The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution elevation data. However, deriving hydrography through flow-routing methods is a complex process that needs to be tailored to different geographic conditions, which can lead to varying solutions. To address this problem, this paper evaluates automated deep learning and its transferability to extract hydrography from interferometric synthetic aperture radar (IfSAR) elevation data spanning a range of geographic conditions in Alaska.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceDate":"August 22-28, 2022","conferenceLocation":"Florence, Italy","language":"English","publisher":"International Society for Photogrammetry and Remote Sensing (ISPRS)","doi":"10.5194/isprs-archives-XLVIII-4-W1-2022-449-2022","usgsCitation":"Stanislawski, L., Shavers, E.J., Duffy, A., Thiem, P.T., Jaroenchai, N., Wang, S., Jiang, Z., Kronenfeld, B.J., and Buttenfield, B.P., 2022, Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska, in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Florence, Italy, August 22-28, 2022, p. 449-456, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-449-2022.","productDescription":"8 p.","startPage":"449","endPage":"456","ipdsId":"IP-143118","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":446603,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-449-2022","text":"Publisher Index Page"},{"id":409352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.48017274535448,\n 67.79791394109961\n ],\n [\n -157.48017274535448,\n 66.00582490875226\n ],\n [\n -152.2946258703545,\n 66.00582490875226\n ],\n [\n -152.2946258703545,\n 67.79791394109961\n ],\n [\n -157.48017274535448,\n 67.79791394109961\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":857001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":857002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duffy, Alexander 0000-0001-6036-0583","orcid":"https://orcid.org/0000-0001-6036-0583","contributorId":299070,"corporation":false,"usgs":false,"family":"Duffy","given":"Alexander","email":"","affiliations":[{"id":64752,"text":"University of Missouri Science & Technology","active":true,"usgs":false}],"preferred":false,"id":857003,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thiem, Philip T. 0000-0002-3324-2589","orcid":"https://orcid.org/0000-0002-3324-2589","contributorId":287990,"corporation":false,"usgs":true,"family":"Thiem","given":"Philip","email":"","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":857004,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaroenchai, Nattapon","contributorId":267318,"corporation":false,"usgs":false,"family":"Jaroenchai","given":"Nattapon","email":"","affiliations":[{"id":38021,"text":"University of Illinois Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":857005,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Shaowen","contributorId":198966,"corporation":false,"usgs":false,"family":"Wang","given":"Shaowen","email":"","affiliations":[],"preferred":false,"id":857006,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jiang, Zhe","contributorId":267317,"corporation":false,"usgs":false,"family":"Jiang","given":"Zhe","email":"","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":857007,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kronenfeld, Barry J. 0000-0002-9518-2462","orcid":"https://orcid.org/0000-0002-9518-2462","contributorId":207104,"corporation":false,"usgs":false,"family":"Kronenfeld","given":"Barry","email":"","middleInitial":"J.","affiliations":[{"id":5043,"text":"Eastern Illinois University","active":true,"usgs":false}],"preferred":false,"id":857008,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Buttenfield, Barbara P. 0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":857009,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236950,"text":"70236950 - 2022 - Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis","interactions":[],"lastModifiedDate":"2024-05-16T15:46:58.587708","indexId":"70236950","displayToPublicDate":"2022-08-31T06:58:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12603,"text":"Journal of the American Society of Agricultural and Biological Engineers","active":true,"publicationSubtype":{"id":10}},"title":"Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis","docAbstract":"The Upper Rio Grande Basin (URGB) is a critical international water resource under pressure from a myriad of climatic, ecological, infrastructural, water-use, and legal constraints. The objective of this study is to provide a comprehensive assessment of the spatial distribution and temporal trends of selected water-budget components (snow processes, evapotranspiration (ET), streamflow processes, and groundwater storage) using integrated analyses, such as watershed modeling and water availability and use data in the URGB over the past three decades. A spatially distributed snow evolution modeling system simulated snowpack processes over 34 years (1984–2017). It highlighted snow water equivalent declines from -35 to -77 mm/decade with widespread variability across elevation zones and land cover types. Gridded actual ET data from the SSEBop model were developed and tested for the URGB and demonstrated that all land-cover types had significant decreasing trends (1986-2015) ranging from -14 to -80 mm/decade. Conductivity-mass-balance (CMB) hydrograph separation results found that baseflow forms a large component of total streamflow, ranging from 29 to 69% (49% average) of total streamflow at 17 URGB sites upstream of Albuquerque, NM. Three of 4 graphical hydrograph separation methods in the U.S. Geological Survey Groundwater Toolbox were found to be inappropriate for estimating baseflow in the URGB; the most promising method, baseflow index (BFI) Standard, was optimized using CMB data and tested at three URGB sites, with resulting overestimation of 0 to 47%. Simulated changes in groundwater storage were extracted from historical and recent groundwater-flow models of select alluvial basins (San Luis, Española, Middle Rio Grande, and Tularosa-Hueco). In general, decreases in groundwater storage were observed from 1903 to 2013 except for the San Luis alluvial basin (Colorado), where periods of recovery are observed. The PRMS hydrologic model was successfully calibrated for 9 near-native subbasins (Nash-Sutcliffe efficiency 0.47 to 0.85) and parameters translated to the remaining subbasins; compared to simulated near-native flows (with minimal influence of reservoirs or diversions), observed Rio Grande streamgage flows demonstrated reductions of 40% or more for New Mexico and Texas areas of the basin. Significant decreasing trends (1980-2015) in precipitation, snowmelt rate, streamflow, and baseflow were observed at many of the 12 streamgage basins studied, which suggests that the decreasing trends for actual ET may be related to overall decreasing water availability in the basin, with negative implications for agricultural production and groundwater abstraction. Water security concerns arise from our findings of higher fraction precipitation as rain, slower snowmelt rates leading to decreasing streamflow production, and an increasing fraction of baseflow, all of which will affect the timing and magnitude of water available for human needs in the basin.
","language":"English","publisher":"American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org","doi":"10.13031/ja.14964","usgsCitation":"Douglas-Mankin, K., Rumsey, C., Sexstone, G., Ivahnenko, T.I., Houston, N., Chavarria, S., Senay, G.B., Foster, L.K., Thomas, J., Flickinger, A.K., Galanter, A.E., Moeser, C.D., Welborn, T.L., Pedraza, D.E., Lambert, P., and Johnson, M.S., 2022, Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis: Journal of the American Society of Agricultural and Biological Engineers, v. 65, no. 4, p. 881-901, https://doi.org/10.13031/ja.14964.","productDescription":"21 p.","startPage":"881","endPage":"901","ipdsId":"IP-133533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":446605,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.13031/ja.14964","text":"Publisher Index Page"},{"id":407213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.7314453125,\n 30.410781790845864\n ],\n [\n -102.21679687500001,\n 30.410781790845864\n ],\n [\n -102.21679687500001,\n 38.30718056188316\n ],\n [\n -109.7314453125,\n 38.30718056188316\n ],\n [\n -109.7314453125,\n 30.410781790845864\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":223378,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":852780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rumsey, Christine 0000-0001-7536-750X crumsey@usgs.gov","orcid":"https://orcid.org/0000-0001-7536-750X","contributorId":146240,"corporation":false,"usgs":true,"family":"Rumsey","given":"Christine","email":"crumsey@usgs.gov","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ivahnenko, Tamara I. 0000-0002-1124-7688 ivahnenk@usgs.gov","orcid":"https://orcid.org/0000-0002-1124-7688","contributorId":2050,"corporation":false,"usgs":true,"family":"Ivahnenko","given":"Tamara","email":"ivahnenk@usgs.gov","middleInitial":"I.","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":false,"id":852783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Houston, Natalie 0000-0002-6071-4545","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":206533,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852784,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chavarria, Shaleene 0000-0001-8792-1010","orcid":"https://orcid.org/0000-0001-8792-1010","contributorId":222578,"corporation":false,"usgs":true,"family":"Chavarria","given":"Shaleene","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852785,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":852786,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Foster, Linzy K. 0000-0002-7373-7017","orcid":"https://orcid.org/0000-0002-7373-7017","contributorId":259186,"corporation":false,"usgs":true,"family":"Foster","given":"Linzy","email":"","middleInitial":"K.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852787,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thomas, Jonathan V. 0000-0003-0903-9713","orcid":"https://orcid.org/0000-0003-0903-9713","contributorId":217874,"corporation":false,"usgs":true,"family":"Thomas","given":"Jonathan V.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852788,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Flickinger, Allison K. 0000-0002-8638-2569","orcid":"https://orcid.org/0000-0002-8638-2569","contributorId":223702,"corporation":false,"usgs":true,"family":"Flickinger","given":"Allison","email":"","middleInitial":"K.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852789,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Galanter, Amy E. 0000-0002-2960-0136","orcid":"https://orcid.org/0000-0002-2960-0136","contributorId":205393,"corporation":false,"usgs":true,"family":"Galanter","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852790,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852791,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Welborn, Toby L. 0000-0003-4839-2405 tlwelbor@usgs.gov","orcid":"https://orcid.org/0000-0003-4839-2405","contributorId":2295,"corporation":false,"usgs":true,"family":"Welborn","given":"Toby","email":"tlwelbor@usgs.gov","middleInitial":"L.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852793,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pedraza, Diana E. 0000-0003-4483-8094","orcid":"https://orcid.org/0000-0003-4483-8094","contributorId":207782,"corporation":false,"usgs":true,"family":"Pedraza","given":"Diana","email":"","middleInitial":"E.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852796,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Lambert, Patrick M. 0000-0001-6808-2303","orcid":"https://orcid.org/0000-0001-6808-2303","contributorId":296913,"corporation":false,"usgs":false,"family":"Lambert","given":"Patrick M.","affiliations":[{"id":32931,"text":"USGS - Retired","active":true,"usgs":false}],"preferred":false,"id":852794,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Johnson, Michael Scott 0000-0003-2378-7144 johnsonm@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-7144","contributorId":296914,"corporation":false,"usgs":true,"family":"Johnson","given":"Michael","email":"johnsonm@usgs.gov","middleInitial":"Scott","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852795,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70236128,"text":"ofr20221072 - 2022 - ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022","interactions":[],"lastModifiedDate":"2022-09-27T12:21:24.033152","indexId":"ofr20221072","displayToPublicDate":"2022-08-31T06:56:32","publicationYear":"2022","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":"2022-1072","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 1, 2022","title":"ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022","docAbstract":"The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 1 (January–March), 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221072","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022: U.S. Geological Survey Open-File Report 2022–1072, 39 p., https://doi.org/10.3133/ofr20221072.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-140787","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":405985,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221072/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405885,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":405884,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1072/images"},{"id":405883,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1072/ofr20221072.XML"},{"id":405880,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1072/coverthb.jpg"},{"id":405882,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1072/ofr20221072.pdf","text":"Report","size":"4.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1072"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Deepwater Horizon oil spill caused extensive injury to natural resources in the Gulf of Mexico, and Gavia immer (common loon) were negatively affected from the spill. The Open Ocean Trustee Implementation Group funded the project Restoration of Common Loons in Minnesota to restore common loons lost to the spill. In 2020–21, priority lakes in an eight-county region in north-central Minnesota were identified to focus project activities. In 2021, surveys on these lakes were started to monitor common loon territory occupancy, nest success, and chick survival. We surveyed 62 lakes and identified 110 common loon territories that will be included in the project. At least 1 nest attempt was observed in 78 of 110 territories, and a second nest attempt was observed in 23 territories. A third nest attempt was observed in one territory. Successful nesting was observed in 32 of 110 territories. We present no formal data analysis and plan to analyze the data after the collection of all field data in subsequent years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221074","usgsCitation":"Beatty, W.S., Fara, L.J., Houdek, S.C., Kenow, K.P., and Gray, B.R., 2022, Restoration of Gavia immer (common loon) in Minnesota—2021 annual report: U.S. Geological Survey Open-File Report 2022–1074, 7 p., https://doi.org/10.3133/ofr20221074.","productDescription":"vi, 7 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-139232","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":435709,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LA536E","text":"USGS data release","linkHelpText":"Summary of Detection Data for Breeding Common Loons in North-central Minnesota (2021-2022) (ver. 1.1, August 2024)"},{"id":405938,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221074/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405361,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1074/ofr20221074.XML"},{"id":405360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1074/ofr20221074.pdf","text":"Report","size":"2.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1074"},{"id":405359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1074/coverthb.jpg"},{"id":405362,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1074/images"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.78955078125,\n 46.27103747280261\n ],\n [\n -93.240966796875,\n 46.27103747280261\n ],\n [\n -93.240966796875,\n 48.821332549646634\n ],\n [\n -96.78955078125,\n 48.821332549646634\n ],\n [\n -96.78955078125,\n 46.27103747280261\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
Surveys and monitoring for the coastal Cactus Wren (Campylorhynchus brunneicapillus) were completed in San Diego County between March 2015 and July 2019. A total of 383 plots were surveyed across 3 genetic clusters (Otay, Lake Jennings, and Sweetwater/Encanto). From 2015 to 2019, 317 plots were surveyed 8 times (twice per year in 2015, 2017–19). Additional plots were added in later years as wrens were discovered in new locations. We found differences in the proportion of plots occupied in the genetic clusters, with a lower proportion of plots occupied in the Otay cluster than in the Lake Jennings and Sweetwater/Encanto clusters in all years. Plot occupancy increased each year in the Otay and Sweetwater/Encanto clusters but not in the Lake Jennings cluster. The number of Cactus Wren territories increased from 2015 through 2018, and then decreased in 2019 in all three genetic clusters.
We monitored nesting activities for two populations of Cactus Wrens in southern San Diego County. The Otay population consisted of two sites within the Otay genetic cluster, and the San Diego population consisted of two sites within the Sweetwater/Encanto and Lake Jennings genetic clusters. Nest monitoring occurred at 10–13 territories per year in the Otay population and 14–18 territories in the San Diego population from 2015 through 2019. All territories were occupied by pairs except two territories in 2015, five in 2016, and two in 2019. Between 46 and 74 Cactus Wren nests were monitored each year, which totaled 295 monitored nests from 2015 to 2019. To evaluate the direct influence of precipitation on breeding success, bio-year precipitation (“precipitation”) was calculated from July 1 of the prior year through June 30 of the breeding season year. Overall apparent nest success was positively influenced by precipitation with the lowest apparent nest success of 50 percent in 2015 and the highest apparent nest success of 72 percent in 2017, corresponding to the second lowest and the highest precipitation years, respectively. Apparent nest success also was higher in the Otay population than in the San Diego population. The number of brood nests initiated per pair and the number of renesting attempts per pair also were higher in years with more precipitation. Other metrics of Cactus Wren nesting success and productivity were positively influenced by the amount of precipitation, including clutch size and egg hatching success. The percent of hatchlings that fledged was greater in the Otay population than in the San Diego population but was not influenced by precipitation. The number of fledglings per pair was higher in years with more precipitation and was greater in the Otay population than in the San Diego population. Predation was the predominant cause of nest failure in both populations.
Analysis of Cactus Wren daily nest survival rate indicated that there was a population, and possibly a precipitation effect on nest survival, with the daily survival rate for the Otay population significantly higher than for the San Diego population and weak increase in the daily survival rate with more precipitation.
A total of 629 Cactus Wrens were banded during the course of the study, 360 in the San Diego population and 269 in the Otay population. Between 2015 and 2019, we resighted 301 color-banded adult birds that ranged between 1 and 8 years old. One additional color-banded bird was resighted in San Pasqual Valley (as part of a separate study); this bird originated in the San Diego population and was excluded from our analyses.
Annual survival was higher for adult Cactus Wrens (ranging from 60 to 70 percent) than for first-year wrens (ranging from 20 to 28 percent) and varied by year. Annual survival was also weakly but positively correlated with precipitation. Annual survival was higher for first year and adult Cactus Wrens following years with increased precipitation. We found no evidence that survival differed by population.
Banding also allowed us to examine whether there were differences in movement of adult and first-year Cactus Wrens by year or by population. We found that average dispersal distance for first-year Cactus Wrens was 1.9 kilometers in the Otay population and 1.6 kilometers in the San Diego population and did not differ by population or year. Dispersal between populations was not common. We detected five instances of movement of first-year wrens between the San Diego and Otay populations. All movements into and out of the San Diego population were from or into territories in the Sweetwater area. We detected no movement between the Lake Jennings site and either of the Sweetwater or Otay sites; however, we did detect one wren that dispersed from Lake Jennings to the San Pasqual Valley population in 2019, which was a distance of 26.4 kilometers. Adult Cactus Wrens were site-faithful, with 87 percent of adults remaining on the same territory between breeding seasons. Precipitation may be a weak driver of movement for adult Cactus Wrens, with adults more likely to remain on the same territory following years of increased precipitation. There was no difference in adult movement between populations.
Arthropods were collected in pitfall traps and by vacuum in 23 Cactus Wren territories during 3 sampling periods in 2016 (early nesting, peak nesting, and late nesting). Arthropods of 19 orders and at least 128 families were collected. Analysis of 43 Cactus Wren fecal samples identified 10 arthropod orders that were present in more than 10 percent of fecal samples. The most abundant arthropod order collected was Hymenoptera; however, Cactus Wrens consumed arthropods in the order Hymenoptera significantly less than their availability, suggesting that this order was avoided. No other orders were significantly selected or avoided; however, selection indices of arthropod families identified that two families of arthropods (Isopoda Porcellionidae [woodlice] and Hymenoptera Formicidae [ants]) were avoided. After excluding the taxa that were avoided or not represented in fecal samples, 95 percent of Cactus Wren prey items were collected in pitfall traps and 5 percent were collected by vacuum. The most abundant prey orders captured were Diptera, Coleoptera, Hemiptera, Hymenoptera, and Aranea.
Analysis of the abundance of Cactus Wren prey items by vegetation type and sampling period indicated that vegetation type by itself was not a significant predictor of arthropod abundance but interacted with sampling period. Seasonal availability of arthropods was highest in the peak nesting period, followed by early and late nesting periods for California sagebrush (Artemisia californica), lemonadeberry (Rhus integrifolia), non-native grass, and bare ground, whereas availability increased from early to late nesting periods for blue elderberry (Sambucus mexicana spp. caerulea), cactus (Opuntia spp. and Cylindropuntia spp.), California buckwheat (Eriogonum fasciculatum), native bunch grasses, and black mustard (Brassica nigra). During the early nesting period, arthropods were most abundant in native bunch grasses and least abundant in lemonadeberry. During the peak nesting period, arthropods were most abundant in native bunch grasses and in areas of bare ground and were least abundant in cactus and blue elderberry. During late nesting, arthropods were most abundant in blue elderberry and non-native grass and least abundant in lemonadeberry and mustard.
Each year from 2015 to 2019, vegetation data were collected at the same 23 territories where arthropods were sampled: 9 territories in the Otay population and 14 territories in the San Diego population. Cactus, California buckwheat, and non-native grasses were detected within at least 60 percent of sampling points in the Otay population. Cactus, California sagebrush, California buckwheat, non-native grass, and black mustard each were detected within an average of 40 percent of sampling points in the San Diego population. No native bunch grass or lemonadeberry were recorded at the Lake Jennings site within the San Diego population. The cover of shrub species was relatively stable throughout the 5 years. Cover of herbaceous species and bare ground had greater annual variation than shrub species.
We found that vegetation cover varied widely among territories, with territory accounting for 69 percent of the variation in vegetation cover. Redundancy analysis allowed us to identify the vegetation types that accounted for the most variation. We used the top scores from the redundancy analysis to identify six vegetation types to be used in generalized linear mixed models analyzing the relationships between vegetation type, precipitation, and Cactus Wren breeding productivity. Three vegetation variables influenced the number of fledglings produced per pair. California sagebrush had a positive effect on the number of fledglings per pair whereas non-native grass and black mustard had a negative effect.
Breeding productivity, survival, and movements of adult and first-year Cactus Wrens indicated that the Otay population behaved similarly to, if not out-performed, the San Diego population during the span of our project, suggesting that the driving forces behind low numbers of Cactus Wrens in the Otay population before 2015 were no longer in effect. The Cactus Wren populations in Otay and San Diego reached a peak in 2018, which followed a year of high productivity and survivorship, both of which were correlated with high precipitation. This peak in population size was consistent with reproductive timing and productivity in other bird populations in semi-arid ecosystems that were linked to precipitation and arthropod abundance. We did not find a strong link among arthropod abundance, vegetation composition, and Cactus Wren breeding productivity, likely in part because arthropod abundance varied by vegetation type and sampling period, suggesting that different vegetation types provided important sources of prey at different periods of the breeding season. Arthropod abundance also may not represent arthropod availability when vegetation structure discourages the ground foraging behavior of species such as Cactus Wrens. Cover of non-native grass negatively influenced breeding productivity, although arthropods were abundant in non-native grass. Other factors that could have influenced differential breeding productivity between the Otay and San Diego populations were habitat restoration, control of annual herbaceous vegetation, human disturbance, lingering effects of wildfire, and nest predation. Overall, precipitation appeared to be a driver of Cactus Wren breeding productivity and possibly survival, potentially obscuring proximate effects of arthropod or vegetation composition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221044","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Lynn, S., Houston, A., and Kus, B.E., 2022, Distribution and demography of Coastal Cactus Wrens in Southern California, 2015–19: U.S. Geological Survey Open-File Report 2022-1044, 44 p., https://doi.org/10.3133/ofr20221044.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-136839","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":435711,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P143ZTB2","text":"USGS data release","linkHelpText":"Cactus Wren Invertebrate Diet Derived from Sequencing of Nestling Fecal Samples in San Diego County, California"},{"id":405951,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221044/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Open-File Report 2022-1044"},{"id":405841,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1044/images"},{"id":405840,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.xml"},{"id":405839,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.pdf","text":"Report","size":"4 MB"},{"id":405838,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1044/covrthb.jpg"},{"id":405842,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76H4FK5","text":"Surveys and Monitoring of Coastal Cactus Wren in Southern San Diego County","description":"Kus, B.E., and Lynn, S., 2022, Surveys and monitoring of Coastal Cactus Wren in southern San Diego County: U.S. Geological Survey data release, https://doi.org/10.5066/F76H4FK5."}],"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 -117.3065185546875,\n 32.52365781569917\n ],\n [\n -116.630859375,\n 32.52365781569917\n ],\n [\n -116.630859375,\n 32.983324091837474\n ],\n [\n -117.3065185546875,\n 32.983324091837474\n ],\n [\n -117.3065185546875,\n 32.52365781569917\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Extreme heat is one of the most important pathways illustrating the connection between climate and human health, and climate change is expected to exacerbate this public health issue. This study first used a case-crossover analysis to characterize the historical (1980–2018) association between summertime heat and non-traumatic mortality in Washington State. A separate analysis was conducted for each of the state’s ten climate divisions to produce distinct exposure–response curves expressing odds of mortality as a function of humidex. Stratified analyses were used to assess the impact of age, sex, race/ethnicity, and select causes of death, and the reported results are pooled across all divisions using meta-analysis. The historical heat–mortality relationship was combined with climate projections to estimate the impact of climate change on heat-related deaths in 2030, 2050, and 2080 under two warming scenarios. The odds ratio (OR) and 95% confidence intervals of mortality at the 99th percentile of humidex compared to the 50th percentile did not include the null value in four climate divisions (E Olympic Cascade Foothills, NE Olympic San Juan, Northeastern, and Puget Sound Lowlands). The statewide odds of mortality are 8% higher (6%, 10%) on 99th percentile days compared to 50th percentile days, driven primarily by an OR of 1.09 (1.06, 1.11) in the Puget Sound Lowlands. Risk is higher for women than men and for Blacks than Whites. Risk increases with age and for diabetic, circulatory, cardiovascular, ischemic, cerebrovascular, and respiratory deaths. The 95% confidence intervals of projected heat-attributable mortality did not overlap with zero in three climate divisions (E Olympic Cascade Foothills, NE Olympic San Juan, and Puget Sound Lowlands). In these three divisions, the average percent increase in heat-attributable mortality across both warming scenarios is 35%, 35%, and 603% in 2030, 2050, and 2080, respectively. This research is the most extensive study of heat-related mortality in Washington to date and can help inform public health initiatives aiming to improve present and future health outcomes in the state.
","language":"English","publisher":"MDPI","doi":"10.3390/atmos13091392","usgsCitation":"Arnold, L., Scheuerell, M.D., and Isaksen, T., 2022, Mortality associated with extreme heat in Washington State: The historical and projected public health burden: Atmosphere, v. 13, no. 9, 1392, 21 p., https://doi.org/10.3390/atmos13091392.","productDescription":"1392, 21 p.","ipdsId":"IP-144123","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":446610,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/atmos13091392","text":"Publisher Index Page"},{"id":430218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-122.519535,48.288314],[-122.551793,48.281512],[-122.584086,48.297987],[-122.618466,48.294159],[-122.626757,48.288991],[-122.620748,48.282961],[-122.623779,48.269431],[-122.652639,48.265081],[-122.66921,48.240614],[-122.668385,48.223967],[-122.628352,48.222467],[-122.606406,48.208262],[-122.588138,48.18594],[-122.558205,48.119579],[-122.571853,48.102143],[-122.54512,48.05255],[-122.513994,48.059077],[-122.511081,48.075301],[-122.525422,48.096537],[-122.513276,48.097538],[-122.491104,48.094242],[-122.431266,48.045001],[-122.376259,48.034457],[-122.373263,48.000791],[-122.353611,47.981433],[-122.349597,47.958796],[-122.358812,47.93742],[-122.376837,47.923703],[-122.380497,47.904023],[-122.397349,47.912401],[-122.431035,47.914732],[-122.445519,47.930226],[-122.44076,47.951845],[-122.446682,47.963155],[-122.47266,47.988449],[-122.501257,47.987089],[-122.521219,47.972997],[-122.546824,47.967215],[-122.552053,47.973644],[-122.542924,47.996404],[-122.58178,48.010386],[-122.607342,48.030992],[-122.593621,48.0472],[-122.594922,48.056318],[-122.614028,48.072788],[-122.598301,48.110616],[-122.609568,48.15186],[-122.633167,48.163281],[-122.677337,48.154587],[-122.693084,48.181509],[-122.763042,48.215342],[-122.770045,48.224395],[-122.752563,48.260061],[-122.732022,48.279425],[-122.72259,48.304268],[-122.673731,48.354683],[-122.664928,48.374823],[-122.664659,48.401508],[-122.634991,48.404244],[-122.63582,48.395128],[-122.609715,48.411565],[-122.60198,48.409907],[-122.595351,48.3972],[-122.585038,48.395166],[-122.585162,48.353304],[-122.551334,48.342138],[-122.506568,48.310041],[-122.505828,48.297677],[-122.519535,48.288314]]],[[[-122.474684,47.511068],[-122.452399,47.503471],[-122.460027,47.48686],[-122.433385,47.46643],[-122.439415,47.458633],[-122.437656,47.407424],[-122.395054,47.399277],[-122.373628,47.388718],[-122.437809,47.365606],[-122.453997,47.343337],[-122.469702,47.344623],[-122.493122,47.330253],[-122.51885,47.33332],[-122.528128,47.345542],[-122.517797,47.368678],[-122.526733,47.398581],[-122.514703,47.414048],[-122.513328,47.449106],[-122.497862,47.475915],[-122.482739,47.483133],[-122.474684,47.511068]]],[[[-122.695907,48.737273],[-122.663259,48.697077],[-122.644901,48.691389],[-122.618225,48.670721],[-122.609576,48.645018],[-122.635299,48.651846],[-122.673538,48.680809],[-122.691795,48.711498],[-122.718833,48.716818],[-122.722262,48.731624],[-122.715709,48.748672],[-122.695907,48.737273]]],[[[-123.035393,49.002154],[-123.021459,48.977299],[-123.028091,48.973943],[-123.083834,48.976139],[-123.090546,49.001976],[-123.035393,49.002154]]],[[[-122.800217,48.60169],[-122.804869,48.595932],[-122.801096,48.585425],[-122.770349,48.558106],[-122.788503,48.530393],[-122.787347,48.523012],[-122.777467,48.517799],[-122.779124,48.508911],[-122.817912,48.483888],[-122.81973,48.458843],[-122.803521,48.428748],[-122.812208,48.422326],[-122.874135,48.418196],[-122.893646,48.422655],[-122.889016,48.435947],[-122.913888,48.443231],[-122.928004,48.439966],[-122.91646,48.453263],[-122.926901,48.460874],[-122.962009,48.451161],[-123.039156,48.460003],[-123.067675,48.479497],[-123.119451,48.492576],[-123.141478,48.505291],[-123.163234,48.529544],[-123.16147,48.547618],[-123.176266,48.562131],[-123.173061,48.579086],[-123.184941,48.58697],[-123.197754,48.586216],[-123.203026,48.596178],[-123.178425,48.622115],[-123.107362,48.622451],[-123.098462,48.612834],[-123.101552,48.59782],[-123.074611,48.591816],[-123.048403,48.569216],[-123.015046,48.560821],[-122.987296,48.561895],[-122.98611,48.569984],[-122.995026,48.578162],[-123.016647,48.580244],[-123.034101,48.591767],[-123.023433,48.599477],[-123.04653,48.61149],[-123.048652,48.621002],[-123.023495,48.634001],[-123.009924,48.655064],[-122.949116,48.693398],[-122.942367,48.706723],[-122.894599,48.71503],[-122.833124,48.698173],[-122.800267,48.67962],[-122.743049,48.661991],[-122.755031,48.649512],[-122.792147,48.633502],[-122.809622,48.619035],[-122.800217,48.60169]]],[[[-123.197953,48.68466],[-123.186076,48.684917],[-123.14799,48.668001],[-123.106165,48.633473],[-123.134956,48.63724],[-123.215917,48.669352],[-123.237148,48.683466],[-123.236567,48.68895],[-123.212892,48.689713],[-123.197953,48.68466]]],[[[-123.025486,48.717966],[-123.007511,48.718863],[-123.005086,48.694342],[-123.021215,48.681416],[-123.042337,48.675663],[-123.03636,48.69008],[-123.070427,48.699971],[-123.040179,48.717296],[-123.025486,48.717966]]],[[[-122.649405,48.588457],[-122.610841,48.561146],[-122.578856,48.54813],[-122.572967,48.529028],[-122.599948,48.536904],[-122.635738,48.526021],[-122.649256,48.528769],[-122.654342,48.537956],[-122.649405,48.588457]]],[[[-122.714512,48.60878],[-122.694672,48.596602],[-122.670638,48.568812],[-122.68944,48.543903],[-122.722407,48.540606],[-122.73048,48.565602],[-122.73944,48.573893],[-122.739898,48.583949],[-122.714512,48.60878]]],[[[-122.699266,48.621115],[-122.674173,48.629944],[-122.657016,48.609891],[-122.676796,48.610055],[-122.699266,48.621115]]],[[[-122.334524,48.018916],[-122.321721,48.019977],[-122.303455,48.005603],[-122.326115,48.010295],[-122.334524,48.018916]]],[[[-122.418268,47.320614],[-122.324833,47.348521],[-122.328434,47.400621],[-122.348035,47.415921],[-122.355135,47.441921],[-122.383136,47.450521],[-122.368036,47.459221],[-122.361336,47.481421],[-122.396538,47.51522],[-122.398338,47.55012],[-122.421139,47.57602],[-122.387139,47.59572],[-122.370167,47.583087],[-122.342937,47.59122],[-122.339513,47.599113],[-122.344937,47.60912],[-122.367819,47.624213],[-122.414645,47.639766],[-122.429841,47.658919],[-122.393248,47.701602],[-122.38044,47.709119],[-122.37314,47.729219],[-122.382641,47.749119],[-122.380241,47.758519],[-122.396422,47.777927],[-122.394944,47.803318],[-122.33595,47.852306],[-122.328546,47.897917],[-122.311927,47.923703],[-122.307048,47.949117],[-122.249007,47.959507],[-122.230046,47.970917],[-122.226346,47.976417],[-122.232391,47.987713],[-122.224979,48.016626],[-122.231761,48.029876],[-122.281087,48.049793],[-122.326119,48.092877],[-122.343241,48.097631],[-122.363842,48.12393],[-122.370253,48.164809],[-122.362044,48.187568],[-122.372492,48.193022],[-122.395499,48.228551],[-122.433767,48.23655],[-122.449605,48.232598],[-122.45371,48.228859],[-122.449513,48.214736],[-122.441731,48.211776],[-122.45493,48.196639],[-122.478535,48.188087],[-122.479008,48.175703],[-122.442383,48.130841],[-122.379481,48.087384],[-122.358375,48.056133],[-122.377114,48.057568],[-122.400692,48.085255],[-122.4675,48.130353],[-122.489986,48.120617],[-122.512031,48.133931],[-122.53722,48.183745],[-122.538916,48.209683],[-122.530996,48.249821],[-122.503786,48.257045],[-122.480925,48.251706],[-122.463962,48.270541],[-122.406516,48.25183],[-122.395328,48.257187],[-122.392058,48.269628],[-122.371693,48.287839],[-122.408718,48.326413],[-122.442678,48.337934],[-122.475529,48.359912],[-122.507437,48.364666],[-122.533452,48.383409],[-122.539449,48.39719],[-122.554536,48.40604],[-122.558403,48.426758],[-122.551221,48.439465],[-122.557298,48.444438],[-122.575254,48.443333],[-122.581607,48.429244],[-122.61448,48.41488],[-122.649839,48.408526],[-122.674158,48.424726],[-122.678928,48.439466],[-122.654844,48.454087],[-122.657753,48.47294],[-122.664623,48.478128],[-122.689121,48.476849],[-122.700603,48.457632],[-122.712322,48.464143],[-122.712981,48.47879],[-122.701644,48.497622],[-122.684521,48.509123],[-122.671386,48.50398],[-122.606961,48.522152],[-122.599951,48.520946],[-122.598469,48.512169],[-122.568071,48.50821],[-122.537355,48.466749],[-122.500721,48.460887],[-122.471832,48.470724],[-122.46967,48.474975],[-122.483501,48.49243],[-122.485288,48.528106],[-122.498463,48.556206],[-122.504428,48.564775],[-122.531978,48.568644],[-122.534719,48.574246],[-122.495904,48.575927],[-122.478431,48.559303],[-122.44456,48.570115],[-122.425271,48.599522],[-122.448702,48.622624],[-122.46425,48.625717],[-122.500308,48.656163],[-122.519172,48.713095],[-122.495301,48.737328],[-122.490401,48.751128],[-122.510902,48.757728],[-122.535803,48.776128],[-122.567498,48.779185],[-122.596844,48.771492],[-122.637146,48.735708],[-122.626287,48.72093],[-122.612562,48.714932],[-122.605733,48.701066],[-122.615169,48.693839],[-122.630422,48.696625],[-122.673472,48.733082],[-122.647443,48.773998],[-122.646777,48.785011],[-122.693683,48.804475],[-122.699303,48.789063],[-122.709815,48.786205],[-122.709169,48.817829],[-122.717073,48.84719],[-122.793175,48.892927],[-122.751289,48.911239],[-122.746596,48.930731],[-122.766096,48.941955],[-122.787539,48.931702],[-122.821631,48.941369],[-122.817226,48.95597],[-122.774276,48.991038],[-122.756318,48.996881],[-122.75802,49.002357],[-121.751252,48.997399],[-117.032351,48.999188],[-117.042623,47.761223],[-117.039813,46.425425],[-117.034696,46.418318],[-117.046915,46.379577],[-117.062785,46.365287],[-117.062748,46.353624],[-117.055983,46.345531],[-117.023844,46.335976],[-117.020663,46.314793],[-116.986688,46.296662],[-116.991134,46.276342],[-116.966742,46.256923],[-116.955264,46.23088],[-116.965841,46.203417],[-116.92187,46.167808],[-116.950276,46.123464],[-116.955263,46.102237],[-116.976957,46.09667],[-116.982479,46.089389],[-116.978938,46.080007],[-116.957372,46.075449],[-116.942656,46.061],[-116.91718,45.996575],[-118.987129,45.999855],[-119.027056,45.969134],[-119.12612,45.932859],[-119.19553,45.92787],[-119.25715,45.939926],[-119.322509,45.933183],[-119.364396,45.921605],[-119.450256,45.917354],[-119.487829,45.906307],[-119.524632,45.908605],[-119.571584,45.925456],[-119.600549,45.919581],[-119.623393,45.905639],[-119.669877,45.856867],[-119.772927,45.845578],[-119.802655,45.84753],[-119.907461,45.828135],[-119.965744,45.824365],[-120.07015,45.785152],[-120.141352,45.773152],[-120.170453,45.761951],[-120.210754,45.725951],[-120.282156,45.72125],[-120.40396,45.699249],[-120.482362,45.694449],[-120.505863,45.700048],[-120.559465,45.738348],[-120.591166,45.746547],[-120.634968,45.745847],[-120.68937,45.715847],[-120.855674,45.671545],[-120.895575,45.642945],[-120.913476,45.640045],[-120.943977,45.656445],[-120.983478,45.648344],[-121.06437,45.652549],[-121.084933,45.647893],[-121.120064,45.623134],[-121.117052,45.618117],[-121.145534,45.607886],[-121.183841,45.606441],[-121.196556,45.616689],[-121.200367,45.649829],[-121.215779,45.671238],[-121.33777,45.704949],[-121.372574,45.703111],[-121.401739,45.692887],[-121.423592,45.69399],[-121.462849,45.701367],[-121.499153,45.720846],[-121.533106,45.726541],[-121.631167,45.704657],[-121.668362,45.705082],[-121.707358,45.694809],[-121.735104,45.694039],[-121.811304,45.706761],[-121.867167,45.693277],[-121.901855,45.670716],[-121.900858,45.662009],[-121.908267,45.654399],[-121.935149,45.644169],[-121.955734,45.643559],[-121.963547,45.632784],[-121.983038,45.622812],[-122.044374,45.609516],[-122.101675,45.583516],[-122.183695,45.577696],[-122.2017,45.564141],[-122.266701,45.543841],[-122.331502,45.548241],[-122.352802,45.569441],[-122.380302,45.575941],[-122.438674,45.563585],[-122.548149,45.596768],[-122.675008,45.618039],[-122.76381,45.657138],[-122.774511,45.680437],[-122.760108,45.734413],[-122.761451,45.759163],[-122.769532,45.780583],[-122.795605,45.81],[-122.785026,45.867699],[-122.81151,45.912725],[-122.806193,45.932416],[-122.813998,45.960984],[-122.837638,45.98082],[-122.856158,46.014469],[-122.878092,46.031281],[-122.884478,46.06028],[-122.904119,46.083734],[-122.962681,46.104817],[-123.004233,46.133823],[-123.041297,46.146351],[-123.115904,46.185268],[-123.166414,46.188973],[-123.280166,46.144843],[-123.371433,46.146372],[-123.430847,46.181827],[-123.427629,46.229348],[-123.474844,46.267831],[-123.501245,46.271004],[-123.547659,46.259109],[-123.547636,46.265595],[-123.613544,46.259988],[-123.669501,46.266832],[-123.679125,46.272502],[-123.680574,46.296025],[-123.700764,46.305278],[-123.724273,46.301161],[-123.727913,46.289661],[-123.741478,46.290274],[-123.766682,46.273499],[-123.806139,46.283588],[-123.875525,46.239787],[-123.919581,46.251217],[-123.954353,46.277001],[-123.974509,46.303063],[-124.001264,46.31326],[-124.020551,46.315737],[-124.029924,46.308312],[-124.044018,46.275925],[-124.060961,46.278761],[-124.080671,46.267239],[-124.064624,46.326899],[-124.057425,46.409315],[-124.057024,46.493338],[-124.068655,46.634879],[-124.062715,46.642582],[-124.048444,46.645827],[-124.035874,46.630822],[-124.052708,46.622796],[-124.050842,46.617421],[-124.023566,46.582559],[-124.031737,46.496375],[-124.026032,46.462978],[-123.990615,46.463019],[-123.99268,46.488617],[-123.983688,46.498542],[-123.968044,46.473497],[-123.943667,46.477197],[-123.921192,46.507731],[-123.896703,46.522665],[-123.894254,46.537028],[-123.920247,46.567343],[-123.928861,46.588875],[-123.955556,46.60357],[-123.960642,46.636364],[-123.921913,46.650262],[-123.923269,46.672708],[-123.851356,46.70256],[-123.84621,46.716795],[-123.87668,46.730657],[-123.898641,46.750205],[-123.916371,46.741322],[-123.91285,46.730647],[-123.916874,46.726739],[-123.948683,46.725369],[-123.968564,46.736106],[-123.974994,46.733391],[-123.979655,46.724658],[-123.966886,46.705184],[-123.994242,46.707929],[-124.003458,46.702337],[-124.063117,46.733664],[-124.092176,46.741624],[-124.108078,46.836388],[-124.138225,46.905534],[-124.110641,46.91252],[-124.093392,46.901168],[-124.089286,46.867716],[-124.073113,46.861493],[-124.055085,46.870429],[-124.046344,46.893972],[-124.01366,46.90363],[-123.985082,46.921916],[-123.957493,46.921261],[-123.86018,46.948556],[-123.898245,46.971927],[-123.939214,46.969739],[-123.959185,46.981759],[-124.012218,46.985176],[-124.019727,46.991189],[-124.005248,47.003915],[-124.017035,47.011717],[-124.026345,47.030187],[-124.065856,47.04114],[-124.122057,47.04165],[-124.141517,47.035142],[-124.151288,47.021112],[-124.138035,46.970959],[-124.124386,46.94387],[-124.180111,46.926357],[-124.169113,46.994508],[-124.182802,47.134041],[-124.236349,47.287287],[-124.25359,47.30248],[-124.271193,47.305025],[-124.299943,47.34836],[-124.319379,47.355559],[-124.336724,47.415996],[-124.355955,47.545698],[-124.382215,47.632302],[-124.412106,47.691199],[-124.425195,47.738434],[-124.453927,47.765334],[-124.47657,47.769671],[-124.489737,47.816988],[-124.539927,47.836967],[-124.562363,47.866216],[-124.625512,47.887963],[-124.645442,47.935338],[-124.672427,47.964414],[-124.67083,47.982366],[-124.682157,48.035987],[-124.696542,48.069274],[-124.695114,48.087096],[-124.687101,48.098657],[-124.733174,48.163393],[-124.731746,48.169997],[-124.704153,48.184422],[-124.696111,48.198599],[-124.690389,48.219745],[-124.705031,48.238774],[-124.684677,48.255228],[-124.676319,48.295143],[-124.665908,48.299324],[-124.65894,48.331057],[-124.670072,48.341341],[-124.696703,48.349748],[-124.727022,48.371101],[-124.731828,48.381157],[-124.716947,48.389776],[-124.653243,48.390691],[-124.631108,48.376522],[-124.599278,48.381035],[-124.395593,48.288772],[-124.361351,48.287582],[-124.272017,48.25441],[-124.250882,48.264773],[-124.238582,48.262471],[-124.101773,48.216883],[-124.107215,48.200082],[-124.050734,48.177747],[-123.981032,48.164761],[-123.880068,48.160621],[-123.858821,48.154273],[-123.778122,48.155466],[-123.728736,48.1628],[-123.71835,48.158713],[-123.702743,48.166783],[-123.651408,48.156952],[-123.628819,48.139279],[-123.590839,48.134949],[-123.551131,48.151382],[-123.507235,48.131807],[-123.440128,48.142014],[-123.441972,48.124259],[-123.424668,48.118065],[-123.332699,48.11297],[-123.288265,48.121036],[-123.239129,48.118217],[-123.21719,48.127203],[-123.1644,48.165894],[-123.133445,48.177276],[-123.143229,48.156633],[-123.116479,48.150208],[-123.085154,48.127137],[-123.06621,48.120469],[-123.038727,48.081138],[-122.979413,48.09594],[-122.929095,48.096244],[-122.917942,48.091535],[-122.927975,48.06665],[-122.918602,48.058238],[-122.877641,48.047025],[-122.849273,48.053808],[-122.857727,48.065774],[-122.878255,48.076072],[-122.882013,48.100779],[-122.876282,48.110877],[-122.833173,48.134406],[-122.760448,48.14324],[-122.748911,48.117026],[-122.778466,48.106135],[-122.801399,48.087561],[-122.766648,48.04429],[-122.74229,48.049324],[-122.739271,48.069153],[-122.747389,48.070795],[-122.733257,48.091232],[-122.698465,48.103102],[-122.68724,48.101662],[-122.69222,48.087081],[-122.682264,48.042723],[-122.668942,48.032026],[-122.669868,48.017217],[-122.686898,48.008305],[-122.70184,48.016106],[-122.723374,48.008095],[-122.718082,47.987739],[-122.701294,47.972979],[-122.6788,47.96793],[-122.676215,47.958743],[-122.68445,47.939593],[-122.657722,47.931156],[-122.651063,47.920985],[-122.655085,47.905058],[-122.646494,47.894771],[-122.610341,47.887343],[-122.631857,47.874815],[-122.63636,47.866186],[-122.69376,47.868002],[-122.681602,47.850405],[-122.683742,47.838773],[-122.748061,47.800546],[-122.758498,47.746036],[-122.781682,47.70392],[-122.811929,47.679861],[-122.832139,47.695511],[-122.790619,47.792597],[-122.812616,47.840029],[-122.820178,47.835904],[-122.815027,47.807493],[-122.845612,47.777474],[-122.880462,47.720643],[-122.896524,47.674838],[-122.97244,47.6149],[-123.106486,47.45817],[-123.15598,47.355745],[-123.140169,47.347496],[-123.111298,47.362619],[-123.120234,47.39149],[-122.967284,47.585685],[-122.917103,47.620743],[-122.856611,47.649615],[-122.804498,47.653363],[-122.754186,47.671612],[-122.740159,47.736228],[-122.722686,47.748827],[-122.714801,47.768176],[-122.690562,47.778372],[-122.682015,47.800882],[-122.623192,47.836199],[-122.608105,47.856728],[-122.573672,47.857582],[-122.573098,47.874081],[-122.588235,47.912923],[-122.620316,47.931553],[-122.617022,47.938987],[-122.603861,47.940478],[-122.592184,47.922519],[-122.549072,47.919072],[-122.527593,47.905882],[-122.513986,47.880807],[-122.506122,47.831745],[-122.482529,47.815511],[-122.485214,47.804128],[-122.495346,47.79704],[-122.495458,47.786692],[-122.471402,47.765965],[-122.470333,47.757109],[-122.471844,47.749819],[-122.488491,47.743605],[-122.554454,47.745704],[-122.543161,47.710941],[-122.53094,47.704814],[-122.511196,47.708715],[-122.504604,47.699136],[-122.518277,47.65132],[-122.493205,47.635122],[-122.500357,47.617816],[-122.49824,47.598242],[-122.493933,47.588963],[-122.479089,47.583654],[-122.518367,47.57408],[-122.543118,47.556326],[-122.546611,47.52355],[-122.52305,47.524],[-122.494882,47.510265],[-122.530514,47.469041],[-122.531889,47.428827],[-122.551136,47.394456],[-122.537044,47.375896],[-122.575985,47.32642],[-122.547521,47.285344],[-122.578211,47.254804],[-122.589454,47.227618],[-122.602541,47.217506],[-122.611464,47.2181],[-122.668571,47.270449],[-122.697378,47.283969],[-122.671256,47.343774],[-122.632463,47.376394],[-122.671486,47.366876],[-122.725738,47.33047],[-122.74525,47.297158],[-122.749621,47.276408],[-122.718124,47.250045],[-122.648941,47.214531],[-122.641802,47.205013],[-122.673925,47.174675],[-122.691771,47.141958],[-122.711997,47.127681],[-122.771619,47.167109],[-122.832799,47.243412],[-122.816633,47.276457],[-122.799025,47.289306],[-122.796646,47.341654],[-122.803688,47.355071],[-122.821868,47.363069],[-122.822344,47.319763],[-122.84586,47.298405],[-122.863732,47.270221],[-122.856171,47.233788],[-122.838298,47.208353],[-122.858735,47.167955],[-122.852046,47.164359],[-122.814238,47.179482],[-122.775056,47.123114],[-122.721437,47.103179],[-122.67813,47.103866],[-122.650634,47.132738],[-122.631987,47.140589],[-122.614855,47.169143],[-122.590829,47.178107],[-122.561957,47.244099],[-122.527586,47.291531],[-122.547747,47.316403],[-122.533338,47.31662],[-122.471652,47.277321],[-122.4442,47.266723],[-122.429605,47.269707],[-122.409199,47.288556],[-122.444635,47.300421],[-122.418268,47.320614]]]]},\"properties\":{\"name\":\"Washington\",\"nation\":\"USA \"}}]}","volume":"13","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Arnold, Logan 0000-0001-8903-2735","orcid":"https://orcid.org/0000-0001-8903-2735","contributorId":339402,"corporation":false,"usgs":false,"family":"Arnold","given":"Logan","email":"","affiliations":[],"preferred":false,"id":903609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Isaksen, T.B.","contributorId":338790,"corporation":false,"usgs":false,"family":"Isaksen","given":"T.B.","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":903611,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236156,"text":"70236156 - 2022 - Simulated global coastal ecosystem responses to a half-century increase in river nitrogen loads","interactions":[],"lastModifiedDate":"2022-08-30T14:07:18.106436","indexId":"70236156","displayToPublicDate":"2022-08-30T09:01:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Simulated global coastal ecosystem responses to a half-century increase in river nitrogen loads","docAbstract":"Coastal ecosystems are increasingly threatened by anthropogenic stressors such as harmful algal blooms and hypoxia projected to intensify through the combined effects of eutrophication and warming. As a major terrestrial nitrogen (N) source to the ocean, rivers play a critical role in shaping both coastal and global biogeochemical cycling. Combining an enhanced-resolution (1/4°), global ocean physical-biogeochemical model with dynamic river inputs, we estimate that elevated river nitrogen loads alone resulted in an increase of 16.6 Tg (+5.8%) in the global coastal nitrogen inventory (CNI) over the half century between 1961 and 2010. This change was accompanied by increases in coastal net primary productivity (NPP, +4.6%) and benthic detrital flux (BDF, +7.3%), the latter of which is indicative of an overall higher oxygen demand in coastal sediments. After normalization by area, the ecosystems most sensitive to added river nitrogen (g N m-2 yr-1) were those with long residence times and strong nitrogen limitation. While even enhanced-resolution global models remain limited in their capacity to resolve near-shore responses, these basic sensitivity factors provide two relevant axes for frameworks assessing the comparative susceptibility of globally distributed coastal ecosystems to enhanced nitrogen loading, and the effectiveness of mitigation strategies.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GL094367","usgsCitation":"Liu, X., Stock, C., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., and Milly, P.C., 2022, Simulated global coastal ecosystem responses to a half-century increase in river nitrogen loads: Geophysical Research Letters, v. 48, no. 17, e2021GL094367, 14 p., https://doi.org/10.1029/2021GL094367.","productDescription":"e2021GL094367, 14 p.","ipdsId":"IP-114921","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":446613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gl094367","text":"Publisher Index Page"},{"id":405903,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"17","noUsgsAuthors":false,"publicationDate":"2021-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Xiao","contributorId":295963,"corporation":false,"usgs":false,"family":"Liu","given":"Xiao","email":"","affiliations":[],"preferred":false,"id":850285,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stock, Charles A.","contributorId":217586,"corporation":false,"usgs":false,"family":"Stock","given":"Charles A.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":850268,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunne, John P.","contributorId":88995,"corporation":false,"usgs":true,"family":"Dunne","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":850269,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, Minjin","contributorId":177261,"corporation":false,"usgs":false,"family":"Lee","given":"Minjin","email":"","affiliations":[],"preferred":false,"id":850270,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shevliakova, Elena","contributorId":201589,"corporation":false,"usgs":false,"family":"Shevliakova","given":"Elena","email":"","affiliations":[{"id":36211,"text":"GFDL/NOAA","active":true,"usgs":false}],"preferred":false,"id":850271,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Malyshev, Sergey","contributorId":189177,"corporation":false,"usgs":false,"family":"Malyshev","given":"Sergey","affiliations":[],"preferred":false,"id":850272,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Milly, Paul C. D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":176836,"corporation":false,"usgs":true,"family":"Milly","given":"Paul","email":"cmilly@usgs.gov","middleInitial":"C. D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":850273,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70238704,"text":"70238704 - 2022 - Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia","interactions":[],"lastModifiedDate":"2022-12-06T13:08:24.04841","indexId":"70238704","displayToPublicDate":"2022-08-30T07:03:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia","docAbstract":"The Glacier Bay area in southeastern Alaska and British Columbia, encompassing Glacier Bay National Park and Preserve, has experienced rapid glacier retreat since the end of the Little Ice Age in the mid-1800s. The impact that rapid deglaciation has had on the slope stability of valley walls and on the sedimentation of fans and deltas adjacent to fjords and inlets is an ongoing research topic. Using 3-year (2018–2020) Sentinel-1 datasets, and an automated time-series persistent scatterer interferometric synthetic aperture radar (PSInSAR) processing method, we detected landslides or delta subsidence at 27 sites within a vast 180 × 180 km remote region encompassing Glacier Bay proper. Most of the sites that we identified had not been previously identified. We categorized the hazard source areas that we identified into three general types:1) slow-moving landslides on steep rocky slopes not near (> 2 km away from) present-day glacier termini (e.g., Tidal Inlet landslide), 2) slow-moving landslides directly adjacent to (< 2 km away from), and associated with glacier thinning and retreat, and 3) subsidence of glacial outwash fan deltas. In categories 1 and 2, we observed 22 landslides moving at velocities ranging from 0.5 to 4 cm/yr. In category 3, we detected five fan deltas subsiding at velocities ranging from 0.5 to 6 cm/yr. Within our measurement error, these velocities were consistent during the monitoring period. Because acceleration was not observed, the issuance of warnings of imminent rapid failure is not warranted, however, continued remote monitoring is warranted. Our interferometric synthetic aperture radar (InSAR) results could be combined with other data sets including field observations, subaerial and submarine landslide inventories, bedrock fabric mapping from newly available light detection and ranging (lidar) data, and geologic maps to produce an inherent susceptibility map for landslides in bedrock and fan deltas. This map could be used to forecast susceptibility for both earthquake and climatically induced landslides.
Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.
This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (Rana pipiens) to (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.
Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.
STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.
While the R. pipiens populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01515-8","usgsCitation":"Waraniak, J.M., Mushet, D., and Stockwell, C.A., 2022, Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region: Landscape Ecology, v. 37, p. 2877-2893, https://doi.org/10.1007/s10980-022-01515-8.","productDescription":"17 p.","startPage":"2877","endPage":"2893","ipdsId":"IP-137156","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446618,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-022-01515-8","text":"Publisher Index Page"},{"id":406585,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.39257812499999,\n 45.920587344733654\n ],\n [\n -96.85546875,\n 45.920587344733654\n ],\n [\n -96.85546875,\n 48.574789910928864\n ],\n [\n -102.39257812499999,\n 48.574789910928864\n ],\n [\n -102.39257812499999,\n 45.920587344733654\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Waraniak, Justin M.","contributorId":211882,"corporation":false,"usgs":false,"family":"Waraniak","given":"Justin","email":"","middleInitial":"M.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":851527,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":851528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stockwell, Craig A.","contributorId":194252,"corporation":false,"usgs":false,"family":"Stockwell","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":851529,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70246958,"text":"70246958 - 2022 - Estimating the effect of tidal marsh restoration on housing prices: A hedonic analysis in the Nisqually National Wildlife Refuge, Washington, USA","interactions":[],"lastModifiedDate":"2023-07-20T11:42:22.401021","indexId":"70246958","displayToPublicDate":"2022-08-30T06:37:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the effect of tidal marsh restoration on housing prices: A hedonic analysis in the Nisqually National Wildlife Refuge, Washington, USA","docAbstract":"Streamflow drought is a recurring challenge, and understanding spatiotemporal patterns of past droughts is needed to manage future water resources. We examined regional patterns in streamflow drought metrics and compared these metrics to low flow timing and magnitude using long-term daily records for 555 minimally disturbed watersheds. For each streamgage, we calculated streamflow drought duration (number of days) and deficit (flow volume below a specified threshold) for each climate year (April 1–March 31). We identified drought using five thresholds (2%–30%) and two approaches: variable thresholds with unique values for each day of the year, and a fixed threshold based on all period-of-record flows. We then analyzed drought trends using the Mann-Kendall test with persistence adjustment for 1921–2020, 1951–2020, and 1981–2020, and computed correlations between annual streamflow drought metrics and climate metrics using values from a monthly water balance model. Spatial patterns in drought metrics were consistent between variable and fixed approaches, though fixed threshold durations were typically longer and variable threshold deficits larger. High interannual variability in drought duration emerged in the central, interior west, and southwestern U.S., with high deficit variability in the interior west. Drought metrics were weakly correlated with low flow magnitude and timing, providing unique information. Drought duration and deficit increased in the southern and western U.S. for both 1951–2020 and 1981–2020, particularly using fixed thresholds, and paralleled trends in aridity. Projections of continued aridification for the southern and western U.S. may increase drought durations and deficits and intensify water availability impacts.
The National Gap Analysis Project created species habitat distribution models for all terrestrial vertebrates in the United States to support conservation assessments and explore patterns of species richness. Those models link species to specific habitats throughout the range of each species. For most vertebrates, there are not enough occurrence data to drive inductive, range-wide species habitat distribution models at high spatial and thematic resolution. However, it is possible to use occurrence data for model evaluation. The combination of citizen science, formal species survey work, and digitized specimen archives are making millions of observations available to the scientific community. Our challenge is to combine the mostly unstructured data into metrics that help us characterize and understand patterns of biodiversity. In this work, we propose two model-evaluation metrics. The first, a buffer proportion assessment, is based on the proportion of habitat in the range relative to the mean proportion of habitat around each of the species’ occurrence records. The second is a measure of the sensitivity (proportion of true presence) to buffer distances around occurrence records. The buffer proportion is a modification of model prevalence versus point prevalence metric, whereby comparison to a null model allows us to determine if the model performs better or worse than random.
In this report, we describe the workflow used to compile and filter the species occurrence records from online resources (for example, the Global Biodiversity Information Facility) and show results for a single species, Desmognathus quadramaculatus (black-bellied salamander). For the salamander, 222 occurrence points met our criteria for inclusion in the evaluation. We found the model performed better than random with a buffer proportion index of 1.745, indicating about 5 times as much habitat was found adjacent to known occurrence records than would be expected from randomly located sites throughout the range. Sensitivity increased with larger buffer distances and leveled off to around 0.7 between 1,000- and 2,000-meter buffer distances, indicating the model is likely best suited for scales exceeding 1,000 meters. We plan to report the buffer proportion assessment and sensitivity metrics along with the full species model reports to increase understanding of the model’s performance and to use the metrics to help prioritize revisions to the models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm2A19","collaboration":"Prepared in cooperation with North Carolina State University, North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied Ecology","usgsCitation":"Rubino, M.J., McKerrow, A.J., Tarr, N.M., and Williams, S.G., 2022, Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data: U.S. Geological Survey Techniques and Methods 2-A19, 13 p., https://doi.org/10.3133/tm2A19.","productDescription":"Report: vi, 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-124954","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":405205,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/a19/images"},{"id":405206,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/a19/tm2a19.xml"},{"id":405202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a19/tm2a19.pdf","text":"Report","size":"1.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2A-19"},{"id":405201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a19/coverthb.jpg"},{"id":405204,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H1308B","text":"USGS data release","linkHelpText":"Black-bellied Salamander (Desmognathus quadramaculatus) aBESAx_CONUS_2001v1 Habitat Map"}],"country":"United States","state":"Alabama, Georgia, North Carolina, Tennessee, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.9580078125,\n 36.73888412439431\n ],\n [\n -82.41943359375,\n 36.73888412439431\n ],\n [\n -83.1884765625,\n 36.43896124085945\n ],\n [\n -84.04541015625,\n 36.13787471840729\n ],\n [\n -84.92431640625,\n 35.99578538642032\n ],\n [\n -85.7373046875,\n 35.38904996691167\n ],\n [\n -86.24267578125,\n 35.11990857099681\n ],\n [\n -86.220703125,\n 34.50655662164561\n ],\n [\n -85.58349609375,\n 34.361576287484176\n ],\n [\n -84.17724609375,\n 34.79576153473033\n ],\n [\n -82.81494140625,\n 35.02999636902566\n ],\n [\n -82.08984375,\n 35.28150065789119\n ],\n [\n -81.10107421874999,\n 35.44277092585766\n ],\n [\n -80.26611328125,\n 36.2265501474709\n ],\n [\n -80.068359375,\n 36.77409249464195\n ],\n [\n -79.7607421875,\n 37.37015718405753\n ],\n [\n -79.453125,\n 37.71859032558816\n ],\n [\n -79.2333984375,\n 38.09998264736481\n ],\n [\n -79.25537109375,\n 38.59970036588819\n ],\n [\n -79.65087890624999,\n 38.54816542304656\n ],\n [\n -80.04638671875,\n 38.42777351132902\n ],\n [\n -80.5517578125,\n 38.39333888832238\n ],\n [\n -80.83740234375,\n 37.996162679728116\n ],\n [\n -81.18896484375,\n 37.405073750176925\n ],\n [\n -81.49658203125,\n 36.914764288955936\n ],\n [\n -81.9580078125,\n 36.73888412439431\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Core Science Analytics and Synthesis
U.S. Geological Survey
Box 25046, Mail Stop 302
Denver, CO 80225
Unselective fishing involves activities that target the entire assemblage rather than specific fish species, size classes, or trophic levels. This common fishing approach has been in practice for decades in inland waters in China but its implications for biodiversity remain unclear. We addressed this issue by studying fish assemblages in freshwater lakes (five fishing lakes, one reference lake, and a total of 51 sampling sites) between pre- and post-fishing time-periods in Eastern China during 2017–2019. The effects of lake, fishing period, and their interactions on fish abundance, biomass, and diversity indices were assessed. Multivariate analysis was conducted to test for differences in fish assemblages among lakes and between fishing periods. After the implementation of fishing activities, significant reductions in fish species richness, abundance, biomass, and all three life-history strategies (opportunistic, equilibrium, and periodic) were observed in fishing lakes, whereas opposite trends were observed in the reference lake. Compositional similarity of fish assemblages among fishing lakes increased over the three-year monitoring period. Our results suggest that unselective fishing reduces fish diversity and homogenizes fish assemblage structure in lakes. These findings have important implications for protecting both biodiversity and fisheries in inland waters in China and are applicable to other countries or regions that rely on fish as a major food source.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watbs.2022.100055","usgsCitation":"Liu, H., Chen, Y., Gozlan, R., Qu, X., Xia, W., Cheng, F., Wang, L., Paukert, C.P., Olden, J., and Xie, S., 2022, Fish diversity reduction and assemblage structure homogenization in lakes: A case study on unselective fishing in China: Water Biology and Security, v. 1, 100055, 8 p., https://doi.org/10.1016/j.watbs.2022.100055.","productDescription":"100055, 8 p.","ipdsId":"IP-129830","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446626,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watbs.2022.100055","text":"Publisher Index Page"},{"id":433314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 115,\n 37\n ],\n [\n 115,\n 32\n ],\n [\n 122,\n 32\n ],\n [\n 122,\n 37\n ],\n [\n 115,\n 37\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Han","contributorId":341500,"corporation":false,"usgs":false,"family":"Liu","given":"Han","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":908513,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Yushun","contributorId":341501,"corporation":false,"usgs":false,"family":"Chen","given":"Yushun","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":908514,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gozlan, Rodolphe E.","contributorId":341502,"corporation":false,"usgs":false,"family":"Gozlan","given":"Rodolphe E.","affiliations":[{"id":81747,"text":"Université de Montpellier","active":true,"usgs":false}],"preferred":false,"id":908515,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qu, Xiao","contributorId":341503,"corporation":false,"usgs":false,"family":"Qu","given":"Xiao","email":"","affiliations":[{"id":27775,"text":"University of Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":908516,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xia, Wentong","contributorId":341504,"corporation":false,"usgs":false,"family":"Xia","given":"Wentong","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":908517,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheng, Fei","contributorId":341505,"corporation":false,"usgs":false,"family":"Cheng","given":"Fei","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":908518,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Lizhu","contributorId":341506,"corporation":false,"usgs":false,"family":"Wang","given":"Lizhu","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":908519,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908520,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Olden, Julian D.","contributorId":341507,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":908521,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Xie, Songguang","contributorId":341508,"corporation":false,"usgs":false,"family":"Xie","given":"Songguang","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":908522,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236305,"text":"70236305 - 2022 - Diminishing Arctic lakes","interactions":[],"lastModifiedDate":"2022-09-01T12:13:35.958674","indexId":"70236305","displayToPublicDate":"2022-08-29T07:12:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Diminishing Arctic lakes","docAbstract":"The Arctic is home to the largest surface water fraction of any terrestrial biome, containing thousands of low-lying lakes. Now, it appears that some Arctic lakes are drying due to rising air temperatures and autumn rains, causing permafrost to thaw and water bodies to drain.
Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data.
Features:
The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.
","language":"English","publisher":"Chapman and Hall/CRC","doi":"10.1201/9781351018784","usgsCitation":"Qian, S.S., Dufour, M.R., and Alameddine, I., 2022, Bayesian applications in environmental and ecological studies with R and Stan, 415 p., https://doi.org/10.1201/9781351018784.","productDescription":"415 p.","ipdsId":"IP-134860","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":407695,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Qian, Song S.","contributorId":198934,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":842387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":842388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alameddine, Ibrahim","contributorId":244836,"corporation":false,"usgs":false,"family":"Alameddine","given":"Ibrahim","affiliations":[{"id":40455,"text":"American University of Beirut","active":true,"usgs":false}],"preferred":false,"id":842389,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236495,"text":"70236495 - 2022 - Geoelectric constraints on the Precambrian assembly and architecture of southern Laurentia","interactions":[],"lastModifiedDate":"2022-09-09T13:17:21.375872","indexId":"70236495","displayToPublicDate":"2022-08-27T08:10:01","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geoelectric constraints on the Precambrian assembly and architecture of southern Laurentia","docAbstract":"Using images from an updated and expanded three-dimensional electrical conductivity synthesis model for the contiguous United States (CONUS), we highlight the key continent-scale geoelectric structures that are associated with the Precambrian assembly of southern Laurentia. Conductivity anomalies are associated with the Trans-Hudson orogen, the Penokean suture, the ca. 1.8–1.7 Ga Cheyenne belt and Spirit Lake tectonic zone, and the Grenville suture zone; the geophysical characteristics of these structures indicate that the associated accretionary events involved the closure of ancient ocean basins along discrete, large-scale structures. In contrast, we observe no large-scale conductivity anomalies through the portion of southern Laurentia that is generally viewed as composed of late Paleoproterozoic–early Mesoproterozoic accretionary crust. The lack of through-going conductors places constraints on the structure, petrology, and geodynamic history of crustal growth in southern Laurentia during that time period. Overall, our model highlights the enigmatic nature of the concealed Precambrian basement of much of southern Laurentia, as it in some places supports and in other places challenges prevailing models of Laurentian assembly. The revised CONUS electrical conductivity model thus provides important constraints for testing new models of Precambrian tectonism in this region.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Laurentia: Turning points in the evolution of a continent","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2022.1220(13)","usgsCitation":"Murphy, B.S., Bedrosian, P.A., and Kelbert, A., 2022, Geoelectric constraints on the Precambrian assembly and architecture of southern Laurentia, chap. of Laurentia: Turning points in the evolution of a continent, v. 220, 18 p., https://doi.org/10.1130/2022.1220(13).","productDescription":"18 p.","ipdsId":"IP-131709","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":446629,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2022.1220(13)","text":"Publisher Index Page"},{"id":406446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"continental 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 -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"220","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Benjamin Scott 0000-0001-7636-3711","orcid":"https://orcid.org/0000-0001-7636-3711","contributorId":242928,"corporation":false,"usgs":true,"family":"Murphy","given":"Benjamin","email":"","middleInitial":"Scott","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":851252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":851253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":851254,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235983,"text":"ofr20221067 - 2022 - Updates for Wake Atoll biosecurity management, biological control, survey, and management, and integrated pest management plans","interactions":[],"lastModifiedDate":"2026-03-30T20:22:47.148148","indexId":"ofr20221067","displayToPublicDate":"2022-08-26T10:52:23","publicationYear":"2022","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":"2022-1067","displayTitle":"Updates for Wake Atoll Biosecurity Management, Biological Control, Survey, and Management, and Integrated Pest Management Plans","title":"Updates for Wake Atoll biosecurity management, biological control, survey, and management, and integrated pest management plans","docAbstract":"Pests and invasive species have been defined as any organism that can have real or perceived adverse effects on operations, or the well-being of personnel, native plants, animals, their environment and ecosystem processes; attack or damage real property, supplies, equipment, or are otherwise undesirable (paraphrased from many sources including 53 Federal Register [FR] 15975, May 4, 1988, as amended at 78 FR 13507, February 28, 2013). Biosecurity programs and pest management plans can be developed and implemented with the goals of preventing the arrival of or eradication or control of pests and invasive species to reduce the potential for adverse effects. Such plans have been developed for Wake Atoll (U.S. Air Force, unpub. data 2017). Periodic plan reviews are an integral step for evaluating plan efficacy and updating plans with new information for improving plan effectiveness. This report summarizes an evaluation of past, current, and potential biosecurity and pest management for Wake with the intent this information can be used for updating existing plans. This document was prepared in cooperation with the U.S. Air Force (USAF) and surveys were performed for the 611th Civil Engineer Squadron Natural Resources Program ACES PROJECT no. YGFZ17002 under agreement number F2MUAA7116GW01 between the USAF and the U.S. Geological Survey’s Western Ecological Research Center (USGS-WERC).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221067","collaboration":"Prepared in cooperation with the U.S. Air Force","programNote":"Ecosystems Mission Area—Land Management Research and Species Management Research Programs","usgsCitation":"Hathaway, S.A., Jacobi, J.D., Peck, R., and Fisher, R.N., 2022, Updates for Wake Atoll biosecurity management, biological control, survey, and management, and integrated pest management plans: U.S. Geological Survey Open-File Report 2022-1067, 56 p., https://doi.org/10.3133/ofr20221067.","productDescription":"viii, 56 p.","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-136103","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"links":[{"id":405629,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1067/ofr20221067.xml"},{"id":405628,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1067/ofr20221067.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":405627,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1067/covrthb.jpg"},{"id":501820,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113419.htm","linkFileType":{"id":5,"text":"html"}},{"id":405630,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1067/images"}],"contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Vampire bat transmitted rabies (VBR) is a continuing burden to public health and agricultural sectors in Latin America, despite decades-long efforts to control the disease by culling bat populations. Culling has been shown to disperse bats, leading to an increased spread of rabies. Thus, non-lethal strategies to control VBR, such as vaccination, are desired. Here, we evaluated the safety and efficacy of a viral-vectored recombinant mosaic glycoprotein rabies vaccine candidate (RCN-MoG) in vampire bats (Desmodus rotundus) of unknown history of rabies exposure captured in México and transported to the United States. Vaccination with RCN-MoG was demonstrated to be safe, even in pregnant females, as no evidence of lesions or adverse effects were observed. We detected rabies neutralizing antibodies in 28% (8/29) of seronegative bats post-vaccination. Survival proportions of adult bats after rabies virus (RABV) challenge ranged from 55–100% and were not significantly different among treatments, pre- or post-vaccination serostatus, and route of vaccination, while eight pups (1–2.5 months of age) used as naïve controls all succumbed to challenge (P<0.0001). Importantly, we found that vaccination with RCN-MoG appeared to block viral shedding, even when infection proved lethal. Using real-time PCR, we did not detect RABV nucleic acid in the saliva samples of 9/10 vaccinated bats that succumbed to rabies after challenge (one was inconclusive). In contrast, RABV nucleic acid was detected in saliva samples from 71% of unvaccinated bats (10/14 sampled, plus one inconclusive) that died of the disease, including pups. Low seroconversion rates post-vaccination and high survival of non-vaccinated bats, perhaps due to earlier natural exposure, limited our conclusions regarding vaccine efficacy. However, our findings suggest a potential transmission-blocking effect of vaccination with RCN-MoG that could provide a promising strategy for controlling VBR in Latin America beyond longstanding culling programs.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pntd.0010699","usgsCitation":"Cardenas-Canales, E.M., Velasco-Villa, A., Ellison, J.A., Satheshkumar, P., Osario, J.E., and Rocke, T.E., 2022, A recombinant rabies vaccine that prevents viral shedding in rabid common vampire bats (Desmodus rotundus): PLoS Neglected Tropical Diseases, v. 16, no. 8, e0010699, 21 p., https://doi.org/10.1371/journal.pntd.0010699.","productDescription":"e0010699, 21 p.","ipdsId":"IP-142109","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":446632,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pntd.0010699","text":"Publisher Index Page"},{"id":435713,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KNHW1P","text":"USGS data release","linkHelpText":"Serology, survival, and detection of rabies data from vaccination trials in the common vampire bat (Desmodus rotundus) using a recombinant raccoon poxvirus-vectored mosaic rabies vaccine candidate"},{"id":406143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","city":"San Luis Potosi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.173095703125,\n 21.963424936844223\n ],\n [\n -100.799560546875,\n 21.963424936844223\n ],\n [\n -100.799560546875,\n 22.334833457530486\n ],\n [\n -101.173095703125,\n 22.334833457530486\n ],\n [\n -101.173095703125,\n 21.963424936844223\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Cardenas-Canales, Elsa M.","contributorId":192489,"corporation":false,"usgs":false,"family":"Cardenas-Canales","given":"Elsa","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":850658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Velasco-Villa, Andres","contributorId":174760,"corporation":false,"usgs":false,"family":"Velasco-Villa","given":"Andres","email":"","affiliations":[{"id":16974,"text":"US Centers for Disease Control and Prevention (CDC)","active":true,"usgs":false}],"preferred":false,"id":850659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellison, James A.","contributorId":197066,"corporation":false,"usgs":false,"family":"Ellison","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":850660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Satheshkumar, Panayampalli S.","contributorId":296099,"corporation":false,"usgs":false,"family":"Satheshkumar","given":"Panayampalli S.","affiliations":[{"id":63980,"text":"Poxvirus and Rabies Branch, Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America","active":true,"usgs":false}],"preferred":false,"id":850661,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Osario, Jorge E.","contributorId":296100,"corporation":false,"usgs":false,"family":"Osario","given":"Jorge","email":"","middleInitial":"E.","affiliations":[{"id":63982,"text":"Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America","active":true,"usgs":false}],"preferred":false,"id":850662,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":850663,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236043,"text":"70236043 - 2022 - TSAW — An exercise in consensus","interactions":[],"lastModifiedDate":"2022-08-26T16:31:30.881212","indexId":"70236043","displayToPublicDate":"2022-08-26T08:59:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1222,"text":"Chemistry International","active":true,"publicationSubtype":{"id":10}},"title":"TSAW — An exercise in consensus","docAbstract":"No abstract available.
","language":"English","publisher":"DeGruyter","doi":"10.1515/ci-2022-0306","usgsCitation":"Coplen, T.B., 2022, TSAW — An exercise in consensus: Chemistry International, v. 44, no. 3, p. 23-23, https://doi.org/10.1515/ci-2022-0306.","productDescription":"1 p.","startPage":"23","endPage":"23","ipdsId":"IP-138579","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":446634,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1515/ci-2022-0306","text":"Publisher Index Page"},{"id":405679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":849780,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70236273,"text":"70236273 - 2022 - Confirmation that eagle fatalities can be reduced by automated curtailment of wind turbines","interactions":[],"lastModifiedDate":"2022-08-31T12:24:53.280063","indexId":"70236273","displayToPublicDate":"2022-08-26T07:24:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9977,"text":"Ecological Solutions and Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Confirmation that eagle fatalities can be reduced by automated curtailment of wind turbines","docAbstract":"The biomagnification of toxic methylmercury (MeHg) and selenium (Se) through aquatic food webs using nitrogen stable isotopes (δ15N) varies among ecosystems but underlying mechanisms are yet unexplained. Given the strong links between MeHg and thiol-containing amino acids and proteins containing selenocysteine, our hypothesis was that cysteine content is a better predictor of MeHg and Se transfer through lake food webs than δ15N. Food web samples were collected from six lakes in Kejimkujik National Park, Nova Scotia, Canada, and the regression slopes of log MeHg or Se versus protein-bound cysteine or bulk δ15N were compared. Across all six lakes, MeHg varied by a factor of 10 among taxa and was significantly and positively related to both cysteine (R2 = 0.65–0.80, p < 0.001) and δ15N (R2 = 0.88–0.94, p < 0.001), with no among-system differences in these slopes. In contrast, total Se concentrations varied by less than a factor of 2 among taxa in four lakes and were significantly related to cysteine in only two food webs (R2 = 0.20 & 0.37, p = 0.014 & < 0.001); however, δ15N was not a predictor of Se in any lake (p = 0.052–0.777). Overall, these novel results indicate that cysteine content predicts MeHg, and sometimes Se, across trophic levels, providing a potential mechanism for among-system differences in their biomagnification.
In the United States, national chemical water quality criteria for the protection of aquatic life assume that aquatic ecosystems have sufficient resiliency to recover from criteria exceedences occurring up to once every 3 years. This resiliency assumption was critically reviewed through two approaches: 1) synthesis of case studies and 2) population modeling. The population modeling examined differences in recovery of species with widely different life histories. One invertebrate (Hyalella azteca) and four fish species were modeled (fathead minnow, brook trout, lake trout, and shortnose sturgeon) with various disturbance magnitudes and intervals. The synthesis of ecosystem case studies showed generally faster recoveries for insect communities rather than fish, and recoveries from pulse (acute) disturbances were often faster than recoveries from press (chronic) disturbances. When the recovery dataset excluded severe disturbances that seemed unrepresentative of common facility discharge upsets that might cause criteria exceedences, the median recovery time was 1 year, 81% of the cases were considered recovered within 3 years, and 95% were considered recovered within 10 years. The modeling projected that short-lived fish species with high recovery times could thrive despite enduring 50% mortality disturbances every other year. However, long-lived fish species had longer recovery times and declined under the 1 disturbance every 3 years scenario. Overall, the analyses did not refute the long-standing judgements that 3 years is generally sufficient for recovery from non-repetitive, moderate intensity disturbances of a magnitude up to 2X the chronic criteria in waters without other pollution sources or stresses. However, these constraints may not always be met and if long-lived fish species are a concern, longer return intervals such as 5 to 10 years could be indicated.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.5471","usgsCitation":"Mebane, C.A., 2022, The capacity of freshwater ecosystems to recover from exceedances of aquatic life criteria: Environmental Toxicology and Chemistry, v. 41, no. 12, p. 2887-2910, https://doi.org/10.1002/etc.5471.","productDescription":"24 p.","startPage":"2887","endPage":"2910","ipdsId":"IP-125552","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":446640,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5471","text":"Publisher Index Page"},{"id":406944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852244,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70235866,"text":"fs20223068 - 2022 - Database of biodiversity, habitat, and aquatic-resource quantification tools used in market-based conservation — 2022 update","interactions":[],"lastModifiedDate":"2022-09-27T13:31:01.57704","indexId":"fs20223068","displayToPublicDate":"2022-08-26T06:25:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3068","displayTitle":"Database of Biodiversity, Habitat, and Aquatic-Resource Quantification Tools Used in Market-Based Conservation — 2022 Update","title":"Database of biodiversity, habitat, and aquatic-resource quantification tools used in market-based conservation — 2022 update","docAbstract":"Market-based conservation makes use of economic incentives to promote actions that avoid, minimize, or compensate for detrimental effects on natural resources and the environment. Examples of market-based conservation mechanisms include aquatic-resource (such as, streams, wetlands, and estuaries) compensatory mitigation, conservation banking, habitat exchanges, and payments for ecosystem services. A critical component in the operation of these market-based conservation mechanisms is the methods (hereafter referred to as “quantification tools”) used to assess existing (sometimes referred to as “baseline”) or potential site conditions. Quantification tools are used to assign values to the benefits provided by preservation, restoration, or enhancement actions, as well as the negative effects of human activities (for example, infrastructure development, energy extraction, and anthropogenic disasters).
In 2018, the U.S. Geological Survey (USGS) published a database describing the attributes of 69 quantification tools developed for United States conservation markets. The database focused on tools used for species-based mitigation, payments for ecosystem services, and ecolabel programs (Chiavacci and Pindilli, 2020). Recently, the USGS, in collaboration with the U.S. Environmental Protection Agency, revised the original database by updating the existing tool information, adding newly developed tools, and broadening the scope to include tools developed for compensatory mitigation under the Clean Water Act Section 404 Regulatory Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223068","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Chiavacci, S.J., French, E.D., and Morgan, J.A., 2022, Database of biodiversity, habitat, and aquatic-resource quantification tools used in market-based conservation — 2022 update: U.S. Geological Survey Fact Sheet 2022–3068, 2 p., https://doi.org/10.3133/fs20223068.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-140564","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":405533,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223068/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3068"},{"id":406471,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79G5M3X","text":"USGS data release","linkHelpText":"Database of Biodiversity, Habitat, and Aquatic Resource Quantification Tools Used for Market-based Conservation in the United States (ver. 2.0, June 2022)"},{"id":405535,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3068/fs20223068.XML"},{"id":405534,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3068/images/"},{"id":405509,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3068/fs20223068.pdf","text":"Report","size":"558 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3068"},{"id":405508,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3068/coverthb3.jpg"}],"contact":"Science and Decisions Center
12201 Sunrise Valley Drive
Reston, VA 20192