this publication show the value of those collaborations.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203026","usgsCitation":"Powers, E.M., and Williams, D.M., 2020, Citizen science collaboration with the U.S. Geological Survey in Alaska: U.S. Geological Survey Fact Sheet 2020-3026, 2 p., https://doi.org/10.3133/fs20203026.","productDescription":"2 p.","onlineOnly":"Y","ipdsId":"IP-114073","costCenters":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"links":[{"id":376751,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3026/fs20203026.pdf","text":"Report","size":"428 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3026"},{"id":376750,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3026/coverthb.jpg"}],"country":"United States","state":"Alaska","county":"Anchorage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.314453125,\n 59.0405546167585\n ],\n [\n -147.12890625,\n 59.0405546167585\n ],\n [\n -147.12890625,\n 62.14497603754045\n ],\n [\n -152.314453125,\n 62.14497603754045\n ],\n [\n -152.314453125,\n 59.0405546167585\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Science Coordinator, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska, 99508

","tableOfContents":"","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2020-07-27","noUsgsAuthors":false,"publicationDate":"2020-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Powers, Elizabeth 0000-0002-4688-1195","orcid":"https://orcid.org/0000-0002-4688-1195","contributorId":221171,"corporation":false,"usgs":true,"family":"Powers","given":"Elizabeth","email":"","affiliations":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":793993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Dee 0000-0003-0400-479X","orcid":"https://orcid.org/0000-0003-0400-479X","contributorId":221172,"corporation":false,"usgs":true,"family":"Williams","given":"Dee","email":"","affiliations":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":793994,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211353,"text":"fs20203039 - 2020 - A not so sudden impact—Historical relations between conifers and insects can help predict damage by nonnative insects","interactions":[],"lastModifiedDate":"2020-09-01T13:51:45.951306","indexId":"fs20203039","displayToPublicDate":"2020-07-27T12:00:14","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":"2020-3039","displayTitle":"A Not So Sudden Impact—Historical Relations Between Conifers and Insects Can Help Predict Damage by Nonnative Insects","title":"A not so sudden impact—Historical relations between conifers and insects can help predict damage by nonnative insects","docAbstract":"

The arrival and establishment of nonnative insects in North America is increasingly problematic. International trade has created opportunities to move wood products and nursery stock worldwide, which has increased the risk of insect introduction to regions or countries where they are not native. One group of researchers, the High-impact Insect Invasions Working Group (HIIWG), has developed a predictive model that can be used to estimate the likelihood that a newly arriving nonnative insect may significantly impact North American conifers. The HIIWG examined several traits and factors associated with nonnative insects feeding on conifers (a conifer specialist) already established in North America. Using these data, the HIIWG identified which combination of factors best predicted the risk that a conifer specialist would have a high impact. The researchers then developed a statistical model to predict the probability that a conifer specialist yet to arrive in North America would cause significant damage to conifers if the insect became established. Using three factors, the model calculates the odds of any particular conifer specialist having a high impact on a North American conifer in a range between 1 in 6.5 to 1 in 2,858. This model is a valuable tool to help identify invading insects with the potential to be the most damaging if the insect becomes established in North America. In addition, application of tools like this model can increase positive environmental outcomes for land managers by focusing their efforts on conifer populations that are deemed most vulnerable to extensive mortality

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203039","usgsCitation":"Durden, L.A., Schulz, A.N., Mech, A., and Thomas, K.A., 2020, A not so sudden impact—Historical relations between conifers and insects can help predict damage by nonnative insects: U.S. Geological Survey Fact Sheet 2020-3039, 4 p., https://doi.org/10.3133/fs20203039.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-117878","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":376753,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3039/fs20203039.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376752,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3039/covrthb.jpg"}],"contact":"
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2020-07-27","noUsgsAuthors":false,"publicationDate":"2020-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Durden, Lekeah A. 0000-0002-8145-6354","orcid":"https://orcid.org/0000-0002-8145-6354","contributorId":229702,"corporation":false,"usgs":true,"family":"Durden","given":"Lekeah","email":"","middleInitial":"A.","affiliations":[],"preferred":true,"id":793995,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schulz, Ashley N.","contributorId":219894,"corporation":false,"usgs":false,"family":"Schulz","given":"Ashley","email":"","middleInitial":"N.","affiliations":[{"id":40088,"text":"Department of Biological Sciences, Arkansas State University, Jonesboro, AR","active":true,"usgs":false}],"preferred":false,"id":793996,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mech, Angela M.","contributorId":219892,"corporation":false,"usgs":false,"family":"Mech","given":"Angela","email":"","middleInitial":"M.","affiliations":[{"id":40087,"text":"School of Environmental and Forest Sciences, University of Washington, Seattle, WA. Corresponding email: ammech@wcu.edu. Present address: Department of Geosciences and Natural Resources, Western Carolina University, Cullowhee, NC","active":true,"usgs":false}],"preferred":false,"id":793997,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thomas, Kathryn A. 0000-0002-7131-8564 kathryn_a_thomas@usgs.gov","orcid":"https://orcid.org/0000-0002-7131-8564","contributorId":167,"corporation":false,"usgs":true,"family":"Thomas","given":"Kathryn","email":"kathryn_a_thomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":793998,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211317,"text":"ofr20201059 - 2020 - Chemical constituent concentrations in stream water, streambed sediment, and soils of Fort Belvoir, Virginia—A characterization of ambient conditions in 2019","interactions":[],"lastModifiedDate":"2020-07-28T14:34:33.855203","indexId":"ofr20201059","displayToPublicDate":"2020-07-27T11:05:00","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":"2020-1059","displayTitle":"Chemical Constituent Concentrations in Stream Water, Streambed Sediment, and Soils of Fort Belvoir, Virginia— A Characterization of Ambient Conditions in 2019","title":"Chemical constituent concentrations in stream water, streambed sediment, and soils of Fort Belvoir, Virginia—A characterization of ambient conditions in 2019","docAbstract":"

Introduction

The U.S. Army Fort Belvoir (FTBL) installation is on the banks of the Potomac River in Fairfax County, northeastern Virginia. The installation was founded by the U.S. Army during World War I. It has been home to a variety of military organizations over the course of its more than 100-year history and currently houses more than 145 mission partners. The installation consists of two noncontiguous units, the Main Post, and a smaller area to the northwest, Fort Belvoir North Area (FTNA). FTBL encompasses 8.91 square miles.

There is concern that activities on FTBL, including a long history of training, operations, and maintenance, may have resulted in contamination of stream water, streambed sediment, and (or) soils. Of particular concern is the U.S. Environmental Protection Agency (EPA) Target Analyte List (TAL). TAL refers to “the list of inorganic compounds/elements designated for analysis as contained in the version of the EPA Contract Laboratory Program Statement of Work for Inorganics Analysis, Multi-Media, Multi-Concentration in effect as of the date on which the laboratory is performing the analysis” (https://www.nj.gov/dep/srp/guidance/tcl_tal/). Because of the potential for TAL contamination at FTBL, the U.S. Geological Survey (USGS), in cooperation with U.S. Army Fort Belvoir, conducted a survey of FTBL’s stream water, streambed sediment, and soils during calendar year 2019.

The terminology “ambient concentrations” is used in this report to represent the concentrations of the TAL and other constituents at the time of sampling. This is in contrast to “background concentrations,” a term that “refers to areas in which the concentrations of chemicals have not been elevated by site activities”. Although some of the samples collected for this project may represent “background concentrations,” there is no assurance that they do, so all data collected are described as having “ambient concentrations.”

The purpose of the study was to obtain environmental data to characterize ambient concentrations of EPA TAL constituents in stream water, streambed sediment, and soils in FTBL, Virginia. This report describes methods and results of sampling stream water, streambed sediment, and soils during 2019. The purpose of this report is four-fold: (1) to describe the field sampling methods used to collect stream water, streambed sediment, and soils; (2) to describe the laboratory methods used to analyze the samples; (3) to report summaries of the field and laboratory results; and (4) to report the quality assurance and quality control results.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201059","collaboration":"Prepared in cooperation with the U.S. Army, Fort Belvoir","usgsCitation":"Rice, K.C., and Chambers, D.B., 2020, Chemical constituent concentrations in stream water, streambed sediment, and soils of Fort Belvoir, Virginia—A characterization of ambient conditions in 2019: U.S. Geological Survey Open-File Report 2020–1059, 20 p., https://doi.org/10.3133/ofr20201059.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-116519","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":376670,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91P7OZJ","text":"USGS data release","linkHelpText":"Fort Belvoir, Virginia, stream-water, streambed-sediment, and soil data collected in 2019"},{"id":376668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1059/coverthb.jpg"},{"id":376669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1059/ofr20201059.pdf","text":"Report","size":"6.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1059"}],"country":"United States","state":"Virginia","city":"Fort Belvoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.19388961791992,\n 38.71096464102174\n ],\n [\n -77.20212936401367,\n 38.69033348573663\n ],\n [\n -77.12900161743163,\n 38.67117065551123\n ],\n [\n -77.11492538452148,\n 38.695290864945804\n ],\n [\n -77.12608337402344,\n 38.741766321754575\n ],\n [\n -77.14050292968749,\n 38.753012320665185\n ],\n [\n -77.16659545898438,\n 38.74390855335671\n ],\n [\n -77.18238830566406,\n 38.73051855149164\n ],\n [\n -77.19388961791992,\n 38.71096464102174\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.2184371948242,\n 38.73935623438332\n ],\n [\n -77.17826843261717,\n 38.73935623438332\n ],\n [\n -77.17826843261717,\n 38.7591700932071\n ],\n [\n -77.2184371948242,\n 38.7591700932071\n ],\n [\n -77.2184371948242,\n 38.73935623438332\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2020-07-27","noUsgsAuthors":false,"publicationDate":"2020-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Rice, Karen C. 0000-0002-9356-5443 kcrice@usgs.gov","orcid":"https://orcid.org/0000-0002-9356-5443","contributorId":178269,"corporation":false,"usgs":true,"family":"Rice","given":"Karen","email":"kcrice@usgs.gov","middleInitial":"C.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":793750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chambers, Douglas B. 0000-0002-5275-5427 dbchambe@usgs.gov","orcid":"https://orcid.org/0000-0002-5275-5427","contributorId":2520,"corporation":false,"usgs":true,"family":"Chambers","given":"Douglas B.","email":"dbchambe@usgs.gov","affiliations":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793751,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209000,"text":"sir20205022 - 2020 - Groundwater quality in relation to drinking water health standards and geochemical characteristics for 54 domestic wells in Clinton County, Pennsylvania, 2017","interactions":[],"lastModifiedDate":"2020-07-27T15:15:44.798988","indexId":"sir20205022","displayToPublicDate":"2020-07-27T10:30:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5022","displayTitle":"Groundwater Quality in Relation to Drinking Water Health Standards and Geochemical Characteristics for 54 Domestic Wells in Clinton County, Pennsylvania, 2017","title":"Groundwater quality in relation to drinking water health standards and geochemical characteristics for 54 domestic wells in Clinton County, Pennsylvania, 2017","docAbstract":"

Despite the reliance on groundwater by approximately 2.4 million rural Pennsylvania residents, publicly available data to characterize the quality of private well water are limited. As part of a regional effort to characterize groundwater in rural areas of Pennsylvania, samples from 54 domestic wells in Clinton County were collected and analyzed in 2017. The samples were evaluated for a wide range of constituents and compared to drinking-water health standards and geochemical characteristics. The sampled wells were completed to depths ranging from 46 to 500 feet in bedrock that was of predominantly sandstone, shale, or carbonate lithology. Results of this study show that the sampled groundwater quality in Clinton County generally met most drinking-water standards that apply to public water supplies. However, a percentage of samples exceeded drinking-water maximum contaminant levels (MCLs) for total coliform bacteria (57.4 percent), Escherichia coli (E. coli) (25.9 percent), nitrate (1.9 percent), and arsenic (1.9 percent); and secondary maximum contaminant levels (SMCLs) for pH (31.5 percent), manganese (29.6 percent), iron (13 percent), total dissolved solids (7.4 percent), aluminum (1.9 percent), and chloride (1.9 percent). Sodium concentrations exceeded the U.S. Environmental Protection Agency drinking-water advisory recommendation in 16.7 percent of the samples. Radon-222 activities exceeded the proposed drinking-water standard of 300 picocuries per liter (pCi/L) in 59.3 percent of the samples. The only volatile organic compounds (VOCs) detected were acetone and methyl ethyl ketone in two separate samples; neither constituent exceeded drinking-water standards.

Higher median nitrate concentrations were found in the carbonate (3.26 milligrams per liter [mg/L]) versus shale (less than 0.04 mg/L) and sandstone (0.27 mg/L) aquifer subsets. Most of the elevated nitrate concentrations were associated with E. coli detections in the carbonate aquifers, where transmissive bedrock can facilitate groundwater contamination by human activities at the land surface.

The median pH of groundwater from the sandstone aquifers (6.53) was less than those for the shale aquifers (7.31) and carbonate aquifers (7.43). Generally, the lower pH samples had greater potential for elevated concentrations of dissolved metals, including beryllium, copper, lead, nickel, and zinc, whereas the higher pH samples had greater potential for elevated concentrations of total dissolved solids, sodium, fluoride, boron, and uranium. Near-neutral samples (pH 6.5 to 7.5) had greater hardness and alkalinity concentrations than other samples with pH outside this range. Many samples from the shale or sandstone aquifers, particularly those with pH less than 6.5, were identified as having serious potential corrosivity based on the combination of the calcite saturation index and the chloride to sulfate mass ratio; however, none of the samples from the carbonate aquifers was identified as seriously corrosive.

Groundwater from 3.7 percent of the wells had concentrations of methane greater than the Pennsylvania action level of 7 mg/L, and 48 of the 54 wells (88.9 percent) had detectable concentrations of methane greater than the 0.0002 mg/L detection limit. Greater methane concentrations were found more frequently in groundwater sampled from the shale aquifers than the carbonate or sandstone aquifers in the study area. Most of the samples containing elevated methane (greater than 0.2 mg/L) were located outside the area of the Appalachian Plateaus. The elevated concentrations of methane generally were associated with suboxic groundwater (dissolved oxygen less than 0.5 mg/L) that had near-neutral to alkaline pH and were correlated with concentrations of iron, manganese, ammonia, sodium, lithium, barium, fluoride, and boron. The stable carbon and hydrogen isotopic compositions of methane in two of four samples analyzed for isotopes were consistent with compositions reported for mud-gas logging samples from gas-bearing geologic units (thermogenic gas) in the Appalachian Plateaus region, whereas two others were consistent with methane of microbial origin or a mixture of microbial and thermogenic gas.

Forty-two percent of samples had chloride concentrations greater than 20 mg/L with variable bromide concentrations. Corresponding chloride/bromide ratios are consistent with low-bromide sources such as road-deicing salt and septic effluent or animal waste, or, in a few cases, high-bromide brine. Brines characterized by relatively high bromide are naturally present in deeper parts of the regional groundwater system and, in some cases, may be mobilized by gas drilling. The chloride, bromide, and other constituents in road-deicing salt or brine solutions tend to be diluted by mixing with fresh groundwater in shallow aquifers used for water supply. One of the four groundwater samples with methane concentrations greater than 4 mg/L had chloride and bromide concentrations and a chloride/bromide ratio that indicates mixing with a salinity source such as road-deicing salt, whereas the chloride and bromide concentrations and ratios for the other three high-methane samples indicate mixing with a small amount of brine (0.03 percent or less). In two other eastern Pennsylvania county studies where gas drilling is absent, groundwater with comparable chloride/bromide ratios, bromide, and chloride concentrations plus other element associations have been reported. Additional sampling and analysis, such as isotopic analysis of the dissolved gas, fracture analysis, and more detailed evaluation of surrounding land uses, may be warranted to better understand the origin of the methane and brine constituents in groundwater at specific locations.

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Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2020-05-14","revisedDate":"2020-07-27","noUsgsAuthors":false,"publicationDate":"2020-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Clune, John W. 0000-0002-3563-1975","orcid":"https://orcid.org/0000-0002-3563-1975","contributorId":205148,"corporation":false,"usgs":true,"family":"Clune","given":"John W.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":216591,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles","suffix":"III","email":"","middleInitial":"A.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784468,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228654,"text":"70228654 - 2020 - The role of phosphorus and nitrogen on chlorophyll a: Evidence from hundreds of lakes","interactions":[],"lastModifiedDate":"2022-02-16T15:29:53.80312","indexId":"70228654","displayToPublicDate":"2020-07-27T09:26:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The role of phosphorus and nitrogen on chlorophyll a: Evidence from hundreds of lakes","title":"The role of phosphorus and nitrogen on chlorophyll a: Evidence from hundreds of lakes","docAbstract":"

The effect of nutrients on phytoplankton biomass in lakes continues to be a subject of debate by aquatic scientists. However, determining whether or not chlorophyll a (CHL) is limited by phosphorus (P) and/or nitrogen (N) is rarely considered using a probabilistic method in studies of hundreds of lakes across broad spatial extents. Several studies have applied a unified CHL-nutrient relationship to determine nutrient limitation, but pose a risk of ecological fallacy because they neglect spatial heterogeneity in ecological contexts. To examine whether or not CHL is limited by P, N, or both nutrients in hundreds of lakes and across diverse ecological settings, a probabilistic machine learning method, Bayesian Network, was applied. Spatial heterogeneity in ecological context was accommodated by the probabilistic nature of the results. We analyzed data from 1382 lakes in 17 US states to evaluate the cause-effect relationships between CHL and nutrients. Observations of CHL, total phosphorus (TP), and total nitrogen (TN) were discretized into three trophic states (oligo-mesotrophic, eutrophic, and hypereutrophic) to train the model. We found that although both nutrients were related to CHL trophic state, TP was more related to CHL than TN, especially under oligo-mesotrophic and eutrophic CHL conditions. However, when the CHL trophic state was hypereutrophic, both TP and TN were important. These results provide additional evidence that P-limitation is more likely under oligo-mesotrophic or eutrophic CHL conditions and that co-limitation of P and N occurs under hypereutrophic CHL conditions. We also found a decreasing pattern of the TN/TP ratio with increasing CHL concentrations, which might be a key driver for the role change of nutrients. Previous work performed at smaller scales support our findings, indicating potential for extension of our findings to other regions. Our findings enhance the understanding of nutrient limitation at macroscales and revealed that the current debate on the limiting nutrient might be caused by failure to consider CHL trophic state. Our findings also provide prior information for the site-specific eutrophication management of unsampled or data-limited lakes.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2020.116236","usgsCitation":"Liang, Z., Soranno, P., and Wagner, T., 2020, The role of phosphorus and nitrogen on chlorophyll a: Evidence from hundreds of lakes: Water Research, v. 185, 116236, 9 p., https://doi.org/10.1016/j.watres.2020.116236.","productDescription":"116236, 9 p.","ipdsId":"IP-113421","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":455860,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2020.116236","text":"Publisher Index Page"},{"id":396014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liang, Zhongyao","contributorId":279427,"corporation":false,"usgs":false,"family":"Liang","given":"Zhongyao","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":834941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soranno, Patricia A.","contributorId":279428,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":834942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":834940,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211311,"text":"70211311 - 2020 - A guidebook to spatial datasets for conservation planning under climate change in the Pacific Northwest","interactions":[],"lastModifiedDate":"2021-01-12T16:10:06.706657","indexId":"70211311","displayToPublicDate":"2020-07-27T08:58:19","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"A guidebook to spatial datasets for conservation planning under climate change in the Pacific Northwest","docAbstract":"This guidebook provides user-friendly overviews of a variety of spatial datasets relevant to conservation and management of natural resources in the face of climate change in the Pacific Northwest, United States. 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Collectively, this information provides natural-resource managers with “snapshots” of a variety of datasets representing diverse processes and conditions, including climate projections, changes in hydrologic conditions, vegetation and fire-regime shifts, animal habitat changes, species movements, and topographic and soil conditions relevant to climate change. 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Ice cover plays a critical role in physical, biogeochemical, and ecological processes in lakes. Despite its importance, winter limnology remains relatively understudied. Here, we provide a primer on the predominant drivers of freshwater lake ice cover and the current methodologies used to study lake ice, including in situ and remote sensing observations, physical based models, and experiments. We highlight opportunities for future research by integrating these four disciplines to address key knowledge gaps in our understanding of lake ice dynamics in changing winters. Advances in technology, data integration, and interdisciplinary collaboration will allow the field to move toward developing global forecasts of lake ice cover for small to large lakes across broad spatial and temporal scales, quantifying ice quality and ice thickness, moving from binary to continuous ice records, and determining how winter ice conditions and quality impact ecosystem processes in lakes over winter. Ultimately, integrating disciplines will improve our ability to understand the impacts of changing winters on lake ice.

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In order to comply with a current U.S. Environmental Protection Agency watershed-based National Pollutant Discharge Elimination System permit, the City of Albuquerque required a better understanding of the rainfall-runoff processes in its small urban watersheds. That requirement prompted the initiation of the assessment of three existing watershed models that were developed to simulate those processes. Three existing rainfall-runoff modeling software packages—Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) (using two sets of methods), Program for Predicting Polluting Particle Passage Through Pits, Puddles, and Ponds (P8), and Arid-Lands Hydrologic Model (AHYMO)—were compared to determine which provided the best balance of accuracy and usability for simulating storm runoff in small watersheds in the urbanized area of Albuquerque, New Mexico. Additionally, results of this study could help inform model users who have interest in simulating storm runoff in similar urban areas throughout the United States. Each model was used to simulate storm runoff in the Hahn Arroyo watershed, an urbanized watershed with concrete-lined arroyo channels in the northeastern quadrant of Albuquerque that exhibits flashy, monsoonal-driven storm runoff. Model results were compared to observed discharge data, according to literature-recommended performance measures and performance evaluation criteria. The HEC-HMS model using the Soil Conservation Service (SCS) curve number (CN) and SCS unit hydrograph methods ranked the highest when averaging the individual performance measures (Nash-Sutcliffe Efficiency, percent bias, and coefficient of determination) rankings together across the hourly calibration and validation periods, followed by P8, which was tied with the HEC-HMS initial and constant approach. For daily rankings using the same rank-averaging approach, the HEC-HMS CN-based model and P8 were tied for the highest ranking, followed by the HEC-HMS initial and constant approach. Alternatively, rating performance using validation period results as an indication of the expected confidence in forecasted results for future conditions, the P8 model performed best for both hourly and daily time-steps, followed by the HEC-HMS CN-based model and the HEC-HMS initial and constant-based model. However, based on the literature performance evaluation criteria, the HEC-HMS and P8 models overall had marginally satisfactory performance only for operation at the daily time-step. Direct comparison of the HEC-HMS and P8 models to the AHYMO is difficult, given the different performance assessment criteria used to assess these models separately in this study, as recommended by the literature. The AHYMO results generally lacked precision, given the wide range in the performance assessment values across events in percent error in peak discharge, difference in timing of peak discharge, percent error in total runoff volume, and difference in duration of event relative to observed data. For some events, however, the AHYMO results were fairly accurate, and AHYMO was likely a good predictor of the timing of storm runoff and the shape of the hydrograph. This study did not assess the results for all potential applications of the models in the Albuquerque urbanized area. Further study may be required to assess the model performance capabilities in other modeling applications.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205058","collaboration":"Prepared in cooperation with the City of Albuquerque","usgsCitation":"Shephard, Z.M., and Douglas-Mankin, K.R., 2020, Comparison of storm runoff models for a small watershed in an urban metropolitan area, Albuquerque, New Mexico: U.S. Geological Survey Scientific Investigations Report 2020–5058, 30 p., https://doi.org/10.3133/sir20205058.","productDescription":"Report: viii, 30 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-113457","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":376561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5058/coverthb.jpg"},{"id":376562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5058/sir20205058.pdf","text":"Report","size":"1.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5058"},{"id":376563,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P930WKCH","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input and output data used to compare storm runoff models for a small watershed in an urban metropolitan area, Albuquerque, New Mexico"}],"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 -106.71844482421874,\n 35.08395557927643\n ],\n [\n -106.44996643066406,\n 35.08395557927643\n ],\n [\n -106.45545959472653,\n 35.19345038573419\n ],\n [\n -106.66694641113278,\n 35.19232810975203\n ],\n [\n -106.71844482421874,\n 35.08395557927643\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-07-26","noUsgsAuthors":false,"publicationDate":"2020-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Shephard, Zachary M. 0000-0003-2994-3355","orcid":"https://orcid.org/0000-0003-2994-3355","contributorId":218999,"corporation":false,"usgs":true,"family":"Shephard","given":"Zachary M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":203927,"corporation":false,"usgs":true,"family":"Douglas-Mankin","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793452,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215152,"text":"70215152 - 2020 - Key components and contrasts in the nitrogen budget across a US-Canadian transboundary watershed","interactions":[],"lastModifiedDate":"2020-10-08T12:16:54.101245","indexId":"70215152","displayToPublicDate":"2020-07-24T07:12:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Key components and contrasts in the nitrogen budget across a US-Canadian transboundary watershed","docAbstract":"

Watershed nitrogen (N) budgets provide insights into drivers and solutions for groundwater and surface water N contamination. We constructed a comprehensive N budget for the transboundary Nooksack River Watershed (British Columbia, Canada, and Washington, USA) using locally derived data, national statistics, and standard parameters. Feed imports for dairy (mainly in the United States) and poultry (mainly in Canada) accounted for 30% and 29% of the total N input to the watershed, respectively. Synthetic fertilizer was the next largest source contributing 21% of inputs. Food imports for humans and pets together accounted for 9% of total inputs, lower than atmospheric deposition (10%). N imported by returning salmon representing marine‐derived nutrients accounted for <0.06% of total N input. Quantified N export was 80% of total N input, driven by ammonia emission (32% of exports). Animal product export was the second largest output of N (31%) as milk and cattle in the United States and poultry products in Canada. Riverine export of N was estimated at 28% of total N export. The commonly used crop nitrogen use efficiency (NUE) metric alone did not provide sufficient information on farming activities but in combination with other criteria such as farm‐gate NUE may better represent management efficiency. Agriculture was the primary driver of N inputs to the environment as a result of its regional importance; the N budget information can inform management to minimize N losses. The N budget provides key information for stakeholders across sectors and borders to create environmentally and economically viable and effective solutions.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JG005577","usgsCitation":"Lin, J., Compton, J., Clark, C., Bittman, S., Schwede, D., Homann, P., Kiffney, P., Hooper, D., Bahr, G., and Baron, J., 2020, Key components and contrasts in the nitrogen budget across a US-Canadian transboundary watershed: Journal of Geophysical Research: Biogeosciences, v. 125, no. 9, e2019JG005577, 22 p., https://doi.org/10.1029/2019JG005577.","productDescription":"e2019JG005577, 22 p.","ipdsId":"IP-112961","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":455878,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8318187","text":"External Repository"},{"id":379214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","state":"Washington, British Columbia","otherGeospatial":"Nooksack River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.48632812499999,\n 47.249406957888446\n ],\n [\n -118.037109375,\n 47.249406957888446\n ],\n [\n -118.037109375,\n 49.66762782262194\n ],\n [\n -123.48632812499999,\n 49.66762782262194\n ],\n [\n -123.48632812499999,\n 47.249406957888446\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Lin, Jiajia","contributorId":211160,"corporation":false,"usgs":false,"family":"Lin","given":"Jiajia","email":"","affiliations":[{"id":38185,"text":"USEPA, Corvallis, Oregon","active":true,"usgs":false}],"preferred":false,"id":801006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Compton, Jana","contributorId":145529,"corporation":false,"usgs":false,"family":"Compton","given":"Jana","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":801007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Chris","contributorId":242877,"corporation":false,"usgs":false,"family":"Clark","given":"Chris","email":"","affiliations":[{"id":37648,"text":"Whatcom Conservation District","active":true,"usgs":false}],"preferred":false,"id":801008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bittman, Shabtai","contributorId":242878,"corporation":false,"usgs":false,"family":"Bittman","given":"Shabtai","email":"","affiliations":[{"id":48567,"text":"Food and Agri-Food Canada","active":true,"usgs":false}],"preferred":false,"id":801009,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schwede, Donna","contributorId":242879,"corporation":false,"usgs":false,"family":"Schwede","given":"Donna","email":"","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":801010,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Homann, Peter","contributorId":242880,"corporation":false,"usgs":false,"family":"Homann","given":"Peter","email":"","affiliations":[{"id":48568,"text":"Weatern Washington University","active":true,"usgs":false}],"preferred":false,"id":801011,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kiffney, Peter","contributorId":242881,"corporation":false,"usgs":false,"family":"Kiffney","given":"Peter","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":801012,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hooper, David","contributorId":242882,"corporation":false,"usgs":false,"family":"Hooper","given":"David","affiliations":[{"id":48568,"text":"Weatern Washington University","active":true,"usgs":false}],"preferred":false,"id":801013,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bahr, Gary","contributorId":242884,"corporation":false,"usgs":false,"family":"Bahr","given":"Gary","email":"","affiliations":[{"id":48569,"text":"Washington State Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":801014,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Baron, Jill S. 0000-0002-5902-6251","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":215101,"corporation":false,"usgs":true,"family":"Baron","given":"Jill S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":801015,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70211300,"text":"ofr20201076 - 2020 - Pesticide concentrations associated with augmented flow pulses in the Yolo Bypass and Cache Slough Complex, California","interactions":[],"lastModifiedDate":"2020-07-24T13:56:08.549449","indexId":"ofr20201076","displayToPublicDate":"2020-07-23T13:18:24","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":"2020-1076","displayTitle":"Pesticide Concentrations Associated with Augmented Flow Pulses in the Yolo Bypass and Cache Slough Complex, California","title":"Pesticide concentrations associated with augmented flow pulses in the Yolo Bypass and Cache Slough Complex, California","docAbstract":"

Surface-water and suspended-sediment samples were collected and analyzed by the U.S. Geological Survey for multiple current-use pesticides and pesticide degradates approximately every 2 weeks at up to five sites in the Yolo Bypass and Cache Slough Complex before, during, and after augmented flow pulses in summer and fall 2016 and 2018 as well as during ambient flow conditions in summer and fall 2017 (no flow pulse). In 2016, augmented flows occurred during the summer (July) and required the pumping of Sacramento River water by local Reclamation Districts into the Colusa Basin Drain and Yolo Bypass Toe Drain. In contrast, augmented flows in 2018 occurred in the fall (August–September) and used agricultural tailwater (primarily rice field discharge water) to create the flow pulse. Water samples were analyzed by the U.S. Geological Survey for a suite of 175 current-use pesticides and pesticide degradates using gas chromatography with mass spectrometry and liquid chromatography with tandem mass spectrometry laboratory methods. Suspended sediments filtered from the water samples were analyzed for 143 pesticides and degradates by gas chromatography with mass spectrometry.

During the study, 53 pesticides were detected, and all the samples contained mixtures of multiple pesticides at concentrations ranging from below method detection limits to 8,780 nanograms per liter. Pesticides used in growing rice were the dominant pesticides present at four of the five sites sampled and urban-use pesticides dominated at the remaining site. Overall, total pesticide concentrations tended to be higher at sites in the northern part of the Yolo Bypass and lower at southern sites, except for the farthest downstream site which received additional pesticide inputs from the Sacramento River. Flow-pulse water source influenced total pesticide concentrations in the Yolo Bypass and Cache Slough Complex, and the highest total pesticide concentrations at each site were detected either immediately before or during the flow pulse generated with agricultural tailwater in 2018. Data gathered during this study will aid the California Department of Water Resources and other agencies working in the region in adaptively managing pulse flows in the Yolo Bypass and Cache Slough Complex, as one of several California Natural Resources Agency’s Delta Smelt Resiliency strategies.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201076","collaboration":"Prepared in cooperation with the California Department of Water Resources and the State and Federal Contractors Water Agency","usgsCitation":"Orlando, J.L., De Parsia, M., Sanders, C., Hladik, M., and Frantzich, J., 2020, Pesticide concentrations associated with augmented flow pulses in the Yolo Bypass and Cache Slough Complex, California: U.S. Geological Survey Open-File Report 2020–1076, 101 p., https://doi.org/10.3133/ofr20201076.","productDescription":"Report: vi, 101 p.; Data release","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-109449","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376629,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"U.S. Geological Survey, 2019, National Water Information System: U.S. Geological Survey Web interface"},{"id":376628,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1076/ofr20201076.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376627,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1076/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yolo Bypass and Cache Slough Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.85760498046875,\n 38.45789034424927\n ],\n [\n -121.35772705078125,\n 38.45789034424927\n ],\n [\n -121.35772705078125,\n 39.06184913429154\n ],\n [\n -121.85760498046875,\n 39.06184913429154\n ],\n [\n -121.85760498046875,\n 38.45789034424927\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-07-23","noUsgsAuthors":false,"publicationDate":"2020-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Orlando, James L. 0000-0002-0099-7221 jorlando@usgs.gov","orcid":"https://orcid.org/0000-0002-0099-7221","contributorId":190788,"corporation":false,"usgs":true,"family":"Orlando","given":"James","email":"jorlando@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Parsia, Matt 0000-0001-5806-5403 mdeparsia@usgs.gov","orcid":"https://orcid.org/0000-0001-5806-5403","contributorId":173765,"corporation":false,"usgs":true,"family":"De Parsia","given":"Matt","email":"mdeparsia@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanders, Corey J. 0000-0001-7743-6396 csanders@usgs.gov","orcid":"https://orcid.org/0000-0001-7743-6396","contributorId":4330,"corporation":false,"usgs":true,"family":"Sanders","given":"Corey","email":"csanders@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":793633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hladik, Michelle L. 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":201293,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle L.","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frantzich, Jared","contributorId":229608,"corporation":false,"usgs":true,"family":"Frantzich","given":"Jared","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793635,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211324,"text":"70211324 - 2020 - Application of empirical land-cover changes to construct climate change scenarios in federally managed lands","interactions":[],"lastModifiedDate":"2020-07-24T15:29:27.866317","indexId":"70211324","displayToPublicDate":"2020-07-23T10:16:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Application of empirical land-cover changes to construct climate change scenarios in federally managed lands","docAbstract":"Sagebrush-dominant ecosystems in the western United States are highly vulnerable to climatic variability. To understand how these ecosystems will respond under potential future conditions, we correlated changes in National Land Cover Dataset “Back-in-Time” fractional cover maps from 1985-2018 with Daymet climate data in three federally managed preserves in the sagebrush steppe ecosystem: Beaty Butte Herd Management Area, Hart Mountain National Antelope Refuge, and Sheldon National Wildlife Refuge. Future (2018 to 2050) abundance and distribution of vegetation cover were modeled at a 300-m resolution under a business-as-usual climate (BAU) scenario and a Representative Concentration Pathway (RCP) 8.5 climate change scenario. Spatially explicit map projections suggest that climate influences may make the landscape more homogeneous in the near future. Specifically, projections indicate that pixels with high bare ground cover become less bare ground dominant, pixels with moderate herbaceous cover contain less herbaceous cover, and pixels with low shrub cover contain more shrub cover. General vegetation patterns and composition do not differ dramatically between scenarios despite RCP 8.5 projections of + 1.2 °C mean annual minimum temperatures and +7.6 mm total annual precipitation. Hart Mountain National Antelope Refuge is forecast to undergo the most change, with both models projecting larger declines in bare ground and larger increases in average herbaceous and shrub cover compared to Beaty Butte Herd Management Area and Sheldon National Wildlife Refuge. These scenarios present plausible future outcomes intended to guide federal land managers to identify vegetation cover changes that may affect habitat condition and availability for species of interest.","language":"English","publisher":"MDPI","doi":"10.3390/rs12152360","usgsCitation":"Soulard, C.E., and Rigge, M.B., 2020, Application of empirical land-cover changes to construct climate change scenarios in federally managed lands: Remote Sensing, v. 12, no. 15, 22 p., https://doi.org/10.3390/rs12152360.","productDescription":"22 p.","ipdsId":"IP-118423","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455891,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12152360","text":"Publisher Index Page"},{"id":436862,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LJ1FI4","text":"USGS data release","linkHelpText":"Spatially-explicit land-cover scenarios of federal lands in the northern Great Basin, 2018-2050"},{"id":376686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":793783,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":793784,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}