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 \"}}]}","edition":"Version 1.0: April 13, 2021; Version 1.1: January 23, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey<br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Landsat Helps Characterize the Changing Landscape of the Willamette Valley</li><li>Taking the Mystery out of Irrigation</li><li>Offering a Clearer Picture of Forests</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-04-13","revisedDate":"2023-01-23","noUsgsAuthors":false,"publicationDate":"2021-04-13","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":147999,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":814060,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70262596,"text":"70262596 - 2021 - The productivity of Cascadia aftershock sequences","interactions":[],"lastModifiedDate":"2025-01-21T17:48:40.330278","indexId":"70262596","displayToPublicDate":"2021-04-13T11:44:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"The productivity of Cascadia aftershock sequences","docAbstract":"<p><span>This study addresses questions about the productivity of Cascadia mainshock–aftershock sequences using earthquake catalogs produced by the Geological Survey of Canada and the Pacific Northwest Seismic Network. Questions concern the likelihood that future moderate to large intermediate depth intraslab earthquakes in Cascadia would have as few detectable aftershocks as those documented since 1949. More broadly, for Cascadia, we consider if aftershock productivities vary spatially, if they are outliers among global subduction zones, and if they are consistent with a physical model in which aftershocks are clock‐advanced versions of tectonically driven background seismicity. A practical motivation for this study is to assess the likely accuracy of aftershock forecasts based on productivities derived from global data that are now being issued routinely by the U.S. Geological Survey. For this reason, we estimated productivity following the identical procedures used in those forecasts and described in&nbsp;</span><a class=\"link link-ref xref-bibr\" data-modal-source-id=\"rf22\">Page<span>&nbsp;</span><i>et&nbsp;al.</i><span>&nbsp;</span>(2016)</a><span>. Results indicate that in Cascadia we can say that the next intermediate depth intraslab earthquake will likely have just a few detectable aftershocks and that aftershock productivity appears to be an outlier among global subduction zones, with rates that on average are lower by more than half, except for mainshocks in the upper plate. Our results are consistent with a clock‐advance model; productivities may be related to the proximity of mainshocks to a population of seismogenic fault patches and correlate with background seismicity rates. The latter and a clear correlation between productivities with mainshock depth indicate that both factors may have predictive value for aftershock forecasting.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200344","usgsCitation":"Gomberg, J.S., and Bodin, P., 2021, The productivity of Cascadia aftershock sequences: Bulletin of the Seismological Society of America, v. 111, no. 3, p. 1494-1507, https://doi.org/10.1785/0120200344.","productDescription":"14 p.","startPage":"1494","endPage":"1507","ipdsId":"IP-123911","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":480847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","otherGeospatial":"Cascadia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -130,\n              50\n            ],\n            [\n              -130,\n              40\n            ],\n            [\n              -120,\n              40\n            ],\n            [\n              -120,\n              50\n            ],\n            [\n              -130,\n              50\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"111","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-04-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bodin, Paul","contributorId":339818,"corporation":false,"usgs":false,"family":"Bodin","given":"Paul","affiliations":[],"preferred":false,"id":924646,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70219529,"text":"70219529 - 2021 - The effects of urban land cover dynamics on urban heat Island intensity and temporal trends","interactions":[],"lastModifiedDate":"2021-06-30T18:29:22.544721","indexId":"70219529","displayToPublicDate":"2021-04-12T08:25:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8118,"text":"GIScience & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"The effects of urban land cover dynamics on urban heat Island intensity and temporal trends","docAbstract":"<div class=\"hlFld-Abstract test\"><div class=\"abstractSection abstractInFull\"><p>Assessments of surface urban heat island (UHI) have focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain time period by including land cover information. In this study, we implemented a prototype approach to characterize the spatiotemporal variations of UHI using time series of Landsat land surface temperature products and annual land change information. We analyzed UHI distribution and change in Sioux Falls, South Dakota, in the north-central United States and found that the mean UHI intensity in the region was as large as 2.2°C during the period 1986–2017 with an increasing trend of 0.02°C per year within the area with a 5-km non-urban extent. The UHI intensity associated with high intensity urban land cover usually is stronger than with low intensity urban land cover. We evaluated the impact of different non-urban reference extents on UHI variation using different non-urban buffers. The result also suggests that the overall temporal trends of UHI intensity are almost the same when using a 5-km or 10-km non-urban buffer surrounding the urban core. The prototype approach provides a framework to consistently quantify UHI and monitor its change to a large geographic extent.</p></div></div>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2021.1903282","usgsCitation":"Xian, G.Z., Shi, H., Auch, R.F., Gallo, K., Zhou, Q., Wu, Z., and Kolian, M., 2021, The effects of urban land cover dynamics on urban heat Island intensity and temporal trends: GIScience & Remote Sensing, v. 58, no. 4, p. 501-515, https://doi.org/10.1080/15481603.2021.1903282.","productDescription":"15 p.","startPage":"501","endPage":"515","ipdsId":"IP-121520","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499916,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/b74f3081aca04fe1b1b49320295d5bfd","text":"External Repository"},{"id":385061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -96.95571899414062,\n              43.34365692013493\n            ],\n            [\n              -96.46408081054688,\n              43.34365692013493\n            ],\n            [\n              -96.46408081054688,\n              43.712556891207\n            ],\n            [\n              -96.95571899414062,\n              43.712556891207\n            ],\n            [\n              -96.95571899414062,\n              43.34365692013493\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"58","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814063,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gallo, Kevin 0000-0001-9162-5011","orcid":"https://orcid.org/0000-0001-9162-5011","contributorId":257326,"corporation":false,"usgs":false,"family":"Gallo","given":"Kevin","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":814064,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":223103,"corporation":false,"usgs":true,"family":"Zhou","given":"Qiang","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814065,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":814066,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kolian, Michael 0000-0002-7134-8317","orcid":"https://orcid.org/0000-0002-7134-8317","contributorId":257327,"corporation":false,"usgs":false,"family":"Kolian","given":"Michael","email":"","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":814067,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70219473,"text":"sir20215006 - 2021 - Regression relations and long-term water-quality constituent concentrations, loads, yields, and trends in the North Fork Ninnescah River, south-central Kansas, 1999–2019","interactions":[],"lastModifiedDate":"2021-04-13T11:49:44.944511","indexId":"sir20215006","displayToPublicDate":"2021-04-12T06:54:54","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5006","displayTitle":"Regression Relations and Long-Term Water-Quality Constituent Concentrations, Loads, Yields, and Trends in the North Fork Ninnescah River, South-Central Kansas, 1999–2019","title":"Regression relations and long-term water-quality constituent concentrations, loads, yields, and trends in the North Fork Ninnescah River, south-central Kansas, 1999–2019","docAbstract":"<p>Cheney Reservoir, in south-central Kansas, is the primary water supply for the city of Wichita, Kansas. The North Fork Ninnescah River is the largest tributary to Cheney Reservoir and contributes about 70 percent of the inflow. The U.S. Geological Survey, in cooperation with the City of Wichita, has been continuously monitoring water quality (including water temperature, specific conductance, pH, dissolved oxygen, and turbidity) on the North Fork Ninnescah River upstream from Cheney Reservoir (U.S. Geological Survey site 07144780) since November 1998. Continued data collection would be beneficial to update and describe changing water-quality conditions in the drainage basin and in the reservoir over time.</p><p>Regression models were developed to describe relations between discretely measured constituent concentrations and continuously measured physical properties. The models updated in this report include total suspended solids (TSS), suspended-sediment concentration (SSC), nitrate plus nitrite, nitrate, orthophosphate (OP), total phosphorus (TP), and total organic carbon (TOC).</p><p>Daily computed concentrations for TSS, TP, and nitrate plus nitrite during 1999–2019 were compared with Cheney Reservoir Task Force (CRTF) goals for base-flow and runoff conditions. CRTF goals for base-flow concentrations were exceeded more frequently (70 to 99.9 percent of the time) than runoff goals (0 to 11 percent of the time). Except for 2012, annual mean TSS concentrations exceeded the base-flow goal every year. Nitrate plus nitrite and TP annual mean concentrations exceeded the base-flow goals every year. TSS and nitrate plus nitrite annual mean concentrations during runoff conditions never exceeded the CRTF runoff goal. TP annual mean concentrations during runoff conditions only exceeded the CRTF runoff goal during 2002.</p><p>Sedimentation is progressively reducing the storage capacity of Cheney Reservoir. During 1999–2019, 55 percent of the computed suspended-sediment load was transported during the top 1 percent of loading days (76 days); 22 percent of the total load was transported in the top 10 loading days, indicating that substantial parts of suspended-sediment loads continue to be delivered during disproportionately small periods in Cheney Reservoir. Successful sediment management efforts necessitate reduction techniques that account for these large load events.</p><p>Flow-normalized concentrations and fluxes were computed during 1999 through 2019 using Weighted Regressions on Time, Discharge, and Season (WRTDS) statistical models and WRTDS bootstrap tests. Flow-normalized concentrations of TSS, SSC, OP, TP, and TOC had upward trend probabilities; conversely, nitrate plus nitrite had a downward trend. Flow-normalized fluxes for OP, TP, and TOC had an upward trend. No discernible patterns were identified for flow-normalized flux of TSS or suspended sediment. Nitrate plus nitrite flow-normalized flux indicated a downward trend.</p><p>Flow-normalized concentrations for TSS were less than the CRTF long-term goal of 100 milligrams per liter (mg/L), but the upward trend indicated the long-term goal may be exceeded if no changes are made. Flow-normalized TP concentrations exceeded the CRTF long-term goal (0.1 mg/L) and were assigned a very likely upward trend. Flow-normalized nitrate plus nitrite concentrations exceeded the CRTF long-term goal of 1.2 mg/L during the beginning of the study period, then were less than the CRTF goal for the remainder of the study; however, during 2010–19 flow-normalized concentrations increased by 6 percent.</p><p>Linking water-quality changes to causal factors requires consistent monitoring before, during, and after changes; this presents challenges related to length and frequency of data collection and available concomitant land-use and conservation practice data. As such, attribution of water-quality trends to land-use changes or conservation practices was not possible for this study because of a lack of land-use and conservation practice data. Additionally, because precipitation frequency and intensity are projected to continue to increase in the Great Plains region, accounting for extreme episodic events may be an important consideration in future sediment and nutrient load reduction plans.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215006","collaboration":"Prepared in cooperation with the City of Wichita","usgsCitation":"Kramer, A.R., Klager, B.J., Stone, M.L., and Eslick-Huff, P.J., 2021, Regression relations and long-term water-quality constituent concentrations, loads, yields, and trends in the North Fork Ninnescah River, south-central Kansas, 1999–2019: U.S. Geological Survey Scientific Investigations Report 2021–5006, 51 p., https://doi.org/10.3133/sir20215006.","productDescription":"Report: ix, 51 p.; Appendixes: 24; Dataset","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118868","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":384937,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":384935,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5006/coverthb.jpg"},{"id":384936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5006/sir20215006.pdf","text":"Report","size":"3.80 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5006"},{"id":384938,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5006/downloads/","text":"Appendixes 1–24","description":"SIR 2021–5006 Appendixes 1–24"}],"country":"United States","state":"Kansas","otherGeospatial":"North Fork Ninnescah River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -98.7176513671875,\n              37.60987994374712\n            ],\n            [\n              -97.3663330078125,\n              37.60987994374712\n            ],\n            [\n              -97.3663330078125,\n              38.238180119798635\n            ],\n            [\n              -98.7176513671875,\n              38.238180119798635\n            ],\n            [\n              -98.7176513671875,\n              37.60987994374712\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/kswsc\" href=\"https://www.usgs.gov/centers/kswsc\">Kansas Water Science Center</a> <br>1217 Biltmore Drive <br>Lawrence, KS 66049</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Regression Relations and Water-Quality Trend Results</li><li>Summary</li><li>References Cited</li><li>Appendixes 1–24</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-04-12","noUsgsAuthors":false,"publicationDate":"2021-04-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Kramer, Ariele R. 0000-0002-7075-3310 akramer@usgs.gov","orcid":"https://orcid.org/0000-0002-7075-3310","contributorId":185245,"corporation":false,"usgs":true,"family":"Kramer","given":"Ariele","email":"akramer@usgs.gov","middleInitial":"R.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":813710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klager, Brian J. 0000-0001-8361-6043 bklager@usgs.gov","orcid":"https://orcid.org/0000-0001-8361-6043","contributorId":5543,"corporation":false,"usgs":true,"family":"Klager","given":"Brian","email":"bklager@usgs.gov","middleInitial":"J.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":813711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stone, Mandy L. 0000-0002-6711-1536 mstone@usgs.gov","orcid":"https://orcid.org/0000-0002-6711-1536","contributorId":4409,"corporation":false,"usgs":true,"family":"Stone","given":"Mandy","email":"mstone@usgs.gov","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":813712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eslick-Huff, Patrick J. 0000-0003-2611-6012","orcid":"https://orcid.org/0000-0003-2611-6012","contributorId":257038,"corporation":false,"usgs":true,"family":"Eslick-Huff","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":813713,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70219474,"text":"ofr20211007 - 2021 - Characterization of water-resource threats and needs for U.S. Fish and Wildlife Service National Wildlife Refuges in the Legacy Mountain-Prairie Region, 2020","interactions":[],"lastModifiedDate":"2021-04-09T19:02:12.178637","indexId":"ofr20211007","displayToPublicDate":"2021-04-09T13:15:00","publicationYear":"2021","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":"2021-1007","displayTitle":"Characterization of Water-Resource Threats and Needs for U.S. Fish and Wildlife Service National Wildlife Refuges in the Legacy Mountain-Prairie Region, 2020","title":"Characterization of water-resource threats and needs for U.S. Fish and Wildlife Service National Wildlife Refuges in the Legacy Mountain-Prairie Region, 2020","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service (FWS), began a study in 2019 to complete the compilation and quality assurance of water-resource threats and needs data for the 117 National Wildlife Refuges (NWRs) in the FWS Legacy Mountain-Prairie Region (LMPR) and to characterize the water-resource threats and needs of each refuge and of the LMPR itself. The LMPR encompasses the states of Colorado, Kansas, Montana, Nebraska, North Dakota, South Dakota, Utah, and Wyoming. This report includes the compilation and quality assurance of current (April 2020) water-resource threats and needs data for the refuges in the LMPR and a statistical, graphical, and spatial characterization, including the ranking and prioritization of threat types, threat causes, and needs by the number of occurrences in the LMPR as a whole and by refuges, states, and select U.S. Environmental Protection Agency Level III Ecoregions.</p><p>A total of 540 unique threat occurrences were identified for 109 refuges in the LMPR. No threats were identified for eight refuges. About 43 percent of the threat occurrences, for 59 refuges, had a high-severity threat rating. Of the 10 most common threat types, 8 were also among the most common high-severity threat types. Water-resource threats had 72 different causes. About 83 percent of the overall common causes for threats and for high-severity threats were the same. The most common threat types overall and the most common high-severity threat types were compromised water management capability, habitat shifting/alteration, and altered flow regimes. The 20 water-resource threat types for Long Lake NWR were the most for refuges in the LMPR. Other refuges with the greatest number of threat types included Marais des Cygnes NWR (18) and Arapaho and Lee Metcalf NWRs (16 each). About 54 percent of refuges with threats had high-severity threats. Arapaho and Quivira NWRs each had 10 high-severity threat types, the maximum number of high-severity threat types for LMPR refuges.</p><p>A total of 637 unique need occurrences were identified for 114 refuges. No needs were reported for three refuges. The most common need type, a Water Resource Inventory and Assessment, was reported for 78 refuges. Two of the most common need types, repair and replace water management infrastructure and water supply/quantity monitoring, were the most common high-priority need types. Bear River Migratory Bird Refuge had the most (39) unique water-resource need types for refuges in the LMPR. Other refuges with the greatest number of need types were Baca (38), Alamosa (36), and Monte Vista (36) NWRs. The most high-priority need types for a refuge was 23, at Monte Vista NWR. Alamosa (22), Baca (22), and Lake Andes (19) NWRs were also among the top 4 refuges with the greatest number of high-priority need types.</p><p>An overall ranking scheme was developed to identify refuges that have the highest-ranking priority for conservation efforts to fulfill refuges’ statutory purposes. The count of occurrences of high-severity threats and high-priority needs were summed to determine the overall ranking value for a refuge. The 10 refuges with the highest overall ranking values, in order of ranking from higher to lower, were Alamosa, Baca, and Monte Vista NWRs (tied for highest); Lake Andes NWR, Ouray and Quivira NWRs, Bear River Migratory Bird Refuge and Flint Hills NWR, Cokeville Meadows NWR, and Arapaho NWR.</p><p>About 33 percent of overall threat occurrences were reported as under the control of the FWS to mitigate, as were 37 percent of all threat occurrences with a high-severity rating. The most common overall threat types and high-severity threat types under FWS control were compromised water management capability; habitat shifting/alteration; altered flow regimes; loss/alteration of wetland habitat; and legal challenges or fines for non-compliance with water policy, law, or regulation. A total of 68 percent of overall need occurrences and 67 percent of all high-priority need occurrences were under the control of the FWS. The most common overall need types and high-priority needs types under control were repair or replace water management infrastructure, water supply/quantity monitoring, water quality baseline monitoring, and protect habitat from invasive species. A Water Resource Inventory and Assessment was also a common overall need under FWS control, as was the high-priority need of water level monitoring.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20211007","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Bauch, N.J., Kohn, M.S., and Caruso, B.S., 2021, Characterization of water-resource threats and needs for U.S. Fish and Wildlife Service National Wildlife Refuges in the Legacy Mountain-Prairie Region, 2020: U.S. Geological Survey Open-File Report 2021–1007, 46 p., https://doi.org/10.3133/ofr20211007.","productDescription":"viii, 46 p.","onlineOnly":"Y","ipdsId":"IP-119415","costCenters":[{"id":191,"text":"Colorado Water Science 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25046, MS 415<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Characterization of Water-Resource Threats and Needs</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Table Listing U.S. Wildlife Fish and Wildlife Service Refuges in the Legacy Mountain-Prairie Region and Maps Showing Severity and Priority Ratings for the Most Common Water-Resource Threat Types and Causes and Water-Resource Need Types</li></ul>","publishedDate":"2021-04-09","noUsgsAuthors":false,"publicationDate":"2021-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Bauch, Nancy J. 0000-0002-0302-2892","orcid":"https://orcid.org/0000-0002-0302-2892","contributorId":202707,"corporation":false,"usgs":true,"family":"Bauch","given":"Nancy J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caruso, Brian S. 0000-0002-2184-4961","orcid":"https://orcid.org/0000-0002-2184-4961","contributorId":257039,"corporation":false,"usgs":false,"family":"Caruso","given":"Brian S.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813716,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70219525,"text":"70219525 - 2021 - Exploring the regional dynamics of U.S. irrigated agriculture from 2002 to 2017","interactions":[],"lastModifiedDate":"2021-04-12T13:24:29.760856","indexId":"70219525","displayToPublicDate":"2021-04-09T08:19:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Exploring the regional dynamics of U.S. irrigated agriculture from 2002 to 2017","docAbstract":"<p><span>The United States has a geographically mature and stable land use and land cover system including land used as irrigated cropland; however, changes in irrigation land use frequently occur related to various drivers. We applied a consistent methodology at a 250 m spatial resolution across the lower 48 states to map and estimate irrigation dynamics for four map eras (2002, 2007, 2012, and 2017) and over four 5-year mapping intervals. The resulting geospatial maps (called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset or MIrAD-US) involved inputs from county-level irrigated statistics from the U.S. Department of Agriculture, National Agricultural Statistics Service, agricultural land cover from the U.S. Geological Survey National Land Cover Database, and an annual peak vegetation index derived from expedited MODIS satellite imagery. This study investigated regional and periodic patterns in the amount of change in irrigated agriculture and linked gains and losses to proximal causes and consequences. While there was a 7% overall increase in irrigated area from 2002 to 2017, we found surprising variability by region and by 5-year map interval. Irrigation land use dynamics affect the environment, water use, and crop yields. Regionally, we found that the watersheds with the largest irrigation gains (based on percent of area) included the Missouri, Upper Mississippi, and Lower Mississippi watersheds. Conversely, the California and the Texas–Gulf watersheds experienced fairly consistent irrigation losses during these mapping intervals. Various drivers for irrigation dynamics included regional climate fluctuations and drought events, demand for certain crops, government land or water policies, and economic incentives like crop pricing and land values. The MIrAD-US (Version 4) was assessed for accuracy using a variety of existing regionally based reference data. Accuracy ranged between 70% and 95%, depending on the region.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/land10040394","usgsCitation":"Shrestha, D., Brown, J.F., Benedict, T.D., and Howard, D., 2021, Exploring the regional dynamics of U.S. irrigated agriculture from 2002 to 2017: Land, v. 10, no. 4, https://doi.org/10.3390/land10040394.","productDescription":"394, 16 p.","startPage":"394","ipdsId":"IP-126684","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452730,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land10040394","text":"Publisher Index Page"},{"id":385004,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      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              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       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Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":813936,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":813937,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benedict, Trenton D 0000-0001-8672-2204","orcid":"https://orcid.org/0000-0001-8672-2204","contributorId":256662,"corporation":false,"usgs":false,"family":"Benedict","given":"Trenton","email":"","middleInitial":"D","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":813938,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howard, Daniel 0000-0002-7563-7538","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":256667,"corporation":false,"usgs":false,"family":"Howard","given":"Daniel","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":813939,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70240297,"text":"70240297 - 2021 - Genetic considerations for rewilding the San Joaquin Desert","interactions":[],"lastModifiedDate":"2023-02-03T15:15:55.360989","indexId":"70240297","displayToPublicDate":"2021-04-08T09:10:16","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"8","title":"Genetic considerations for rewilding the San Joaquin Desert","docAbstract":"Genetic data are a powerful and important tool for guiding rewilding efforts and for monitoring the recovery outcomes of those efforts. When used in conjunction with historic species’ distribution records and predictive habitat suitability modeling, genetic information adds a key piece to the puzzle that will increase the probability of successful ecosystem restoration.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Rewilding agricultural landscapes: a California study in rebalancing the needs of people and nature","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Island Press","usgsCitation":"Richmond, J.Q., Wood, D.A., and Matocq, M.D., 2021, Genetic considerations for rewilding the San Joaquin Desert, chap. 8 <i>of</i> Rewilding agricultural landscapes: a California study in rebalancing the needs of people and nature, p. 109-128.","productDescription":"20 p.","startPage":"109","endPage":"128","ipdsId":"IP-125484","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Desert","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -119.17782627056152,\n              35.01010185782188\n            ],\n            [\n              -118.30695417496116,\n              35.621582059868544\n            ],\n            [\n              -119.44808176516588,\n              37.07443079472277\n            ],\n            [\n              -120.97833657128518,\n              36.86695517685743\n            ],\n            [\n              -119.17782627056152,\n              35.01010185782188\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863290,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863291,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matocq, Marjorie D","contributorId":222917,"corporation":false,"usgs":false,"family":"Matocq","given":"Marjorie","email":"","middleInitial":"D","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":863292,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70219526,"text":"70219526 - 2021 - Reconstructing the dynamics of the highly similar May 2016 and June 2019 Iliamna Volcano, Alaska ice–rock avalanches from seismoacoustic data","interactions":[],"lastModifiedDate":"2021-04-12T13:21:07.805475","indexId":"70219526","displayToPublicDate":"2021-04-08T08:08:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7942,"text":"Earth Surface Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing the dynamics of the highly similar May 2016 and June 2019 Iliamna Volcano, Alaska ice–rock avalanches from seismoacoustic data","docAbstract":"<p>Surficial mass wasting events are a hazard worldwide. Seismic and acoustic signals from these often remote processes, combined with other geophysical observations, can provide key information for monitoring and rapid response efforts and enhance our understanding of event dynamics. Here, we present seismoacoustic data and analyses for two very large ice–rock avalanches occurring on Iliamna Volcano, Alaska (USA), on 22 May 2016 and 21 June 2019. Iliamna is a glacier-mantled stratovolcano located in the Cook Inlet, ∼200 km from Anchorage, Alaska. The volcano experiences massive, quasi-annual slope failures due to glacial instabilities and hydrothermal alteration of volcanic rocks near its summit. The May 2016 and June 2019 avalanches were particularly large and generated energetic seismic and infrasound signals which were recorded at numerous stations at ranges from ∼9 to over 600 km. Both avalanches initiated in the same location near the head of Iliamna's east-facing Red Glacier, and their ∼8 km long runout shapes are nearly identical. This repeatability – which is rare for large and rapid mass movements – provides an excellent opportunity for comparison and validation of seismoacoustic source characteristics. For both events, we invert long-period (15–80 s) seismic signals to obtain a force-time representation of the source. We model the avalanche as a sliding block which exerts a spatially static point force on the Earth. We use this force-time function to derive constraints on avalanche acceleration, velocity, and directionality, which are compatible with satellite imagery and observed terrain features. Our inversion results suggest that the avalanches reached speeds exceeding 70 m s−1, consistent with numerical modeling from previous Iliamna studies. We lack sufficient local infrasound data to test an acoustic source model for these processes. However, the acoustic data suggest that infrasound from these avalanches is produced after the mass movement regime transitions from cohesive block-type failure to granular and turbulent flow – little to no infrasound is generated by the initial failure. At Iliamna, synthesis of advanced numerical flow models and more detailed ground observations combined with increased geophysical station coverage could yield significant gains in our understanding of these events.</p>","language":"English","publisher":"Copernicus","doi":"10.5194/esurf-9-271-2021","usgsCitation":"Toney, L., Fee, D., Allstadt, K.E., Haney, M.M., and Matoza, R.S., 2021, Reconstructing the dynamics of the highly similar May 2016 and June 2019 Iliamna Volcano, Alaska ice–rock avalanches from seismoacoustic data: Earth Surface Dynamics, v. 9, p. 271-293, https://doi.org/10.5194/esurf-9-271-2021.","productDescription":"23 p.","startPage":"271","endPage":"293","ipdsId":"IP-122705","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":452741,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/esurf-9-271-2021","text":"Publisher Index Page"},{"id":385003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Iliamna Volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -156.26953125,\n              59.712097173322924\n            ],\n            [\n              -144.8876953125,\n              59.712097173322924\n            ],\n            [\n              -144.8876953125,\n              63.31268278043484\n            ],\n            [\n              -156.26953125,\n              63.31268278043484\n            ],\n            [\n              -156.26953125,\n              59.712097173322924\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Toney, Liam 0000-0003-0167-9433","orcid":"https://orcid.org/0000-0003-0167-9433","contributorId":257264,"corporation":false,"usgs":true,"family":"Toney","given":"Liam","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":813940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fee, David","contributorId":251816,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":813941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":813942,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":813943,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Matoza, Robin S.","contributorId":257265,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","email":"","middleInitial":"S.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":813944,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70220260,"text":"70220260 - 2021 - A food web including parasites for kelp forests of the Santa Barbara Channel, California","interactions":[],"lastModifiedDate":"2021-04-29T12:41:34.184802","indexId":"70220260","displayToPublicDate":"2021-04-08T07:34:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"A food web including parasites for kelp forests of the Santa Barbara Channel, California","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>We built a high-resolution topological food web for the kelp forests of the Santa Barbara Channel, California, USA that includes parasites and significantly improves resolution compared to previous webs. The 1,098 nodes and 21,956 links in the web describe an economically, socially, and ecologically vital system. Nodes are broken into life-stages, with 549 free-living life-stages (492 species from 21 Phyla) and 549 parasitic life-stages (450 species from 10 Phyla). Links represent three kinds of trophic interactions, with 9,352 predator-prey links, 2,733 parasite-host links and 9,871 predator-parasite links. All decisions for including nodes and links are documented, and extensive metadata in the node list allows users to filter the node list to suit their research questions. The kelp-forest food web is more species-rich than any other published food web with parasites, and it&nbsp;has the largest proportion of parasites. Our food web may be used to predict how kelp forests may respond to change, will advance our understanding of parasites in ecosystems, and fosters development of theory that incorporates large networks.</p></div></div>","language":"English","publisher":"Nature","doi":"10.1038/s41597-021-00880-4","usgsCitation":"Morton, D.N., Antonino, C.Y., Broughton, F.J., Dykman, L.N., Kuris, A.M., and Lafferty, K.D., 2021, A food web including parasites for kelp forests of the Santa Barbara Channel, California: Scientific Data, v. 8, 99, 14 p., https://doi.org/10.1038/s41597-021-00880-4.","productDescription":"99, 14 p.","ipdsId":"IP-124484","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":452760,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-021-00880-4","text":"Publisher Index Page"},{"id":385383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Santa Barbara Channel","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.58593749999999,\n              33.55512901742288\n            ],\n            [\n              -118.93249511718749,\n              33.55512901742288\n            ],\n            [\n              -118.93249511718749,\n              34.52918706954935\n            ],\n            [\n              -120.58593749999999,\n              34.52918706954935\n            ],\n            [\n              -120.58593749999999,\n              33.55512901742288\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2021-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Morton, Dana N.","contributorId":224397,"corporation":false,"usgs":false,"family":"Morton","given":"Dana","email":"","middleInitial":"N.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":814916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Antonino, Cristiana Y. 0000-0002-3352-9344","orcid":"https://orcid.org/0000-0002-3352-9344","contributorId":257725,"corporation":false,"usgs":false,"family":"Antonino","given":"Cristiana","email":"","middleInitial":"Y.","affiliations":[{"id":52092,"text":"College of Creative Studies, University of California, Santa Barbara, CA, 93106-6150, USA","active":true,"usgs":false}],"preferred":true,"id":814917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broughton, Farallon J","contributorId":257726,"corporation":false,"usgs":false,"family":"Broughton","given":"Farallon","email":"","middleInitial":"J","affiliations":[{"id":52092,"text":"College of Creative Studies, University of California, Santa Barbara, CA, 93106-6150, USA","active":true,"usgs":false}],"preferred":false,"id":814918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dykman, Lauren N","contributorId":257727,"corporation":false,"usgs":false,"family":"Dykman","given":"Lauren","email":"","middleInitial":"N","affiliations":[{"id":52094,"text":"Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA, 93106-6150, USA","active":true,"usgs":false}],"preferred":false,"id":814919,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kuris, Armand M.","contributorId":189859,"corporation":false,"usgs":false,"family":"Kuris","given":"Armand","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":814920,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":814921,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70219486,"text":"70219486 - 2021 - Effects of midazolam on corticosterone and blood gases in spectacled eiders prior to transmitter implantation","interactions":[],"lastModifiedDate":"2021-06-30T17:59:04.702622","indexId":"70219486","displayToPublicDate":"2021-04-08T07:07:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Effects of midazolam on corticosterone and blood gases in spectacled eiders prior to transmitter implantation","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Stress and physical exertion may affect the physiology and behavior of wildlife during and after capture, and consequently, survival following release. Such effects may reduce the quality and quantity of the data obtained from captured wildlife. We captured spectacled eiders (<i>Somateria fischeri</i>), a species listed as threatened under the United States Endangered Species Act, in western Alaska, USA, during spring 2018 for surgical implantation of satellite transmitters. We evaluated the efficacy of midazolam, a benzodiazepine sedative given at capture, to reduce stress and physical exertion. We dosed spectacled eiders with either midazolam (5 mg/ml,<span>&nbsp;</span><img class=\"section_image\" src=\"https://wildlife.onlinelibrary.wiley.com/cms/asset/1bb0b6c7-a9fa-4aec-add8-744ce6b2209c/jwmg22046-math-0001.png\" alt=\"urn:x-wiley:0022541X:media:jwmg22046:jwmg22046-math-0001\" data-mce-src=\"https://wildlife.onlinelibrary.wiley.com/cms/asset/1bb0b6c7-a9fa-4aec-add8-744ce6b2209c/jwmg22046-math-0001.png\"><span>&nbsp;</span>= 2.2 mg/kg intramuscular;<span>&nbsp;</span><i>n</i> = 20) or saline (0.7 ml intramuscular;<span>&nbsp;</span><i>n</i> = 20) at the point of capture. We assessed sedation level and collected blood samples upon arrival to the field surgery site and at anesthetic induction. We found that midazolam reduced mean corticosterone concentration by 29% and median lactate concentration by 30.3% at the mean arrival time (42 min post‐dosing) relative to the control group. These effects had abated by the mean induction time (99 min post‐dosing). Unexpectedly, blood pH was reduced in the midazolam treatment relative to controls at both arrival and induction, which likely resulted from sedative‐induced respiratory depression that was easily treated with intubation and mechanical ventilation, and administration of the reversal drug, flumazenil. Low blood pH was not associated with negative post‐surgical outcomes, as had been found in spectacled eiders with acidosis caused by anaerobic metabolism typical of physical exertion. Intramuscular injection of midazolam in the field effectively reduced stress and physical exertion in spectacled eiders prior to surgical implantation of transmitters. © 2021 The Wildlife Society.</p></div></div>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22046","usgsCitation":"Spriggs, M., Rizzolo, D., Martin, K., Myers, G.E., and Sexson, M.G., 2021, Effects of midazolam on corticosterone and blood gases in spectacled eiders prior to transmitter implantation: Journal of Wildlife Management, v. 85, no. 5, p. 909-919, https://doi.org/10.1002/jwmg.22046.","productDescription":"11 p.","startPage":"909","endPage":"919","ipdsId":"IP-124966","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384962,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"85","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Spriggs, Maria","contributorId":127662,"corporation":false,"usgs":false,"family":"Spriggs","given":"Maria","email":"","affiliations":[],"preferred":false,"id":813775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rizzolo, Daniel","contributorId":257067,"corporation":false,"usgs":false,"family":"Rizzolo","given":"Daniel","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Kate","contributorId":223948,"corporation":false,"usgs":false,"family":"Martin","given":"Kate","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Myers, Gwen E.","contributorId":89336,"corporation":false,"usgs":false,"family":"Myers","given":"Gwen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":813778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sexson, Matthew G. 0000-0002-1078-0835 msexson@usgs.gov","orcid":"https://orcid.org/0000-0002-1078-0835","contributorId":5544,"corporation":false,"usgs":true,"family":"Sexson","given":"Matthew","email":"msexson@usgs.gov","middleInitial":"G.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":813779,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70227012,"text":"70227012 - 2021 - Diagenesis revealed by fine-scale features at Vera Rubin ridge, Gale crater, Mars","interactions":[],"lastModifiedDate":"2021-12-27T14:06:20.930698","indexId":"70227012","displayToPublicDate":"2021-04-07T08:03:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9967,"text":"JGR Planets","active":true,"publicationSubtype":{"id":10}},"title":"Diagenesis revealed by fine-scale features at Vera Rubin ridge, Gale crater, Mars","docAbstract":"<div class=\"article-section__content en main\"><p>Fine-scale (submillimeter to centimeter) depositional and diagenetic features encountered during the<span>&nbsp;</span><i>Curiosity</i><span>&nbsp;</span>rover's traverse in Gale crater provide a means to understand the geologic history of Vera Rubin ridge (VRR). VRR is a topographically high feature on the lower north slope of Aeolis Mons, a 5-km high stratified mound within Gale crater. We use high-spatial resolution images from the Mars Hand Lens Imager (MAHLI) as well as grain sizes estimated with the Gini index mean score technique that uses ChemCam Laser-Induced Breakdown Spectroscopy (LIBS) chemical data to constrain the postdepositional history of the strata exposed on this ridge. MAHLI images were used to examine the color, grain size, and style of lamination of the host rocks, as well as to explore the occurrence of nodules, diagenetic crystals, pits, and a variety of dark-gray iron-rich features. This survey revealed abundant and widespread diagenetic features within the rocks exposed on VRR and demonstrated that rock targets estimated to be coarser generally contain more diagenetic features than those estimated to have finer grains, which indicate that grain size may have influenced the degree and type of diagenesis. A subset of rocks within VRR are gray in color and exhibit the highest proportion of diagenetic features. We suggest that these targets experienced a different diagenetic history than the other rocks on VRR and hypothesize that redistribution and recrystallization of iron within specific intervals may have resulted in both the gray color and the abundance of dark-gray iron-rich diagenetic features.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JE006311","usgsCitation":"Bennett, K.A., Rivera-Hernandez, F., Tinker, C., Horgan, B.H., Fey, D.M., Edwards, C.S., Edgar, L.A., Kronyak, R., Edgett, K.S., Fraeman, A.A., Kah, L.C., Henderson, M., Stein, N., Dehouck, E., and Williams, A., 2021, Diagenesis revealed by fine-scale features at Vera Rubin ridge, Gale crater, Mars: JGR Planets, v. 126, no. 5, e2019JE006311, 24 p., https://doi.org/10.1029/2019JE006311.","productDescription":"e2019JE006311, 24 p.","ipdsId":"IP-114170","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":452782,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019je006311","text":"External Repository"},{"id":393407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Bennett, Kristen A. 0000-0001-8105-7129","orcid":"https://orcid.org/0000-0001-8105-7129","contributorId":237068,"corporation":false,"usgs":true,"family":"Bennett","given":"Kristen","email":"","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":829200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivera-Hernandez, Frances","contributorId":270378,"corporation":false,"usgs":false,"family":"Rivera-Hernandez","given":"Frances","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":829201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tinker, Connor","contributorId":270379,"corporation":false,"usgs":false,"family":"Tinker","given":"Connor","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":829202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Horgan, Briony H. N. 0000-0001-6314-9724","orcid":"https://orcid.org/0000-0001-6314-9724","contributorId":258276,"corporation":false,"usgs":false,"family":"Horgan","given":"Briony","email":"","middleInitial":"H. N.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":829203,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fey, Deirdra M.","contributorId":214028,"corporation":false,"usgs":false,"family":"Fey","given":"Deirdra","email":"","middleInitial":"M.","affiliations":[{"id":36716,"text":"Malin Space Science Systems","active":true,"usgs":false}],"preferred":false,"id":829204,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, C. S.","contributorId":270383,"corporation":false,"usgs":false,"family":"Edwards","given":"C.","email":"","middleInitial":"S.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":829205,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Edgar, Lauren A. 0000-0001-7512-7813 ledgar@usgs.gov","orcid":"https://orcid.org/0000-0001-7512-7813","contributorId":167501,"corporation":false,"usgs":true,"family":"Edgar","given":"Lauren","email":"ledgar@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":829206,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kronyak, Rachel","contributorId":181503,"corporation":false,"usgs":false,"family":"Kronyak","given":"Rachel","email":"","affiliations":[],"preferred":false,"id":829207,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Edgett, Kenneth S.","contributorId":203786,"corporation":false,"usgs":false,"family":"Edgett","given":"Kenneth","email":"","middleInitial":"S.","affiliations":[{"id":36716,"text":"Malin Space Science Systems","active":true,"usgs":false}],"preferred":false,"id":829208,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fraeman, Abigail A.","contributorId":200404,"corporation":false,"usgs":false,"family":"Fraeman","given":"Abigail","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":829209,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kah, Linda C.","contributorId":181497,"corporation":false,"usgs":false,"family":"Kah","given":"Linda","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":829210,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Henderson, Marie","contributorId":270376,"corporation":false,"usgs":false,"family":"Henderson","given":"Marie","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":829211,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Stein, Nathan","contributorId":167651,"corporation":false,"usgs":false,"family":"Stein","given":"Nathan","affiliations":[{"id":24730,"text":"Department of Earth and Planetary Sciences, Washington University in St. Louis","active":true,"usgs":false}],"preferred":false,"id":829212,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Dehouck, Erwin","contributorId":270386,"corporation":false,"usgs":false,"family":"Dehouck","given":"Erwin","email":"","affiliations":[{"id":56160,"text":"Université de Lyon","active":true,"usgs":false}],"preferred":false,"id":829213,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Williams, Amy","contributorId":176785,"corporation":false,"usgs":false,"family":"Williams","given":"Amy","affiliations":[],"preferred":false,"id":829214,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}}
,{"id":70219482,"text":"70219482 - 2021 - Using enclosed Y-mazes to assess chemosensory behavior in reptiles","interactions":[],"lastModifiedDate":"2021-04-12T11:48:07.072909","indexId":"70219482","displayToPublicDate":"2021-04-07T07:05:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2498,"text":"Journal of Visualized Experiments","active":true,"publicationSubtype":{"id":10}},"title":"Using enclosed Y-mazes to assess chemosensory behavior in reptiles","docAbstract":"Reptiles utilize a variety of environmental cues to inform and drive animal behavior such as chemical scent trails produced by food or conspecifics. Decrypting the scent-trailing behavior of vertebrates, particularly invasive species, enables the discovery of cues that induce exploratory behavior and can aid in the development of valuable basic and applied biological tools. However, pinpointing behaviors dominantly driven by chemical cues versus other competing environmental cues can be challenging. Y-mazes are common tools used in animal behavior research that allow quantification of vertebrate chemosensory behavior across a range of taxa. By reducing external stimuli, Y-mazes remove confounding factors and present focal animals with a binary choice. In our Y-maze studies, a scenting animal is restricted to one arm of the maze to leave a scent trail and is removed once scent-laying parameters have been met. Then, depending on the trial type, either the focal animal is allowed into the maze, or a competing scent trail is created. The result is a record of the focal animal's choice and behavior while discriminating between the chemical cues presented. Here, two Y-maze apparatuses tailored to different invasive reptile species: Argentine black and white tegu lizards (Salvator merianae) and Burmese pythons (Python bivittatus) are described, outlining the operation and cleaning of these Y-mazes. Further, the variety of data produced, experimental drawbacks and solutions, and suggested data analysis frameworks have been summarized.","language":"English","publisher":"Jove","doi":"10.3791/61858","usgsCitation":"Parker, M.R., Currylow, A.F., Tillman, E.A., Robinson, C.J., Josimovich, J.M., Bukovich, I.M., Nazarian, L.A., Nafus, M.G., Kluever, B.M., and Yackel Adams, A.A., 2021, Using enclosed Y-mazes to assess chemosensory behavior in reptiles: Journal of Visualized Experiments, v. 170, e61858, 19 p., https://doi.org/10.3791/61858.","productDescription":"e61858, 19 p.","onlineOnly":"Y","ipdsId":"IP-120440","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":452792,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://digitalcommons.unl.edu/icwdm_usdanwrc/2437","text":"Publisher Index Page"},{"id":384963,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"170","noUsgsAuthors":false,"publicationDate":"2021-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Parker, M. Rockwell 0000-0003-0923-3911","orcid":"https://orcid.org/0000-0003-0923-3911","contributorId":257054,"corporation":false,"usgs":false,"family":"Parker","given":"M.","email":"","middleInitial":"Rockwell","affiliations":[{"id":16809,"text":"James Madison University","active":true,"usgs":false}],"preferred":false,"id":813752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Currylow, Andrea Faye 0000-0003-1631-8964","orcid":"https://orcid.org/0000-0003-1631-8964","contributorId":257055,"corporation":false,"usgs":true,"family":"Currylow","given":"Andrea","email":"","middleInitial":"Faye","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":813753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tillman, Eric A.","contributorId":257056,"corporation":false,"usgs":false,"family":"Tillman","given":"Eric","email":"","middleInitial":"A.","affiliations":[{"id":51974,"text":"US Department of Agriculture, National Wildlife Research Center, Gainesville, FL","active":true,"usgs":false}],"preferred":false,"id":813754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robinson, Charlotte J. 0000-0002-9156-1609","orcid":"https://orcid.org/0000-0002-9156-1609","contributorId":257057,"corporation":false,"usgs":false,"family":"Robinson","given":"Charlotte","email":"","middleInitial":"J.","affiliations":[{"id":51975,"text":"USGS Fort Collins Science Center (formerly)","active":true,"usgs":false}],"preferred":false,"id":813756,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Josimovich, Jillian Maureen 0000-0002-7523-3496 jjosimovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7523-3496","contributorId":257058,"corporation":false,"usgs":true,"family":"Josimovich","given":"Jillian","email":"jjosimovich@usgs.gov","middleInitial":"Maureen","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":813757,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bukovich, Isabella M.G.","contributorId":257059,"corporation":false,"usgs":false,"family":"Bukovich","given":"Isabella","email":"","middleInitial":"M.G.","affiliations":[{"id":16809,"text":"James Madison University","active":true,"usgs":false}],"preferred":false,"id":813759,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nazarian, Lauren A.","contributorId":257060,"corporation":false,"usgs":false,"family":"Nazarian","given":"Lauren","email":"","middleInitial":"A.","affiliations":[{"id":16809,"text":"James Madison University","active":true,"usgs":false}],"preferred":false,"id":813760,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nafus, Melia G. 0000-0002-7325-3055 mnafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7325-3055","contributorId":197462,"corporation":false,"usgs":true,"family":"Nafus","given":"Melia","email":"mnafus@usgs.gov","middleInitial":"G.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":813761,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kluever, Bryan M.","contributorId":257061,"corporation":false,"usgs":false,"family":"Kluever","given":"Bryan","email":"","middleInitial":"M.","affiliations":[{"id":51974,"text":"US Department of Agriculture, National Wildlife Research Center, Gainesville, FL","active":true,"usgs":false}],"preferred":false,"id":813762,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":813763,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70228404,"text":"70228404 - 2021 - The American Kestrel (Falco sparverius) genoscape: Implications for monitoring, management, and subspecies boundaries","interactions":[],"lastModifiedDate":"2022-02-10T16:55:34.103523","indexId":"70228404","displayToPublicDate":"2021-04-06T10:35:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10109,"text":"Ornithology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The American Kestrel (<i>Falco sparverius</i>) genoscape: Implications for monitoring, management, and subspecies boundaries","title":"The American Kestrel (Falco sparverius) genoscape: Implications for monitoring, management, and subspecies boundaries","docAbstract":"<p><span>Identifying population genetic structure is useful for inferring evolutionary process and comparing the resulting structure with subspecies boundaries can aid in species management. The American Kestrel (</span><i>Falco sparverius</i><span>) is a widespread and highly diverse species with 17 total subspecies, only 2 of which are found north of U.S./Mexico border (</span><i>F. s. paulus</i><span>&nbsp;is restricted to southeastern United States, while&nbsp;</span><i>F. s. sparverius</i><span>&nbsp;breeds across the remainder of the U.S. and Canadian distribution). In many parts of their U.S. and Canadian range, American Kestrels have been declining, but it has been difficult to interpret demographic trends without a clearer understanding of gene flow among populations. Here we sequence the first American Kestrel genome and scan the genome of 197 individuals from 12 sampling locations across the United States and Canada in order to identify population structure. To validate signatures of population structure and fill in sampling gaps across the U.S. and Canadian range, we screened 192 outlier loci in an additional 376 samples from 34 sampling locations. Overall, our analyses support the existence of 5 genetically distinct populations of American Kestrels—eastern, western, Texas, Florida, and Alaska. Interestingly, we found that while our genome-wide genetic data support the existence of previously described subspecies boundaries in the United States and Canada, genetic differences across the sampled range correlate more with putative migratory phenotypes (resident, long-distance, and short-distance migrants) rather than a priori described subspecies boundaries per se. Based on our results, we suggest the resulting 5 genetically distinct populations serve as the foundation for American Kestrel conservation and management in the face of future threats.</span></p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/auk/ukaa051","usgsCitation":"Ruegg, K.C., Brinkmeyer, M., Bossu, C.M., Bay, R., Anderson, E.C., Boal, C.W., Dawson, R.D., Eschenbauch, A., McClure, C.J., Miller, K.E., Morrow, L., Morrow, J.R., Oleyar, M.D., Ralph, B., Schulwitz, S., Swem, T., Therrien, J.F., Van Buskirk, R., Smith, T.B., and Heath, J.A., 2021, The American Kestrel (Falco sparverius) genoscape: Implications for monitoring, management, and subspecies boundaries: Ornithology, v. 138, no. 2, ukaa051, https://doi.org/10.1093/auk/ukaa051.","productDescription":"ukaa051","ipdsId":"IP-115588","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":452797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/auk/ukaa051","text":"Publisher Index Page"},{"id":395779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","volume":"138","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-04-24","publicationStatus":"PW","contributors":{"editors":[{"text":"Therrien, J-F.","contributorId":275699,"corporation":false,"usgs":false,"family":"Therrien","given":"J-F.","email":"","affiliations":[{"id":51980,"text":"Hawk Mountain Sanctuary","active":true,"usgs":false}],"preferred":false,"id":834226,"contributorType":{"id":2,"text":"Editors"},"rank":16}],"authors":[{"text":"Ruegg, K. C.","contributorId":275671,"corporation":false,"usgs":false,"family":"Ruegg","given":"K.","email":"","middleInitial":"C.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":834208,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinkmeyer, M.","contributorId":275672,"corporation":false,"usgs":false,"family":"Brinkmeyer","given":"M.","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":834209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bossu, C. M.","contributorId":275674,"corporation":false,"usgs":false,"family":"Bossu","given":"C.","email":"","middleInitial":"M.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":834315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bay, R.","contributorId":275673,"corporation":false,"usgs":false,"family":"Bay","given":"R.","email":"","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":834210,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, E. C.","contributorId":275675,"corporation":false,"usgs":false,"family":"Anderson","given":"E.","email":"","middleInitial":"C.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":834212,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834213,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dawson, R. D.","contributorId":275676,"corporation":false,"usgs":false,"family":"Dawson","given":"R.","email":"","middleInitial":"D.","affiliations":[{"id":49840,"text":"University of Northern British Columbia","active":true,"usgs":false}],"preferred":false,"id":834214,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Eschenbauch, A.","contributorId":275681,"corporation":false,"usgs":false,"family":"Eschenbauch","given":"A.","email":"","affiliations":[{"id":56878,"text":"Central Wisconsin Kestrel Research","active":true,"usgs":false}],"preferred":false,"id":834215,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McClure, C. J. W.","contributorId":275685,"corporation":false,"usgs":false,"family":"McClure","given":"C.","email":"","middleInitial":"J. W.","affiliations":[{"id":56879,"text":"The Pergrine Fund","active":true,"usgs":false}],"preferred":false,"id":834216,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miller, K. E.","contributorId":275688,"corporation":false,"usgs":false,"family":"Miller","given":"K.","email":"","middleInitial":"E.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":834217,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Morrow, L.","contributorId":275692,"corporation":false,"usgs":false,"family":"Morrow","given":"L.","email":"","affiliations":[{"id":56880,"text":"Shenandoah Valley Raptor Study Area","active":true,"usgs":false}],"preferred":false,"id":834218,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Morrow, J. R.","contributorId":58716,"corporation":false,"usgs":false,"family":"Morrow","given":"J.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":834219,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Oleyar, M. D.","contributorId":275693,"corporation":false,"usgs":false,"family":"Oleyar","given":"M.","email":"","middleInitial":"D.","affiliations":[{"id":35596,"text":"HawkWatch International","active":true,"usgs":false}],"preferred":false,"id":834220,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ralph, B.","contributorId":275694,"corporation":false,"usgs":false,"family":"Ralph","given":"B.","email":"","affiliations":[{"id":56883,"text":"Yosemite Area Audubon Society","active":true,"usgs":false}],"preferred":false,"id":834221,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schulwitz, S.","contributorId":275695,"corporation":false,"usgs":false,"family":"Schulwitz","given":"S.","affiliations":[{"id":36583,"text":"The Peregrine Fund","active":true,"usgs":false}],"preferred":false,"id":834222,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Swem, T.","contributorId":275696,"corporation":false,"usgs":false,"family":"Swem","given":"T.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834223,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Therrien, J. F.","contributorId":243502,"corporation":false,"usgs":false,"family":"Therrien","given":"J.","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":834316,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Van Buskirk, Rich","contributorId":275812,"corporation":false,"usgs":false,"family":"Van Buskirk","given":"Rich","email":"","affiliations":[],"preferred":false,"id":834317,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Smith, T. B.","contributorId":275697,"corporation":false,"usgs":false,"family":"Smith","given":"T.","email":"","middleInitial":"B.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":834224,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Heath, J. A.","contributorId":275698,"corporation":false,"usgs":false,"family":"Heath","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":834225,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}}
,{"id":70219423,"text":"70219423 - 2021 - A reassessment of Chao2 estimates for population monitoring of grizzly bears in the Greater Yellowstone Ecosystem","interactions":[],"lastModifiedDate":"2021-04-15T15:26:51.238749","indexId":"70219423","displayToPublicDate":"2021-04-06T10:17:12","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"A reassessment of Chao2 estimates for population monitoring of grizzly bears in the Greater Yellowstone Ecosystem","docAbstract":"<p>The Yellowstone Ecosystem Subcommittee (YES) asked the Interagency Grizzly Bear Study Team (IGBST) to re-assess a technique used in annual population estimation and trend monitoring of grizzly bears in the Greater Yellowstone Ecosystem (GYE). This technique is referred to as the Chao2 approach and estimates the number of females with cubs-of-the-year (hereafter, females with cubs) and, in association with other demographic data, is used by the IGBST to produce annual population estimates. Females with cubs are an easily recognizable population segment, and trends for this reproductive segment of the population are assumed to be representative of trend for the entire population. </p><p>The overarching objective of the analyses presented in this report was to provide a more accurate representation of the GYE grizzly bear population using the current methodologies in place. Specifically, we addressed two limitations of the current Chao2 approach: 1) underestimation bias associated with a distance criterion used to differentiate annual sightings of females with cubs into unique individuals and 2) limitations of the model-averaging approach to effectively distinguish among potential future population trajectories (decline, stability, and growth). </p><p>The first issue addressed in this report is the underestimation bias associated with the rule set that Knight et al. (1995) developed to differentiate sightings of females with cubs into unique individuals (i.e., unique family groups). The rule set was originally designed to be conservative by reducing the risk of identifying more females with cubs than actually existed, primarily through use of a distance criterion of 30 km to separate sightings of unique females. This approach resulted in an underestimation bias, and previous research demonstrated that this bias increases with increasing number of females with cubs. Using location data from radio-marked females with cubs, we evaluated alternative distance criteria by simulating scenarios with varying numbers of true females with cubs and sightings. Findings from these analyses demonstrate that bias in estimates of females with cubs can be substantially reduced by changing the 30-km distance criterion in the rule set to 16 km, which produced relatively unbiased estimates. Findings also indicate, however, the importance of adaptability with regard to the distance criteria because of the complex relationships and biases among the various parameters involved in estimation of unique females with cubs. The total number of annual sightings and the true number of females with cubs play particularly important roles. Whereas these analyses remind us that there is no perfect approach to estimating the number of females with cubs from sightings under various scenarios, they provide us with new tools to determine when and how to adapt the monitoring program. </p><p>The second issue we were tasked to investigate was the potential for improvement of the technique referred to as model-averaging, which serves to smooth relatively high variation in annual estimates. This technique was chosen by YES as the basis for monitoring the Yellowstone grizzly bear population, as described in the 2016 Conservation Strategy. This choice was made in part because the technique has been well documented and population estimates derived from counts of females with cubs are conservative. Using simulations of population trends, we demonstrate why the model-averaging technique currently used cannot distinguish between plausible future trend scenarios. As a suitable alternative to model averaging, we propose the use of generalized additive models (GAMs). Using a suite of simulated trend dynamics relevant to management, we demonstrate GAM performance for tracking trends in females with cubs within the context of the annual monitoring program. We demonstrate the ability to not only document directional changes in population trend but also patterns of stabilization or resiliency after such changes. Furthermore, the proposed monitoring framework provides objective measures useful for early detection of directional changes in trend. The new framework is flexible, allowing retrospective analysis of Chao2-based estimates and future applications to time series of other population metrics, such as vital rates. </p><p>The aforementioned updates provide us with new tools to determine when and how to adapt the monitoring program. Within the context of current monitoring protocols and effort, and considering the full suite of simulations presented in this report and previous studies, the IGBST plans to incorporate the following changes to the population monitoring protocol: 1) modify the distance criterion, starting with 16 km under current sampling conditions and 2) revise the population monitoring framework using GAMs as the basis for smoothing of annual estimates and detecting trends and changes in trend. </p><p>Implementation of the 16-km distance criterion combined with use of GAM techniques would affect some of the population metrics (e.g., annual population size and uncertainty, population trend, mortality rates) used to inform management responses. A primary consideration is that the 16-km distance criterion results in total population estimates derived from the Chao2 estimates that are greater than those we have reported in the past. This increase is due to a change in the implementation of the technique and more accurately represents the number of females with cubs in the GYE grizzly bear population. Additionally, interpretation of retrospective trend patterns may change due to the combination of a different distance criterion and enhanced trend monitoring based on the GAM approach we present here. Implementation will require relatively minor changes in the monitoring protocols described in Appendices B and C of the 2016 Conservation Strategy. Finally, we note that the IGBST has ongoing investigations into the merits of an Integrated Population Model (IPM), for which annual Chao2-based estimates are important input data. The IGBST plans to continue those investigations using the 16-km distance criterion to derive Chao2 estimates.</p>","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"van Manen, F.T., Ebinger, M.R., Haroldson, M.A., Bjornlie, D., Clapp, J., Thompson, D.J., Frey, K.L., Costello, C., Hendricks, C., Nicholson, J., Gunther, K.A., Wilmot, K.R., Cooley, H., Fortin-Noreus, J., Hnilicka, P., and Tyers, D.B., 2021, A reassessment of Chao2 estimates for population monitoring of grizzly bears in the Greater Yellowstone Ecosystem, viii, 77 p.","productDescription":"viii, 77 p.","ipdsId":"IP-126615","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":385125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384856,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/science/interagency-grizzly-bear-study-team?qt-science_center_objects=0#qt-science_center_objects"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone Ecosystem","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.7364501953125,\n              43.265206318396025\n            ],\n            [\n              -108.753662109375,\n              43.265206318396025\n            ],\n            [\n              -108.753662109375,\n              45.59482210127054\n            ],\n            [\n              -111.7364501953125,\n              45.59482210127054\n            ],\n            [\n              -111.7364501953125,\n              43.265206318396025\n            ]\n          ]\n        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,{"id":70219448,"text":"70219448 - 2021 - Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites","interactions":[],"lastModifiedDate":"2021-04-08T13:12:45.82359","indexId":"70219448","displayToPublicDate":"2021-04-06T08:10:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Altered climate, including weather extremes, can cause major shifts in vegetative recovery after disturbances. Predictive models that can identify the separate and combined temporal effects of disturbance and weather on plant communities and that are transferable among sites are needed to guide vulnerability assessments and management interventions. We asked how functional group abundance responded to time since fire and antecedent weather, if long‐term vegetation trajectories were better explained by initial post‐fire weather conditions or by general five‐year antecedent weather, and if weather effects helped predict post‐fire vegetation abundances at a new site. We parameterized models using a 30‐yr vegetation monitoring dataset from burned and unburned areas of the Orchard Training Area (OCTC) of southern Idaho, USA, and monthly PRISM data, and assessed model transferability on an independent dataset from the well‐sampled Soda wildfire area along the Idaho/Oregon border. Sagebrush density increased with lower mean air temperature of the coldest month and slightly increased with higher mean air temperature of the hottest month, and with higher maximum January–June precipitation. Perennial grass cover increased in relation to higher precipitation, measured annually in the first four years after fire and/or in September–November the year of fire. Annual grass increased in relation to higher March–May precipitation in the year after fire, but not with September–November precipitation in the year of fire. Initial post‐fire weather conditions explained 1% more variation in sagebrush density than recent antecedent 5‐yr weather did but did not explain additional variation in perennial or annual grass cover. Inclusion of weather variables increased transferability of models for predicting perennial and annual grass cover from the OCTC to the Soda wildfire regardless of the time period in which weather was considered. In contrast, inclusion of weather variables did not affect transferability of the forecasts of post‐fire sagebrush density from the OCTC to the Soda site. Although model transferability may be improved by including weather covariates when predicting post‐fire vegetation recovery, predictions may be surprisingly unaffected by the temporal windows in which coarse‐scale gridded weather data are considered.</p></div></div>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3446","usgsCitation":"Applestein, C., Caughlin, T., and Germino, M., 2021, Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites: Ecosphere, v. 12, no. 4, e03446, 21 p., https://doi.org/10.1002/ecs2.3446.","productDescription":"e03446, 21 p.","ipdsId":"IP-115515","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":452799,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3446","text":"Publisher Index Page"},{"id":384931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.0703125,\n              42.49640294093705\n            ],\n            [\n              -115.23559570312499,\n              42.49640294093705\n            ],\n            [\n              -115.23559570312499,\n              43.8028187190472\n            ],\n            [\n              -117.0703125,\n              43.8028187190472\n            ],\n            [\n              -117.0703125,\n              42.49640294093705\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":218003,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":813602,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813600,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70220380,"text":"70220380 - 2021 - Machine-learning predictions of high arsenic and high manganese at drinking water depths of the glacial aquifer system, northern continental United States","interactions":[],"lastModifiedDate":"2021-05-10T13:09:02.341417","indexId":"70220380","displayToPublicDate":"2021-04-06T08:01:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Machine-learning predictions of high arsenic and high manganese at drinking water depths of the glacial aquifer system, northern continental United States","docAbstract":"<div class=\"article_abstract\"><div class=\"container container_scaled-down\"><div class=\"row\"><div class=\"col-xs-12\"><div id=\"abstractBox\" class=\"article_abstract-content hlFld-Abstract\"><p class=\"articleBody_abstractText\">Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (&gt;10 μg/L) and Mn (&gt;300 μg/L) in groundwater from the glacial aquifer system (GLAC), which spans 25 states in the northern United States and provides drinking water to 30 million people. Our BRT models’ predictor variables (PVs) included recently developed three-dimensional estimates of a suite of groundwater age metrics, redox condition, and pH. We also demonstrated a successful approach to significantly improve ML prediction sensitivity for imbalanced data sets (small percentage of high values). We present predictions of the probability of high As and high Mn concentrations in groundwater, and uncertainty, at two nonuniform depth surfaces that represent moving median depths of GLAC domestic and public supply wells within the three-dimensional model domain. Predicted high likelihood of anoxic condition (high iron or low dissolved oxygen), predicted pH, relative well depth, several modeled groundwater age metrics, and hydrologic position were all PVs retained in both models; however, PV importance and influence differed between the models. High-As and high-Mn groundwater was predicted with high likelihood over large portions of the central part of the GLAC.</p></div></div></div></div></div>","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c06740","usgsCitation":"Erickson, M., Elliott, S.M., Brown, C., Stackelberg, P.E., Ransom, K.M., Reddy, J.E., and Cravotta, C., 2021, Machine-learning predictions of high arsenic and high manganese at drinking water depths of the glacial aquifer system, northern continental United States: Environmental Science & Technology, v. 9, no. 55, p. 5791-5805, https://doi.org/10.1021/acs.est.0c06740.","productDescription":"15 p.","startPage":"5791","endPage":"5805","ipdsId":"IP-121306","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":452801,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.0c06740","text":"Publisher Index Page"},{"id":436418,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94FCZJ2","text":"USGS data release","linkHelpText":"Groundwater data, predictor variables, and rasters used for predicting the probability of high arsenic and high manganese in the Glacial Aquifer System, northern continental United States"},{"id":385543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n     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0000-0002-6998-7267","orcid":"https://orcid.org/0000-0002-6998-7267","contributorId":202976,"corporation":false,"usgs":true,"family":"Reddy","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":815300,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":815301,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70219455,"text":"70219455 - 2021 - Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states","interactions":[],"lastModifiedDate":"2021-04-08T12:58:16.777994","indexId":"70219455","displayToPublicDate":"2021-04-06T07:56:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states","docAbstract":"<p>Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (<i>Pekania pennanti</i>) based on GPS and accelerometer data.</p>","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-021-00256-8","usgsCitation":"Hance, D., Moriarty, K.M., Hollen, B.A., and Perry, R., 2021, Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states: Movement Ecology, v. 9, 17, 22 p., https://doi.org/10.1186/s40462-021-00256-8.","productDescription":"17, 22 p.","ipdsId":"IP-123520","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":452803,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00256-8","text":"Publisher Index Page"},{"id":384927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.55224609375,\n              41.88592102814744\n            ],\n            [\n              -121.17919921875001,\n              41.88592102814744\n            ],\n            [\n              -121.17919921875001,\n              42.84375132629021\n            ],\n            [\n              -123.55224609375,\n              42.84375132629021\n            ],\n            [\n              -123.55224609375,\n              41.88592102814744\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":813625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moriarty, Katie M.","contributorId":256976,"corporation":false,"usgs":false,"family":"Moriarty","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":51930,"text":"National Council for Air and Stream Improvement, Inc., Corvallis, Oregon, USA","active":true,"usgs":false}],"preferred":false,"id":813626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hollen, Bruce A.","contributorId":256977,"corporation":false,"usgs":false,"family":"Hollen","given":"Bruce","email":"","middleInitial":"A.","affiliations":[{"id":51933,"text":"USDI Bureau of Land Management, Regional Office, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":813627,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":813628,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70219432,"text":"fs20213007 - 2021 - Water resources of St. Martin Parish, Louisiana","interactions":[],"lastModifiedDate":"2021-04-07T11:41:50.260918","indexId":"fs20213007","displayToPublicDate":"2021-04-06T05:37:34","publicationYear":"2021","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":"2021-3007","displayTitle":"Water Resources of St. Martin Parish, Louisiana","title":"Water resources of St. Martin Parish, Louisiana","docAbstract":"<p>Information concerning the availability, use, and quality of water in St. Martin Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 46.99 million gallons per day (Mgal/d) of water were withdrawn in St. Martin Parish, including about 35.91 Mgal/d from groundwater sources and 11.08 Mgal/d from surface-water sources. Withdrawals for agricultural use, composed of aquaculture (32.28 Mgal/d), rice irrigation (6.44 Mgal/d), general irrigation (2.38 Mgal/d), and livestock uses (0.06 Mgal/d), accounted for about 88 percent (41.16 Mgal/d) of the total water withdrawn. Other categories of use included public supply, which accounted for about 10 percent (4.83 Mgal/d), rural domestic, which accounted for about 2 percent (0.81 Mgal/d), and industry, which accounted for less than 1 percent (0.18 Mgal/d). Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicate that water withdrawals in St. Martin Parish peaked in 1985 at more than 68 Mgal/d.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213007","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Lindaman, M.A., and White, V.E., 2021, Water resources of St. Martin Parish, Louisiana: U.S. Geological Survey Fact Sheet 2021–3007, 6 p., https://doi.org/10.3133/fs20213007.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103365","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":384877,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3007/fs20213007.pdf","text":"Report","size":"1.14 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Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-91.657,30.4587],[-91.6453,30.4523],[-91.6432,30.4454],[-91.6405,30.4432],[-91.6374,30.4427],[-91.6373,30.4349],[-91.6379,30.4336],[-91.6326,30.4272],[-91.6273,30.4176],[-91.6267,30.4112],[-91.624,30.4048],[-91.6246,30.3947],[-91.6304,30.3846],[-91.6351,30.3778],[-91.6394,30.3741],[-91.6436,30.3727],[-91.6478,30.3677],[-91.6457,30.365],[-91.6266,30.3586],[-91.6245,30.3545],[-91.6266,30.3426],[-91.6271,30.3357],[-91.6282,30.3302],[-91.6213,30.3105],[-91.6043,30.2895],[-91.59,30.2772],[-91.5879,30.2731],[-91.5911,30.2671],[-91.5911,30.2639],[-91.5884,30.257],[-91.5757,30.2479],[-91.563,30.242],[-91.5429,30.2397],[-91.5271,30.2411],[-91.5176,30.2415],[-91.4922,30.2369],[-91.479,30.2319],[-91.4753,30.2287],[-91.4763,30.2255],[-91.4795,30.2246],[-91.4821,30.2214],[-91.4816,30.2186],[-91.4885,30.2109],[-91.4901,30.2063],[-91.4906,30.2017],[-91.488,30.198],[-91.4827,30.1939],[-91.4774,30.1921],[-91.4742,30.1875],[-91.4774,30.1711],[-91.4753,30.166],[-91.4748,30.1564],[-91.469,30.1486],[-91.4727,30.1431],[-91.4616,30.1317],[-91.4637,30.1253],[-91.4648,30.1235],[-91.4679,30.1212],[-91.4684,30.1184],[-91.4727,30.1143],[-91.4706,30.1097],[-91.4642,30.1029],[-91.3925,30.1028],[-91.3893,30.1024],[-91.3898,30.0996],[-91.3814,30.0978],[-91.3777,30.095],[-91.3766,30.0863],[-91.3719,30.0817],[-91.3698,30.0767],[-91.3688,30.0671],[-91.3725,30.0653],[-91.3725,30.0625],[-91.3688,30.0589],[-91.488,30.0585],[-91.4948,30.0489],[-91.4954,30.0415],[-91.4985,30.0406],[-91.5064,30.0392],[-91.5086,30.0328],[-91.5998,30.0337],[-91.6103,30.0465],[-91.6061,30.058],[-91.6088,30.0694],[-91.6304,30.0721],[-91.6426,30.0835],[-91.6579,30.1064],[-91.6616,30.1101],[-91.6658,30.1101],[-91.6685,30.1124],[-91.6716,30.1123],[-91.6748,30.111],[-91.6764,30.1087],[-91.6753,30.1073],[-91.678,30.105],[-91.6854,30.1096],[-91.6875,30.1137],[-91.6959,30.116],[-91.6991,30.121],[-91.7039,30.1214],[-91.7075,30.1178],[-91.7128,30.1173],[-91.7223,30.12],[-91.726,30.1228],[-91.7313,30.1228],[-91.7366,30.1186],[-91.7397,30.1195],[-91.7466,30.1182],[-91.7524,30.1126],[-91.7587,30.1099],[-91.7682,30.108],[-91.7698,30.0984],[-91.7639,30.0971],[-91.7608,30.0911],[-91.7729,30.0897],[-91.7782,30.0865],[-91.7861,30.0824],[-91.8029,30.0741],[-91.8045,30.0681],[-91.8113,30.0567],[-91.8192,30.0457],[-91.8746,30.0693],[-91.8952,30.0587],[-91.9074,30.0728],[-91.9512,30.0736],[-91.9366,30.1148],[-91.9308,30.1245],[-91.9056,30.1543],[-91.9088,30.157],[-91.912,30.1593],[-91.9126,30.1634],[-91.9089,30.1639],[-91.9163,30.179],[-91.9169,30.1822],[-91.9174,30.1845],[-91.9211,30.1881],[-91.9343,30.1826],[-91.9417,30.1844],[-91.9549,30.1871],[-91.9544,30.1816],[-91.9628,30.176],[-91.9739,30.1774],[-91.9803,30.1824],[-91.9788,30.1933],[-91.9803,30.1943],[-91.9809,30.2011],[-91.9793,30.2057],[-91.9794,30.2094],[-91.9773,30.2112],[-91.9694,30.2117],[-91.9662,30.214],[-91.9583,30.2117],[-91.9525,30.2246],[-91.9441,30.226],[-91.9431,30.2365],[-91.9515,30.2392],[-91.9595,30.2374],[-91.9642,30.241],[-91.9696,30.2488],[-91.9675,30.2524],[-91.9691,30.2602],[-91.977,30.2606],[-91.9518,30.2799],[-91.9551,30.3088],[-91.9625,30.3138],[-91.9663,30.3224],[-91.9685,30.3339],[-91.9616,30.3403],[-91.9569,30.3435],[-91.959,30.3499],[-91.9511,30.3527],[-91.9538,30.36],[-91.9792,30.3635],[-91.9792,30.3663],[-91.9856,30.3672],[-91.9893,30.3704],[-91.9862,30.3754],[-91.9814,30.3818],[-91.9831,30.3923],[-91.9805,30.4043],[-91.9784,30.4079],[-91.9705,30.4121],[-91.9652,30.4107],[-91.9546,30.4066],[-91.9429,30.3998],[-91.9381,30.3971],[-91.9317,30.3971],[-91.9143,30.4027],[-91.9074,30.4045],[-91.8953,30.4087],[-91.8916,30.4101],[-91.8688,30.4184],[-91.8656,30.4166],[-91.8571,30.413],[-91.8545,30.4116],[-91.8497,30.4066],[-91.8433,30.4075],[-91.8327,30.4057],[-91.8301,30.4025],[-91.8205,30.398],[-91.8126,30.3962],[-91.6791,30.3951],[-91.6802,30.3974],[-91.6945,30.4019],[-91.6977,30.412],[-91.6988,30.4147],[-91.7131,30.4321],[-91.719,30.4494],[-91.7227,30.4526],[-91.7365,30.4517],[-91.7397,30.4531],[-91.7423,30.4576],[-91.7439,30.4709],[-91.7509,30.4786],[-91.7546,30.4855],[-91.7568,30.4978],[-91.7011,30.4975],[-91.7016,30.4925],[-91.6926,30.481],[-91.6825,30.4779],[-91.6682,30.4747],[-91.6581,30.4646],[-91.657,30.4587]]],[[[-91.2509,29.9611],[-91.2515,29.9566],[-91.2462,29.957],[-91.2457,29.9533],[-91.2478,29.9524],[-91.2425,29.9428],[-91.2384,29.9387],[-91.2341,29.9373],[-91.2299,29.9336],[-91.23,29.9286],[-91.2326,29.924],[-91.2316,29.9176],[-91.2242,29.9116],[-91.2153,29.9088],[-91.21,29.9029],[-91.2069,29.8978],[-91.2075,29.8869],[-91.2054,29.8795],[-91.1997,29.8612],[-91.185,29.847],[-91.1582,29.8414],[-91.1508,29.8368],[-91.1335,29.8376],[-91.1203,29.828],[-91.1114,29.827],[-91.1067,29.8206],[-91.1062,29.8169],[-91.1036,29.8178],[-91.0994,29.8142],[-91.0957,29.8082],[-91.0963,29.8059],[-91.0926,29.8018],[-91.1,29.7871],[-91.1001,29.7844],[-91.0959,29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Martin\",\"state\":\"LA\"}}]}","contact":"<p><a href=\"mailto:%20gs-w-lmg_center_director@usgs.gov\" data-mce-href=\"mailto:%20gs-w-lmg_center_director@usgs.gov\">Director</a>, <a href=\"https://la.water.usgs.gov/\" data-mce-href=\"https://la.water.usgs.gov/\">Lower Mississippi-Gulf Water Science Center</a><br>U.S. Geological Survey<br>3535 S. Sherwood Forest Blvd., Suite 120 <br>Baton Rouge, LA 70816</p>","tableOfContents":"<ul><li>Introduction</li><li>Groundwater Resources</li><li>Surface-Water Resources</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-04-05","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Lindaman, Maxwell A. 0000-0003-1786-1272","orcid":"https://orcid.org/0000-0003-1786-1272","contributorId":219064,"corporation":false,"usgs":true,"family":"Lindaman","given":"Maxwell A.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813539,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Vincent E. 0000-0002-1660-0102 vwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-1660-0102","contributorId":5388,"corporation":false,"usgs":true,"family":"White","given":"Vincent","email":"vwhite@usgs.gov","middleInitial":"E.","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813540,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70219431,"text":"fs20213014 - 2021 - Water resources of Iberville Parish, Louisiana","interactions":[],"lastModifiedDate":"2021-04-06T12:41:11.538969","indexId":"fs20213014","displayToPublicDate":"2021-04-06T05:35:34","publicationYear":"2021","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":"2021-3014","displayTitle":"Water Resources of Iberville Parish, Louisiana","title":"Water resources of Iberville Parish, Louisiana","docAbstract":"<p>Information concerning the availability, use, and quality of water in Iberville Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 589.87&nbsp;million gallons per day (Mgal/d) of water were withdrawn in Iberville Parish in southeastern Louisiana: 30.86 Mgal/d from groundwater sources and 559.01 Mgal/d from surface-water sources. Withdrawals for industrial use accounted for about 77&nbsp;percent (452.80&nbsp;Mgal/d) of the total water withdrawn in 2016. Other use categories included power generation, which accounted for about 21 percent (124.54&nbsp;Mgal/d), and aquaculture, which accounted for about 1 percent (7.50 Mgal/d). Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicate that water withdrawals peaked in 1980 at 1,429.78 Mgal/d.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213014","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Lindaman, M.A., and White, V.E., 2021, Water resources of Iberville Parish, Louisiana: U.S. Geological Survey Fact Sheet 2021–3014, 6 p., https://doi.org/10.3133/fs20213014.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103366","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":384875,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":384873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3014/coverthb.jpg"},{"id":384874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3014/fs20213014.pdf","text":"Report","size":"1.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3014"}],"country":"United States","state":"Louisiana","county":"Iberville Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.4853,30.4972],[-91.4604,30.4707],[-91.4535,30.4753],[-91.4524,30.4743],[-91.4143,30.4318],[-91.4127,30.4322],[-91.3947,30.4094],[-91.3947,30.3956],[-91.3714,30.3874],[-91.3371,30.3526],[-91.3202,30.3443],[-91.3144,30.3246],[-91.1419,30.3237],[-91.1382,30.3169],[-91.1329,30.315],[-91.1255,30.3127],[-91.1213,30.3132],[-91.1171,30.3145],[-91.1128,30.315],[-91.0943,30.319],[-91.0863,30.3199],[-91.0721,30.3203],[-91.0678,30.3212],[-91.0641,30.3207],[-91.0594,30.3202],[-91.052,30.3184],[-91.0461,30.317],[-91.0398,30.3174],[-91.025,30.3201],[-91.0213,30.3146],[-91.0218,30.3128],[-91.0599,30.2132],[-91.0536,30.2122],[-91.0691,30.1817],[-91.0718,30.162],[-91.0797,30.1634],[-91.0914,30.157],[-91.0904,30.136],[-91.0905,30.125],[-91.09,30.1126],[-91.09,30.109],[-91.1069,30.1081],[-91.1061,30.0628],[-91.2244,30.0256],[-91.2222,30.0307],[-91.2222,30.0394],[-91.2238,30.0407],[-91.2275,30.043],[-91.2306,30.0421],[-91.2391,30.0307],[-91.2423,30.0298],[-91.2544,30.0426],[-91.2591,30.0504],[-91.2638,30.0546],[-91.2686,30.061],[-91.3371,30.0602],[-91.3514,30.0602],[-91.3545,30.0611],[-91.3577,30.057],[-91.3625,30.0543],[-91.3672,30.0547],[-91.3688,30.0589],[-91.3725,30.0625],[-91.3725,30.0653],[-91.3688,30.0671],[-91.3698,30.0767],[-91.3719,30.0817],[-91.3766,30.0863],[-91.3777,30.095],[-91.3814,30.0978],[-91.3898,30.0996],[-91.3893,30.1024],[-91.3925,30.1028],[-91.4642,30.1029],[-91.4706,30.1097],[-91.4727,30.1143],[-91.4684,30.1184],[-91.4679,30.1212],[-91.4648,30.1235],[-91.4637,30.1253],[-91.4616,30.1317],[-91.4727,30.1431],[-91.469,30.1486],[-91.4748,30.1564],[-91.4753,30.166],[-91.4774,30.1711],[-91.4742,30.1875],[-91.4774,30.1921],[-91.4827,30.1939],[-91.488,30.198],[-91.4906,30.2017],[-91.4901,30.2063],[-91.4885,30.2109],[-91.4816,30.2186],[-91.4821,30.2214],[-91.4795,30.2246],[-91.4763,30.2255],[-91.4753,30.2287],[-91.479,30.2319],[-91.4922,30.2369],[-91.5176,30.2415],[-91.5271,30.2411],[-91.5429,30.2397],[-91.563,30.242],[-91.5757,30.2479],[-91.5884,30.257],[-91.5911,30.2639],[-91.5911,30.2671],[-91.5879,30.2731],[-91.59,30.2772],[-91.6043,30.2895],[-91.6213,30.3105],[-91.6282,30.3302],[-91.6271,30.3357],[-91.6266,30.3426],[-91.6245,30.3545],[-91.6266,30.3586],[-91.6457,30.365],[-91.6478,30.3677],[-91.6436,30.3727],[-91.6394,30.3741],[-91.6351,30.3778],[-91.6304,30.3846],[-91.6246,30.3947],[-91.624,30.4048],[-91.6267,30.4112],[-91.6273,30.4176],[-91.6326,30.4272],[-91.6379,30.4336],[-91.6373,30.4349],[-91.6374,30.4427],[-91.6405,30.4432],[-91.6432,30.4454],[-91.6453,30.4523],[-91.657,30.4587],[-91.6581,30.4646],[-91.6682,30.4747],[-91.6825,30.4779],[-91.6926,30.481],[-91.7016,30.4925],[-91.7011,30.4975],[-91.6253,30.4972],[-91.5839,30.4967],[-91.5818,30.4825],[-91.5701,30.4826],[-91.5568,30.483],[-91.5584,30.4885],[-91.5261,30.4972],[-91.4853,30.4972]]]},\"properties\":{\"name\":\"Iberville\",\"state\":\"LA\"}}]}","contact":"<p><a href=\"mailto:%20gs-w-lmg_center_director@usgs.gov\" data-mce-href=\"mailto:%20gs-w-lmg_center_director@usgs.gov\">Director</a>, <a href=\"https://la.water.usgs.gov/\" data-mce-href=\"https://la.water.usgs.gov/\">Lower Mississippi-Gulf Water Science Center</a><br>U.S. Geological Survey<br>3535 S. Sherwood Forest Blvd., Suite 120 <br>Baton Rouge, LA 70816</p>","tableOfContents":"<ul><li>Introduction</li><li>Groundwater Resources</li><li>Surface-Water Resources</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-04-05","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Lindaman, Maxwell A. 0000-0003-1786-1272","orcid":"https://orcid.org/0000-0003-1786-1272","contributorId":219064,"corporation":false,"usgs":true,"family":"Lindaman","given":"Maxwell A.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Vincent E. 0000-0002-1660-0102 vwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-1660-0102","contributorId":5388,"corporation":false,"usgs":true,"family":"White","given":"Vincent","email":"vwhite@usgs.gov","middleInitial":"E.","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813538,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70217845,"text":"fs20203062 - 2021 - 3D Elevation Program—Federal best practices","interactions":[],"lastModifiedDate":"2021-04-06T00:27:02.362495","indexId":"fs20203062","displayToPublicDate":"2021-04-05T20:30:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3062","displayTitle":"3D Elevation Program—Federal Best Practices","title":"3D Elevation Program—Federal best practices","docAbstract":"<p>The goal of the 3D Elevation Program (3DEP) is to complete nationwide data acquisition in 8 years, by 2023, to provide the first-ever national baseline of consistent high-resolution three-dimensional data—including bare earth elevations and three-dimensional point clouds—collected in a timeframe of less than a decade. Successful implementation of 3DEP depends on partnerships and the development and adoption of a unified Federal approach to acquiring data. The purpose of this document is to outline several best practices to aid the Federal 3DEP community in reaching a higher level of coordinated implementation, maximize Federal data investments, and reduce the number of years it will take to complete national coverage. The best practices are provided to Federal agencies as a checklist to assess the level of their participation and to inspire further adoption of Federal enterprise practices that will advance joint 3DEP coverage goals for the benefit of their missions and the Nation as a whole. It is anticipated that additional best practices will be defined and added as the effort matures.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203062","usgsCitation":"Lukas, V., and Baez, V., 2021, 3D Elevation Program—Federal best practices: U.S. Geological Survey Fact Sheet 2020–3062, 2 p., https://doi.org/10.3133/fs20203062.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-118601","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":384879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3062/fs20203062.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3062"},{"id":383062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3062/coverthb.jpg"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/core-science-systems/national-geospatial-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-geospatial-program\">National Geospatial Program</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive, MS 511<br>Reston, VA 20192<br>Email: <a href=\"mailto:3DEP@usgs.gov\" data-mce-href=\"mailto:3DEP@usgs.gov\">3DEP@usgs.gov</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Purpose</li><li>Background</li><li>Reference Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2021-02-09","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Lukas, Vicki 0000-0002-3151-6689 vlukas@usgs.gov","orcid":"https://orcid.org/0000-0002-3151-6689","contributorId":2890,"corporation":false,"usgs":true,"family":"Lukas","given":"Vicki","email":"vlukas@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":809892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baez, Vanessa 0000-0002-3101-8647","orcid":"https://orcid.org/0000-0002-3101-8647","contributorId":248801,"corporation":false,"usgs":true,"family":"Baez","given":"Vanessa","email":"","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":809893,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70219254,"text":"sir20215011 - 2021 - Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017","interactions":[],"lastModifiedDate":"2023-04-10T18:30:08.234211","indexId":"sir20215011","displayToPublicDate":"2021-04-05T11:15:06","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5011","displayTitle":"Aquaculture and Irrigation Water-Use Model (AIWUM) Version 1.0—An Agricultural Water-Use Model Developed for the Mississippi Alluvial Plain, 1999–2017","title":"Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017","docAbstract":"<p>Water use is a critical and often uncertain component of quantifying any water budget and securing reliable and sustainable water supplies. Recent water-level declines in the Mississippi Alluvial Plain (MAP), especially in the central part of the Mississippi Delta, pose a threat to water sustainability. Aquaculture and Irrigation Water-Use Model (AIWUM) 1.0, one of the first national agricultural water-use models that provides water use at the scale of most groundwater models, was developed and compared to other reported and estimated aquaculture and irrigation water-use values within the MAP study area for 1999 through 2017 to improve water-use estimates needed as input to a hydrologic decision-support system in the MAP. Results indicate annual total water-use estimates from 1999 through 2017 ranged from about 5 to 13 billion gallons per day and, on average, a majority of the water use was applied to rice (about 51 percent), followed by soybeans (about 26 percent), and less than (&lt;) 10 percent each was applied to aquaculture, corn, cotton, and other crops. Comparisons indicated that annual total water-use estimates from AIWUM 1.0 were smaller than or comparable to all other sources of water-use data. Although there is disagreement at the monthly timescale in estimates in the Mississippi Delta within each part of the growing season, the annual total water use is comparable between AIWUM 1.0 and the Mississippi Embayment Regional Aquifer Study groundwater model 2.1. Estimates from AIWUM 1.0 could be used in models at all scales (for example, local, regional, national) and could provide a nationally consistent methodology in estimating water use driven by regional crop-specific withdrawal rates.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215011","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality, the Yazoo Mississippi Delta Joint Water Management District, and the Arkansas Natural Resources Commission","usgsCitation":"Wilson, J.L., 2021, Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017: U.S. Geological Survey Scientific Investigations Report 2021–5011, 36 p., https://doi.org/10.3133/sir20215011.","productDescription":"Report: viii, 36 p.; 3 Data releases; 2 Datasets; 1 Software release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-098146","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436420,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YDGJ7L","text":"USGS data release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM)"},{"id":415513,"rank":8,"type":{"id":35,"text":"Software Release"},"url":"https://code.usgs.gov/map/wu/aiwum_1.1","text":"USGS software release","linkHelpText":"—Mississippi Alluvial Plain / wu / AIWUM 1.1"},{"id":415512,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RGZOBZ","text":"USGS data release","linkHelpText":"Aquaculture and irrigation water-Use model (AIWUM) version 1.1 estimates and related datasets for the Mississippi Alluvial Plain"},{"id":384819,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products","text":"USGS National Hydrography web page","linkHelpText":"— National Hydrography Dataset"},{"id":384818,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS Water Data for the Nation"},{"id":384817,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JMO9G4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0 estimates and related datasets for the Mississippi Alluvial Plain, 1999–2017"},{"id":384816,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9MHS","text":"USGS data release","description":"USGS Data Release","linkHelpText":"National 1-kilometer rasters of selected Census of Agriculture statistics allocated to land use for the time period 1950 to 2012"},{"id":384814,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5011/coverthb.jpg"},{"id":384815,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5011/sir20215011.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5011"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -88.9892578125,\n              37.16031654673677\n            ],\n            [\n    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data-mce-href=\"https://www.usgs.gov/centers/cm-water\" href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a> <br>U.S. Geological Survey<br>405 North Goodwin <br>Urbana, IL 61801</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction<br></li><li>Methods</li><li>Comparisons of Estimates with Other Models</li><li>Aquaculture and Irrigation Water-Use in the Mississippi Alluvial Plain, 1999–2017</li><li>Strengths and Weaknesses of AIWUM 1.0</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-04-05","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Jordan L. 0000-0003-0490-9062 jlwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-0490-9062","contributorId":5416,"corporation":false,"usgs":true,"family":"Wilson","given":"Jordan","email":"jlwilson@usgs.gov","middleInitial":"L.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813430,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70219253,"text":"ofr20211018 - 2021 - Linear regression model documentation and updates for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River, Kansas, July 2012 through September 2019","interactions":[],"lastModifiedDate":"2021-04-06T11:34:06.93192","indexId":"ofr20211018","displayToPublicDate":"2021-04-05T10:50:33","publicationYear":"2021","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":"2021-1018","displayTitle":"Linear Regression Model Documentation and Updates for Computing Water-Quality Constituent Concentrations or Densities using Continuous Real-Time Water-Quality Data for the Kansas River, Kansas, July 2012 through September 2019","title":"Linear regression model documentation and updates for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River, Kansas, July 2012 through September 2019","docAbstract":"<p>The Kansas River provides drinking water to about 800,000 people in northeastern Kansas. Water-treatment facilities that use the Kansas River as a water-supply source use chemical and physical processes during water treatment to remove contaminants before public distribution. Advanced notification of changing water-quality conditions near water-supply intakes allows water-treatment facilities to proactively adjust treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, and Johnson County WaterOne, collected water-quality data at the Kansas River at Wamego (USGS site 06887500; hereafter referred to as the “Wamego site”) and De Soto (USGS site 06892350; hereafter referred to as the “De Soto site”) monitoring sites to update previously published regression models relating continuous water-quality sensor measurements, streamflow, and seasonal components to discretely sampled water-quality constituent concentrations or densities. Linear regression analysis was used to update and develop models for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll <i>a</i>, total suspended solids, suspended sediment, and fecal indicator bacteria at the Wamego and De Soto monitoring sites using data collected during July 2012 through September 2019. The water-quality information documented in this report can be used as guidance for water-treatment processes and to characterize changes in water-quality conditions in the Kansas River over time that would not be otherwise possible.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211018","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, and Johnson County WaterOne","usgsCitation":"Williams, T.J., 2021, Linear regression model documentation and updates for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River, Kansas, July 2012 through September 2019: U.S. Geological Survey Open-File Report 2021–1018, 18 p., https://doi.org/10.3133/ofr20211018.","productDescription":"Report: vii, 18 p.; Appendixes: 1–32; Dataset","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120556","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":384812,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1018/downloads","text":"Appendixes 1–32","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1018 Appendixes 1–32"},{"id":384811,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1018/ofr20211018.pdf","text":"Report","size":"1.16 MB","description":"OFR 2021–1018"},{"id":384810,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1018/coverthb.jpg"},{"id":384813,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.66845703124999,\n              38.151837403006766\n            ],\n            [\n              -94.5703125,\n              38.151837403006766\n            ],\n            [\n              -94.5703125,\n              39.977120098439634\n            ],\n            [\n              -97.66845703124999,\n              39.977120098439634\n            ],\n            [\n              -97.66845703124999,\n              38.151837403006766\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/kswsc\" href=\"https://www.usgs.gov/centers/kswsc\">Kansas Water Science Center</a> <br>U.S. Geological Survey<br>1217 Biltmore Drive <br>Lawrence, KS 66049 </p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Description of Study Area</li><li>Methods</li><li>Developed and Updated Regression Models</li><li>Summary</li><li>References Cited</li><li>Appendixes 1–32</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-04-05","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Thomas J. 0000-0003-3124-3243 tjwilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-3124-3243","contributorId":185244,"corporation":false,"usgs":true,"family":"Williams","given":"Thomas","email":"tjwilliams@usgs.gov","middleInitial":"J.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":813421,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70219202,"text":"ofr20211015 - 2021 - Synthesis of geochronologic research on Late Pliocene to Holocene emergent shorelines in the lower Savannah River area of southeastern Georgia, USA","interactions":[],"lastModifiedDate":"2021-04-05T16:30:46.589655","indexId":"ofr20211015","displayToPublicDate":"2021-04-05T10:05:00","publicationYear":"2021","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":"2021-1015","displayTitle":"Synthesis of Geochronologic Research on Late Pliocene to Holocene Emergent Shorelines in the Lower Savannah River Area of Southeastern Georgia, USA","title":"Synthesis of geochronologic research on Late Pliocene to Holocene emergent shorelines in the lower Savannah River area of southeastern Georgia, USA","docAbstract":"<p>Emergent late Pliocene and Pleistocene shoreline deposits, morphologically identifiable Pleistocene shoreline units, and seaward-facing scarps characterize the easternmost Atlantic Coastal Plain (ACP) of the United States of America. In some areas of the ACP, these deposits, units, and scarps have been studied in detail. Within these areas, temporal and spatial data are sufficient for time-depositional frameworks for shoreline-evolution to have been developed and published. For other areas, such as the southeastern Atlantic Coastal Plain (SEACP), available data are conflicting and (or) insufficient to develop such a framework, or to make shoreline correlations. Differential epeirogenic uplift and shoreline deformation, resulting from mantle-flow and climate-induced isostatic adjustments, complicate regional shoreline correlations. In the SEACP, the topographically prominent Orangeburg Scarp (hereafter, the Scarp) rises tens of meters in elevation from southeastern Georgia to southeastern North Carolina. The degree to which the Scarp and shoreline units seaward of the Scarp are deformed continues to be debated, but there is general agreement that the lower Savannah River area (LSRA) of Georgia and South Carolina is the least deformed area of the SEACP.</p><p>This paper synthesizes published and previously unpublished numerical age and stratigraphic data for emergent Pliocene and younger shoreline deposits in the LSRA in Georgia. Age data are applied to these shoreline deposits as they are delineated (map units) on the 1976 geologic map of Georgia by Lawton and others. Age assignments are based on stratigraphic position, fossil content, soil and weathering diagnostic properties, and numerical ages as determined by meteoric Beryllium‑10 paleosol residence time (<sup>10</sup>BePRT), optically stimulated luminescence (OSL), uranium disequilibrium series (U-series), amino acid racemization (AAR), and radiocarbon (<sup>14</sup>C) analyses. These data provide a preliminary Pliocene-Pleistocene geochronology for the Orangeburg Scarp and shoreline deposits seaward of the Scarp in the LSRA of Georgia. Minimum ages and age ranges indicate the following:</p><ul><li>the Orangeburg Scarp formed sometime in the late Pliocene and early Pleistocene, between 3 Ma and 1 Ma;</li><li>three, and possibly four, shoreline complexes were deposited in the middle Pleistocene;</li><li>two shoreline complexes were deposited in the late middle and the late Pleistocene;</li><li>deposition of the youngest shoreline complex began in the late Pleistocene and continues to the present;</li><li>each shoreline complex was modified by multiple sea level highstands over time periods that lasted tens of thousands to hundreds of thousands of years; and</li><li>Pleistocene shoreline chronology differs in part from modeled global sea level highstands.</li></ul>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211015","usgsCitation":"Markewich, H.W., Pavich, M.J., Mahan, S.A., Bierman, P.R., Alemán‑González, W.B., and Schultz, A.P., 2021, Synthesis of geochronologic research on Late Pliocene to Holocene emergent shorelines in the lower Savannah River area of southeastern Georgia, USA: U.S. Geological Survey Open-File Report 2021–1015, 48 p., https://doi.org/10.3133/ofr20211015.","productDescription":"viii, 48 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-116346","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":384768,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1015/ofr20211015.pdf","text":"Report","size":"3.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1015"},{"id":384767,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1015/coverthb.jpg"}],"country":"United States","state":"Georgia, South Carolina","otherGeospatial":"Lower Savannah River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -81.82617187499999,\n              31.606609719226917\n            ],\n            [\n              -80.67260742187499,\n              31.606609719226917\n            ],\n            [\n              -80.67260742187499,\n              33.201924189778936\n            ],\n            [\n              -81.82617187499999,\n              33.201924189778936\n            ],\n            [\n              -81.82617187499999,\n              31.606609719226917\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/fbgc\" data-mce-href=\"https://www.usgs.gov/centers/fbgc\">Florence Bascom Geoscience Center</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive<br>Reston, VA 21092</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>LSRA Shoreline Deposits and Shoreline Complexes—Stratigraphy and Age</li><li>Details for Previously Unpublished Age and Stratigraphic Data</li><li>Summary of Age Data</li><li>General Observations Based on the Age Data</li><li>Concluding Comment</li><li>References Cited</li><li>Glossary</li><li>Appendix 1. Methods Used for Sampling and Analyses</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2021-04-05","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Markewich, Helaine W. 0000-0001-9656-3243 helainem@usgs.gov","orcid":"https://orcid.org/0000-0001-9656-3243","contributorId":2008,"corporation":false,"usgs":true,"family":"Markewich","given":"Helaine","email":"helainem@usgs.gov","middleInitial":"W.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":813207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pavich, Milan J. mpavich@usgs.gov","contributorId":2348,"corporation":false,"usgs":true,"family":"Pavich","given":"Milan","email":"mpavich@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":813208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":813209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bierman, Paul R. 0000-0001-9627-4601","orcid":"https://orcid.org/0000-0001-9627-4601","contributorId":19041,"corporation":false,"usgs":true,"family":"Bierman","given":"Paul","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":813210,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aleman-Gonzalez, Wilma B. 0000-0003-3156-0126 waleman@usgs.gov","orcid":"https://orcid.org/0000-0003-3156-0126","contributorId":2530,"corporation":false,"usgs":true,"family":"Aleman-Gonzalez","given":"Wilma","email":"waleman@usgs.gov","middleInitial":"B.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":813211,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schultz, Arthur P. aschultz@usgs.gov","contributorId":3252,"corporation":false,"usgs":true,"family":"Schultz","given":"Arthur","email":"aschultz@usgs.gov","middleInitial":"P.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":813212,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70219513,"text":"70219513 - 2021 - Half of global methane emissions come from highly variable aquatic ecosystem sources","interactions":[],"lastModifiedDate":"2021-04-12T14:53:03.704996","indexId":"70219513","displayToPublicDate":"2021-04-05T09:50:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Half of global methane emissions come from highly variable aquatic ecosystem sources","docAbstract":"<p><span>Atmospheric methane is a potent greenhouse gas that plays a major role in controlling the Earth’s climate. The causes of the renewed increase of methane concentration since 2007 are uncertain given the multiple sources and complex biogeochemistry. Here, we present a metadata analysis of methane fluxes from all major natural, impacted and human-made aquatic ecosystems. Our revised bottom-up global aquatic methane emissions combine diffusive, ebullitive and/or plant-mediated fluxes from 15 aquatic ecosystems. We emphasize the high variability of methane fluxes within and between aquatic ecosystems and a positively skewed distribution of empirical data, making global estimates sensitive to statistical assumptions and sampling design. We find aquatic ecosystems contribute (median) 41% or (mean) 53% of total global methane emissions from anthropogenic and natural sources. We show that methane emissions increase from natural to impacted aquatic ecosystems and from coastal to freshwater ecosystems. We argue that aquatic emissions will probably increase due to urbanization, eutrophication and positive climate feedbacks and suggest changes in land-use management as potential mitigation strategies to reduce aquatic methane emissions.</span></p>","language":"English","publisher":"Nature Research","doi":"10.1038/s41561-021-00715-2","usgsCitation":"Rosentreter, J.A., Borges, A.V., Deemer, B., Holgerson, M.A., Liu, S., Song, C., Melack, J.M., Raymond, P.A., Duarte, C.M., Allen, G., Olefeldt, D., Poulter, B., Batin, T.I., and Eyre, B.D., 2021, Half of global methane emissions come from highly variable aquatic ecosystem sources: Nature Geoscience, v. 14, p. 225-230, https://doi.org/10.1038/s41561-021-00715-2.","productDescription":"6 p.","startPage":"225","endPage":"230","ipdsId":"IP-112683","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":487210,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://infoscience.epfl.ch/record/284804","text":"External Repository"},{"id":385016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosentreter, Judith A.","contributorId":257244,"corporation":false,"usgs":false,"family":"Rosentreter","given":"Judith","email":"","middleInitial":"A.","affiliations":[{"id":51987,"text":"Centre for Coastal Biogeochemistry, Southern Cross University, Lismore, NSW, 2480, Australia","active":true,"usgs":false}],"preferred":false,"id":813864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Borges, Alberto V.","contributorId":257245,"corporation":false,"usgs":false,"family":"Borges","given":"Alberto","email":"","middleInitial":"V.","affiliations":[{"id":51988,"text":"University of Liege, Chemical Oceanography Unit, Liege, Belgium","active":true,"usgs":false}],"preferred":false,"id":813865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holgerson, Meredith A.","contributorId":257243,"corporation":false,"usgs":false,"family":"Holgerson","given":"Meredith","email":"","middleInitial":"A.","affiliations":[{"id":51986,"text":"Departments of Biology and Environmental Studies, St. Olaf College, Northfield, Minnesota, USA","active":true,"usgs":false}],"preferred":false,"id":813867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liu, Shaoda","contributorId":257246,"corporation":false,"usgs":false,"family":"Liu","given":"Shaoda","email":"","affiliations":[{"id":51989,"text":"Yale School of Forestry and Environmental Studies, 195 Prospect Street, New Haven, CT, USA","active":true,"usgs":false}],"preferred":false,"id":813868,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Song, Chunlin","contributorId":257247,"corporation":false,"usgs":false,"family":"Song","given":"Chunlin","email":"","affiliations":[{"id":51990,"text":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, China","active":true,"usgs":false}],"preferred":false,"id":813869,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Melack, John M.","contributorId":167466,"corporation":false,"usgs":false,"family":"Melack","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":24713,"text":"Bren School of Environmental Science and Management, University of California, Santa Barbara, California, USA","active":true,"usgs":false}],"preferred":false,"id":813870,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Raymond, Peter A.","contributorId":172876,"corporation":false,"usgs":false,"family":"Raymond","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":17883,"text":"Yale School of Forestry and Environmental Studies, New Haven, CT","active":true,"usgs":false}],"preferred":false,"id":813871,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Duarte, Carlos M.","contributorId":222294,"corporation":false,"usgs":false,"family":"Duarte","given":"Carlos","email":"","middleInitial":"M.","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":813872,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Allen, George H.","contributorId":257248,"corporation":false,"usgs":false,"family":"Allen","given":"George H.","affiliations":[{"id":51991,"text":"Department of Geography, Texas A&M University, College Station, TX, USA","active":true,"usgs":false}],"preferred":false,"id":813873,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Olefeldt, David","contributorId":169408,"corporation":false,"usgs":false,"family":"Olefeldt","given":"David","affiliations":[{"id":32365,"text":"Department of Renewable Resources, University of Alberta","active":true,"usgs":false}],"preferred":false,"id":813874,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Poulter, Benjamin 0000-0002-9493-8600","orcid":"https://orcid.org/0000-0002-9493-8600","contributorId":200477,"corporation":false,"usgs":false,"family":"Poulter","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":813875,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Batin, Tom I.","contributorId":257249,"corporation":false,"usgs":false,"family":"Batin","given":"Tom","email":"","middleInitial":"I.","affiliations":[{"id":51992,"text":"Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland","active":true,"usgs":false}],"preferred":false,"id":813876,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Eyre, Bradley D.","contributorId":257250,"corporation":false,"usgs":false,"family":"Eyre","given":"Bradley","email":"","middleInitial":"D.","affiliations":[{"id":51987,"text":"Centre for Coastal Biogeochemistry, Southern Cross University, Lismore, NSW, 2480, Australia","active":true,"usgs":false}],"preferred":false,"id":813877,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}}
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