{"pageNumber":"48","pageRowStart":"1175","pageSize":"25","recordCount":10956,"records":[{"id":70228894,"text":"70228894 - 2021 - Wetland selection by female Ring-Necked Ducks (Aythya collaris) in the Southern Atlantic Flyway","interactions":[],"lastModifiedDate":"2022-02-23T13:28:46.986794","indexId":"70228894","displayToPublicDate":"2021-08-13T07:21:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Wetland selection by female Ring-Necked Ducks (Aythya collaris) in the Southern Atlantic Flyway","docAbstract":"

On the wintering grounds, wetland selection by waterfowl is influenced by spatiotemporal resource distribution. The ring-necked duck (Aythya collaris) winters in the southeastern United States where a disproportionate amount of Atlantic Flyway ring-necked duck harvest occurs. We quantified female ring-necked duck selection for wetland characteristics during and after the 2017–2018 and 2018–2019 waterfowl hunting seasons using discrete choice modeling under a Bayesian framework. Relative probability of selection was primarily influenced by characteristics at the local wetland scale. Relative probability of selection was higher for flooded agriculture and vegetated wetlands than open water and was positively influenced by wetland area during the winter. After the hunting season, the relative probability of selection decreased for flooded agriculture but increased for vegetated wetlands, and the effect of wetland area decreased in magnitude. We attribute changes in selection during and after the hunting season to dietary shifts related to migratory preparation, resource depletion, and reproductive pairing. Understanding the wetland characteristics that wintering waterfowl select, and the spatial scale at which selection occurs, is important for informing effective wetland management and waterfowl harvest practices.

","language":"English","publisher":"Springer","doi":"10.1007/s13157-021-01485-8","usgsCitation":"Mezebish, T.D., Chandler, R., Olsen, G.H., Goodman, M., Rohwer, F., Meng, N.J., and McConnell, M.D., 2021, Wetland selection by female Ring-Necked Ducks (Aythya collaris) in the Southern Atlantic Flyway: Wetlands, v. 41, 84, 13 p., https://doi.org/10.1007/s13157-021-01485-8.","productDescription":"84, 13 p.","ipdsId":"IP-130253","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":396335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.73754882812499,\n 29.99300228455108\n ],\n [\n -81.59545898437499,\n 29.99300228455108\n ],\n [\n -81.59545898437499,\n 31.062345409804433\n ],\n [\n -84.73754882812499,\n 31.062345409804433\n ],\n [\n -84.73754882812499,\n 29.99300228455108\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","noUsgsAuthors":false,"publicationDate":"2021-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Mezebish, Tori D.","contributorId":239496,"corporation":false,"usgs":false,"family":"Mezebish","given":"Tori","email":"","middleInitial":"D.","affiliations":[{"id":27618,"text":"University of Georgia, Warnell School of Forestry and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":835802,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chandler, Richard rchandler@usgs.gov","contributorId":2511,"corporation":false,"usgs":true,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","affiliations":[{"id":13266,"text":"Warnell School of Forestry and Natural Resources, The University of Georgia","active":true,"usgs":false}],"preferred":false,"id":835838,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olsen, Glenn H. 0000-0002-7188-6203","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":238130,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goodman, Michele","contributorId":239497,"corporation":false,"usgs":false,"family":"Goodman","given":"Michele","email":"","affiliations":[{"id":47893,"text":"Elmwood Park Zoo, Norristown, Pennyslvania","active":true,"usgs":false}],"preferred":false,"id":835804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rohwer, Frank C.","contributorId":239498,"corporation":false,"usgs":false,"family":"Rohwer","given":"Frank C.","affiliations":[{"id":47894,"text":"Delta Waterfowl, Bismark North Dakota","active":true,"usgs":false}],"preferred":false,"id":835805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meng, Nicholas J.","contributorId":264806,"corporation":false,"usgs":false,"family":"Meng","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":54559,"text":"Warnell School of Forestry and Natural Resources, University of Georgia,","active":true,"usgs":false}],"preferred":false,"id":835839,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McConnell, Mark D.","contributorId":239499,"corporation":false,"usgs":false,"family":"McConnell","given":"Mark","email":"","middleInitial":"D.","affiliations":[{"id":47895,"text":"College of Forest Resources, Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":835806,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229486,"text":"70229486 - 2021 - Tolerance of northern Gulf of Mexico eastern oysters to chronic warming at extreme salinities","interactions":[],"lastModifiedDate":"2022-03-09T13:10:17.276921","indexId":"70229486","displayToPublicDate":"2021-08-12T07:06:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2476,"text":"Journal of Thermal Biology","active":true,"publicationSubtype":{"id":10}},"title":"Tolerance of northern Gulf of Mexico eastern oysters to chronic warming at extreme salinities","docAbstract":"

The eastern oyster, Crassostrea virginica, provides critical ecosystem services and supports valuable fishery and aquaculture industries in northern Gulf of Mexico (nGoM) subtropical estuaries where it is grown subtidally. Its upper critical thermal limit is not well defined, especially when combined with extreme salinities. The cumulative mortalities of the progenies of wild C. virginica from four nGoM estuaries differing in mean annual salinity, acclimated to low (4.0), moderate (20.0), and high (36.0) salinities at 28.9 °C (84 °F) and exposed to increasing target temperatures of 33.3 °C (92 °F), 35.6 °C (96 °F) or 37.8 °C (100 °F), were measured over a three-week period. Oysters of all stocks were the most sensitive to increasing temperatures at low salinity, dying quicker (i.e., lower median lethal time, LT50) than at the moderate and high salinities and resulting in high cumulative mortalities at all target temperatures. Oysters of all stocks at moderate salinity died the slowest with high cumulative mortalities only at the two highest temperatures. The F1 oysters from the more southern and hypersaline Upper Laguna Madre estuary were generally more tolerant to prolonged higher temperatures (higher LT50) than stocks originating from lower salinity estuaries, most notably at the highest salinity. Using the measured temperatures oysters were exposed to, 3-day median lethal Celsius degrees (LD50) were estimated for each stock at each salinity. The lowest 3-day LD50 (35.1–36.0 °C) for all stocks was calculated at a salinity of 4.0, while the highest 3-day LD50 (40.1–44.0 °C) was calculated at a salinity of 20.0.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jtherbio.2021.103072","usgsCitation":"Marshall, D., Coxe, N., La Peyre, M., Walton, W., Rikard, F.S., Beseres Pollack, J., Kelly, M., and La Peyre, J., 2021, Tolerance of northern Gulf of Mexico eastern oysters to chronic warming at extreme salinities: Journal of Thermal Biology, v. 100, 103072, 7 p., https://doi.org/10.1016/j.jtherbio.2021.103072.","productDescription":"103072, 7 p.","ipdsId":"IP-126113","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":451207,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://digitalcommons.lsu.edu/biosci_pubs/3821","text":"Publisher Index Page"},{"id":396900,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana, Texas","otherGeospatial":"Northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.91015624999999,\n 26.03704188651584\n ],\n [\n -90.52734374999999,\n 26.03704188651584\n ],\n [\n -90.52734374999999,\n 30.826780904779774\n ],\n [\n -97.91015624999999,\n 30.826780904779774\n ],\n [\n -97.91015624999999,\n 26.03704188651584\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"100","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, D.A.","contributorId":287622,"corporation":false,"usgs":false,"family":"Marshall","given":"D.A.","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coxe, N.C.","contributorId":288255,"corporation":false,"usgs":false,"family":"Coxe","given":"N.C.","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":837592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walton, W.C.","contributorId":287624,"corporation":false,"usgs":false,"family":"Walton","given":"W.C.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":837593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rikard, F. Scott","contributorId":288303,"corporation":false,"usgs":false,"family":"Rikard","given":"F.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":837656,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beseres Pollack, J.","contributorId":288257,"corporation":false,"usgs":false,"family":"Beseres Pollack","given":"J.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":837594,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kelly, M.A.","contributorId":221161,"corporation":false,"usgs":false,"family":"Kelly","given":"M.A.","email":"","affiliations":[],"preferred":false,"id":837595,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"La Peyre, J.F.","contributorId":274908,"corporation":false,"usgs":false,"family":"La Peyre","given":"J.F.","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837596,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221895,"text":"ofr20211070 - 2021 - Optimization of salt marsh management at the Long Island National Wildlife Refuge Complex, New York, through use of structured decision making","interactions":[],"lastModifiedDate":"2021-08-11T16:24:11.519939","indexId":"ofr20211070","displayToPublicDate":"2021-08-11T10:25:00","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":"2021-1070","displayTitle":"Optimization of Salt Marsh Management at the Long Island National Wildlife Refuge Complex, New York, Through Use of Structured Decision Making","title":"Optimization of salt marsh management at the Long Island National Wildlife Refuge Complex, New York, through use of structured decision making","docAbstract":"

Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Long Island National Wildlife Refuge Complex in New York. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of five marsh management units within the refuge complex and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge-complex scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to about $24,000, but that further expenditures may yield diminishing return on investment. Potential management actions in optimal portfolios at total costs less than $24,000 consistently included approaches for increasing drainage from the marsh surface within the marsh management units. The potential management benefits were derived from expected improvements in surface-water drainage and capacity for marsh elevation to keep pace with sea-level rise, and presumed increases in numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds. The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Long Island National Wildlife Refuge Complex that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211070","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., and Williams, M.R., 2021, Optimization of salt marsh management at the Long Island National Wildlife Refuge Complex, New York, through use of structured decision making (ver. 1.1, August 2021): U.S. Geological Survey Open-File Report 2021–1070, 34 p., https://doi.org/10.3133/ofr20211070.","productDescription":"Report: vi, 34 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126538","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387845,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1070/versionHist.txt","size":"640 B","linkFileType":{"id":2,"text":"txt"}},{"id":387151,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1070/ofr20211070.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1070"},{"id":387150,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1070/coverthb2.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0478515625,\n 40.576412521044425\n ],\n [\n -73.6138916015625,\n 40.54720023441049\n ],\n [\n -73.1854248046875,\n 40.60978237983301\n ],\n [\n -72.66357421875,\n 40.77638178482896\n ],\n [\n -72.015380859375,\n 40.96330795307353\n ],\n [\n -71.795654296875,\n 41.091772220976644\n ],\n [\n -72.2625732421875,\n 41.18278832811288\n ],\n [\n -72.7294921875,\n 41.02964338716638\n ],\n [\n -73.245849609375,\n 40.94256444133327\n ],\n [\n -73.4820556640625,\n 40.967455873296714\n ],\n [\n -73.707275390625,\n 40.8595252289932\n ],\n [\n -73.8775634765625,\n 40.79301881008675\n ],\n [\n -74.0203857421875,\n 40.693134153308065\n ],\n [\n -74.0478515625,\n 40.576412521044425\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: July 13, 2021; Version 1.1: August 11, 2021","contact":"

Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-07-13","revisedDate":"2021-08-11","noUsgsAuthors":false,"publicationDate":"2021-07-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Neckles, Hilary A. 0000-0002-5662-2314 hneckles@usgs.gov","orcid":"https://orcid.org/0000-0002-5662-2314","contributorId":3821,"corporation":false,"usgs":true,"family":"Neckles","given":"Hilary","email":"hneckles@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819237,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819238,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nagel, Jessica L. 0000-0002-4437-0324 jnagel@usgs.gov","orcid":"https://orcid.org/0000-0002-4437-0324","contributorId":3976,"corporation":false,"usgs":true,"family":"Nagel","given":"Jessica","email":"jnagel@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819239,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adamowicz, Susan C.","contributorId":174712,"corporation":false,"usgs":false,"family":"Adamowicz","given":"Susan","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":true,"id":819240,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mikula, Toni","contributorId":208473,"corporation":false,"usgs":false,"family":"Mikula","given":"Toni","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":819241,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, Monica R.","contributorId":261000,"corporation":false,"usgs":false,"family":"Williams","given":"Monica","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":819242,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70225757,"text":"70225757 - 2021 - Estimates of abundance and harvest rates of female black bears across a large spatial extent","interactions":[],"lastModifiedDate":"2021-11-10T13:15:40.907975","indexId":"70225757","displayToPublicDate":"2021-08-10T07:10:58","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":"Estimates of abundance and harvest rates of female black bears across a large spatial extent","docAbstract":"

American black bears (Ursus americanus) are an iconic wildlife species in the southern Appalachian highlands of the eastern United States and have increased in number and range since the early 1980s. Given an increasing number of human-bear conflicts in the region, many management agencies have liberalized harvest regulations to reduce bear populations to socially acceptable levels. Wildlife managers need reliable population data for assessing the effects of management actions for this high-profile species. Our goal was to use DNA extracted from hair collected at barbed-wire enclosures (i.e., hair traps) to identify individual bears and then use spatially explicit capture-recapture methods to estimate female black bear density, abundance, and harvest rate. We established 888 hair traps across 66,678 km2 of the southern Appalachian highlands in Georgia, North Carolina, South Carolina, and Tennessee, USA, in 2017 and 2018, arranged in 174 clusters of 2–9 traps/cluster. We collected 9,113 hair samples from those sites over 6 weeks of sampling, of which 1,954 were successfully genotyped to 462 individual female bears. Our spatially explicit estimator included a percent forest covariate to explain inhomogeneous bear density across the region. Densities ranged up to 0.410 female bears/km2 and regional abundance was 5,950 (95% CI = 4,988–7,098) female bears. Based on hunter kill data from 2016 to 2018, mean annual harvest rates for females were 12.7% in Georgia, 17.6% in North Carolina, 17.6% in South Carolina, and 22.8% in Tennessee. Our estimated harvest rates for most states approached or exceeded theoretical maximum sustainable levels, and population trend data (i.e., bait-station indices) indicated decreasing growth rates since about 2009. These data suggest that the increased harvest goals and poor hard mast production over a series of prior years reduced bear population abundance in many states. We were able to obtain reasonable population abundance and density estimates because of spatially explicit capture-recapture methods, cluster sampling, and a large spatial extent. Continued monitoring of bear populations (e.g., annual bait-station surveys and periodic population estimation using spatially explicit methods) by state jurisdictions would help to ensure that population trajectories are consistent with management goals. © 2021 The Wildlife Society.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22104","usgsCitation":"Humm, J., and Clark, J.D., 2021, Estimates of abundance and harvest rates of female black bears across a large spatial extent: Journal of Wildlife Management, v. 85, no. 7, p. 1321-1331, https://doi.org/10.1002/jwmg.22104.","productDescription":"11 p.","startPage":"1321","endPage":"1331","ipdsId":"IP-123233","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":391565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, North Carolina, South Carolina, Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.7060546875,\n 36.50963615733049\n ],\n [\n -82.06787109374999,\n 36.59788913307022\n ],\n [\n -84.0673828125,\n 35.871246850027966\n ],\n [\n -84.6826171875,\n 35.37113502280101\n ],\n [\n -84.7705078125,\n 33.96158628979907\n ],\n [\n -83.8037109375,\n 33.52307880890422\n ],\n [\n -82.265625,\n 33.46810795527896\n ],\n [\n -80.44189453125,\n 34.27083595165\n ],\n [\n -79.365234375,\n 35.15584570226544\n ],\n [\n -78.486328125,\n 35.94243575255426\n ],\n [\n -78.42041015625,\n 36.33282808737917\n ],\n [\n -78.7060546875,\n 36.50963615733049\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Humm, Jacob","contributorId":268358,"corporation":false,"usgs":false,"family":"Humm","given":"Jacob","email":"","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":826512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":826513,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223273,"text":"70223273 - 2021 - Changes in organic carbon source and storage with sea level rise-induced transgression in a Chesapeake Bay marsh","interactions":[],"lastModifiedDate":"2021-08-19T15:34:52.390789","indexId":"70223273","displayToPublicDate":"2021-08-09T10:30:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Changes in organic carbon source and storage with sea level rise-induced transgression in a Chesapeake Bay marsh","docAbstract":"

Organic matter (OM) accumulation in marsh soils affects marsh survival under rapid sea-level rise (SLR). This work describes the changing organic geochemistry of a salt marsh located in the Blackwater National Wildlife Refuge on the eastern shore of Chesapeake Bay that has transgressed inland with SLR over the past 35–75 years. Marsh soils and vegetation were sampled along an elevation gradient from the intertidal zone to the adjacent forest, representing a space-for-time substitution of the process of marsh transgression. Stable carbon isotope analysis of bulk OM gives evidence for a transition from C3 upland-sourced OM to C4-dominated marsh vegetation over time. The vegetative source of the OM changes along a marsh-upland mixing line from herbaceous angiosperm-sourced lignin in the lower elevation marsh to a woody gymnosperm signature at the upper border of the marsh. The results of stable isotope and lignin analyses illustrate that landward encroachment of marsh grasses results in deposition of herbaceous tissues exhibiting relatively little decay. This presents a possible mechanism for OM stabilization as marshes migrate inland.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2021.107550","usgsCitation":"Van Allen, R., Schreiner, K.M., Guntenspergen, G.R., and Carlin, J.A., 2021, Changes in organic carbon source and storage with sea level rise-induced transgression in a Chesapeake Bay marsh: Estuaries and Coasts, v. 261, 107550, 11 p., https://doi.org/10.1016/j.ecss.2021.107550.","productDescription":"107550, 11 p.","ipdsId":"IP-103097","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":436246,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97H1N4E","text":"USGS data release","linkHelpText":"Changes in Organic Carbon Source and Storage with Sea Level Rise-Induced Transgression in a Chesapeake Bay Marsh"},{"id":388155,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Blackwater National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.19430541992188,\n 38.37396220263095\n ],\n [\n -75.99655151367188,\n 38.37396220263095\n ],\n [\n -75.99655151367188,\n 38.47509432050245\n ],\n [\n -76.19430541992188,\n 38.47509432050245\n ],\n [\n -76.19430541992188,\n 38.37396220263095\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"261","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Van Allen, Rachel","contributorId":264468,"corporation":false,"usgs":false,"family":"Van Allen","given":"Rachel","email":"","affiliations":[{"id":34699,"text":"University of Minnesota-Duluth","active":true,"usgs":false}],"preferred":false,"id":821564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schreiner, Kathryn M.","contributorId":201540,"corporation":false,"usgs":false,"family":"Schreiner","given":"Kathryn","email":"","middleInitial":"M.","affiliations":[{"id":36192,"text":"Large Lakes Observatory, University of Minnesota Duluth, Duluth, Minnesota, USA.","active":true,"usgs":false}],"preferred":false,"id":821565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":821566,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carlin, Joseph A.","contributorId":200295,"corporation":false,"usgs":false,"family":"Carlin","given":"Joseph","email":"","middleInitial":"A.","affiliations":[{"id":13544,"text":"California State University, Fullerton","active":true,"usgs":false}],"preferred":false,"id":821567,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223880,"text":"70223880 - 2021 - Evaluating the migration mortality hypothesis using monarch tagging data","interactions":[],"lastModifiedDate":"2021-09-14T11:35:01.272469","indexId":"70223880","displayToPublicDate":"2021-08-07T08:39:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the migration mortality hypothesis using monarch tagging data","docAbstract":"

The decline in the eastern North American population of the monarch butterfly population since the late 1990s has been attributed to the loss of milkweed during the summer breeding season and the consequent reduction in the size of the summer population that migrates to central Mexico to overwinter (milkweed limitation hypothesis). However, in some studies the size of the summer population was not found to decline and was not correlated with the size of the overwintering population. The authors of these studies concluded that milkweed limitation could not explain the overwintering population decline. They hypothesized that increased mortality during fall migration was responsible (migration mortality hypothesis). We used data from the long-term monarch tagging program, managed by Monarch Watch, to examine three predictions of the migration mortality hypothesis: (1) that the summer population size is not correlated with the overwintering population size, (2) that migration success is the main determinant of overwintering population size, and (3) that migration success has declined over the last two decades. As an index of the summer population size, we used the number of wild-caught migrating individuals tagged in the U.S. Midwest from 1998 to 2015. As an index of migration success we used the recovery rate of Midwest tagged individuals in Mexico. With regard to the three predictions: (1) the number of tagged individuals in the Midwest, explained 74% of the variation in the size of the overwintering population. Other measures of summer population size were also correlated with overwintering population size. Thus, there is no disconnection between late summer and winter population sizes. (2) Migration success was not significantly correlated with overwintering population size, and (3) migration success did not decrease during this period. Migration success was correlated with the level of greenness of the area in the southern U.S. used for nectar by migrating butterflies. Thus, the main determinant of yearly variation in overwintering population size is summer population size with migration success being a minor determinant. Consequently, increasing milkweed habitat, which has the potential of increasing the summer monarch population, is the conservation measure that will have the greatest impact.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2020.00264","usgsCitation":"Taylor, O.R., Pleasants, J., Grundel, R., Pecoraro, S., Lovett, J.P., and Ryan, A., 2021, Evaluating the migration mortality hypothesis using monarch tagging data: Frontiers in Ecology and Evolution, v. 8, 264, 13 p., https://doi.org/10.3389/fevo.2020.00264.","productDescription":"264, 13 p.","ipdsId":"IP-106646","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":451254,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2020.00264","text":"Publisher Index Page"},{"id":389143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.0,\n 40\n ],\n [\n -60.0,\n 40\n ],\n [\n -60.00,\n 50.0\n ],\n [\n -100.0,\n 50.0\n ],\n [\n -100.0,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Orley R.","contributorId":168617,"corporation":false,"usgs":false,"family":"Taylor","given":"Orley","email":"","middleInitial":"R.","affiliations":[{"id":25342,"text":"Department of Ecology and Evolutionary Biology, University of Kansas","active":true,"usgs":false}],"preferred":false,"id":823073,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pleasants, John M.","contributorId":168616,"corporation":false,"usgs":false,"family":"Pleasants","given":"John M.","affiliations":[{"id":25341,"text":"Department of Ecology, Evolution, and Organismal Biology, Iowa State University","active":true,"usgs":false}],"preferred":false,"id":823074,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grundel, Ralph 0000-0002-2949-7087 rgrundel@usgs.gov","orcid":"https://orcid.org/0000-0002-2949-7087","contributorId":2444,"corporation":false,"usgs":true,"family":"Grundel","given":"Ralph","email":"rgrundel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":823075,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pecoraro, Samuel 0000-0002-3435-649X","orcid":"https://orcid.org/0000-0002-3435-649X","contributorId":221137,"corporation":false,"usgs":true,"family":"Pecoraro","given":"Samuel","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":823076,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lovett, James P.","contributorId":265598,"corporation":false,"usgs":false,"family":"Lovett","given":"James","email":"","middleInitial":"P.","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":823077,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ryan, Ann","contributorId":265599,"corporation":false,"usgs":false,"family":"Ryan","given":"Ann","email":"","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":823078,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222522,"text":"sir20215055 - 2021 - Groundwater quality and age of secondary bedrock aquifers in the glaciated portion of eastern Nebraska, 2016–18","interactions":[],"lastModifiedDate":"2021-08-05T09:52:41.030548","indexId":"sir20215055","displayToPublicDate":"2021-08-04T08:23:21","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":"2021-5055","displayTitle":"Groundwater Quality and Age of Secondary Bedrock Aquifers in the Glaciated Portion of Eastern Nebraska, 2016–18","title":"Groundwater quality and age of secondary bedrock aquifers in the glaciated portion of eastern Nebraska, 2016–18","docAbstract":"

The Eastern Nebraska Water Resources Assessment (ENWRA) project was initiated in 2006 to assist water managers by developing a hydrogeologic framework and water budget for the glaciated portion of eastern Nebraska. Within the ENWRA area, the primary groundwater sources for municipal, domestic, and irrigation water needs are provided by withdrawals from alluvial, buried paleovalley, and the High Plains aquifer (where present). Generally, other bedrock aquifers are considered a secondary water source. However, in some areas, such as parts of Sarpy and Nemaha Counties, these secondary bedrock aquifers are the only source of water within glaciated upland areas. To improve the understanding of the quality, geochemistry, and age of groundwater from bedrock aquifers, the U.S. Geological Survey (USGS), in cooperation with the ENWRA group, which includes the Lewis and Clark, Lower Elkhorn, Lower Platte North, Lower Platte South, Nemaha, and Papio-Missouri River Natural Resources Districts, designed a study to sample 31 wells completed in the secondary bedrock aquifers and analyze samples for major ions, physical properties, nutrients, stable isotopes, and selected age tracers. Of the 31 samples collected for this report, 22 samples were collected from the Dakota aquifer contained in the Dakota Sandstone, 3 from the Niobrara aquifer contained in the Niobrara Formation of Colorado Group, and 6 from Paleozoic aquifers contained in undifferentiated Paleozoic-age units.

The results of this study indicate that major ion data collected from the Dakota aquifer can be used for assessing the quality, recharge source, and age of groundwater. Calcium bicarbonate dominant samples were characterized as modern or mixed, indicating that, in these areas, groundwater is unconfined and is recharged by precipitation and (or) surface water. If groundwater extraction rates exceed recharge rates, total dissolved solid concentrations may increase as a result of upwelling of groundwater from deeper units or formations, which can adversely affect groundwater quality. Sampling results presented in this report indicate water quality is good, but that groundwater in the Dakota aquifer with calcium bicarbonate water type may be vulnerable to surface contamination. In contrast, groundwater sampled from the Dakota aquifer, having a dominant water type other than calcium bicarbonate, generally has low dissolved oxygen and nitrate concentrations, and higher concentrations of total dissolved solids and trace elements, including iron and strontium. The geochemical characteristics of noncalcium bicarbonate samples from the Dakota aquifer indicated confining conditions and limited groundwater recharge from local precipitation. Apparent groundwater ages estimated from radiocarbon (carbon-14) sampling of noncalcium bicarbonate samples from the Dakota aquifer indicated that the time of groundwater recharge to the Dakota aquifer occurred during Pleistocene time. Depleted stable isotopes results indicate recharge during a colder climate. Groundwater under confined conditions is not easily recharged from precipitation or surface water. Future groundwater-level monitoring in locations where the Dakota aquifer appears to be confined could provide information to evaluate whether groundwater supplies remain sufficient to meet future municipal, domestic, and irrigation needs.

For the Niobrara aquifer and Paleozoic aquifers, the dominant water type was not a diagnostic indicator of recharge source, age, and groundwater quality as with the Dakota aquifer. Most likely this is because the host formation was dominated by calcium-carbonate-rich rocks; however, few samples were collected from these aquifers to be able to confirm this interpretation. Samples collected from wells completed in the Niobrara aquifer and Paleozoic aquifers and characterized as calcium sulfate water type have statistically significantly higher concentrations of total dissolved solids compared to other samples from the Niobrara aquifer and Paleozoic aquifers characterized as calcium bicarbonate. Given that six of the nine of samples collected from the Niobrara and Paleozoic aquifers indicated modern recharge, these secondary bedrock aquifers are reliant on precipitation to sustain groundwater levels and may be vulnerable to a multiyear drought. Well yields of the Niobrara and Paleozoic aquifers are dependent on the presence of secondary porosity and these units offer little storage. Samples collected from wells completed in Paleozoic aquifers were the most isotopically enriched and similar to modern precipitation and had the highest concentrations of nitrate, indicating that groundwater is affected by agricultural activities. Future groundwater sampling would be beneficial to characterize groundwater-quality changes within the Niobrara and Paleozoic aquifers over time.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215055","collaboration":"Prepared in cooperation with the Eastern Nebraska Water Resources Assessment","usgsCitation":"Hobza, C.M., and Flynn, A.T., 2021, Groundwater quality and age of secondary bedrock aquifers in the glaciated portion of eastern Nebraska, 2016–18: U.S. Geological Survey Scientific Investigations Report 2021–5055, 42 p., https://doi.org/10.3133/sir20215055.","productDescription":"Report: viii, 42 p.; Dataset","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-122775","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":387641,"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":387643,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5055/sir20215055.XML","linkFileType":{"id":8,"text":"xml"}},{"id":387642,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5055/images"},{"id":387640,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5055/sir20215055.pdf","text":"Report","size":"2.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5055"},{"id":387639,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5055/coverthb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.9541015625,\n 42.779275360241904\n ],\n [\n -97.7783203125,\n 42.261049162113856\n ],\n [\n -97.58056640625,\n 41.52502957323801\n ],\n [\n -97.05322265625,\n 41.07935114946899\n ],\n [\n -96.87744140625,\n 40.64730356252251\n ],\n [\n -96.591796875,\n 39.99395569397331\n ],\n [\n -95.2734375,\n 39.977120098439634\n ],\n [\n -95.49316406249999,\n 40.3130432088809\n ],\n [\n -95.80078125,\n 40.713955826286046\n ],\n [\n -95.91064453125,\n 41.31082388091818\n ],\n [\n -96.13037109375,\n 42.00032514831621\n ],\n [\n -96.5478515625,\n 42.65012181368022\n ],\n [\n -97.0751953125,\n 42.87596410238256\n ],\n [\n -97.822265625,\n 42.89206418807337\n ],\n [\n -97.9541015625,\n 42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-08-04","noUsgsAuthors":false,"publicationDate":"2021-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Hobza, Christopher M. 0000-0002-6239-934X cmhobza@usgs.gov","orcid":"https://orcid.org/0000-0002-6239-934X","contributorId":2393,"corporation":false,"usgs":true,"family":"Hobza","given":"Christopher","email":"cmhobza@usgs.gov","middleInitial":"M.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":820456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flynn, Amanda T. 0000-0001-9768-2076 aflynn@usgs.gov","orcid":"https://orcid.org/0000-0001-9768-2076","contributorId":176644,"corporation":false,"usgs":true,"family":"Flynn","given":"Amanda","email":"aflynn@usgs.gov","middleInitial":"T.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":820457,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70222506,"text":"sir20215060 - 2021 - Groundwater assessment for petroleum hydrocarbon compounds associated with Fuels Area C, Ellsworth Air Force Base, South Dakota, 2014–18","interactions":[],"lastModifiedDate":"2021-08-03T11:56:55.430548","indexId":"sir20215060","displayToPublicDate":"2021-08-02T12:17:00","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":"2021-5060","displayTitle":"Groundwater Assessment for Petroleum Hydrocarbon Compounds Associated with Fuels Area C, Ellsworth Air Force Base, South Dakota, 2014–18","title":"Groundwater assessment for petroleum hydrocarbon compounds associated with Fuels Area C, Ellsworth Air Force Base, South Dakota, 2014–18","docAbstract":"

In 2013, the U.S. Geological Survey began a study in cooperation with the Defense Logistics Agency and the U.S. Air Force to estimate groundwater-flow direction, install groundwater monitoring wells, and collect soil and groundwater samples for petroleum hydrocarbon compounds to identify the presence of hydrocarbon contamination at Ellsworth Air Force Base, South Dakota, specifically around Fuels Area C. Several fuel spills of diesel fuel, jet fuel, and other petroleum products were documented on or near Fuels Area C and several studies have been done to determine the extent of petroleum hydrocarbon contamination in the subsurface.

Two-dimensional electrical resistivity tomography surveys were completed at Fuels Area C in 2014 to characterize subsurface materials and determine the depth to bedrock along survey lines. The depth to the top of the Pierre Shale from land surface along the four electrical resistivity tomography survey lines in Fuels area C ranged from about 5.4 to 8.7 meters. Resistivity lines and lithologic logs in wells in the area indicated mostly clay material with minor occurrences of sand and gravel.

Discrete groundwater levels were collected between November 2014 and June 2018 at 14 monitoring wells for use in generating a potentiometric surface in the study area around Fuels Area C. The potentiometric contours indicated that groundwater flow was from the west to east or southwest to southeast around Fuels Area C.

Soil and groundwater samples were collected at selected locations from 2014 to 2018 to better understand the presence and movement of petroleum hydrocarbons in the study area around Fuels Area C. Soil samples were collected at eight wells during installation in 2014 and three wells during installation in 2016. Groundwater samples were collected from 14 wells and a recovery sump around Fuels Area C from 2014 to 2018.

Several petroleum hydrocarbon compounds were detected, but below action levels, in soil samples collected in 2014 and 2016. Benzene and toluene were not detected in any of the soil samples from the 11 monitoring well sites. Ethylbenzene and total xylenes were detected at sites 1 and 7. Naphthalene was detected in samples from five sites (sites 1, 5, 7, 8, and 9), but concentrations were less than the Tier 1 action level of 25 milligrams per kilogram.

Gasoline-range organic compounds were detected in all soil samples collected during the installation of 11 groundwater monitoring wells within or near Fuels Area C in 2014 and 2016. Diesel-range organic compounds were detected in 9 out of the 11 soil samples collected at the 11 monitoring wells. Gasoline-range organic compound concentrations exceeded the Tier 2 assessment level for total petroleum hydrocarbons in soil samples from site 1 (5,200 milligrams per kilogram), site 5 (580 milligrams per kilogram), and site 9 (1,800 milligrams per kilogram); the remaining sites had concentrations below the Tier 2 assessment level for total petroleum hydrocarbons. The highest concentrations of diesel-range organic compounds in soil samples were from site 1 (3,600 milligrams per kilogram), site 5 (440 milligrams per kilogram), and site 14 (330 milligrams per kilogram), and only the sample from site 1 exceeded the Tier 2 assessment level for total petroleum hydrocarbons.

Petroleum hydrocarbon concentrations were measured in samples collected from 14 groundwater monitoring wells and 1 recovery sump between 2014 and 2018 in the study area around Fuels Area C. Benzene, toluene, ethylbenzene, and xylene (BTEX) compounds were detected in at least one sample collected from 10 of the 15 sites sampled in the study area from 2014 to 2018. Samples from monitoring well sites 2, 3, 6, 8, and 9 did not have any quantifiable concentrations of BTEX compounds. Multiple BTEX compounds were detected consistently in samples collected from sites 10 and 11. Few BTEX compounds were detected at sites outside of and downgradient from Fuels Area C (sites 12–14). Naphthalene was detected in 8 of the 15 sites sampled in the study area in 2014–18. Measurable concentrations of naphthalene generally were less than 5 micrograms per liter in wells sampled in the study area in 2014–18 except for samples collected at sites 5, 7, and 11.

The variability of the presence of BTEX compounds and naphthalene in wells sampled in the study area during 2014–18 likely is caused by the variability in the subsurface material, local groundwater flow, operational fueling activities, and historical spills and releases in the area. The spatial and temporal variability in the BTEX compounds and naphthalene concentrations from samples collected from 2014 to 2018 do not indicate a consistent pattern of subsurface flow or contaminate movement that would be expected if a contaminant plume migrated with the flow and movement of groundwater.

Gasoline-range organic and diesel-range organic compounds were detected in most of the groundwater samples collected in the study area around Fuels Area C in 2014–18; however, concentrations were often less than the laboratory reporting level. Median gasoline-range organic compound concentrations were greater than the laboratory reporting level at sites 1, 5, 9, 10, and 11. The highest concentrations of gasoline-range organic and diesel-range organic compounds generally were observed in samples collected from sites 10 and 11. Gasoline-range organic compound concentrations ranged from 1,500 to 9,700 micrograms per liter at site 10 and from less than 100 to 13,000 micrograms per liter at site 11. Diesel-range organic compound concentrations ranged from 9,600 to 55,000 micrograms per liter at site 10 and from 560 to 7,300 micrograms per liter at site 11.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215060","collaboration":"Prepared in cooperation with Defense Logistics Agency and Ellsworth Air Force Base","usgsCitation":"Bender, D.A., Galloway, J.M., and Medler, C.J., 2021, Groundwater assessment for petroleum hydrocarbon compounds associated with Fuels Area C, Ellsworth Air Force Base, South Dakota, 2014–18: U.S. Geological Survey Scientific Investigations Report 2021–5060, 37 p., https://doi.org/10.3133/sir20215060.","productDescription":"Report: vi, 37 p.; Appendix Table; Data Release; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-123385","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":387602,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5060/coverthb.jpg"},{"id":387603,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5060/sir20215060.pdf","text":"Report","size":"6.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5060"},{"id":387606,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJH17","text":"USGS data release","linkHelpText":"Electrical Resistivity Tomography (ERT) and Horizontal-to-Vertical Spectral Ratio (HVSR) data collected within and near Ellsworth Air Force Base, South Dakota, from 2014 to 2019"},{"id":387605,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5060/sir20215060_table2.1.csv","text":"Table 2.1","size":"28.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2021–5060 Appendix Table 2.1","linkHelpText":"— Appendix table 2.1 Water-quality results for groundwater samples collected from 14 monitoring wells in the study area around Fuels Area C, 2014–18"},{"id":387604,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5060/sir20215060_table2.1.xlsx","text":"Table 2.1","size":"42.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2021–5060 Appendix Table 2.1","linkHelpText":"— Appendix table 2.1 Water-quality results for groundwater samples collected from 14 monitoring wells in the study area around Fuels Area C, 2014–18"},{"id":387607,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"South Dakota","otherGeospatial":"Ellsworth Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.15235137939453,\n 44.104598040381106\n ],\n [\n -103.03321838378906,\n 44.104598040381106\n ],\n [\n -103.03321838378906,\n 44.17974184575526\n ],\n [\n -103.15235137939453,\n 44.17974184575526\n ],\n [\n -103.15235137939453,\n 44.104598040381106\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-08-02","noUsgsAuthors":false,"publicationDate":"2021-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Bender, David A. 0000-0002-1269-0948 dabender@usgs.gov","orcid":"https://orcid.org/0000-0002-1269-0948","contributorId":985,"corporation":false,"usgs":true,"family":"Bender","given":"David","email":"dabender@usgs.gov","middleInitial":"A.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":820331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":820332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Medler, Colton J. 0000-0001-6119-5065","orcid":"https://orcid.org/0000-0001-6119-5065","contributorId":201463,"corporation":false,"usgs":true,"family":"Medler","given":"Colton","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":820333,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216953,"text":"ofr20201097 - 2021 - Forest area to support landbird population goals for the Mississippi Alluvial Valley","interactions":[],"lastModifiedDate":"2024-03-04T18:26:39.337253","indexId":"ofr20201097","displayToPublicDate":"2021-08-02T10:40:00","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":"2020-1097","displayTitle":"Forest Area to Support Landbird Population Goals for the Mississippi Alluvial Valley","title":"Forest area to support landbird population goals for the Mississippi Alluvial Valley","docAbstract":"

Historically, the Mississippi Alluvial Valley (MAV) (Partners in Flight Bird Conservation Region #26) was predominantly bottomland hardwood forest, but natural vegetation has been cleared from about 80 percent of this ecoregion and converted primarily to agriculture. Because most bird species that are of conservation concern in this region are dependent on forested wetlands, bottomland hardwood forest is the habitat of greatest conservation concern in the MAV. Past conservation planning for forest-dwelling birds in this region has focused on habitat objectives with presumptions regarding bird population goals being met through habitat provision. To better define population objectives, we estimated current populations of silvicolous birds on the basis of detections during 10 years of North American Breeding Bird Surveys (BBS). For each species, we used their estimated population and historical (1966–2015) change in their relative abundance, as assessed from BBS data, to establish regional population goals. We used the variance associated with historical BBS trends to estimate the minimum forest area required to sustain greater than or equal to (≥) 25 breeding pairs, which we combined with predicted probability of occupancy to identify sustainable forested habitat. For 54 species, we used published empirical density estimates, as affected by forest management, to estimate the proportion of the population objective that could be provisioned within sustainable forest patches. The area of presumed population-sustaining habitat, under existing forest management, was sufficient to support the species’ population objective for 23 species. We estimated that the target populations of seven additional species (Black-and-white Warbler, Brown Thrasher, Cerulean Warbler, Eastern Towhee, Indigo Bunting, Wood Thrush, and Yellow-breasted Chat) could be supported by current forest area through widespread changes in forest management. Target populations of seven other species (American Robin, Barred Owl, Boat-tailed Grackle, Chipping Sparrow, Eastern Phoebe, Mississippi Kite, and Red-headed Woodpecker) were accommodated within the MAV when populations in both forest and nonforest habitats are considered. For the remaining 20 species, we estimated the population increase needed to achieve their population goals. For these species, we estimated the additional area of forest restoration required to achieve their population goal within sustainable forest patches or, alternatively, the additional area of occupied habitat required to support their population goal within both forest and nonforest habitat. An additional 700,000 hectares of sustainable forest habitat may be enough to attain the forest-dependent population goals for most bird species within the MAV.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201097","collaboration":"Prepared in cooperation with the Lower Mississippi Valley Joint Venture","usgsCitation":"Twedt, D.J., and Mini, A., 2021, Forest area to support landbird population goals for the Mississippi Alluvial Valley (ver. 1.1, August 2021): U.S. Geological Survey Open-File Report 2020–1097, 84 p., https://doi.org/10.3133/ofr20201097.","productDescription":"Report: vi, 75 p.; 2 Appendixes; Version History","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112336","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":436253,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YMSM8I","text":"USGS data release","linkHelpText":"Eastern Ecological Science CenterxLegacy Data ReleasesPatuxent Wildlife Research CenterPredicted Avian Species Occupancy, Area of Sustainable Forest Habitat, and Area of Occupied Habitat within the Mississippi Alluvial Valley Bird Conservation Region Predicted Avian Species Occupancy, Area of Sustainable Forest Habitat, and Area of Occupied Habitat within the Mississippi Alluvial Valley Bird Conservation Region"},{"id":436252,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AFKXXK","text":"USGS data release","linkHelpText":"Stop locations along Breeding Bird Survey routes in the Gulf Coastal Plains & Ozarks region"},{"id":381438,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1097/ofr20201097.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1097"},{"id":387552,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1097/versionHist.txt","size":"519 B","linkFileType":{"id":2,"text":"txt"}},{"id":381541,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://doi.org/10.5066/P9YMSM8I","text":"Appendixes 7, 8, and 9","linkHelpText":"- Predicted avian species occupancy"},{"id":381539,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://doi.org/10.5066/P9AFKXXK","text":"Appendixes 2 and 3","linkHelpText":"- Bird detections during North American Breeding Bird Surveys"},{"id":381437,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1097/coverthb3.jpg"}],"country":"United States","state":"Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Alluvial Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.76953125,\n 36.932330061503144\n ],\n [\n -89.80224609374999,\n 37.142803443716836\n ],\n [\n -90.06591796875,\n 37.055177106660814\n ],\n [\n -92.04345703125,\n 34.63320791137959\n ],\n [\n -91.91162109375,\n 32.47269502206151\n ],\n [\n -92.197265625,\n 30.41078179084589\n ],\n [\n -90.06591796875,\n 29.22889003019423\n ],\n [\n -89.4287109375,\n 30.012030680358613\n ],\n [\n -91.1865234375,\n 31.372399104880525\n ],\n [\n -90.63720703125,\n 32.565333160841035\n ],\n [\n -89.7802734375,\n 33.46810795527896\n ],\n [\n -89.7802734375,\n 34.615126683462194\n ],\n [\n -88.76953125,\n 36.932330061503144\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: August 2021; Version 1.0: February 2021","contact":"

Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2021-02-05","revisedDate":"2021-08-02","noUsgsAuthors":false,"publicationDate":"2021-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Twedt, Daniel J. 0000-0003-1223-5045 dtwedt@usgs.gov","orcid":"https://orcid.org/0000-0003-1223-5045","contributorId":398,"corporation":false,"usgs":true,"family":"Twedt","given":"Daniel","email":"dtwedt@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":807062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mini, Anne","contributorId":171716,"corporation":false,"usgs":false,"family":"Mini","given":"Anne","affiliations":[{"id":26934,"text":"Lower Mississippi Valley Joint Venture and American Bird Conservancy, 193 Business Park Drive, Suite E, Ridgeland, MS 39157","active":true,"usgs":false}],"preferred":false,"id":807063,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70222930,"text":"70222930 - 2021 - Establishment of a microsatellite genetic baseline for North American Atlantic sturgeon (Acipenser o. oxyrhinchus) and range-wide analysis of population genetics","interactions":[],"lastModifiedDate":"2021-10-18T14:23:58.805746","indexId":"70222930","displayToPublicDate":"2021-08-02T09:52:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Establishment of a microsatellite genetic baseline for North American Atlantic sturgeon (Acipenser o. oxyrhinchus) and range-wide analysis of population genetics","title":"Establishment of a microsatellite genetic baseline for North American Atlantic sturgeon (Acipenser o. oxyrhinchus) and range-wide analysis of population genetics","docAbstract":"

Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) is a long-lived, anadromous species that is broadly distributed along the Atlantic coast of North America. Historic overharvest and habitat degradation resulted in significant declines to Atlantic sturgeon populations and, following decades of limited recovery, the species was listed under the Endangered Species Act of the United States in 2012. Given continued threats to recovery and limited information about population demography, there is a need for new tools to assist in Atlantic sturgeon conservation. Here, we present a range-wide microsatellite genetic baseline for North American Atlantic sturgeon that is comprised of 2510 individuals from 18 genetically distinct groups collected in 13 rivers and one estuary. Analysis of this baseline suggested that populations from the northern range of Atlantic sturgeon were more highly differentiated than those from the southern extent, where patterns of differentiation were complicated by rivers with genetically distinct spring and fall spawning runs and less geographic distance separating populations. Despite significant demographic bottleneck events, all populations showed at least moderate levels of genetic diversity across a suite of metrics. Additionally, individual-based assignment tests had over 80% accuracy for assigning individuals to their river of origin, highlighting the utility of this baseline for characterizing the composition of mixed-stock aggregations and understanding stock-specific vulnerability and recovery. The expanded spatial coverage of this baseline dataset enabled novel inferences about patterns of genetic differentiation and spawning phenology in Atlantic sturgeon which can be used to support conservation and management efforts.

","language":"English","publisher":"Springer","doi":"10.1007/s10592-021-01390-x","usgsCitation":"White, S.L., Kazyak, D., Darden, T.L., Farrae, D.J., Lubinski, B.A., Johnson, R.L., Eackles, M.S., Balazik, M., Brundage, H., Fox, A.G., Fox, D.A., Hager, C.H., Kahn, J.E., and Wirgin, I.I., 2021, Establishment of a microsatellite genetic baseline for North American Atlantic sturgeon (Acipenser o. oxyrhinchus) and range-wide analysis of population genetics: Conservation Genetics, v. 22, p. 977-992, https://doi.org/10.1007/s10592-021-01390-x.","productDescription":"16 p.","startPage":"977","endPage":"992","ipdsId":"IP-124759","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387815,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.38671875,\n 29.99300228455108\n ],\n [\n -74.35546875,\n 35.460669951495305\n ],\n [\n -74.00390625,\n 38.75408327579141\n ],\n [\n -71.806640625,\n 40.245991504199026\n ],\n [\n -69.78515625,\n 40.64730356252251\n ],\n [\n -69.2578125,\n 43.068887774169625\n ],\n [\n -65.390625,\n 45.27488643704891\n ],\n [\n -66.796875,\n 48.3416461723746\n ],\n [\n -68.5546875,\n 48.980216985374994\n ],\n [\n -75.146484375,\n 45.336701909968134\n ],\n [\n -75.05859375,\n 41.244772343082076\n ],\n [\n -76.46484375,\n 39.977120098439634\n ],\n [\n -80.5078125,\n 36.24427318493909\n ],\n [\n -83.056640625,\n 33.358061612778876\n ],\n [\n -83.935546875,\n 32.02670629333614\n ],\n [\n -81.38671875,\n 29.99300228455108\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","noUsgsAuthors":false,"publicationDate":"2021-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Darden, Tanya L.","contributorId":263425,"corporation":false,"usgs":false,"family":"Darden","given":"Tanya","email":"","middleInitial":"L.","affiliations":[{"id":53977,"text":"SC DNR","active":true,"usgs":false}],"preferred":false,"id":820835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farrae, Daniel J.","contributorId":263426,"corporation":false,"usgs":false,"family":"Farrae","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":53977,"text":"SC DNR","active":true,"usgs":false}],"preferred":false,"id":820836,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820837,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820838,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820839,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Balazik, 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Resources, Delaware State University","active":true,"usgs":false}],"preferred":false,"id":820843,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hager, Chris H","contributorId":263429,"corporation":false,"usgs":false,"family":"Hager","given":"Chris","email":"","middleInitial":"H","affiliations":[{"id":53979,"text":"Chesapeake Scientific","active":true,"usgs":false}],"preferred":false,"id":820844,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kahn, Jason E","contributorId":263430,"corporation":false,"usgs":false,"family":"Kahn","given":"Jason","email":"","middleInitial":"E","affiliations":[{"id":53980,"text":"NMFS","active":true,"usgs":false}],"preferred":false,"id":820845,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wirgin, Isaac I","contributorId":263431,"corporation":false,"usgs":false,"family":"Wirgin","given":"Isaac","email":"","middleInitial":"I","affiliations":[{"id":53981,"text":"NYU","active":true,"usgs":false}],"preferred":false,"id":820846,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70222414,"text":"sim3472 - 2021 - Geologic and geophysical maps of the Santa Maria and part of the Point Conception 30'×60' quadrangles, California","interactions":[{"subject":{"id":17516,"text":"ofr92189 - 1992 - Preliminary geologic map of Santa Maria 30' x 60' quadrangle, California","indexId":"ofr92189","publicationYear":"1992","noYear":false,"title":"Preliminary geologic map of Santa Maria 30' x 60' quadrangle, California"},"predicate":"SUPERSEDED_BY","object":{"id":70222414,"text":"sim3472 - 2021 - Geologic and geophysical maps of the Santa Maria and part of the Point Conception 30'×60' quadrangles, California","indexId":"sim3472","publicationYear":"2021","noYear":false,"title":"Geologic and geophysical maps of the Santa Maria and part of the Point Conception 30'×60' quadrangles, California"},"id":1},{"subject":{"id":21108,"text":"ofr9525 - 1995 - Preliminary digital geologic map of the Santa Maria 30' x 60' Quadrangle, California, in ARC/INFO, with exploration well locations and subsurface formation depths","indexId":"ofr9525","publicationYear":"1995","noYear":false,"title":"Preliminary digital geologic map of the Santa Maria 30' x 60' Quadrangle, California, in ARC/INFO, with exploration well locations and subsurface formation depths"},"predicate":"SUPERSEDED_BY","object":{"id":70222414,"text":"sim3472 - 2021 - Geologic and geophysical maps of the Santa Maria and part of the Point Conception 30'×60' quadrangles, California","indexId":"sim3472","publicationYear":"2021","noYear":false,"title":"Geologic and geophysical maps of the Santa Maria and part of the Point Conception 30'×60' quadrangles, California"},"id":2}],"lastModifiedDate":"2021-08-03T11:47:53.611967","indexId":"sim3472","displayToPublicDate":"2021-08-02T09:30:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3472","displayTitle":"Geologic and Geophysical Maps of the Santa Maria and Part of the Point Conception 30'×60' Quadrangles, California","title":"Geologic and geophysical maps of the Santa Maria and part of the Point Conception 30'×60' quadrangles, California","docAbstract":"This report presents digital geologic, gravity, and aeromagnetic maps for the onshore parts of the Santa Maria and Point Conception 30'x60' quadrangles at a compilation scale of 1:100,000. The map depicts the distribution of bedrock units, surficial deposits, paleontological data, geophysical data and structural features in the Santa Maria basin and the Santa Ynez Mountains to the south, an area corresponding to 26 contiguous 7.5-minute quadrangles. The map also includes offshore faults from the Hosgri fault, a major structural feature, east to the shoreline. This new map revises and supersedes two earlier versions of the 30'x60' Santa Maria quadrangle that were produced as part of the U.S. Geological Survey’s investigations of onshore oil and gas resources of the Santa Maria province (Keller, 1995). The first map was released as a scanned black-and-white image of hand-drawn compilation (Tennyson, 1992); the second map was a digital release that is no longer available (Tennyson and others, 1995). This new map also includes the geology of the onshore part of the adjacent Point Conception 30'x60' quadrangle that encompasses the Santa Ynez Mountains of the western Transverse Ranges. The digital database also contains magnetic and gravity data for the entire region, paleontological data, and interpretation of major offshore structural features that bear on the continuity and connection of the mapped onshore structures.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3472","usgsCitation":"Sweetkind, D.S., Langenheim, V.E., McDougall-Reid, K., Sorlien, C.C., Demas, S.C., Tennyson, M.E., and Johnson, S.Y., 2021, Geologic and geophysical maps of the Santa Maria and part of the Point Conception 30'×60' quadrangles, California: U.S. Geological Survey Scientific Investigations Map 3472, 1 sheet, scale 1:100,000, 58-p. pamphlet, https://doi.org/10.3133/sim3472. [Supersedes USGS Open-File Reports 95–25 and 92–189.]","productDescription":"Report: vi, 58 p.; 9 Sheets: 57.14 x 35.04 inches or smaller; Data Release; ReadMe; Related Works","onlineOnly":"Y","ipdsId":"IP-054681","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":387551,"rank":16,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181024","linkHelpText":"California State Waters Map Series — Offshore of Point Conception, California"},{"id":387550,"rank":15,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3319","linkHelpText":"California State Waters Map Series: offshore of Refugio Beach, 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MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3472 Multilayered, interactive, geospatial PDF","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC to access interactive layers."}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.16271972656249,\n 34.266296360583546\n ],\n [\n -119.8443603515625,\n 34.266296360583546\n ],\n [\n -119.8443603515625,\n 34.93548199355901\n ],\n [\n -121.16271972656249,\n 34.93548199355901\n ],\n [\n -121.16271972656249,\n 34.266296360583546\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046

","tableOfContents":"","publishedDate":"2021-08-02","noUsgsAuthors":false,"publicationDate":"2021-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Sweetkind, Donald S. 0000-0003-0892-4796","orcid":"https://orcid.org/0000-0003-0892-4796","contributorId":210808,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":819967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langenheim, Victoria E. 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":217134,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":819968,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McDougall-Reid, Kristin 0000-0002-8788-3664","orcid":"https://orcid.org/0000-0002-8788-3664","contributorId":216211,"corporation":false,"usgs":true,"family":"McDougall-Reid","given":"Kristin","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":819969,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sorlien, Christopher C. 0000-0002-2359-9592","orcid":"https://orcid.org/0000-0002-2359-9592","contributorId":197404,"corporation":false,"usgs":false,"family":"Sorlien","given":"Christopher","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":819970,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Demas, Shiera C.","contributorId":261398,"corporation":false,"usgs":false,"family":"Demas","given":"Shiera","email":"","middleInitial":"C.","affiliations":[{"id":52841,"text":"Valdez International Corporation, Denver, Colo","active":true,"usgs":false}],"preferred":false,"id":819971,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tennyson, Marilyn E. 0000-0002-5166-2421","orcid":"https://orcid.org/0000-0002-5166-2421","contributorId":202544,"corporation":false,"usgs":true,"family":"Tennyson","given":"Marilyn E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819972,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Samuel Y. 0000-0001-7972-9977","orcid":"https://orcid.org/0000-0001-7972-9977","contributorId":221270,"corporation":false,"usgs":true,"family":"Johnson","given":"Samuel Y.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":819973,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237591,"text":"70237591 - 2021 - Integrating high-resolution coastal acidification monitoring data across seven United States estuaries","interactions":[],"lastModifiedDate":"2022-10-14T13:46:19.52335","indexId":"70237591","displayToPublicDate":"2021-08-01T15:52:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Integrating high-resolution coastal acidification monitoring data across seven United States estuaries","docAbstract":"

Beginning in 2015, the United States Environmental Protection Agency’s (EPA’s) National Estuary Program (NEP) started a collaboration with partners in seven estuaries along the East Coast (Barnegat Bay; Casco Bay), West Coast (Santa Monica Bay; San Francisco Bay; Tillamook Bay), and the Gulf of Mexico (GOM) Coast (Tampa Bay; Mission-Aransas Estuary) of the United States to expand the use of autonomous monitoring of partial pressure of carbon dioxide (pCO2) and pH. Analysis of high-frequency (hourly to sub-hourly) coastal acidification data including pCO2, pH, temperature, salinity, and dissolved oxygen (DO) indicate that the sensors effectively captured key parameter measurements under challenging environmental conditions, allowing for an initial characterization of daily to seasonal trends in carbonate chemistry across a range of estuarine settings. Multi-year monitoring showed that across all water bodies temperature and pCO2 covaried, suggesting that pCO2 variability was governed, in part, by seasonal temperature changes with average pCO2 being lower in cooler, winter months and higher in warmer, summer months. Furthermore, the timing of seasonal shifts towards increasing (or decreasing) pCO2 varied by location and appears to be related to regional climate conditions. Specifically, pCO2 increases began earlier in the year in warmer water, lower latitude water bodies in the GOM (Tampa Bay; Mission-Aransas Estuary) as compared with cooler water, higher latitude water bodies in the northeast (Barnegat Bay; Casco Bay), and upwelling-influenced West Coast water bodies (Tillamook Bay; Santa Monica Bay; San Francisco Bay). Results suggest that both thermal and non-thermal influences are important drivers of pCO2 in Tampa Bay and Mission-Aransas Estuary. Conversely, non-thermal processes, most notably the biogeochemical structure of coastal upwelling, appear to be largely responsible for the observed pCO2 values in West Coast water bodies. The co-occurrence of high salinity, high pCO2, low DO, and low temperature water in Santa Monica Bay and San Francisco Bay characterize the coastal upwelling paradigm that is also evident in Tillamook Bay when upwelling dominates freshwater runoff and local processes. These data demonstrate that high-quality carbonate chemistry observations can be recorded from estuarine environments using autonomous sensors originally designed for open-ocean settings.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.679913","usgsCitation":"Rosenau, N.A., Galavotti, H., Yates, K.K., Bohlen, C., Hunt, C.W., Liebman, M., Brown, A.C., Pacella, S.R., John L. Largier, Nielsen, K., Hu, X., McCutcheon, M., Vasslides, J., Poach, M., Ford, T., Johnston, K., and Steele, A., 2021, Integrating high-resolution coastal acidification monitoring data across seven United States estuaries: Frontiers in Marine Science, v. 8, 679913, 21 p., https://doi.org/10.3389/fmars.2021.679913.","productDescription":"679913, 21 p.","ipdsId":"IP-122630","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":451308,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2021.679913","text":"Publisher Index Page"},{"id":408298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Florida, Maine, New Jersey, Oregon, Texas,","otherGeospatial":"Aransas Estuary, Barnegat Bay, Casco Bay Estuary, Coastal Bend Bay, San Francisco Bay Estuary, Santa Monica Bay, Tampa Bay 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The Wind River Basin in Wyoming is one of many structural and sedimentary basins that formed in the Rocky Mountain foreland during the Laramide orogeny in the latest Cretaceous through the early Eocene. The basin (bounded by the Washakie, Owl Creek, and Bighorn uplifts on the north, the Casper arch on the east, the Granite Mountains uplift on the south, and Wind River uplift on the west) encompasses about 7,400 square miles in central Wyoming.

The two stratigraphic cross sections presented in this report were constructed as part of a project carried out by the U.S. Geological Survey to characterize and evaluate the undiscovered continuous (unconventional) oil and gas resources of the Mowry Shale in the Wind River Basin in central Wyoming. The purpose of the cross sections is to show the stratigraphic relationship of the Mowry Shale and associated Lower and lowermost Upper Cretaceous strata in the Wind River Basin. These two cross sections were constructed using borehole geophysical logs from 41 wells drilled for oil and gas exploration and production, and one research well that was cored and logged by the U.S. Geological Survey. Both lines originate at Sheldon Dome in the northwestern part of the basin and end near Bates Creek in the extreme southeastern part of the basin. The stratigraphic interval extends from the uppermost part of the Upper Jurassic Morrison Formation to the basal part of the Upper Cretaceous Frontier Formation. The datum is the top of the Clay Spur Bentonite Bed, a distinctive bed at the top of the Upper Cretaceous Mowry Shale. A gamma ray and (or) spontaneous potential log was used in combination with a resistivity log to identify and correlate units.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3476","usgsCitation":"Finn, T.M., 2021, Stratigraphic cross sections of the Mowry Shale and associated strata in the Wind River Basin, Wyoming: U.S. Geological Survey Scientific Investigations Map 3476, 1 sheet,14-p. pamphlet, https://doi.org/10.3133/sim3476.","productDescription":"Report: iv, 14 p.; 1 Sheet: 59.67 x 28.80 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-122529","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":387326,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y7SLB6","text":"USGS data release","linkHelpText":"Tops file for the Mowry Shale and associated strata in the Wind River Basin, Wyoming"},{"id":387325,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3476/sim3476_sheet.pdf","text":"Sheet—","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3476 Sheet","linkHelpText":"Stratigraphic Cross Sections of the Mowry Shale and Associated Strata in the Wind River Basin, Wyoming"},{"id":387324,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3476/sim3476_pamphlet.pdf","text":"Report","size":"4.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3476 Pamphlet"},{"id":387323,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3476/coverthb_sheet.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.35791015625,\n 43.644025847699496\n ],\n [\n -109.5556640625,\n 43.75522505306928\n ],\n [\n -109.8193359375,\n 43.644025847699496\n ],\n [\n -109.31396484375,\n 42.73087427928485\n ],\n [\n -109.13818359375,\n 42.48830197960227\n ],\n [\n -108.12744140625,\n 42.24478535602799\n ],\n [\n -107.8857421875,\n 42.48830197960227\n ],\n [\n -107.07275390625,\n 42.342305278572816\n ],\n [\n -106.23779296875,\n 42.114523952464246\n ],\n [\n -105.97412109375,\n 42.049292638686836\n ],\n [\n -105.71044921875,\n 42.16340342422401\n ],\n [\n -105.75439453125,\n 42.4234565179383\n ],\n [\n -106.01806640624999,\n 42.66628070564928\n ],\n [\n -106.63330078125,\n 43.08493742707592\n ],\n [\n -108.08349609375,\n 43.51668853502906\n ],\n [\n -109.35791015625,\n 43.644025847699496\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046

","tableOfContents":"","publishedDate":"2021-07-28","noUsgsAuthors":false,"publicationDate":"2021-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Finn, Thomas M. 0000-0001-6396-9351 finn@usgs.gov","orcid":"https://orcid.org/0000-0001-6396-9351","contributorId":778,"corporation":false,"usgs":true,"family":"Finn","given":"Thomas","email":"finn@usgs.gov","middleInitial":"M.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819631,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70222434,"text":"70222434 - 2021 - The spatial-temporal relationship of blue-winged teal to domestic poultry: Movement state modeling of a highly mobile avian influenza host","interactions":[],"lastModifiedDate":"2021-10-18T14:21:49.694371","indexId":"70222434","displayToPublicDate":"2021-07-26T09:16:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The spatial-temporal relationship of blue-winged teal to domestic poultry: Movement state modeling of a highly mobile avian influenza host","docAbstract":"

1. Migratory waterfowl facilitate long distance dispersal of zoonotic pathogens and are increasingly recognized as contributing to the geographic spread of avian influenza viruses (AIV). AIV are globally distributed and have the potential to produce highly contagious poultry disease, economically impact both large-scale and backyard poultry producers, and raise the specter of epidemics and pandemics in human populations.

2. Because migratory waterfowl behavior varies across multiple spatial and temporal scales, the timing and distribution of wild bird AIV introductions to poultry are also heterogeneous in time and space. To help reduce economic impacts to the poultry industry and enable poultry producers to better anticipate when and where poultry outbreaks may occur, it is critically important to consider the movement ecology of the waterfowl species transporting and transmitting AIV.

3. We used telemetry for a geographically widespread and common AIV host, blue-winged teal (Spatula discors; BWTE), to model reservoir host movement states with respect to backyard and commercial poultry facilities in the United States. Our modeling framework enabled us to estimate wild bird proximity to poultry facilities while concurrently assessing the influence of poultry facilities on BWTE movement state transition. Our primary objective was to estimate the likelihood of duck and poultry overlap by estimating when and where BWTE were geographically closest to poultry.

4. Synthesis and applications. Migratory waterfowl facilitate dispersal of the avian influenza viruses that cause highly contagious poultry disease. Movement analysis of blue-winged teal indicates that spatio-temporal overlap between wild birds and poultry facilities varies by season, the poultry type produced (e.g., turkey, chicken), and if the facility is a commercial or backyard operation. These findings are broadly applicable to disease ecology research and can be applied by poultry producers to improve bio-security, enhance poultry management, and prioritize disease surveillance efforts.

","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13963","usgsCitation":"Humphreys, J.M., Douglas, D.C., Ramey, A.M., Mullinax, J.M., Soos, C., Link, P.T., Walther, P., and Prosser, D., 2021, The spatial-temporal relationship of blue-winged teal to domestic poultry: Movement state modeling of a highly mobile avian influenza host: Journal of Applied Ecology, v. 58, no. 10, p. 2040-2052, https://doi.org/10.1111/1365-2664.13963.","productDescription":"13 p.","startPage":"2040","endPage":"2052","ipdsId":"IP-118863","costCenters":[{"id":107,"text":"Alaska Climate Science Center","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":451405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13963","text":"Publisher Index Page"},{"id":387599,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Humphreys, John M.","contributorId":217932,"corporation":false,"usgs":false,"family":"Humphreys","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":820044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":820046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":820045,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":820047,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soos, Catherine","contributorId":177909,"corporation":false,"usgs":false,"family":"Soos","given":"Catherine","email":"","affiliations":[],"preferred":false,"id":820048,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Link, Paul T.","contributorId":53611,"corporation":false,"usgs":false,"family":"Link","given":"Paul","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":820049,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Walther, Patrick","contributorId":213915,"corporation":false,"usgs":false,"family":"Walther","given":"Patrick","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":820050,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":820051,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70223494,"text":"70223494 - 2021 - Water–rock interaction and the concentrations of major, trace, and rare earth elements in hydrocarbon-associated produced waters of the United States","interactions":[],"lastModifiedDate":"2024-09-16T16:35:32.635392","indexId":"70223494","displayToPublicDate":"2021-07-26T07:46:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9161,"text":"Environmental Science: Processes & Impacts","active":true,"publicationSubtype":{"id":10}},"title":"Water–rock interaction and the concentrations of major, trace, and rare earth elements in hydrocarbon-associated produced waters of the United States","docAbstract":"

Studies of co-produced waters from hydrocarbon extraction across multiple energy-producing basins have generally focused on major ions or a few select tracers, and studies that examine trace elements and involve laboratory experiments have generally been basin specific. Here, new perspective is sought through a broad analysis of concentration data for 26 elements from three hydrocarbon well types using the U.S. Geological Survey National Produced Waters Geochemical Database (v2.3). Those data are compared to leachates (water, hydrochloric acid, and artificial brine) from 12 energy-resource related shales from across the United States. Both lower pH and higher ionic strength were associated with greater concentrations of many trace elements in produced waters. However, individual effects were difficult to distinguish because higher ionic strengths drive decreases in pH. Water–rock interactions in the leaching experiments generally replicated produced water concentrations for trace elements including Al, As, Cd, Co, Cu, Mo, Ni, Pb, Sb, Si, and Zn. Enhanced middle rare earth element (REE) mobilization relative to shale REE content occurred with low pH leachates. Produced water concentrations of Li, Sr, and Ba were not replicated by the leaching experiments. Patterns of high Li, Sr, and Ba concentrations and ratios relative to other elements across produced waters types indicate controls on these elements in many settings related to pore space pools of salts, brines, and ion-exchange sites affected by diagenetic processes. The size of those pools is diluted and masked by other water–rock interaction processes at the water–rock ratios necessitated by laboratory experiments. The results broadly link water–rock interaction processes and environmental patterns across a wide variety of produced waters and host formations and thus provide context for trace element data from other environmental and laboratory studies of such waters.

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Resources Program","active":true,"usgs":true}],"preferred":true,"id":822174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":822175,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221896,"text":"sir20215026 - 2021 - Hydrogeology of the Susquehanna River valley-fill aquifer system in the towns of Conklin and Kirkwood, Broome County, New York","interactions":[],"lastModifiedDate":"2024-06-26T19:36:08.52945","indexId":"sir20215026","displayToPublicDate":"2021-07-23T10:10:00","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":"2021-5026","displayTitle":"Hydrogeology of the Susquehanna River Valley-Fill Aquifer System in the Towns of Conklin and Kirkwood, Broome County, New York","title":"Hydrogeology of the Susquehanna River valley-fill aquifer system in the towns of Conklin and Kirkwood, Broome County, New York","docAbstract":"

The hydrogeology of the Susquehanna River valley-fill aquifer system and adjacent areas in south-central Broome County, New York, was investigated in cooperation with the New York State Department of Environmental Conservation. The study area encompasses roughly 55.5 square miles and includes the towns of Conklin and Kirkwood. Multiple small, perhaps discontinuous, valley-fill aquifers of unknown extent and hydraulic interconnection underlie the Susquehanna River valley from easternmost Binghamton south to Riverside, New York, near the Pennsylvania border. The hydrogeologic framework of these aquifers is described in this report on the basis of existing descriptions of surficial materials, especially those related to deglaciation, and subsurface data extracted from well and boring logs. A compilation of surficial geology, the descriptions of the spatial distribution of confined and unconfined aquifers, hydrogeologic sections, and well locations is provided as an oversized map plate and in a U.S. Geological Survey data release.

Residential households are one of the principal consumers of groundwater in the study area. Approximately half of these households are served by public water-supply systems that obtain water from wells, chiefly from highly productive but small and likely discontinuous surficial deposits of sand and gravel, while others obtain water from sand-and-gravel aquifers beneath till and (or) fine-grained lacustrine deposits, and a few from bedrock. Residents outside the public-supply service areas rely on private wells. In till-mantled upland areas, nearly all private wells tap bedrock. Water-resource potential is likely greatest north of Kirkwood Center, New York, where the valley is narrowest, and local aquifers are in thick stratified glacial deposits. Well yields are highest in this part of the valley, and the local aquifer system is likely replenished through induced infiltration from the Susquehanna River and numerous small tributaries. The area between Langdon and Kirkwood is filled with a mixture of stratified and unstratified glacial sediments and contains one high-yield well. This area likely has moderate water-resource potential, but limited well data make this difficult to verify. Well yields from suitable stratified glacial sediments generally decrease southward toward Riverside, New York.

Characterizing potential groundwater resources is also helpful for prioritizing source-water-protection efforts. Water resources throughout New York are at risk of contamination from commercial and industrial surface activities. As in many valley areas throughout the Susquehanna River watershed in south-central New York, valley wells with depths greater than roughly 100 to 150 feet are susceptible to contamination by naturally occurring saltwater and methane. New York currently has a moratorium on hydraulic fracturing, but the study area is underlain by rocks suitable for unconventional methods of gas production that would likely be initiated if the moratorium were to be lifted.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215026","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Van Hoesen, J.G., Heisig, P.M., and Fisher, S.R., 2021, Hydrogeology of the Susquehanna River valley-fill aquifer system in the towns of Conklin and Kirkwood, Broome County, New York: U.S. Geological Survey Scientific Investigations Report 2021–5026, 29 p., 1 pl., https://doi.org/10.3133/sir20215026.","productDescription":"Report: vii, 29 p.; 1 Plate 30.25 x 31.25 inches; Data Release","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118763","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":387155,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5026/sir20215026_plate1.pdf","text":"Plate 1","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Detailed aquifer mapping of the Susquehanna River valley in south-central Broome County, towns of Conklin and Kirkwood, New York"},{"id":387154,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O1EAV7","text":"USGS data release","linkHelpText":"Digital datasets for the hydrogeology of the Susquehanna River Valley in south-central Broome County, towns of Conklin and Kirkwood, New York"},{"id":387153,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5026/sir20215026.pdf","text":"Report","size":"8.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5026"},{"id":387152,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5026/coverthb2.jpg"}],"country":"United States","state":"New York","county":"Broome County","otherGeospatial":"Susquehanna River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.91690063476561,\n 42.001345689029755\n ],\n [\n -75.74970245361328,\n 42.001345689029755\n ],\n [\n -75.74970245361328,\n 42.08803181932636\n ],\n [\n -75.91690063476561,\n 42.08803181932636\n ],\n [\n -75.91690063476561,\n 42.001345689029755\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-07-23","noUsgsAuthors":false,"publicationDate":"2021-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Hoesen, John G. 0000-0003-2531-3794 jvanhoesen@usgs.gov","orcid":"https://orcid.org/0000-0003-2531-3794","contributorId":261007,"corporation":false,"usgs":true,"family":"Van Hoesen","given":"John","email":"jvanhoesen@usgs.gov","middleInitial":"G.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819244,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heisig, Paul M. 0000-0003-0338-4970 pmheisig@usgs.gov","orcid":"https://orcid.org/0000-0003-0338-4970","contributorId":793,"corporation":false,"usgs":true,"family":"Heisig","given":"Paul","email":"pmheisig@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Shannon R. 0000-0001-8700-8504 srfisher@usgs.gov","orcid":"https://orcid.org/0000-0001-8700-8504","contributorId":261008,"corporation":false,"usgs":true,"family":"Fisher","given":"Shannon","email":"srfisher@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819246,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":58027,"text":"ofr20041348 - 2021 - Hazard analysis of landslides triggered by Typhoon Chata’an on July 2, 2002, in Chuuk State, Federated States of Micronesia","interactions":[],"lastModifiedDate":"2025-01-29T20:22:29.930774","indexId":"ofr20041348","displayToPublicDate":"2021-07-21T12:00:00","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":"2004-1348","displayTitle":"Hazard Analysis of Landslides Triggered by Typhoon Chata’an on July 2, 2002, in Chuuk State, Federated States of Micronesia","title":"Hazard analysis of landslides triggered by Typhoon Chata’an on July 2, 2002, in Chuuk State, Federated States of Micronesia","docAbstract":"

More than 250 landslides were triggered across the eastern volcanic islands of Chuuk State in the Federated States of Micronesia by torrential rainfall from tropical storm Chata’an on July 2, 2002. Landslides triggered during nearly 20 inches of rainfall in less than 24 hours caused 43 fatalities and the destruction or damage of 231 structures, including homes, schools, community centers, and medical dispensaries. Landslides also buried roads, crops, and water supplies. The landslides ranged in volume from a few cubic meters to more than 1 million cubic meters. Most of the failures began as slumps and transformed into debris flows, some of which traveled several hundred meters across coastal flatlands into populated areas. A landslide-inventory map produced after the storm shows that the island of Tonoas had the largest area affected by landslides, although the islands of Weno, Fefan, Etten, Uman, Siis, Udot, Eot, and Fanapanges also had significant landslides. Based on observations since the storm, we estimate the continuing hazard from landslides triggered by Chata’an to be relatively low. However, tropical storms and typhoons similar to Chata’an frequently develop in Micronesia and are likely to affect the islands of Chuuk in the future.

To assess the landslide hazard from future tropical storms, we produced a hazard map that identifies landslide-source areas of high, moderate, and low hazard. This map can be used to identify relatively safe areas for relocating structures or establishing areas where people could gather for shelter in relative safety during future typhoons or tropical storms similar to Chata’an.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20041348","productDescription":"Report: 22 p.; 2 Plates: 35.71 x 40.02 inches","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387302,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2004/1348/ofr20041348_plate1_Revision.pdf","text":"Plate 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2004-1348 Plate 1","linkHelpText":"Landslide Inventory Map of Chuuk Islands Affected by Typhoon Chata'an"},{"id":182255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2004/1348/coverthb.jpg"},{"id":387301,"rank":2,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2004/1348/versionHist.txt","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2004-1348 version history"},{"id":387300,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2004/1348/ofr20041348_pamphlet_Revision.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2004-1348 Pamphlet"},{"id":387303,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2004/1348/ofr20041348_plate2_Revision.pdf","text":"Plate 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2004-1348 Plate 2","linkHelpText":"Debris-Flow Hazard Map of Chuuk Islands Affected by Typhoon Chata’an"},{"id":391875,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_69259.htm"}],"country":"Federated States of Micronesia","state":"Chuuk State","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 151.675,\n 7.2789\n ],\n [\n 151.9022,\n 7.2789\n ],\n [\n 151.9022,\n 7.4689\n ],\n [\n 151.675,\n 7.4689\n ],\n [\n 151.675,\n 7.2789\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: July 21, 2021","contact":"

Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225

","tableOfContents":"","publishedDate":"2004-10-11","revisedDate":"2021-07-21","noUsgsAuthors":false,"publicationDate":"2004-10-11","publicationStatus":"PW","scienceBaseUri":"4f4e4a6be4b07f02db63d776","contributors":{"authors":[{"text":"Harp, Edwin L. harp@usgs.gov","contributorId":1290,"corporation":false,"usgs":true,"family":"Harp","given":"Edwin","email":"harp@usgs.gov","middleInitial":"L.","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":false,"id":258170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":258169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michael, John A. jmichael@usgs.gov","contributorId":1877,"corporation":false,"usgs":true,"family":"Michael","given":"John","email":"jmichael@usgs.gov","middleInitial":"A.","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":false,"id":258171,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223114,"text":"70223114 - 2021 - Incorporation of non-native species in the diets of cisco (Coregonus artedi) from eastern Lake Ontario","interactions":[],"lastModifiedDate":"2021-08-11T12:50:04.835719","indexId":"70223114","displayToPublicDate":"2021-07-19T07:46:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Incorporation of non-native species in the diets of cisco (Coregonus artedi) from eastern Lake Ontario","docAbstract":"

Cisco Coregonus artedi was once an important native fish in Lake Ontario; however, after multiple population crashes, the cisco stock has yet to recover to historic abundances. Rehabilitation of cisco in Lake Ontario is a fish community management objective, but the extent to which recent non-native species and pelagic food web changes have influenced cisco is not well understood. We described cisco diets in contemporary Lake Ontario following the addition and spread of non-native zooplankton species. We collected 618 cisco and processed 178 for full diet analysis in eastern Lake Ontario using mid-water trawls and bottom-set gill nets from 2016 to 2020. We found that Lake Ontario cisco were mostly zooplanktivorous, and non-native zooplankton dominated their diet during July and September. Cisco smaller than 300 mm had a more diverse diet including both native and non-native zooplankton, while cisco larger than 300 mm fed almost exclusively on non-native predatory cladocerans Bythotrephes longimanus and Cercopagis pengoi (98.9% consumed prey dry mass). We also found fish eggs, presumed to be of coregonine origin in 75% of non-empty December-collected cisco diets, suggesting eggs subsidize cisco diets when available. Juvenile round goby Neogobius melanostomus, alewife Alosa pseudoharengus and rainbow smelt Osmerus mordax were found in 2% of all analyzed non-empty stomachs. Lake Ontario cisco diet appears to be more similar to zooplanktivorous Lake Superior cisco than Lake Michigan where piscivory is prevalent. Lake Ontario cisco diets reflected zooplankton community changes indicating that non-native predatory cladocerans are now an important energy source supporting this native species.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.05.007","usgsCitation":"Gatch, A., Weidel, B., Gorsky, D., O’Malley, B., Connerton, M., Holden, J., Holeck, K.T., Goertzke, J., and Karboski, C.T., 2021, Incorporation of non-native species in the diets of cisco (Coregonus artedi) from eastern Lake Ontario: Journal of Great Lakes Research, v. 47, no. 4, p. 1135-1145, https://doi.org/10.1016/j.jglr.2021.05.007.","productDescription":"11 p.","startPage":"1135","endPage":"1145","ipdsId":"IP-127290","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":387841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Eastern Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.2998046875,\n 43.50075243569041\n ],\n [\n -75.849609375,\n 43.50075243569041\n ],\n [\n -75.849609375,\n 44.378839759088585\n ],\n [\n -77.2998046875,\n 44.378839759088585\n ],\n [\n -77.2998046875,\n 43.50075243569041\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gatch, Alexander","contributorId":264161,"corporation":false,"usgs":false,"family":"Gatch","given":"Alexander","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":821017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":821018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorsky, Dimitry","contributorId":251650,"corporation":false,"usgs":false,"family":"Gorsky","given":"Dimitry","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":821019,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Malley, Brian 0000-0001-5035-3080 bomalley@usgs.gov","orcid":"https://orcid.org/0000-0001-5035-3080","contributorId":216560,"corporation":false,"usgs":true,"family":"O’Malley","given":"Brian","email":"bomalley@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":821020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Connerton, Michael","contributorId":251649,"corporation":false,"usgs":false,"family":"Connerton","given":"Michael","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":821021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holden, Jeremy","contributorId":139654,"corporation":false,"usgs":false,"family":"Holden","given":"Jeremy","affiliations":[{"id":12864,"text":"OMNRF","active":true,"usgs":false}],"preferred":false,"id":821022,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holeck, Kristen T.","contributorId":105549,"corporation":false,"usgs":false,"family":"Holeck","given":"Kristen","email":"","middleInitial":"T.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":821023,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goertzke, J.A.","contributorId":264162,"corporation":false,"usgs":false,"family":"Goertzke","given":"J.A.","email":"","affiliations":[{"id":39079,"text":"NYSDEC","active":true,"usgs":false}],"preferred":false,"id":821024,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Karboski, Curtis T.","contributorId":191251,"corporation":false,"usgs":false,"family":"Karboski","given":"Curtis","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":821025,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70222350,"text":"70222350 - 2021 - Model estimated baseflow for streams with endangered Atlantic Salmon in Maine, USA","interactions":[],"lastModifiedDate":"2021-11-16T15:32:29.208233","indexId":"70222350","displayToPublicDate":"2021-07-18T09:08:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Model estimated baseflow for streams with endangered Atlantic Salmon in Maine, USA","docAbstract":"

We present a regression model for estimating mean August baseflow per square kilometer of drainage area to help resource managers assess relative amounts of baseflow in Maine streams with Atlantic Salmon habitat. The model was derived from mean August baseflows computed at 31 USGS streamflow gages in Maine. We use an ordinary least squares regression model to estimate mean August baseflow per unit drainage area from two explanatory variables: percentage of the basin underlain by sand and gravel aquifers and mean July precipitation in the basin. This model provides the ability to estimate mean August baseflow in cubic meters per second per square kilometer of basin area on user-selected, ungaged sites throughout Maine south of 46° 21′55″ N latitude. The model has an adjusted R2 of 0.78 and a mean 95% prediction interval of plus or minus 0.002 cubic meters per second per square kilometer. A map of the Narraguagus watershed in eastern coastal Maine shows reaches color coded by relative amounts of baseflow predicted by the model as an example of how this method could be applied throughout Maine. The map can be used to identify reaches with relatively higher amounts of baseflow during summer low flows for habitat conservation and restoration work. These areas have the potential to be high-quality habitat for Atlantic salmon and other cold-water fish because baseflows are known to moderate stream temperatures in summer low-flow periods.

","language":"English","publisher":"Wiley","doi":"10.1002/rra.3835","usgsCitation":"Lombard, P.J., Dudley, R., Collins, M.J., Saunders, R., and Atkinson, E., 2021, Model estimated baseflow for streams with endangered Atlantic Salmon in Maine, USA: River Research and Applications, v. 37, no. 9, p. 1254-1264, https://doi.org/10.1002/rra.3835.","productDescription":"11 p.","startPage":"1254","endPage":"1264","ipdsId":"IP-124443","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":451480,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.3835","text":"Publisher Index Page"},{"id":436271,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KRSNU7","text":"USGS data release","linkHelpText":"Spatial Coverage for Estimated Baseflow for Streams Containing Endangered Atlantic Salmon in Maine, USA (version 1.1, June 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\"}}]}","volume":"37","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-07-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Lombard, Pamela J. 0000-0002-0983-1906","orcid":"https://orcid.org/0000-0002-0983-1906","contributorId":203509,"corporation":false,"usgs":true,"family":"Lombard","given":"Pamela","email":"","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819723,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dudley, Robert W. 0000-0002-0934-0568","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":220211,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert W.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819724,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Matthias J. 0000-0003-4238-2038","orcid":"https://orcid.org/0000-0003-4238-2038","contributorId":196365,"corporation":false,"usgs":false,"family":"Collins","given":"Matthias","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":819725,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saunders, Rory","contributorId":261311,"corporation":false,"usgs":false,"family":"Saunders","given":"Rory","email":"","affiliations":[{"id":52809,"text":"NOAA, National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":819726,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atkinson, Ernie","contributorId":261312,"corporation":false,"usgs":false,"family":"Atkinson","given":"Ernie","email":"","affiliations":[{"id":52810,"text":"Maine Department of Marine Resources, Division of Sea-run Fisheries","active":true,"usgs":false}],"preferred":false,"id":819727,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70222387,"text":"70222387 - 2021 - Experimental evaluation of spatial capture–recapture study design","interactions":[],"lastModifiedDate":"2021-10-06T15:34:25.246554","indexId":"70222387","displayToPublicDate":"2021-07-18T07:24:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Experimental evaluation of spatial capture–recapture study design","docAbstract":"

A principal challenge impeding strong inference in analyses of wild populations is the lack of robust and long-term data sets. Recent advancements in analytical tools used in wildlife science may increase our ability to integrate smaller data sets and enhance the statistical power of population estimates. One such advancement, the development of spatial capture–recapture (SCR) methods, explicitly accounts for differences in spatial study designs, making it possible to equate multiple study designs in one analysis. SCR has been shown to be robust to variation in design as long as minimal sampling guidance is adhered to. However, these expectations are based on simulation and have yet to be evaluated in wild populations. Here we conduct a rigorously designed field experiment by manipulating the arrangement of artificial cover objects (ACOs) used to collect data on red-backed salamanders (Plethodon cinereus) to empirically evaluate the effects of design configuration on inference made using SCR. Our results suggest that, using SCR, estimates of space use and detectability are sensitive to study design configuration, namely the spacing and extent of the array, and that caution is warranted when assigning biological interpretation to these parameters. However, estimates of population density remain robust to design except when the configuration of detectors grossly violates existing recommendations.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2419","usgsCitation":"Fleming, J.E., Campbell Grant, E.H., Sterrett, S., and Sutherland, C., 2021, Experimental evaluation of spatial capture–recapture study design: Ecological Applications, v. 31, no. 7, e02419, 11 p., https://doi.org/10.1002/eap.2419.","productDescription":"e02419, 11 p.","ipdsId":"IP-118474","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":451481,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.2419","text":"External Repository"},{"id":387463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Wendell State Forest","geographicExtents":"{\n \"type\": 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The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.

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2021 - Regeneration trends along climate gradients in Taxodium distichum forests of the southeastern United States","interactions":[],"lastModifiedDate":"2023-06-09T14:09:27.431715","indexId":"70222933","displayToPublicDate":"2021-07-16T09:23:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Regeneration trends along climate gradients in Taxodium distichum forests of the southeastern United States","title":"Regeneration trends along climate gradients in Taxodium distichum forests of the southeastern United States","docAbstract":"

The development of relict vegetation at the edges of some ecosystems has taken place particularly in environments where the regeneration of foundational species is declining. As an important stage of regeneration in the Taxodium distichum, this study explored the relationship of cone volume and seed number across environmental gradients in the Mississippi River Alluvial Valley (MRAV) and northern Gulf of Mexico Coast (GOM) in a long-term network of forested wetlands (North American Baldcypress Swamp Network (NABCSN)) from 2007 to 2019. Resembling spheroids, the volumes of Taxodium distichum cones were based on the measured dimensions of the cones collected in swamps across southeastern environmental gradients. Cones with larger volumes also had larger numbers of seeds (r2 = 0.423, F = 113.9, p < 0.0001; Linear regression equation: Seed number per cone = 9.8925223 + 0.8854056* Cone volume cm3). Mean cone volumes were related to water availability with highest volumes in locations with moderate amounts of total annual precipitation (e.g., White River National Wildlife Refuge (NWR) Arkansas, Tensas NWR Louisiana, and Morgan Brake NWR Mississippi), and longer periods of annual percent time of site drawdown. Cone volume was high in 2018 following the 2017 mega-flood in the Mississippi River Alluvial Valley (MRAV) generated by Hurricane Harvey. Mean annual air temperature was not related to cone volume. Along the Gulf Coast, mean cone volume increased from east to west from Florida to Texas. Especially near the edge of the range of T. distichum forests, smaller cones may be related to regeneration failure and lower seed numbers to support regeneration. A better understanding of regeneration constraints can inform managers of the emergence of relict status in these forests.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119485","usgsCitation":"Middleton, B., Lei, T., Villegas, O., and Liu, X., 2021, Regeneration trends along climate gradients in Taxodium distichum forests of the southeastern United States: Forest Ecology and Management, v. 497, 119485, 10 p.; Data Release, https://doi.org/10.1016/j.foreco.2021.119485.","productDescription":"119485, 10 p.; Data Release","ipdsId":"IP-125636","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":387812,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417855,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H7WGM5"}],"country":"United States","state":"Arkansas, Florida, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.76953125,\n 37.43997405227057\n ],\n [\n -89.9560546875,\n 37.26530995561875\n ],\n [\n -91.4501953125,\n 33.8339199536547\n ],\n [\n -91.62597656249999,\n 31.765537409484374\n ],\n [\n -91.8896484375,\n 30.44867367928756\n ],\n [\n -90.087890625,\n 29.19053283229458\n ],\n [\n -89.384765625,\n 30.06909396443887\n ],\n [\n -90.4833984375,\n 30.44867367928756\n ],\n [\n -89.3408203125,\n 32.84267363195431\n ],\n [\n -88.76953125,\n 37.43997405227057\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.814453125,\n 29.6880527498568\n ],\n [\n -83.6279296875,\n 29.878755346037977\n ],\n [\n -83.671875,\n 30.675715404167743\n ],\n [\n -85.1220703125,\n 30.600093873550072\n ],\n [\n -84.814453125,\n 29.6880527498568\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"497","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Middleton, Beth 0000-0002-1220-2326","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":206922,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":820867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lei, Ting","contributorId":245022,"corporation":false,"usgs":false,"family":"Lei","given":"Ting","affiliations":[{"id":40912,"text":"Beijing Forestry","active":true,"usgs":false}],"preferred":false,"id":820868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villegas, Omag 0000-0003-0169-895X","orcid":"https://orcid.org/0000-0003-0169-895X","contributorId":263439,"corporation":false,"usgs":false,"family":"Villegas","given":"Omag","email":"","affiliations":[{"id":53989,"text":"Universidad Juarez del Estado de Durango, Mexico","active":true,"usgs":false}],"preferred":false,"id":820869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liu, Xiaohui","contributorId":263440,"corporation":false,"usgs":false,"family":"Liu","given":"Xiaohui","email":"","affiliations":[{"id":53990,"text":"NE Institute of Geography and Agroecology, Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":820870,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70222137,"text":"70222137 - 2021 - Timing of iceberg scours and massive ice-rafting events in the subtropical North Atlantic","interactions":[],"lastModifiedDate":"2021-07-22T13:10:48.697227","indexId":"70222137","displayToPublicDate":"2021-07-16T07:01:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Timing of iceberg scours and massive ice-rafting events in the subtropical North Atlantic","docAbstract":"

High resolution seafloor mapping shows extraordinary evidence that massive (>300 m thick) icebergs once drifted >5,000 km south along the eastern United States, with >700 iceberg scours now identified south of Cape Hatteras. Here we report on sediment cores collected from several buried scours that show multiple plow marks align with Heinrich Event 3 (H3), ~31,000 years ago. Numerical glacial iceberg simulations indicate that the transport of icebergs to these sites occurs during massive, but short-lived, periods of elevated meltwater discharge. Transport of icebergs to the subtropics, away from deep water formation sites, may explain why H3 was associated with only a modest increase in ice-rafting across the subpolar North Atlantic, and implies a complex relationship between freshwater forcing and climate change. Stratigraphy from subbottom data across the scour marks shows there are additional features that are both older and younger, and may align with other periods of elevated meltwater discharge.

","language":"English","publisher":"Nature","doi":"10.1038/s41467-021-23924-0","usgsCitation":"Condron, A., and Hill, J.C., 2021, Timing of iceberg scours and massive ice-rafting events in the subtropical North Atlantic: Nature Communications, v. 12, 3668, 14 p., https://doi.org/10.1038/s41467-021-23924-0.","productDescription":"3668, 14 p.","ipdsId":"IP-120103","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":451504,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-021-23924-0","text":"Publisher Index Page"},{"id":387321,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia, South Carolina","otherGeospatial":"Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.37695312499999,\n 34.88593094075317\n ],\n [\n -77.82714843749999,\n 34.161818161230386\n ],\n [\n -79.365234375,\n 33.137551192346145\n ],\n [\n -81.0791015625,\n 31.653381399664\n ],\n [\n -81.474609375,\n 30.259067203213018\n ],\n [\n -79.6728515625,\n 26.86328062676624\n ],\n [\n -75.76171875,\n 27.916766641249065\n ],\n [\n -73.0810546875,\n 28.92163128242129\n ],\n [\n -71.71875,\n 34.161818161230386\n ],\n [\n -76.37695312499999,\n 34.88593094075317\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2021-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Condron, Alan 0000-0002-7337-1713","orcid":"https://orcid.org/0000-0002-7337-1713","contributorId":229547,"corporation":false,"usgs":false,"family":"Condron","given":"Alan","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":819626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hill, Jenna C. 0000-0002-7475-357X","orcid":"https://orcid.org/0000-0002-7475-357X","contributorId":21987,"corporation":false,"usgs":true,"family":"Hill","given":"Jenna","email":"","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":819627,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224570,"text":"70224570 - 2021 - Down to Earth with nuclear electromagnetic pulse: Realistic surface impedance affects mapping of the E3 geoelectric hazard","interactions":[],"lastModifiedDate":"2021-09-28T12:22:22.806471","indexId":"70224570","displayToPublicDate":"2021-07-15T07:20:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9361,"text":"Earth and Space Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Down to Earth with nuclear electromagnetic pulse: Realistic surface impedance affects mapping of the E3 geoelectric hazard","docAbstract":"

An analysis is made of Earth-surface geoelectric fields and voltages on electricity transmission power-grids induced by a late-phase E3 nuclear electromagnetic pulse (EMP). A hypothetical scenario is considered of an explosion of several hundred kilotons set several hundred kilometers above the eastern-midcontinental United States. Ground-level E3 geoelectric fields are estimated by convolving a standard parameterization of E3 geomagnetic field variation with magnetotelluric Earth-surface impedance tensors derived from wideband measurements acquired across the study region during a recent survey. These impedance tensors are a function of subsurface three-dimensional electrical conductivity structure. Results, presented as a movie-map, demonstrate that localized differences in surface impedance strongly distort the amplitude, polarization, and variational phase of induced E3 geoelectric fields. Locations with a high degree of E3 geoelectric polarization tend to have high geoelectric amplitude. Uniform half-space models and one-dimensional, depth-dependent models of Earth-surface impedance, such as those widely used in government and industry reports informing power-grid vulnerability assessment projects, do not provide accurate estimates of the E3 geoelectric hazard in complex geological settings. In particular, for the Eastern-Midcontinent, half-space models can lead to (order-one) overestimates/underestimates of EMP-induced geovoltages on parts of the power grid by as much as \"urn:x-wiley:23335084:media:ess2899:ess2899-math-0001\"1,000 volts (a range of 2,000 volts)—comparable to the amplitudes of the geovoltages themselves.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021EA001792","usgsCitation":"Love, J.J., Lucas, G., Murphy, B.S., Bedrosian, P.A., Rigler, E.J., and Kelbert, A., 2021, Down to Earth with nuclear electromagnetic pulse: Realistic surface impedance affects mapping of the E3 geoelectric hazard: Earth and Space Sciences, v. 8, no. 8, e2021EA001792, 25 p., https://doi.org/10.1029/2021EA001792.","productDescription":"e2021EA001792, 25 p.","ipdsId":"IP-128556","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":451509,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021ea001792","text":"Publisher Index Page"},{"id":389861,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":824100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucas, Greg M. 0000-0003-1331-1863","orcid":"https://orcid.org/0000-0003-1331-1863","contributorId":223556,"corporation":false,"usgs":false,"family":"Lucas","given":"Greg M.","affiliations":[{"id":6605,"text":"USGS","active":true,"usgs":false}],"preferred":false,"id":824101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Benjamin Scott 0000-0001-7636-3711","orcid":"https://orcid.org/0000-0001-7636-3711","contributorId":242928,"corporation":false,"usgs":true,"family":"Murphy","given":"Benjamin","email":"","middleInitial":"Scott","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":824102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":824103,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rigler, E. Joshua 0000-0003-4850-3953 erigler@usgs.gov","orcid":"https://orcid.org/0000-0003-4850-3953","contributorId":4367,"corporation":false,"usgs":true,"family":"Rigler","given":"E.","email":"erigler@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":824104,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":824105,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221835,"text":"sir20215031 - 2021 - Optimization of the Idaho National Laboratory water-quality aquifer monitoring network, southeastern Idaho","interactions":[],"lastModifiedDate":"2021-07-16T12:31:02.274219","indexId":"sir20215031","displayToPublicDate":"2021-07-15T07:17:18","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":"2021-5031","displayTitle":"Optimization of the Idaho National Laboratory Water-Quality Aquifer Monitoring Network, Southeastern Idaho","title":"Optimization of the Idaho National Laboratory water-quality aquifer monitoring network, southeastern Idaho","docAbstract":"

Long-term monitoring of water-quality data collected from wells at the Idaho National Laboratory (INL) has provided essential information for delineating the movement of radiochemical and chemical wastes in the eastern Snake River Plain aquifer, southeastern Idaho. Since 1949, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, has maintained as many as 200 wells in the INL water-quality monitoring network. A network design tool, distributed as an R package, was developed to evaluate and optimize groundwater monitoring in the existing network based on water-quality data collected at 153 sampling sites since January 1, 1989. The objective of the optimization design tool is to reduce well monitoring redundancy while retaining sufficient data to reliably characterize water-quality conditions in the aquifer. A spatial optimization was used to identify a set of wells whose removal leads to the smallest increase in the deviation between interpolated concentration maps using the existing and reduced monitoring networks while preserving significant long-term trends and seasonal components in the data. Additionally, a temporal optimization was used to identify reductions in sampling frequencies by minimizing the redundancy in sampling events.

Spatial optimization uses an islands genetic algorithm to identify near-optimal network designs removing 10, 20, 30, 40, and 50 wells from the existing monitoring network. With this method, choosing a greater number of wells to remove results in greater cost savings and decreased accuracy of the average relative difference between interpolated maps of the reduced-dataset and the full-dataset. The genetic search algorithm identified reduced networks that best capture the spatial patterns of the average concentration plume while preserving long-term temporal trends at individual wells. Concentration data for 10 analyte types are integrated in a single optimization so that all datasets may be evaluated simultaneously. A constituent was selected for inclusion in the spatial optimization problem when the observations were sufficient to (1) establish a two-range variability model, (2) classify at least one concentration time series as a continuous record block, and (3) make a prediction using the quantile-kriging interpolation method. The selected constituents include sodium, chloride, sulfate, nitrate, carbon tetrachloride, 1,1-dichloroethylene, 1,1,1-trichloroethane, trichloroethylene, tritium, strontium-90, and plutonium-238.

In temporal optimization, an iterative-thinning method was used to find an optimal sampling frequency for each analyte-well pair. Optimal frequencies indicate that for many of the wells, samples may be collected less frequently and still be able to characterize the concentration over time. The optimization results indicated that the sample-collection interval may be increased by an of average of 273 days owing to temporal redundancy.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215031","collaboration":"DOE/ID-22252
Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Fisher, J.C., Bartholomay, R.C., Rattray, G.W., and Maimer, N.V., 2021, Optimization of the Idaho National Laboratory water-quality aquifer monitoring network, southeastern Idaho: U.S. Geological Survey Scientific Investigations Report 2021–5031 (DOE/ID-22252), 63 p., https://doi.org/10.3133/sir20215031.","productDescription":"Report: vii, 63 p.; Appendix 1-12; 2 Software Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071486","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":387046,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app02.html","text":"Appendix 2","size":"854 KB","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5031 Appendix 2"},{"id":387045,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app01.html","text":"Appendix 1","size":"6.3 MB","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5031 Appendix 1"},{"id":387058,"rank":16,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9X71CSU","text":"USGS software release —","description":"USGS software release","linkHelpText":"ObsNetQW—Assessment of a water-quality aquifer monitoring network"},{"id":387057,"rank":15,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9PP9UXZ","text":"USGS software release —","description":"USGS software release","linkHelpText":"inldata—Collection of datasets for the U.S. Geological Survey-Idaho National Laboratory aquifer monitoring networks"},{"id":387056,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app12.pdf","text":"Appendix 12","size":"116 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031 Appendix 12"},{"id":387054,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app10.pdf","text":"Appendix 10","size":"171 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031 Appendix 10"},{"id":387053,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app09.pdf","text":"Appendix 9","size":"12.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031 Appendix 9"},{"id":387052,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app08.pdf","text":"Appendix 8","size":"138 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031 Appendix 8"},{"id":387051,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app07.pdf","text":"Appendix 7","size":"7.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031 Appendix 7"},{"id":387047,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app03.pdf","text":"Appendix 3","size":"354 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031 Appendix 3"},{"id":387043,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5031/coverthb.jpg"},{"id":387048,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app04.pdf","text":"Appendix 4","size":"14.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031 Appendix 4"},{"id":387049,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app05.pdf","text":"Appendix 5","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031 Appendix 5"},{"id":387050,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app06.pdf","text":"Appendix 6","size":"154 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031 Appendix 6"},{"id":387044,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031.pdf","text":"Report","size":"14.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031"},{"id":387055,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5031/sir20215031_app11.pdf","text":"Appendix 11","size":"21.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5031 Appendix 11"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.4393310546875,\n 43.45291889355465\n ],\n [\n -112.4725341796875,\n 43.432977075795606\n ],\n [\n -112.43957519531251,\n 44.06390660801777\n ],\n [\n -113.389892578125,\n 44.09547572946637\n ],\n [\n -113.4393310546875,\n 43.45291889355465\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520

","tableOfContents":"","publishedDate":"2021-07-15","noUsgsAuthors":false,"publicationDate":"2021-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Fisher, Jason C. 0000-0001-9032-8912 jfisher@usgs.gov","orcid":"https://orcid.org/0000-0001-9032-8912","contributorId":2523,"corporation":false,"usgs":true,"family":"Fisher","given":"Jason","email":"jfisher@usgs.gov","middleInitial":"C.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartholomay, Roy C. 0000-0002-4809-9287 rcbarth@usgs.gov","orcid":"https://orcid.org/0000-0002-4809-9287","contributorId":1131,"corporation":false,"usgs":true,"family":"Bartholomay","given":"Roy","email":"rcbarth@usgs.gov","middleInitial":"C.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818875,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rattray, Gordon W. 0000-0002-1690-3218 grattray@usgs.gov","orcid":"https://orcid.org/0000-0002-1690-3218","contributorId":2521,"corporation":false,"usgs":true,"family":"Rattray","given":"Gordon","email":"grattray@usgs.gov","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818876,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maimer, Neil V. 0000-0003-3047-3282 nmaimer@usgs.gov","orcid":"https://orcid.org/0000-0003-3047-3282","contributorId":5659,"corporation":false,"usgs":true,"family":"Maimer","given":"Neil","email":"nmaimer@usgs.gov","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818877,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}