{"pageNumber":"412","pageRowStart":"10275","pageSize":"25","recordCount":166003,"records":[{"id":70221717,"text":"70221717 - 2022 - Status and trends of the Lake Huron prey fish community, 1976-2019","interactions":[],"lastModifiedDate":"2022-04-20T16:44:39.860251","indexId":"70221717","displayToPublicDate":"2022-03-01T11:39:58","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Status and trends of the Lake Huron prey fish community, 1976-2019","docAbstract":"<p>The USGS Great Lakes Science Center has assessed annual changes in the offshore prey fish community of Lake Huron since 1973. Assessments are based on a bottom trawl survey conducted in October of each year and an acoustics-midwater trawl survey, which began in 2004 and is conducted in September-October. Both surveys were completed in their entirety in 2019. Prey fish biomass in Lake Huron in 2019 was dominated by two species, Bloater (<i>Coregonus hoyi</i>) and Rainbow Smelt (<i>Osmerus mordax</i>). In the main basin, prey fish biomass remained below levels observed prior to community-wide declines that began in the early to mid 1990s. Bloater was the most abundant prey fish species in the main basin, whereas Rainbow Smelt was the most abundant prey species in the North Channel and in Georgian Bay. Both surveys suggested that Bloater biomass is increasing in the main basin. Low biomass of invasive species like Alewife (<i>Alosa pseudoharengus</i>) and Rainbow Smelt is consistent with fish community objectives focused on restoration of native fish communities. Abundance of invasive Round Goby (Neogobius melanostomus) in 2019 was low relative to 2018. Biomass of the native Cisco (<i>Coregonus artedi</i>) continued to increase in the North Channel and Georgian Bay. Biomass of slimy sculpin (<i>Cottus cognatus</i>) and Deepwater Sculpin (<i>Myoxocephalus thompsoni</i>) in 2019 was down from 2018 but within the range observed over the past decade. Reduced lake productivity, predation by a recovering piscivore community, and shifts in food web dynamics that favor fish production in nearshore environments may prevent prey fish biomass in offshore areas from returning to levels observed prior to the early 1990’s. However, increased biomass of Bloater and Cisco suggests that lake conditions may favor recovery of native coregonids.</p>","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Hondorp, D.W., O’Brien, T.P., Esselman, P., and Roseman, E., 2022, Status and trends of the Lake Huron prey fish community, 1976-2019, 34 p.","productDescription":"34 p.","ipdsId":"IP-126930","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":399168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399163,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/"}],"country":"Canada, United States","state":"Lake Huron","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n           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dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":818508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Brien, Timothy P. 0000-0003-4502-5204 tiobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-4502-5204","contributorId":2662,"corporation":false,"usgs":true,"family":"O’Brien","given":"Timothy","email":"tiobrien@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":818509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":818510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":818511,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70229221,"text":"70229221 - 2022 - Monitoring for adaptive management of burned sagebrush-steppe rangelands: addressing variability and uncertainty on the 2015 Soda Megafire","interactions":[],"lastModifiedDate":"2022-03-03T17:03:57.559584","indexId":"70229221","displayToPublicDate":"2022-03-01T10:59:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring for adaptive management of burned sagebrush-steppe rangelands: addressing variability and uncertainty on the 2015 Soda Megafire","docAbstract":"<p>• Use of adaptive management supported by robust monitoring is vital to solving severe rangeland problems, such as the exotic annual grass invasion and fire cycle in sagebrush-steppe rangelands.</p><p>• Uncertainty in post-fire plant-community composition and plant response to treatments poses a challenge to land management and research but can be addressed with a high density of observations over short time frames.</p><p>• The monitoring for adaptive management of the 2015 Soda Megafire area (113,000 Ha) sampled up to 2000 observation plots in each of five post-fire years, and provided important insights on challenges, solutions, and insights that can be applied to monitoring future burned areas.</p><p><br data-mce-bogus=\"1\"></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rala.2021.12.002","usgsCitation":"Germino, M., Torma, P., Fisk, M., and Applestein, C., 2022, Monitoring for adaptive management of burned sagebrush-steppe rangelands: addressing variability and uncertainty on the 2015 Soda Megafire: Rangelands, v. 44, no. 1, p. 99-110, https://doi.org/10.1016/j.rala.2021.12.002.","productDescription":"12 p.","startPage":"99","endPage":"110","ipdsId":"IP-125468","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448647,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rala.2021.12.002","text":"Publisher Index Page"},{"id":396711,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Soda Megafire area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.564453125,\n              42.00848901572399\n            ],\n            [\n              -116.20239257812499,\n              42.00848901572399\n            ],\n            [\n              -116.20239257812499,\n              44.12702800650004\n            ],\n            [\n              -118.564453125,\n              44.12702800650004\n            ],\n            [\n              -118.564453125,\n              42.00848901572399\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"44","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":836967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torma, Peter","contributorId":287638,"corporation":false,"usgs":false,"family":"Torma","given":"Peter","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":836968,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisk, Matthew 0000-0002-2250-0116","orcid":"https://orcid.org/0000-0002-2250-0116","contributorId":218005,"corporation":false,"usgs":true,"family":"Fisk","given":"Matthew","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":836969,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":205748,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":836970,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70230867,"text":"70230867 - 2022 - Why are human-black bear conflicts increasing? Assessing the mechanisms driving conflicts in Durango, Colorado","interactions":[],"lastModifiedDate":"2022-10-04T16:16:34.229119","indexId":"70230867","displayToPublicDate":"2022-03-01T10:58:50","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10772,"text":"International Bear News","active":true,"publicationSubtype":{"id":30}},"title":"Why are human-black bear conflicts increasing? Assessing the mechanisms driving conflicts in Durango, Colorado","docAbstract":"<p>No abstract available.</p>","language":"English","publisher":"International Association for Bear Research and Management (IBA) and the IUCN/SSC Bear Specialist Group","usgsCitation":"Johnson, H.E., 2022, Why are human-black bear conflicts increasing? Assessing the mechanisms driving conflicts in Durango, Colorado: International Bear News, v. 31, no. 1, p. 22-24.","productDescription":"3 p.","startPage":"22","endPage":"24","ipdsId":"IP-136095","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":407861,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399732,"type":{"id":15,"text":"Index Page"},"url":"https://www.bearbiology.org/iba-publications/iba-newsletter/"}],"country":"United States","state":"Colorado","city":"Durango","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.95166015624999,\n              37.233061907375195\n            ],\n            [\n              -107.80059814453125,\n              37.233061907375195\n            ],\n            [\n              -107.80059814453125,\n              37.31447530414411\n            ],\n            [\n              -107.95166015624999,\n              37.31447530414411\n            ],\n            [\n              -107.95166015624999,\n              37.233061907375195\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"31","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":841509,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70262533,"text":"70262533 - 2022 - Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains","interactions":[],"lastModifiedDate":"2025-01-23T17:03:28.007798","indexId":"70262533","displayToPublicDate":"2022-03-01T10:58:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains","docAbstract":"<p>In the central Appalachians of Virginia and West Virginia, the Virginia northern flying squirrel (<i>Glaucomys sabrinus fuscus</i>; VNFS) is a subspecies of northern flying squirrel generally associated with red spruce (<i>Picea rubens</i>)-dominated forests at high elevations. Listed as endangered by the</p><p>U.S. Fish and Wildlife Service from 1985 to 2013, the VNFS currently is the subject of a 10-year post-delisting assessment. Still considered a state-listed species in Virginia and a species of greatest conservation need in West Virginia, the VNFS serves as a focal target for red spruce restoration activities in the High Allegheny Region (HAR) of the two states. Owing to the cryptic nature of VNFS and its low detection probability in live-capture surveys, managers in the region rely on habitat models to assess probable presence. Using long-term nest-box, live-trapping, and radio-telemetry data matched with updated high elevation forest-type coverage data for the region, we created a new VNFS resource selection function and spatial coverage map. Inputting red spruce cover, increasing elevation, and decreasing landform index (increasing site shelteredness) composed the best model explaining VNFS occurrence. The calculated amount of low-quality habitat was congruent with previous modeling efforts; however, inclusion of more VNFS occurrence records in the current effort indicated that previous efforts substantially underestimated the amount (&gt;400%) of extant high quality VNFS habitat. We estimate the HAR to contain approximately 197,952 ha with ≥0.50 predicted probability of occurrence of VNFS. In addition to potentially improving current and future VNFS live-capture surveys, with this model managers may better target forests for red spruce restoration to increase high&nbsp;elevation forest ecological integrity and to improve habitat patch connectedness for VNFS.</p>","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Ford, W., Diggins, C., De La Cruz, J., and Silvis, A., 2022, Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 9, p. 168-175.","productDescription":"8 p.","startPage":"168","endPage":"175","ipdsId":"IP-129565","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia, West Virginia","otherGeospatial":"High Allegheny Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -80.54094480023322,\n              36.94509861616578\n            ],\n            [\n              -79.74649202772338,\n              36.97475579082641\n            ],\n            [\n              -77.97606039422519,\n              38.33504579423493\n            ],\n            [\n              -77.55782664323384,\n              39.373334368570056\n            ],\n            [\n              -78.88106642468261,\n              39.62337985209078\n            ],\n            [\n              -80.73280550358555,\n              38.17478880030487\n            ],\n            [\n              -80.94924239314835,\n              37.25410993243544\n            ],\n            [\n              -80.54094480023322,\n              36.94509861616578\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ford, W. 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Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":924488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diggins, Corinne A.","contributorId":270602,"corporation":false,"usgs":false,"family":"Diggins","given":"Corinne A.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":924489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De La Cruz, Jesse L.","contributorId":349583,"corporation":false,"usgs":false,"family":"De La Cruz","given":"Jesse L.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":924490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":924491,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70231751,"text":"70231751 - 2022 - Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions","interactions":[],"lastModifiedDate":"2022-05-25T15:50:59.036384","indexId":"70231751","displayToPublicDate":"2022-03-01T10:50:05","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions","docAbstract":"Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized weight-of-evidence and logistic regression methods to estimate resource favorability, but these analyses relied upon some expert decisions. While expert decisions can add confidence to aspects of the modeling process by ensuring only reasonable models are employed, expert decisions also introduce human bias into assessments. This bias presents a source of error that may affect the performance of the models and resulting resource estimates. Our study aims to reduce expert input through robust data-driven analyses and better-suited data science techniques, with the goals of saving time, reducing bias, and improving predictive ability. We present six favorability maps for geothermal resources in the western United States created using two strategies applied to three modern machine learning algorithms (logistic regression, support-vector machines, and XGBoost). To provide a direct comparison to previous assessments, we use the same input data as the 2008 U.S. Geological Survey (USGS) conventional moderate- to high-temperature geothermal resource assessment. The six new favorability maps required far less expert decision-making, but broadly agree with the previous assessment. Despite the fact that the 2008 assessment results employed linear methods, the non-linear machine learning algorithms (i.e., support-vector machines and XGBoost) produced greater agreement with the previous assessment than the linear machine learning algorithm (i.e., logistic regression). It is not surprising that geothermal systems depend on non-linear combinations of features, and we postulate that the expert decisions during the 2008 assessment accounted for system non-linearities. Substantial challenges to applying machine learning algorithms to predict geothermal resource favorability include severe class imbalance (i.e., there are very few known geothermal systems compared to the large area considered), and while there are known geothermal systems (i.e., positive labels), all other sites have an unknown status (i.e., they are unlabeled), instead of receiving a negative label (i.e., the known/proven absence of a geothermal resource). We address both challenges through a custom undersampling strategy that can be used with any algorithm and then evaluated using F1 scores.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 47th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"47th Stanford Geothermal Workshop","conferenceDate":"Feb 7-9, 2022","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Mordensky, S.P., Lipor, J., DeAngelo, J., Burns, E., and Lindsey, C.R., 2022, Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions, <i>in</i> Proceedings, 47th workshop on geothermal reservoir engineering, Stanford, CA, Feb 7-9, 2022, 18 p.","productDescription":"18 p.","ipdsId":"IP-135047","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science 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,{"id":70256683,"text":"70256683 - 2022 - Interannual colony exchange among breeding Eastern Brown Pelicans","interactions":[],"lastModifiedDate":"2024-08-30T15:49:16.568962","indexId":"70256683","displayToPublicDate":"2022-03-01T10:40:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2284,"text":"Journal of Field Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Interannual colony exchange among breeding Eastern Brown Pelicans","docAbstract":"<p><span>Colonial seabirds often display high rates of interannual site fidelity to breeding locations, especially as adults. Species using more nearshore or coastal systems, however, may display comparatively less fidelity than highly pelagic species. We used long-term GPS tracking data to assess the frequency of interannual colony switching in Eastern Brown Pelicans (</span><i>Pelecanus occidentalis carolinensis</i><span>) in the South Atlantic Bight, a species about which adult site fidelity is poorly understood. By using recursive behavioral patterns to identify nest locations, we report 36% of reproductive attempts occurred at colonies that were different from the colony used in a prior year. Colony switching was especially prevalent in female pelicans, and distances between switched colonies ranged from 56 to 592 km. We suggest that long-term telemetry may be a reliable tool for assessing interannual breeding site fidelity, especially when compared to more traditional band resighting efforts.</span></p>","language":"English","publisher":"Association of Field Ornithologists","doi":"10.5751/JFO-00074-930105","usgsCitation":"Wilkinson, B.P., and Jodice, P.G., 2022, Interannual colony exchange among breeding Eastern Brown Pelicans: Journal of Field Ornithology, v. 93, no. 1, 5, 7 p., https://doi.org/10.5751/JFO-00074-930105.","productDescription":"5, 7 p.","ipdsId":"IP-132768","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":486872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/jfo-00074-930105","text":"Publisher Index Page"},{"id":433376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, South Carolina","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -81.13454549531326,\n              31.631315558373984\n            ],\n            [\n              -81.43899581910412,\n              31.496130645736173\n            ],\n            [\n              -81.50118238985,\n              31.063680609712733\n            ],\n            [\n              -81.19097593931667,\n              31.13972861864322\n            ],\n            [\n              -81.10767658389439,\n              31.37880444773341\n            ],\n            [\n              -81.13454549531326,\n              31.631315558373984\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -79.60338045917963,\n              33.00055249145659\n            ],\n            [\n              -79.87467500389812,\n              32.93930111981804\n            ],\n            [\n              -79.91864664343109,\n              32.75250095830276\n            ],\n            [\n              -79.7883919304639,\n              32.717250473356934\n            ],\n            [\n              -79.63407742975072,\n              32.81666930938573\n            ],\n            [\n              -79.54820887693526,\n              32.926718586458584\n            ],\n            [\n              -79.60338045917963,\n              33.00055249145659\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"93","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wilkinson, Bradley P.","contributorId":341576,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Bradley","email":"","middleInitial":"P.","affiliations":[{"id":81755,"text":"Department of Forestry and Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":908639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908640,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70229135,"text":"70229135 - 2022 - Pharmaceutical pollution of the world’s rivers","interactions":[],"lastModifiedDate":"2022-03-01T17:37:55.020596","indexId":"70229135","displayToPublicDate":"2022-03-01T10:21:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Pharmaceutical pollution of the world’s rivers","docAbstract":"<p>Environmental exposure to active pharmaceutical ingredients (APIs) can have negative effects on the health of ecosystems and humans. While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world’s rivers, representing the environmental influence of 471.4 million people across&nbsp;137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals.</p>","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2113947119","usgsCitation":"Wilkinson, J., Boxall, A.B., Kolpin, D., Leung, K.M., Lai, R.W., Galban-Malagon, C., Adell, A.D., Mondon, J., Metian, M., Marchant, R., Bouzas-Monroy, A., Cuni-Sanchez, A., Coors, A., P., C., Rojo, M., Gordon, C., Cara, M., Moermond, M., Luarte, T., Petrosyan, V., Perikhanyan, Y., Mahon, C.S., McGurk, C.J., Hofmann, T., Kormoker, T., Iniguez, V., Guzman-Otazo, J., Tavares, J.L., de Figueiredo, F.G., Razzolini, M.T., Dougnon, V., Gbaguidi, G., Traore, O., Blais, J., Kimpe, L.E., Wong, M., Wong, D., Ntchantcho, R., Pizarro, J., Ying, G., Chen, C., Paez, M., Martinez-Lara, J., Otamonga, J., Pote, J., Ifo, S.A., Wilson, P.J., Echeverria-Saenz, S., Udikovic-Kolic, N., Milakovic, M., Fatta-Kassinos, D., Ioannou-Ttofa, L., Belusova, V., Vymazal, J., Cardenas-Bustamante, M., Kassa, B.A., Garric, J., Chaumot, A., Gibba, P., Kunchulia, I., S., S., Lyberatos, G., Halldórsson, H., Melling, M., Shashidhar, T., Lamba, M., Nastiti, A., Supriatin, A., Pourang, N., Abedini, A., Abdullah, O., Gharbia, S.S., Pilla, F., Chefetz, B., Topaz, T., Yao, K.M., Aubakirova, B., Beisenova, R., Olaka, L., Mulu, J., Chatanga, P., Ntuli, V., Blama, N.T., Sherif, S., Aris, A.Z., Looi, L.J., Niang, M., Traore, S.T., Oldenkamp, R., Ogunbanwo, O., Ashfaq, M., Iqbal, M., Abdeen, Z., O’Dea, A., Morales-Saldana, J.M., Custodio, M., de la Cruz, H., Navarrete, I., Carvalho, F., Gogra, A.B., Koroma, B.M., Cerkvenik-Flajs, V., Gombac, M., Thwala, M., Choi, K., Kang, H., Celestino Ladu, J.L., Rico, A., Amerashinghe, P., Sobek, A., Horlitz, G., Zenker, A.K., King, A.C., Jiang, J., Kariuki, R., Tumbo, M., Tezel, U., Onay, T.T., Lejju, J.B., Vystavna, Y., Vergeles, Y., Heinzen, H., Perez-Parada, A., Sims, D.B., Figy, M., Good, D., and Teta, C., 2022, Pharmaceutical pollution of the world’s rivers: Proceedings of the National Academy of Sciences, v. 119, no. 8, p. 1-10, https://doi.org/10.1073/pnas.2113947119.","productDescription":"e2113947119, 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-129923","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":448650,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2113947119","text":"Publisher Index Page"},{"id":396606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Earth","volume":"119","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-02-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilkinson, John L.","contributorId":287305,"corporation":false,"usgs":false,"family":"Wilkinson","given":"John L.","affiliations":[{"id":35536,"text":"University of York","active":true,"usgs":false}],"preferred":false,"id":836613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boxall, Alistair B. 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,{"id":70229139,"text":"70229139 - 2022 - Global and regional sea level rise scenarios for the United States","interactions":[],"lastModifiedDate":"2025-09-10T13:11:55.613409","indexId":"70229139","displayToPublicDate":"2022-03-01T09:54:01","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":91,"text":"Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NOS.01","title":"Global and regional sea level rise scenarios for the United States","docAbstract":"<p>This report and accompanying datasets from the U.S. Sea Level Rise and Coastal Flood Hazard Scenarios and Tools Interagency Task Force provide 1) sea level rise scenarios to 2150 by decade that include estimates of vertical land motion and 2) a set of extreme water level probabilities for various heights along the U.S. coastline. These data are available at 1-degree grids along the U.S. coastline and downscaled specifically at NOAA tide-gauge locations. Estimates of flood exposure are assessed using contemporary U.S. coastal flood-severity thresholds for current conditions (e.g., sea levels and infrastructure footprint) and for the next 30 years (out to year 2050), assuming no additional risk reduction measures are enacted.</p><p>This effort builds upon the 2017 Task Force report (Sweet et al., 2017). In particular, the set of global mean sea level rise scenarios from that report are updated and downscaled with output directly from the United Nations Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6; IPCC, 2021a), through the efforts of the NASA Sea Level Change Team; updates include adjustments to the temporal trajectories and exceedance probabilities of these scenarios based upon end-of-century global temperatures. As with the 2017 report, these global mean sea level rise scenarios are regionalized for the U.S. coastline. In addition, methodology supporting the U.S. Department of Defense Regional Sea Level (DRSL) database (Hall et al., 2016) is adapted for the extreme water level dataset newly developed for this report.</p><p>This report will be a key technical input for the Fifth National Climate Assessment (NCA5). These data and information are being incorporated into current and planned agency tools and services, such as NOAA’s Sea Level Rise Viewer and Inundation Dashboard, NASA’s Sea Level Change Portal, and others. Although the intent of this report is not to provide authoritative guidance or design specifications for a specific project, it is intended to help inform Federal agencies, state and local governments, and stakeholders in coastal communities about current and future sea level rise to help contextualize its effects for decision-making purposes. </p>","language":"English","publisher":"National Oceanic and Atmospheric Administration","usgsCitation":"Sweet, W., Hamlington, B., Kopp, R.E., Weaver, C., Barnard, P.L., Bekaert, D., Brooks, W., Craghan, M., Dusek, G., Frederikse, T., Garner, G., Genz, A.S., Krasting, J.P., Larour, E., Marcy, D., Marra, J.J., Obeysekera, J., Osler, M., Pendleton, M., Roman, D., Schmied, L., Veatch, W., White, K., and Zuzak, C., 2022, Global and regional sea level rise scenarios for the United States: Technical Report NOS.01, xiv, 95 p.","productDescription":"xiv, 95 p.","ipdsId":"IP-135630","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science 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,{"id":70236380,"text":"70236380 - 2022 - The Coles Hill uranium deposit, Virginia, USA: Geology, geochemistry, geochronology, and genetic model","interactions":[],"lastModifiedDate":"2022-09-22T18:58:36.948329","indexId":"70236380","displayToPublicDate":"2022-03-01T09:48:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"The Coles Hill uranium deposit, Virginia, USA: Geology, geochemistry, geochronology, and genetic model","docAbstract":"<p>The Coles Hill uranium deposit with an indicated resource of about 130 million lbs. of U<sub>3</sub>O<sub>8</sub> is the largest unmined uranium deposit in the United States. The deposit is hosted in the Taconian (approximately 480 – 450 Ma) Martinsville igneous complex, which consists of the Ordovician Leatherwood Granite (granodiorite) and Silurian Rich Acres Formation (diorite). The host rock was metamorphosed to orthogneiss during the Alleghanian orogeny (approximately 325 – 260 Ma) when it also underwent dextral strike-slip movement along the Brookneal shear zone. During the Triassic, extensional tectonics led to the development of the Dan River Basin that lies east of Coles Hill. The mineralized zone is hosted in brittle structures in the footwall of the Triassic Chatham fault that forms the western edge of the basin. Within brittle fracture zones, uranium silicate and uranium-bearing fluorapatite with traces of brannerite form veins and breccia fill with chlorite, quartz, calcite, titanium oxide, pyrite, and calcite. Uranium silicates also coat and replace primary titanite, zircon, ilmenite, and sulfides. Sodium metasomatism preceded and accompanied uranium mineralization, pervasively altering host rock, and forming albite from primary feldspar, depositing limpid albite rims on feldspar, altering titanite to titanium oxide and calcite and forming riebeckite. Various geothermometers suggest temperatures of less than approximately <span>~200°C</span> during mineralization. In situ U-Pb analyses of titanite, Ti-oxide, and apatite, and Rb/Sr and U/Pb isotope-systematics of whole rock samples resolve the timing of geologic processes affecting Coles Hill. The host Leatherwood granite containing primary euhedral titanite is dated at 450 – 445 Ma, in agreement with previously obtained ages from zircon in the Martinsville igneous complex. A regional metamorphic event at 330 – 310 Ma formed anhedral titanite and some apatite, re-equilibrated whole rock Rb/Sr and U-Pb isotopes and is interpreted to have coincided with movement along the Brookneal shear zone. During shearing and metamorphism primary refractory uranium-bearing minerals including titanite, zircon, and uranothorite were recrystallized and uranium was liberated and incorporated locally into hematite, clay, and other fine-grained minerals. Uranium mineralization was accompanied by a metasomatic episode between 250 and 200 Ma that reset the Rb-Sr and U-Pb isotope systems, forming titanite and apatite that are associated and in places intimately intergrown with uranium silicate dating mineralization. This event coincides with rifting that formed the Dan River Basin and was a precursor to the breakup of Pangea. Based on the close spatial and temporal association of uranium with apatite, we conclude that uranium was carried as a uranyl-phosphate complex. The release of calcium during sodium metasomatic alteration of primary calcic feldspar and titanite in the host rock initiated successive reactions in which uranium and phosphate in mineralizing fluids combined with calcium to form U-enriched fluorapatite. Excess uranium was locally reduced by coupled redox reactions involving ferrous iron and sulfide minerals in the host rock, forming uranium silicates. Based on the deposit mineralogy, oxygen isotope geochemistry and trace element characteristics of uranium silicate and gangue minerals the primary mineralizing fluids likely included connate and/or meteoric water sourced from the local Dan River Basin. High heat flow related to Mesozoic rifting may have driven these (P-Na-F-rich) brines through local aquifers and into basin margin faults, transporting uranium from the basin or mobilizing uranium from previously formed U-minerals in the Brookneal shear zone, or from U-enriched older basement rock.</p>","language":"English","publisher":"Geoscience World","doi":"10.5382/econgeo.4874","usgsCitation":"Hall, S., Beard, J., Potter, C.J., Bodnar, R., Neymark, L.A., Paces, J.B., Johnson, C.A., Breit, G., Zielinski, R.A., and Aylor, G.J., 2022, The Coles Hill uranium deposit, Virginia, USA: Geology, geochemistry, geochronology, and genetic model: Economic Geology, v. 117, no. 2, p. 273-304, https://doi.org/10.5382/econgeo.4874.","productDescription":"32 p.","startPage":"273","endPage":"304","ipdsId":"IP-114752","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science 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,{"id":70237201,"text":"70237201 - 2022 - USGS invasive carp database management and integration support","interactions":[],"lastModifiedDate":"2022-10-04T14:55:20.0136","indexId":"70237201","displayToPublicDate":"2022-03-01T09:47:50","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"USGS invasive carp database management and integration support","docAbstract":"<p>Bigheaded carps (Bighead Carp and Silver Carp) tracking, monitoring, and contracted removal will continue throughout the Illinois River and Upper Mississippi River as part of an adaptive management effort to mitigate, control, and contain bigheaded carps. Other fish will also be tracked to maintain a holistic view of the transmitter distribution in the Upper Illinois River Waterway. To facilitate these actions, a need to compile and analyze invasive carp-related data from all agencies exists. Invasive carp-related data include all data sources that could inform the MRWG objectives or projects. These data, often in disparate formats, must be integrated into a common format that allows all agencies the opportunity to assess invasive carp monitoring, control, and removal efforts. Ensuring the interoperability of these datasets allows for their use in various analyzes and modeling efforts. Implementing an interoperable data management framework provides the mechanisms for end users to find and use integrated data. Integrating data for use in modeling and analysis furthers the partnership’s collective understanding of bigheaded carp life history, distribution, and movement and can be used to facilitate adaptive management actions (e.g., directing monitoring, sampling, and removal efforts, assessing invasive carp abundance to support modeling efforts, informing deployment of control actions, etc.). An effective data management strategy will streamline the data update process, providing all agencies with timely data and analyses in support of informed decision-making processes. </p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2021 Invasive carp interim summary report","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Invasive Carp Regional Coordinating Committee","usgsCitation":"Harrison, T.J., Brey, M.K., and Stone, J., 2022, USGS invasive carp database management and integration support, 4 p.","productDescription":"4 p.","startPage":"109","endPage":"112","ipdsId":"IP-139244","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":407860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":407845,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Illinois River, upper Mississippi River system","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.14306640625,\n              36.421282443649496\n            ],\n            [\n              -88.11035156249999,\n              40.6306300839918\n            ],\n            [\n              -87.73681640625,\n              41.95131994679697\n            ],\n            [\n              -88.681640625,\n              43.644025847699496\n            ],\n            [\n              -89.12109375,\n              46.118941506107056\n            ],\n            [\n              -91.0986328125,\n              46.51351558059737\n            ],\n            [\n              -93.1640625,\n              46.483264729155586\n            ],\n            [\n              -93.80126953124999,\n              47.754097979680026\n            ],\n            [\n              -94.833984375,\n              48.06339653776211\n            ],\n            [\n              -96.3720703125,\n              46.73986059969267\n            ],\n            [\n              -96.26220703125,\n              44.809121700077355\n            ],\n            [\n              -94.8779296875,\n              42.21224516288584\n            ],\n            [\n              -92.548828125,\n              39.57182223734374\n            ],\n            [\n              -91.1865234375,\n              37.37015718405753\n            ],\n            [\n              -90.10986328125,\n              36.98500309285596\n            ],\n            [\n              -90.28564453124999,\n              36.63316209558658\n            ],\n            [\n              -89.80224609374999,\n              36.12012758978146\n            ],\n            [\n              -89.14306640625,\n              36.421282443649496\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":853617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":853659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stone, Jayme 0000-0002-0512-3072","orcid":"https://orcid.org/0000-0002-0512-3072","contributorId":251712,"corporation":false,"usgs":false,"family":"Stone","given":"Jayme","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":853618,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70264283,"text":"70264283 - 2022 - Wind River subbasin restoration: Annual Report of U.S. Geological Survey activities January 2020 through December 2020","interactions":[],"lastModifiedDate":"2025-03-10T15:02:41.714948","indexId":"70264283","displayToPublicDate":"2022-03-01T09:41:36","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Wind River subbasin restoration: Annual Report of U.S. Geological Survey activities January 2020 through December 2020","docAbstract":"<p>We sampled juvenile wild Steelhead Trout Oncorhynchus mykiss in headwater streams of the Wind River, WA, to characterize population attributes and investigate life-history metrics, particularly migratory patterns, and early life-stage survival. We used passive integrated transponder (PIT) tagging and a series of instream PIT-tag interrogation systems (PTISs) to track juveniles and adults. The Wind River subbasin is considered a wild Steelhead refuge by Washington Department of Fish and Wildlife (WDFW). No hatchery Steelhead Trout have been released in the Wind River subbasin since 1997, and hatchery adults are estimated at less than one percent of spawners in most years. Over twenty years of Steelhead Trout status and trend monitoring and research in the subbasin is contributing to understanding of population response to numerous restoration actions in the subbasin, including removal of Hemlock Dam from Trout Creek in 2009, which had an outdated adult ladder and contributed to increased water temperatures reducing performance of juvenile Steelhead Trout. </p><p>Data from our study, and companion work by Washington Department of Fish and Wildlife, are contributing to Bonneville Power Administration’s (BPA) Research, Monitoring, and Evaluation (RM&amp;E) Program Strategy of Fish Population Status Monitoring (https://www.cbfish.org/ProgramStrategy.mvc/Index). Specifically, this work addresses the substrategies of 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and Uncertainties Research regarding differing life histories of a wild Steelhead Trout population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives. </p><p>During summer and fall 2020, we PIT-tagged 1,415 Steelhead parr (age-0 and age-1) in the Trout Creek and upper Wind River watersheds. Recaptures and detections of PIT-tagged Steelhead Trout parr happened through repeat headwater sampling, smolt trap operations, and instream PTISs and Columbia River PIT-tag detection infrastructure. Throughout the year, we maintained a series of six instream PTISs to monitor movement of tagged Steelhead Trout parr, smolts, and adults, providing data to population assessments, and life-cycle research and modeling. </p><p>Detection data from PIT-tagged adult Steelhead Trout at PTISs allow assessment of adult escapement to tributary watersheds within the Wind River subbasin. Adult Steelhead Trout detection efficiency estimates at our primary PTIS in Trout Creek have been greater than 92 percent during eight of the past nine years and have exceeded 90% at our primary PTIS in the Wind River the past three years. Adult escapement estimates to tributary watersheds are helping evaluate the efficacy of the 2009 removal of Hemlock Dam from rkm 2.0 of Trout Creek. The dam had potential negative effects on Steelhead Trout populations in Trout Creek due to hydrologic impairment, increased temperatures, and adult passage issues. Hemlock Dam was laddered for adult passage, but not to modern standards, which likely resulted in avoidance by some adult Steelhead Trout. </p><p>We continue to improve our PTISs in the Wind River subbasin. The improvements in siting and addition of grid power to the upper Wind River PTIS (site code WRU, rkm 27.6) during 2016 and 2017, and the addition of the Mine Reach site (site code MIN, rkm 36.0) have much improved PIT-tagged fish monitoring in the upper Wind River watershed. The paired PTIS design in the upper Wind River watershed (sites WRU and MIN), matches that in the Trout Creek watershed (sites TRC and TC4) and will allow comparisons of Steelhead Trout population metrics between the two watersheds as response to Hemlock Dam removal continues and future restoration efforts occur in Trout Creek. We installed two new PTISs during 2020. Both were installed downstream of our primary interrogation sites on Trout Creek and in the mainstem Wind River. We hope the two new sites will provide interrogations information that will allow us to better estimate detection efficiencies of downstream moving juvenile Steelhead Trout at the primary interrogation sites. The additional interrogations will be particularly important for those fish tagged with 9-mm PIT tags as less information from downstream locations is available from them. These sites and other status and trend data will allow evaluation of further planned restoration within the watershed, particularly that proposed for the headwaters of Trout Creek. </p><p>Detections at the instream PTISs have demonstrated trends of age-0 and age-1 parr emigration from natal areas during summer and fall, in addition to the expected movement of parr and smolts in spring. We have estimated that from 15 to 51% of parr tagged as age-0 fish in headwater areas make downstream migrations at age 1 for additional rearing during both spring and fall. We have estimated that up to 27% of Steelhead Trout parr, tagged as age-1 fish, make downstream migrations during fall. These findings raise questions about where parr most successfully rear and whether migrations are density or habitat quality driven. Broader monitoring programs would give a more comprehensive understanding of juvenile Steelhead Trout production and rearing and productivity contribution. </p><p>Repeat sampling at consistent locations in the subbasin has enabled assessment of juvenile Steelhead Trout growth patterns. Growth rates (relative change in weight) of age-0 PITtagged parr during summer were similar across the subbasin but lower for age-1 parr in the Trout Creek watershed than the upper Wind River watershed. Yearly relative growth for parr tagged at age-0 is similar across the subbasin. </p><p>Non-native Brook Trout Salvelinus confluentus are present in the subbasin, chiefly the Trout Creek watershed, and repeat sampling has allowed us to index their prevalence. Mean percent-of-catch that is Brook Trout, at four sample sites in Trout Creek, has declined from the period 1998 – 2003 to the period 2011 – 2020. Percent-of-catch and number of Brook Trout at the Trout Creek sites from 2011 through 2020 declined, though both metrics increased in 2018. </p><p>Evaluation and planning of restoration efforts are critical to ensure efficient use of resources. Assessing Steelhead Trout life history variation in the Wind River subbasin will inform research and tracking of many populations and help inform habitat restoration and water allocation planning. Movement of Steelhead Trout parr from natal areas to other rearing areas raises questions regarding juvenile abundance, origin, and habitat use within watersheds. Improved PTISs and focused PIT tagging of age-0 and age-1 Steelhead Trout parr allow investigation of such questions. Increasingly detailed viable salmonid population information, such as that provided by PIT-tagging and instream PTIS networks like those in the Wind River can provide data to inform fisheries policy and management and understand life-history strategies and limiting factors. Such efforts also provide assessment of long-term effects of habitat restoration actions such as the removal of Hemlock Dam on Trout Creek, and the proposed Stage-0 restoration effort for upper Trout Creek, which would be a large-scale effort to reset sections of stream within their floodplain, restoring connectivity and interaction with surrounding landscape.&nbsp;</p>","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Jezorek, I., 2022, Wind River subbasin restoration: Annual Report of U.S. Geological Survey activities January 2020 through December 2020, 71 p.","productDescription":"71 p.","ipdsId":"IP-137356","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":483143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":483119,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P190880","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Wind River subbasin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.1275,\n              46\n            ],\n            [\n              -122.1275,\n              45.75\n            ],\n            [\n              -121.8,\n              45.75\n            ],\n            [\n              -121.8,\n              46\n            ],\n            [\n              -122.1275,\n              46\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian 0000-0002-3842-3485","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":217811,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930257,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70237373,"text":"70237373 - 2022 - Modeling functional flows in California rivers","interactions":[],"lastModifiedDate":"2022-10-12T14:30:29.820543","indexId":"70237373","displayToPublicDate":"2022-03-01T09:17:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Modeling functional flows in California rivers","docAbstract":"Environmental flows are critical to the recovery and conservation of freshwater ecosystems worldwide. However, estimating\ndesired ranges of environmental flows across large, diverse landscapes is challenging. To advance protections of environmental flows for streams in California, USA, we developed a statewide modeling approach focused on functional components of the natural flow regime. Functional flow components in California streams—fall pulse flows, wet season peak flows and base flows, the spring flow recession, and dry season baseflows—support essential physical and ecological processes in riverine ecosystems. These functional flow components can be represented by functional flow metrics (FFMs) and quantified by their magnitude, timing, frequency, duration, and rate-of-change from daily streamflow records. After quantifying FFMs at reference-quality streamflow gages in California, we used machine-learning methods to estimate their natural range of values for all stream reaches in the state based on physical watershed characteristics and climatic factors. We found that the models performed well in predicting FFMs in streams across a diversity of landscape and climate contexts, according to several model performance criteria. Using the predicted FFM values, we established initial estimates of ecological flows that are expected to support critical functions and are broadly protective of ecosystem health. Modeling functional flows statewide offers a pathway for increasing the pace and scale of environmental flow protections in California and beyond.","language":"English","publisher":"Frontiers in Environmental Science","doi":"10.3389/fenvs.2022.787473","usgsCitation":"Grantham, T.E., Carlisle, D.M., Howard, J., Lane, B., Lusardi, R., Obester, A., Sandoval-Solis, S., Stanford, B., Stein, E.D., Taniguchi-Quan, K.T., Yarnell, S.M., and Zimmerman, J.K., 2022, Modeling functional flows in California rivers: Frontiers in Environmental Science, v. 10, 787473, 11 p., https://doi.org/10.3389/fenvs.2022.787473.","productDescription":"787473, 11 p.","ipdsId":"IP-132706","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448653,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2022.787473","text":"Publisher Index Page"},{"id":435941,"rank":0,"type":{"id":30,"text":"Data 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California--Davis","active":true,"usgs":false}],"preferred":false,"id":854318,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Zimmerman, Julie K. 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,{"id":70229699,"text":"70229699 - 2022 - Strength and memory of precipitation's control over streamflow across the conterminous United States","interactions":[],"lastModifiedDate":"2022-03-15T14:19:39.099966","indexId":"70229699","displayToPublicDate":"2022-03-01T09:13:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Strength and memory of precipitation's control over streamflow across the conterminous United States","docAbstract":"<p><span>How precipitation (P) is translated into streamflow (Q) and over what timescales (i.e., “memory”) is difficult to predict without calibration of site-specific models or using geochemical approaches, posing barriers to prediction in ungauged basins or advancement of general theories. Here, we used a data-driven approach to identify regional patterns and exogenous controls on P–Q interactions. We applied an information flow analysis, which quantifies uncertainty reduction, to a daily time series of P and Q from 671 watersheds across the conterminous United States. We first demonstrated that information transfer from P to Q primarily reflects the quickflow component of water-budgets, based on a watershed model. Readily quantifiable information flows show a functional relationship with model parameters, suggesting utility for model calibration. Second, applied to real watersheds, P–Q information flows exhibit seasonally varying behavior within regions in a manner consistent with dominant runoff generation mechanisms. However, the timing and the magnitude of information flows also reflect considerable subregional heterogeneity, likely attributable to differences in watershed size, baseflow contributions, and variation in aerial coverage of preferential flow paths. A regression analysis showed that a combination of climate and watershed characteristics are predictive of P–Q information flows. Though information flows cannot, in most cases, uniquely determine dominant runoff mechanisms, they provide a means to quantify the heterogeneous outcomes of those mechanisms within regions, thereby serving as a benchmarking tool for models developed at the regional scale. Last, information flows characterize regionally specific ways in which catchment connectivity changes from the wet to dry season.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR030186","usgsCitation":"Moges, E., Ruddell, B., Zhang, L., Driscoll, J.M., and Larsen, L., 2022, Strength and memory of precipitation's control over streamflow across the conterminous United States: Water Resources Research, v. 58, no. 3, e2021WR030186, 20 p., https://doi.org/10.1029/2021WR030186.","productDescription":"e2021WR030186, 20 p.","ipdsId":"IP-128702","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":448657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr030186","text":"Publisher Index Page"},{"id":397106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70231897,"text":"70231897 - 2022 - Eagle Ford Group in southwest Texas","interactions":[],"lastModifiedDate":"2022-06-03T14:13:23.838554","indexId":"70231897","displayToPublicDate":"2022-03-01T09:09:49","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10812,"text":"AAPG Wiki","active":true,"publicationSubtype":{"id":30}},"title":"Eagle Ford Group in southwest Texas","docAbstract":"<p>No abstract available.</p>","language":"English","publisher":"American Association of Petroleum Geologists Energy Minerals Division","usgsCitation":"Birdwell, J.E., 2022, Eagle Ford Group in southwest Texas: AAPG Wiki, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-130760","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":401683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":401519,"type":{"id":15,"text":"Index Page"},"url":"https://wiki.aapg.org/Eagle_Ford_Group,_southwest_Texas"}],"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              -104.0625,\n              28.613459424004414\n            ],\n            [\n              -94.21875,\n              28.613459424004414\n            ],\n            [\n              -94.21875,\n              33.87041555094183\n            ],\n            [\n              -104.0625,\n              33.87041555094183\n            ],\n            [\n              -104.0625,\n              28.613459424004414\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":844052,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70229540,"text":"70229540 - 2022 - Identification of fresh submarine groundwater off the coast of San Diego, USA, using electromagnetic methods","interactions":[],"lastModifiedDate":"2022-05-13T14:53:35.170768","indexId":"70229540","displayToPublicDate":"2022-03-01T09:01:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Identification of fresh submarine groundwater off the coast of San Diego, USA, using electromagnetic methods","docAbstract":"<p><span>Climate change has a pronounced effect on water resources in many semiarid climates, causing populated areas such as San Diego County (USA), to become more vulnerable to water shortages in the coming decades. To prepare for decreased water supply, San Diego County is adopting policies to decrease water use and to develop additional local sources of water. One new local source of freshwater is produced by a desalination facility that purifies brackish groundwater from the coastal San Diego Formation. This formation has been studied extensively onshore, but little is known about the geology or groundwater quality offshore in the adjacent continental shelf. Because most groundwater systems are interconnected and complex, further analysis is needed to identify offshore geology, possible sequestration of freshwater in the shelf, and potential pathways for saltwater intrusion. This comprehensive understanding is important because seawater intrusion may limit use of the San Diego Formation and longevity of desalination facilities. Controlled-source electromagnetic methods are uniquely suited to detecting offshore groundwater as they are sensitive to changes in pore fluids such as the transition from fresh to brackish groundwater. This paper describes results from surface-towed electromagnetic surveys that mapped the pore-fluid salinity and possible fluid pathways in the continental shelf off the coast of San Diego. The results indicate a considerable volume of fresh-to-brackish groundwater sequestered in the shelf, both in continuous lenses and isolated pockets, that appear influenced by fault systems and shallow stratigraphy.</span></p>","language":"English","publisher":"Springer Link","doi":"10.1007/s10040-022-02463-y","usgsCitation":"King, R.B., Danskin, W.R., Constable, S., and Maloney, J.M., 2022, Identification of fresh submarine groundwater off the coast of San Diego, USA, using electromagnetic methods: Hydrogeology Journal, v. 30, p. 965-973, https://doi.org/10.1007/s10040-022-02463-y.","productDescription":"9 p,","startPage":"965","endPage":"973","ipdsId":"IP-132993","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":448661,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-022-02463-y","text":"Publisher Index Page"},{"id":396992,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Baja California, California","city":"San Diego","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.333984375,\n              32.400834826722196\n            ],\n            [\n              -116.97967529296874,\n              32.442567075075075\n            ],\n            [\n              -116.43035888671875,\n              32.11980111179328\n            ],\n            [\n              -116.180419921875,\n              32.001088607540446\n            ],\n            [\n              -116.11175537109376,\n              32.05231681645636\n            ],\n            [\n              -116.14196777343749,\n              32.20815332547324\n            ],\n            [\n              -116.45233154296874,\n              32.669436832605314\n            ],\n            [\n              -116.71874999999999,\n              33.05932046347212\n            ],\n            [\n              -116.82586669921874,\n              33.123750829710225\n            ],\n            [\n              -117.1307373046875,\n              32.960281958039836\n            ],\n            [\n              -117.41638183593749,\n              32.76880048488168\n            ],\n            [\n              -117.49053955078125,\n              32.74339241542703\n            ],\n            [\n              -117.333984375,\n              32.400834826722196\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"30","noUsgsAuthors":false,"publicationDate":"2022-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"King, Roslynn B. 0000-0001-9944-2463","orcid":"https://orcid.org/0000-0001-9944-2463","contributorId":288371,"corporation":false,"usgs":false,"family":"King","given":"Roslynn","email":"","middleInitial":"B.","affiliations":[{"id":61739,"text":"Scripps Institution of Oceanography, University of California San Diego and Department of Geological Sciences, San Diego State University","active":true,"usgs":false}],"preferred":false,"id":837801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Danskin, Wesley R. 0000-0001-8672-5501 wdanskin@usgs.gov","orcid":"https://orcid.org/0000-0001-8672-5501","contributorId":1034,"corporation":false,"usgs":true,"family":"Danskin","given":"Wesley","email":"wdanskin@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Constable, Steven","contributorId":9178,"corporation":false,"usgs":false,"family":"Constable","given":"Steven","email":"","affiliations":[{"id":16196,"text":"Scripps Institution of Oceanography, La Jolla, CA","active":true,"usgs":false}],"preferred":false,"id":837803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maloney, Jillian M. 0000-0001-8223-4676","orcid":"https://orcid.org/0000-0001-8223-4676","contributorId":261208,"corporation":false,"usgs":false,"family":"Maloney","given":"Jillian","email":"","middleInitial":"M.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":837804,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70234588,"text":"70234588 - 2022 - USGS tools perform gas source analysis in the field","interactions":[],"lastModifiedDate":"2022-08-12T14:06:26.373273","indexId":"70234588","displayToPublicDate":"2022-03-01T08:59:58","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":11603,"text":"Fire in the Ice","active":true,"publicationSubtype":{"id":30}},"title":"USGS tools perform gas source analysis in the field","docAbstract":"Field studies of gas hydrate rely on gas sampling and analysis tools to determine the origins and alteration of methane and other hydrocarbons. The conventional strategy for deciphering gas origins has been to collect gas and other related samples in the field then pack, ship, store, and later analyze these discrete samples in a laboratory using instruments that require specialized training and dedicated operators. Two critical drawbacks to the conventional strategy are that: 1) measurements made and analyzed after completing the field program often highlight where data should have been collected; 2) discrete sampling limits the spatial or temporal measurement density, increasing the likelihood of missing small-scale natural features.","language":"English","publisher":"U.S. Department of Energy National Energy Technology Laboratory","usgsCitation":"Pohlman, J., Casso, M., Boze, L., and Bergeron, E., 2022, USGS tools perform gas source analysis in the field: Fire in the Ice, v. 22, no. 1, p. 11-14.","productDescription":"4 p.","startPage":"11","endPage":"14","ipdsId":"IP-137796","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":405117,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":405115,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.netl.doe.gov/fire-in-the-ice","linkFileType":{"id":5,"text":"html"}}],"volume":"22","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pohlman, John 0000-0002-3563-4586","orcid":"https://orcid.org/0000-0002-3563-4586","contributorId":220804,"corporation":false,"usgs":true,"family":"Pohlman","given":"John","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":848887,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casso, Michael 0000-0002-6990-9090 mcasso@usgs.gov","orcid":"https://orcid.org/0000-0002-6990-9090","contributorId":2904,"corporation":false,"usgs":true,"family":"Casso","given":"Michael","email":"mcasso@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boze, Lee-Gray 0000-0003-1853-7888 lboze@usgs.gov","orcid":"https://orcid.org/0000-0003-1853-7888","contributorId":220996,"corporation":false,"usgs":true,"family":"Boze","given":"Lee-Gray","email":"lboze@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848889,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergeron, Emile 0000-0002-6157-2171","orcid":"https://orcid.org/0000-0002-6157-2171","contributorId":294785,"corporation":false,"usgs":true,"family":"Bergeron","given":"Emile","email":"","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848890,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70241168,"text":"70241168 - 2022 - Real-time telemetry and multi-state modeling","interactions":[],"lastModifiedDate":"2023-03-14T13:46:09.843378","indexId":"70241168","displayToPublicDate":"2022-03-01T08:39:37","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Real-time telemetry and multi-state modeling","docAbstract":"<p>This project will result in real-time data and other invasive carp movement information to inform realtime management decisions and refine the SEICarP model. 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,{"id":70240638,"text":"70240638 - 2022 - Great Lakes lake trout thiamine monitoring program annual report","interactions":[],"lastModifiedDate":"2023-02-10T14:43:47.798215","indexId":"70240638","displayToPublicDate":"2022-03-01T08:34:59","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":7577,"text":"Annual Report","active":true,"publicationSubtype":{"id":4}},"title":"Great Lakes lake trout thiamine monitoring program annual report","docAbstract":"The U.S. Geological Survey’s Great Lakes Science Center (GLSC), Eastern Ecological Science Center, and Columbia Environmental Research Center (CERC), and the State University of New York (SUNY) Brockport have conducted in collaboration with partner agencies a cooperative program to monitor thiamine concentrations in lake trout eggs since the late 1990s. In 2021, egg thiamine concentrations were highly variable at each sampling site. No eggs samples with thiamine concentrations less than the 4 nmol/g threshold recommended for successful lake trout reproduction were collected in Lakes Superior, Huron, Erie, and Champlain. In contrast, every site in Lakes Michigan and Ontario and Cayuga Lake had some lake trout eggs below 4 nmol/g. Time series of mean lake trout egg thiamine concentrations showed high temporal and spatial variability within the Great Lakes region.","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Rinchard, J., Blowers, T., and Lantry, B.F., 2022, Great Lakes lake trout thiamine monitoring program annual report: Annual Report, 14 p.","productDescription":"14 p.","ipdsId":"IP-138978","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":412945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412925,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -92.50168188088435,\n              50.145656635677284\n            ],\n            [\n              -92.50168188088435,\n              41.53090693545596\n            ],\n            [\n              -71.88727532711619,\n              41.53090693545596\n            ],\n            [\n              -71.88727532711619,\n              50.145656635677284\n            ],\n            [\n              -92.50168188088435,\n              50.145656635677284\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rinchard, Jacques","contributorId":302335,"corporation":false,"usgs":false,"family":"Rinchard","given":"Jacques","affiliations":[{"id":65405,"text":"Brockport State University of New York","active":true,"usgs":false}],"preferred":false,"id":864056,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blowers, Thomas","contributorId":302336,"corporation":false,"usgs":false,"family":"Blowers","given":"Thomas","affiliations":[{"id":65405,"text":"Brockport State University of New York","active":true,"usgs":false}],"preferred":false,"id":864057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lantry, Brian F. 0000-0001-8797-3910 bflantry@usgs.gov","orcid":"https://orcid.org/0000-0001-8797-3910","contributorId":3435,"corporation":false,"usgs":true,"family":"Lantry","given":"Brian","email":"bflantry@usgs.gov","middleInitial":"F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":864058,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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0000-0001-9452-6596 shoudek@usgs.gov","orcid":"https://orcid.org/0000-0001-9452-6596","contributorId":4423,"corporation":false,"usgs":true,"family":"Houdek","given":"Steven","email":"shoudek@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":905585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brian R. Lubinski","contributorId":339916,"corporation":false,"usgs":false,"family":"Brian R. Lubinski","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":905587,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70230685,"text":"70230685 - 2022 - Natural and anthropogenic influences on benthic cyanobacteria in streams of the northeastern United States","interactions":[],"lastModifiedDate":"2022-05-13T15:16:03.306357","indexId":"70230685","displayToPublicDate":"2022-03-01T06:53:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Natural and anthropogenic influences on benthic cyanobacteria in streams of the northeastern United States","docAbstract":"<div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0045\">Benthic cyanobacteria are widespread in streams and rivers and have the potential to release toxins. In large numbers, these microorganisms and their toxins present a risk to human health. Cyanobacterial abundance in stream biofilms is typically related to single or a limited set of environmental factors, mainly light availability, water temperature, and nutrient concentrations. However, these factors may act synergistically with watershed characteristics and other stressors, such as anthropogenic pollutants, to affect cyanobacteria. We investigated the influence of multiple regional and local variables on the abundance of benthic cyanobacterial genera in streams using all subsets generalized additive modeling. We examined watershed factors (topography, geology, and climate) alongside in-stream factors (geomorphology, hydrology, pH, specific conductance, nutrients, organic contaminants, and dissolved metals) from 76 sites along an urban gradient in the northeast United States. Each genus responded to a distinct combination of environmental variables, demonstrating strong intergeneric variation in environmental selection of realized niches. Four of the 7 potentially toxigenic genera that we modeled were positively influenced by water temperature or nutrients. Nonetheless, watershed characteristics, streamflow, and/or other water quality pollutants were equally or more influential for the potentially toxigenic genera. Additionally, the relationships between cyanobacterial abundance and environmental factors varied in shape and direction across many genera. In particular, with increasing concentrations of herbicides, polychlorinated biphenyls, or metals, the abundance of roughly half of the affected genera decreased, while the others increased. These results likely demonstrate novel toxic effects of the pollutants on cyanobacterial genera in the environment, while indicating that unmeasured biotic interactions may lead to positive responses for other genera. Our results emphasize the need to consider variables beyond those that are most frequently measured or implicated (e.g., water temperature and nutrients) to more fully understand the environmental conditions that influence the distributions and abundance of potentially harmful cyanobacteria.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.154241","usgsCitation":"Schulte, N.O., Carlisle, D.M., and Spaulding, S., 2022, Natural and anthropogenic influences on benthic cyanobacteria in streams of the northeastern United States: Science of the Total Environment, v. 826, 154241, 13 p., https://doi.org/10.1016/j.scitotenv.2022.154241.","productDescription":"154241, 13 p.","ipdsId":"IP-136038","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":399391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Massachusetts, New Hampshire. New York, Rhode Island, Vermont","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -79.7607421875,\n              40.81380923056963\n            ],\n            [\n              -70.224609375,\n              40.81380923056963\n            ],\n            [\n              -70.224609375,\n              44.55916341529182\n            ],\n            [\n              -79.7607421875,\n              44.55916341529182\n            ],\n            [\n              -79.7607421875,\n              40.81380923056963\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"826","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schulte, Nicholas O. 0000-0001-6284-4987","orcid":"https://orcid.org/0000-0001-6284-4987","contributorId":290510,"corporation":false,"usgs":false,"family":"Schulte","given":"Nicholas","email":"","middleInitial":"O.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":841152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":290511,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":841153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spaulding, Sarah A. 0000-0002-9787-7743","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":223186,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":841154,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70235703,"text":"70235703 - 2022 - Limited land base and competing land uses force societal tradeoffs when siting energy development","interactions":[],"lastModifiedDate":"2022-08-16T17:25:06.545554","indexId":"70235703","displayToPublicDate":"2022-02-28T16:04:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Limited land base and competing land uses force societal tradeoffs when siting energy development","docAbstract":"<p>As human populations grow, decisions regarding use of the world's finite land base become increasingly complex. We adopted a land use–conflict scenario involving renewable energy to illustrate one potential cause of these conflicts and resulting tradeoff decisions. Renewable energy industries wishing to expand operations in the United States are limited by multijurisdictional regulations in finding developable land. Interest groups entreat industries to avoid land for various reasons, including avoidance of prime wildlife habitat in accordance with an “avoidance-first” mitigation strategy. By applying a uniform set of rules for renewable energy facilities to the Prairie Pothole Region and portions of the Northern Great Plains, we evaluated the effects of regulations and avoidance of prime wildlife habitat on the amount of land available for development. In our scenario, existing regulations excluded 39% of the project area from potential development, with human infrastructure accounting for 30% (10–66% among states), whereas federally protected species accounted for &lt; 1% at project area and state levels. Unregulated lands accounted for 61% of the project area, with conservation areas predicted as high-quality sites for breeding grassland birds and waterfowl and for migrating whooping cranes <i>Grus americana</i> accounting for 19% within the project area (6–27% among states). This model demonstrated a limited land base available for new development when accounting for regulations and concerns of a subset of societal interest groups. Additional interest groups likely will have different and competing concerns, further emphasizing the complexity of future land-use decisions as the available land base for development diminishes.</p>","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-21-036","usgsCitation":"Shaffer, J.A., Niemuth, N.D., Loesch, C.R., Derby, C.E., Pearse, A.T., Barnes, K.W., Shaffer, T.L., and Ryba, A.J., 2022, Limited land base and competing land uses force societal tradeoffs when siting energy development: Journal of Fish and Wildlife Management, v. 13, no. 1, p. 106-123, https://doi.org/10.3996/JFWM-21-036.","productDescription":"18 p.","startPage":"106","endPage":"123","ipdsId":"IP-122448","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":448664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-036","text":"Publisher Index Page"},{"id":405160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Shaffer, Jill A. 0000-0003-3172-0708 jshaffer@usgs.gov","orcid":"https://orcid.org/0000-0003-3172-0708","contributorId":3184,"corporation":false,"usgs":true,"family":"Shaffer","given":"Jill","email":"jshaffer@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":848973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Niemuth, Neal D. 0009-0006-9637-5588","orcid":"https://orcid.org/0009-0006-9637-5588","contributorId":204334,"corporation":false,"usgs":false,"family":"Niemuth","given":"Neal","email":"","middleInitial":"D.","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":848974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loesch, Charles R. 0000-0003-3090-1566","orcid":"https://orcid.org/0000-0003-3090-1566","contributorId":213437,"corporation":false,"usgs":false,"family":"Loesch","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":848975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Derby, Clayton E.","contributorId":295253,"corporation":false,"usgs":false,"family":"Derby","given":"Clayton","email":"","middleInitial":"E.","affiliations":[{"id":38051,"text":"Western EcoSystems Technology, Inc.","active":true,"usgs":false}],"preferred":false,"id":848976,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":848977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnes, Kevin W.","contributorId":295254,"corporation":false,"usgs":false,"family":"Barnes","given":"Kevin","email":"","middleInitial":"W.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":848978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shaffer, Terry L. 0000-0001-6950-8951 tshaffer@usgs.gov","orcid":"https://orcid.org/0000-0001-6950-8951","contributorId":3192,"corporation":false,"usgs":true,"family":"Shaffer","given":"Terry","email":"tshaffer@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":848979,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ryba, Adam J.","contributorId":204335,"corporation":false,"usgs":false,"family":"Ryba","given":"Adam","email":"","middleInitial":"J.","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":848980,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70229031,"text":"ofr20221015 - 2022 - Floods of June 21–July 1, 2018, in the Floyd River and Little Sioux River Basins, northwestern Iowa","interactions":[],"lastModifiedDate":"2026-03-27T19:52:19.500576","indexId":"ofr20221015","displayToPublicDate":"2022-02-28T13:24:18","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1015","displayTitle":"Floods of June 21–July 1, 2018, in the Floyd River and Little Sioux River Basins, Northwestern Iowa","title":"Floods of June 21–July 1, 2018, in the Floyd River and Little Sioux River Basins, northwestern Iowa","docAbstract":"<p>The Floyd River and Little Sioux River Basins in northwestern Iowa flooded on June 21–July 1, 2018, after sustained rainfall on June 14–27, 2018. Within the Floyd River Basin, rainfall totals from June 14 to 21 preceding flooding were 3.01 inches (in.) at Le Mars, 4.50 in. at Orange City, and 7.44 in. at Sheldon. Within the Little Sioux River Basin, rainfall amounts for the 2-week period from June 14 to 27 preceding flooding were 11.29 in. at Lake Park, 12.95 in. at Milford, 5.56 in. at Spencer, 7.71 in. at Sioux Rapids, and 6.13 in. at Cherokee. Flooding in the Floyd River Basin resulted in a recorded maximum peak discharge of 14,300 cubic feet per second (ft<sup>3</sup>/s; annual exceedance probability [AEP] estimate between 4 and 10 percent) at the U.S. Geological Survey (USGS) streamgage Floyd River at Alton, Iowa (06600100), and a recorded maximum peak discharge of 9,180 ft<sup>3</sup>/s (AEP estimate greater than 10 percent) at the USGS streamgage Floyd River at James, Iowa (06600500). Flooding in the Little Sioux River Basin resulted in a recorded maximum peak discharge of 16,300 ft<sup>3</sup>/s (AEP estimate between 4 and 10 percent) at the USGS streamgage Little Sioux River at Linn Grove, Iowa (06605850), and maximum peak discharges of 18,700 ft<sup>3</sup>/s (AEP estimate greater than 10 percent) and 20,000 ft<sup>3</sup>/s (AEP estimate greater than 10 percent) were recorded at the USGS streamgages Little Sioux River at Correctionville, Iowa (06606600), and Little Sioux River near Turin, Iowa (06607500), respectively. High-water mark elevations were surveyed at 19 locations along the Floyd River and 22 locations along the Little Sioux River to develop 2 flood profiles: a 52.5-mile profile along the Floyd River from State Highway 3 at Le Mars to U.S. Highway 18 at Sheldon that includes the USGS streamgage Floyd River at Alton and a 101-mile profile along the Little Sioux River from U.S. Highway 59 at Cherokee to U.S. Highway 18 north of Spencer that includes the USGS streamgage Little Sioux River at Linn Grove.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221015","collaboration":"Prepared in cooperation with the Iowa Department of Transportation and the Iowa Highway Research Board (Project HR–140)","usgsCitation":"O’Shea, P.S., Wilson, J.L., Vegrzyn, J.C., and Barnes, K.K., 2022, Floods of June 21–July 1, 2018, in the Floyd River and Little Sioux River Basins, northwestern Iowa: U.S. Geological Survey Open-File Report 2022–1015, 35 p., https://doi.org/10.3133/ofr20221015.","productDescription":"Report: ix, 35 p.; 2 Data Releases; Dataset","numberOfPages":"48","onlineOnly":"N","ipdsId":"IP-111461","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":396505,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1015/coverthb.jpg"},{"id":396510,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V8NO0H","text":"USGS data release","linkHelpText":"Peak-flow frequency analysis for seven selected U.S. Geological Survey streamgages in the Floyd and Little Sioux River Basins, Iowa, based on data through water year 2019"},{"id":396508,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1015/images"},{"id":396507,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1015/ofr20221015.XML","size":"189 kB","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2022–1015 XML"},{"id":396506,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1015/ofr20221015.pdf","text":"Report","size":"9.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1015"},{"id":501760,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112527.htm","linkFileType":{"id":5,"text":"html"}},{"id":396512,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":396511,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W7VP64","text":"USGS data release","linkHelpText":"Peak-flow frequency analysis for three selected streamgages in the Cedar and Little Sioux River Basins, Iowa, based on data through water year 2019"}],"country":"United States","state":"Iowa","otherGeospatial":"Floyd River and Little Sioux River Basins","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -96.6192626953125,\n              42.407234661551875\n            ],\n            [\n              -95.701904296875,\n              42.407234661551875\n            ],\n            [\n              -95.701904296875,\n              43.5326204268101\n            ],\n            [\n              -96.6192626953125,\n              43.5326204268101\n            ],\n            [\n              -96.6192626953125,\n              42.407234661551875\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/cm-water\" href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a><br>U.S. Geological Survey<br>400 South Clinton Street, Suite 269 <br>Iowa City, IA 52240</p><p><a data-mce-href=\"../contact\" href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Study Basins</li><li>Floods of June–July 2018</li><li>Flood Event Viewer</li><li>Summary</li><li>References Cited</li><li>Appendix 1. List of Bench Marks and Reference Points</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-02-28","noUsgsAuthors":false,"publicationDate":"2022-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Shea, Padraic S. 0000-0001-9005-8289 poshea@usgs.gov","orcid":"https://orcid.org/0000-0001-9005-8289","contributorId":196742,"corporation":false,"usgs":true,"family":"O’Shea","given":"Padraic","email":"poshea@usgs.gov","middleInitial":"S.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Jordan L. 0000-0003-0490-9062 jlwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-0490-9062","contributorId":5416,"corporation":false,"usgs":true,"family":"Wilson","given":"Jordan","email":"jlwilson@usgs.gov","middleInitial":"L.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vegrzyn, Jared C. 0000-0002-9018-1823","orcid":"https://orcid.org/0000-0002-9018-1823","contributorId":258186,"corporation":false,"usgs":true,"family":"Vegrzyn","given":"Jared","email":"","middleInitial":"C.","affiliations":[],"preferred":true,"id":836237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnes, Kimberlee K. 0000-0002-8917-7165 kkbarnes@usgs.gov","orcid":"https://orcid.org/0000-0002-8917-7165","contributorId":2683,"corporation":false,"usgs":true,"family":"Barnes","given":"Kimberlee","email":"kkbarnes@usgs.gov","middleInitial":"K.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836238,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70262183,"text":"70262183 - 2022 - Comparison of methods for estimating density and population trends for low-density Asian bears","interactions":[],"lastModifiedDate":"2025-01-15T17:38:27.828111","indexId":"70262183","displayToPublicDate":"2022-02-28T11:28:42","publicationYear":"2022","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":"Comparison of methods for estimating density and population trends for low-density Asian bears","docAbstract":"<p><span>Populations of bears in Asia are vulnerable to extinction and effective monitoring is critical to measure and direct conservation efforts. Population abundance (local density) or growth&nbsp;</span><i>(λ</i><span>) are the most sensitive metrics to change. We discuss the value in implementing spatially explicit capture-recapture (SCR), the current gold standard for density estimation, and open population SCR (OPSCR) to monitor changes in density over time. We provide guidance for designing studies to provide estimates with sufficient power to detect changes. Because of the wide availability of&nbsp;camera traps&nbsp;and interest in their use, we consider six density estimation methods and their extensions developed for use with camera traps, with specific consideration of assumptions and applications for monitoring Asian bears. We conducted a power analysis to calculate the precision in estimates needed to detect changes in populations with reference to IUCN Red List criteria. We performed a&nbsp;systematic review&nbsp;of empirical studies implementing camera trap abundance estimation methods and considered sample sizes, effort, and model assumptions required to achieve adequate precision for population monitoring. We found SCR and OPSCR, reliant on “marked” individuals, are currently the only methods with enough power to reliably detect even moderate to major (20–80%) declines. Camera trap methods with unmarked individuals rarely achieved precision sufficient to detect even large declines (80–90%), although with some exceptions (e.g., situations with moderate population densities, large number of sampling sites, or inclusion of ancillary local&nbsp;telemetry&nbsp;data. We describe additional estimation options including line transects, direct observations, monitoring age-specific survival and reproductive rates, and hybrid/integrated methodologies that may have potential to work for some Asian bear populations. We conclude monitoring changes in abundance or density is possible for most Asian bear populations but will require collaboration among researchers over broad spatial extents and extensive financial investment to overcome biological and logistical constraints. We strongly encourage practitioners to consider study design and sampling effort required to meet objectives by conducting simulations, power analyses, and assumption checks prior to implementing monitoring efforts, and reporting standardized dispersion measures such as coefficients of variation to allow for assessment of precision. Our guidance is relevant to other low-density and wide-ranging species.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02058","usgsCitation":"Morin, D., Boulanger, J., Bischof, R., Lee, D., Ngoprasert, D., Fuller, A.K., McLellan, B., Steinmetz, R., Sharma, S., Garshelis, D., Gopalaswamy, A.M., Nawaz, M.A., and Karanth, U., 2022, Comparison of methods for estimating density and population trends for low-density Asian bears: Global Ecology and Conservation, e02058, 21 p., https://doi.org/10.1016/j.gecco.2022.e02058.","productDescription":"e02058, 21 p.","ipdsId":"IP-135458","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467198,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02058","text":"Publisher Index Page"},{"id":466442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Morin, Dana J.","contributorId":348354,"corporation":false,"usgs":false,"family":"Morin","given":"Dana J.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":923389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boulanger, John","contributorId":348355,"corporation":false,"usgs":false,"family":"Boulanger","given":"John","affiliations":[{"id":83347,"text":"Integrated Ecological Research","active":true,"usgs":false}],"preferred":false,"id":923390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bischof, Richard","contributorId":348356,"corporation":false,"usgs":false,"family":"Bischof","given":"Richard","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":923391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, David C.","contributorId":348357,"corporation":false,"usgs":false,"family":"Lee","given":"David C.","affiliations":[{"id":83348,"text":"University of South Wales","active":true,"usgs":false}],"preferred":false,"id":923392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ngoprasert, Dusit","contributorId":348358,"corporation":false,"usgs":false,"family":"Ngoprasert","given":"Dusit","affiliations":[{"id":83349,"text":"Program, King Mongkut’s University of Technology Thonburi","active":true,"usgs":false}],"preferred":false,"id":923393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":923388,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McLellan, Bruce","contributorId":348359,"corporation":false,"usgs":false,"family":"McLellan","given":"Bruce","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":923394,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Steinmetz, Robert","contributorId":348360,"corporation":false,"usgs":false,"family":"Steinmetz","given":"Robert","affiliations":[{"id":83350,"text":"WWF Thailand","active":true,"usgs":false}],"preferred":false,"id":923395,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sharma, Sandeep","contributorId":348362,"corporation":false,"usgs":false,"family":"Sharma","given":"Sandeep","affiliations":[{"id":62105,"text":"University of Goettingen","active":true,"usgs":false}],"preferred":false,"id":923396,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Garshelis, Dave","contributorId":348363,"corporation":false,"usgs":false,"family":"Garshelis","given":"Dave","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":923397,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gopalaswamy, Arjun M.","contributorId":199394,"corporation":false,"usgs":false,"family":"Gopalaswamy","given":"Arjun","email":"","middleInitial":"M.","affiliations":[{"id":35775,"text":"Indian Statistical Institute, Bangalore, India","active":true,"usgs":false},{"id":20302,"text":"Univeristy of Oxford","active":true,"usgs":false}],"preferred":false,"id":923608,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Nawaz, Muhammad Ali","contributorId":300024,"corporation":false,"usgs":false,"family":"Nawaz","given":"Muhammad","email":"","middleInitial":"Ali","affiliations":[{"id":65003,"text":"Department of Biological and Environmental Sciences, Qatar University","active":true,"usgs":false}],"preferred":false,"id":923609,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Karanth, Ullas","contributorId":348577,"corporation":false,"usgs":false,"family":"Karanth","given":"Ullas","affiliations":[],"preferred":false,"id":923610,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70229694,"text":"70229694 - 2022 - Drivers of flight performance of California Condors (Gymnogyps californianus)","interactions":[],"lastModifiedDate":"2022-03-15T14:33:09.650195","indexId":"70229694","displayToPublicDate":"2022-02-28T09:28:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Drivers of flight performance of California Condors (<i>Gymnogyps californianus</i>)","title":"Drivers of flight performance of California Condors (Gymnogyps californianus)","docAbstract":"<p><span>Flight behavior of soaring birds depends on a complex array of physiological, social, demographic, and environmental factors. California Condors (</span><i>Gymnogyps californianus</i><span>) rely on thermal and orographic updrafts to subsidize extended bouts of soaring flight, and their soaring flight performance is expected to vary in response to environmental variation and, potentially, with experience. We collected 6298 flight tracks described by high-frequency GPS telemetry data from five birds ranging in age from 1 to 19 yr old and followed over 32 d in summer 2016. Using these data, we tested the hypothesis that climb rate, an indicator of flight performance, would be related to the topographic and meteorological variables the bird experienced, and also to its age. Climb rate was greater when condors were flying in faster winds and during environmental conditions that were conducive to updraft development. However, we found no effect of age on climb rate. Although many of these relationships were expected based on flight theory, the lack of an effect of age was unexpected. Our work expands understanding of the relationship condors have with the environment, and it also suggests the potential for as-yet unexplored complexity to this relationship. As such, this study provides insight into avian flight behavior and, because flight performance influences bird behavior and exposure to anthropogenic risk, it has potential consequences for development of conservation management plans.</span></p>","language":"English","publisher":"Raptor Research Foundation","doi":"10.3356/JRR-20-94","usgsCitation":"Bonner, S.R., Poessel, S.A., Brandt, J.C., Astell, M.T., Belthoff, J.R., and Katzner, T.E., 2022, Drivers of flight performance of California Condors (Gymnogyps californianus): Journal of Raptor Research, v. 56, no. 1, p. 17-27, https://doi.org/10.3356/JRR-20-94.","productDescription":"11 p.","startPage":"17","endPage":"27","ipdsId":"IP-120507","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448665,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr-20-94","text":"Publisher Index Page"},{"id":397109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Bitter Creek and Hopper Mountain National Wildlife Refuges","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.5,\n              34\n            ],\n            [\n              -118.4,\n              34\n            ],\n            [\n              -118.4,\n              36\n            ],\n            [\n              -119.5,\n              36\n            ],\n            [\n              -119.5,\n              34\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bonner, Sophie R.","contributorId":288473,"corporation":false,"usgs":false,"family":"Bonner","given":"Sophie","email":"","middleInitial":"R.","affiliations":[{"id":61767,"text":"Department of Geography, University of Texas at Austin, Austin, TX","active":true,"usgs":false}],"preferred":false,"id":837974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":837975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, Joseph C.","contributorId":288474,"corporation":false,"usgs":false,"family":"Brandt","given":"Joseph","email":"","middleInitial":"C.","affiliations":[{"id":61768,"text":"U.S. Fish and Wildlife Service, Hopper Mountain National Wildlife Refuge Complex, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":837976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Astell, Molly T.","contributorId":288475,"corporation":false,"usgs":false,"family":"Astell","given":"Molly","email":"","middleInitial":"T.","affiliations":[{"id":61768,"text":"U.S. Fish and Wildlife Service, Hopper Mountain National Wildlife Refuge Complex, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":837977,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belthoff, James R. 0000-0002-6051-2353","orcid":"https://orcid.org/0000-0002-6051-2353","contributorId":190592,"corporation":false,"usgs":false,"family":"Belthoff","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":837978,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191909,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd","email":"tkatzner@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":838016,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70229057,"text":"70229057 - 2022 - Water chemistry, exposure routes and metal forms determine the bioaccumulation dynamics of silver (ionic and nanoparticulate) in Daphnia magna","interactions":[],"lastModifiedDate":"2022-02-28T15:04:24.769878","indexId":"70229057","displayToPublicDate":"2022-02-28T08:49:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Water chemistry, exposure routes and metal forms determine the bioaccumulation dynamics of silver (ionic and nanoparticulate) in <i>Daphnia magna</i>","title":"Water chemistry, exposure routes and metal forms determine the bioaccumulation dynamics of silver (ionic and nanoparticulate) in Daphnia magna","docAbstract":"<p>Treatment wetlands utilize various physical and biological processes to reduce levels of organic contaminants, metals, bacteria, and suspended solids. Silver nanoparticles (AgNPs) are one type of contaminant that can enter treatment wetlands and impact the overall treatment efficacy. Grazing by filter-feeding zooplankton, such as<span>&nbsp;</span><i>Daphnia magna</i>, is critical to treatment wetland functioning; but the effects of AgNPs on zooplankton are not fully understood, especially at environmentally relevant concentrations. We characterized the bioaccumulation kinetics of dissolved and nanoparticulate (citrate-coated)<span>&nbsp;</span><sup>109</sup>Ag in<span>&nbsp;</span><i>D. magna</i><span>&nbsp;</span>exposed to environmentally relevant<span>&nbsp;</span><sup>109</sup>Ag concentrations (i.e., 0.2–23 nmol L<sup>−1</sup><span>&nbsp;</span>Ag) using a stable isotope as a tracer of Ag. Both aqueous and nanoparticulate forms of<span>&nbsp;</span><sup>109</sup>Ag were bioavailable to<span>&nbsp;</span><i>D. magna</i><span>&nbsp;</span>after exposure. Water chemistry affected<span>&nbsp;</span><sup>109</sup>Ag influx from<span>&nbsp;</span><sup>109</sup>AgNP but not from<span>&nbsp;</span><sup>109</sup>AgNO<sub>3</sub>. Silver retention was greater for citrate-coated<span>&nbsp;</span><sup>109</sup>AgNP than dissolved<span>&nbsp;</span><sup>109</sup>Ag, indicating a greater potential for bioaccumulation from nanoparticulate Ag. Feeding inhibition was observed at higher dietary<span>&nbsp;</span><sup>109</sup>Ag concentrations, which could lead to reduced treatment wetland performance. Our results illustrate the importance of using environmentally relevant concentrations and media compositions when predicting Ag bioaccumulation and provide insight into potential effects on filter feeders critical to the function of treatment wetlands.<span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span></span></span></p>","language":"English","publisher":"ACS Publications","doi":"10.1002/etc.5271","usgsCitation":"Lesser, E., Sheikh, F.N., Sikder, M., Croteau, M.N., Franklin, N., Baalousha, M., and Ismail, N.S., 2022, Water chemistry, exposure routes and metal forms determine the bioaccumulation dynamics of silver (ionic and nanoparticulate) in Daphnia magna: Environmental Toxicology and Chemistry, v. 41, no. 3, p. 726-738, https://doi.org/10.1002/etc.5271.","productDescription":"13 p.","startPage":"726","endPage":"738","ipdsId":"IP-131554","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":396548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Lesser, Emma","contributorId":286941,"corporation":false,"usgs":false,"family":"Lesser","given":"Emma","email":"","affiliations":[{"id":47946,"text":"Smith College","active":true,"usgs":false}],"preferred":false,"id":836370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheikh, Fatima Noor","contributorId":286942,"corporation":false,"usgs":false,"family":"Sheikh","given":"Fatima","email":"","middleInitial":"Noor","affiliations":[{"id":47946,"text":"Smith College","active":true,"usgs":false}],"preferred":false,"id":836371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sikder, Mithun 0000-0002-6295-0939","orcid":"https://orcid.org/0000-0002-6295-0939","contributorId":255449,"corporation":false,"usgs":false,"family":"Sikder","given":"Mithun","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":836372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":836373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Franklin, Natasha","contributorId":286944,"corporation":false,"usgs":false,"family":"Franklin","given":"Natasha","email":"","affiliations":[],"preferred":false,"id":836374,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baalousha, Mohammed","contributorId":239642,"corporation":false,"usgs":false,"family":"Baalousha","given":"Mohammed","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":836375,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ismail, Niveen S.","contributorId":286947,"corporation":false,"usgs":false,"family":"Ismail","given":"Niveen","email":"","middleInitial":"S.","affiliations":[{"id":47946,"text":"Smith College","active":true,"usgs":false}],"preferred":false,"id":836376,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
]}