The Round Goby (Neogobius melanostomus) is an invasive benthic fish indigenous to the Ponto-Caspian region of Eurasia. It recently colonized the Great Lakes and has expanded eastward through the New York State Canal System over the past decade. The species was first documented in the Mohawk River watershed in 2014 and was found in the Hudson River in 2021. Round Goby can adversely affect aquatic ecosystems in many ways such as outcompeting native benthic fishes, consuming the eggs of nest-building species such as Smallmouth Bass (Micropterus dolomieu), and transferring contaminants to higher trophic levels (e.g., desirable gamefish). They can also carry the viral hemorrhagic septicemia (VHS) virus which has been linked to fish kills in New York and some evidence suggests Round Goby are an important vector in avian botulism outbreaks. However, the presence of Round Goby has also been linked to faster growth rate and larger maximum size of some predators such as Smallmouth Bass. ed watersheds of the northeastern United States.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Mohawk Watershed Symposium 2023 abstracts and program","largerWorkSubtype":{"id":15,"text":"Monograph"},"conferenceTitle":"Mohawk Watershed Symposium 2023","conferenceDate":"March 17, 2023","conferenceLocation":"Schenectady, NY","language":"English","publisher":"Union College","usgsCitation":"George, S.D., Baldigo, B., Rees, C., Bartron, M.L., Pendleton, R., and Pearson, S., 2023, Invasive Round Goby in the Mohawk and Hudson Rivers: What’s the latest?, in Mohawk Watershed Symposium 2023 abstracts and program, Schenectady, NY, March 17, 2023, p. 21-23.","productDescription":"3 p.","startPage":"21","endPage":"23","ipdsId":"IP-148828","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":414370,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":414368,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://minerva.union.edu/garverj/mws/2023/symposium.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Hudson River, Mohawk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.2056012272887,\n 44.87799282933736\n ],\n [\n -76.62652177537367,\n 44.87799282933736\n ],\n [\n -76.62652177537367,\n 41.997425542519494\n ],\n [\n -73.2056012272887,\n 41.997425542519494\n ],\n [\n -73.2056012272887,\n 44.87799282933736\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rees, Christopher B.","contributorId":196308,"corporation":false,"usgs":false,"family":"Rees","given":"Christopher B.","affiliations":[],"preferred":false,"id":866854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartron, Meredith L.","contributorId":149109,"corporation":false,"usgs":false,"family":"Bartron","given":"Meredith","email":"","middleInitial":"L.","affiliations":[{"id":26874,"text":"USFWS, Lamar, PA","active":true,"usgs":false},{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":866855,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pendleton, Richard M.","contributorId":273135,"corporation":false,"usgs":false,"family":"Pendleton","given":"Richard M.","affiliations":[{"id":56428,"text":"New York Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":866856,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearson, Steven","contributorId":303228,"corporation":false,"usgs":false,"family":"Pearson","given":"Steven","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":866857,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70242878,"text":"70242878 - 2023 - Stream restoration produces transitory, not permanent, changes to fish assemblages at compensatory mitigation sites","interactions":[],"lastModifiedDate":"2023-07-24T16:43:22.658419","indexId":"70242878","displayToPublicDate":"2023-03-20T06:46:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Stream restoration produces transitory, not permanent, changes to fish assemblages at compensatory mitigation sites","docAbstract":"There is inconsistent evidence that stream restoration projects lead to recovery of ecosystem attributes, especially stream biota. While some assessments have documented desired changes in fish community metrics in the first years following restoration, longer-term studies have not always corroborated these findings. In this study, we used data and monitoring reports submitted to federal regulators by stream mitigation consultants to examine whether in-stream restoration activities led to changes in fish community attributes at 23 compensatory mitigation projects representing 53 sampling sites in Georgia, United States over 7 years of post-restoration monitoring. Modeling results indicated that abundance and species richness of fishes generally increased in the first years after restoration before decreasing to baseline levels by the seventh year. This pattern was consistent for models considering sensitive fish taxa, as well as at sites across a range of agricultural and forested land cover percentages. However, the effect of restoration on species richness was dampened in larger streams and at more urbanized locations. A community trajectory analysis supported the findings that fish community change was transitory at most sites. Remote estimation of canopy cover change at restoration sites suggested that the hump-shaped response may be driven by increased light availability during the immediate-post restoration period, followed by subsequent re-shading of stream channels by riparian plantings. Our analysis indicates that reach-level manipulation of streams should not be expected to induce long-term changes in fish communities, and that publicly available monitoring reports may be leveraged to address questions of stream restoration efficacy.
The utility of a functional immune assay for smallmouth bass (Micropterus dolomieu) lymphocyte mitogenesis was evaluated. Wild populations in the Potomac River have faced disease and mortality with immunosuppression from exposure to chemical contaminants a suspected component. However, a validated set of immune parameters to screen for immunosuppression in wild fish populations is not available. Prior to use in ecotoxicology studies, ancillary factors influencing the mitogenic response need to be understood. The assay was field-tested with fish collected from three sites in West Virginia as part of health assessments occurring in spring (pre-spawn; April–May) and fall (recrudescence; October–November). Anterior kidney leukocytes were exposed to lipopolysaccharide (LPS) from E.coli O111:B4 or mitogen-free media and proliferation was measured using imaging flow cytometry with advanced machine learning to distinguish lymphocytes. An anti-smallmouth bass IgM monoclonal antibody was used to identify IgM+ lymphocytes. Lymphocyte mitogenesis, or proliferative responses, varied by site and season and positively and negatively correlated with factors such as sex, age, tissue parasites, and macrophage aggregates. Background proliferation of IgM− lymphocytes was negatively correlated to LPS-induced proliferation in both seasons at all sites, but only in spring for IgM+ lymphocytes. The results demonstrate that many factors, in addition to chemical contaminants, may influence lymphocyte proliferation.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes8030159","usgsCitation":"Smith, C.R., Ottinger, C., Walsh, H.L., Mazik, P.M., and Blazer, V., 2023, Application of a lipopolysaccharide (LPS)-stimulated mitogenesis assay in smallmouth bass (Micropterus dolomieu) to augment wild fish health studies: Fishes, v. 8, no. 3, 159, 18 p., https://doi.org/10.3390/fishes8030159.","productDescription":"159, 18 p.","ipdsId":"IP-138884","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":444243,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes8030159","text":"Publisher Index Page"},{"id":435416,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FTUPPX","text":"USGS data release","linkHelpText":"Immune Function of Wild Smallmouth Bass Collected from Sites within the Chesapeake Bay Watershed, 2016-2021"},{"id":414615,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia, West Virginia","otherGeospatial":"Cheat River, South Branch Potomac River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.18698999878868,\n 39.52897283606595\n ],\n [\n -80.18698999878868,\n 38.97427210045825\n ],\n [\n -78.74995409613122,\n 38.97427210045825\n ],\n [\n -78.74995409613122,\n 39.52897283606595\n ],\n [\n -80.18698999878868,\n 39.52897283606595\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Cheyenne R. 0000-0002-7226-1774","orcid":"https://orcid.org/0000-0002-7226-1774","contributorId":219236,"corporation":false,"usgs":true,"family":"Smith","given":"Cheyenne","email":"","middleInitial":"R.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true},{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":true,"id":867150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ottinger, Christopher 0000-0003-2551-1985","orcid":"https://orcid.org/0000-0003-2551-1985","contributorId":205874,"corporation":false,"usgs":true,"family":"Ottinger","given":"Christopher","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":867151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Heather L. 0000-0001-6392-4604 hwalsh@usgs.gov","orcid":"https://orcid.org/0000-0001-6392-4604","contributorId":4696,"corporation":false,"usgs":true,"family":"Walsh","given":"Heather","email":"hwalsh@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":867152,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":867153,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":867154,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241022,"text":"sir20235007 - 2023 - Status of water-level altitudes and long-term and short-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2022","interactions":[],"lastModifiedDate":"2026-03-02T18:04:22.136258","indexId":"sir20235007","displayToPublicDate":"2023-03-07T13:51:09","publicationYear":"2023","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":"2023-5007","displayTitle":"Status of Water-Level Altitudes and Long-Term and Short-Term Water-Level Changes in the Chicot and Evangeline (Undifferentiated) and Jasper Aquifers, Greater Houston Area, Texas, 2022","title":"Status of water-level altitudes and long-term and short-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2022","docAbstract":"Since the early 1900s, groundwater withdrawn from the primary aquifers that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers—has been an important source of water in the greater Houston area, Texas. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting the status of water-level altitudes and water-level changes in these aquifers in the greater Houston area.
In this report, the Chicot and Evangeline aquifers are treated as a single hydrogeologic unit for the purposes of providing annual assessments of regional-scale water-level altitudes and changes over time. In 2022, shaded depictions of water-level altitudes for the Chicot and Evangeline aquifers (undifferentiated) ranged from about 270 feet (ft) below the North American Vertical Datum of 1988 (NAVD 88) to about 195 ft above NAVD 88. The largest decline in water-level altitudes indicated by the 1977–2022 long-term water-level-change map was southeast of The Woodlands. In comparison, the 1990–2022 long-term water-level-change map depicts declines in water-level altitudes in localized areas at or near certain wells in parts of northwestern Harris County and southern Montgomery County. The largest rise in water-level altitudes for 1977–2022 was observed in a relatively large area in southeastern Harris County, whereas the largest rise in water-level altitudes for 1990–2022 was observed in a relatively small area in central Harris County. The 5-year short-term water-level-change map depicts the largest declines at three wells in northern Fort Bend County and the largest rises at five wells in southwestern and central Harris County. The 1-year short-term water-level-change map depicts the largest decline at a well in north-central Harris County and the largest rises at a well in north-central Fort Bend County and a well in southwestern Harris County.
In 2022, shaded depictions of water-level altitudes for the Jasper aquifer ranged from about 213 ft below NAVD 88 to about 285 ft above NAVD 88. The 2000–22 long-term water-level-change map depicts water-level declines throughout most of the study area where water-level-measurement data from the aquifer were collected, with the largest decline in north-central Harris County and south-central Montgomery County south of The Woodlands. The 5-year short-term water-level-change map depicts the largest decline at a well in Conroe and the largest rise at a well in south-central Montgomery County. The 1-year short-term water-level-change map depicts the largest decline at a well in south-central Montgomery County east of The Woodlands and the largest rise at a well in Conroe.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235007","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District","usgsCitation":"Ramage, J.K., and Braun, C.L., 2023, Status of water-level altitudes and long-term and short-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2022: U.S. Geological Survey Scientific Investigations Report 2023–5007, 26 p., https://doi.org/10.3133/sir20235007.","productDescription":"Report: v, 26 p.; 2 Data Releases; Dataset","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-144568","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":413744,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F32XDT","text":"USGS data release","linkHelpText":"Groundwater-level altitudes and long-term groundwater-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2022"},{"id":413743,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P967ZHTU","text":"USGS data release","linkHelpText":"Depth to groundwater measured from wells completed in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2022"},{"id":413745,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":413736,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5007/sir20235007.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":413852,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235007/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":413730,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5007/coverthb.jpg"},{"id":413735,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5007/sir20235007.pdf","text":"Report","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5007"},{"id":413742,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5007/images"},{"id":500687,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114444.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Texas","city":"Houston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.20508784641805,\n 29.65321984264439\n ],\n [\n -94.58944610142002,\n 30.08175651239739\n ],\n [\n -95.09460266513673,\n 30.461126573452134\n ],\n [\n -95.76448419528316,\n 30.669153528428893\n ],\n [\n -96.01706247714125,\n 30.57465112178049\n ],\n [\n -96.32454908114292,\n 30.091258611624937\n ],\n [\n -96.14884245028512,\n 29.605491239224605\n ],\n [\n -95.84135584628345,\n 29.098182928087823\n ],\n [\n -95.29227262485227,\n 28.92531664791001\n ],\n [\n -95.0616576718509,\n 28.94453828891595\n ],\n [\n -94.87496937656448,\n 29.126965830599318\n ],\n [\n -94.58944610142002,\n 29.414351031110456\n ],\n [\n -94.19410618198944,\n 29.54818708251109\n ],\n [\n -94.20508784641805,\n 29.65321984264439\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
This report updates groundwater-storage and groundwater-level trends presented in U.S. Geological Survey (USGS) Scientific Investigations Report 2019–5060, in the Big Chino Subbasin, Yavapai County, Arizona. This earlier geophysical investigation of groundwater-storage change in the Big Chino Subbasin was conducted by the U.S. Geological Survey, in cooperation with the City of Prescott, the Town of Prescott Valley, and the Salt River Project from 2010 to 2017 to understand groundwater-level and groundwater-storage changes. Conclusions were based on precipitation, streamflow, groundwater level, and repeat microgravity data; the latter is a direct measurement of groundwater-storage change. This report focuses on the southern part of the Big Chino Subbasin for water years 2018–2021. These more recent data show relatively small changes in groundwater storage, consistent with the earlier monitoring presented in U.S. Geological Survey Scientific Investigations Report 2019–5060. In the Big Chino Water Ranch area, water levels have increased gradually owing to discontinued pumping for irrigation during summer months, and an in-channel recharge event in summer 2021. In the Paulden, Arizona, area, gradual water level declines have continued a downward trend that started in the 1990s. Seasonal variation is present in the Paulden area, with higher water levels in the winter months when pumping for irrigation and agricultural use is reduced. Two wells showed groundwater-level increases consistent with in-channel recharge in 2018 and 2021, whereas groundwater levels in a well screened in the deeper, confined to semi-confined carbonate aquifer showed no such discrete recharge events. In the area west of Big Chino Wash and east of the Juniper Mountains and Santa Maria Mountains, groundwater levels continued long-term declines, but storage changes were minimal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225117","collaboration":"Prepared in Cooperation with the City of Prescott, the Town of Prescott Valley, and Salt River Project","usgsCitation":"Kennedy, J.R., 2022, Aquifer storage change, 2018–2021, in the Big Chino Subbasin, Yavapai County, Arizona: U.S. Geological Survey Scientific Investigations Report 2022–5117, 16 p., https://doi.org/10.3133/sir20225117.","productDescription":"vii, 16 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-134054","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":500461,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114443.htm","linkFileType":{"id":5,"text":"html"}},{"id":413723,"rank":3,"type":{"id":34,"text":"Image 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U.S. Geological Survey
520 N. Park Avenue
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Species distribution models enable resource managers to avoid and mitigate impacts to, or enhance habitat of, target species at the landscape level. Persistent declines of northern long-eared bats (Myotis septentrionalis) due to white-nose syndrome have made acquisition of contemporary data difficult. Therefore, use of legacy data may be necessary for creation of species distribution models. We used historical roost and capture records, both individually and in combination, to assess the distribution and availability of northern long-eared bat habitat across the 670,000-ha Monongahela Na- tional Forest (MNF), West Virginia, USA. We created random forest presence/pseudo-absence models to examine influences of various biotic and abi- otic predictors on both roosting and foraging presence locations of northern long-eared bats. Predicted northern long-eared bat habitat was abundant (43.1% of the MNF) and widely dispersed. Generally, all models suggested that northern long-eared bat habitat was characterized by interior forests containing linear edge features. We observed only 3.4% spatial overlap of habitat based on complete model agreement, but 38.5% of all habitat areas resulted from agreement between capture-only and combination models. Our models provide important assessments of habitat availability necessary for addressing state and federal conservation requirements on the MNF and adjacent eastern West Virginia mountains.
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Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":924991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, S. 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At the time of writing this report, Hilcorp Alaska LLC has received BOEM approval for an oil and gas Development and Production Plan (DPP) that includes the construction of the Liberty Drilling Island (LDI) in Foggy Island Bay, situated within Stefansson Sound circa 30 km east of Prudhoe Bay (Figure 1.1). The aim of this study is to investigate how longer periods of open water (defined as < 15% ice cover), decreased sea ice cover, and changes in ocean and atmospheric conditions might affect wave and storm surge conditions, sediment transport patterns, and coastal erosion rates within Foggy Island Bay as well as the modeled influence of the offshore artificial island on sediment transport patterns.","language":"English","publisher":"Bureau of Ocean and Energy Management (BOEM)","usgsCitation":"Erikson, L.H., Nederhoff, C.M., Engelstad, A.C., Kasper, J., and Bieniek, P.A., 2023, Central Beaufort Sea Wave and Hydrodynamic Modeling Study; Report 2: Modeled waves, hydrodynamics, and sediment transport within Foggy Island Bay: OCS Study BOEM 2022-079, 64 p.","productDescription":"64 p.","ipdsId":"IP-147575","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491591,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://espis.boem.gov/final%20reports/BOEM_2022-079.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":491739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Foggy Island Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.9566685211152,\n 70.37889977965969\n ],\n [\n -147.9566685211152,\n 70.1704960051022\n ],\n [\n -147.22144502523884,\n 70.1704960051022\n ],\n [\n -147.22144502523884,\n 70.37889977965969\n ],\n [\n -147.9566685211152,\n 70.37889977965969\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nederhoff, Cornelis M. 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":265889,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Cornelis","email":"","middleInitial":"M.","affiliations":[{"id":33886,"text":"Deltares USA","active":true,"usgs":false}],"preferred":true,"id":941726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engelstad, Anita C 0000-0002-0211-4189","orcid":"https://orcid.org/0000-0002-0211-4189","contributorId":268303,"corporation":false,"usgs":true,"family":"Engelstad","given":"Anita","email":"","middleInitial":"C","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kasper, Jeremy L. 0000-0003-0975-6114","orcid":"https://orcid.org/0000-0003-0975-6114","contributorId":208630,"corporation":false,"usgs":false,"family":"Kasper","given":"Jeremy L.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":941728,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bieniek, Peter A.","contributorId":210907,"corporation":false,"usgs":false,"family":"Bieniek","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":941729,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262463,"text":"70262463 - 2023 - Maximum likelihood estimator and nightly acoustic count values as weight of evidence of bat maternity activity","interactions":[],"lastModifiedDate":"2025-01-23T16:23:50.220837","indexId":"70262463","displayToPublicDate":"2023-03-01T10:23:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Maximum likelihood estimator and nightly acoustic count values as weight of evidence of bat maternity activity","docAbstract":"Since the spread of white-nose syndrome in North America, several bat species have shown precipitous declines in abundance and distribution. With lower netting detection probabilities for the currently threatened but proposed endangered northern long-eared bat (Myotis septentrionalis) and endangered Indiana bats (Myotis sodalis), determination of presence or absence for regulatory clearance often has shifted to the use of acoustic sur- veys. However, acoustic surveys are unable to differentiate between non-reproductive individuals versus a maternity colony. We used recorded nightly echolocation pass counts of bat species-specific probabilities with maximum likelihood estimator (MLE) scores to determine thresholds by cover type and reproductive period whereby the potential for northern long-eared bat or Indiana bat maternity colonies occurs. Where nightly MLE P-values were
<0.05, mean predicted nightly pass counts were significantly higher in areas of known northern long-eared bat and Indiana bat maternity colonies versus sites that were in either species’ distribution but with no maternity activity known. Nightly pass counts (MLE P < 0.05) were higher for sites with observed maternity activity for both bat species across forest, forest-field edge, and riparian areas versus sites where no maternity activity was known. For northern long-eared bats, nightly pass counts were highest in the juvenile volancy period (after 15 July) whereas, for Indiana bats, nightly pass counts were highest in the lactation period (16 June to 15 July). Except for edge conditions for northern long-eared bats, a MLE P < 0.05 combined with nightly pass counts above thresholds developed from surveys at known maternity colony sites for both species may indicate potential presence of a maternity colony locally and provide a tool to more efficiently use targeted mist-netting for further determination.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Ford, W., Thorne, E., Silvis, A., Barr, E., Armstrong, M., and King, R.A., 2023, Maximum likelihood estimator and nightly acoustic count values as weight of evidence of bat maternity activity: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 100, p. 100-106.","productDescription":"7 p.","startPage":"100","endPage":"106","ipdsId":"IP-141830","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480708,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/maximum-likelihood-estimator-and-nightly-acoustic-count-values-weight-evidence-bat"},{"id":481004,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.52841950928277,\n 45.986380216998896\n ],\n [\n -95.52841950928277,\n 27.24529883757546\n ],\n [\n -72.57107363673734,\n 27.24529883757546\n ],\n [\n -72.57107363673734,\n 45.986380216998896\n ],\n [\n -95.52841950928277,\n 45.986380216998896\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"100","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":924264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thorne, Emily D.","contributorId":342150,"corporation":false,"usgs":false,"family":"Thorne","given":"Emily D.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":924265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silvis, Alexander","contributorId":264896,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","affiliations":[{"id":54472,"text":"RES Inc.","active":true,"usgs":false}],"preferred":false,"id":924266,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barr, Elaine L.","contributorId":343161,"corporation":false,"usgs":false,"family":"Barr","given":"Elaine L.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":924267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Armstrong, Michael P.","contributorId":343338,"corporation":false,"usgs":false,"family":"Armstrong","given":"Michael P.","affiliations":[{"id":39892,"text":"Massachusetts Division of Marine Fisheries","active":true,"usgs":false}],"preferred":false,"id":924268,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, R. Andrew","contributorId":342609,"corporation":false,"usgs":false,"family":"King","given":"R.","email":"","middleInitial":"Andrew","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":924269,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70244009,"text":"70244009 - 2023 - Decision making for Centennial Valley Arctic Grayling conservation on Red Rocks Lake National Wildlife Refuge","interactions":[],"lastModifiedDate":"2023-05-31T14:25:15.030454","indexId":"70244009","displayToPublicDate":"2023-03-01T09:06:27","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Decision making for Centennial Valley Arctic Grayling conservation on Red Rocks Lake National Wildlife Refuge","docAbstract":"This report describes a decision analysis process that was conducted in support of a U.S. Fish and Wildlife Service Environmental Assessment on Arctic grayling (Thymallus arcticus) on Red Rocks Lake National Wildlife Refuge in the Centennial Valley, Montana.
","language":"English","publisher":"U.S. fish and Wildlife Service","collaboration":"USFWS, Montana Fish, Wildlife & Parks","usgsCitation":"Cook, J.D., Flynn, K., Bell, D.A., Jaeger, M.E., Warren, J., Kreiner, R., Payne, J., Andrews, J., Brummond, A., Campbell Grant, E.H., and Sells, S.N., 2023, Decision making for Centennial Valley Arctic Grayling conservation on Red Rocks Lake National Wildlife Refuge, 75 p.","productDescription":"75 p.","ipdsId":"IP-149939","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":417578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417552,"rank":1,"type":{"id":15,"text":"Index 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systems","interactions":[],"lastModifiedDate":"2023-06-08T14:50:49.582397","indexId":"70241940","displayToPublicDate":"2023-03-01T08:50:12","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Decision science as a framework for combining geomorphological and ecological modeling for the management of coastal systems","docAbstract":"The loss of ecosystem services due to climate change and coastal development is projected to have significant impacts on local economies and conservation of natural resources. Consequently, there has been an increase in coastal management activities such as living shorelines, oyster reef restoration, marsh restoration, beach and dune nourishment, and revegetation projects. Coastal management decisions are complex and include challenging trade-offs. Decision science offers a useful framework to address such complex problems. Here, we provide a synthesis about how decision science can help to integrate research from multiple disciplines (physical and life sciences) with management of coastal and marine systems. Specifically, we discuss the importance of considering concepts and techniques from ecology, coastal geology, geomorphology, climate science, oceanography, and decision analysis when developing conservation plans for coastal restoration. We illustrate the process with several coastal restoration studies. Our capstone example is based on a recent barrier island restoration assessment project at Dauphin Island, Alabama, which included the development of geomorphological and ecological models. We show how decision science can be used as a framework to combine geomorphological and ecological modeling to help inform management decisions while considering uncertainty about system changes and risk tolerance. We also build on our examples through a review of recently developed techniques for spatial conservation planning for land acquisition decisions and the application of adaptive management for sequential decisions.
","language":"English","publisher":"The Resilience Alliance","doi":"10.5751/ES-13696-280150","usgsCitation":"Martin, J., Richardson, M.S., Passeri, D., Enwright, N., Yurek, S., Flocks, J., Eaton, M.J., Zeigler, S., Charkhgard, H., Udell, B.J., and Irwin, E.R., 2023, Decision science as a framework for combining geomorphological and ecological modeling for the management of coastal systems: Ecology and Society, v. 28, no. 1, 50, 42 p.; Data Release, https://doi.org/10.5751/ES-13696-280150.","productDescription":"50, 42 p.; Data Release","ipdsId":"IP-131634","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science 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Texas","interactions":[],"lastModifiedDate":"2026-02-05T14:42:08.024436","indexId":"fs20233001","displayToPublicDate":"2023-02-28T12:30:00","publicationYear":"2023","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":"2023-3001","displayTitle":"Flood Warning Toolset for the Sabinal River Near Utopia, Texas","title":"Flood warning toolset for the Sabinal River near Utopia, Texas","docAbstract":"Floods are one of the most frequent and expensive natural disasters that occur across the United States. Rapid, high-water events that occur in local areas—flash floods—are especially difficult for emergency managers to predict and provide advance warning to the public, and insufficient data can hamper postflood recovery efforts. Central Texas is hilly, and it is known as a “flash flood alley” because of its high-intensity rains, shallow soils, and steep terrain, all of which combined can result in loss of life and property damage. For example, the flash flood event during July 2002 claimed 12 lives in central Texas, including 1 in the town of Utopia, which is on the east bank of the Sabinal River in a flash-flood-prone area along the Balcones Escarpment. During the flood event, the peak discharge recorded on July 5, 2002, at U.S. Geological Survey (USGS) streamgage 08198000 Sabinal River near Sabinal, Tex. (hereinafter referred to as the “Sabinal gage”), was 108,000 cubic feet per second (corresponding to a stream stage [also called gage height] of 33.74 feet). To put the 2002 flood into context, during a typical year the median daily discharge in the Sabinal River at the Sabinal gage is only about 23 cubic feet per second. In 2021, the USGS, in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, developed a flood warning toolset for the Sabinal River near Utopia. This study builds on earlier USGS flood work on the Medina River in Bandera County. The newly developed toolset consists of a newly installed USGS streamgage to collect continuous stream stage data (streamgage 08197970 Sabinal River at Utopia, Tex.; hereinafter referred to as the “Utopia gage”) 13 miles upstream from the Sabinal gage, a hydraulic model developed for the Sabinal River near Utopia, and an online library of digital flood-inundation maps referenced to the stream stage at the Utopia gage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233001","issn":"2327-6932 (online)","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., 2023, Flood warning toolset for the Sabinal River near Utopia, Texas: U.S. Geological Survey Fact Sheet 2023–3001 (ver. 2.0, September 2023), 4 p., https://doi.org/10.3133/fs20233001.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-136337","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":421082,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2023/3001/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":499562,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114429.htm","linkFileType":{"id":5,"text":"html"}},{"id":421081,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20235001","text":"Scientific Investigations Report 2023–5001","description":"SIR 2023-5001","linkHelpText":"- Flood-Inundation Maps Created Using a Synthetic Rating Curve for a 10-Mile Reach of the Sabinal River and a 7-Mile Reach of the West Sabinal River Near Utopia, Texas, 2021"},{"id":421080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3001/fs20233001.pdf","text":"Report","size":"1.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3001"},{"id":414773,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3001/coverthb.jpg"}],"country":"United States","state":"Texas","city":"Utopia","otherGeospatial":"Sabinal River, West Sabinal River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.6333,\n 29.75\n ],\n [\n -99.6333,\n 29.6\n ],\n [\n -99.5,\n 29.6\n ],\n [\n -99.5,\n 29.75\n ],\n [\n -99.6333,\n 29.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: February 2023; Version 2.0: September 2023","contact":"Director, Oklahoma-Texas Water Science Center
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Overview
Creation of Flood Warning Toolset
References Cited
Paleoclimate reconstructions can provide a window into the environmental conditions in Earth history when atmospheric carbon dioxide concentrations were higher than today. In the eastern USA, paleoclimate reconstructions are sparse, because terrestrial sedimentary deposits are rare. Despite this, the eastern USA has the largest population and population density in North America, and understanding the effects of current and future climate change is of vital importance. Here, we provide terrestrial paleoclimate reconstructions of the eastern USA from Miocene fossil floras. Additionally, we compare proxy paleoclimate reconstructions from the warmest period in the Miocene, the Miocene Climatic Optimum (MCO), to those of an MCO Earth System Model. Reconstructed Miocene temperatures and precipitation north of 35°N are higher than modern. In contrast, south of 35°N, temperatures and precipitation are similar to today, suggesting a poleward amplification effect in eastern North America. Reconstructed Miocene rainfall seasonality was predominantly higher than modern, regardless of latitude, indicating greater variability in intra-annual moisture transport. Reconstructed climates are almost uniformly in the temperate seasonal forest biome, but heterogeneity of specific forest types is evident. Reconstructed Miocene terrestrial temperatures from the eastern USA are lower than modeled temperatures and coeval Atlantic sea surface temperatures. However, reconstructed rainfall is consistent with modeled rainfall. Our results show that during the Miocene, climate was most different from modern in the northeastern states, and may suggest a drastic reduction in the meridional temperature gradient along the North American east coast compared to today.
The objective of the work presented in this report is to develop equations that can be used to extend the base flow record at multiple partial-record streamgaging stations at Acadia National Park in eastern coastal Maine based on nearby continuous-record streamgaging stations. Daily mean streamflow values at U.S. Geological Survey continuous-record streamgaging station Otter Creek near Bar Harbor, Maine (station 01022840) had stronger correlations with instantaneous measurements during base flow conditions from 2006 to 2020 at 14 partial-record streamgaging stations at Acadia National Park than the other four continuous-record streamgaging stations tested for use as index stations. Index stations are continuous-record stations on hydrologically similar streams that have the potential to be used to extend the record at the partial-record station. Base flow is that part of streamflow that is sustained primarily by groundwater discharge. It is not attributable to direct precipitation or melting snow. Five of the partial-record stations had strong correlations with Otter Creek (correlation coefficient greater than 0.90) and relatively low root mean square errors (from 0.04 to 0.19). An additional four partial-record stations had fair correlations with Otter Creek (correlation coefficient from 0.79 to 0.9) and relatively low root mean square errors (from 0.05 to 0.19). For these 10 stations, maintenance of variance extension type 1 (MOVE.1) record extension equations computed in this report provide a reasonable method for extending the partial record, estimating summer monthly means and medians, and estimating daily mean streamflow values at these sites on days with no streamflow (discharge) measurements. Four of the partial-record stations have weak correlations (less than 0.78) or high root mean square error values (greater than 9) or both, indicating that record extension techniques are not appropriate for these partial-record stations using currently [2022] available data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225126","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Lombard, P.J., 2023, Estimating streamflow for base flow conditions at partial-record streamgaging stations at Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2022–5126, 13 p., https://doi.org/10.3133/sir20225126.","productDescription":"Report: vi, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-143769","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":413317,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZP8XHG","text":"USGS data release","linkHelpText":"Data and code to support MOVE.1 regression equations for streamflow at partial-record streamgaging stations at Acadia National Park, Maine:"},{"id":413315,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5126/sir20225126.XML"},{"id":413312,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5126/coverthb.jpg"},{"id":413313,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5126/sir20225126.pdf","text":"Report","size":"1.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5126"},{"id":413316,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5126/images/"},{"id":413864,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225126/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5126"},{"id":500481,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114380.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maine","otherGeospatial":"Acadia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.16726259768689,\n 44.32624433734378\n ],\n [\n -68.17739845831488,\n 44.36973371484888\n ],\n [\n -68.21954229987337,\n 44.38307924264697\n ],\n [\n -68.23981402112935,\n 44.41090441296549\n ],\n [\n -68.28035746364178,\n 44.41928748605292\n ],\n [\n -68.29422758871185,\n 44.406712425764226\n ],\n [\n -68.33050330043281,\n 44.35371506667144\n ],\n [\n -68.38971806515461,\n 44.35562227826236\n ],\n [\n -68.40305472387581,\n 44.31937464393428\n ],\n [\n -68.39025153150348,\n 44.27890346604781\n ],\n [\n -68.3390387620142,\n 44.21661505589944\n ],\n [\n -68.3112985118746,\n 44.217762064180675\n ],\n [\n -68.2883594588743,\n 44.24566571236582\n ],\n [\n -68.30329651664208,\n 44.285396004838105\n ],\n [\n -68.24514868461804,\n 44.281958867796675\n ],\n [\n -68.1982036459197,\n 44.29532438221068\n ],\n [\n -68.16726259768689,\n 44.32662596339111\n ],\n [\n -68.16726259768689,\n 44.32624433734378\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
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Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (a) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios, (b) sensitivity, and (c) adaptive capacity. The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increases are expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Strong variation in predicted estuarine nutrient loads was observed as a function of climate model projections, while projected LULC changes were more consistently associated with elevated loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries.
The U.S. Geological Survey (USGS) launched the Earth Mapping Resources Initiative (Earth MRI) to modernize the surface and subsurface geologic mapping of the United States, with a focus on identifying areas that may have the potential to contain critical mineral resources. EarthMRI can inform strategies to ensure secure and reliable domestic critical mineral supplies for the United States as mandated by Executive Order 13817 and the Infrastructure and Jobs Act of 2021 (Public Law 117–58, 135 Stat. 529). Earth MRI is a collaborative effort between the USGS and the State geological surveys as represented by the Association of American State Geologists to identify, prioritize, and acquire new geoscience data for geographic areas, or focus areas, across the Nation that have potential to host critical mineral resources. Mapping of focus areas was based on a framework of mineral systems and their associated mineral deposit types that could possibly host critical minerals. Using readily available geologic, geophysical, geochemical, and mineral deposit data, teams of USGS scientists worked with representatives of State geological surveys in a series of workshops to outline focus areas that contain evidence of key features for one or more mineral systems. These areas can be used to guide future efforts to collect new geologic, geophysical, geochemical, and topographic data that focus on critical minerals through Earth MRI.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233007","collaboration":"Prepared in cooperation with the Association of American State Geologists","usgsCitation":"Hammarstrom, J.M., Kreiner, D.C., Dicken, C.L., and Woodruff, L.G., 2023, National map of focus areas for potential critical mineral resources in the United States: U.S. Geological Survey Fact Sheet 2023–3007, 4 p., https://doi.org/10.3133/fs20233007.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-147615","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":499567,"rank":3,"type":{"id":36,"text":"NGMDB Index 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Mineral Resources Program
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: minerals@usgs.gov
Study region
The Southern Hills aquifer system in the Louisiana Capital Area Groundwater Conservation District (CAGCD), USA.
Study focus
The Southern Hills aquifer system provides abundant groundwater for public and industrial supplies in the CAGCD. Groundwater depletion, saltwater intrusion, and land subsidence are potential concerns due to prolonged excessive groundwater withdrawals. This study develops a high-fidelity groundwater flow model utilizing a complex unstructured grid to investigate groundwater flow and storage responses to excessive groundwater withdrawals for the Southern Hills aquifer system in the CAGCD. The groundwater model incorporates the Mississippi River alluvial aquifer down to the Miocene sands extending to depths around 1 km.
New hydrological insights
Groundwater modeling results indicate large cones of depression in the Evangeline and Jasper formations in the Baton Rouge area due to prolonged groundwater withdrawals. Low-permeability faults are inferred by significant groundwater level difference across the faults. While local groundwater storage depletion in deeper aquifers is evident, overall estimated groundwater storage changes of the Southern Hills aquifer system in the CAGCD are close to zero in the past two decades, indicating insignificant groundwater storage changes. This is attributed to dominant interactions between the major rivers and the shallower alluvial aquifer. In addition, the simulated groundwater storage changes exhibit patterns similar to those derived by the Gravity Recovery and Climate Experiment (GRACE) model that has been used in evaluation of groundwater depletion in many regional studies.
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U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
In coordination with the U.S. Department of the Interior (DOI) and in response to the Bipartisan Infrastructure Law (BIL), the U.S. Geological Survey (USGS) produced a documented unplugged orphaned oil and gas well dataset (called the DOW dataset hereafter) that contains the location and status of these wells nationwide as of 2022. The DOW dataset includes 117,672 wells across 27 states. The data were compiled from publicly available, and often online, state agency sources. The USGS reformatted, produced coordinate locations when needed, and conducted quality control evaluations on the source material. The DOW dataset has numerous potential uses related to the topic of orphaned well locations. For example, the DOW dataset has contributed to the template behind the DOI's future orphaned well program database. Until the DOI database is complete, the USGS's DOW dataset may serve as an interim product, helping the DOI fulfill obligations in the BIL legislation.
The DOW dataset can also be utilized in future USGS estimates of greenhouse gas emissions from Federal lands. The DOW dataset is available as a USGS data release with metadata describing the sources and processing steps used in its production. An online map is also available for exploration and interaction with the DOW data. In addition to describing the DOW dataset and its generation, this report includes analyses of orphaned well completion year and well depth, produced by combining the DOW dataset with the proprietary IHS Markit database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1167","usgsCitation":"Merrill, M.D., Grove, C.A., Gianoutsos, N.J., and Freeman, P.A., 2023, Analysis of the United States documented unplugged orphaned oil and gas well dataset (ver. 1.1, April 2023): U.S. Geological Survey Data Report 1167, 10 p., https://doi.org/10.3133/dr1167.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-145262","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":499551,"rank":8,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
956 National Center
Reston, VA 20192
Population estimates derived from monitoring efforts can be sensitive to the survey method selected, potentially leading to biased estimates and low precision relative to true population size. While small unmanned aerial systems (UAS) present a unique opportunity to survey avian populations while limiting disturbance, relatively little is known about how this method compares with more traditional approaches. In this study we compared population estimates of Snowy (Egretta thula) and Cattle Egrets (Bubulcus ibis) in a mixed-species colony in the Chesapeake Bay (Maryland, USA) derived from UAS photo counts, flush counts, flight-line surveys, and in-colony nest counts along with the time required to derive an estimate via each approach. We found that UAS counts and flush counts produced lower pair estimates than nest counts and flight-line surveys (P < 0.05), and required dramatically less time (x̄ = 6, 8, 84 and 90 min, respectively). These results suggest that while UAS have the potential to collect valuable survey data from breeding colonies that are hard to reach or are especially sensitive to the disturbance inherent in other methods, inherent biases should be considered and caution should be used when comparing results between survey types.
Measuring river response to dam removal affords a rare, important opportunity to study fluvial response to sediment pulses on a large field scale. We present a before–after/control–impact study of the Carmel River, California, measuring fluvial geomorphic and grain-size evolution over 8 years, six of which postdated removal of a 32 m-high dam (one of the largest dams removed worldwide) and included 11 flow events exceeding the 2-year flood magnitude. We find that the reservoir-sediment pulse following dam removal was relatively small (97 000 ± 24 000 t over 4 years), owing to deliberate reservoir-sediment stabilization. Scaled to the size of the Carmel River watershed and compared against long-term bedrock denudation rates, the post-dam-removal sediment release was slightly less than the annualized long-term sediment export from this basin. New sediment transited >30 km to the river mouth in less than 2 years, assisted by floods 2 and 4 years after dam removal. The sediment pulse fined the downstream riverbed while causing mostly low-magnitude bed-elevation changes: commonly 0.5 to 1 m or smaller, occurring as discontinuous sediment patches or interstitial deposits, aside from the filling and subsequent partial scour of deep pools. There was no major geomorphic reset downstream from the dam site. Geomorphic changes were driven almost entirely by flow rather than by the modest increase in sediment supply, in contrast to recent examples from other large dam removals. The relatively minor disturbance caused by dam removal on the Carmel River is likely analogous to many future dam removals: a relatively small sediment pulse after deliberate limitation of reservoir-sediment erosion, and with an upstream dam remaining in place. Thus, a large dam removal need not lead to major downstream impacts.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5561","usgsCitation":"East, A.E., Harrison, L.R., Smith, D.P., Logan, J.B., and Bond, R., 2023, Six years of fluvial response to a large dam removal on the Carmel River, California, USA: Earth Surface Processes and Landforms, v. 48, no. 8, p. 1487-1501, https://doi.org/10.1002/esp.5561.","productDescription":"15 p.","startPage":"1487","endPage":"1501","ipdsId":"IP-146182","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":444561,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5561","text":"Publisher Index Page"},{"id":415160,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Carmel River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.62577522165773,\n 36.36105374788829\n ],\n [\n -121.58724434610056,\n 36.40905968854021\n ],\n [\n -121.61843600726598,\n 36.46293870830412\n ],\n [\n -121.750541866319,\n 36.53594773620604\n ],\n [\n -121.8532908678046,\n 36.61551540625254\n ],\n [\n -121.90741757394426,\n 36.588265345279865\n ],\n [\n -121.93261880515863,\n 36.54806169091083\n ],\n [\n -121.88413981443409,\n 36.520771377870034\n ],\n [\n -121.79850327393265,\n 36.50047233869989\n ],\n [\n -121.7839886060512,\n 36.469663631874326\n ],\n [\n -121.74712134963194,\n 36.40894399256348\n ],\n [\n -121.68209557572075,\n 36.36804816657221\n ],\n [\n -121.62606895769773,\n 36.3610353099688\n ],\n [\n -121.62577522165773,\n 36.36105374788829\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":868544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":868545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Douglas P.","contributorId":201716,"corporation":false,"usgs":false,"family":"Smith","given":"Douglas","email":"","middleInitial":"P.","affiliations":[{"id":35924,"text":"California State University, Monterey Bay","active":true,"usgs":false}],"preferred":false,"id":868546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Logan, Joshua B. 0000-0002-6191-4119 jlogan@usgs.gov","orcid":"https://orcid.org/0000-0002-6191-4119","contributorId":2335,"corporation":false,"usgs":true,"family":"Logan","given":"Joshua","email":"jlogan@usgs.gov","middleInitial":"B.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":868547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bond, Rosealea","contributorId":201717,"corporation":false,"usgs":false,"family":"Bond","given":"Rosealea","affiliations":[{"id":12520,"text":"NOAA National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":868548,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240647,"text":"70240647 - 2023 - Hawai‘i residents’ perceptions of Kīlauea’s 2018 eruption information","interactions":[],"lastModifiedDate":"2023-02-10T13:19:04.275323","indexId":"70240647","displayToPublicDate":"2023-02-05T07:17:13","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7593,"text":"Volcanica","active":true,"publicationSubtype":{"id":10}},"title":"Hawai‘i residents’ perceptions of Kīlauea’s 2018 eruption information","docAbstract":"The 2018 eruption of Kīlauea Volcano was notable for its variety of large and spatially distinct hazards, simultaneously affecting three geographically disparate, culturally diverse regions in Hawaiʻi. We conducted a pilot study, consisting of 18 semi-structured interviews, two survey responses, and several informal conversations with Hawaiʻi residents to learn which sources/messengers of eruption information were deemed most trusted and credible. Participants' perceptions of the U.S. Geological Survey Hawaiian Volcano Observatory (HVO), community-based messengers, and traditional news media can be examined across four themes: relevance, expertise, sincerity, and pace. Among our interview participants, Lower East Rift Zone (LERZ) residents placed the highest trust in their community messengers, summit residents deemed HVO most trustworthy, and Kaʻū residents trusted information from both HVO and local news media. Our findings suggest that future official eruption communications would benefit from 1) designating communications personnel to act as community liaisons and 2) increasing pace and relevance of information delivery.
A pilot study among farming households in eastern Iowa was conducted to assess human exposure to neonicotinoids (NEOs). The study was in a region with intense crop and livestock production and where groundwater is vulnerable to surface-applied contaminants. In addition to paired outdoor (hydrant) water and indoor (tap) water samples from private wells, urine samples were collected from 47 adult male pesticide applicators along with the completions of dietary and occupational surveys. Estimated Daily Intake (EDI) were then calculated to examine exposures for different aged family members. NEOs were detected in 53% of outdoor and 55% of indoor samples, with two or more NEOs in 13% of samples. Clothianidin was the most frequently detected NEO in water samples. Human exposure was ubiquitous in urine samples. A median of 10 different NEOs and/or metabolites were detected in urine, with clothianidin, nitenpyram, thiamethoxam, 6-chloronicotinic acid, and thiacloprid amide detected in every urine samples analyzed. Dinotefuran, imidaclothiz, acetamiprid-N-desmethyl, and N-desmethyl thiamethoxam were found in ≥70% of urine samples. Observed water intake for study participants and EDIs were below the chronic reference doses (CRfD) and acceptable daily intake (ADI) standards for all NEOs indicating minimal risk from ingestion of tap water. The study results indicate that while the consumption of private well tap water provides a human exposure pathway, the companion urine results provide evidence that diet and/or other exposure pathways (e.g., occupational, house dust) may contribute to exposure more than water contamination. Further biomonitoring research is needed to better understand the scale of human exposure from different sources.