{"pageNumber":"199","pageRowStart":"4950","pageSize":"25","recordCount":46670,"records":[{"id":70221475,"text":"70221475 - 2021 - Relative risk of groundwater-quality degradation near California (USA) oil fields estimated from 3H, 14C, and 4He","interactions":[],"lastModifiedDate":"2021-06-17T11:56:09.830879","indexId":"70221475","displayToPublicDate":"2021-06-05T06:52:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Relative risk of groundwater-quality degradation near California (USA) oil fields estimated from 3H, 14C, and 4He","docAbstract":"

Relative risks of groundwater-quality degradation near selected California oil fields are estimated by examining spatial and temporal patterns in chemical and isotopic data in the context of groundwater-age categories defined by tritium and carbon-14. In the Coastal basins, western San Joaquin Valley (SJV), and eastern SJV; 82, 76, and 0% of samples are premodern (pre-1953 recharge), respectively; and 3, 0, and 31% are modern (recharged during or after 1953), respectively. Carbon-14 and helium-4 data indicate most premodern samples are 1000 to 10,000 (33%) or >10,000 (50%) years old. Organic chemicals that could be associated with deeper hydrocarbon reservoirs (e.g. thermogenic gases and benzene) occur most frequently in premodern groundwater, suggesting premodern groundwater has a higher risk of degradation from upward migration of hydrocarbons than modern and mixed-age groundwater. Low sulfate concentrations in some premodern groundwater containing high thermogenic-methane concentrations (>28 mg/L) indicate methane attenuation associated with sulfate reduction can be limited in premodern groundwater. The more common occurrence of manufactured compounds, like tetrachloroethene, in modern and mixed-age groundwater than in premodern groundwater indicates modern and mixed-age groundwater has a higher risk of degradation from land-surface sources than premodern groundwater. Time-series data for chloride in groundwater affected by disposal of oil-field water in unlined ponds indicate some modern and mixed-age groundwater are susceptible to chemical migration within 2–3 km of surface sources. Timescales for diluting chloride concentrations in groundwater with fresh recharge once disposal ponds are decommissioned are shorter in mixed-age groundwater with large fractions of modern water (9–14 years in one example) than in mixed-age groundwater with large fractions of premodern water (no evidence of dilution after 12 years of monitoring in one example). The presence of predominantly premodern groundwater in the Coastal basins and western SJV indicates these areas have relatively high risk from upward migration of hydrocarbons, reduced methane attenuation capacity, and long dilution times, whereas predominantly modern- and mixed-age groundwater in the eastern SJV indicates this area has relatively high risk from chemical migration from land-surface sources and subsequent extensive spreading. Age-based characterizations of relative risk could inform the design of groundwater-monitoring programs near oil fields in terms of the spatial distribution of monitoring points relative to source areas and monitoring frequency and duration.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2021.105024","usgsCitation":"McMahon, P.B., Landon, M.K., Davis, T., Wright, M., Rosecrans, C.Z., Anders, R., Land, M., Kulongoski, J.T., and Hunt, A., 2021, Relative risk of groundwater-quality degradation near California (USA) oil fields estimated from 3H, 14C, and 4He: Applied Geochemistry, v. 131, 105024, 15 p., https://doi.org/10.1016/j.apgeochem.2021.105024.","productDescription":"105024, 15 p.","ipdsId":"IP-120473","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":452009,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2021.105024","text":"Publisher Index Page"},{"id":386566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -120.95947265624999,\n 33.96158628979907\n ],\n [\n -117.99316406249999,\n 33.96158628979907\n ],\n [\n -117.99316406249999,\n 35.30840140169162\n ],\n [\n -120.95947265624999,\n 35.30840140169162\n ],\n [\n -120.95947265624999,\n 33.96158628979907\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817785,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landon, Matthew K. 0000-0002-5766-0494 landon@usgs.gov","orcid":"https://orcid.org/0000-0002-5766-0494","contributorId":392,"corporation":false,"usgs":true,"family":"Landon","given":"Matthew","email":"landon@usgs.gov","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817786,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Tracy 0000-0003-0253-6661 tadavis@usgs.gov","orcid":"https://orcid.org/0000-0003-0253-6661","contributorId":176921,"corporation":false,"usgs":true,"family":"Davis","given":"Tracy","email":"tadavis@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817787,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wright, Michael 0000-0003-0653-6466 mtwright@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-6466","contributorId":151031,"corporation":false,"usgs":true,"family":"Wright","given":"Michael","email":"mtwright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817788,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosecrans, Celia Z. 0000-0003-1456-4360 crosecrans@usgs.gov","orcid":"https://orcid.org/0000-0003-1456-4360","contributorId":187542,"corporation":false,"usgs":true,"family":"Rosecrans","given":"Celia","email":"crosecrans@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":817789,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anders, Robert 0000-0002-2363-9072 randers@usgs.gov","orcid":"https://orcid.org/0000-0002-2363-9072","contributorId":1210,"corporation":false,"usgs":true,"family":"Anders","given":"Robert","email":"randers@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817790,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Land, Michael 0000-0001-5141-0307 mtland@usgs.gov","orcid":"https://orcid.org/0000-0001-5141-0307","contributorId":171938,"corporation":false,"usgs":true,"family":"Land","given":"Michael","email":"mtland@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817791,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817792,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hunt, Andrew G. 0000-0002-3810-8610","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":206197,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":817793,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70229435,"text":"70229435 - 2021 - Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using rangewide monitoring data","interactions":[],"lastModifiedDate":"2022-03-08T12:41:49.267311","indexId":"70229435","displayToPublicDate":"2021-06-05T06:40:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using rangewide monitoring data","docAbstract":"

Climate change is expected to alter the distribution and abundance of tree species, impacting ecosystem structure and function. Yet, anticipating where this will occur is often hampered by a lack of understanding of how demographic rates, most notably recruitment, vary in response to climate and competition across a species range. Using large-scale monitoring data on two dry woodland tree species (Pinus edulis and Juniperus osteosperma), we develop an approach to infer recruitment, survival, and growth of both species across their range. In doing so, we account for ecological and statistical dependencies inherent in large-scale monitoring data. We find that drying and warming conditions generally lead to declines in recruitment and survival, but the strength of responses varied between species. These climate conditions point to geographic regions of high vulnerability for particular species, such as Pinus edulis in northern Arizona, where both survival and recruitment are low. Our approach provides a path forward for leveraging emerging large-scale monitoring and remotely sensed data to anticipate the impacts of global change on species distributions.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3425","usgsCitation":"Shriver, R.K., Yackulic, C., Bell, D.M., and Bradford, J., 2021, Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using rangewide monitoring data: Ecology, v. 102, no. 8, e03425, 12 p., https://doi.org/10.1002/ecy.3425.","productDescription":"e03425, 12 p.","ipdsId":"IP-118123","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":452012,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2020.04.03.024497","text":"External Repository"},{"id":396845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"102","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Shriver, Robert K 0000-0002-4590-4834","orcid":"https://orcid.org/0000-0002-4590-4834","contributorId":222834,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert","email":"","middleInitial":"K","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":837435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":837436,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, David M.","contributorId":191003,"corporation":false,"usgs":false,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":837437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":837438,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221174,"text":"ofr20211049 - 2021 - Deposit classification scheme for the Critical Minerals Mapping Initiative Global Geochemical Database","interactions":[],"lastModifiedDate":"2021-06-07T11:43:05.163242","indexId":"ofr20211049","displayToPublicDate":"2021-06-04T16:00:00","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":"2021-1049","displayTitle":"Deposit Classification Scheme for the Critical Minerals Mapping Initiative Global Geochemical Database","title":"Deposit classification scheme for the Critical Minerals Mapping Initiative Global Geochemical Database","docAbstract":"

A challenge for the global economy is to meet the growing demand for commodities used in today’s advanced technologies. Critical minerals are commodities (for example, elements, compounds, minerals) deemed vital to the economic and national security of individual countries that are vulnerable to supply disruption. The national geological agencies of Australia, Canada, and the United States recently joined forces to advance understanding and foster development of critical mineral resources in their respective countries through the Critical Minerals Mapping Initiative (CMMI). An initial goal of the CMMI is to fill the knowledge gap on the abundance of critical minerals in ores. To do this, the CMMI compiled modern multielement geochemical data generated by each agency on ore samples collected from historical and active mines and prospects from around the world. To identify relationships between critical minerals, deposit types, deposit environments, and mineral systems, a unified deposit classification scheme was needed. This report describes the scheme developed by the CMMI to classify the initial release of geochemical data. In 2021, the resulting database—along with basic query, statistical analysis, and display tools—will be served to the public through a web-based portal managed by Geoscience Australia. The database will enable users to trace critical minerals through mineral systems and identify individual deposits or deposit types that are potential sources of critical minerals.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211049","issn":"2331-1258","collaboration":"Prepared as part of a joint research program between the U.S. Geological Survey, Geological Survey of Canada, Geological Survey of Queensland, and Geoscience Australia","usgsCitation":"Hofstra, A., Lisitsin, V., Corriveau, L., Paradis, S., Peter, J., Lauzière, K., Lawley, C., Gadd, M., Pilote, J., Honsberger, I., Bastrakov, E., Champion, D., Czarnota, K., Doublier, M., Huston, D., Raymond, O., VanDerWielen, S., Emsbo, P., Granitto, M., and Kreiner, D., 2021, Deposit classification scheme for the Critical Minerals Mapping Initiative Global Geochemical Database: U.S. Geological Survey Open-File Report 2021–1049, 60 p., https://doi.org/10.3133/ofr20211049.","productDescription":"Report: v, 60 p.; 1 Table","onlineOnly":"Y","ipdsId":"IP-127680","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":386206,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2021/1049/ofr20211049_table2.pdf","text":"Table 2—Deposit classification scheme","size":"224 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1049 Table 1"},{"id":386204,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1049/coverthb.jpg"},{"id":386205,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1049/ofr20211049.pdf","text":"Report","size":"1.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1049"}],"contact":"

Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
MS 973, Box 25046
Denver, CO 80225

","tableOfContents":"","publishedDate":"2021-06-04","noUsgsAuthors":false,"publicationDate":"2021-06-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Hofstra, Albert H. 0000-0002-2450-1593 ahofstra@usgs.gov","orcid":"https://orcid.org/0000-0002-2450-1593","contributorId":1302,"corporation":false,"usgs":true,"family":"Hofstra","given":"Albert","email":"ahofstra@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":816952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lisitsin, Vladimir","contributorId":259280,"corporation":false,"usgs":false,"family":"Lisitsin","given":"Vladimir","email":"","affiliations":[{"id":52346,"text":"Geological Survey of Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":816953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Corriveau, Louise","contributorId":259281,"corporation":false,"usgs":false,"family":"Corriveau","given":"Louise","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":816954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paradis, Suzanne","contributorId":259282,"corporation":false,"usgs":false,"family":"Paradis","given":"Suzanne","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":816955,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peter, Jan","contributorId":259283,"corporation":false,"usgs":false,"family":"Peter","given":"Jan","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":816956,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lauziere, Kathleen","contributorId":259284,"corporation":false,"usgs":false,"family":"Lauziere","given":"Kathleen","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":816957,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lawley, Christopher","contributorId":259285,"corporation":false,"usgs":false,"family":"Lawley","given":"Christopher","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":816958,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gadd, Michael","contributorId":259286,"corporation":false,"usgs":false,"family":"Gadd","given":"Michael","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":816959,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pilote, Jean-Luc","contributorId":259287,"corporation":false,"usgs":false,"family":"Pilote","given":"Jean-Luc","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":816960,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Honsberger, Ian","contributorId":259288,"corporation":false,"usgs":false,"family":"Honsberger","given":"Ian","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":816961,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bastrakov, Evgeniy","contributorId":259289,"corporation":false,"usgs":false,"family":"Bastrakov","given":"Evgeniy","email":"","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":816962,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Champion, David C.","contributorId":259290,"corporation":false,"usgs":false,"family":"Champion","given":"David","middleInitial":"C.","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":816963,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Czarnota, Karol","contributorId":259291,"corporation":false,"usgs":false,"family":"Czarnota","given":"Karol","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":816964,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Doublier, Michael P.","contributorId":259292,"corporation":false,"usgs":false,"family":"Doublier","given":"Michael","middleInitial":"P.","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":816965,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Huston, David L.","contributorId":259293,"corporation":false,"usgs":false,"family":"Huston","given":"David","middleInitial":"L.","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":816966,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Raymond, Oliver","contributorId":259294,"corporation":false,"usgs":false,"family":"Raymond","given":"Oliver","email":"","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":816967,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"VanDerWielen, Simon","contributorId":259295,"corporation":false,"usgs":false,"family":"VanDerWielen","given":"Simon","email":"","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":816968,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Emsbo, Poul 0000-0001-9421-201X pemsbo@usgs.gov","orcid":"https://orcid.org/0000-0001-9421-201X","contributorId":997,"corporation":false,"usgs":true,"family":"Emsbo","given":"Poul","email":"pemsbo@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":816969,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Granitto, Matthew 0000-0003-3445-4863 granitto@usgs.gov","orcid":"https://orcid.org/0000-0003-3445-4863","contributorId":1224,"corporation":false,"usgs":true,"family":"Granitto","given":"Matthew","email":"granitto@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":816972,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Kreiner, Douglas C. 0000-0002-4405-1403","orcid":"https://orcid.org/0000-0002-4405-1403","contributorId":220474,"corporation":false,"usgs":true,"family":"Kreiner","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":816973,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70221792,"text":"70221792 - 2021 - Sea star wasting disease pathology in Pisaster ochraceus shows a basal-to-surface process affecting color phenotypes differently","interactions":[],"lastModifiedDate":"2021-07-07T14:27:46.714793","indexId":"70221792","displayToPublicDate":"2021-06-03T19:43:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Sea star wasting disease pathology in Pisaster ochraceus shows a basal-to-surface process affecting color phenotypes differently","title":"Sea star wasting disease pathology in Pisaster ochraceus shows a basal-to-surface process affecting color phenotypes differently","docAbstract":"

Sea star wasting disease (SSWD) refers to a suite of poorly described non-specific clinical signs including abnormal posture, epidermal ulceration, and limb autotomy (sloughing) causing mortalities of over 20 species of sea stars and subsequent ecological shifts throughout the northeastern Pacific. While SSWD is widely assumed to be infectious, with environmental conditions facilitating disease progression, few data exist on cellular changes associated with the disease. This is unfortunate, because such observations could inform mechanisms of disease pathogenesis and host susceptibility. Here, we replicated SSWD by exposing captive Pisaster ochraceus to a suite of non-infectious organic substances and show that development of gross lesions is a basal-to-surface process involving inflammation (e.g. infiltration of coelomocytes) of ossicles and mutable collagenous tissue, leading to epidermal ulceration. Affected sea stars also manifest increases in a heretofore undocumented coelomocyte type, spindle cells, that might be a useful marker of inflammation in this species. Finally, compared to purple morphs, orange P. ochraceus developed more severe lesions but survived longer. Longer-lived, and presumably more visible, severely-lesioned orange sea stars could have important demographic implications in terms of detectability of lesioned animals in the wild and measures of apparent prevalence of disease.

","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/dao03598","usgsCitation":"Work, T.M., Weatherby, T.M., DeRito, C.M., Besemer, R.M., and Hewson, I., 2021, Sea star wasting disease pathology in Pisaster ochraceus shows a basal-to-surface process affecting color phenotypes differently: Diseases of Aquatic Organisms, v. 145, p. 21-33, https://doi.org/10.3354/dao03598.","productDescription":"Article: 13 p.; Data Release","startPage":"21","endPage":"33","ipdsId":"IP-126265","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":452021,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/dao03598","text":"Publisher Index Page"},{"id":386979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":386991,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGH5ZF"}],"volume":"145","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":818734,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weatherby, Tina M.","contributorId":260782,"corporation":false,"usgs":false,"family":"Weatherby","given":"Tina","email":"","middleInitial":"M.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":818735,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeRito, Christopher M.","contributorId":260783,"corporation":false,"usgs":false,"family":"DeRito","given":"Christopher","email":"","middleInitial":"M.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":818736,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Besemer, Ryan M.","contributorId":260784,"corporation":false,"usgs":false,"family":"Besemer","given":"Ryan","email":"","middleInitial":"M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":818737,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hewson, Ian","contributorId":260785,"corporation":false,"usgs":false,"family":"Hewson","given":"Ian","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":818738,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221140,"text":"fs20213026 - 2021 - Water resources of St. Landry Parish, Louisiana","interactions":[],"lastModifiedDate":"2021-06-04T11:55:23.587754","indexId":"fs20213026","displayToPublicDate":"2021-06-03T11:08:01","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3026","displayTitle":"Water Resources of St. Landry Parish, Louisiana","title":"Water resources of St. Landry Parish, Louisiana","docAbstract":"

Information concerning the availability, use, and quality of water in St. Landry Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 116.75 million gallons per day (Mgal/d) of water were withdrawn in St. Landry Parish: about 98.13 Mgal/d from groundwater sources and 18.62 Mgal/d from surface-water sources. Withdrawals for agricultural use, composed of general irrigation, rice irrigation, aquaculture, and livestock uses, accounted for about 90 percent (105.31 Mgal/d) of the total water withdrawn. Other categories of use included public supply, which accounted for about 8 percent of the total water withdrawn (9.77 Mgal/d), industry which accounted for about 1 percent (1.03 Mgal/d), and rural domestic which accounted for about 1 percent (0.65 Mgal/d). Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 1965 at 194.57 Mgal/d due to a large reported surface-water withdrawal of 144.00 Mgal/d for power generation that was not reported for other years.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213026","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Lindaman, M.A.., and White, V.E., 2021, Water resources of St. Landry Parish, Louisiana: U.S. Geological Survey Fact Sheet 2021–3026, 6 p., https://doi.org/10.3133/fs20213026.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103363","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":386161,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3026/coverthb.jpg"},{"id":386162,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3026/fs20213026.pdf","text":"Report","size":"2.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3026"},{"id":386163,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS data release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"}],"country":"United States","state":"Louisiana","county":"St. Landry Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.2132,30.8487],[-92.1078,30.8503],[-91.8154,30.8483],[-91.7978,30.8442],[-91.7977,30.8337],[-91.8041,30.8291],[-91.8078,30.8204],[-91.8089,30.8145],[-91.8067,30.8104],[-91.7987,30.8104],[-91.787,30.7976],[-91.7779,30.794],[-91.7688,30.7858],[-91.7613,30.7675],[-91.7565,30.7607],[-91.7554,30.7534],[-91.7576,30.7493],[-91.7581,30.7415],[-91.7485,30.7301],[-91.7468,30.7237],[-91.7372,30.7118],[-91.7335,30.7018],[-91.7366,30.6794],[-91.7323,30.6725],[-91.7328,30.668],[-91.7445,30.6625],[-91.7466,30.6588],[-91.7444,30.6401],[-91.7412,30.6327],[-91.7449,30.6254],[-91.7508,30.6231],[-91.7539,30.6176],[-91.755,30.6126],[-91.7512,30.5994],[-91.7544,30.5861],[-91.7549,30.5742],[-91.757,30.5687],[-91.7575,30.5628],[-91.7559,30.5596],[-91.7425,30.5317],[-91.733,30.5203],[-91.7319,30.5125],[-91.7324,30.5102],[-91.7361,30.5084],[-91.7472,30.5093],[-91.7525,30.5079],[-91.7568,30.4978],[-91.7546,30.4855],[-91.7509,30.4786],[-91.7439,30.4709],[-91.7423,30.4576],[-91.7397,30.4531],[-91.7365,30.4517],[-91.7227,30.4526],[-91.719,30.4494],[-91.7131,30.4321],[-91.6988,30.4147],[-91.6977,30.412],[-91.6945,30.4019],[-91.6802,30.3974],[-91.6791,30.3951],[-91.8126,30.3962],[-91.8205,30.398],[-91.8301,30.4025],[-91.8327,30.4057],[-91.8433,30.4075],[-91.8497,30.4066],[-91.8545,30.4116],[-91.8571,30.413],[-91.8656,30.4166],[-91.8688,30.4184],[-91.8916,30.4101],[-91.8953,30.4087],[-91.9074,30.4045],[-91.9143,30.4027],[-91.9317,30.3971],[-91.9381,30.3971],[-91.9429,30.3998],[-91.9546,30.4066],[-91.9652,30.4107],[-91.9705,30.4121],[-91.9784,30.4079],[-91.9805,30.4043],[-91.9831,30.3923],[-91.9814,30.3818],[-91.9862,30.3754],[-91.9893,30.3704],[-91.9962,30.3708],[-92.0015,30.3671],[-92.0052,30.3662],[-92.0089,30.3684],[-92.0126,30.3716],[-92.0226,30.3643],[-92.0258,30.361],[-92.0295,30.3606],[-92.0353,30.3642],[-92.0407,30.3692],[-92.0423,30.3751],[-92.0465,30.3792],[-92.0497,30.3801],[-92.0545,30.3769],[-92.0582,30.3737],[-92.0634,30.3714],[-92.0724,30.3645],[-92.0814,30.3612],[-92.0851,30.3575],[-92.086,30.3475],[-92.0881,30.3356],[-92.0933,30.3314],[-92.0986,30.3305],[-92.1423,30.2991],[-92.1419,30.3151],[-92.1419,30.317],[-92.1419,30.3206],[-92.1504,30.321],[-92.1589,30.321],[-92.1584,30.332],[-92.1591,30.3503],[-92.1755,30.3511],[-92.1757,30.3717],[-92.1763,30.3762],[-92.1764,30.3932],[-92.1759,30.4005],[-92.1773,30.438],[-92.2117,30.4382],[-92.2446,30.4371],[-92.2447,30.4517],[-92.245,30.4805],[-92.2588,30.4804],[-92.2688,30.4808],[-92.455,30.4816],[-92.4942,30.4818],[-92.4874,30.4878],[-92.4805,30.4924],[-92.471,30.4939],[-92.4657,30.4967],[-92.4637,30.5008],[-92.4659,30.5108],[-92.4622,30.5163],[-92.4592,30.5246],[-92.4508,30.532],[-92.4397,30.5362],[-92.4285,30.5363],[-92.4227,30.5386],[-92.4148,30.5405],[-92.2795,30.5388],[-92.263,30.5385],[-92.2622,30.5682],[-92.2113,30.569],[-92.2117,30.6129],[-92.2107,30.6198],[-92.2064,30.6216],[-92.2038,30.6257],[-92.206,30.6299],[-92.2044,30.6331],[-92.2055,30.6353],[-92.2034,30.6372],[-92.205,30.639],[-92.2008,30.6477],[-92.2009,30.6564],[-92.1945,30.6596],[-92.1887,30.6647],[-92.1861,30.667],[-92.1797,30.6661],[-92.175,30.6762],[-92.1729,30.6758],[-92.1694,30.7677],[-92.1774,30.7685],[-92.1816,30.7694],[-92.187,30.7758],[-92.1918,30.7785],[-92.1977,30.7798],[-92.2073,30.7848],[-92.2079,30.7889],[-92.2127,30.7948],[-92.2132,30.8487]]]},\"properties\":{\"name\":\"Saint Landry\",\"state\":\"LA\"}}]}","contact":"

Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-06-03","noUsgsAuthors":false,"publicationDate":"2021-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Lindaman, Maxwell A. 0000-0003-1786-1272","orcid":"https://orcid.org/0000-0003-1786-1272","contributorId":219064,"corporation":false,"usgs":true,"family":"Lindaman","given":"Maxwell A.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Vincent E. 0000-0002-1660-0102 vwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-1660-0102","contributorId":5388,"corporation":false,"usgs":true,"family":"White","given":"Vincent","email":"vwhite@usgs.gov","middleInitial":"E.","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816834,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221138,"text":"sir20215013 - 2021 - Workflow for using unmanned aircraft systems and traditional geospatial data to delineate agricultural drainage tiles at edge-of-field sites","interactions":[],"lastModifiedDate":"2021-06-04T11:49:06.69849","indexId":"sir20215013","displayToPublicDate":"2021-06-03T10:27:40","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":"2021-5013","displayTitle":"Workflow for Using Unmanned Aircraft Systems and Traditional Geospatial Data to Delineate Agricultural Drainage Tiles at Edge-of-Field Sites","title":"Workflow for using unmanned aircraft systems and traditional geospatial data to delineate agricultural drainage tiles at edge-of-field sites","docAbstract":"

Managing nutrient and sediment runoff from fields that drain to the Great Lakes is key to mitigating harmful algal blooms. Implementation of best management practices on agricultural land is considered a critical step to improving water quality in these streams, however the effect of these best management practices is difficult to quantify. The purpose of this study was to use a suite of high-resolution imagery acquired with unmanned aircraft systems (including a combination of visible, multispectral, and thermal cameras) to better characterize edge-of-field (EOF) sites in Michigan and Wisconsin that are monitored in cooperation with the Great Lakes Restoration Initiative. This high-resolution imagery (2.5–12-centimeter ground resolution) was used to delineate artificial subsurface drainage (tile-drain) networks and surface water flow paths that indicate contributing areas (that is, all area that drains to a monitored point) at these EOF sites, providing better characterization of each study site. Contributing areas for these sites ranged from 2.86 to 5.07 hectares and, among the sites, tile drains were identified as those that followed soil properties and those that were more densely patterned networks. These surveys also indicated that the contributing area monitored at the EOF sites may cross field boundaries and is not always coincident with the area underlain by subsurface drainage.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215013","usgsCitation":"Webber, J.J., and Williamson, T.N., 2021, Workflow for using unmanned aircraft systems and traditional geospatial data to delineate agricultural drainage tiles at edge-of-field sites: U.S. Geological Survey Scientific Investigations Report 2021–5013, 18 p., https://doi.org/10.3133/sir20215013.","productDescription":"Report: vii, 18 p.; Data Releases: 4","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-118324","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":386180,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5013/images"},{"id":386154,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5013/coverthb.jpg"},{"id":386155,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5013/sir20215013.pdf","text":"Report","size":"17.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5013"},{"id":386156,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EXXX2O","text":"USGS data release","description":"USGS data release","linkHelpText":"Low-altitude visible, multispectral, and thermal-infrared imagery from edge-of-field monitoring sites for Great Lakes Restoration Initiative - Michigan Flume 2"},{"id":386157,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N8ELYZ","text":"USGS data release","description":"USGS data release","linkHelpText":"Low-altitude visible, multispectral, and thermal-infrared imagery from edge-of-field monitoring sites for Great Lakes Restoration Initiative - Wisconsin Surface Water 3"},{"id":386158,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DNURMT","text":"USGS data release","description":"USGS data release","linkHelpText":"Low-altitude visible, multispectral, and thermal-infrared imagery from edge-of-field monitoring sites for Great Lakes Restoration Initiative - Wisconsin Surface Water 4 and 5"},{"id":386159,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93R270D","text":"USGS data release","description":"USGS data release","linkHelpText":"Low-altitude visible, multispectral, and thermal-infrared imagery from edge-of-field monitoring sites for Great Lakes Restoration Initiative - 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Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278

","tableOfContents":"","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2021-06-03","noUsgsAuthors":false,"publicationDate":"2021-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Webber, J. Jeremy 0000-0002-2512-2448","orcid":"https://orcid.org/0000-0002-2512-2448","contributorId":259209,"corporation":false,"usgs":true,"family":"Webber","given":"J.","email":"","middleInitial":"Jeremy","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816830,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816831,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259656,"text":"70259656 - 2021 - Raising the West: Mid-Cenozoic Colorado-plano related to subvolcanic batholith assembly in the Southern Rocky Mountains (USA)?","interactions":[],"lastModifiedDate":"2024-10-18T12:18:33.059578","indexId":"70259656","displayToPublicDate":"2021-06-03T07:16:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Raising the West: Mid-Cenozoic Colorado-plano related to subvolcanic batholith assembly in the Southern Rocky Mountains (USA)?","docAbstract":"

The Southern Rocky Mountains of Colorado, United States, have the highest regional elevation in North America, but present-day crustal thickness (∼42–47 km) is no greater than for the adjacent, topographically lower High Plains and Colorado Plateau. The chemistry of continental-arc rocks of the mid-Cenozoic Southern Rocky Mountain volcanic field, calibrated to compositions and Moho depths at young arcs, suggests that paleocrustal thickness may have been 20%–35% greater than at present and elevations accordingly higher. Thick mid-Cenozoic Rocky Mountain crust and high paleo-elevations, comparable to those inferred for the Nevadaplano farther west in the United States from analogous volcanic chemistry, could be consistent with otherwise-perplexing evidence for widespread rapid erosion during volcanism. Variable mid-Cenozoic crustal thickening and uplift could have resulted from composite batholith growth during volcanism, superimposed on prior crustal thickening during early Cenozoic (Laramide) compression. Alternatively, the arc–crustal thickness calibration may be inappropriate for high-potassium continental arcs, in which case other published interpretations using similar methods may also be unreliable.

","language":"English","publisher":"Geological Society of America","doi":"10.1130/G48963.1","usgsCitation":"Lipman, P.W., 2021, Raising the West: Mid-Cenozoic Colorado-plano related to subvolcanic batholith assembly in the Southern Rocky Mountains (USA)?: Geology, v. 49, no. 9, p. 1107-1111, https://doi.org/10.1130/G48963.1.","productDescription":"5 p.","startPage":"1107","endPage":"1111","ipdsId":"IP-128279","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462997,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico","otherGeospatial":"Southern Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.23198775685327,\n 39.97501601168983\n ],\n [\n -108.23198775685327,\n 35.24337666638678\n ],\n [\n -103.79351119435339,\n 35.24337666638678\n ],\n [\n -103.79351119435339,\n 39.97501601168983\n ],\n [\n -108.23198775685327,\n 39.97501601168983\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Lipman, Peter W. 0000-0001-9175-6118","orcid":"https://orcid.org/0000-0001-9175-6118","contributorId":203612,"corporation":false,"usgs":true,"family":"Lipman","given":"Peter","email":"","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":916160,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70254573,"text":"70254573 - 2021 - Effects of climate and irrigation on GRACE-based estimates of water storage changes in major US aquifers","interactions":[],"lastModifiedDate":"2024-06-03T11:50:37.905478","indexId":"70254573","displayToPublicDate":"2021-06-03T06:45:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate and irrigation on GRACE-based estimates of water storage changes in major US aquifers","docAbstract":"

Understanding climate and human impacts on water storage is critical for sustainable water-resources management. Here we assessed climate and human drivers of total water storage (TWS) variability from Gravity Recovery and Climate Experiment (GRACE) satellites compared with drought severity and irrigation water use in 14 major aquifers in the United States. Results show that long-term variability in TWS tracked by GRACE satellites is dominated by interannual variability in most of the 14 major US aquifers. Low TWS trends in the humid eastern U.S. are linked to low drought intensity. Although irrigation pumpage in the humid Mississippi Embayment aquifer exceeded that in the semi-arid California Central Valley, a surprising lack of TWS depletion in the Mississippi Embayment aquifer is attributed to extensive streamflow capture. Marked storage depletion in the semi-arid southwestern Central Valley and south-central High Plains totaled ∼90 km3, about three times greater than the capacity of Lake Mead, the largest U.S. reservoir. Depletion in the Central Valley was driven by long-term droughts (⩽5 yr) amplified by switching from mostly surface water to groundwater irrigation. Low or slightly rising TWS trends in the northwestern (Columbia and Snake Basins) US are attributed to dampening drought impacts by mostly surface water irrigation. GRACE satellite data highlight synergies between climate and irrigation, resulting in little impact on TWS in the humid east, amplified TWS depletion in the semi-arid southwest and southcentral US, and dampened TWS deletion in the northwest and north central US Sustainable groundwater management benefits from conjunctive use of surface water and groundwater, inefficient surface water irrigation promoting groundwater recharge, efficient groundwater irrigation minimizing depletion, and increasing managed aquifer recharge. This study has important implications for sustainable water development in many regions globally.

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Pickle Research Campus, Bldg. 130, 10100 Burnet Rd., Austin, TX 78758-4445","active":true,"usgs":false}],"preferred":false,"id":901930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rateb, Ahsraf 0000-0002-8875-1508","orcid":"https://orcid.org/0000-0002-8875-1508","contributorId":337082,"corporation":false,"usgs":false,"family":"Rateb","given":"Ahsraf","affiliations":[{"id":80965,"text":"Bureau of Economic Geology, University of Texas","active":true,"usgs":false}],"preferred":false,"id":901931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pool, Donald R. 0001-1234-4321-0505","orcid":"https://orcid.org/0001-1234-4321-0505","contributorId":337083,"corporation":false,"usgs":false,"family":"Pool","given":"Donald R.","affiliations":[{"id":80967,"text":"Retired USGS, Arizona Water Science Center","active":true,"usgs":false}],"preferred":false,"id":901932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":337084,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":901933,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Save, Himanshu","contributorId":187510,"corporation":false,"usgs":false,"family":"Save","given":"Himanshu","email":"","affiliations":[],"preferred":false,"id":902001,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sun, Alexander Y. 0000-0002-6365-8526","orcid":"https://orcid.org/0000-0002-6365-8526","contributorId":302987,"corporation":false,"usgs":false,"family":"Sun","given":"Alexander","email":"","middleInitial":"Y.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":902002,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Long, Di","contributorId":187511,"corporation":false,"usgs":false,"family":"Long","given":"Di","email":"","affiliations":[],"preferred":false,"id":902003,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fuchs, Brian","contributorId":192359,"corporation":false,"usgs":false,"family":"Fuchs","given":"Brian","email":"","affiliations":[],"preferred":false,"id":902004,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70260971,"text":"70260971 - 2021 - Benzotriazole concentrations in airport runoff are reduced following changes in airport deicer formulations","interactions":[],"lastModifiedDate":"2024-11-19T19:07:08.734559","indexId":"70260971","displayToPublicDate":"2021-06-02T12:45:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2006,"text":"Integrated Environmental Assessment and Management","active":true,"publicationSubtype":{"id":10}},"title":"Benzotriazole concentrations in airport runoff are reduced following changes in airport deicer formulations","docAbstract":"

A comparison of the presence of additives in airport deicers commonly used in the United States and in airport runoff was conducted with data collected before and after changes in deicer formulations. Three isomers of benzotriazoles (BTs)—4-methyl-1H-benzotriazole (4-MeBT), 5-methyl-1H-benzotriazole (5-MeBT), and 1H-benzotriazole (1H-BT)—are corrosion inhibitors added to some formulations of airport deicers and are reported to be a source of aquatic toxicity in streams receiving airport runoff. Concentrations of BT in aircraft deicers and anti-icing fluids (ADAF) were reduced over time but were not reduced in potassium acetate airfield-pavement deicer material (PDM) that was used throughout the study period. Streams receiving runoff from Milwaukee Mitchell International Airport, Milwaukee, Wisconsin, USA, were monitored from 2004 to 2019 for BTs, with concentrations of 4-MeBT varying from <0.35 to 4600 µg/L, 5-MeBT varying from <0.25 to 6600 µg/L, and 1H-BT varying from <0.25 to 150 µg/L. Median 4-MeBT concentrations at sites downstream from the airport decreased by approximately 74%, 5-MeBT by 69%, and 1H-BT by 82% following reduction in BTs in ADAF formulations, resulting in a reduction in the potential for aquatic toxicity in receiving streams. A change in residuals from regression analysis between freezing point depressants and BTs indicate that the reduction in BT concentrations in airport runoff was a result of BT reduction in ADAF formulations, but PDM may still be a substantial source of BTs in airport runoff. Because BTs are a source of aquatic toxicity in airport deicers, the reductions in BTs in the common deicers observed in this study can be used to demonstrate the potential for a reduction in the effects to aquatic organisms in airport runoff, resulting in greater likelihood of meeting aquatic toxicity requirements in airport stormwater permits, and potentially driving airports, airlines, and permit holders to advocate further reduction or elimination of BTs and other harmful contaminants in airport deicers.

","language":"English","publisher":"Wiley","doi":"10.1002/ieam.4468","usgsCitation":"Olds, H., Corsi, S., and Rutter, T.D., 2021, Benzotriazole concentrations in airport runoff are reduced following changes in airport deicer formulations: Integrated Environmental Assessment and Management, v. 18, no. 1, p. 245-257, https://doi.org/10.1002/ieam.4468.","productDescription":"13 p.","startPage":"245","endPage":"257","ipdsId":"IP-119323","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467241,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ieam.4468","text":"Publisher Index Page"},{"id":464297,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","city":"Milwaukee","otherGeospatial":"Milwaukee Mitchell International Airport","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.93312340482309,\n 42.95913686851276\n ],\n [\n -87.93312340482309,\n 42.93182296880775\n ],\n [\n -87.87946797516405,\n 42.93182296880775\n ],\n [\n -87.87946797516405,\n 42.95913686851276\n ],\n [\n -87.93312340482309,\n 42.95913686851276\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Olds, Hayley T. 0000-0002-6701-6459 htolds@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":215837,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley","email":"htolds@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rutter, Troy D. 0000-0001-5130-204X tdrutter@usgs.gov","orcid":"https://orcid.org/0000-0001-5130-204X","contributorId":2081,"corporation":false,"usgs":true,"family":"Rutter","given":"Troy","email":"tdrutter@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918761,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221076,"text":"sir20215024 - 2021 - Use of dissolved oxygen monitoring to evaluate phosphorus loading in Connecticut streams, 2015–18","interactions":[],"lastModifiedDate":"2021-06-02T17:25:11.979978","indexId":"sir20215024","displayToPublicDate":"2021-06-02T08:11:17","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":"2021-5024","displayTitle":"Use of Dissolved Oxygen Monitoring to Evaluate Phosphorus Loading in Connecticut Streams, 2015–18","title":"Use of dissolved oxygen monitoring to evaluate phosphorus loading in Connecticut streams, 2015–18","docAbstract":"

The Connecticut Department of Energy and Environmental Protection (CT DEEP) has developed an interim phosphorus reduction strategy to establish water-quality-based phosphorus limits in nontidal freshwaters for industrial and municipal water pollution control facilities. A recommendation in the strategy included the addition of diurnal dissolved oxygen (DO) sampling to the sampling of diatom communities collected by CT DEEP. The chemistry data coupled with biological data will help to examine the effects of phosphorus loading in streams. The U.S. Geological Survey (USGS), in cooperation with the CT DEEP and New England Interstate Water Pollution Control Commission, implemented a summer DO monitoring program from 2015 to 2018 to examine the effects of phosphorus loading in streams. Continuous DO data were collected at 18 sites in streams with varying concentrations of phosphorus throughout the State of Connecticut. Discrete water-quality nutrient data were collected by the USGS at 11 of the 18 sites. All continuous and discrete data collected from June to September for the 4 years were examined for all sites. This report documents a pattern of diurnal DO for monitoring sites across 4 years and presents estimated daily gross primary productivity (GPP), ecosystem respiration (ER), and a standardized rate coefficient for gas exchange for selected streams. Relations of phosphorus concentrations to the diurnal DO response and stream metabolism are described. Interannual variability in average annual total phosphorus (TP) concentrations and maximum daily DO concentrations were evaluated among sites in years of the study. Streams identified as impaired by CT DEEP such as Naugatuck River at Beacon Falls (USGS station 01208500), Still River at Route 7 at Brookfield Center (USGS station 01201487), and Quinnipiac River at Wallingford (USGS station 01196500) had higher TP concentrations (greater than 0.10 milligram per liter [mg/L]) throughout the study. Reference streams considered unimpaired had lower concentrations of TP (less than 0.10 mg/L). The range in daily DO concentrations remained less than 4 mg/L for most of the sites during the study except for Naugatuck River at Beacon Falls and Still River at Route 7 at Brookfield Center. Daily GPP and ER were summarized for 11 sites using the maximum likelihood estimation model of the streamMetabolizer package in the R statistical program. The models indicated that most sites had an estimated negative net primary productivity, based on the daily estimates of GPP and ER, which indicates the systems are heterotrophic and dominated by respiration. The high variation of GPP and ER reported for several sites can be affected by many physical, chemical, and biological factors, including the abundance and community composition of phytoplankton, periphyton, and macrophyte algae present. The variability in mean GPP was similar to the variability in maximum DO concentrations when plotted against annual average TP concentrations for the maximum likelihood estimation model in streamMetabolizer. The concept that phosphorus loading can affect the stream metabolism requires more detailed knowledge of stream geomorphic variables (canopy cover, stream velocity, water depth) and algal communities to help improve the scientific basis for managing phosphorus loading.

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Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

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","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-06-02","noUsgsAuthors":false,"publicationDate":"2021-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Izbicki, Brittney 0000-0002-9161-0415 bizbicki@usgs.gov","orcid":"https://orcid.org/0000-0002-9161-0415","contributorId":207391,"corporation":false,"usgs":true,"family":"Izbicki","given":"Brittney","email":"bizbicki@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":816705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morrison, Jonathan 0000-0002-1756-4609 jmorriso@usgs.gov","orcid":"https://orcid.org/0000-0002-1756-4609","contributorId":2274,"corporation":false,"usgs":true,"family":"Morrison","given":"Jonathan","email":"jmorriso@usgs.gov","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816706,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221164,"text":"70221164 - 2021 - Short‐period surface‐wave tomography in the continental United States— A resource for research","interactions":[],"lastModifiedDate":"2021-11-01T15:22:56.646029","indexId":"70221164","displayToPublicDate":"2021-06-02T07:32:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Short‐period surface‐wave tomography in the continental United States— A resource for research","docAbstract":"

The variation of phase and group velocity dispersion of Love and Rayleigh waves was determined for the continental United States and adjacent Canada. By processing ambient noise from the broadband channels of the Transportable Array (TA) of USArray and several Program for the Array Seismic Studies of the Continental Lithosphere experiments and using some earthquake recordings, the effort was focused on determining dispersion down to periods as short as 2 s. The relatively short distances between TA stations permitted the use of a 25&#x2009;&#x2009;km&#xD7;25&#x2009;&#x2009;km\">25  km×25  km grid for the four independent tomographic inversions (Love and Rayleigh and phase and group velocity). One reason for trying to obtain short‐period dispersion was to have a data set capable of constraining upper crust velocity models for use in determining regional moment tensors. The benefit of focusing on short‐period dispersion is apparent in the tomography maps—shallow geologic structures such as the Mid‐Continent Rift, and the Michigan, Illinois, Anadarko, Arkoma, and Appalachian basins are imaged. In our processing, we noted that the phase velocities were more robustly determined than the group velocities. We also noted that the inability to obtain dispersion at short periods shows distinct regional patterns that may be related to the local upper crust structure.

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J.","affiliations":[{"id":52339,"text":"Department of Geosciences, Penn State, 440 Deike Building, University Park, PA 16802","active":true,"usgs":false}],"preferred":false,"id":816918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":816919,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aziz-Zanjani, A.","contributorId":259272,"corporation":false,"usgs":false,"family":"Aziz-Zanjani","given":"A.","email":"","affiliations":[{"id":52342,"text":"Department of Earth and Atmospheric Sciences, Saint Louis University, 3642 Lindell Boulevard, St. Louis, MO 63108 USA","active":true,"usgs":false}],"preferred":false,"id":816920,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boschelli, J.","contributorId":259273,"corporation":false,"usgs":false,"family":"Boschelli","given":"J.","affiliations":[{"id":52342,"text":"Department of Earth and Atmospheric Sciences, Saint Louis University, 3642 Lindell Boulevard, St. Louis, MO 63108 USA","active":true,"usgs":false}],"preferred":false,"id":816921,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221082,"text":"sir20215037 - 2021 - Sediment concentrations and loads upstream from and through John Redmond Reservoir, east-central Kansas, 2010–19","interactions":[],"lastModifiedDate":"2021-06-02T13:05:23.095316","indexId":"sir20215037","displayToPublicDate":"2021-06-02T06:12:32","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":"2021-5037","displayTitle":"Sediment Concentrations and Loads Upstream from and through John Redmond Reservoir, East-Central Kansas, 2010–19","title":"Sediment concentrations and loads upstream from and through John Redmond Reservoir, east-central Kansas, 2010–19","docAbstract":"

Streambank erosion and reservoir sedimentation are primary concerns of resource managers in Kansas and throughout many regions of the United States and negatively affect flood control, water supply, and recreation. The Cottonwood and upper Neosho Rivers drain into John Redmond Reservoir, and since reservoir completion in 1964, there has been substantial conservation-pool sedimentation and storage loss in John Redmond Reservoir, causing storage capacity losses more rapidly than most other Federal reservoirs in Kansas. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office, has monitored water quality (temperature, specific conductance, and turbidity) on the Cottonwood River (upstream from the reservoir) and Neosho River (upstream and downstream from the reservoir) since 2007 with additional sites added in 2009. The purpose of this report is to quantify suspended-sediment concentrations, loads, and yields entering and exiting John Redmond Reservoir during January 1, 2010, through December 31, 2019.

Three water-quality monitoring sites were upstream from the reservoir (Cottonwood River near Plymouth, Kansas [USGS site 07182250; hereinafter referred to as “Cottonwood”]; Neosho River at Burlingame Road near Emporia, Kans. [USGS site 07179750; hereinafter referred to as “Burlingame”]; and Neosho River at Neosho Rapids, Kans. [USGS site 07182390; hereinafter referred to as “Neosho Rapids”]), and one water-quality monitoring site was downstream from the reservoir (Neosho River at Burlington, Kans. [USGS site 07182510; hereinafter referred to as “Burlington”]). The Neosho Rapids streamgage is downstream from the confluence of the Cottonwood and upper Neosho Rivers and has a contributing drainage area accounting for 91 percent of the total contributing drainage area to John Redmond Reservoir.

Continuously measured streamflow, water quality, and discrete water-quality data were used to develop updated regression models to compute suspended-sediment concentrations, loads, and yields upstream and downstream from John Redmond Reservoir in east-central Kansas. Several turbidity sensors were deployed during the analysis period, and there are no established relations between the sensors; therefore, individual models for each sensor were developed. Model statistics for the turbidity and suspended-sediment concentration linear regression models were better (based on the coefficient of determination, root mean square error, and model standard percentage error) than the streamflow and suspended-sediment concentration linear regression models, indicating better model performance. Computed concentrations, loads, and yields do not account for the ungaged 9 percent of the drainage basin downstream from the Neosho Rapids streamgage.

Mean daily suspended-sediment loads upstream from the reservoir were largest at Neosho Rapids (2,250 tons), second largest at Cottonwood (2,180 tons), and smallest at Burlingame (624 tons). Streamflow at Burlington was predominately regulated by reservoir releases, and mean daily suspended-sediment loads were smaller (286 tons) than at upstream sites. Among the upstream sites, Cottonwood had the largest mean daily suspended-sediment concentration (179 milligrams per liter [mg/L]), followed by Neosho Rapids (162 mg/L), and Burlingame (108 mg/L). Burlington had the smallest mean daily suspended-sediment concentration of all sites (46 mg/L).

Annual reservoir trapping efficiency ranged from 82 to 94 percent, and the largest sediment mass trapped was during 2019 (2,230,000 tons). Reservoir storage decreased an estimated 7,750 acre-feet during 2010 and 2014–19. Using the mean trapping efficiency to estimate suspended-sediment loads during years with missing data (2011–13), the total estimated reservoir storage lost to sedimentation for the analysis period (2010–19) was 8,690 acre-feet, about 17 percent of the remaining storage space reported in 2007. The mean annual sedimentation rate during the analysis period (747 acre-feet per year) was about 85 percent larger than the design sedimentation rate (404 acre-feet per year) originally projected during construction. Different reservoir outflow management strategies, including operating near normal capacity as opposed to higher flood pool levels, could reduce the total reservoir storage lost by 3 percent (about 261 acre-feet), which is equal to 14 percent of the total sediment removed during the dredging operation in 2016.

During the study period, about 56 percent of the total suspended-sediment load was transported during streamflows greater than the National Weather Service flood action stage at the upstream sites (0.1–5 percent of the record; Cottonwood mean: 48 percent; Burlingame mean: 40 percent; Neosho Rapids mean: 78 percent). Disproportionately large sediment loads were delivered during short periods of time, and localized efforts of stream erosion protection (streambank stabilization, riparian buffers) were likely to be overwhelmed. Precipitation frequency and intensity are projected to continue to increase in this region; therefore, future sediment reduction strategies that account for extreme episodic events may be beneficial. Changes to reservoir outflow management could also minimize sediment accumulation while still preserving flood control. Continued investigation of sediment reduction measures is necessary for future mitigation with the understanding that sedimentation rate is largely driven by high flows. Results from this study can be used to calibrate sediment models, explore sediment reduction strategies, highlight the importance of continued water-quality monitoring to determine effectiveness and changes in sediment transport, and assess the ability of John Redmond Reservoir to support designated uses into the future.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215037","collaboration":"Prepared in cooperation with the Kansas Water Office","usgsCitation":"Kramer, A.R., Peterman-Phipps, C.L., Mahoney, M.D., and Lukasz, B.S., 2021, Sediment concentrations and loads upstream from and through John Redmond Reservoir, east-central Kansas, 2010–19: U.S. Geological Survey Scientific Investigations Report 2021–5037, 49 p., https://doi.org/10.3133/sir20215037.","productDescription":"Report: ix, 50 p; Appendixes: 12; Dataset","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119997","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":386084,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix09.pdf","text":"Appendix 9","size":"457 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 9","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07182250, Cottonwood River near Plymouth, Kansas, during January 1, 2010, through December 31, 2019"},{"id":386074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5037/coverthb.jpg"},{"id":386075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037"},{"id":386076,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix01.pdf","text":"Appendix 1","size":"408 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 1","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07182250, Cottonwood River near Plymouth, Kansas, during January 1, 2010, through April 22, 2015"},{"id":386078,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix03.pdf","text":"Appendix 3","size":"432 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 3","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07182390, Neosho River at Neosho Rapids, Kansas, during January 1, 2010, through September 24, 2015"},{"id":386079,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix04.pdf","text":"Appendix 4","size":"455 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 4","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07182510, Neosho River at Burlington, Kansas, during January 1, 2010, through October 16, 2015"},{"id":386088,"rank":15,"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"},{"id":386087,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix12.pdf","text":"Appendix 12","size":"451 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 12","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07182510, Neosho River at Burlington, Kansas, during January 1, 2010, through December 31, 2019"},{"id":386086,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix11.pdf","text":"Appendix 11","size":"449 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 11","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07182390, Neosho River at Neosho Rapids, Kansas, during January 1, 2010, through December 31, 2019"},{"id":386083,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix08.pdf","text":"Appendix 8","size":"427 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 8","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07182510, Neosho River at Burlington, Kansas, during October 23, 2015, through December 31, 2019"},{"id":386082,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix07.pdf","text":"Appendix 7","size":"391 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 7","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07182390, Neosho River at Neosho Rapids, Kansas, during November 13, 2015, through December 31, 2019"},{"id":386085,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix10.pdf","text":"Appendix 10","size":"418 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 10","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07179750, Neosho River at Burlingame Road near Emporia, Kansas, during January 1, 2010, through December 31, 2019"},{"id":386080,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix05.pdf","text":"Appendix 5","size":"376 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 5","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07182250, Cottonwood River near Plymouth, Kansas, during April 22, 2015, through December 31, 2019"},{"id":386081,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix06.pdf","text":"Appendix 6","size":"399 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 6","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07179750, Neosho River at Burlingame Road near Emporia, Kansas, during May 2, 2015, through December 31, 2019"},{"id":386077,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix02.pdf","text":"Appendix 2","size":"414 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 2","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07179750, Neosho River at Burlingame Road near Emporia, Kansas, during January 1, 2010, through December 16, 2012"}],"country":"United States","state":"Kansas","otherGeospatial":"John Redmond Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.52838134765624,\n 38.01131226070673\n ],\n [\n -95.49041748046875,\n 38.01131226070673\n ],\n [\n -95.49041748046875,\n 39.27266344858914\n ],\n [\n -97.52838134765624,\n 39.27266344858914\n ],\n [\n -97.52838134765624,\n 38.01131226070673\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-06-02","noUsgsAuthors":false,"publicationDate":"2021-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Kramer, Ariele R. 0000-0002-7075-3310 akramer@usgs.gov","orcid":"https://orcid.org/0000-0002-7075-3310","contributorId":185245,"corporation":false,"usgs":true,"family":"Kramer","given":"Ariele","email":"akramer@usgs.gov","middleInitial":"R.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":816715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterman-Phipps, Cara L. 0000-0003-1822-2552","orcid":"https://orcid.org/0000-0003-1822-2552","contributorId":259166,"corporation":false,"usgs":true,"family":"Peterman-Phipps","given":"Cara","email":"","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":816716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mahoney, Matthew D. 0000-0002-9008-7132","orcid":"https://orcid.org/0000-0002-9008-7132","contributorId":206054,"corporation":false,"usgs":true,"family":"Mahoney","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":816717,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lukasz, Bradley S. 0000-0001-5438-5901","orcid":"https://orcid.org/0000-0001-5438-5901","contributorId":225021,"corporation":false,"usgs":true,"family":"Lukasz","given":"Bradley","email":"","middleInitial":"S.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":816718,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229123,"text":"70229123 - 2021 - Accelerating ecological sciences from above: Spatial contrastive learning for remote sensing","interactions":[],"lastModifiedDate":"2022-03-02T00:57:00.423902","indexId":"70229123","displayToPublicDate":"2021-06-01T18:50:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10143,"text":"Proceedings of the AAAI Conference on Artificial Intelligence","active":true,"publicationSubtype":{"id":10}},"title":"Accelerating ecological sciences from above: Spatial contrastive learning for remote sensing","docAbstract":"

The rise of neural networks has opened the door for automatic analysis of remote sensing data. A challenge to using this machinery for computational sustainability is the necessity of massive labeled data sets, which can be cost-prohibitive for many non-profit organizations. The primary motivation for this work is one such problem; the efficient management of invasive species -- invading flora and fauna that are estimated to cause damages in the billions of dollars annually. As an ongoing collaboration with the New York Natural Heritage Program, we consider the use of unsupervised deep learning techniques for dimensionality reduction of remote sensing images, which can reduce sample complexity for downstream tasks and decreases the need for large labeled data sets. We consider spatially augmenting contrastive learning by training neural networks to correctly classify two nearby patches of a landscape as such. We demonstrate that this approach improves upon previous methods and naive classification for a large-scale data set of remote sensing images derived from invasive species observations obtained over 30 years. Additionally, we simulate deployment in the field via active learning and evaluate this method on another important challenge in computational sustainability -- landcover classification -- and again find that it outperforms previous baselines.

","language":"English","publisher":"Association for the Advancement of Artificial Intelligence","usgsCitation":"Bjorck, J., Shi, Q., Rapazzo, B.H., Dean, J., Fuller, A.K., Brown-Lima, C., and Gomes, C., 2021, Accelerating ecological sciences from above: Spatial contrastive learning for remote sensing: Proceedings of the AAAI Conference on Artificial Intelligence, v. 35, no. 17, 10 p.","productDescription":"10 p.","ipdsId":"IP-122790","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":396619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bjorck, Johan","contributorId":287231,"corporation":false,"usgs":false,"family":"Bjorck","given":"Johan","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":836576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Qinru","contributorId":287233,"corporation":false,"usgs":false,"family":"Shi","given":"Qinru","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":836577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rapazzo, Brendan H.","contributorId":287234,"corporation":false,"usgs":false,"family":"Rapazzo","given":"Brendan","email":"","middleInitial":"H.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":836578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dean, Jennifer","contributorId":287236,"corporation":false,"usgs":false,"family":"Dean","given":"Jennifer","affiliations":[{"id":61506,"text":"New York Natural Heritage Program","active":true,"usgs":false}],"preferred":false,"id":836579,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836575,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown-Lima, Carrie","contributorId":287237,"corporation":false,"usgs":false,"family":"Brown-Lima","given":"Carrie","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":836580,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gomes, Carla","contributorId":287239,"corporation":false,"usgs":false,"family":"Gomes","given":"Carla","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":836581,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70220688,"text":"ofr20211046 - 2021 - Synthetic aperture radar and optical mapping used to monitor change and replacement of Phragmites australis marsh in the Lower Mississippi River Delta, Louisiana","interactions":[],"lastModifiedDate":"2021-06-02T11:49:04.647575","indexId":"ofr20211046","displayToPublicDate":"2021-06-01T15:52:30","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":"2021-1046","displayTitle":"Synthetic Aperture Radar and Optical Mapping Used to Monitor Change and Replacement of Phragmites australis Marsh in the Lower Mississippi River Delta, Louisiana","title":"Synthetic aperture radar and optical mapping used to monitor change and replacement of Phragmites australis marsh in the Lower Mississippi River Delta, Louisiana","docAbstract":"

Synthetic aperture radar (SAR) mapping of density as an enhancement of Phragmites australis optical live fractional cover (LFC) mapping was carried out in the lower Mississippi Delta during 2016 to 2019. Also, as part of the study, the replacement of P. australis with elephant-ear was analyzed. To that end, yearly maps from 2016 to 2019 of L-band SAR horizontal send, vertical receive (HV) data representing marsh density were produced for the lower Mississippi River Delta. The mapping indicated high local variability within broad yearly density change in P. australis marsh. LFC mapping indicated a similar pattern of broad yearly change. That overall density and LFC linear correspondence was confirmed with regressions of P. australis marsh HV-density data and optical-LFC data. Local differences reflected as high scatter in the plots. Based on those results, a combined LFC and HV-density assessment tracker of P. australis condition was developed. Major findings from the use of the trajectory tool were the high decrease in HV density from 2016 to 2017, the identification of severely degraded P. australis marsh and European P. australis marsh in some areas, and indications of linkage between the density decline from 2016 to 2017 and the elephant-ear replacement from 2018 to 2019. The trajectory tool application also indicated an inverse relationship between elephant-ear occurrence and HV-density changes from 2018 to 2019. A similar but weaker relationship was found between elephant-ear and LFC. These relationships may provide a means for early detection of replacement of P. australis marsh by elephant-ear and other unwanted plant species.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211046","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ramsey, E.W., III, and Rangoonwala, A., 2021, Synthetic aperture radar and optical mapping used to monitor change and replacement of Phragmites australis marsh in the lower Mississippi River Delta, Louisiana: U.S. Geological Survey Open-File Report 2021–1046, 19 p., https://doi.org/10.3133/ofr20211046.","productDescription":"vii, 19 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-122730","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":385955,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1046/images"},{"id":385911,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1046/coverthb.jpg"},{"id":385912,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1046/ofr20211046.pdf","text":"Report","size":"3.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1046"}],"country":"United States","state":"Louisiana","otherGeospatial":"Lower Mississippi River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.41497802734375,\n 29.054969241647125\n ],\n [\n -88.97689819335938,\n 29.054969241647125\n ],\n [\n -88.97689819335938,\n 29.41208667100814\n ],\n [\n -89.41497802734375,\n 29.41208667100814\n ],\n [\n -89.41497802734375,\n 29.054969241647125\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, Louisiana 70506 

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-06-01","noUsgsAuthors":false,"publicationDate":"2021-06-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramsey III, Elijah W. 0000-0002-4518-5796","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":214746,"corporation":false,"usgs":true,"family":"Ramsey III","given":"Elijah W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":816420,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rangoonwala, Amina 0000-0002-0556-0598","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":212072,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":816421,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221885,"text":"70221885 - 2021 - Multivariate classification of the crude oil petroleum systems in southeast Texas, USA, using conventional and compositional data analysis of biomarkers","interactions":[],"lastModifiedDate":"2021-07-13T18:57:42.166406","indexId":"70221885","displayToPublicDate":"2021-06-01T13:53:23","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Multivariate classification of the crude oil petroleum systems in southeast Texas, USA, using conventional and compositional data analysis of biomarkers","docAbstract":"

Chemically, petroleum is an extraordinarily complex mixture of different types of hydrocarbons that are now possible to isolate and identify because of advances in geochemistry. Here, we use biomarkers and carbon isotopes to establish genetic differences and similarities among oil samples. Conventional approaches for evaluating biomarker and carbon isotope relative abundances include statistical techniques such as principal component and cluster analysis. Considering that proportions of the different hydrocarbon molecules are relative parts of a laboratory sample, the data are compositional in nature, thus requiring the use of log-ratio approaches for adequate mathematical modeling. We apply both traditional and compositional modeling approaches to crude oil samples from an onshore area of about 50,000 square miles in southeast Texas. The data comprise 177 crude oil samples from producing oil fields that include key biomarkers, elemental, and isotopic values commonly used in source rock correlation studies. Our results indicate that compositional modeling has higher discriminating power and lower uncertainty than the traditional approach, allowing the identification of up to 16 clusters. Each cluster represents one oil family from a source rock organofacies ranging from Carboniferous to Paleogene. The families provide new insights into important petroleum systems in the Texas onshore region of the Gulf of Mexico sedimentary basin.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Advances in compositional data analysis—Festschrift in honor of Vera-Pawlowsky-Glahn","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-71175-7_16","usgsCitation":"Olea, R., Martin-Fernandez, J.A., and Craddock, W.H., 2021, Multivariate classification of the crude oil petroleum systems in southeast Texas, USA, using conventional and compositional data analysis of biomarkers, chap. of Advances in compositional data analysis—Festschrift in honor of Vera-Pawlowsky-Glahn, p. 303-307, https://doi.org/10.1007/978-3-030-71175-7_16.","productDescription":"5 p.","startPage":"303","endPage":"307","ipdsId":"IP-112995","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":387163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.119140625,\n 25.97779895546436\n ],\n [\n -96.767578125,\n 27.68352808378776\n ],\n [\n -95.11962890625,\n 28.497660832963472\n ],\n [\n -93.8232421875,\n 29.49698759653577\n ],\n [\n -93.97705078125,\n 30.20211367909724\n ],\n [\n -95.55908203125,\n 30.240086360983426\n ],\n [\n -97.3388671875,\n 28.9600886880068\n ],\n [\n -98.23974609375,\n 27.586197857692664\n ],\n [\n -97.91015624999999,\n 26.13571361317392\n ],\n [\n -97.119140625,\n 25.97779895546436\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-06-02","publicationStatus":"PW","contributors":{"editors":[{"text":"Fitzmoser, Peter","contributorId":261055,"corporation":false,"usgs":false,"family":"Fitzmoser","given":"Peter","email":"","affiliations":[],"preferred":false,"id":819247,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Hron, Karel","contributorId":261056,"corporation":false,"usgs":false,"family":"Hron","given":"Karel","email":"","affiliations":[],"preferred":false,"id":819248,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Martin-Fernandez, Josep Antoni","contributorId":208528,"corporation":false,"usgs":false,"family":"Martin-Fernandez","given":" Josep Antoni","affiliations":[],"preferred":false,"id":819249,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Palarea-Albaladejo, Javier","contributorId":120518,"corporation":false,"usgs":true,"family":"Palarea-Albaladejo","given":"Javier","email":"","affiliations":[],"preferred":false,"id":819250,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":224285,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin-Fernandez, J. A 0000-0003-2366-1592","orcid":"https://orcid.org/0000-0003-2366-1592","contributorId":260957,"corporation":false,"usgs":false,"family":"Martin-Fernandez","given":"J.","email":"","middleInitial":"A","affiliations":[{"id":28183,"text":"University of Girona","active":true,"usgs":false}],"preferred":false,"id":819214,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Craddock, William H. 0000-0002-4181-4735 wcraddock@usgs.gov","orcid":"https://orcid.org/0000-0002-4181-4735","contributorId":3411,"corporation":false,"usgs":true,"family":"Craddock","given":"William","email":"wcraddock@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819215,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221088,"text":"ds1138 - 2021 - Distribution and demography of coastal cactus wrens (Campylorhynchus brunneicapillus) in southern San Diego County, California—2020 data summary","interactions":[],"lastModifiedDate":"2021-06-02T11:41:30.374625","indexId":"ds1138","displayToPublicDate":"2021-06-01T13:17:25","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1138","displayTitle":"Distribution and Demography of Coastal Cactus Wrens (Campylorhynchus brunneicapillus) in Southern San Diego County, California—2020 Data Summary","title":"Distribution and demography of coastal cactus wrens (Campylorhynchus brunneicapillus) in southern San Diego County, California—2020 data summary","docAbstract":"

Executive Summary

Surveys for coastal Cactus Wren (Campylorhynchus brunneicapillus) were done in 378 established plots in southern San Diego County in 2020, encompassing three genetic clusters (Otay, Lake Jennings, and Sweetwater/Encanto genetic clusters). Two surveys were completed at each plot between March 1 and July 31. Cactus Wrens were detected in 131 plots (35 percent of plots). This is a slight increase over the proportion of occupied plots in 2019. One hundred and nine Cactus Wren territories were detected across all survey plots in 2020, an increase from 83 in 2019. At least 85 percent of Cactus Wren territories were occupied by pairs, and 62 fledglings were observed in 2020.

There were 89 color-banded Cactus Wrens observed in 2020, 84 of which we could identify to individual. Adults of known age ranged from 1 to at least 6 years old. Adult Cactus Wrens moved on average 0.2 kilometers (km; maximum 3.8 km) from their 2019 territories to their 2020 territories. Cactus Wrens that fledged in 2019 moved on average 1.2 km (maximum 9.9 km) to their 2020 territories. No known-identity Cactus Wrens moved between genetic clusters from 2019 to 2020.

Vegetation at Cactus Wren plots typically was dominated by coastal sage scrub shrubs such as California sagebrush (Artemisia californica), lemonadeberry (Rhus integrifolia), California buckwheat (Eriogonum fasciculatum), and broom baccharis (Baccharis sarothroides). Very little dead or unhealthy cactus was observed within Cactus Wren survey plots. Thirty-eight percent of plots had at least 25 percent of the cactus crowded or overtopped by vines and shrubs. Non-native annual cover was greater than 25 percent at 35 percent of plots.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1138","usgsCitation":"Lynn, S., and Kus, B.E., 2021, Distribution and demography of coastal cactus wrens (Campylorhynchus brunneicapillus) in southern San Diego County, California—2020 data summary: U.S. Geological Survey Data Series 1138, 12 p., https://doi.org/10.3133/ds1138.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"12","onlineOnly":"Y","ipdsId":"IP-126296","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":386066,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76H4FK5","linkHelpText":"Surveys and Monitoring of Coastal Cactus Wren in Southern San Diego County"},{"id":386065,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ds/1138/images"},{"id":386064,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/ds/1138/ds1138.xml"},{"id":386063,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1138/ds1138.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1138/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Southern San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.32299804687499,\n 32.48196313217176\n ],\n [\n -116.7132568359375,\n 32.48196313217176\n ],\n [\n -116.7132568359375,\n 32.8334428466495\n ],\n [\n -117.32299804687499,\n 32.8334428466495\n ],\n [\n -117.32299804687499,\n 32.48196313217176\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

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

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-06-01","noUsgsAuthors":false,"publicationDate":"2021-06-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Lynn, Suellen 0000-0003-1543-0209 suellen_lynn@usgs.gov","orcid":"https://orcid.org/0000-0003-1543-0209","contributorId":3843,"corporation":false,"usgs":true,"family":"Lynn","given":"Suellen","email":"suellen_lynn@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":816720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":816721,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219911,"text":"70219911 - 2021 - Fisheries research and monitoring activities of the Lake Erie Biological Station, 2020","interactions":[],"lastModifiedDate":"2021-09-17T16:44:29.465926","indexId":"70219911","displayToPublicDate":"2021-06-01T11:44:02","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":8434,"text":"Lake Erie Biological Station Annual Report","active":true,"publicationSubtype":{"id":4}},"title":"Fisheries research and monitoring activities of the Lake Erie Biological Station, 2020","docAbstract":"This report presents biomass-based summaries of fish communities in the West Basin of Lake Erie derived from USGS bottom trawl surveys conducted from 2013 to 2020 during June and September. The survey design provided temporal and spatial coverage that did not exist in the interagency trawl database, and thus complemented the August ODNR-OMNRF effort to reinforce stock assessments with more robust data. Analyses herein evaluated trends in: total biomass, abundance of dominant predator and forage species, non-native species composition, biodiversity and community structure. Data from this effort can be explored interactively online\n(https://lebs.shinyapps.io/western-basin/), and are accessible for download (https://www.sciencebase.gov/catalog/item/6013031fd34e162231fed756, Keretz et al. 2021). Annual survey data are added to these sources as the data become available.","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Keretz, K.R., Kocovsky, P., Kraus, R., Roberts, J., and Schmitt, J., 2021, Fisheries research and monitoring activities of the Lake Erie Biological Station, 2020: Lake Erie Biological Station Annual Report, 12 p.","productDescription":"12 p.","ipdsId":"IP-126685","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":389405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389403,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.sealamprey.org/pubs/lake_committees/common_docs/2020%20LEC%20report_Final.pdf"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": 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pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814381,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814384,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220465,"text":"70220465 - 2021 - Status and trends of pelagic and benthic prey fish populations in Lake Michigan, 2020","interactions":[],"lastModifiedDate":"2021-09-17T16:15:30.362165","indexId":"70220465","displayToPublicDate":"2021-06-01T11:13:08","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":9350,"text":"Lake Michigan Forage Report","active":true,"publicationSubtype":{"id":3}},"title":"Status and trends of pelagic and benthic prey fish populations in Lake Michigan, 2020","docAbstract":"Lakewide acoustic (AC) and bottom trawl (BT) surveys are conducted annually to generate indices of pelagic and benthic prey fish densities in Lake Michigan. The BT survey had been conducted each fall from 1973 through 2019 using 12-m trawls at depths ranging from 9 to 110 m and included 70 fixed locations distributed across seven transects; this survey estimates densities of seven prey fish species (i.e., alewife, bloater, rainbow smelt, deepwater sculpin, slimy sculpin, round goby, ninespine stickleback) as well as for age-0 yellow perch and large burbot. The AC survey, which serves to estimate densities of three prey fish species (i.e., alewife, bloater, and rainbow smelt), had been conducted each late summer/early fall from 2004-2019. The data generated from these surveys are used to estimate various population parameters that are, in turn, used by state and tribal agencies in managing Lake Michigan fish stocks. \n\nThe 2020 COVID-19 pandemic severely limited the Lake Michigan pelagic and benthic prey fish surveys. While the AC survey was not conducted, 32 tows across three of seven standard BT transects (Saugatuck, Waukegan and Port Washington) were completed during an abbreviated survey. Total prey fish biomass density from the abbreviated BT survey was 1.91 kg/ha, continuing a recent trend of historically low estimates below the long-term (i.e., 1973-2020) average of 34.94 kg/ha. Mean biomass of yearling and older (YAO) alewives in 2020 was 0.025 ± 0.017 kg/ha, tied for the lowest ever recorded on the BT survey. No age-0 alewife were captured in the bottom trawl and of the limited number (n=16) of alewife collected, none were older than age four. Bloater (1.39 kg/ha) and deepwater sculpin (0.47 kg/ha) accounted for greatest proportion of biomass in the BT survey, while biomass density of slimy sculpin, round goby and rainbow smelt were all ≤ 0.01 kg/ha. While caution must be taken when interpreting the results of the abbreviated BT survey, the estimates suggest that prey fish densities remain well below historical values.","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Tingley, R.W., Bunnell, D.B., Warner, D., Madenjian, C.P., and Dieter, P., 2021, Status and trends of pelagic and benthic prey fish populations in Lake Michigan, 2020: Lake Michigan Forage Report, 19 p.","productDescription":"19 p.","ipdsId":"IP-127217","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":389398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389397,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.glfc.org/pubs/lake_committees/common_docs/LM_Forage_Report_2021_For_Dissemination.pdf"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":815602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dieter, Patricia 0000-0003-1686-2679","orcid":"https://orcid.org/0000-0003-1686-2679","contributorId":217345,"corporation":false,"usgs":true,"family":"Dieter","given":"Patricia","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":815603,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229039,"text":"70229039 - 2021 - Coffee plantations, hurricanes and avian resiliency: Insights from occupancy, and local colonization and extinction rates in Puerto Rico","interactions":[],"lastModifiedDate":"2022-03-01T12:17:19.293477","indexId":"70229039","displayToPublicDate":"2021-06-01T10:31:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Coffee plantations, hurricanes and avian resiliency: Insights from occupancy, and local colonization and extinction rates in Puerto Rico","docAbstract":"Insights on impacts and resiliency of avian species with respect to hurricanes in the Caribbean have largely focused on responses measured in protected habitats. We assessed avian responses in non-protected habitat, specifically shade-restored coffee plantations, because their structural complexity retains many attributes of secondary forests, and may contribute to landscape scale species resiliency. We tallied species richness, and estimated occupancy probability of 12 resident avian species, after adjusting for imperfect detection, to assess the impact of hurricane Maria (20 September 2017) in the context of two stages of shade-restored coffee plantations. For 5 of those species, we also estimated local colonization and extinction probabilities to assess their prospect of rebounding (resiliency). We used survey data collected March-June 2015-2017 (pre-hurricane) and 2018 (post-hurricane) in 58 coffee farms and satellite imagery to assess vegetation structure. Restored farms were grouped into two categories based on time-since-restoration: newly-restored and fully-restored. We predicted that mean percent forest cover in fully-restored farms (~30-40%) would revert to levels in newly-restored farms (<15%), with concomitant changes in occupancy by avian species. As predicted, mean percent forest cover (16.17 4.27%) in fully-restored farms post-hurricane reverted to pre-hurricane levels in newly-restored farms (15.00 5.61%). The loss represented 30-38% relative to the pre-hurricane cover levels. Detections of focal species dropped an average of 41% post-hurricane, with associated reductions in occupancy for 9/11 species. Occupancy of the Puerto Rican Bullfinch and Puerto Rican Spindalis reverted to levels detected in newly-restored plantations prior to the hurricane as predicted. Prospects of rebounding were more likely for species with invariant or increases in colonization probability (e.g., Yellow-faced Grassquit, Northern Mockingbird, Puerto Rican Spindalis). Rebounding for frugivores like the Puerto Rican Bullfinch would be protracted given that colonization rates dropped from 0.56 0.12 (pre-hurricane) to 0.04 0.2 (post-hurricane), regardless of restoration stage. Our work showed that the avian community associated with restored coffee farms exhibited as high degree of ecological resistance as the similarity in species composition before and after the hurricane was 81%, and all 12 focal species continued to occupy farms under both restoration stages. The prospect of the focal species to rebound (resiliency) was specific-specific, and in some cases, mediated by their affinity to a particular farm restoration stage. The strength of hurricanes is projected to intensify with global warming. Pockets of undamaged or partially damaged shade-grown or fully-restored coffee plantations may contribute to species resiliency by increasing landscape level habitat redundancy, and facilitate habitat shifts to secure food resources or harbor source populations to colonize recovering, hurricane-damaged habitat tracts.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2021.e01579","usgsCitation":"Irizarry, A.D., Collazo, J.A., Vandermeer, J., and Perfecto, I., 2021, Coffee plantations, hurricanes and avian resiliency: Insights from occupancy, and local colonization and extinction rates in Puerto Rico: Global Ecology and Conservation, v. 27, e01579, 12 p., https://doi.org/10.1016/j.gecco.2021.e01579.","productDescription":"e01579, 12 p.","ipdsId":"IP-120553","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":452043,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2021.e01579","text":"Publisher Index Page"},{"id":396563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Puerto Rico","otherGeospatial":"Greater Antilles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.08251953125,\n 17.955219304287816\n ],\n [\n -66.42059326171874,\n 17.955219304287816\n ],\n [\n -66.42059326171874,\n 18.424896202842426\n ],\n [\n -67.08251953125,\n 18.424896202842426\n ],\n [\n -67.08251953125,\n 17.955219304287816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Irizarry, Amarilys D.","contributorId":205434,"corporation":false,"usgs":false,"family":"Irizarry","given":"Amarilys","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":836342,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandermeer, J.","contributorId":286880,"corporation":false,"usgs":false,"family":"Vandermeer","given":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":836344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perfecto, I.","contributorId":286882,"corporation":false,"usgs":false,"family":"Perfecto","given":"I.","email":"","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":836345,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226922,"text":"70226922 - 2021 - A multi-tracer and well-bore flow profile approach to determine occurrence, movement, and sources of perchlorate in groundwater","interactions":[],"lastModifiedDate":"2021-12-21T14:59:34.058138","indexId":"70226922","displayToPublicDate":"2021-06-01T08:43:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"A multi-tracer and well-bore flow profile approach to determine occurrence, movement, and sources of perchlorate in groundwater","docAbstract":"The purpose of this study is to determine the occurrence, movement and sources of perchlorate in groundwater using a comprehensive set of environmental tracers coupled with discreet borehole data. Potential sources of perchlorate to groundwater at the study site have been attributed to waste disposal and industrial activities as well as to past agricultural operations. Perchlorate concentrations in samples ranged from <1 to 40 g/l, with a median of 6.1 g/l. Concentrations were relativity consistent with depth except at one site where dilution may be occurring due to the infiltration of surface water from Pyrite Creek. Well-bore flow profiles indicated that perchlorate redistribution was occurring via intra-well bore flow at one site where up to 14,000 mg/year of perchlorate could be moving from the shallower to the deeper zones of the alluvial aquifer. Natural attenuation processes of perchlorate do not appear to be widespread in groundwater but does occur in portions of the aquifer adjacent to the Santa Ana River, likely limiting the mobility of perchlorate from the southernmost extent of the mapped plume to areas further down-gradient. Age dating tracers indicate that perchlorate originating from the waste disposal ponds has largely moved through the zones of the aquifer sampled. Age distributions, noble gas temperature, delta neon values and stable isotopes of water indicate that a substantial fraction of perchlorate in groundwater may have been mobilized from the unsaturated zone and/or is from the infiltration of storm water runoff originating from Pyrite Canyon.","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2021.104959","usgsCitation":"Wright, M., Izbicki, J.A., and Jurgens, B.C., 2021, A multi-tracer and well-bore flow profile approach to determine occurrence, movement, and sources of perchlorate in groundwater: Applied Geochemistry, v. 129, p. 1-18, https://doi.org/10.1016/j.apgeochem.2021.104959.","productDescription":"104959, 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-116219","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":452054,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2021.104959","text":"Publisher Index Page"},{"id":393189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Jurupa Valley","otherGeospatial":"Jurupa Mountains, Mira Loma Hills, Pedley Hills, San Sevaine Channel, Santa Ana River, Stringfellow Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.66082763671875,\n 33.945638452963024\n ],\n [\n -117.14241027832031,\n 33.945638452963024\n ],\n [\n -117.14241027832031,\n 34.34343606848294\n ],\n [\n -117.66082763671875,\n 34.34343606848294\n ],\n [\n -117.66082763671875,\n 33.945638452963024\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"129","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Wang, Z. Zimeng","contributorId":270243,"corporation":false,"usgs":false,"family":"Wang","given":"Z.","email":"","middleInitial":"Zimeng","affiliations":[],"preferred":false,"id":828813,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Wright, Michael 0000-0003-0653-6466 mtwright@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-6466","contributorId":151031,"corporation":false,"usgs":true,"family":"Wright","given":"Michael","email":"mtwright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Izbicki, John A. 0000-0003-0816-4408 jaizbick@usgs.gov","orcid":"https://orcid.org/0000-0003-0816-4408","contributorId":152474,"corporation":false,"usgs":true,"family":"Izbicki","given":"John","email":"jaizbick@usgs.gov","middleInitial":"A.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828800,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127842,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","email":"bjurgens@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828801,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221599,"text":"70221599 - 2021 - Watersheds and drainage networks","interactions":[],"lastModifiedDate":"2021-06-25T12:49:28.165348","indexId":"70221599","displayToPublicDate":"2021-06-01T07:47:23","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Watersheds and drainage networks","docAbstract":"

This topic is an overview of basic concepts about how the distribution of water on the Earth, with specific regard to watersheds, stream and river networks, and waterbodies are represented by geographic data. The flowing and non-flowing bodies of water on the earth’s surface vary in extent largely due to seasonal and annual changes in climate and precipitation. Consequently, modeling the detailed representation of surface water using geographic information is important. The area of land that collects surface runoff and other flowing water and drains to a common outlet location defines a watershed. Terrain and surface features can be naturally divided into watersheds of various sizes. Drainage networks are important data structures for modeling the distribution and movement of surface water over the terrain.  Numerous tools and methods exist to extract drainage networks and watersheds from digital elevation models (DEMs). The cartographic representations of surface water are referred to as hydrographic features and consist of a snapshot at a specific time. Hydrographic features can be assigned general feature types, such as lake, pond, river, and ocean. Hydrographic features can be stored, maintained, and distributed for use through vector geospatial databases, such as the National Hydrography Dataset (NHD) for the United States.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The geographic information science & technology body of knowledge","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University Consortium for Geographic Information Science","doi":"10.22224/gistbok/2021.2.1","usgsCitation":"Stanislawski, L., and Shavers, E.J., 2021, Watersheds and drainage networks, chap. of The geographic information science & technology body of knowledge, https://doi.org/10.22224/gistbok/2021.2.1.","ipdsId":"IP-125926","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":452062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.22224/gistbok/2021.2.1","text":"Publisher Index Page"},{"id":386732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":818251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":818252,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70222359,"text":"70222359 - 2021 - Quarterly wildlife mortality report April 2021","interactions":[],"lastModifiedDate":"2023-10-13T13:31:04.206897","indexId":"70222359","displayToPublicDate":"2021-05-31T11:09:23","publicationYear":"2021","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":9359,"text":"Wildlife Disease Association Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"Quarterly wildlife mortality report April 2021","docAbstract":"The USGS National Wildlife Health Center (NWHC) Quarterly Mortality Report provides brief summaries of epizootic mortality and morbidity events by quarter. The write-ups, highlighting epizootic events and other wildlife disease topics of interest, are published in the Wildlife Disease Association quarterly newsletter. A link is provided in this WDA newsletter to the Wildlife Health Information Sharing Partnership event reporting system (WHISPers) so readers can view associated data.","language":"English","publisher":"Wildlife Disease Association","usgsCitation":"Richards, B.J., Russell, R., and Ballmann, A., 2021, Quarterly wildlife mortality report April 2021: Wildlife Disease Association Newsletter, p. 9-12.","productDescription":"4 p.","startPage":"9","endPage":"12","ipdsId":"IP-128295","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":389735,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.wildlifedisease.org/PersonifyEbusiness/Resources/Publications/Newsletter/Archive"},{"id":389741,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Richards, Bryan J. 0000-0001-9955-2523","orcid":"https://orcid.org/0000-0001-9955-2523","contributorId":219535,"corporation":false,"usgs":true,"family":"Richards","given":"Bryan","email":"","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":819751,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":819752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ballmann, Anne 0000-0002-0380-056X aballmann@usgs.gov","orcid":"https://orcid.org/0000-0002-0380-056X","contributorId":140319,"corporation":false,"usgs":true,"family":"Ballmann","given":"Anne","email":"aballmann@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":819753,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70219918,"text":"70219918 - 2021 - 3-D Modeling of the Duluth Complex from geophysical data","interactions":[],"lastModifiedDate":"2021-09-17T15:49:58.370459","indexId":"70219918","displayToPublicDate":"2021-05-31T10:43:45","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"3-D Modeling of the Duluth Complex from geophysical data","docAbstract":"The Mesoproterozoic Duluth Complex in northeastern Minnesota is one of the major plutonic components of the Midcontinent Rift System and hosts a variety of copper-nickel sulfide and platinum-group element deposits. The Duluth Complex is composed of a series of individual mafic and felsic intrusions emplaced 1110-1098 Ma within Paleoproterozoic sedimentary rocks of the Animikie basin and volcanic flows of the Midcontinent Rift. Prior work has included 2-D modeling and qualitative geologic interpretations of gravity and magnetic data (e.g., Chandler, 1990; Chandler and Ferderer, 1989), much of which is still preliminary (V. Chandler, written commun., 2020). Three-dimensional modeling has been limited, with only one 3-D model created using Bouguer gravity data constrained by seismic-reflection interpretations as part of a PhD thesis (Allen, 1994). Given the complex geology of the area, 3-D modeling is useful for providing a complete picture of the variable densities, susceptibilities, and electrical resistivities throughout the Duluth Complex and associated volcanic rocks as well as their depth extent beneath sedimentary cover. Models of these geophysical properties at depth enable more accurate geologic mapping in the subsurface which can lead to an improved understanding of the formation history of the Duluth Complex. \nIn this study, we use aeromagnetic data acquired between 1979-1991 (Chandler, 2007), Bouguer gravity data collected since 1950 (Chandler and Lively, 2019), and magnetotelluric data collected in 2019 to create new 2-D and 3-D geophysical models of the Duluth Complex constrained by seismic reflection, geologic, and rock property data. An inversion of the Bouguer gravity data for thickness of the Duluth Complex using constant densities of 3110 kg/m3 and 2670 kg/m3 for the Duluth Complex and surrounding crustal rocks, respectively, results in thicknesses ranging from ~3-28 km for the Duluth Complex and related intrusions and volcanic rocks (Figure 1A). A 3-D model of the magnetotelluric data reveals low resistivity anomalies at ~5-10 km depth below the northern margin of the Duluth Complex and below the Greenwood Lake intrusion (Figure 1B). We expect to encounter low resistivities at depth associated with the Paleoproterozoic Animikie basin, which makes up the floor of the Duluth Complex, and therefore interpret these anomalies as either the base of the complex or as fragments of Animikie sediments interfingered with igneous intrusive rocks. Finally, 3-D voxel models of density and susceptibility illuminate the subsurface distribution of rock properties below the Duluth Complex which, in combination with resistivity and thickness models, can be used to create a 3-D geologic map of this area.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"67th Institute on Lake Superior Geology Proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Institute on Lake Superior Geology","usgsCitation":"Peterson, D.E., Bedrosian, P.A., and Finn, C., 2021, 3-D Modeling of the Duluth Complex from geophysical data, in 67th Institute on Lake Superior Geology Proceedings, p. 52-53.","productDescription":"2 p.","startPage":"52","endPage":"53","ipdsId":"IP-128596","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":389395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389394,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.lakesuperiorgeology.org/Virtual2021/index.html"}],"country":"United States","state":"Minnesota","otherGeospatial":"Duluth Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.31787109374999,\n 47.29413372501023\n ],\n [\n -88.48388671874999,\n 47.29413372501023\n ],\n [\n -88.48388671874999,\n 48.46563710044979\n ],\n [\n -93.31787109374999,\n 48.46563710044979\n ],\n [\n -93.31787109374999,\n 47.29413372501023\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, Dana E. 0000-0002-1941-265X","orcid":"https://orcid.org/0000-0002-1941-265X","contributorId":225536,"corporation":false,"usgs":true,"family":"Peterson","given":"Dana","email":"","middleInitial":"E.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":814394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":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":814395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finn, Carol A. 0000-0002-6178-0405","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":205010,"corporation":false,"usgs":true,"family":"Finn","given":"Carol A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":814396,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223180,"text":"70223180 - 2021 - Alaska landbird montoring survey: Alaska regional protocol framework for monitoring landbirds using point counts","interactions":[],"lastModifiedDate":"2021-08-17T14:13:12.185743","indexId":"70223180","displayToPublicDate":"2021-05-31T09:03:14","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5856,"text":"Regional Protocol Framework","active":true,"publicationSubtype":{"id":1}},"title":"Alaska landbird montoring survey: Alaska regional protocol framework for monitoring landbirds using point counts","docAbstract":"

Alaska provides habitat for 143 species of landbirds that occur regularly in the state, about half of which breed predominantly north of the border between the contiguous United States and Canada. The road-based North American Breeding Bird Survey (BBS) provides some data on population trends in Alaska, but most northern populations are inadequately monitored by this program because of a paucity of roads. To remedy this deficiency, Boreal Partners in Flight developed the Alaska Landbird Monitoring Survey (ALMS) to monitor breeding populations of landbirds in off-road areas of Alaska in tandem with data collected from the roadside BBS. The primary objective of ALMS is to monitor long-term population trends of landbirds and other species that can be monitored by diurnal point counts during the breeding season, including many shorebirds and aquatic birds. A secondary objective is to estimate landbird densities by habitat, which can be used to model avian distribution and abundance across Alaska. ALMS is a collaborative program whereby agencies and other entities conduct standardized surveys of breeding birds and their habitats on the lands they manage and then contribute the data to the U.S. Geological Survey Alaska Science Center for storage and analysis.

The short-term implementation goal of ALMS is to monitor birds systematically within each of 100 randomly selected survey blocks, thereby matching the number of BBS surveys conducted in each of Alaska's five Bird Conservation Regions (BCRs). Each block has a mini-grid of 15−25 points that are surveyed biennially, with half of the blocks surveyed in alternating years. Survey blocks are stratified by accessibility and cost-effectiveness. Refuges may opt to limit sites to those accessible by foot, vehicle, boat, or fixed-wing aircraft, as these can be surveyed more inexpensively and reliably over time. Observers survey each point within a survey block for birds using a 10-min point count once per summer on a biennial basis. They collect corresponding habitat data during the first visit and at subsequent 10-year intervals or whenever a disturbance (e.g., fire, wind) has caused a significant change. USGS analyzes ALMS data jointly with BBS data to test for differences between off-road and roadside areas and to increase power to detect statewide trends. Additional blocks can be surveyed in areas that are more difficult and expensive to access as resources become available in the future. Long-term monitoring enables detection of change in bird populations in relation to fire, disease and insect damage, resource development, climate-related change, and other landscape-level disturbances across Alaska. Results from ALMS can also help prioritize conservation and research towards species before they become endangered and require expensive recovery programs.

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Despite its successes, the U.S. Endangered Species Act (ESA) has proven challenging to implement due to funding limitations, workload backlog, and other problems. As threats to species survival intensify and as more species come under threat, the need for the ESA and similar conservation laws and policies in other countries to function efficiently has grown. Attempts by the U.S. Fish and Wildlife Service (USFWS) to streamline ESA decisions include multispecies recovery plans and habitat conservation plans. We address species status assessment (SSA), a USFWS process to inform ESA decisions from listing to recovery, within the context of multispecies and ecosystem planning. Although existing SSAs have a single-species focus, ecosystem-based research can efficiently inform multiple SSAs within a region and provide a foundation for transition to multispecies SSAs in the future. We considered at-risk grassland species and ecosystems within the southeastern United States, where a disproportionate number of rare and endemic species are associated with grasslands. To initiate our ecosystem-based approach, we used a combined literature-based and structured World Café workshop format to identify science needs for SSAs. Discussions concentrated on 5 categories of threats to grassland species and ecosystems, consistent with recommendations to make shared threats a focus of planning under the ESA: (1) habitat loss, fragmentation, and disruption of functional connectivity; (2) climate change; (3) altered disturbance regimes; (4) invasive species; and (5) localized impacts. For each threat, workshop participants identified science and information needs, including database availability, research priorities, and modeling and mapping needs. Grouping species by habitat and shared threats can make the SSA process and other planning processes for conservation of at-risk species worldwide more efficient and useful. We found a combination of literature review and structured discussion effective for identifying the scientific information and analysis needed to support the development of multiple SSAs.

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