{"pageNumber":"251","pageRowStart":"6250","pageSize":"25","recordCount":184733,"records":[{"id":70247783,"text":"70247783 - 2023 - Ooencyrtus pitosina (Hymenoptera: Encyrtidae)–A natural enemy of Samoan swallowtail butterfly Papilio godeffroyi (Lepidoptera: Papilionidae)","interactions":[],"lastModifiedDate":"2023-08-17T16:12:25.957408","indexId":"70247783","displayToPublicDate":"2023-08-09T11:07:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"<i>Ooencyrtus pitosina</i> (Hymenoptera: Encyrtidae)–A natural enemy of Samoan swallowtail butterfly <i>Papilio godeffroyi</i> (Lepidoptera: Papilionidae)","title":"Ooencyrtus pitosina (Hymenoptera: Encyrtidae)–A natural enemy of Samoan swallowtail butterfly Papilio godeffroyi (Lepidoptera: Papilionidae)","docAbstract":"<p><span>A new species of encyrtid wasp,&nbsp;</span><i>Ooencyrtus pitosina</i><span>&nbsp;Polaszek, Noyes &amp; Fusu&nbsp;</span><strong>sp. n.,</strong><span>&nbsp;(Hymenoptera: Encyrtidae: Encyrtinae) is described as a gregarious parasitoid in the eggs of the endemic Samoan swallowtail butterfly&nbsp;</span><i>Papilio godeffroyi</i><span>&nbsp;(Lepidoptera: Papilionidae) in the Samoan archipelago. It is described here because it is an important natural enemy of this butterfly, and to facilitate identification for future work with this parasitoid and its host.</span></p>","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0288306","usgsCitation":"Polaszek, A., Noyes, J., Lugli, E., Schmaedick, M., Peck, R., Banko, P.C., and Fusu, L., 2023, Ooencyrtus pitosina (Hymenoptera: Encyrtidae)–A natural enemy of Samoan swallowtail butterfly Papilio godeffroyi (Lepidoptera: Papilionidae): PLoS ONE, v. 18, no. 8, e0288306, 14 p., https://doi.org/10.1371/journal.pone.0288306.","productDescription":"e0288306, 14 p.","ipdsId":"IP-151649","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":442468,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0288306","text":"Publisher Index Page"},{"id":419892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"American Samoa","otherGeospatial":"Tutuila Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -170.5479745945281,\n              -14.211795172366578\n            ],\n            [\n              -170.85124653357516,\n              -14.211795172366578\n            ],\n            [\n              -170.85124653357516,\n              -14.3876752038625\n            ],\n            [\n              -170.5479745945281,\n              -14.3876752038625\n            ],\n            [\n              -170.5479745945281,\n              -14.211795172366578\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"18","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Polaszek, Andrew","contributorId":328501,"corporation":false,"usgs":false,"family":"Polaszek","given":"Andrew","email":"","affiliations":[{"id":37250,"text":"Natural History Museum, London","active":true,"usgs":false}],"preferred":false,"id":880442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noyes, John","contributorId":328502,"corporation":false,"usgs":false,"family":"Noyes","given":"John","email":"","affiliations":[{"id":37250,"text":"Natural History Museum, London","active":true,"usgs":false}],"preferred":false,"id":880443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lugli, Elena","contributorId":328503,"corporation":false,"usgs":false,"family":"Lugli","given":"Elena","email":"","affiliations":[{"id":78381,"text":"Core Research Laboratories, Natural History Museum, London","active":true,"usgs":false}],"preferred":false,"id":880444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmaedick, Mark","contributorId":140009,"corporation":false,"usgs":false,"family":"Schmaedick","given":"Mark","affiliations":[{"id":13353,"text":"American Samoa Community College","active":true,"usgs":false}],"preferred":false,"id":880445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peck, Robert W. 0000-0002-8739-9493","orcid":"https://orcid.org/0000-0002-8739-9493","contributorId":193088,"corporation":false,"usgs":false,"family":"Peck","given":"Robert W.","affiliations":[],"preferred":false,"id":880446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":880447,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fusu, Lucian","contributorId":328505,"corporation":false,"usgs":false,"family":"Fusu","given":"Lucian","email":"","affiliations":[{"id":78383,"text":"Cuza University, Iasi, Romania","active":true,"usgs":false}],"preferred":false,"id":880448,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70257359,"text":"70257359 - 2023 - Prey ration, temperature, and predator species influence digestion rates of prey DNA inferred from qPCR and metabarcoding","interactions":[],"lastModifiedDate":"2024-08-23T17:08:42.832471","indexId":"70257359","displayToPublicDate":"2023-08-09T10:00:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Prey ration, temperature, and predator species influence digestion rates of prey DNA inferred from qPCR and metabarcoding","docAbstract":"<p><span>Diet analysis is a vital tool for understanding trophic interactions and is frequently used to inform conservation and management. Molecular approaches can identify diet items that are impossible to distinguish using more traditional visual-based methods. Yet, our understanding of how different variables, such as predator species or prey ration size, influence molecular diet analysis is still incomplete. Here, we conducted a large feeding trial to assess the impact that ration size, predator species, and temperature had on digestion rates estimated with visual identification, qPCR, and metabarcoding. Our trial was conducted by feeding two rations of Chinook salmon (</span><i>Oncorhynchus tshawytscha</i><span>) to two piscivorous fish species (largemouth bass [</span><i>Micropterus salmoides</i><span>] and channel catfish [</span><i>Ictalurus punctatus</i><span>]) held at two different temperatures (15.5 and 18.5°C) and sacrificed at regular intervals up to 120 h from the time of ingestion to quantify the prey contents remaining in the digestive tract. We found that ration size, temperature, and predator species all influenced digestion rate, with some indication that ration size had the largest influence. DNA-based analyses were able to identify salmon smolt prey in predator gut samples for much longer than visual analysis (~12 h for visual analysis vs. ~72 h for molecular analyses). Our study provides evidence that modelling the persistence of prey DNA in predator guts for molecular diet analyses may be feasible using a small set of controlling variables for many fish systems.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.13849","usgsCitation":"Dick, C., Larson, W., Karpan, K., Baetscher, D.S., Shi, Y., Sethi, S., Fangue, N., and Henderson, M., 2023, Prey ration, temperature, and predator species influence digestion rates of prey DNA inferred from qPCR and metabarcoding: Molecular Ecology Resources, v. 00, p. 1-17, https://doi.org/10.1111/1755-0998.13849.","productDescription":"17 p.","startPage":"1","endPage":"17","ipdsId":"IP-148203","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":442471,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/1755-0998.13849","text":"External Repository"},{"id":433116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"00","noUsgsAuthors":false,"publicationDate":"2023-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Dick, Cory","contributorId":342431,"corporation":false,"usgs":false,"family":"Dick","given":"Cory","email":"","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":910100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, Wesley A.","contributorId":342433,"corporation":false,"usgs":false,"family":"Larson","given":"Wesley A.","affiliations":[{"id":37482,"text":"National Oceanographic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":910101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karpan, Kirby","contributorId":342435,"corporation":false,"usgs":false,"family":"Karpan","given":"Kirby","email":"","affiliations":[{"id":37482,"text":"National Oceanographic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":910102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baetscher, Diana S.","contributorId":342437,"corporation":false,"usgs":false,"family":"Baetscher","given":"Diana","email":"","middleInitial":"S.","affiliations":[{"id":37482,"text":"National Oceanographic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":910103,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shi, Yue","contributorId":342439,"corporation":false,"usgs":false,"family":"Shi","given":"Yue","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":910104,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910105,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fangue, Nann A.","contributorId":342441,"corporation":false,"usgs":false,"family":"Fangue","given":"Nann A.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":910106,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":910107,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70252793,"text":"70252793 - 2023 - Application of the technology readiness levels framework to natural resource management tools","interactions":[],"lastModifiedDate":"2024-04-05T14:57:41.408788","indexId":"70252793","displayToPublicDate":"2023-08-09T09:55:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1657,"text":"Fisheries","onlineIssn":"1548-8446","printIssn":"0363-2415","active":true,"publicationSubtype":{"id":10}},"title":"Application of the technology readiness levels framework to natural resource management tools","docAbstract":"<p><span>Technology advancements in fisheries science can provide useful tools to support natural resource management and conservation. However, new technologies may also present challenges for decision makers due to the lack of a standardized process to assess technologies for consideration within management plans. Future technology development in fishery and water resources could benefit from a framework that assigns an appropriate technology development stage and defines the readiness for implementation. Technology Readiness Levels (TRL) are one established research and development scale used throughout engineering and related disciplines that could be applied to natural resource management tools. The TRL assess the maturity of a technology from nascent idea through a fully developed technology. Steps within this scale could provide a general framework for researchers to follow when planning and conducting studies, while similarly providing a standard scale for resource managers to assess readiness for technology transfer and implementation. The goal of this paper is to describe TRL in the context of natural resource management tools and offer this existing framework as one option to facilitate communication between researchers and managers.</span></p>","language":"English","publisher":"American fisheries Society","doi":"10.1002/fsh.10982","usgsCitation":"Cupp, A.R., Fritts, A.K., Brey, M.K., Woodley, C., Smith, D., Cornish, M., McGovern, A., Simmonds, R., and Jackson, N., 2023, Application of the technology readiness levels framework to natural resource management tools: Fisheries, v. 48, no. 11, p. 474-479, https://doi.org/10.1002/fsh.10982.","productDescription":"6 p.","startPage":"474","endPage":"479","ipdsId":"IP-153278","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":442473,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fsh.10982","text":"Publisher Index Page"},{"id":427515,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Cupp, Aaron R. 0000-0001-5995-2100 acupp@usgs.gov","orcid":"https://orcid.org/0000-0001-5995-2100","contributorId":5162,"corporation":false,"usgs":true,"family":"Cupp","given":"Aaron","email":"acupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":898233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fritts, Andrea K. 0000-0003-2142-3339","orcid":"https://orcid.org/0000-0003-2142-3339","contributorId":204594,"corporation":false,"usgs":true,"family":"Fritts","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":898234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":898235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woodley, Christa M.","contributorId":301986,"corporation":false,"usgs":false,"family":"Woodley","given":"Christa M.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":898236,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, David 0000-0001-6074-9257","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":1989,"corporation":false,"usgs":false,"family":"Smith","given":"David","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":898237,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cornish, Mark","contributorId":203379,"corporation":false,"usgs":false,"family":"Cornish","given":"Mark","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":898238,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGovern, Amy","contributorId":335384,"corporation":false,"usgs":false,"family":"McGovern","given":"Amy","email":"","affiliations":[{"id":80390,"text":"USFWS Regional Office","active":true,"usgs":false}],"preferred":false,"id":898239,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Simmonds, Rob","contributorId":317890,"corporation":false,"usgs":false,"family":"Simmonds","given":"Rob","email":"","affiliations":[{"id":68344,"text":"U.S. Fish and Wildlife Service (USFWS)","active":true,"usgs":false}],"preferred":false,"id":898240,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jackson, Neal","contributorId":203382,"corporation":false,"usgs":false,"family":"Jackson","given":"Neal","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":898241,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70248274,"text":"70248274 - 2023 - Visitation to national parks in California shows annual and seasonal change during extreme drought and wet years","interactions":[],"lastModifiedDate":"2023-09-06T13:46:49.08371","indexId":"70248274","displayToPublicDate":"2023-08-09T08:41:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16703,"text":"PLOS Climate","active":true,"publicationSubtype":{"id":10}},"title":"Visitation to national parks in California shows annual and seasonal change during extreme drought and wet years","docAbstract":"<p><span>This study examines the influence of drought indicators on recreational visitation patterns to National Park Service units in California (USA) from 1980 to 2019. We considered mountain, arid, and coastal park types across a climate gradient where seasonal recreational opportunities are directly or indirectly dependent on water resources. Significant departures from the normal hydroclimate, reflected by drought or unusually wet conditions, can lead visitors to change their behavior, including recreating at a different time or place. Drought conditions can facilitate earlier seasonal access at higher elevation parks, but displace visitors in other seasons and parks. Wetter-than-average conditions can displace visitors due to snowpack or flooding, but also facilitate other activities. We found a decrease in annual visitation at popular mountain parks including Yosemite (-8.6%) and Sequoia and Kings Canyon (-8.2%) during extreme drought years due to lower-than-average attendance in peak summer and fall months. Extreme wet years also had significantly reduced annual visitation in Sequoia and Kings (-8.5%) and Lassen Volcanic (-13.9%) due to declines in spring and summer use as snowpack restricts road access. For arid parks, drought status did not have a statistically significant effect on annual visitation, although extreme drought led to less use during the hottest months of summer at Death Valley, and extreme wet conditions at Pinnacles led to less visitation throughout the year (-16.6%), possibly from impacts to infrastructure associated with flooding. For coastal park units, extreme drought led to year-round higher levels of use at Redwood (+27.7%), which is typically wet, and less year-round use at Channel Islands (-23.6%), which is relatively dry, while extreme wet years led to higher levels of annual use at Channel Islands (+29.4%). Collectively, these results indicate the effect of extreme drought or wet years on park visitation varies by park depending on geography and recreational activities offered.</span></p>","language":"English","publisher":"PLoS","doi":"10.1371/journal.pclm.0000260","usgsCitation":"Jenkins, J.S., Abatzoglou, J.T., Wilkins, E.J., and Perry, E.E., 2023, Visitation to national parks in California shows annual and seasonal change during extreme drought and wet years: PLOS Climate, v. 2, no. 8, e0000260, 19 p., https://doi.org/10.1371/journal.pclm.0000260.","productDescription":"e0000260, 19 p.","ipdsId":"IP-143303","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":442476,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pclm.0000260","text":"Publisher Index 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 \"}}]}","volume":"2","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Jeffrey S. 0000-0003-2860-9654","orcid":"https://orcid.org/0000-0003-2860-9654","contributorId":329398,"corporation":false,"usgs":false,"family":"Jenkins","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[{"id":16805,"text":"University of California, Merced","active":true,"usgs":false}],"preferred":false,"id":882199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abatzoglou, John T.","contributorId":329399,"corporation":false,"usgs":false,"family":"Abatzoglou","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":16805,"text":"University of California, Merced","active":true,"usgs":false}],"preferred":false,"id":882200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilkins, Emily J. 0000-0003-3055-4808","orcid":"https://orcid.org/0000-0003-3055-4808","contributorId":328409,"corporation":false,"usgs":true,"family":"Wilkins","given":"Emily","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":882201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Elizabeth E. 0000-0002-7992-6345","orcid":"https://orcid.org/0000-0002-7992-6345","contributorId":329400,"corporation":false,"usgs":false,"family":"Perry","given":"Elizabeth","email":"","middleInitial":"E.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":882202,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70247464,"text":"fs20233032 - 2023 - Predicting water quality in the Clark Fork near Grant-Kohrs Ranch National Historic Site, southwestern Montana","interactions":[],"lastModifiedDate":"2026-02-09T17:36:47.99778","indexId":"fs20233032","displayToPublicDate":"2023-08-09T07:34:31","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3032","displayTitle":"Predicting Water Quality in the Clark Fork near Grant-Kohrs Ranch National Historic Site, Southwestern Montana","title":"Predicting water quality in the Clark Fork near Grant-Kohrs Ranch National Historic Site, southwestern Montana","docAbstract":"<p>The U.S. Geological Survey (USGS) provides a wide range of streamflow, groundwater, and water-quality data to Government, commercial, academic, and public users. The USGS has a record of success with using optical turbidity sensors to predict suspended-sediment concentrations in rivers and streams. Turbidity sensors collect backscatter signals from suspended particles in water, which can be accurately measured and linked closely to hazardous contaminants that travel on the surfaces of suspended particles. Contaminant concentrations derived from the statistical relations between turbidity and contaminants like copper and lead can then be measured in real-time. </p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233032","usgsCitation":"Ellison, C.A., 2023, Predicting water quality in the Clark Fork near Grant-Kohrs Ranch National Historic Site, southwestern Montana: U.S. Geological Survey Fact Sheet 2023–3032, 4 p., https://doi.org/10.3133/fs20233032.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-149634","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":499691,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115178.htm","linkFileType":{"id":5,"text":"html"}},{"id":419659,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233032/full"},{"id":419605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3032/fs20233032.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3032"},{"id":419604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3032/coverthb.jpg"},{"id":419606,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3032/fs20233032.XML"},{"id":419607,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3032/images"},{"id":419608,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9330BXM","text":"USGS data release","linkHelpText":"Water quality and streamflow data for the Clark Fork near Grant-Kohrs Ranch National Historic Site in southwestern Montana, water years 2019–2020"}],"country":"United States","state":"Montana","otherGeospatial":"Clark Fork, Grant-Kohrs Ranch National Historic Site","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -112.8301717989599,\n              46.5276604287545\n            ],\n            [\n              -112.8301717989599,\n              46.38873569479347\n            ],\n            [\n              -112.66374787281434,\n              46.38873569479347\n            ],\n            [\n              -112.66374787281434,\n              46.5276604287545\n            ],\n            [\n              -112.8301717989599,\n              46.5276604287545\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/wy-mt-water/\" data-mce-href=\"https://www.usgs.gov/centers/wy-mt-water/\">Wyoming-Montana Water Science Center</a><br>U.S. Geological Survey<br>3162 Bozeman Avenue <br>Helena, MT 59601</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Grant-Kohrs Ranch National Historic Site</li><li>Water-Quality Monitoring using Surrogate Technology</li><li>USGS and NPS Collaborative Study</li><li>Results of the Study</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-08-09","noUsgsAuthors":false,"publicationDate":"2023-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Ellison, Christopher A. 0000-0002-5886-6654 cellison@usgs.gov","orcid":"https://orcid.org/0000-0002-5886-6654","contributorId":4891,"corporation":false,"usgs":true,"family":"Ellison","given":"Christopher","email":"cellison@usgs.gov","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":879758,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70247756,"text":"70247756 - 2023 - Initial comparison of pollen counting methods using precipitation and ambient air samples and automated artificial intelligence to support national monitoring objectives","interactions":[],"lastModifiedDate":"2023-10-11T15:44:40.402223","indexId":"70247756","displayToPublicDate":"2023-08-09T06:55:41","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":667,"text":"Aerobiologia","active":true,"publicationSubtype":{"id":10}},"title":"Initial comparison of pollen counting methods using precipitation and ambient air samples and automated artificial intelligence to support national monitoring objectives","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Given the endemic nature of pollen throughout the environment, the impact upon human health, and the need for more extensive and better measurements of pollen in the USA, a preliminary project within the National Atmospheric Deposition Program’s (NADP) National Trends Network (NTN) was developed. Pollen was measured in ambient air by several methods and in precipitation wet deposition samples at three monitoring sites in the NTN. A method for counting pollen on filters was developed and provided pollen counts for NADP atmospheric wet-deposition samples and high-volume ambient air samplers (HVAS) for comparison with co-located traditional National Allergy Bureau microscopy samples and a commercially available pollen sensor (PS) counting method during the 2021 pollen season. The goals of this project were to test the potential of available air-monitoring infrastructures to obtain improved spatial measurements of aeroallergens, compare pollen counting results from the various methods, and to determine the suitability of using wet deposition samples for pollen collection. The onset and senescence of pollen seasons for general categories of genera compared favorably for each method at each site, indicating that pollen monitoring using wet-deposition and ambient air sampling filters could provide useful information to inform scientific studies, but not likely for public health objectives. Pollen counts were log transformed for Pearson product moment correlation. Tree pollen counts were correlated at all sites for daily PS data and traditional counting data (<i>R</i> = 0.69–0.84), but statistical correlations between methods for grass and weed pollen were weak (0.40 &lt; <i>R</i> &lt; 0.60) or considered not correlated (<i>R</i> &lt; 0.40). Total pollen counts in NADP precipitation samples were correlated with traditional and PS counts at only one of three sites. Pollen counts for the weekly HVAS filter samples were correlated with PS counts for trees (<i>R</i> = 0.62) and with NAB counts for trees (<i>R</i> = 0.68) and weeds (<i>R</i> = 0.72). Correlations in the data between methods suggest that, given further methods development, a variety of techniques could be integrated to expand and enhance existing pollen monitoring networks. Improved ambient air and atmospheric deposition sampling methods specifically targeted for pollen capture and analysis could support the collection of accurate and efficient meaningful aeroallergen data from existing atmospheric monitoring networks.</p></div></div>","language":"English","publisher":"Springer Nature","doi":"10.1007/s10453-023-09794-7","usgsCitation":"Wetherbee, G.A., Gay, D.A., Uram, E., Williams, T., and Johnson, A., 2023, Initial comparison of pollen counting methods using precipitation and ambient air samples and automated artificial intelligence to support national monitoring objectives: Aerobiologia, v. 39, p. 303-325, https://doi.org/10.1007/s10453-023-09794-7.","productDescription":"23 p.","startPage":"303","endPage":"325","ipdsId":"IP-142176","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":419877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","noUsgsAuthors":false,"publicationDate":"2023-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Wetherbee, Gregory A. 0000-0002-6720-2294","orcid":"https://orcid.org/0000-0002-6720-2294","contributorId":215100,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory","email":"","middleInitial":"A.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":880286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gay, David A. 0000-0003-4690-7962","orcid":"https://orcid.org/0000-0003-4690-7962","contributorId":328478,"corporation":false,"usgs":false,"family":"Gay","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":880287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Uram, Eric 0000-0001-5913-1791","orcid":"https://orcid.org/0000-0001-5913-1791","contributorId":328479,"corporation":false,"usgs":false,"family":"Uram","given":"Eric","email":"","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":880288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Terri 0000-0003-1605-7389","orcid":"https://orcid.org/0000-0003-1605-7389","contributorId":328480,"corporation":false,"usgs":false,"family":"Williams","given":"Terri","email":"","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":880289,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Andrew 0000-0003-0027-8056","orcid":"https://orcid.org/0000-0003-0027-8056","contributorId":328481,"corporation":false,"usgs":false,"family":"Johnson","given":"Andrew","affiliations":[{"id":78376,"text":"Maine Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":880290,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70247499,"text":"70247499 - 2023 - SaTSeaD: Satellite Triangulated Sea Depth open-source bathymetry module for NASA Ames Stereo Pipeline","interactions":[],"lastModifiedDate":"2023-08-10T11:47:28.20107","indexId":"70247499","displayToPublicDate":"2023-08-09T06:45:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"SaTSeaD: Satellite Triangulated Sea Depth open-source bathymetry module for NASA Ames Stereo Pipeline","docAbstract":"<div class=\"html-p\">We developed the first-ever bathymetric module for the NASA Ames Stereo Pipeline (ASP) open-source topographic software called Satellite Triangulated Sea Depth, or SaTSeaD, to derive nearshore bathymetry from stereo imagery. Correct bathymetry measurements depend on water surface elevation, and whereas previous methods considered the water surface horizontal, our bathymetric module accounts for the curvature of the Earth in the imagery. The process is semiautomatic, reliable, and repeatable, independent of any external bathymetry data eliminating user bias in selecting bathymetry calibration points, and it can generate a fully integrated and seamless topo-bathymetry digital elevation model (TBDEM) in the same coordinate system, comparable with the band-ratio method irrespective of the regression method used for the band-ratio algorithm. The ASP output can be improved by applying a camera bundle adjustment to minimize reprojection errors and by alignment to a more accurate topographic (above water) surface without any bathymetric input since the derived TBDEM is a rigid surface. These procedures can decrease bathymetry root mean square errors from 30 to 80 percent, depending on environmental conditions, the quality of satellite imagery, and the spectral band used (e.g., blue, green, or panchromatic).</div>","language":"English","publisher":"MDPI","doi":"10.3390/rs15163950","usgsCitation":"Palaseanu-Lovejoy, M., Alexandrov, O., Danielson, J.J., and Storlazzi, C.D., 2023, SaTSeaD: Satellite Triangulated Sea Depth open-source bathymetry module for NASA Ames Stereo Pipeline: Remote Sensing, v. 15, no. 16, 3950, 28 p., https://doi.org/10.3390/rs15163950.","productDescription":"3950, 28 p.","ipdsId":"IP-154732","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":442479,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs15163950","text":"Publisher Index Page"},{"id":419692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"16","noUsgsAuthors":false,"publicationDate":"2023-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":305576,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","affiliations":[],"preferred":true,"id":879885,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alexandrov, Oleg","contributorId":299745,"corporation":false,"usgs":false,"family":"Alexandrov","given":"Oleg","affiliations":[],"preferred":false,"id":879886,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":879887,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879888,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70247917,"text":"70247917 - 2023 - Functional gene composition and metabolic potential of deep-sea coral-associated microbial communities","interactions":[],"lastModifiedDate":"2023-10-11T15:49:50.8594","indexId":"70247917","displayToPublicDate":"2023-08-09T06:39:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1338,"text":"Coral Reefs","active":true,"publicationSubtype":{"id":10}},"title":"Functional gene composition and metabolic potential of deep-sea coral-associated microbial communities","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Over the past decade, an abundance of 16S rRNA gene surveys have provided microbiologists with data regarding the prokaryotes present in a coral-associated microbial community. Functional gene studies that provide information regarding what those microbes might do are fewer, particularly for non-tropical corals. Using the GeoChip 5.0S microarray, we present a functional gene study of microbiomes from five species of cold-water corals collected from depths of 296–1567&nbsp;m. These species included two octocorals,<span>&nbsp;</span><i>Acanthogorgia aspera</i><span>&nbsp;</span>and<span>&nbsp;</span><i>Acanthogorgia spissa</i>, and three stony corals:<span>&nbsp;</span><i>Desmophyllum dianthus</i>,<span>&nbsp;</span><i>Desmophyllum pertusum</i><span>&nbsp;</span>(formerly<span>&nbsp;</span><i>Lophelia pertusa</i>), and<span>&nbsp;</span><i>Enallopsammia profunda</i>. A total of 24,281 gene sequences (representing different microbial taxa) encoding for 383 functional gene families and representing 9 metabolic gene categories were identified. Gene categories included metabolism of carbon, nitrogen, phosphorus, and sulfur, as well as virulence, organic remediation, metal homeostasis, secondary metabolism and phylogeny. We found that microbiomes from<span>&nbsp;</span><i>Acanthogorgia</i><span>&nbsp;</span>spp. were the most functionally distinct but also least diverse compared against those from stony corals.<span>&nbsp;</span><i>Desmophyllum</i><span>&nbsp;</span>spp. microbiomes were more similar to each other than to<span>&nbsp;</span><i>E. profunda</i>. Of 383 total gene families detected in this study, less than 20% were significantly different among these deep-water coral species. Similarly, out of 59 metabolic sub-categories for which we were able to make a direct comparison to microbiomes of tropical corals, only 7 were notably different: anaerobic ammonium oxidation (anammox), chitin degradation, and dimethylsulfoniopropionate (DMSP) degradation, all of which had higher representations in deep-water corals; and chromium homeostasis/resistance, copper homeostasis/resistance, antibiotic resistance, and methanogenesis, all of which had higher representation in tropical corals. This implies a broad-scale convergence of the microbial functional genes present within the coral holobiont, independent of coral species, depth, symbiont status, and morphology.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s00338-023-02409-0","usgsCitation":"Pratte, Z.A., Stewart, F.J., and Kellogg, C.A., 2023, Functional gene composition and metabolic potential of deep-sea coral-associated microbial communities: Coral Reefs, v. 42, p. 1011-1023, https://doi.org/10.1007/s00338-023-02409-0.","productDescription":"13 p.","startPage":"1011","endPage":"1023","ipdsId":"IP-144192","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":442481,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00338-023-02409-0","text":"Publisher Index Page"},{"id":435229,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RPE8YX","text":"USGS data release","linkHelpText":"Functional Gene Microarray Data From Cold-water Corals (Acanthogorgia spp., Desmophyllum dianthus, Desmophyllum pertusum, and Enallopsammia profunda) from the Atlantic Ocean off the Southeast Coast of the United States-Raw Data"},{"id":420106,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","noUsgsAuthors":false,"publicationDate":"2023-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Pratte, Zoe A.","contributorId":214260,"corporation":false,"usgs":false,"family":"Pratte","given":"Zoe","email":"","middleInitial":"A.","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":881003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, Frank J.","contributorId":328672,"corporation":false,"usgs":false,"family":"Stewart","given":"Frank","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":881004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kellogg, Christina A. 0000-0002-6492-9455 ckellogg@usgs.gov","orcid":"https://orcid.org/0000-0002-6492-9455","contributorId":391,"corporation":false,"usgs":true,"family":"Kellogg","given":"Christina","email":"ckellogg@usgs.gov","middleInitial":"A.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":881005,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70246990,"text":"fs20233021 - 2023 - Assessment of continuous oil and gas resources in the Upper Jurassic Smackover Formation of the onshore U.S. Gulf Coast, 2022","interactions":[],"lastModifiedDate":"2026-02-09T17:26:23.426867","indexId":"fs20233021","displayToPublicDate":"2023-08-08T11:50:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3021","displayTitle":"Assessment of Continuous Oil and Gas Resources in the Upper Jurassic Smackover Formation of the Onshore U.S. Gulf Coast, 2022","title":"Assessment of continuous oil and gas resources in the Upper Jurassic Smackover Formation of the onshore U.S. Gulf Coast, 2022","docAbstract":"<p>Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean continuous resources of 0.8 billion barrels of oil and 16 trillion cubic feet of gas in the Upper Jurassic Smackover Formation of the onshore U.S. Gulf Coast region.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20233021","programNote":"National and Global Petroleum Assessment","usgsCitation":"Whidden, K.J., Birdwell, J.E., Gardner, R.D. Kinney, S.A., Paxton, S.T., Pitman, J.K., and Schenk, C.J., 2023, Assessment of continuous oil and gas resources in the Upper Jurassic Smackover Formation of the onshore U.S. Gulf Coast, 2022:  U.S. Geological Survey Fact Sheet, 2023–3021, 4 p., https://doi.org/10.3133/fs20233021.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-142174","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":419215,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VUEV4S","text":"USGS data release","linkHelpText":"USGS Gulf Coast Geologic Energy Assessments and Research Project-Upper Jurassic Smackover Formation Continuous Resources: Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":419711,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2023-3021"},{"id":419603,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3021/fs20233021.xml"},{"id":419203,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3021/coverthb.jpg"},{"id":419205,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3021/fs20233021.pdf","text":"Report","size":"960 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3021"},{"id":419602,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3021/images"},{"id":499687,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115127.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Alabama, Arkansas, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -99.63954266217267,\n              33.987977712674066\n            ],\n            [\n              -100.05858425781297,\n              27.930954901716802\n            ],\n            [\n              -83.70971819105422,\n              29.49496018600658\n            ],\n            [\n              -86.11747118404872,\n              32.090935524736096\n            ],\n            [\n              -99.63954266217267,\n              33.987977712674066\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/central-energy-resources-science-center\" data-mce-href=\"https://www.usgs.gov/centers/central-energy-resources-science-center\">Central Energy Resources Science Center</a><br>U.S. Geological Survey<br>Box 25046, MS-939<br>Denver, CO 80225-0046</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"https://pubs.er.usgs.gov/contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Total Petroleum System and Assessment Units</li><li>Undiscovered Resources Summary</li><li>Acknowledgment</li><li>References Cited</li></ul>","publishedDate":"2023-08-08","noUsgsAuthors":false,"publicationDate":"2023-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Whidden, Katherine J. 0000-0002-7841-2553 kwhidden@usgs.gov","orcid":"https://orcid.org/0000-0002-7841-2553","contributorId":3960,"corporation":false,"usgs":true,"family":"Whidden","given":"Katherine","email":"kwhidden@usgs.gov","middleInitial":"J.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":878483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":878484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gardner, Rand 0000-0001-8711-5334","orcid":"https://orcid.org/0000-0001-8711-5334","contributorId":316831,"corporation":false,"usgs":true,"family":"Gardner","given":"Rand","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":878485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kinney, Scott A. 0000-0001-5008-5813 skinney@usgs.gov","orcid":"https://orcid.org/0000-0001-5008-5813","contributorId":1395,"corporation":false,"usgs":true,"family":"Kinney","given":"Scott","email":"skinney@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":878486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paxton, Stanley T. 0000-0002-9098-1740 spaxton@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-1740","contributorId":739,"corporation":false,"usgs":true,"family":"Paxton","given":"Stanley","email":"spaxton@usgs.gov","middleInitial":"T.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":878487,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pitman, Janet K. 0000-0002-0441-779X","orcid":"https://orcid.org/0000-0002-0441-779X","contributorId":228982,"corporation":false,"usgs":true,"family":"Pitman","given":"Janet K.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":878488,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schenk, Christopher J. 0000-0002-0248-7305 schenk@usgs.gov","orcid":"https://orcid.org/0000-0002-0248-7305","contributorId":826,"corporation":false,"usgs":true,"family":"Schenk","given":"Christopher","email":"schenk@usgs.gov","middleInitial":"J.","affiliations":[{"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":878489,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70247097,"text":"70247097 - 2023 - Bighorn sheep associations: Understanding tradeoffs of sociality and implications for disease transmission","interactions":[],"lastModifiedDate":"2023-08-10T16:29:14.149297","indexId":"70247097","displayToPublicDate":"2023-08-08T11:16:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Bighorn sheep associations: Understanding tradeoffs of sociality and implications for disease transmission","docAbstract":"<p><span>Sociality directly influences mating success, survival rates, and disease, but ultimately likely evolved for its fitness benefits in a challenging environment. The tradeoffs between the costs and benefits of sociality can operate at multiple scales, resulting in different interpretations of animal behavior. We investigated the influence of intrinsic (</span><i>e.g.</i><span>, relatedness, age) and extrinsic factors (</span><i>e.g.</i><span>, land cover type, season) on direct contact (simultaneous GPS locations ≤ 25 m) rates of bighorn sheep (</span><i>Ovis canadensis</i><span>) at multiple scales near the Waterton-Glacier International Peace Park. During 2002–2012, male and female bighorn were equipped with GPS collars. Indirect contact (GPS locations ≤ 25 m regardless of time) networks identified two major breaks whereas direct contact networks identified an additional barrier in the population, all of which corresponded with prior disease exposure metrics. More direct contacts occurred between same-sex dyads than female-male dyads and between bighorn groups with overlapping summer home ranges. Direct contacts occurred most often during the winter-spring season when bighorn traveled at low speeds and when an adequate number of bighorn were collared in the area. Direct contact probabilities for all dyad types were inversely related to habitat quality, and differences in contact probability were driven by variables related to survival such as terrain ruggedness, distance to escape terrain, and canopy cover. We provide evidence that probabilities of association are higher when there is greater predation risk and that contact analysis provides valuable information for understanding fitness tradeoffs of sociality and disease transmission potential.</span></p>","language":"English","publisher":"PeerJ Inc.","doi":"10.7717/peerj.15625","usgsCitation":"Tosa, M., Biel, M., and Graves, T.A., 2023, Bighorn sheep associations: Understanding tradeoffs of sociality and implications for disease transmission: PeerJ, v. 11, e15625, 28 p., https://doi.org/10.7717/peerj.15625.","productDescription":"e15625, 28 p.","ipdsId":"IP-097793","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":442484,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.7717/peerj.15625","text":"Publisher Index Page"},{"id":435230,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ANTBOF","text":"USGS data release","linkHelpText":"Glacier Waterton International Peace Park bighorn sheep (Ovis canadensis), 2002-2012"},{"id":419710,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, British Columbia, Montana","otherGeospatial":"Blackfeet Indian Reservation, Waterton-Glacier International Peace Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -114.21954038442665,\n              49.16160622487246\n            ],\n            [\n              -113.83879589961161,\n              48.6100611857764\n            ],\n            [\n              -112.89302799949076,\n              48.57343225752126\n            ],\n            [\n              -113.59820762202632,\n              49.252735925458836\n            ],\n            [\n              -114.21954038442665,\n              49.16160622487246\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Tosa, Marie","contributorId":317263,"corporation":false,"usgs":false,"family":"Tosa","given":"Marie","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":878870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biel, Mark","contributorId":317264,"corporation":false,"usgs":false,"family":"Biel","given":"Mark","email":"","affiliations":[{"id":68985,"text":"GNP","active":true,"usgs":false}],"preferred":false,"id":878871,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graves, Tabitha A. 0000-0001-5145-2400 tgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":5898,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha","email":"tgraves@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":878872,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70249411,"text":"70249411 - 2023 - The blue carbon reservoirs from Maine to Long Island, NY","interactions":[],"lastModifiedDate":"2023-10-10T15:33:18.522773","indexId":"70249411","displayToPublicDate":"2023-08-08T10:23:52","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"The blue carbon reservoirs from Maine to Long Island, NY","docAbstract":"<p>In response to the New England Governor and Eastern Canadian Premier 2017 Climate Change Action Plan recommendation to “manage blue carbon resources to preserve and enhance their existing carbon reservoirs,” the U.S. Environmental Protection Agency (EPA) convened a New England Blue Carbon Inventory Workgroup, comprised of a variety of federal, state, academic, and non-profit organizations to develop an inventory of blue carbon stocks from Maine to Long Island, New York. The Workgroup focused its inventory efforts on salt marshes and eelgrass meadows, leveraging existing habitat maps for geographic data. Existing data for soil organic carbon stocks were then used to calculate blue carbon stock estimates. For visual display purposes, sediment carbon heat maps were developed to highlight areas of greatest carbon accumulation. The habitat distribution and sediment carbon heat maps can be accessed on the Northeast Ocean Data Portal (www.northeastoceandata.org/eelgrass) which is a public source of expert-reviewed, interactive maps and data on the ocean ecosystem, economy, and culture of the northeastern United States and can be used to facilitate decision making by government agencies, tribal nations, businesses, non-governmental organizations (NGOs), academic institutions, and individuals. Based on available data and Workgroup calculations, the target geographic area has an estimated 218,222 acres of eelgrass meadows, salt marsh and saline Phragmites, which are estimated to provide a reservoir of 7,523,568 megagrams of blue carbon, or the equivalent to the annual carbon emissions from over 5,944,024 passenger vehicles. Due to data limitations, the carbon stock estimate represents a mere fraction of the actual quantity of accumulated carbon in these habitats. The findings from the Workgroup’s efforts and the resulting map products can help inform land and coastal management policies, fisheries management, and climate change mitigation practices. Further refinements and expansion of data are needed, including more detailed habitat maps, deeper soil core data for soil organic carbon content, and inclusion of more marine flora into calculations. </p>","language":"English","publisher":"Environmental Protection Agency","usgsCitation":"Colarusso, P., Libohova, Z., Shumchenia, E., Eagle, M.J., Christian, M., Vincent, R., and Johnson, B., 2023, The blue carbon reservoirs from Maine to Long Island, NY, 31 p.","productDescription":"31 p.","ipdsId":"IP-146585","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":421823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421698,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.northeastoceandata.org/files/metadata/Themes/Habitat/EPABlueCarbonReport.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -67.61511760727777,\n              45.22754478031186\n            ],\n            [\n              -69.46772958832273,\n              44.45391256542621\n            ],\n            [\n              -70.41808310369574,\n              44.0249641018155\n            ],\n            [\n              -71.18235185260586,\n              43.43128866248264\n            ],\n            [\n              -71.38130070335747,\n              41.926291445574776\n            ],\n            [\n              -72.88727252479508,\n              41.59585766120409\n            ],\n            [\n              -74.23639888538038,\n              41.298612606793824\n            ],\n            [\n              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D.","contributorId":218700,"corporation":false,"usgs":false,"family":"Colarusso","given":"Philip D.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":885521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Libohova, Zamir","contributorId":330648,"corporation":false,"usgs":false,"family":"Libohova","given":"Zamir","email":"","affiliations":[{"id":63834,"text":"United States Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":885522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shumchenia, Emily","contributorId":330649,"corporation":false,"usgs":false,"family":"Shumchenia","given":"Emily","email":"","affiliations":[{"id":78947,"text":"Northeast Regional Ocean Council","active":true,"usgs":false}],"preferred":false,"id":885523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":885524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christian, Megan","contributorId":330651,"corporation":false,"usgs":false,"family":"Christian","given":"Megan","email":"","affiliations":[{"id":63834,"text":"United States Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":885525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vincent, Robert","contributorId":330652,"corporation":false,"usgs":false,"family":"Vincent","given":"Robert","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":885526,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Beverly","contributorId":330653,"corporation":false,"usgs":false,"family":"Johnson","given":"Beverly","email":"","affiliations":[{"id":33413,"text":"Bates College","active":true,"usgs":false}],"preferred":false,"id":885527,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70247801,"text":"70247801 - 2023 - Evolutionary fire ecology: An historical account and future directions","interactions":[],"lastModifiedDate":"2023-08-18T11:50:47.619613","indexId":"70247801","displayToPublicDate":"2023-08-08T06:50:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":997,"text":"BioScience","active":true,"publicationSubtype":{"id":10}},"title":"Evolutionary fire ecology: An historical account and future directions","docAbstract":"<p class=\"chapter-para\">The idea that fire acts as an evolutionary force contributing to shaping species traits started a century ago, but had not been widely recognized until very recently. Among the first to realize this force were<span>&nbsp;</span><i>E</i>dward<span>&nbsp;</span><i>B</i>. Poulton,<span>&nbsp;</span><i>R. D</i>ale Guthrie, and<span>&nbsp;</span><i>E</i>dwin<span>&nbsp;</span><i>V</i>. Komarek in animals and<span>&nbsp;</span><i>W</i>illis<span>&nbsp;</span><i>L</i>. Jepson,<span>&nbsp;</span><i>W</i>alter<span>&nbsp;</span><i>W</i>. Hough,<span>&nbsp;</span><i>T</i>om<span>&nbsp;</span><i>M</i>. Harris,<span>&nbsp;</span><i>P</i>hilip<span>&nbsp;</span><i>V</i>. Wells, and<span>&nbsp;</span><i>R</i>obert<span>&nbsp;</span><i>W</i>. Mutch in plants. They were all ahead of their time in their evolutionary thinking. Since then, evolutionary fire ecology has percolated very slowly into the mainstream ecology and evolutionary biology; in fact, this topic is still seldom mentioned in textbooks of ecology or evolution. Currently, there is plenty of evidence suggesting that we cannot understand the biodiversity of our planet without considering the key evolutionary role of fire. But there is still research to be done in order to fully understand fire's contribution to species evolution and to predicting species responses to rapid global changes.</p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biad059","usgsCitation":"Pausas, J.G., and Keeley, J., 2023, Evolutionary fire ecology: An historical account and future directions: BioScience, biad059, 7 p., https://doi.org/10.1093/biosci/biad059.","productDescription":"biad059, 7 p.","ipdsId":"IP-151839","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":442489,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biad059","text":"Publisher Index Page"},{"id":419922,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2023-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Pausas, Juli G.","contributorId":197439,"corporation":false,"usgs":false,"family":"Pausas","given":"Juli","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":880500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":880501,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70247684,"text":"70247684 - 2023 - Fuel treatments in shrublands experiencing pinyon and juniper expansion result in trade-offs between desired vegetation and increased fire behavior","interactions":[],"lastModifiedDate":"2023-08-11T16:24:22.45893","indexId":"70247684","displayToPublicDate":"2023-08-07T09:32:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fuel treatments in shrublands experiencing pinyon and juniper expansion result in trade-offs between desired vegetation and increased fire behavior","docAbstract":"<h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Background</h3><p>Native pinyon (<i>Pinus</i><span>&nbsp;</span>spp.) and juniper (<i>Juniperus</i><span>&nbsp;</span>spp.) trees are expanding into shrubland communities across the Western United States. These trees often outcompete with native sagebrush (<i>Artemisia</i><span>&nbsp;</span>spp.) associated species, resulting in increased canopy fuels and reduced surface fuels. Woodland expansion often results in longer fire return intervals with potential for high severity crown fire. Fuel treatments are commonly used to prevent continued tree infilling and growth and reduce fire risk, increase ecological resilience, improve forage quality and quantity, and/or improve wildlife habitat. Treatments may present a trade-off; they restore shrub and herbaceous cover and decrease risk of canopy fire but may increase surface fuel load and surface fire potential. We measured the accumulation of surface and canopy fuels over 10 years from ten sites across the Intermountain West in the Sagebrush Steppe Treatment Evaluation Project woodland network (<a href=\"http://www.sagestep.org/\" data-mce-href=\"http://www.sagestep.org/\">www.SageSTEP.org</a>), which received prescribed fire or mechanical (cut and drop) tree reduction treatments. We used the field data and the Fuel Characteristic Classification System (FCCS) in the Fuel and Fire Tools (FFT) application to estimate surface and canopy fire behavior in treated and control plots in tree expansion phases I, II, and III.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Results</h3><p>Increased herbaceous surface fuel following prescribed fire treatments increased the modeled rate of surface fire spread (ROS) 21-fold and nearly tripled flame length (FL) by year ten post-treatment across all expansion phases. In mechanical treatments, modeled ROS increased 15-fold, FL increased 3.8-fold, and reaction intensity roughly doubled in year ten post-treatment compared to pretreatment and untreated controls. Treatment effects were most pronounced at 97th percentile windspeeds, with modeled ROS up to 82&nbsp;m min<sup>−1</sup><span>&nbsp;</span>in mechanical and 106&nbsp;m min<sup>−1</sup><span>&nbsp;</span>in prescribed fire treatments by 10 years post-treatment compared to 5&nbsp;m min<sup>−1</sup><span>&nbsp;</span>in untreated controls. Crown fire transmissivity risk was eliminated by both fuel treatments.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Conclusions</h3><p>While prescribed fire and mechanical treatments in shrublands experiencing tree expansion restored understory vegetation and prevented continued juniper and pinyon infilling and growth, these fuel treatments also increased modeled surface fire behavior. Thus, management tradeoffs occur between desired future vegetation and wildfire risk after fuel treatments.</p>","language":"English","publisher":"Springer","doi":"10.1186/s42408-023-00201-7","usgsCitation":"Williams, C.L., Ellsworth, L., Strand, E., Reeves, M.C., Shaff, S.E., Short, K., Chambers, J., Newingham, B., and Tortorelli, C., 2023, Fuel treatments in shrublands experiencing pinyon and juniper expansion result in trade-offs between desired vegetation and increased fire behavior: Fire Ecology, v. 19, 46, 21 p., https://doi.org/10.1186/s42408-023-00201-7.","productDescription":"46, 21 p.","ipdsId":"IP-154672","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":442492,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-023-00201-7","text":"Publisher Index Page"},{"id":419748,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada, Oregon, Utah","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -112.242321499979,\n              37.26614922694803\n            ],\n            [\n              -111.73150627380747,\n              40.33982762810214\n            ],\n            [\n              -117.02080093108165,\n              40.473878098349985\n            ],\n            [\n              -116.74789132368525,\n              38.56409342781134\n            ],\n            [\n              -112.242321499979,\n              37.26614922694803\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": 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State University","active":true,"usgs":false}],"preferred":false,"id":880021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellsworth, Lisa M.","contributorId":328375,"corporation":false,"usgs":false,"family":"Ellsworth","given":"Lisa M.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":880022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Strand, Eva","contributorId":328376,"corporation":false,"usgs":false,"family":"Strand","given":"Eva","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":880023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reeves, Matt C.","contributorId":328377,"corporation":false,"usgs":false,"family":"Reeves","given":"Matt","email":"","middleInitial":"C.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":880024,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shaff, Scott E. 0000-0001-8978-9260","orcid":"https://orcid.org/0000-0001-8978-9260","contributorId":219813,"corporation":false,"usgs":true,"family":"Shaff","given":"Scott","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":880025,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Short, Karen","contributorId":328378,"corporation":false,"usgs":false,"family":"Short","given":"Karen","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":880026,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chambers, Jeanne C.","contributorId":328379,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne C.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":880027,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Newingham, Beth","contributorId":328380,"corporation":false,"usgs":false,"family":"Newingham","given":"Beth","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":880028,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tortorelli, Claire","contributorId":328381,"corporation":false,"usgs":false,"family":"Tortorelli","given":"Claire","email":"","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":880029,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70249617,"text":"70249617 - 2023 - Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment-bound bacterial communities in agricultural streams","interactions":[],"lastModifiedDate":"2024-09-16T16:08:11.965666","indexId":"70249617","displayToPublicDate":"2023-08-07T09:15:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment-bound bacterial communities in agricultural streams","docAbstract":"<p><span>Agriculture is the most dominant land use globally and is projected to increase in the future to support a growing human population but also threatens ecosystem structure and services. Bacteria mediate numerous biogeochemical pathways within ecosystems. Therefore, identifying linkages between stressors associated with agricultural land use and responses of bacterial diversity is an important step in understanding and improving resource management. Here, we use the Mississippi Alluvial Plain (MAP) ecoregion, a highly modified agroecosystem, as a case study to better understand agriculturally associated drivers of stream bacterial diversity and assembly mechanisms. In the MAP, we found that planktonic bacterial communities were strongly influenced by salinity. Tolerant taxa increased with increasing ion concentrations, likely driving homogenous selection which accounted for ~90% of assembly processes. Sediment bacterial phylogenetic diversity increased with increasing agricultural land use and was influenced by sediment particle size, with assembly mechanisms shifting from homogenous to variable selection as differences in median particle size increased. Within individual streams, sediment heterogeneity was correlated with bacterial diversity and a subsidy-stress relationship along the particle size gradient was observed. Planktonic and sediment communities within the same stream also diverged as sediment particle size decreased. Nutrients including carbon, nitrogen, and phosphorus, which tend to be elevated in agroecosystems, were also associated with detectable shifts in bacterial community structure. Collectively, our results establish that two understudied variables, salinity and sediment texture, are the primary drivers of bacterial diversity within the studied agroecosystem, whereas nutrients are secondary drivers. Although numerous macrobiological communities respond negatively, we observed increasing bacterial diversity in response to agricultural stressors including salinization and sedimentation. Elevated taxonomic and phylogenetic bacterial diversity likely increases the probability of detecting community responses to stressors. Thus, bacteria community responses may be more reliable for establishing water quality goals within highly modified agroecosystems that have experienced shifting baselines.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16905","usgsCitation":"DeVilbiss, S.E., Taylor, J.M., and Hicks, M.B., 2023, Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment-bound bacterial communities in agricultural streams: Global Change Biology, v. 29, no. 19, p. 5615-5633, https://doi.org/10.1111/gcb.16905.","productDescription":"19 p.","startPage":"5615","endPage":"5633","ipdsId":"IP-147797","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":442494,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.16905","text":"Publisher Index Page"},{"id":421998,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Mississippi Alluvial Plain ecoregion","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -91.5140953224701,\n              31.05055950304515\n            ],\n            [\n              -90.8551102985312,\n              32.36507651262579\n            ],\n            [\n              -89.83254733034994,\n              33.34811466354185\n            ],\n            [\n              -89.71892922277418,\n              33.94399111104305\n            ],\n            [\n              -90.08250716701652,\n              35.020996431931664\n            ],\n            [\n              -90.25293432837982,\n              34.97445988995358\n            ],\n            [\n              -90.4347233005013,\n              34.82536524774929\n            ],\n            [\n              -90.54834140807708,\n              34.67600021318641\n            ],\n            [\n              -90.61651227262215,\n              34.376461081479576\n            ],\n            [\n              -90.76421581247044,\n              34.29202179657514\n            ],\n            [\n              -90.95736659534946,\n              34.13229367636508\n            ],\n            [\n              -91.0255374598945,\n              33.91570969239025\n            ],\n            [\n              -91.15051737822778,\n              33.54719821260997\n            ],\n            [\n              -91.11643194595524,\n              33.06291844822118\n            ],\n            [\n              -91.13915556747027,\n              32.709900045262685\n            ],\n            [\n              -90.95736659534946,\n              32.38426808119267\n            ],\n            [\n              -91.57090437625834,\n              31.361530547535793\n            ],\n            [\n              -91.6731606730759,\n              31.0213536083742\n            ],\n            [\n              -91.5140953224701,\n              31.05055950304515\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"29","issue":"19","noUsgsAuthors":false,"publicationDate":"2023-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"DeVilbiss, Stephen E.","contributorId":316291,"corporation":false,"usgs":false,"family":"DeVilbiss","given":"Stephen","email":"","middleInitial":"E.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":886463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Jason M.","contributorId":100678,"corporation":false,"usgs":true,"family":"Taylor","given":"Jason","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":886464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hicks, Matthew B. 0000-0001-5516-0296 mhicks@usgs.gov","orcid":"https://orcid.org/0000-0001-5516-0296","contributorId":3778,"corporation":false,"usgs":true,"family":"Hicks","given":"Matthew","email":"mhicks@usgs.gov","middleInitial":"B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886465,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70247476,"text":"70247476 - 2023 - Why are larger fish farther upstream? Testing multiple hypotheses using Silver Chub in two Midwestern United States riverscapes","interactions":[],"lastModifiedDate":"2023-11-07T15:27:31.071457","indexId":"70247476","displayToPublicDate":"2023-08-07T06:59:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Why are larger fish farther upstream? Testing multiple hypotheses using Silver Chub in two Midwestern United States riverscapes","docAbstract":"<h3 id=\"nafm10903-sec-1000-title\" class=\"article-section__sub-title section1\">Objective</h3><p>Three competing hypotheses might explain the widely documented intrapopulation larger-fish-upstream phenomenon. The age-phased recruitment hypothesis posits that fish spawn downstream and move upstream as they age and grow, the static population with growth and mortality gradients hypothesis posits that fish spawn throughout a riverscape and growth is greater upstream while recruitment is greater downstream, and the colonization cycle hypothesis posits that fish spawn upstream, larvae drift downstream, and individuals move upstream as they age and grow.</p><h3 id=\"nafm10903-sec-1001-title\" class=\"article-section__sub-title section1\">Methods</h3><p>We tested for the larger-fish-upstream pattern using populations of Silver Chub<span>&nbsp;</span><i>Macrhybopsis storeriana</i><span>&nbsp;</span>in the Arkansas and Ohio rivers, as well as investigated longitudinal variation in reproductive investment (Arkansas River), age structure for adult fish (Arkansas River), and number and occurrence of age-0 fish (Ohio River).</p><h3 id=\"nafm10903-sec-1002-title\" class=\"article-section__sub-title section1\">Result</h3><p>The larger-fish-upstream pattern was temporally persistent in both riverscapes. In the Arkansas River, reproductive investment was greatest upstream, where initiation of spawning likely occurred based on gonadosomatic indices. Adult fish were most numerous in the Arkansas River 125–175 km upstream from Kaw Reservoir, with age-2 fish numbers peaking farther upstream compared with age-1 fish. In the Ohio River, age-0 fish counts increased downstream and were rare among the shortest river fragments (&lt;100 km) between lock-and-dam structures. These findings are inconsistent with the age-phased recruitment hypothesis based on upstream spawning in the Arkansas River and inconsistent with the static population with growth and mortality gradients hypothesis based on virtual absence of age-2 fish downstream (Arkansas River) and age-0 fish upstream (Ohio River). The most likely explanation for longitudinal variation in Silver Chub size distribution is downstream drift of ichthyoplankton followed by net upstream movement (i.e., colonization cycle hypothesis), but formal assessments of movement and ova characteristics require more research.</p><h3 id=\"nafm10903-sec-1004-title\" class=\"article-section__sub-title section1\">Conclusion</h3><p>Managing multidimensional riverscapes requires insight into the mechanisms that regulate upstream-to-downstream patterns in fish populations, and our work underscores a potential size-related benefit to maintaining broadscale longitudinal connectivity.</p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10903","usgsCitation":"Perkin, J., Kocovsky, P.M., Steffensmeier, Z.D., and Gido, K.B., 2023, Why are larger fish farther upstream? Testing multiple hypotheses using Silver Chub in two Midwestern United States riverscapes: North American Journal of Fisheries Management, v. 43, no. 5, p. 1225-1245, https://doi.org/10.1002/nafm.10903.","productDescription":"21 p.","startPage":"1225","endPage":"1245","ipdsId":"IP-137978","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":419658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Arkansas River, Ohio River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -80.13429431625079,\n              42.033412294725736\n            ],\n            [\n              -89.83662411202006,\n              38.68964800967032\n            ],\n            [\n              -90.424027695692,\n              36.14679269166915\n            ],\n            [\n              -80.71262443169684,\n              38.24466463362809\n            ],\n            [\n              -79.88744096795848,\n              40.15147528018017\n            ],\n            [\n              -80.13429431625079,\n              42.033412294725736\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -98.08402461622501,\n              38.05521661406516\n            ],\n            [\n              -98.10568817604296,\n              36.76504675151496\n            ],\n            [\n              -96.73029240586655,\n              36.49642782881216\n            ],\n            [\n              -96.73029240586655,\n              38.12463929495229\n            ],\n            [\n              -98.08402461622501,\n              38.05521661406516\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Perkin, Joshuah S.","contributorId":238286,"corporation":false,"usgs":false,"family":"Perkin","given":"Joshuah S.","affiliations":[{"id":47708,"text":"Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX","active":true,"usgs":false}],"preferred":false,"id":879821,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"preferred":true,"id":879822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steffensmeier, Zachary D 0000-0003-3323-5968","orcid":"https://orcid.org/0000-0003-3323-5968","contributorId":317973,"corporation":false,"usgs":false,"family":"Steffensmeier","given":"Zachary","email":"","middleInitial":"D","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":879823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gido, Keith B.","contributorId":198487,"corporation":false,"usgs":false,"family":"Gido","given":"Keith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":879824,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70247508,"text":"70247508 - 2023 - A spatially explicit modeling framework to guide management of subsidized avian predator densities","interactions":[],"lastModifiedDate":"2023-08-10T11:43:08.209226","indexId":"70247508","displayToPublicDate":"2023-08-07T06:39:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"A spatially explicit modeling framework to guide management of subsidized avian predator densities","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>Anthropogenic resource subsidization across western ecosystems has contributed to widespread increases in generalist avian predators, including common ravens (<i>Corvus corax</i>; hereafter, raven). Ravens are adept nest predators and can negatively impact species of conservation concern. Predation effects from ravens are especially concerning for greater sage-grouse (<i>Centrocercus urophasianus</i>; hereafter, sage-grouse), which have experienced prolonged population decline. Our objectives were to quantify spatiotemporal patterns in raven density, evaluate sage-grouse nest success concurrent with fluctuating raven densities, and demonstrate a spatially explicit decision support tool to guide management applications to appropriate conflict areas. We combined ~28,000 raven point count surveys with data from more than 900 sage-grouse nests between 2009 and 2019 within the Great Basin, USA. We modeled variation in raven density using a Bayesian hierarchical distance sampling approach with environmental covariates on detection and abundance. Concurrently, we modeled sage-grouse nest survival using a hierarchical frailty model as a function of raven density and other environmental covariates that influence the risk of nest failure. Raven density commonly exceeded 0.5 ravens km<sup>−2</sup><span>&nbsp;</span>and increased at low elevations with more anthropogenic development and/or agriculture. Reduced sage-grouse nest survival was strongly associated with elevated raven density (e.g., &gt;0.5 ravens km<sup>−2</sup>) and varied with topographic ruggedness, shrub cover, and burned areas. For conservation application, we developed a spatially explicit planning tool that predicts nest survival under current and reduced raven numbers within the Great Basin to help direct management actions to localized areas where sage-grouse nests are at highest risk of failure. Our modeling framework can be generalized to multiple species where spatially registered abundance and demographic data are available.</p></div></div>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4618","usgsCitation":"O’Neil, S.T., Coates, P.S., Webster, S.C., Brussee, B.E., Dettenmaier, S.J., Tull, J.C., Jackson, P.J., Casazza, M.L., and Espinosa, S.P., 2023, A spatially explicit modeling framework to guide management of subsidized avian predator densities: Ecosphere, v. 14, no. 8, e4618, 20 p., https://doi.org/10.1002/ecs2.4618.","productDescription":"e4618, 20 p.","ipdsId":"IP-145230","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":442498,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4618","text":"Publisher Index Page"},{"id":435232,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96DW3EH","text":"USGS data release","linkHelpText":"Code for a hierarchical model of raven densities linked with sage-grouse nest survival to help guide management of subsidized avian predators, version 1.0"},{"id":435231,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BLVJTS","text":"USGS data release","linkHelpText":"Data to Support Hierarchical Models and Decision Support Maps to Guide Management of Subsidized Avian Predator Densities"},{"id":419691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Great Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -120.2868540455589,\n              42.470077280121444\n            ],\n            [\n              -120.24292757983179,\n              38.118170774915555\n            ],\n            [\n              -117.21200144466887,\n              36.332112780767496\n            ],\n            [\n              -114.35678117241328,\n              36.93261209362862\n            ],\n            [\n              -114.35678117241328,\n              42.470077280121444\n            ],\n            [\n              -120.2868540455589,\n              42.470077280121444\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"14","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":879908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":879909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Webster, Sarah C. 0000-0003-4981-2010","orcid":"https://orcid.org/0000-0003-4981-2010","contributorId":302117,"corporation":false,"usgs":true,"family":"Webster","given":"Sarah","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":879910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":879911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dettenmaier, Seth J. 0000-0001-6325-8808","orcid":"https://orcid.org/0000-0001-6325-8808","contributorId":302087,"corporation":false,"usgs":true,"family":"Dettenmaier","given":"Seth","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":879912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tull, John C. 0000-0002-0680-008X","orcid":"https://orcid.org/0000-0002-0680-008X","contributorId":201650,"corporation":false,"usgs":false,"family":"Tull","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":879913,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":879914,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":879915,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Espinosa, Shawn P.","contributorId":195583,"corporation":false,"usgs":false,"family":"Espinosa","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":879916,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70247807,"text":"70247807 - 2023 - Spatial and temporal overlap between hatchery- and natural-origin steelhead and Chinook salmon during spawning in the Klickitat River, Washington, USA","interactions":[],"lastModifiedDate":"2024-01-24T17:30:18.876165","indexId":"70247807","displayToPublicDate":"2023-08-07T06:39:16","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal overlap between hatchery- and natural-origin steelhead and Chinook salmon during spawning in the Klickitat River, Washington, USA","docAbstract":"<div id=\"article__content\" class=\"col-sm-12 col-md-8 col-lg-8 article__content article-row-left\"><div class=\"article__body \"><div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>A goal of many segregated salmonid hatchery programs is to minimize potential interbreeding between hatchery- and natural-origin fish. To assess this on the Klickitat River, Washington, USA, we used radiotelemetry during 2009–2014 to evaluate spatiotemporal spawning overlap between hatchery-origin and natural-origin steelhead<span>&nbsp;</span><i>Oncorhynchus mykiss</i><span>&nbsp;</span>and spring Chinook Salmon<span>&nbsp;</span><i>O. tshawytscha.</i><span>&nbsp;</span>We estimated percentages of tagged fish that spawned naturally in the Klickitat River subbasin, emigrated from the Klickitat River, or died before spawning. For steelhead, 12% of hatchery-origin and 50% of natural-origin fish spawned naturally. For spring Chinook Salmon, 18% of hatchery-origin and 44% of natural-origin fish spawned naturally. Tag loss may result in underestimates in these percentages. Most hatchery-origin steelhead (90%) spawned downstream of rkm 32 and 75% spawned from November to mid-March, while the majority of natural-origin steelhead (64%) spawned upstream of rkm 32 and 75% spawned from mid-March to late May. Spawn timing of hatchery-origin Chinook Salmon (early August to mid-September) overlapped with that of natural-origin Chinook Salmon (late July to late September), and fish of both origins spawned in the same 30-km reach of the river. We estimated the percent of hatchery-origin spawners on the natural spawning grounds (pHOS) to be 12% for steelhead and 40% for spring Chinook Salmon across all study years. A kernel density analysis was used to estimate probability of spatiotemporal overlap between hatchery- and natural-origin spawners. For steelhead, we estimated this overlap to be 25% (95% CI 22.5–28%). For spring Chinook Salmon, tight spatial clustering of hatchery-origin fish resulted in a lower overlap estimate of 21% (13%-31%). We suggest adjusting pHOS estimates using these overlap estimates or similar spatiotemporal data on actual spawner proximity and possible interactions, and that these types of analyses be used in conjunction with gene flow analysis to accurately evaluate effects of individual hatchery programs.</p></div></div></div></div>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10945","usgsCitation":"Zendt, J.S., Allen, B., Kock, T.J., Perry, R., and Pope, A., 2023, Spatial and temporal overlap between hatchery- and natural-origin steelhead and Chinook salmon during spawning in the Klickitat River, Washington, USA: North American Journal of Fisheries Management, v. 43, no. 6, p. 1687-1701, https://doi.org/10.1002/nafm.10945.","productDescription":"15 p.","startPage":"1687","endPage":"1701","ipdsId":"IP-150095","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":442500,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10945","text":"Publisher Index Page"},{"id":419920,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Klickitat River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -120.61464912453181,\n              45.72771938418035\n            ],\n            [\n              -120.82413483062325,\n              46.49480247077369\n            ],\n            [\n              -121.44510336977899,\n              46.52093875212026\n            ],\n            [\n              -121.51897546658203,\n              45.721330175550776\n            ],\n            [\n              -121.32915952789224,\n              45.676278902772864\n            ],\n            [\n              -121.23425155854733,\n              45.65807648481219\n            ],\n            [\n              -121.18872659764212,\n              45.5967150188734\n            ],\n            [\n              -121.03749113429595,\n              45.642554957636634\n            ],\n            [\n              -120.61464912453181,\n              45.72771938418035\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"43","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Zendt, Joseph S.","contributorId":328537,"corporation":false,"usgs":false,"family":"Zendt","given":"Joseph","email":"","middleInitial":"S.","affiliations":[{"id":78390,"text":"Yakama Nation Fisheries Program, 1575 Horseshoe Bend Road, Klickitat, WA 98628","active":true,"usgs":false}],"preferred":false,"id":880531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allen, Brady","contributorId":328538,"corporation":false,"usgs":false,"family":"Allen","given":"Brady","affiliations":[{"id":78392,"text":"Bonneville Power Administration, 905 NE 11th Avenue, Portland, OR 97232","active":true,"usgs":false}],"preferred":false,"id":880532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kock, Tobias J. 0000-0001-8976-0230","orcid":"https://orcid.org/0000-0001-8976-0230","contributorId":214550,"corporation":false,"usgs":true,"family":"Kock","given":"Tobias","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":880533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":880534,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pope, Adam C. 0000-0002-7253-2247","orcid":"https://orcid.org/0000-0002-7253-2247","contributorId":223237,"corporation":false,"usgs":true,"family":"Pope","given":"Adam","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":880535,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70247785,"text":"70247785 - 2023 - Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud-based analysis","interactions":[],"lastModifiedDate":"2023-11-20T17:36:37.180823","indexId":"70247785","displayToPublicDate":"2023-08-06T10:59:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5456,"text":"Limnology and Oceanography Letters","active":true,"publicationSubtype":{"id":10}},"title":"Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud-based analysis","docAbstract":"<p><span>Originally developed for terrestrial science and applications, the US Geological Survey Landsat surface reflectance (SR) archive spanning ~ 40 yr of observations has been increasingly utilized in large-scale water-quality studies. These products, however, have not been rigorously validated using in situ measured reflectance. This letter quantifies and demonstrates the quality of the SR products by harnessing a sizeable global dataset (</span><i>N</i><span> = 1100). We found that the Landsat 8/9 SR in the green and red bands marginally meet the targeted accuracy requirements (30%), whereas the uncertainties in the blue and coastal-aerosol bands ranged from 48% to 110%. We further observed &gt; +25% biases in the visible bands of Landsat 5/7 SR, which can introduce an apparent downward trend when applied in time-series analyses combined with Landsat 8/9. Users must exercise caution when using this archive for trend analyses, and progress in atmospheric correction is required to foster advanced applications of the Landsat archive for aquatic science.</span></p>","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lol2.10344","usgsCitation":"Maciel, D.A., Pahlevan, N., Barbosa, C.C., de Moraes de Novo, E.M., Paulino, R.S., Martins, V.S., Vermote, E., and Crawford, C., 2023, Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud-based analysis: Limnology and Oceanography Letters, v. 8, no. 6, p. 820-858, https://doi.org/10.1002/lol2.10344.","productDescription":"9 p.","startPage":"820","endPage":"858","ipdsId":"IP-149014","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":442503,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.10344","text":"Publisher Index Page"},{"id":419891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Maciel, Daniel Andrade","contributorId":328506,"corporation":false,"usgs":false,"family":"Maciel","given":"Daniel","email":"","middleInitial":"Andrade","affiliations":[{"id":78384,"text":"INPE","active":true,"usgs":false}],"preferred":false,"id":880451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pahlevan, Nima","contributorId":328507,"corporation":false,"usgs":false,"family":"Pahlevan","given":"Nima","affiliations":[{"id":78385,"text":"NASA GSFC/ SSAI","active":true,"usgs":false}],"preferred":false,"id":880452,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barbosa, Claudio Clemente Faria","contributorId":328508,"corporation":false,"usgs":false,"family":"Barbosa","given":"Claudio","email":"","middleInitial":"Clemente Faria","affiliations":[{"id":78384,"text":"INPE","active":true,"usgs":false}],"preferred":false,"id":880453,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Moraes de Novo, Evlyn Marcia Leao","contributorId":328509,"corporation":false,"usgs":false,"family":"de Moraes de Novo","given":"Evlyn","email":"","middleInitial":"Marcia Leao","affiliations":[{"id":78384,"text":"INPE","active":true,"usgs":false}],"preferred":false,"id":880454,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paulino, Rejane Souza","contributorId":328510,"corporation":false,"usgs":false,"family":"Paulino","given":"Rejane","email":"","middleInitial":"Souza","affiliations":[{"id":78384,"text":"INPE","active":true,"usgs":false}],"preferred":false,"id":880455,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martins, Vitor Souza","contributorId":328511,"corporation":false,"usgs":false,"family":"Martins","given":"Vitor","email":"","middleInitial":"Souza","affiliations":[{"id":78386,"text":"Missippssii State University","active":true,"usgs":false}],"preferred":false,"id":880456,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vermote, Eric","contributorId":328512,"corporation":false,"usgs":false,"family":"Vermote","given":"Eric","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":880457,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":880458,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70247452,"text":"70247452 - 2023 - Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources","interactions":[],"lastModifiedDate":"2023-08-08T11:41:46.30057","indexId":"70247452","displayToPublicDate":"2023-08-06T06:39:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources","docAbstract":"<p><span>Geostatistics is the most commonly used probabilistic approach for modeling earth systems, including quality parameters of various geoenergy resources. In geostatistics, estimates, either on a point or block support, are generated as a spatially-weighted average of surrounding samples. The optimal weights are determined through the stationary variogram model which accounts for the spatial structure of the samples. Recently, efficient modeling workflows using various machine learning algorithms (MLAs) have been expanded to the spatial context for modeling geological heterogeneity. The flexible use of MLAs as a spatial estimation tool stems mainly from the fact that unlike kriging, they do not require any variogram, nor do they depend strongly on a prior stationarity assumption (i.e., second order stationarity). This study evaluates the performance of two MLAs (ensemble super learner and elliptical radial basis neural network), ordinary kriging, and hybrid spatial modeling approaches using ordinary intrinsic collocated cokriging. The aforementioned modeling techniques are compared for estimating resources for four coal variables (wash yield, ash yield, calorific value and thickness) as an example. The results suggest that MLAs, when implemented alone, do not outperform ordinary kriging, but the estimation accuracy of the final model, measured by the root mean squared error tends to subtly improve (</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2023.104328","usgsCitation":"Erdogan Erten, G., Erten, O., Karacan, C.O., Boisvert, J., and Deutsch, C.V., 2023, Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources: International Journal of Coal Geology, v. 276, 104328, 16 p., https://doi.org/10.1016/j.coal.2023.104328.","productDescription":"104328, 16 p.","ipdsId":"IP-149602","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":419585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -82.33693408888138,\n              37.563642070051216\n            ],\n            [\n              -82.33693408888138,\n              37.039521964862686\n            ],\n            [\n              -81.70000033583997,\n              37.039521964862686\n            ],\n            [\n              -81.70000033583997,\n              37.563642070051216\n            ],\n            [\n              -82.33693408888138,\n              37.563642070051216\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"276","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erdogan Erten, Gamze","contributorId":317909,"corporation":false,"usgs":false,"family":"Erdogan Erten","given":"Gamze","email":"","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erten, Oktay","contributorId":300145,"corporation":false,"usgs":false,"family":"Erten","given":"Oktay","email":"","affiliations":[],"preferred":false,"id":879701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":879702,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boisvert, Jeff","contributorId":317910,"corporation":false,"usgs":false,"family":"Boisvert","given":"Jeff","email":"","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879703,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deutsch, Clayton V.","contributorId":317911,"corporation":false,"usgs":false,"family":"Deutsch","given":"Clayton","email":"","middleInitial":"V.","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879704,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70247514,"text":"70247514 - 2023 - A brave new world: Managing for biodiversity conservation under ecosystem transformation","interactions":[],"lastModifiedDate":"2023-08-10T11:55:02.303425","indexId":"70247514","displayToPublicDate":"2023-08-05T06:51:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"A brave new world: Managing for biodiversity conservation under ecosystem transformation","docAbstract":"<div class=\"html-p\">Traditional conservation practices have primarily relied on maintaining biodiversity by preserving species and habitats in place. Many regions are experiencing unprecedented environmental conditions, shifts in species distribution and habitats, and high turnover in species composition, resulting in ecological transformation. Natural resource managers have lacked tools for identifying and selecting strategies to manage ecosystem transformation. A recently formalized decision support framework provides a way for managers to resist, accept, or direct (RAD) the trajectory of change. We begin by identifying how historical conservation practices are built into the RAD framework. Next, we describe how RAD can be used to implement climate change adaptation actions, using examples from the Mojave Desert to provide ecological context. Third, we discuss how the RAD framework can assist with the creation of conservation portfolios, facilitating the maintenance of overall biodiversity across a landscape. Preserving species assemblages in their current state, or restoring them to historical conditions, will not always be possible, and RAD allows for explicit deliberation about when and where to prioritize scarce resources. We conclude with a set of guidelines for conservation practitioners or managers moving forward. Although operating under an increasingly uncertain future is daunting, managers can utilize RAD to conserve biodiversity and effectively handle ecosystem transformation.</div>","language":"English","publisher":"MDPI","doi":"10.3390/land12081556","usgsCitation":"Wilkening, J.L., Magness, D.R., Thompson, L., and Lynch, A., 2023, A brave new world: Managing for biodiversity conservation under ecosystem transformation: Land, v. 12, no. 8, 1556, 14 p., https://doi.org/10.3390/land12081556.","productDescription":"1556, 14 p.","ipdsId":"IP-152003","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":442507,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land12081556","text":"Publisher Index Page"},{"id":419694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilkening, Jennifer L. 0000-0001-8748-4578","orcid":"https://orcid.org/0000-0001-8748-4578","contributorId":127685,"corporation":false,"usgs":false,"family":"Wilkening","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7111,"text":"U. Colorado, Boulder, Dept. Ecology & Evol.Biol., PhD Student","active":true,"usgs":false}],"preferred":false,"id":879932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magness, Dawn Robin","contributorId":318208,"corporation":false,"usgs":false,"family":"Magness","given":"Dawn","email":"","middleInitial":"Robin","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":879933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":207364,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":879934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":879935,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70256432,"text":"70256432 - 2023 - Effects of sucker gigging on fish populations in Oklahoma scenic rivers","interactions":[],"lastModifiedDate":"2024-09-09T15:31:56.213633","indexId":"70256432","displayToPublicDate":"2023-08-04T10:26:42","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-151-2023","title":"Effects of sucker gigging on fish populations in Oklahoma scenic rivers","docAbstract":"<p><span>Suckers (Catostomidae) are ecologically important, and some support popular fisheries, despite not being considered ‘sport fish’ in most states. Gigging suckers is a popular and culturally significant pastime in the Ozark Highlands, but little is known about the effect of gigging harvest on population dynamics of suckers. Therefore, research is needed to determine safe levels of sucker harvest that ensure sustainability of sucker gigging and protect overall ecosystem function. The objectives of this study were to: 1) determine the spatial distribution of common sucker species during spawning season (when sucker gigging is most effective), 2) determine the population size, age&nbsp;</span><span class=\"glossify-tooltip-link glossify-tooltip-popup\" aria-label=\"Something temporarily or permanently constructed, built, or placed; and constructed of natural or manufactured parts including, but not limited to, a building, shed, cabin, porch, bridge, walkway, stair steps, sign, landing, platform, dock, rack, fence, telecommunication device, antennae, fish cleaning table, satellite dish/mount, or well head.\">structure<span>&nbsp;</span></span><span>, and total mortality rate for common sucker species, and 3) model the effects of different harvest rates on sucker populations to determine the harvest rate at which growth overfishing and recruitment overfishing begin. &nbsp;Suckers were sampled using electrofishing, modified fyke netting, gillnetting, hoop netting, and seining and marked with passive integrated transponder (PIT) tags to provide information about population size, demographics, and coarse-scale movement patterns. &nbsp;A subset of fish sampled using the above gears and additional fish collected during gigging tournaments in 2017-2019 and 2021-2022 (no tournament was held in 2020) were used for age analyses. Tournament data collected prior to the initiation of this project were obtained from the state agency. Data from gigging tournaments indicated Golden Redhorse Moxostoma erythrurum, Black Redhorse M. duquesnei, White Sucker Catostomus commersonii, and Spotted Sucker Minytrema melanops were vulnerable to gigging harvest. Selection by giggers for larger individuals was apparent for all species except Golden Redhorse in 2019. Spotted Suckers constituted most fish harvested, but the proportion of each species harvested still varied among years. A total of 943 fish were aged from samples obtained from 2017 to 2022 and results from subsequent analyses indicated a high degree of variation in growth rates within and among species. Over 4,700 suckers were tagged with PIT tags and over 400 recaptures of these tagged fish were made since autumn 2018. Preliminary analyses indicate survival was consistent across samples and species, and detection rates varied by sampling event (3-month periods). Our most likely top multistrata model suggested that a large portion of fish within the upper Spavinaw, lower Spavinaw, and reservoir sections remain in these locations year-round (means: 0.46 – 0.67). Despite this, transition probabilities are still high for movement from upper Spavinaw to lower Spavinaw (mean: 0.32) and from lower Spavinaw to upper Spavinaw (mean: 0.38). Likewise, transition probabilities were high for movement from lower Spavinaw to the reservoir (mean: 0.15) and from the reservoir to lower Spavinaw (mean: 0.32). Transition probabilities between upper Spavinaw and the reservoir were low in both directions (means &lt; 0.01). Population sizes, growth trajectories and length-weight relationships varied among species. Preliminary harvest models suggest species-specific regulation may be scientifically appropriate; however, it may be difficult for giggers to identify species while gigging. Based on our model results, there appears to be little risk of recruitment or growth overfishing for any species at current exploitation levels.</span></p>","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Zetner, D., Shoup, D., and Brewer, S., 2023, Effects of sucker gigging on fish populations in Oklahoma scenic rivers: Cooperator Science Series FWS/CSS-151-2023, ii, 60 p.","productDescription":"ii, 60 p.","ipdsId":"IP-153396","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":431783,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/effects-sucker-gigging-fish-populations-oklahoma-scenic-rivers"},{"id":433623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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E.","contributorId":242905,"corporation":false,"usgs":false,"family":"Shoup","given":"D. E.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907354,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":340552,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907355,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70247418,"text":"sir20235093 - 2023 - Analysis of high-resolution single channel seismic data for use in sediment resource evaluation, eastern Texas and western Louisiana Continental Shelf, Gulf of Mexico","interactions":[],"lastModifiedDate":"2026-03-12T21:16:59.60233","indexId":"sir20235093","displayToPublicDate":"2023-08-04T08:46:47","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5093","displayTitle":"Analysis of High-Resolution Single Channel Seismic Data for Use in Sediment Resource Evaluation, Eastern Texas and Western Louisiana Continental Shelf, Gulf of Mexico","title":"Analysis of high-resolution single channel seismic data for use in sediment resource evaluation, eastern Texas and western Louisiana Continental Shelf, Gulf of Mexico","docAbstract":"<p>Shallow subsurface geologic data recorded as high-resolution seismic profiles are used to interpret the geology of coastal and marine systems. These data were originally recorded on paper rolls that are stored in geophysical archives. Data collection has since converted to entirely digital formats, yet the analog data are still useful for geologic interpretation. This report describes the process of recovering analog copies of seismic profiles from physical archives, electronically scanning, and converting them to industry-standard digital format. The recovered data are also reviewed and assessed for potential sediment resources. The data recovered in this study were collected from the Gulf of Mexico continental shelf offshore of East Texas and West Louisiana. The project is a collaborative study between the U.S. Geological Survey and the Bureau of Ocean Energy Management.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235093","issn":"2328-0328","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management","programNote":"Coastal and Marine Hazards and Resources Program","usgsCitation":"Flocks, J., Forde, A., and Bosse, S., 2023, Analysis of high-resolution single channel seismic data for use in sediment resource evaluation, eastern Texas and western Louisiana Continental Shelf, Gulf of Mexico: U.S. Geological Survey Scientific Investigations Report 2023–5093, 18 p., https://doi.org/10.3133/sir20235093.","productDescription":"Report: viii, 18 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-144157","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":419532,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235093/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5093 HTML"},{"id":419531,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5093/sir20235093.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5093 XML"},{"id":419530,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5093/sir20235093.pdf","size":"4.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5093 pdf"},{"id":419529,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5093/images"},{"id":419528,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5093/coverthb.jpg"},{"id":419533,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EFUAN3","text":"USGS data release—Archive of digitized analog boomer seismic reflection data collected from the northern Gulf of Mexico—Intersea 1980"},{"id":501060,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115126.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana, Texas","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -91.86243995309941,\n              29.6\n            ],\n            [\n              -94.1,\n              29.6\n            ],\n            [\n              -94.1,\n              28.4\n            ],\n            [\n              -91.86243995309941,\n              28.4\n            ],\n            [\n              -91.86243995309941,\n              29.6\n            ]\n          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jflocks@usgs.gov","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":816,"corporation":false,"usgs":true,"family":"Flocks","given":"James","email":"jflocks@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forde, Arnell S. 0000-0002-5581-2255 aforde@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-2255","contributorId":376,"corporation":false,"usgs":true,"family":"Forde","given":"Arnell","email":"aforde@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bosse, Stephen T. 0000-0001-6110-2973 sbosse@usgs.gov","orcid":"https://orcid.org/0000-0001-6110-2973","contributorId":189712,"corporation":false,"usgs":true,"family":"Bosse","given":"Stephen","email":"sbosse@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":879520,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70247436,"text":"70247436 - 2023 - DisasterNet: Causal Bayesian networks with normalizing flows for cascading hazards","interactions":[],"lastModifiedDate":"2023-08-08T13:37:18.248038","indexId":"70247436","displayToPublicDate":"2023-08-04T08:30:10","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"DisasterNet: Causal Bayesian networks with normalizing flows for cascading hazards","docAbstract":"<p><span>Sudden-onset hazards like earthquakes often induce cascading secondary hazards (e.g., landslides, liquefaction, debris flows, etc.) and subsequent impacts (e.g., building and infrastructure damage) that cause catastrophic human and economic losses. Rapid and accurate estimates of these hazards and impacts are critical for timely and effective post-disaster responses. Emerging remote sensing techniques provide pre- and post-event satellite images for rapid hazard estimation. However, hazards and damage often co-occur or colocate with underlying complex cascading geophysical processes, making it challenging to directly differentiate multiple hazards and impacts from satellite imagery using existing single-hazard models. We introduce DisasterNet, a novel family of causal Bayesian networks to model processes that a major hazard triggers cascading hazards and impacts and further jointly induces signal changes in remotely sensed observations. We integrate normalizing flows to effectively model the highly complex causal dependencies in this cascading process. A triplet loss is further designed to leverage prior geophysical knowledge to enhance the identifiability of our highly expressive Bayesian networks. Moreover, a novel stochastic variational inference with normalizing flows is derived to jointly approximate posteriors of multiple unobserved hazards and impacts from noisy remote sensing observations. Integrating with the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system, our framework is evaluated in recent global earthquake events. Evaluation results show that DisasterNet significantly improves multiple hazard and impact estimation compared to existing USGS products.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"KDD '23: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"ACM SIGKDD Conference on Knowledge Discovery & Data Mining","conferenceDate":"August 6-10, 2023","conferenceLocation":"Long Beach, CA","language":"English","publisher":"Association for Computing Machinery","doi":"10.1145/3580305.3599807","usgsCitation":"Li, X., Burgi, P.M., Ma, W., Noh, H., Wald, D.J., and Xu, S., 2023, DisasterNet: Causal Bayesian networks with normalizing flows for cascading hazards, <i>in</i> KDD '23: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, v. 29, Long Beach, CA, August 6-10, 2023, p. 4391-4403, https://doi.org/10.1145/3580305.3599807.","productDescription":"13 p.","startPage":"4391","endPage":"4403","ipdsId":"IP-149697","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":419595,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","noUsgsAuthors":false,"publicationDate":"2023-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Xuechun","contributorId":317874,"corporation":false,"usgs":false,"family":"Li","given":"Xuechun","email":"","affiliations":[{"id":69176,"text":"Stonybrook University","active":true,"usgs":false}],"preferred":false,"id":879620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burgi, Paula Madeline 0000-0003-3001-5759","orcid":"https://orcid.org/0000-0003-3001-5759","contributorId":317875,"corporation":false,"usgs":true,"family":"Burgi","given":"Paula","email":"","middleInitial":"Madeline","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":879621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ma, Wei","contributorId":317876,"corporation":false,"usgs":false,"family":"Ma","given":"Wei","email":"","affiliations":[{"id":37969,"text":"Hong Kong Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":879622,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noh, Haeyoung","contributorId":317877,"corporation":false,"usgs":false,"family":"Noh","given":"Haeyoung","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":879623,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":879624,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xu, Susu","contributorId":300127,"corporation":false,"usgs":false,"family":"Xu","given":"Susu","email":"","affiliations":[{"id":65025,"text":"Stony Brook University, NY, USA","active":true,"usgs":false}],"preferred":false,"id":879625,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70247753,"text":"70247753 - 2023 - Assessment of recovery potential for the American horseshoe crab (Limulus polyphemus): An application of the IUCN green status process","interactions":[],"lastModifiedDate":"2023-11-07T15:32:17.216838","indexId":"70247753","displayToPublicDate":"2023-08-04T07:15:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":862,"text":"Aquatic Conservation: Marine and Freshwater Ecosystems","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Assessment of recovery potential for the American horseshoe crab (<i>Limulus polyphemus</i>): An application of the IUCN green status process","title":"Assessment of recovery potential for the American horseshoe crab (Limulus polyphemus): An application of the IUCN green status process","docAbstract":"<ol class=\"\"><li>According to an International Union for Conservation of Nature (IUCN) Red List assessment (RLA), the American horseshoe crab (<i>Limulus polyphemus</i>), an iconic coastal species, is at risk of extirpation in some regions within its range where small and vulnerable populations occur. However, the RLA does not consider future status beyond viability and does not attempt to identify the conservation necessary to effectively mitigate threats and recover the species to full ecological functionality. To aid in conservation planning for vulnerable species, the IUCN developed the Green Status of Species assessment (GSA) process to complement the RLA.</li><li>This paper describes the application of the GSA process to assess the recovery potential of the American horseshoe crab. First, specific<span>&nbsp;</span><i>Limulus</i><span>&nbsp;</span>populations within spatial units for conservation were delineated, and their statuses were defined based on viability and ecological functionality. Then conservation actions were identified that would promote recovery and affect their near- and long-term population status under different conservation scenarios.</li><li>Horseshoe crab conservation has relied on, and will continue to depend on, effective harvest regulation. However, as currently conceived, conservation is not expected to mitigate habitat loss at the scale required to restore range-wide ecological functionality, primarily because habitat loss is widespread and affected by climate change. Thus, the GSA results, while indicating that there is potential for near-term recovery gains, reveal that long-term recovery is in doubt owing to expected loss of habitat.</li><li>To conserve critical habitats for spawning and early life stages and achieve ecological functionality, it is imperative to identify and develop conservation plans at appropriate spatial scales. Unfortunately, such plans do not currently exist and need to be established. The GSA Green Score can then serve as a metric for monitoring recovery and gauging the effectiveness of conservation implementation.</li></ol>","language":"English","publisher":"Wiley","doi":"10.1002/aqc.3990","usgsCitation":"Smith, D.R., Brockmann, H.J., Carmichael, R.H., Hallerman, E., Watson, W., and Zaldivar-Rae, J., 2023, Assessment of recovery potential for the American horseshoe crab (Limulus polyphemus): An application of the IUCN green status process: Aquatic Conservation: Marine and Freshwater Ecosystems, v. 33, no. 11, p. 1175-1199, https://doi.org/10.1002/aqc.3990.","productDescription":"25 p.","startPage":"1175","endPage":"1199","ipdsId":"IP-150297","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":442511,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/aqc.3990","text":"Publisher Index Page"},{"id":419881,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":880277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brockmann, H. Jane","contributorId":199472,"corporation":false,"usgs":false,"family":"Brockmann","given":"H.","email":"","middleInitial":"Jane","affiliations":[{"id":12558,"text":"University of Florida, Gainesville","active":true,"usgs":false}],"preferred":false,"id":880278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carmichael, Ruth H.","contributorId":23420,"corporation":false,"usgs":false,"family":"Carmichael","given":"Ruth","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":880279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hallerman, Eric M.","contributorId":279474,"corporation":false,"usgs":false,"family":"Hallerman","given":"Eric M.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":880280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Watson, W.M.","contributorId":189601,"corporation":false,"usgs":false,"family":"Watson","given":"W.M.","email":"","affiliations":[],"preferred":false,"id":880281,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zaldivar-Rae, Jaime","contributorId":328477,"corporation":false,"usgs":false,"family":"Zaldivar-Rae","given":"Jaime","affiliations":[{"id":78374,"text":"Universidad Anahuc Mayab","active":true,"usgs":false}],"preferred":false,"id":880282,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70247383,"text":"sir20235073 - 2023 - Response in the water quality of Delavan Lake, Wisconsin, to changes in phosphorus loading—Setting new goals for loading from its drainage basin","interactions":[],"lastModifiedDate":"2026-03-12T20:43:24.534885","indexId":"sir20235073","displayToPublicDate":"2023-08-03T14:21:59","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5073","displayTitle":"Response in the Water Quality of Delavan Lake, Wisconsin, to Changes in Phosphorus Loading—Setting New Goals for Loading from its Drainage Basin","title":"Response in the water quality of Delavan Lake, Wisconsin, to changes in phosphorus loading—Setting new goals for loading from its drainage basin","docAbstract":"<p>During 1989–92, an extensive rehabilitation project was completed in and around Delavan Lake, Wisconsin, to improve the lake’s water quality. However, in 2016, the lake was listed by the Wisconsin Department of Natural Resources as impaired for excessive algal growth (high chlorophyll <i>a</i> concentrations), and high phosphorus input was listed as its likely cause. In addition, the recent (2017–21) mean summer water clarity (as measured with a Secchi disk) was shallower than the goal set by the community (3.0 meters). Based primarily on flow and water-quality data collected in Jackson Creek, which is the main tributary of the lake, the mean annual phosphorus loading to the lake during water years (WYs) 2017–21 was 6,570 kilograms per year (kg/yr), and 306 kg/yr came from uncontrollable sources (atmospheric deposition and groundwater). Phosphorus loading during these years was about 48 percent higher than the long-term mean loading from WY 1984 to WY 2021. Based on results from Canfield-Bachmann phosphorus models, Carlson trophic state index relations, and the Jones and Bachmann chlorophyll <i>a</i> relation, external phosphorus loading would need to be decreased from 6,570 to 5,270 kg/yr (a 21-percent reduction in the potentially controllable external phosphorus load from the base period of WYs 2017–21) for chlorophyll <i>a</i> concentrations greater than 20 micrograms per liter to be detected no more than 5.0 percent of the time (the Wisconsin Department of Natural Resources criterion for chlorophyll <i>a</i> impairment for the lake). Based on Carlson trophic state index relations, external loading would need to be decreased from 6,570 to 4,380 kg/yr (a 35-percent reduction in the potentially controllable external phosphorus load) for summer mean Secchi depths to increase to 3.0 meters. Therefore, for Delavan Lake to reach the water-quality criteria for impairment and the goals for all three water-quality constituents, a 35-percent reduction in the potentially controllable phosphorus load is needed, which equates to a reduction in total phosphorus loading from 6,570 to 4,380 kg/yr. A 35-percent reduction in phosphorus loading to improve the water quality of Delavan Lake is less than the 49-percent reduction in phosphorus loading required for the area near Delavan Lake to improve the water quality of the Rock River and its tributaries indicated in the Rock River total maximum daily load.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235073","collaboration":"Prepared in cooperation with the Town of Delavan and the Delavan Lake Sanitary District","usgsCitation":"Robertson, D.M., Siebers, B.J., and Fredrick, R.A., 2023, Response in the water quality of Delavan Lake, Wisconsin, to changes in phosphorus loading—Setting new goals for loading from its drainage basin: U.S. Geological Survey Scientific Investigations Report 2023–5073, 28 p., https://doi.org/10.3133/sir20235073.","productDescription":"Report: viii, 28 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-148703","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501038,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115123.htm","linkFileType":{"id":5,"text":"html"}},{"id":419534,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235073/full","text":"Report"},{"id":419467,"rank":6,"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":419466,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H85BK0","text":"USGS data release","linkHelpText":"Eutrophication models to simulate changes in the water quality of Green Lake, Wisconsin in response to changes in phosphorus loading, with supporting water-quality data for the lake, its tributaries, and atmospheric deposition"},{"id":419465,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5073/images/"},{"id":419464,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5073/sir20235073.XML","linkFileType":{"id":8,"text":"xml"}},{"id":419463,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5073/sir20235073.pdf","text":"Report","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5073"},{"id":419462,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5073/coverthb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Delavan Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -88.69602097806326,\n              42.574403384923556\n            ],\n            [\n              -88.52306051926304,\n              42.574403384923556\n            ],\n            [\n              -88.52306051926304,\n              42.66531414833537\n            ],\n            [\n              -88.69602097806326,\n              42.66531414833537\n            ],\n            [\n              -88.69602097806326,\n              42.574403384923556\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/umid-water\" data-mce-href=\"https://www.usgs.gov/centers/umid-water\">Upper Midwest Water Science Center</a><br>U.S. Geological Survey<br>1 Gifford Pinchot Drive<br>Madison, WI 53726</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Study Site</li><li>General Approach</li><li>Methods of Data Collection, Flow and Load Estimation, and Eutrophication Modeling</li><li>Lake Water Quality</li><li>Water and Phosphorus Loading to Delavan Lake</li><li>Response in Near-Surface Water Quality to Changes in Phosphorus Loading</li><li>Summary and Conclusions</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-08-03","noUsgsAuthors":false,"publicationDate":"2023-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siebers, Benjamin J. 0000-0002-2900-5169","orcid":"https://orcid.org/0000-0002-2900-5169","contributorId":206518,"corporation":false,"usgs":true,"family":"Siebers","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fredrick, Reed A. 0000-0002-7771-0655","orcid":"https://orcid.org/0000-0002-7771-0655","contributorId":317831,"corporation":false,"usgs":true,"family":"Fredrick","given":"Reed","email":"","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879393,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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