{"pageNumber":"92","pageRowStart":"2275","pageSize":"25","recordCount":41032,"records":[{"id":70252216,"text":"sir20245010 - 2024 - Evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes region, New York, 2019 and 2020","interactions":[],"lastModifiedDate":"2026-02-02T22:18:32.06379","indexId":"sir20245010","displayToPublicDate":"2024-03-26T10:10:00","publicationYear":"2024","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":"2024-5010","displayTitle":"Evaluation of Sensors for Continuous Monitoring of Harmful Algal Blooms in the Finger Lakes Region, New York, 2019 and 2020","title":"Evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes region, New York, 2019 and 2020","docAbstract":"<p>In response to the increasing frequency of cyanobacterial harmful algal blooms (CyanoHABs) in the Finger Lakes region of New York State, a pilot study by the U.S. Geological Survey, in collaboration with the New York State Department of Environmental Conservation, was conducted to enhance CyanoHAB monitoring and understanding. High-frequency sensors were deployed on open water monitoring-station platforms at Seneca Lake in 2019–20, at Owasco Lake in 2019–20, and at Skaneateles Lake in 2019. One of the goals of this study was to evaluate the ability of in-place sensors to make representative measurements of dissolved organic matter, nutrients, and algal pigments (as indicators of phytoplankton biomass) while collecting routine field parameters (water temperature, specific conductance, pH, dissolved oxygen, turbidity, weather, and light) to provide additional information about environmental conditions.</p><p>Despite challenges like power issues and sensor fouling, the sensors performed well overall. However, correlation analyses between sensor readings and laboratory measurements revealed variable performance. Results indicate the relation between the fluorescent dissolved organic matter sensor and laboratory-measured dissolved organic carbon was weak at all study lakes. The nitrate sensors can be sensitive to ambient temperature and have a substantial power requirement, and the relation between sensor- and laboratory-measured nitrate values differed among lakes. The orthophosphate sensors, which were complex and prone to data loss, yielded results that were difficult to interpret because orthophosphate detections are rare in the study lakes. The multichannel fluorometer was also complex to use and required several unique procedures for its operation.</p><p>Chlorophyll measurements from the fluorometers correlated moderately well with laboratory-measured chlorophyll-<i>a</i>, although relations with total phytoplankton biovolume were weaker. Relations between phycocyanin concentration measurements from the dual-channel fluorometers and cyanobacterial biovolume were not significant; however, the cyanobacterial biovolume correlation was moderately strong with chlorophyll contribution from cyanobacteria measurements from the multichannel fluorometer. Of all collected parameters, water temperature was among the strongest correlated with chlorophyll-<i>a</i>, total phytoplankton biovolume, and cyanobacterial biovolume.</p><p>Stepwise regression analysis was used to identify the best parameters for modeling variance in laboratory measures of phytoplankton biomass. This analysis included factors such as chlorophyll fluorescence, pH, water temperature, and others, which varied by lake. Overall, the models had limited explanatory power for chlorophyll-<i>a</i> and other biovolumes, possibly due to the absence of CyanoHABs at the open-water monitoring locations. Multivariate models did not outperform simple fluorescence-based models. Notably, turbidity was a more significant indicator of cyanobacterial biovolume variability than phycocyanin from dual-channel fluorometers.</p><p>The study concludes that while single and multivariate models based on sensor data are useful, they did not explain any more variance than fluorescence-based models. Broader data collection, including more CyanoHAB events, is necessary to refine these models. Integrating machine learning could leverage large, complex datasets to improve CyanoHAB predictions, thereby enhancing the management and understanding of these blooms.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245010","usgsCitation":"Johnston, B.D., Finkelstein, K.M., Gifford, S.R., Stouder, M.D., Nystrom, E.A., Savoy, P.R., Rosen, J.J., and Jennings, M.B., 2024, Evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes region, New York, 2019 and 2020: U.S. Geological Survey Scientific Investigations Report 2024–5010, 54 p., https://doi.org/10.3133/sir20245010.","productDescription":"Report: vii, 54 p.; 2 Data Releases","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151193","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":426806,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TP9T1D","text":"USGS data release","linkHelpText":"Phytoplankton data from Owasco, Seneca, and Skaneateles Lakes, Finger Lakes region, New York, 2019–2020 (ver. 2.1, June 2023)"},{"id":426805,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9046YOS","text":"USGS data release","linkHelpText":"Field data for an evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes, New York, 2019 and 2020"},{"id":426804,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5010/images/"},{"id":426803,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5010/sir20245010.XML"},{"id":426802,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245010/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5010 HTML"},{"id":499423,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116207.htm","linkFileType":{"id":5,"text":"html"}},{"id":426800,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5010/coverthb.jpg"},{"id":426801,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5010/sir20245010.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5010 PDF"}],"country":"United States","state":"New York","otherGeospatial":"Finger Lakes Region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -78.18392460037663,\n              43.28424086245511\n            ],\n            [\n              -78.18392460037663,\n              42.10263922827107\n            ],\n  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Learned</li><li>Sensor Performance and Evaluation Discussion</li><li>Informing Future Monitoring and Research Approaches</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2024-03-26","noUsgsAuthors":false,"publicationDate":"2024-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnston, Brett D. 0000-0003-2991-4976","orcid":"https://orcid.org/0000-0003-2991-4976","contributorId":206233,"corporation":false,"usgs":true,"family":"Johnston","given":"Brett","email":"","middleInitial":"D.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finkelstein, Kaitlyn M. 0000-0003-1588-3312","orcid":"https://orcid.org/0000-0003-1588-3312","contributorId":202727,"corporation":false,"usgs":true,"family":"Finkelstein","given":"Kaitlyn","email":"","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896957,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gifford, Sabina R. 0000-0002-0724-4986","orcid":"https://orcid.org/0000-0002-0724-4986","contributorId":310415,"corporation":false,"usgs":true,"family":"Gifford","given":"Sabina","email":"","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896958,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stouder, Michael D.W. 0000-0002-0446-2574","orcid":"https://orcid.org/0000-0002-0446-2574","contributorId":301805,"corporation":false,"usgs":true,"family":"Stouder","given":"Michael","middleInitial":"D.W.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896959,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nystrom, Elizabeth A. 0000-0002-0886-3439 nystrom@usgs.gov","orcid":"https://orcid.org/0000-0002-0886-3439","contributorId":1072,"corporation":false,"usgs":true,"family":"Nystrom","given":"Elizabeth","email":"nystrom@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896960,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Savoy, Philip 0000-0002-6075-837X","orcid":"https://orcid.org/0000-0002-6075-837X","contributorId":300288,"corporation":false,"usgs":true,"family":"Savoy","given":"Philip","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":896964,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rosen, Joshua J. 0000-0001-5420-033X","orcid":"https://orcid.org/0000-0001-5420-033X","contributorId":332009,"corporation":false,"usgs":true,"family":"Rosen","given":"Joshua","email":"","middleInitial":"J.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896962,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jennings, Matthew B. 0000-0002-3783-5875 mbjennin@usgs.gov","orcid":"https://orcid.org/0000-0002-3783-5875","contributorId":334696,"corporation":false,"usgs":true,"family":"Jennings","given":"Matthew","email":"mbjennin@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896963,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70252694,"text":"70252694 - 2024 - Controls on in-stream nitrogen loss in western Lake Erie tributaries","interactions":[],"lastModifiedDate":"2024-04-03T14:15:02.853271","indexId":"70252694","displayToPublicDate":"2024-03-26T07:00:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Controls on in-stream nitrogen loss in western Lake Erie tributaries","docAbstract":"<p>Management efforts to reduce cyanobacterial harmful algal blooms (cHABs) in the Great Lakes have focused on decreasing tributary inputs of phosphorus (P). Recent research has indicated that reduction of both P and nitrogen (N) can lessen cHABs severity. Microbially mediated N cycling in streambed sediment may reduce N riverine loads, yet little is known about in-stream N processing rates in the Maumee River Basin, a major source of nutrients to Lake Erie. During summer of 2019 and 2021, we sampled streambed sediment to measure potential nitrification and denitrification rates using the acetylene block method at 78 sites throughout the Maumee River network. We used structural equation models to identify indirect and direct drivers of denitrification. Precipitation was greater in 2019, resulting in a 67 % increase in mean discharge, 41 % of farm fields to be fallow, and a 50 % reduction in fertilizer use. During summer field surveys, median stream-water nitrate concentrations were not different between 2019 and 2021. Median denitrification rates were 13.3 mg N/m2/h and 31.2 mg N/m2/h, respectively, indicating high potential to remove N. Nitrate concentrations and nitrification rates were strong direct drivers of denitrification, especially in 2019 when coupled nitrification–denitrification sustained denitrification. Nitrate concentrations varied with land use. Notably, nitrate concentrations increased with the area of fallow land, which may indicate the presence of a legacy N source. These findings indicate that promoting streambed denitrification could reduce N loads to Lake Erie, but legacy N currently stored in the system may mask N reduction efforts.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102284","usgsCitation":"Kreiling, R.M., Bartsch, L., Perner, P.M., Breckner, K.J., Williamson, T.N., Hood, J.M., Manning, N., and Johnson, L.T., 2024, Controls on in-stream nitrogen loss in western Lake Erie tributaries: Journal of Great Lakes Research, v. 50, no. 2, 102284, https://doi.org/10.1016/j.jglr.2024.102284.","productDescription":"102284","ipdsId":"IP-155751","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":427346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana, Michigan, Ohio","otherGeospatial":"Maumee River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -85.1,\n              42\n            ],\n            [\n              -85.1,\n              40.52\n            ],\n            [\n              -83,\n              40.52\n            ],\n            [\n              -83,\n              42\n            ],\n            [\n              -85.1,\n              42\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"50","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kreiling, Rebecca M. 0000-0002-9295-4156","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":202193,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perner, Patrik Mathis 0000-0002-6142-518X","orcid":"https://orcid.org/0000-0002-6142-518X","contributorId":261675,"corporation":false,"usgs":true,"family":"Perner","given":"Patrik","email":"","middleInitial":"Mathis","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breckner, Kenna Jean 0000-0002-8358-7825","orcid":"https://orcid.org/0000-0002-8358-7825","contributorId":301096,"corporation":false,"usgs":true,"family":"Breckner","given":"Kenna","email":"","middleInitial":"Jean","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hood, James M.","contributorId":267332,"corporation":false,"usgs":false,"family":"Hood","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":897946,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Manning, Nathan F.","contributorId":211818,"corporation":false,"usgs":false,"family":"Manning","given":"Nathan F.","affiliations":[],"preferred":false,"id":897947,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Laura T.","contributorId":301097,"corporation":false,"usgs":false,"family":"Johnson","given":"Laura","email":"","middleInitial":"T.","affiliations":[{"id":16990,"text":"Heidelberg University","active":true,"usgs":false}],"preferred":false,"id":897948,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70252248,"text":"sir20245011 - 2024 - A conceptual site model of contaminant transport pathways from the Bremerton Naval Complex to Sinclair Inlet, Washington, 2011–21","interactions":[],"lastModifiedDate":"2026-02-02T22:20:20.744408","indexId":"sir20245011","displayToPublicDate":"2024-03-26T05:50:08","publicationYear":"2024","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":"2024-5011","displayTitle":"A Conceptual Site Model of Contaminant Transport Pathways from the Bremerton Naval Complex to Sinclair Inlet, Washington, 2011–21","title":"A conceptual site model of contaminant transport pathways from the Bremerton Naval Complex to Sinclair Inlet, Washington, 2011–21","docAbstract":"<p>Historical activities on the Bremerton Naval Complex (BNC) in Puget Sound, Washington, have resulted in Sinclair Inlet sediments with elevated concentrations of contaminants, including organic contaminants such as polychlorinated biphenyls and trace elements including mercury. Six U.S. Geological Survey–U.S. Navy datasets have been collected since the last major assessment, in 2013, of soil and groundwater contaminant transport pathways and mercury loading estimates from the BNC to Sinclair Inlet. These include:</p><ol><li>mercury isotope analysis to support sourcing of mercury in Sinclair Inlet;</li><li>mercury sampling within the dry dock systems;</li><li>nearshore thermal surveys to identify potential groundwater discharge locations to Sinclair Inlet;</li><li>time-series monitoring in nearshore wells to understand the inland extent and dynamics of the tidal mixing zone;</li><li>tidal studies of mercury in nearshore monitoring wells in an area of contaminated fill material called Site 1; and</li><li>a spatial survey of trace elements and other parameters in nearshore monitoring wells, pore water, seeps, surface water, and sediment along unwalled shorelines in the western part of the BNC.</li></ol><p>The results were incorporated into an updated Conceptual Site Model and used to update contaminant load estimates from the terrestrial BNC to Sinclair Inlet. The results from these studies provide data to the U.S. Navy to support prioritization of on-going remediation actions to manage contamination on the BNC that reduce potential impacts to Sinclair Inlet sediment, surface water, and fish and shellfish tissue.</p><p>Mercury isotope analysis of surface sediments and particulate material indicated that a similar industrial mercury profile is present throughout Puget Sound, including terrestrial and marine BNC samples and in other Sinclair Inlet sediments and persists across regions with low and elevated mercury concentrations. Two sources of mercury at the BNC are Sites 1 and 2 subsurface soils/fill material, with total mercury concentrations in particulates collected from the bottom of monitoring wells drilled in these materials ranging from 18,000 to 44,000 nanograms per gram (as compared to the Washington State Marine Sediment Cleanup Screening Level of 590 nanograms per gram).</p><p>Contaminants are transported from the terrestrial BNC to Sinclair Inlet via three primary pathways, (1) stormwater outfalls, (2) dry dock discharges, and (3) direct discharge along unwalled shorelines.</p><p>Previous loading estimates (based on filtered total mercury) ranked stormwater outfalls, particularly outfall PSNS015 in Site 2 soils, as the largest soil and groundwater contaminant transport pathway from the terrestrial BNC to Sinclair Inlet. Updated loading estimates in this report suggest that the dry dock systems may be a larger pathway of mercury from the terrestrial BNC to Sinclair Inlet than previously thought, within the same order of magnitude as the PSNS015 storm-drain system.</p><p>Trace-element loads via direct shoreline discharge are difficult to estimate due to the large and dynamic tidal mixing zone of groundwater and seawater in the nearshore along unwalled shorelines. However, current best estimated ranges suggest that direct shoreline discharge is one of the three main pathways and may contribute smaller mercury loads than the stormwater and the dry dock systems. Along unwalled shorelines, direct groundwater discharge of terrestrial contaminants may be less important than recirculating seawater in the nearshore mixing zone that can extract contaminants from nearshore subsurface material. Total estimated mercury loads from the terrestrial BNC to Sinclair Inlet range from approximately 40 to 200 grams of filtered total mercury per year and a minimum of 70–350 grams of particulate total mercury per year, for a minimum total of 110–525 grams of whole (filtered plus particulate) total mercury per year. Data gaps are identified that, if filled, would further refine the Conceptual Site Model and contaminant loading estimates from the terrestrial BNC to Sinclair Inlet.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245011","collaboration":"Prepared in cooperation with U.S. Department of the Navy","usgsCitation":"Conn, K.E., Janssen, S.E., Opatz, C.C., and Bright, V.A.L., 2024, A conceptual site model of contaminant transport pathways from the Bremerton Naval Complex to Sinclair Inlet, Washington, 2011–21 (ver. 1.1): U.S. Geological Survey Scientific Investigations Report 2024–5011, 111 p., https://doi.org/10.3133/sir20245011.","productDescription":"Report: x, 111 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-142440","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499424,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116205.htm","linkFileType":{"id":5,"text":"html"}},{"id":426852,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5011/sir20245011.XML"},{"id":426851,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5011/images"},{"id":427851,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2024/5011/VersionHistory.txt","description":"Version History"},{"id":426850,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K9P8G2","text":"USGS data release","description":"USGS data release","linkHelpText":"Particulate mercury isotope results, fiber optic thermal survey data, and nearshore surface sediment results at the Bremerton Naval Complex, Washington, USA, 2020-21"},{"id":426849,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94FCYGV","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-NWT model to simulate the groundwater flow system at Puget Sound Naval Shipyard, Naval Base Kitsap, Bremerton, Washington"},{"id":426848,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245011/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5011"},{"id":426847,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5011/sir20245011.pdf","text":"Report","size":"36.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5011"},{"id":426846,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5011/sir20245011.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Sinclair Inlet","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.75191725882988,\n              47.580405375032115\n            ],\n            [\n              -122.75191725882988,\n              47.50997977336348\n            ],\n            [\n              -122.60639127716968,\n              47.50997977336348\n            ],\n            [\n              -122.60639127716968,\n              47.580405375032115\n            ],\n            [\n              -122.75191725882988,\n              47.580405375032115\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_wa@usgs.gov\" data-mce-href=\"mailto:dc_wa@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/washington-water-science-center\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/washington-water-science-center\">Washington Water Science Center</a><br>U.S. Geological Survey<br>934 Broadway, Suite 300<br>Tacoma, Washington 98402</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Quality Assurance and Control and Data Management</li><li>Mercury in Sediments and in the Dry Dock System</li><li>Groundwater/Surface-Water Interactions along Unwalled Shorelines</li><li>Updated Conceptual Site Model and Mercury Loading Estimates</li><li>Summary</li><li>References Cited</li><li>Appendixes 1–5</li></ul>","publishedDate":"2024-03-26","revisedDate":"2024-04-17","noUsgsAuthors":false,"publicationDate":"2024-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Conn, Kathleen E. 0000-0002-2334-6536 kconn@usgs.gov","orcid":"https://orcid.org/0000-0002-2334-6536","contributorId":3923,"corporation":false,"usgs":true,"family":"Conn","given":"Kathleen E.","email":"kconn@usgs.gov","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Opatz, Chad C. 0000-0002-5272-0195 copatz@usgs.gov","orcid":"https://orcid.org/0000-0002-5272-0195","contributorId":48857,"corporation":false,"usgs":true,"family":"Opatz","given":"Chad","email":"copatz@usgs.gov","middleInitial":"C.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":false,"id":897038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bright, Valerie A.L. 0000-0002-7627-8004","orcid":"https://orcid.org/0000-0002-7627-8004","contributorId":294970,"corporation":false,"usgs":true,"family":"Bright","given":"Valerie","email":"","middleInitial":"A.L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897039,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70252416,"text":"sir20245015 - 2024 - Evaluation of groundwater resources in the Upper White River Basin within Mount Rainier National Park, Washington State, 2020","interactions":[],"lastModifiedDate":"2026-02-03T14:20:57.829693","indexId":"sir20245015","displayToPublicDate":"2024-03-25T10:05:35","publicationYear":"2024","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":"2024-5015","displayTitle":"Evaluation of Groundwater Resources in the Upper White River Basin within Mount Rainier National Park, Washington State, 2020","title":"Evaluation of groundwater resources in the Upper White River Basin within Mount Rainier National Park, Washington State, 2020","docAbstract":"<p>The U.S. Geological Survey (USGS), in cooperation with the National Park Service, investigated groundwater gains and losses on the upper White River within Mount Rainier National Park in Washington. This investigation was conducted using stream discharge measurements at 14 locations within 7 reaches over a 6.5-mile river length from near the White River’s origin at the terminus of the Emmons Glacier on Mount Rainier to the White River Entrance near the northeast boundary of Mount Rainier National Park. Locations selected for the stream discharge measurements were on the main channel of the White River and on tributary streams near their confluence with the White River.</p><p>A soil-water-balance (SWB) model analysis was also performed on the White River basin to estimate groundwater recharge throughout the basin during the time of the study. Analyses were made for the White River basin at the sub-basin (zone) scale to determine groundwater input to the stream for individual stream reaches. The gridded SWB model was simulated at a 10-meter (m) horizontal resolution, where recharge simulations were constructed using five spatially distributed datasets. Daily climate data as input for the simulation included gridded daily precipitation and air temperature.</p><p>Upon analysis of the seepage run results, three of the seven reaches showed groundwater gains in this study. The SWB model results were used in conjunction with the baseflow gain totals in the reaches to estimate the length of time for recharge to become base flow. Further analysis estimated the rates of groundwater flow in the zones with adjacent gaining reaches. A streamflow gain curve was created from a simple flow model for each of the zones to relate the recharge from the zones to the adjacent reaches on the White River and tributaries. The fit of the streamflow gain curve to the calculated streamflow gain during the seepage run was used to analyze where the recharge from each zone resulted as streamflow gain. Consecutive reach losses from zones D and L were immediately followed downstream by a relatively large gain in zone GH, indicating that the gain in the reach adjacent to zone GH could be from the recharge in zones D and L.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245015","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fuhrig, L.T., Long, A.J., and Headman, A.O., 2024, Evaluation of groundwater resources in the Upper White River Basin within Mount Rainier National Park, Washington State, 2020 (ver. 1.1, March 2024): U.S. Geological Survey Scientific Investigations Report 2024–5015, 19 p., https://doi.org/10.3133/sir20245015.","productDescription":"Report: vi, 19 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-148848","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":499425,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116204.htm","linkFileType":{"id":5,"text":"html"}},{"id":426941,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5015/sir20245015.XML"},{"id":426940,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5015/images"},{"id":426939,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KI310W","text":"USGS data release","description":"USGS data release","linkHelpText":"Soil water balance model of the White River basin, Mount Rainier National Park, Washington, USA"},{"id":427249,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2024/5015/versionHistory.txt"},{"id":426938,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245015/full","linkFileType":{"id":5,"text":"html"}},{"id":426937,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5015/sir20245015.pdf","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5015"},{"id":426936,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5015/sir20245015.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.66827303334932,\n              47.34261069492973\n            ],\n            [\n              -122.66827303334932,\n              46.07710849497087\n            ],\n            [\n              -120.72369295522444,\n              46.07710849497087\n            ],\n            [\n              -120.72369295522444,\n              47.34261069492973\n            ],\n            [\n              -122.66827303334932,\n              47.34261069492973\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","edition":"Version 1.0: March 25, 2024; Version 1.1: March 29, 2024","contact":"<p><a href=\"mailto:dc_wa@usgs.gov\" data-mce-href=\"mailto:dc_wa@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/washington-water-science-center\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/washington-water-science-center\">Washington Water Science Center</a><br>U.S. Geological Survey<br>934 Broadway, Suite 300<br>Tacoma, Washington 98402</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Description of Study Area</li><li>Methods and Results</li><li>Discussion</li><li>Limitations and Additional Assumptions</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2024-03-25","revisedDate":"2024-03-29","noUsgsAuthors":false,"publicationDate":"2024-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Fuhrig, Leland T. 0000-0001-5694-9061 lfuhrig@usgs.gov","orcid":"https://orcid.org/0000-0001-5694-9061","contributorId":195830,"corporation":false,"usgs":true,"family":"Fuhrig","given":"Leland","email":"lfuhrig@usgs.gov","middleInitial":"T.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897144,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Andrew J. 0000-0001-7385-8081 ajlong@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-8081","contributorId":989,"corporation":false,"usgs":true,"family":"Long","given":"Andrew","email":"ajlong@usgs.gov","middleInitial":"J.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Headman, Alexander O. 0000-0003-0034-3970","orcid":"https://orcid.org/0000-0003-0034-3970","contributorId":334972,"corporation":false,"usgs":false,"family":"Headman","given":"Alexander O.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":false,"id":897146,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70252535,"text":"70252535 - 2024 - Noise constraints on global body‐wave measurement thresholds","interactions":[],"lastModifiedDate":"2024-07-30T14:19:55.830125","indexId":"70252535","displayToPublicDate":"2024-03-25T06:44:14","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Noise constraints on global body‐wave measurement thresholds","docAbstract":"<p><span>Intermediate sized earthquakes (≈</span><i>M</i><span>4–6.5) are often measured using the teleseismic body‐wave magnitude (</span><span class=\"inline-formula no-formula-id\">⁠\uD835\uDC5Ab⁠</span><span>).&nbsp;</span><span class=\"inline-formula no-formula-id\">\uD835\uDC5Ab</span><span>&nbsp;measurements are especially critical at the lower end of this range when teleseismic waveform modeling techniques (i.e., moment tensor analysis) are difficult. The U.S. Geological Survey National Earthquake Information Center (NEIC) determines the location and magnitude of all&nbsp;</span><i>M</i><span>&nbsp;5 and greater earthquakes worldwide within 20&nbsp;min of the rupture time, and therefore accurate&nbsp;</span><span class=\"inline-formula no-formula-id\">\uD835\uDC5Ab</span><span>&nbsp;magnitude estimates are essential to fulfill its mission. To better understand how network geometry and noise levels affect the global response capabilities, we developed a method to spatially estimate the minimum measurable&nbsp;</span><span class=\"inline-formula no-formula-id\">\uD835\uDC5Ab⁠</span><span>. To do this, we compare expected&nbsp;</span><span class=\"inline-formula no-formula-id\">\uD835\uDC5Ab</span><span>&nbsp;amplitudes at every station to the station’s background noise level. We find that using NEIC’s current network geometry and these idealized thresholds, NEIC can potentially estimate&nbsp;</span><span class=\"inline-formula no-formula-id\">\uD835\uDC5Ab</span><span>&nbsp;magnitudes down to&nbsp;</span><i>M</i><span>&nbsp;4.5 globally. Low‐latitude regions in the Southern Hemisphere present the biggest opportunity to improve monitoring capabilities. However, logistically they also present the biggest hurdles for network operators. Finally, to test the resiliency of the network we removed the 20 most important stations and found the&nbsp;</span><span class=\"inline-formula no-formula-id\">\uD835\uDC5Ab</span><span>&nbsp;threshold remains&nbsp;</span><span class=\"inline-formula no-formula-id\">\uD835\uDC5Ab</span><span>&nbsp;4.5. However, the region where only&nbsp;</span><span class=\"inline-formula no-formula-id\">\uD835\uDC5Ab</span><span>&nbsp;4.5 and greater can be estimated increases and is again restricted to the Southern Hemisphere.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230246","usgsCitation":"Ringler, A.T., Wilson, D.C., Earle, P.S., Yeck, W.L., Mason, D.B., and Wilgus, J., 2024, Noise constraints on global body‐wave measurement thresholds: Bulletin of the Seismological Society of America, v. 114, no. 4, p. 1765-1776, https://doi.org/10.1785/0120230246.","productDescription":"13 p.","startPage":"1765","endPage":"1776","ipdsId":"IP-160503","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":427200,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"114","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897439,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897440,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897441,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":897442,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mason, David B. 0000-0003-0313-3370 dmason@usgs.gov","orcid":"https://orcid.org/0000-0003-0313-3370","contributorId":265781,"corporation":false,"usgs":true,"family":"Mason","given":"David","email":"dmason@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897443,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilgus, Justin T.","contributorId":206263,"corporation":false,"usgs":false,"family":"Wilgus","given":"Justin T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":897444,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70269056,"text":"70269056 - 2024 - Limited evidence of late Quaternary tectonic surface deformation in the eastern Tennessee seismic zone, USA","interactions":[],"lastModifiedDate":"2025-07-15T15:24:44.875998","indexId":"70269056","displayToPublicDate":"2024-03-25T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Limited evidence of late Quaternary tectonic surface deformation in the eastern Tennessee seismic zone, USA","docAbstract":"<p>The ~300-km-long eastern Tennessee seismic zone (ETSZ), USA, is the second-most seismically active region east of the Rocky Mountains. Seismicity generally occurs below the Paleozoic fold-and-thrust belt within the Mesoproterozoic basement, at depths of 5–26 km, and earthquake magnitudes during the instrumental record have been moment magnitude (<strong><i>M</i><sub>w</sub></strong>)≤4.8. Evidence of surface deformation may not exist or be difficult to detect because of the vegetated and soil-mantled landscape, landslides, locally steep topography, anthropogenic landscape modification, or long, irregular recurrence intervals between surface-rupturing earthquakes. Despite the deep seismicity, analog models indicate that accumulation of strike-slip or oblique-slip displacement at depth could be expected to propagate upward through the Paleozoic section, producing a detectable surficial signal of distributed faulting. To identify potential surface deformation, we interrogated the landscape at different spatial scales. We evaluated morphotectonic and channel metrics, such as channel sinuosity and catchment-scale hypsometry. Additionally, we mapped possible fault-related topographic features on 1-m lidar. Finally, we integrated our observations with available bedrock and Quaternary surficial mapping and subsurface geophysical data. At a regional scale, most morphotectonic and channel metrics have a strong lithologic control. Within smaller regions of similar lithology, we observe changes in landscape metrics like channel sinuosity and catchment-scale hypsometry that spatially correlate with new lineaments identified in this study and previously mapped east–west Cenozoic faults. These faults have apparent left-lateral offsets, are optimally oriented to slip in the current stress field, and match kinematics from recent focal mechanisms, but do not clearly preserve evidence of late Pleistocene or Holocene tectonic surface deformation. Most newly mapped lineaments might be explained by either tectonic or non-tectonic origins, such as fluvial or karst processes. We also re-evaluated a previously described paleoseismic site and interpret that the exposure does not record evidence of late Pleistocene faulting but instead is explained by fluvial stratigraphy.</p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230094","usgsCitation":"Jobe, J.A., Briggs, R.W., Gold, R.D., Bauer, L., and Collett, C., 2024, Limited evidence of late Quaternary tectonic surface deformation in the eastern Tennessee seismic zone, USA: Bulletin of the Seismological Society of America, v. 114, no. 4, p. 1920-1940, https://doi.org/10.1785/0120230094.","productDescription":"21 p.","startPage":"1920","endPage":"1940","ipdsId":"IP-154193","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"eastern Tennessee","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -86.17028696488822,\n              36.643021837336434\n            ],\n            [\n              -86.17028696488822,\n              35.0924378490018\n            ],\n            [\n              -82.53465655915278,\n              35.0924378490018\n            ],\n            [\n              -82.53465655915278,\n              36.643021837336434\n            ],\n            [\n              -86.17028696488822,\n              36.643021837336434\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"114","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson Jobe, Jessica A. 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Thompson Jobe","given":"Jessica","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943170,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bauer, Laurel","contributorId":266056,"corporation":false,"usgs":false,"family":"Bauer","given":"Laurel","email":"","affiliations":[{"id":54872,"text":"Office of Nuclear Reactor Regulation, U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":943171,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collett, Camille 0000-0003-4836-0243","orcid":"https://orcid.org/0000-0003-4836-0243","contributorId":310393,"corporation":false,"usgs":false,"family":"Collett","given":"Camille","affiliations":[{"id":67175,"text":"Formerly: U.S. Geological Survey, Geologic Hazards Science Center","active":true,"usgs":false}],"preferred":false,"id":943172,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70253018,"text":"70253018 - 2024 - Fair graph learning using constraint-aware priority adjustment and graph masking in river networks","interactions":[],"lastModifiedDate":"2024-04-16T16:15:32.996155","indexId":"70253018","displayToPublicDate":"2024-03-24T11:08:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10143,"text":"Proceedings of the AAAI Conference on Artificial Intelligence","active":true,"publicationSubtype":{"id":10}},"title":"Fair graph learning using constraint-aware priority adjustment and graph masking in river networks","docAbstract":"<p><span>Accurate prediction of water quality and quantity is crucial for sustainable development and human well-being. However, existing data-driven methods often suffer from spatial biases in model performance due to heterogeneous data, limited observations, and noisy sensor data. To overcome these challenges, we propose Fair-Graph, a novel graph-based recurrent neural network that leverages interrelated knowledge from multiple rivers to predict water flow and temperature within large-scale stream networks. Additionally, we introduce node-specific graph masks for information aggregation and adaptation to enhance prediction over heterogeneous river segments. To reduce performance disparities across river segments, we introduce a centralized coordination strategy that adjusts training priorities for segments. We evaluate the prediction of water temperature within the Delaware River Basin, and the prediction of streamflow using simulated data from U.S. National Water Model in the Houston River network. The results showcase improvements in predictive performance and highlight the proposed model's ability to maintain spatial fairness over different river segments.</span></p>","language":"English","publisher":"Association for the Advancement of Artificial Intelligence","doi":"10.1609/aaai.v38i20.30212","usgsCitation":"He, E., Xie, Y., Sun, A.Y., Zwart, J.A., Yang, J., Jin, Z., Wang, Y., Karimi, H.A., and Jia, X., 2024, Fair graph learning using constraint-aware priority adjustment and graph masking in river networks: Proceedings of the AAAI Conference on Artificial Intelligence, v. 38, no. 20, p. 22087-22095, https://doi.org/10.1609/aaai.v38i20.30212.","productDescription":"9 p.","startPage":"22087","endPage":"22095","ipdsId":"IP-158367","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":440050,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1609/aaai.v38i20.30212","text":"Publisher Index Page"},{"id":427821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Delaware River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -76.19185307052085,\n              38.71562965387949\n            ],\n            [\n              -74.4374506373952,\n              38.7070541171185\n            ],\n            [\n              -74.13043021159795,\n              41.597382404326765\n            ],\n            [\n              -75.720357416618,\n              41.63871233834436\n            ],\n            [\n              -76.19185307052085,\n              38.71562965387949\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"38","issue":"20","noUsgsAuthors":false,"publicationDate":"2024-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"He, Erhu","contributorId":329980,"corporation":false,"usgs":false,"family":"He","given":"Erhu","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":898944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xie, Yiqun","contributorId":297447,"corporation":false,"usgs":false,"family":"Xie","given":"Yiqun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":898945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sun, Alexander Y. 0000-0002-6365-8526","orcid":"https://orcid.org/0000-0002-6365-8526","contributorId":302987,"corporation":false,"usgs":false,"family":"Sun","given":"Alexander","email":"","middleInitial":"Y.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":898946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":898947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yang, Jie","contributorId":335648,"corporation":false,"usgs":false,"family":"Yang","given":"Jie","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":898948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jin, Zhenong","contributorId":297865,"corporation":false,"usgs":false,"family":"Jin","given":"Zhenong","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":898949,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Yang","contributorId":173071,"corporation":false,"usgs":false,"family":"Wang","given":"Yang","email":"","affiliations":[],"preferred":false,"id":898950,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Karimi, Hassan Ali","contributorId":335649,"corporation":false,"usgs":false,"family":"Karimi","given":"Hassan","email":"","middleInitial":"Ali","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":898951,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":898952,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70253890,"text":"70253890 - 2024 - Association of water arsenic with incident diabetes in U.S. adults: The Multi-Ethnic Study of Atherosclerosis and The Strong Heart Study","interactions":[],"lastModifiedDate":"2024-07-01T14:43:11.240846","indexId":"70253890","displayToPublicDate":"2024-03-24T10:11:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17625,"text":"Diabetes Care","active":true,"publicationSubtype":{"id":10}},"title":"Association of water arsenic with incident diabetes in U.S. adults: The Multi-Ethnic Study of Atherosclerosis and The Strong Heart Study","docAbstract":"<div class=\"title -title\">OBJECTIVE</div><p>We examined the association of arsenic in federally regulated community water systems (CWSs) and unregulated private wells with type 2 diabetes (T2D) incidence in the Strong Heart Family Study (SHFS), a prospective study of American Indian communities, and the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of racially and ethnically diverse urban U.S. communities.</p><div class=\"title -title\">RESEARCH DESIGN AND METHODS</div><p>We evaluated 1,791 participants from SHFS and 5,777 participants from MESA who had water arsenic estimates available and were free of T2D at baseline (2001–2003 and 2000–2002, respectively). Participants were followed for incident T2D until 2010 (SHFS cohort) or 2019 (MESA cohort). We used Cox proportional hazards mixed-effects models to account for clustering by family and residential zip code, with adjustment for sex, baseline age, BMI, smoking status, and education.</p><div class=\"title -title\">RESULTS</div><p>T2D incidence was 24.4 cases per 1,000 person-years (mean follow-up, 5.6 years) in SHFS and 11.2 per 1,000 person-years (mean follow-up, 14.0 years) in MESA. In a meta-analysis across the SHFS and MESA cohorts, the hazard ratio (95% CI) per doubling in CWS arsenic was 1.10 (1.02, 1.18). The corresponding hazard ratio was 1.09 (0.95, 1.26) in the SHFS group and 1.10 (1.01, 1.20) in the MESA group. The corresponding hazard ratio (95% CI) for arsenic in private wells and incident T2D in SHFS was 1.05 (0.95, 1.16). We observed statistical interaction and larger magnitude hazard ratios for participants with BMI &lt;25 kg/m<sup>2</sup><span>&nbsp;</span>and female participants.</p><div class=\"title -title\">CONCLUSIONS</div><p>Low to moderate water arsenic levels (&lt;10 µg/L) were associated with T2D incidence in the SHFS and MESA cohorts.</p>","language":"English","publisher":"American Diabetes Association","doi":"10.2337/dc23-2231","usgsCitation":"Spaur, M., Galvez-Fernandez, M., Chen, Q., Lombard, M.A., Bostick, B., Factor-Litvak, P., Fretts, A., Shea, S., Navas-Acien, A., and Nigra, A., 2024, Association of water arsenic with incident diabetes in U.S. adults: The Multi-Ethnic Study of Atherosclerosis and The Strong Heart Study: Diabetes Care, v. 47, no. 7, p. 1143-1151, https://doi.org/10.2337/dc23-2231.","productDescription":"9 p.","startPage":"1143","endPage":"1151","ipdsId":"IP-159826","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":440052,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.2337/dc23-2231","text":"External Repository"},{"id":428359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Spaur, Maya","contributorId":257947,"corporation":false,"usgs":false,"family":"Spaur","given":"Maya","email":"","affiliations":[{"id":52179,"text":"Columbia University Mailman School of Public Health","active":true,"usgs":false}],"preferred":false,"id":900006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Galvez-Fernandez, Marta","contributorId":336125,"corporation":false,"usgs":false,"family":"Galvez-Fernandez","given":"Marta","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":900007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chen, Qixuan","contributorId":318224,"corporation":false,"usgs":false,"family":"Chen","given":"Qixuan","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":900008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lombard, Melissa A. 0000-0001-5924-6556 mlombard@usgs.gov","orcid":"https://orcid.org/0000-0001-5924-6556","contributorId":198254,"corporation":false,"usgs":true,"family":"Lombard","given":"Melissa","email":"mlombard@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900009,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bostick, Benjamin","contributorId":257949,"corporation":false,"usgs":false,"family":"Bostick","given":"Benjamin","affiliations":[{"id":40291,"text":"Lamont-Doherty Earth Observatory of Columbia University","active":true,"usgs":false}],"preferred":false,"id":900010,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Factor-Litvak, Pam","contributorId":336127,"corporation":false,"usgs":false,"family":"Factor-Litvak","given":"Pam","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":900011,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fretts, Amanda","contributorId":336128,"corporation":false,"usgs":false,"family":"Fretts","given":"Amanda","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":900012,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shea, Steven","contributorId":336129,"corporation":false,"usgs":false,"family":"Shea","given":"Steven","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":900013,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Navas-Acien, Ana","contributorId":257950,"corporation":false,"usgs":false,"family":"Navas-Acien","given":"Ana","email":"","affiliations":[{"id":52179,"text":"Columbia University Mailman School of Public Health","active":true,"usgs":false}],"preferred":false,"id":900014,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nigra, Anne E","contributorId":257951,"corporation":false,"usgs":false,"family":"Nigra","given":"Anne E","affiliations":[{"id":52179,"text":"Columbia University Mailman School of Public Health","active":true,"usgs":false}],"preferred":false,"id":900015,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70256586,"text":"70256586 - 2024 - Plant-derived products selectively suppress growth of the harmful alga Prymnesium parvum","interactions":[],"lastModifiedDate":"2024-08-22T16:38:45.585477","indexId":"70256586","displayToPublicDate":"2024-03-23T11:38:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Plant-derived products selectively suppress growth of the harmful alga <i>Prymnesium parvum</i>","title":"Plant-derived products selectively suppress growth of the harmful alga Prymnesium parvum","docAbstract":"<p><span class=\"html-italic\">Prymnesium parvum</span><span>&nbsp;is a harmful alga found in brackish waters worldwide whose toxins can be lethal to aquatic organisms. Established field methods to control blooms of this species, however, are unavailable. Earlier studies showed that various extracts of giant reed (</span><span class=\"html-italic\">Arundo donax</span><span>) can suppress&nbsp;</span><span class=\"html-italic\">P. parvum</span><span>&nbsp;growth and that ellipticine, an allelochemical present in giant reed, is a potent algicide against this species. The unintended effects of giant reed products on nontarget organisms, however, are not fully understood. This study determined the effects of giant reed leachate (aqueous extract of dried chips) and ellipticine on growth of&nbsp;</span><span class=\"html-italic\">P. parvum</span><span>&nbsp;and the green microalga&nbsp;</span><span class=\"html-italic\">Chlorella sorokiniana</span><span>; survival and reproduction of the planktonic crustacean&nbsp;</span><span class=\"html-italic\">Daphnia pulex</span><span>; and hatching success, larval survival, and larval swimming behavior of the teleost fish&nbsp;</span><span class=\"html-italic\">Danio rerio</span><span>. Leachate made with 3 g chips L</span><sup>−1</sup><span>&nbsp;was lethally toxic to&nbsp;</span><span class=\"html-italic\">P. parvum</span><span>&nbsp;and&nbsp;</span><span class=\"html-italic\">D. pulex</span><span>, stimulated&nbsp;</span><span class=\"html-italic\">C. sorokiniana</span><span>&nbsp;growth, and impaired&nbsp;</span><span class=\"html-italic\">D. rerio</span><span>&nbsp;behavior. Leachate at 1 g L</span><sup>−1</sup><span>&nbsp;fully suppressed&nbsp;</span><span class=\"html-italic\">P. parvum</span><span>&nbsp;growth, had moderate effects on&nbsp;</span><span class=\"html-italic\">D. pulex</span><span>&nbsp;reproductive output, and had no effects on&nbsp;</span><span class=\"html-italic\">D. rerio</span><span>. Ellipticine at 0.01 mg L</span><sup>−1</sup><span>&nbsp;irreversibly inhibited&nbsp;</span><span class=\"html-italic\">P. parvum</span><span>&nbsp;growth, acutely but reversibly inhibited&nbsp;</span><span class=\"html-italic\">C. sorokiniana</span><span>&nbsp;growth, slightly delayed&nbsp;</span><span class=\"html-italic\">D. pulex</span><span>&nbsp;reproduction, and had no effects on&nbsp;</span><span class=\"html-italic\">D. rerio</span><span>. These observations suggest that when applied at appropriate concentrations, natural products derived from giant reed can be used as tools to specifically control&nbsp;</span><span class=\"html-italic\">P. parvum</span><span>&nbsp;growth with minimal effects on nontarget species.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/w16070930","usgsCitation":"Mary, M.A., Tabora-Sarmiento, S., Nash, S., Mayer, G.D., Crago, J., and Patino, R., 2024, Plant-derived products selectively suppress growth of the harmful alga Prymnesium parvum: Water, v. 16, no. 7, 930, 12 p., https://doi.org/10.3390/w16070930.","productDescription":"930, 12 p.","ipdsId":"IP-159702","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440054,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w16070930","text":"Publisher Index Page"},{"id":433072,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Mary, Mousumi A.","contributorId":341256,"corporation":false,"usgs":false,"family":"Mary","given":"Mousumi","email":"","middleInitial":"A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tabora-Sarmiento, Shisbeth","contributorId":341257,"corporation":false,"usgs":false,"family":"Tabora-Sarmiento","given":"Shisbeth","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nash, Sarah","contributorId":341258,"corporation":false,"usgs":false,"family":"Nash","given":"Sarah","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908153,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mayer, Gregory D.","contributorId":172783,"corporation":false,"usgs":false,"family":"Mayer","given":"Gregory","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":908154,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crago, Jordan","contributorId":341260,"corporation":false,"usgs":false,"family":"Crago","given":"Jordan","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908155,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908156,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70261324,"text":"70261324 - 2024 - Exploring and integrating differences in niche characteristics across regional and global scales to better understand plant invasions in Hawaiʻi","interactions":[],"lastModifiedDate":"2024-12-05T15:54:04.297803","indexId":"70261324","displayToPublicDate":"2024-03-23T09:50:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Exploring and integrating differences in niche characteristics across regional and global scales to better understand plant invasions in Hawaiʻi","docAbstract":"<p><span>The spread of ecosystem modifying invasive plant (EMIP) species is one of the largest threats to native ecosystems in Hawaiʻi. However, differences in niche characteristics between Hawaiʻi’s isolated insular environment and the wider global distribution of these species have not been carefully examined. We used species distribution modeling (SDM) methods to assess similarities and differences in niche characteristics between global and regional scales for 17 EMIPs present in Hawaiʻi. With a clearer understanding of the global context of regional plant invasion, we combined two SDM methods to better understand the potential future regional spread: (1) a nested modeling approach to integrate global and regional invasive species distribution projections; and (2) integrating all available agency and citizen science data to minimize the effect of monitoring gaps and biases. Our results show there are multiple similarities in niche characteristics across regional and global scales for most species, such as similar sets of climatic determinants of distribution, similar responses along environmental gradients, and moderate to high niche overlap between global and regional models. However, some differences were apparent and likely due to several factors including incomplete regional spread, community assembly or diversity effects. Invaders that established earlier showed a higher degree of niche overlap and similar environmental gradient responses when comparing global and regional models. This pattern, coupled with the tendency for regionally-based projections to predict narrower distributions than global projections, indicates a potential for continued spread of several invasive species across the Hawaiian landscape. Our study has broader implications for understanding the distribution and spread of invasive species in other regions, as similar analyses and models, including a novel way to characterize environmental gradient response differences across regions or scales, can likely provide valuable information for conservation and management efforts.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10530-024-03284-8","usgsCitation":"Fortini, L., Kaiser, L.R., Daehler, C., Jacobi, J.D., Dimson, M., and Gillespie, T., 2024, Exploring and integrating differences in niche characteristics across regional and global scales to better understand plant invasions in Hawaiʻi: Biological Invasions, v. 26, p. 1827-1843, https://doi.org/10.1007/s10530-024-03284-8.","productDescription":"17 p.","startPage":"1827","endPage":"1843","ipdsId":"IP-154054","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":464808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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W","contributorId":304639,"corporation":false,"usgs":false,"family":"Gillespie","given":"Thomas W","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":920389,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70252341,"text":"ofr20241018 - 2024 - Greater sage-grouse habitat of Nevada and northeastern California—Integrating space use, habitat selection, and survival indices to guide areas for habitat management","interactions":[],"lastModifiedDate":"2024-03-26T16:43:36.240165","indexId":"ofr20241018","displayToPublicDate":"2024-03-22T13:06:41","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1018","displayTitle":"Greater Sage-Grouse Habitat of Nevada and Northeastern California—Integrating Space Use, Habitat Selection, and Survival Indices to Guide Areas for Habitat Management","title":"Greater sage-grouse habitat of Nevada and northeastern California—Integrating space use, habitat selection, and survival indices to guide areas for habitat management","docAbstract":"<h1>Executive Summary</h1><p>Greater sage-grouse populations (<i>Centrocercus urophasianus</i>; hereafter sage-grouse) are threatened by a suite of disturbances and anthropogenic factors that have contributed to a net loss of sagebrush-dominant shrub cover in recent decades. Declines in sage-grouse populations are largely linked to habitat loss across their range. A key component of conservation and land use planning efforts for sage-grouse involves the continued monitoring and modeling of habitat requirements and suitability across its range. The Bureau of Land Management (BLM) is addressing the management of sage-grouse habitats on BLM-authorized public lands throughout the western United States through a land use planning amendment and associated environmental impact statement (86 FR 66331). More than 25 percent of the range-wide distribution of sage-grouse is within Nevada and northeastern California, and information on sage-grouse distribution and habitat requirements is important to guide appropriate management decisions. Therefore, the BLM has identified the need for updated spatially explicit information on sage-grouse habitat in Nevada and northeastern California to guide the land use planning amendment and associated management decisions.</p><p>To address this need, researchers with the U.S. Geological Survey, in close cooperation with multiple State and Federal resource agency partners, including BLM, Nevada Department of Wildlife (NDOW) and California Department of Fish and Wildlife (CDFW), sought to map sage-grouse distribution and produce example habitat designations in these states. Herein, we report results of our primary study objective, which was to map sage-grouse habitat and create example habitat management areas, based on more than a decade of location and survival data collected from marked sage-grouse across the study region coupled with lek count survey data managed by the NDOW and the CDFW.</p><p>We expanded on previously developed methodology to incorporate information on habitat selection and survival during reproductive life stages and specific seasons with updated sage-grouse location and known fate datasets, while also including brood-rearing areas that are understood to be threatened and important for population persistence. We combined predictive habitat map surfaces for each life stage and season with updated information on current occupancy patterns to classify habitat based on its suitability and probability of occupancy. We carried out additional steps to delineate specific example habitat management areas, specifically (1) incorporated corridors connecting key nesting and brood-rearing habitat, (2) corrected outputs for pre-wildfire habitat conditions within areas burned in the last 16 years, and (3) masked out areas of anthropogenic development. Our methodological example of deriving habitat management areas was intended to help inform decisions by BLM and other land managers regarding conservation and management of sage-grouse. Associated data products in the form of habitat maps provide updated, detailed, and comprehensive information about the status of habitats and can be useful to partner agencies in their efforts to designate and rank habitats for this species of high conservation concern in Nevada and California, with full recognition that on-the-ground field data and local sources of information and expertise should be used in conjunction with inferences from these models.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241018","collaboration":"Prepared in cooperation with the Bureau of Land Management, Nevada Department of Wildlife, and California Department of Fish and Wildlife","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Milligan, M.C., Coates, P.S., O’Neil, S.T., Brussee, B.E., Chenaille, M.P., Friend, D., Steele, K., Small, J.R., Bowden, T.S., Kosic, A.D., and Miller, K., 2024, Greater sage-grouse habitat of Nevada and northeastern California—Integrating space use, habitat selection, and survival indices to guide areas for habitat management: U.S. Geological Survey Open-File Report 2024–1018, 70 p., https://doi.org/10.3133/ofr20241018.","productDescription":"Report: viii, 70 p.: Data Release","numberOfPages":"70","onlineOnly":"Y","ipdsId":"IP-157608","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":427111,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241018/full"},{"id":426917,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P933VE6W","text":"USGS Data Release","description":"Coates, P.S., Milligan, M.C., O’Neil, S.T., Brussee, B.E., and Chenaille, M.P., 2024, Rasters representing Greater sage-grouse space use, habitat selection, and survival to inform habitat management: U.S. Geological Survey data release, https://doi.org/10.5066/P933VE6W.","linkHelpText":"Rasters representing Greater sage-grouse space use, habitat selection, and survival to inform habitat management"},{"id":426916,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1018/images"},{"id":426914,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1018/ofr20241018.xml"},{"id":426913,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1018/ofr20241018.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426912,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1018/covrthb.jpg"}],"country":"United States","state":"California, Idaho, Nevada","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -114,\n              43\n            ],\n            [\n              -121,\n              43\n            ],\n            [\n              -121,\n              38\n            ],\n            [\n              -114,\n              38\n            ],\n            [\n              -114,\n              43\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/werc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/werc\">Western Ecological Research Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>3020 State University Drive East<br>Sacramento, California 95819</p>","tableOfContents":"<ul><li>Preface</li><li>Acknowledgements</li><li>Executive Summary</li><li>Background</li><li>Study Area</li><li>Methods</li><li>Results</li><li>Interpretation and Synthesis</li><li>References Cited</li><li>Appendix 1. Model Specifications for Habitat Selection Models</li><li>Appendix 2. Model Specifications for Survival Models</li><li>Appendix 3. Review and Revision Process</li><li>Appendix 4. Results of Variable Reduction Analyses</li><li>&nbsp;Appendix 5. Mapping Habitat Suitability with the Abundance and Space Use Index</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2024-03-22","noUsgsAuthors":false,"publicationDate":"2024-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Milligan, Megan C. 0000-0001-8466-7803","orcid":"https://orcid.org/0000-0001-8466-7803","contributorId":296042,"corporation":false,"usgs":true,"family":"Milligan","given":"Megan","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":897125,"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":897126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":897127,"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":897128,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chenaille, Michael P. 0000-0003-3387-7899 mchenaille@usgs.gov","orcid":"https://orcid.org/0000-0003-3387-7899","contributorId":194661,"corporation":false,"usgs":true,"family":"Chenaille","given":"Michael","email":"mchenaille@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":897129,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Friend, Derek 0000-0002-6909-8769","orcid":"https://orcid.org/0000-0002-6909-8769","contributorId":255288,"corporation":false,"usgs":false,"family":"Friend","given":"Derek","email":"","affiliations":[{"id":51512,"text":"Department of Geography, University of Nevada, Reno, 1664 N Virginia St, Reno, NV 89557, USA","active":true,"usgs":false}],"preferred":true,"id":897130,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Steele, Kathleen","contributorId":334965,"corporation":false,"usgs":false,"family":"Steele","given":"Kathleen","email":"","affiliations":[{"id":17737,"text":"Nevada Sagebrush Ecosystem Program; Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":true,"id":897131,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Small, Justin R.","contributorId":78146,"corporation":false,"usgs":false,"family":"Small","given":"Justin","email":"","middleInitial":"R.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":true,"id":897132,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bowden, Timothy S.","contributorId":334966,"corporation":false,"usgs":false,"family":"Bowden","given":"Timothy","email":"","middleInitial":"S.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":true,"id":897133,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kosic, Arlene D.","contributorId":334967,"corporation":false,"usgs":false,"family":"Kosic","given":"Arlene","email":"","middleInitial":"D.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":true,"id":897134,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Miller, Katherine","contributorId":259248,"corporation":false,"usgs":false,"family":"Miller","given":"Katherine","email":"","affiliations":[{"id":6952,"text":"California Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":true,"id":897135,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70252455,"text":"70252455 - 2024 - Seismic attenuation and stress on the San Andreas Fault at Parkfield: Are we critical yet?","interactions":[],"lastModifiedDate":"2024-03-25T14:10:12.17114","indexId":"70252455","displayToPublicDate":"2024-03-22T08:53:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Seismic attenuation and stress on the San Andreas Fault at Parkfield: Are we critical yet?","docAbstract":"<p><span>The Parkfield transitional segment of the San Andreas Fault (SAF) is characterized by the production of frequent quasi-periodical M6 events that break the very same asperity. The last Parkfield mainshock occurred on 28 September 2004, 38 years after the 1966 earthquake, and after the segment showed a ∼22 years average recurrence time. The main reason for the much longer interevent period between the last two earthquakes is thought to be the reduction of the Coulomb stress from the M6.5 Coalinga earthquake of 2 May 1983, and the M6 Nuñez events of June 11th and 22 July 1983. Plausibly, the transitional segment of the SAF at Parkfield is now in the late part of its seismic cycle and current observations may all be relative to a state of stress close to criticality. However, the behavior of the attenuation parameter in the last few years seems substantially different from the one that characterized the years prior to the 2004 mainshock. A few questions arise: (i) Does a detectable preparation phase for the Parkfield mainshocks exist, and is it the same for all events? (ii) How dynamically/kinematically similar are the quasi-periodic occurrences of the Parkfield mainshocks? (iii) Are some dynamic/kinematic characteristics of the next mainshock predictable from the analysis of current data? (e.g., do we expect the epicenter of the next failure to be co-located to that of 2004?) (iv) Should we expect the duration of the current interseismic period to be close to the 22-year “undisturbed” average value? We respond to the questions listed above by analyzing the non-geometric attenuation of direct S-waves along the transitional segment of the SAF at Parkfield, in the close vicinity of the fault plane, between January 2001 and November 2023. Of particular interest is the preparatory behavior of the attenuation parameter as the 2004 mainshock approached, on both sides of the SAF. We also show that the non-volcanic tremor activity modulates the seismic attenuation in the area, and possibly the seismicity along the Parkfield fault segment, including the occurrence of the mainshocks.</span></p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2024.1349425","usgsCitation":"Malagnini, L., Nadeau, R., and Parsons, T.E., 2024, Seismic attenuation and stress on the San Andreas Fault at Parkfield: Are we critical yet?: Frontiers in Earth Science, v. 12, 1349425; 16 p., https://doi.org/10.3389/feart.2024.1349425.","productDescription":"1349425; 16 p.","ipdsId":"IP-161211","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":440062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3389/feart.2024.1349425","text":"Publisher Index Page"},{"id":426966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Parkfield","otherGeospatial":"Sand Andreas Fault","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -120.8,\n              36.2\n            ],\n            [\n              -120.8,\n              35.7\n            ],\n            [\n              -120.2,\n              35.7\n            ],\n            [\n              -120.2,\n              36.2\n            ],\n            [\n              -120.8,\n              36.2\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Malagnini, Luca 0000-0001-5809-9945","orcid":"https://orcid.org/0000-0001-5809-9945","contributorId":245308,"corporation":false,"usgs":false,"family":"Malagnini","given":"Luca","email":"","affiliations":[{"id":5113,"text":"INGV","active":true,"usgs":false}],"preferred":false,"id":897204,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nadeau, Robert M. 0000-0003-1255-0643","orcid":"https://orcid.org/0000-0003-1255-0643","contributorId":264609,"corporation":false,"usgs":false,"family":"Nadeau","given":"Robert M.","affiliations":[{"id":54514,"text":"Berkeley Seismological Laboratory, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":897205,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":897206,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70253037,"text":"70253037 - 2024 - Improved efficient physics-based computational modeling of regional wave-driven coastal flooding for reef-lined coastlines","interactions":[],"lastModifiedDate":"2024-04-17T11:41:22.684054","indexId":"70253037","displayToPublicDate":"2024-03-22T06:39:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17460,"text":"Journal of Marine Science & Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Improved efficient physics-based computational modeling of regional wave-driven coastal flooding for reef-lined coastlines","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif text-s\"><div id=\"abs0002\" class=\"abstract author\"><div id=\"abss0002\"><p id=\"spara012\">Coastal flooding affects low-lying communities worldwide and is expected to increase with climate change, especially along reef-lined coasts, where wave-driven flooding is particularly prevalent. However, current regional modeling approaches are either insufficient or too computationally expensive to accurately assess risks in these complex environments. This study introduces and validates an improved computationally efficient and physics-based approach to compute dynamic wave-driven regional flooding on reef-lined coasts. We coupled a simplified-physics flood model (SFINCS) with a one-dimensional wave transformation model (XBeach-1D). To assess the performance of the proposed approach, we compared its results with results from a fully resolving two-dimensional wave transformation model (XBeach-2D). We applied this approach for a range of storms and sea-level rise scenarios for two contrasting reef-lined coastal geomorphologies: one low relief area and one high relief area. Our findings reveal that SFINCS coupled with XBeach-1D generates flood extents comparable to those produced by XBeach-2D, with a hit rate of 92%. However, this method tends to underpredict the flood extent of weaker, high-frequency storms and overpredict stronger, low-frequency storms. Across scenarios, our approach overpredicted the mean flood water depth, with a positive bias of 7 cm and root mean square difference of 15 cm. Offering approximately 100 times greater computational efficiency than its two-dimensional XBeach counterpart, this flood modeling technique is recommended for wave-driven flood modeling in scenarios with high computational demands, such as modeling numerous scenarios or undertaking detailed regional-scale modeling.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ocemod.2024.102358","usgsCitation":"Gaido-Lassarre, C., Nederhoff, K., Storlazzi, C.D., Reguero, B., and Beck, M.W., 2024, Improved efficient physics-based computational modeling of regional wave-driven coastal flooding for reef-lined coastlines: Journal of Marine Science & Engineering, v. 198, 102358, 14 p., https://doi.org/10.1016/j.ocemod.2024.102358.","productDescription":"102358, 14 p.","ipdsId":"IP-154224","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":440065,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ocemod.2024.102358","text":"Publisher Index Page"},{"id":427837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"198","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gaido-Lassarre, Camila","contributorId":335650,"corporation":false,"usgs":false,"family":"Gaido-Lassarre","given":"Camila","email":"","affiliations":[{"id":17620,"text":"UCSC","active":true,"usgs":false}],"preferred":false,"id":898993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nederhoff, Kees 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":334091,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Kees","affiliations":[{"id":39963,"text":"Deltares-USA","active":true,"usgs":false}],"preferred":true,"id":898994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":898995,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reguero, Borja","contributorId":264485,"corporation":false,"usgs":false,"family":"Reguero","given":"Borja","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":898996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":898997,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70252249,"text":"sir20245017 - 2024 - Groundwater model of the Harney Basin, southeastern Oregon","interactions":[],"lastModifiedDate":"2026-02-02T22:27:22.663466","indexId":"sir20245017","displayToPublicDate":"2024-03-22T06:23:53","publicationYear":"2024","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":"2024-5017","displayTitle":"Groundwater Model of the Harney Basin, Southeastern Oregon","title":"Groundwater model of the Harney Basin, southeastern Oregon","docAbstract":"<p>Groundwater development, mainly for large-scale irrigation, has increased substantially in the Harney Basin of southeastern Oregon since 2010. Concurrently, some areas of the basin experienced groundwater-level declines of more than 100 feet, and some shallow wells have gone dry. The Oregon Water Resources Department has limited new groundwater development in the basin until an improved understanding of the groundwater-flow system is available. The groundwater resources report by Gingerich and others (2022, U.S. Geological Survey Scientific Investigations Report 2021–5103, <a data-mce-href=\"https://doi.org/10.3133/sir20215103\" href=\"https://doi.org/10.3133/sir20215103\">https://doi.org/10.3133/sir20215103</a>) provides that understanding. This report describes the development of a numerical groundwater-flow model that can be used as a tool to help improve that understanding. The Harney Basin Groundwater Model was developed using the finite-difference groundwater-modeling software U.S. Geological Survey modular finite-difference groundwater-flow model (MODFLOW 6) and associated Python pre- and post-processing routines. The groundwater model encompasses the entire 5,240-square-mile Harney Basin and adjacent areas and is calibrated to the hydrologic conditions from 1930 to 2018. The model has a uniform grid consisting of 78,064 nearly square cells, each covering 2,005 by 2,007 feet (about 92 acres) and has 10 layers (780,640 total cells) representing the vertical distribution of hydrogeologic units. The results from the calibrated model simulations indicate that groundwater pumpage exceeded recharge since about the mid-1980s, resulting in an estimated net cumulative depletion of groundwater storage (discharge minus recharge) of about 840,000 acre-feet and also indicated declines in groundwater evapotranspiration and spring and stream discharge. Model simulations show as much as 100 feet of groundwater-level decline in some areas and more than 40 feet of decline in widespread areas in recent decades. Model simulations are consistent with field observations of groundwater levels through time.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245017","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Gingerich, S.B., Boschmann, D.E., Grondin, G.H., and Schibel, H.J., 2024, Groundwater model of the Harney Basin, southeastern Oregon: U.S. Geological Survey Scientific Investigations Report 2024–5017, 104 p., https://doi.org/10.3133/sir20245017.","productDescription":"Report: xii, 104 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-152081","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":499430,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116178.htm","linkFileType":{"id":5,"text":"html"}},{"id":426855,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5017/sir20245017.jpg"},{"id":426856,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5017/sir20245017.pdf","text":"Report","size":"47.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5017"},{"id":426869,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OEKEIO","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW 6 model used to simulate groundwater flow in the Harney Basin, southeastern Oregon"},{"id":426859,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5017/sir20245017.XML"},{"id":426858,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5017/images"}],"country":"United States","state":"Oregon","otherGeospatial":"Harney Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -121.44430319880412,\n              44.965776942074626\n            ],\n            [\n              -121.44430319880412,\n              42.262073209475204\n            ],\n            [\n              -117.31344382380401,\n              42.262073209475204\n            ],\n            [\n              -117.31344382380401,\n              44.965776942074626\n            ],\n            [\n              -121.44430319880412,\n              44.965776942074626\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_or@usgs.gov\" data-mce-href=\"mailto:dc_or@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/oregon-water-science-center\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/oregon-water-science-center\">Oregon Water Science Center</a><br>U.S. Geological Survey<br>601 SW 2nd Avenue, Suite 1950<br>Portland, OR 97204</p>","tableOfContents":"<ul><li>Acknowledgements</li><li>Abstract</li><li>Introduction</li><li>Model Boundaries and Discretization</li><li>Hydraulic Properties</li><li>Simulation of Recharge</li><li>Simulation of Discharge</li><li>Model Calibration and Results</li><li>Model Sensitivity to Parameters</li><li>Simulated Groundwater Budget</li><li>Future Scenarios</li><li>Model Uncertainty, Limitations, and Improvements</li><li>Summary and Conclusions</li><li>References Cited</li><li>Appendix 1. Hydrostratigraphic Units in the Harney Basin Groundwater Model</li></ul>","publishedDate":"2024-03-22","noUsgsAuthors":false,"publicationDate":"2024-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Gingerich, Stephen B. 0000-0002-4381-0746 sbginger@usgs.gov","orcid":"https://orcid.org/0000-0002-4381-0746","contributorId":1426,"corporation":false,"usgs":true,"family":"Gingerich","given":"Stephen","email":"sbginger@usgs.gov","middleInitial":"B.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boschmann, Darrick E. 0000-0001-8662-9261","orcid":"https://orcid.org/0000-0001-8662-9261","contributorId":289547,"corporation":false,"usgs":false,"family":"Boschmann","given":"Darrick","email":"","middleInitial":"E.","affiliations":[{"id":34888,"text":"Oregon Water Resources Department","active":true,"usgs":false}],"preferred":false,"id":897041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grondin, Gerald H. 0000-0002-8930-6967","orcid":"https://orcid.org/0000-0002-8930-6967","contributorId":289548,"corporation":false,"usgs":false,"family":"Grondin","given":"Gerald","email":"","middleInitial":"H.","affiliations":[{"id":34888,"text":"Oregon Water Resources Department","active":true,"usgs":false}],"preferred":false,"id":897042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schibel, Halley J 0000-0002-9562-9340","orcid":"https://orcid.org/0000-0002-9562-9340","contributorId":290740,"corporation":false,"usgs":false,"family":"Schibel","given":"Halley","email":"","middleInitial":"J","affiliations":[{"id":34888,"text":"Oregon Water Resources Department","active":true,"usgs":false}],"preferred":false,"id":897043,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70252625,"text":"70252625 - 2024 - Warm places, warm years, and warm seasons increase parasitizing of moose by winter ticks","interactions":[],"lastModifiedDate":"2024-04-01T11:19:27.37329","indexId":"70252625","displayToPublicDate":"2024-03-21T06:14:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Warm places, warm years, and warm seasons increase parasitizing of moose by winter ticks","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>Observed links between parasites, such as ticks, and climate change have aroused concern for human health, wildlife population dynamics, and broader ecosystem effects. The one-host life history of the winter tick (<i>Dermacentor albipictus</i>) links each annual cohort to environmental conditions during three specific time periods when they are predictably vulnerable: spring detachment from hosts, summer larval stage, and fall questing for hosts. We used mixed-effects generalized linear models to investigate the drivers of tick loads carried by moose (<i>Alces alces</i>) relative to these time periods and across 750 moose, 10 years, and 16 study areas in the western United States. We tested for the effects of biotic factors (moose density, shared winter range, vegetation, migratory behavior) and weather conditions (temperature, snow, humidity) during each seasonal period when ticks are vulnerable and off-host. We found that warm climatic regions, warm seasonal periods across multiple partitions of the annual tick life cycle, and warm years relative to long-term averages each contributed to increased tick loads. We also found important effects of snow and other biotic factors such as host density and vegetation. Tick loads in the western United States were, on average, lower than those where tick-related die-offs in moose populations have occurred recently, but loads carried by some individuals may be sufficient to cause mortality. Lastly, we found interannual variation in tick loads to be most correlated with spring snowpack, suggesting this environmental component may have the highest potential to induce change in tick load dynamics in the immediate future of this region.</p></div></div>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4799","usgsCitation":"DeCesare, N.J., Harris, R.B., Atwood, M., Bergman, E.A., , C., Cross, P., Fralick, G., Hersey, K., Hurley, M., Koser, T., Levine, R.L., Monteith, K., Newby, J., Peterson, C., Robertson, S.M., and Wise, B., 2024, Warm places, warm years, and warm seasons increase parasitizing of moose by winter ticks: Ecosphere, v. 15, no. 3, e4799, 15 p., https://doi.org/10.1002/ecs2.4799.","productDescription":"e4799, 15 p.","ipdsId":"IP-156720","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":440077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4799","text":"Publisher Index Page"},{"id":427260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"DeCesare, Nicholas J.","contributorId":200237,"corporation":false,"usgs":false,"family":"DeCesare","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":897745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Richard B.","contributorId":198309,"corporation":false,"usgs":false,"family":"Harris","given":"Richard","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":897746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atwood, M. P.","contributorId":335215,"corporation":false,"usgs":false,"family":"Atwood","given":"M. P.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":897747,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergman, Eric A. 0000-0002-7069-8286","orcid":"https://orcid.org/0000-0002-7069-8286","contributorId":84513,"corporation":false,"usgs":false,"family":"Bergman","given":"Eric","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":897748,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":" Courtemanch","contributorId":204813,"corporation":false,"usgs":false,"given":"Courtemanch","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":897749,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":218820,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":897750,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fralick, Gary","contributorId":287797,"corporation":false,"usgs":false,"family":"Fralick","given":"Gary","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":897751,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hersey, Kent","contributorId":99873,"corporation":false,"usgs":false,"family":"Hersey","given":"Kent","affiliations":[{"id":6763,"text":"Utah Division of Wildlife Resources, Salt Lake City, Utah","active":true,"usgs":false}],"preferred":false,"id":897752,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hurley, Mark A.","contributorId":287804,"corporation":false,"usgs":false,"family":"Hurley","given":"Mark A.","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":897753,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Koser, T","contributorId":335217,"corporation":false,"usgs":false,"family":"Koser","given":"T","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":897754,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Levine, Rebecca L.","contributorId":296705,"corporation":false,"usgs":false,"family":"Levine","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":897755,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Monteith, Kevin","contributorId":279766,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":897756,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Newby, Jesse","contributorId":335218,"corporation":false,"usgs":false,"family":"Newby","given":"Jesse","affiliations":[{"id":40948,"text":"Montana Fish Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":897757,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Peterson, Collin","contributorId":335220,"corporation":false,"usgs":false,"family":"Peterson","given":"Collin","affiliations":[{"id":40948,"text":"Montana Fish Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":897758,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Robertson, Sarah M.","contributorId":335069,"corporation":false,"usgs":false,"family":"Robertson","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":897759,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wise, Benjamin","contributorId":189800,"corporation":false,"usgs":false,"family":"Wise","given":"Benjamin","affiliations":[],"preferred":false,"id":897760,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70252736,"text":"70252736 - 2024 - Novel insights about petroleum systems from source and reservoir rock characterization, Cretaceous Deposits, Babouri-Figuil Basin, Northern Cameroon","interactions":[],"lastModifiedDate":"2024-04-04T16:07:33.665151","indexId":"70252736","displayToPublicDate":"2024-03-20T10:57:42","publicationYear":"2024","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":"Novel insights about petroleum systems from source and reservoir rock characterization, Cretaceous Deposits, Babouri-Figuil Basin, Northern Cameroon","docAbstract":"<p><span>The Babouri-Figuil Basin (BFB) is a frontier basin for petroleum in Cameroon. It belongs to the series of Cretaceous rift basins of the West and Central Rift System (WCARS), the origin of which is related to the opening of the South Atlantic. Within the same rift system, commercial hydrocarbon accumulations have been discovered in Chad, Sudan, Niger and, more recently, in Nigeria (Gongola Basin). The study of the geology of the BFB just recently received considerable attention, mainly because of its presumed hydrocarbon potential. In the pursuit of researching possible petroleum systems in the BFB, the current study provides a first look into the characterization of source and reservoir rock and its integration into a 2D lithostratigraphic model. The study was solely based on outcrop samples. Black shale and massive claystone are good to excellent hydrocarbon source rocks [e.g., up to 38&nbsp;wt% total organic carbon (TOC), up to 943&nbsp;mg/g hydrogen index, up to 85&nbsp;m thickness, up to 20–30&nbsp;km lateral extension], with moderate to high values of extractable organic matter (e.g., &gt;10,000&nbsp;ppm). Calcareous claystone, on the other hand, are poor source rocks [e.g., &lt;0.20&nbsp;wt% TOC]. The samples are thermally immature, except for those located close to volcanic intrusion at Golombe that have reached the threshold for oil generation (Tmax &gt;435&nbsp;°C, production index &gt;0.1). The petrographic analysis of sandstone revealed that they are fine-grained to coarse-grained, poorly to moderately sorted, texturally and compositionally immature to submature, subarkosic to arkosic arenites. The main diagenetic processes that affected sandstones are as follows: moderate to intense compaction characterized by the development of long, concavo-convex, and sutured contacts between grains; cementation through calcite, iron oxide, and quartz cements; alteration of mica and feldspar grains; partial to complete dissolution of feldspar, mica, amphibole grains, and calcite cement; and the replacement of feldspar and mica grains by clay minerals. Alteration and dissolution increase the porosity of sandstone through the creation of secondary pores. However, mechanical compaction through the development of a pseudomatrix and cementation as pore-filling materials have significantly reduced the quality of sandstone beds as conventional petroleum reservoirs. Hence, the best reservoir-quality sandstones in the basin are generally located in the upper portion of the basin in terms of its lithostratigraphic model. They are the cleanest sandstones with the smallest amount of cement and the lowest ductile grain content (pseudomatrix), with a thickness that varies from 3&nbsp;m to 120&nbsp;m and a lateral extension of 20&nbsp;km. The lithostratigraphic model of the basin is characterized by an extensive lacustrine environment that provided a thick sequence of organic-rich formations; sand deposited as extensive reservoirs sandwiched between shale/claystone beds; the development of stratigraphic traps through lateral facies change; and the widespread deposition of lacustrine and floodplain claystone that provide regional seals. The similarities between the Babouri-Figuil Basin and proven petroleum systems in other WCARS rift basins suggest that the basin may host at least one petroleum system where actively generating source rocks are present.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2024.104491","usgsCitation":"Gaspard, M., Hatcherian, J.J., Hackley, P.C., Bessong, M., Bapowa, C., Pougue, H., and Meying, A., 2024, Novel insights about petroleum systems from source and reservoir rock characterization, Cretaceous Deposits, Babouri-Figuil Basin, Northern Cameroon: International Journal of Coal Geology, v. 285, 104491, 21 p., https://doi.org/10.1016/j.coal.2024.104491.","productDescription":"104491, 21 p.","ipdsId":"IP-158392","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":427402,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Cameroon","otherGeospatial":"Babouri-Figuil Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              13.666,\n              9.85\n            ],\n            [\n              13.666,\n              9.633\n            ],\n            [\n              14.033,\n              9.633\n            ],\n            [\n              14.033,\n              9.85\n            ],\n            [\n              13.666,\n              9.85\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"285","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gaspard, Manga","contributorId":335306,"corporation":false,"usgs":false,"family":"Gaspard","given":"Manga","email":"","affiliations":[],"preferred":false,"id":898040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":898041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":898039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bessong, Moise","contributorId":335307,"corporation":false,"usgs":false,"family":"Bessong","given":"Moise","email":"","affiliations":[],"preferred":false,"id":898042,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bapowa, Carole","contributorId":335308,"corporation":false,"usgs":false,"family":"Bapowa","given":"Carole","email":"","affiliations":[],"preferred":false,"id":898043,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pougue, Henry","contributorId":335309,"corporation":false,"usgs":false,"family":"Pougue","given":"Henry","email":"","affiliations":[],"preferred":false,"id":898044,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meying, Arsene","contributorId":335311,"corporation":false,"usgs":false,"family":"Meying","given":"Arsene","email":"","affiliations":[],"preferred":false,"id":898045,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70261502,"text":"70261502 - 2024 - Changes in landscape and climate in Mexico and Texas reveal small effects on migratory habitat of monarch butterflies (Danaus plexippus)","interactions":[],"lastModifiedDate":"2024-12-13T14:14:04.429932","indexId":"70261502","displayToPublicDate":"2024-03-20T08:19:36","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Changes in landscape and climate in Mexico and Texas reveal small effects on migratory habitat of monarch butterflies (Danaus plexippus)","docAbstract":"<p><span>The decline of the iconic monarch butterfly (</span><i>Danaus plexippus</i><span>) in North America has motivated research on the impacts of land use and land cover (LULC) change and climate variability on monarch habitat and population dynamics. We investigated spring and fall trends in LULC, milkweed and nectar resources over a 20-year period, and ~ 30&nbsp;years of climate variables in Mexico and Texas, U.S. This region supports spring breeding, and spring and fall migration during the annual life cycle of the monarch. We estimated a − 2.9% decline in milkweed in Texas, but little to no change in Mexico. Fall and spring nectar resources declined &lt; 1% in both study extents. Vegetation greenness increased in the fall and spring in Mexico while the other climate variables did not change in both Mexico and Texas. Monarch habitat in Mexico and Texas appears relatively more intact than in the midwestern, agricultural landscapes of the U.S. Given the relatively modest observed changes in nectar and milkweed, the relatively stable climate conditions, and increased vegetation greenness in Mexico, it seems unlikely that habitat loss (quantity or quality) in Mexico and Texas has caused large declines in population size or survival during migration.</span></p>","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-024-56693-z","usgsCitation":"Diffendorfer, J., Botello, F., Drummond, M.A., Ancona, Z.H., Corro, L.M., Thogmartin, W.E., Ibsen, P.C., Moreno-Sanchez, R., Lukens, L., and Sanchez-Cordero, V., 2024, Changes in landscape and climate in Mexico and Texas reveal small effects on migratory habitat of monarch butterflies (Danaus plexippus): Scientific Reports, v. 14, 6703, 13 p., https://doi.org/10.1038/s41598-024-56693-z.","productDescription":"6703, 13 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Understanding the effects of climate change on hydrology is important to State and community officials to evaluate the potential effects on infrastructure and water systems. To better understand the effects of climate change on hydrology, the U.S. Geological Survey, in partnership with Cornell University and Tufts University, conducted a study in cooperation with the Massachusetts Executive Office of Energy and Environmental Affairs to develop tools for projecting 21st-century climate and hydrologic characteristics in Massachusetts.</p><p>A stochastic weather generator was developed to project future climatic characteristics for Massachusetts. The stochastic weather generator estimates daily precipitation, minimum temperature, and maximum temperature for 17 warming scenarios (from 0 to 8 degrees Celsius, in 0.5-degree increments). To project future hydrologic characteristics, the stochastic weather generator output data were input to the Precipitation-Watershed Modeling System deterministic watershed model for the Squannacook River watershed, which is the watershed selected as the pilot study location for investigating future hydrologic characteristics. Hydrologic data output from the deterministic watershed model were then input to a stochastic watershed model developed for this study to correct model errors (model errors are often observed in the output from deterministic models at the high- and low-flow extremes). The output from the stochastic watershed model was then used to characterize hydrology for the 17 warming scenarios. For the Squannacook River watershed, the results project more extreme flood and low streamflows under the warming scenarios.</p><p>Output from the tools allows the characterization of future streamflows for the years 2030, 2050, 2070, and 2090, which expands our understanding of 21st-century climatic and hydrologic risk in Massachusetts. These tools could improve Federal, State, and community officials’ ability to mitigate the effects of climate change over the next several decades.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235134","collaboration":"Prepared in cooperation with the Massachusetts Executive Office of Energy and Environmental Affairs","usgsCitation":"Olson, S.A., Shabestanipour, G., Lamontagne, J., and Steinschneider, S., 2024, Characterizing future streamflows in Massachusetts using stochastic modeling—A pilot study: U.S. Geological Survey Scientific Investigations Report 2023–5134, 19 p., https://doi.org/10.3133/sir20235134.","productDescription":"Report: v, 19 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-149673","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":499396,"rank":7,"type":{"id":36,"text":"NGMDB Index 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 \"}}]}","contact":"<p><a href=\"mailto:dc_nweng@usgs.gov\" data-mce-href=\"mailto:dc_nweng@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Study Methodology</li><li>Characterizing Future Streamflows for the Squannacook River Using Stochastic Modeling Methods</li><li>Limitations</li><li>Database of Project Results</li><li>Summary</li><li>Selected References</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2024-03-19","noUsgsAuthors":false,"publicationDate":"2024-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Olson, Scott A. 0000-0002-1064-2125","orcid":"https://orcid.org/0000-0002-1064-2125","contributorId":210173,"corporation":false,"usgs":true,"family":"Olson","given":"Scott A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shabestanipour, Ghazal","contributorId":303810,"corporation":false,"usgs":false,"family":"Shabestanipour","given":"Ghazal","email":"","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":896588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamontagne, Jonathan","contributorId":303813,"corporation":false,"usgs":false,"family":"Lamontagne","given":"Jonathan","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":896589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steinschneider, Scott 0000-0002-8882-1908","orcid":"https://orcid.org/0000-0002-8882-1908","contributorId":206359,"corporation":false,"usgs":false,"family":"Steinschneider","given":"Scott","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":896590,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70261729,"text":"70261729 - 2024 - Managing ecosystems with resist-accept-direct (RAD)","interactions":[],"lastModifiedDate":"2024-12-20T17:02:36.796663","indexId":"70261729","displayToPublicDate":"2024-03-19T10:59:52","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Managing ecosystems with resist-accept-direct (RAD)","docAbstract":"<ol class=\"\"><li>In recent years considerable interest has been generated in a new approach known as<span>&nbsp;</span><i>resist</i>–<i>accept</i>–<i>direct</i>, or RAD, for managing ecosystems in the face of climate change. Under RAD, strategic responses to climate change are described in terms of three broad categories: resisting climate transformation, accepting the transformation and continuing to manage as best one can, and directing the transformed system toward novel ecological conditions. In particular, the potential for integrating RAD and adaptive management has been broadly considered, though absent a decision-making framework needed for implementation.</li><li>We propose a hierarchical decision scheme for RAD that accounts for strategy selection among the three RAD options, as well as adaptive decision making within each option. We use stochastic models and uncertainties about ecosystem processes to account for the dynamics of climate-transformed ecosystems, and show how these features can be used to inform RAD strategies. Operationally, the approach involves decisions at two levels: one level involves choosing a policy for each strategy, and the second level involves deciding which strategy has the greatest policy value.</li><li>The structure described here extends recent work in climate change adaptation, by including Markovian decisions under climate change, strategy-specific policies, and value functions for assessing and selecting RAD strategies. We provide a hierarchical accounting of decisions and responses, and develop rules for the timing of those decisions.</li><li>Combining RAD and adaptive management can help to organize thinking about ecological conservation under climate change, and focus attention on mechanisms for making decisions. We believe the structure presented here can facilitate conservation efforts under the non-stationary climate conditions we are sure to face for the foreseeable future.</li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.14309","usgsCitation":"Williams, B.K., and Brown, E., 2024, Managing ecosystems with resist-accept-direct (RAD): Methods in Ecology and Evolution, v. 15, no. 5, p. 796-805, https://doi.org/10.1111/2041-210X.14309.","productDescription":"10 p.","startPage":"796","endPage":"805","ipdsId":"IP-162393","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":467023,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.14309","text":"Publisher Index Page"},{"id":465405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Byron K. 0000-0001-7644-1396","orcid":"https://orcid.org/0000-0001-7644-1396","contributorId":207067,"corporation":false,"usgs":true,"family":"Williams","given":"Byron","email":"","middleInitial":"K.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":921610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Ellie 0000-0001-7798-830X ebrown@usgs.gov","orcid":"https://orcid.org/0000-0001-7798-830X","contributorId":200491,"corporation":false,"usgs":true,"family":"Brown","given":"Ellie","email":"ebrown@usgs.gov","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":false,"id":921611,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70258316,"text":"70258316 - 2024 - Partitioning of chemicals in aquatic organisms","interactions":[],"lastModifiedDate":"2024-09-11T14:55:55.782266","indexId":"70258316","displayToPublicDate":"2024-03-19T09:53:16","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Partitioning of chemicals in aquatic organisms","docAbstract":"<p><span>Human populations have utilized heavy metals and persistent organic pollutants for their physiochemical properties in industrial, agricultural, and consumer goods for decades. Limited knowledge on their persistence and toxicological effects has resulted in organisms being exposed to some of the most problematic compounds ever generated by humans. Although overlap in exposure paradigms exists for historical and emerging contaminants, the different physiochemical properties, sources into the environment, and bioactivity of contaminants of emerging concern (CECs) have highlighted the importance of characterizing their risk to aquatic wildlife under chronic low-dose exposure scenarios. This chapter defines the fundamental terminology associated with characterizing the exposure paradigm in ecological risk assessment. The different sources and fate, routes of exposure, and biotransformation of common contaminants are covered using model chemicals to emphasize important factors that affect their partitioning among different environmental matrices. Finally, this chapter concludes with a discussion about bioaccumulation models and an example of how two similar CECs demonstrate different clearance rates and bioaccumulation potentials in fish.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Aquatic ecotoxicology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-031-53130-9_8","usgsCitation":"Saari, G.N., Siddiqui, S., and Brander, S.M., 2024, Partitioning of chemicals in aquatic organisms, chap. <i>of</i> Aquatic ecotoxicology, p. 115-130, https://doi.org/10.1007/978-3-031-53130-9_8.","productDescription":"16 p.","startPage":"115","endPage":"130","ipdsId":"IP-146203","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":433696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-03-19","publicationStatus":"PW","contributors":{"editors":[{"text":"Siddiqui, Samreen","contributorId":298402,"corporation":false,"usgs":false,"family":"Siddiqui","given":"Samreen","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":912940,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Brander, Susanne M.","contributorId":187546,"corporation":false,"usgs":false,"family":"Brander","given":"Susanne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":912941,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Saari, Gavin N. 0000-0002-3593-5127 gsaari@usgs.gov","orcid":"https://orcid.org/0000-0002-3593-5127","contributorId":289203,"corporation":false,"usgs":true,"family":"Saari","given":"Gavin","email":"gsaari@usgs.gov","middleInitial":"N.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":912888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siddiqui, Samreen","contributorId":298402,"corporation":false,"usgs":false,"family":"Siddiqui","given":"Samreen","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":912889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brander, Susanne M.","contributorId":187546,"corporation":false,"usgs":false,"family":"Brander","given":"Susanne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":912890,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70252168,"text":"ofr20241011 - 2024 - Summary of Creepmeter Data from 1980 to 2020—Measurements Spanning the Hayward, Calaveras, and San Andreas Faults in Northern and Central California","interactions":[],"lastModifiedDate":"2026-01-28T18:05:08.13879","indexId":"ofr20241011","displayToPublicDate":"2024-03-18T11:51:10","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1011","displayTitle":"Summary of Creepmeter Data from 1980 to 2020—Measurements Spanning the Hayward, Calaveras, and San Andreas Faults in Northern and Central California","title":"Summary of Creepmeter Data from 1980 to 2020—Measurements Spanning the Hayward, Calaveras, and San Andreas Faults in Northern and Central California","docAbstract":"<p>This report is an update to the presentation by Schulz (1989) introducing potential users to the creepmeter data collected between the publication of Schulz’s report and mid-2020. The creepmeter network monitors aseismic, surface slip at various locations on the Hayward, Calaveras, and San Andreas Faults in northern and central California. There are different designs of creepmeters and these are briefly described. For a majority of the creepmeters, these data are automatically sent to the U.S. Geological Survey (USGS) offices where they are stored and processed. In addition, for most of the creepmeters, occasional manual measurements are made and these are compared with digitally recorded data. For some sites, the comparisons indicated degradation of the electronic sensor and consequently corrections are made to the digital data. The largest transient deformation is that which followed the 2004, <i>M6</i>, Parkfield earthquake. Various functions found in the literature that have been used to model postseismic slip were tested with the observed postseismic behavior seen on the creepmeters in the vicinity of Parkfield, California. No single function adequately fit all the data from these Parkfield instruments. This report is a discussion and analysis of data from creepmeters deployed by the USGS. The discussion primarily focuses on instruments that are currently operating in 2020 or have operated quite recently but are no longer in service.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241011","usgsCitation":"Langbein, J., Bilham, R.G., Snyder, H.A., and Ericksen, T., 2024, Summary of Creepmeter Data from 1980 to 2020—Measurements Spanning the Hayward, Calaveras, and San Andreas Faults in Northern and Central California: U.S. Geological Survey Report 2024–1011, 110 p., https://doi.org/10.3133/ofr20241011.","productDescription":"vi, 110 p.","numberOfPages":"110","onlineOnly":"Y","ipdsId":"IP-143918","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":499206,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116172.htm","linkFileType":{"id":5,"text":"html"}},{"id":426750,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1011/ofr20241011.pdf","text":"Report","size":"60 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426749,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1011/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.83784784258054,\n              37.99394764431494\n            ],\n            [\n              -122.83784784258054,\n              34.52234572819374\n            ],\n            [\n              -119.36616815508066,\n              34.52234572819374\n            ],\n            [\n              -119.36616815508066,\n              37.99394764431494\n            ],\n            [\n              -122.83784784258054,\n              37.99394764431494\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/earthquake-science-center\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/earthquake-science-center\">Earthquake Science Center</a><br><a href=\"https://www.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>350 N. Akron Rd.<br>Moffett Field, CA 94035</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Instrumentation, Methods, and Data; Data Summary</li><li>Parkfield Postseismic Creep</li><li>Discussion</li><li>References Cited</li><li>Appendix 1. Site Summaries for Creepmeter Sites for the Hayward, Calaveras, and San Andreas Faults in Northern and Central California</li><li>Appendix 2. Calculation of Fault Slip from Creepmeter Data—Effect of Obliquity and Fault-Normal Displacements</li><li>Appendix 3. Creepmeter Calibration Issues Addressing the Potential Scaling Problem for the Gold Hill (XGH1) Creepmeter Located on the San Andreas Fault Southeast of Parkfield, California</li><li>Appendix 4. Seasonal Variations in Creep for the Hayward, Calaveras, and San Andreas Faults in Northern and Central California</li><li>Appendix 5. Creep, Earthquakes, and Strain for the Hayward, Calaveras, and San Andreas Faults in Northern and Central California</li><li>Appendix 6. Earth Tide Effects on Selected Creepmeters in Northern and Central California</li><li>Appendix 7. Propagating Creep Events for Selected Creepmeters in Northern and Central California</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2024-03-18","noUsgsAuthors":false,"publicationDate":"2024-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Langbein, John 0000-0002-7821-8101","orcid":"https://orcid.org/0000-0002-7821-8101","contributorId":202336,"corporation":false,"usgs":true,"family":"Langbein","given":"John","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":896831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bilham, Roger G. 0000-0002-5547-4102","orcid":"https://orcid.org/0000-0002-5547-4102","contributorId":48200,"corporation":false,"usgs":true,"family":"Bilham","given":"Roger","email":"","middleInitial":"G.","affiliations":[],"preferred":true,"id":896832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snyder, Hollice A.","contributorId":59530,"corporation":false,"usgs":true,"family":"Snyder","given":"Hollice","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":896833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ericksen, Todd 0000-0001-9340-575X tericksen@usgs.gov","orcid":"https://orcid.org/0000-0001-9340-575X","contributorId":198145,"corporation":false,"usgs":true,"family":"Ericksen","given":"Todd","email":"tericksen@usgs.gov","affiliations":[],"preferred":true,"id":896834,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70252641,"text":"70252641 - 2024 - Evaluation and refinement of chlorophyll-a algorithms for high-biomass blooms in San Francisco Bay (USA)","interactions":[],"lastModifiedDate":"2024-04-02T14:52:29.703617","indexId":"70252641","displayToPublicDate":"2024-03-18T09:45:43","publicationYear":"2024","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":"Evaluation and refinement of chlorophyll-a algorithms for high-biomass blooms in San Francisco Bay (USA)","docAbstract":"<p><span>A massive bloom of the raphidophyte&nbsp;</span><i><span class=\"html-italic\">Heterosigma akashiwo</span></i><span>&nbsp;occurred in summer 2022 in San Francisco Bay, causing widespread ecological impacts including events of low dissolved oxygen and mass fish kills. The rapidly evolving bloom required equally rapid management response, leading to the use of near-real-time image analysis of chlorophyll from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3. Standard algorithms failed to adequately capture the bloom, signifying a need to refine a two-band algorithm developed for coastal and inland waters that relates the red-edge part of the remote sensing reflectance spectrum to chlorophyll. While the bloom was the initial motivation for optimizing this algorithm, an extensive dataset of in-water validation measurements from both bloom and non-bloom periods was used to evaluate performance over a range of concentrations and community composition. The modified red-edge algorithm with a simplified atmospheric correction scheme outperformed existing standard products across diverse conditions, and given the modest computational requirements, was found suitable for operational use and near-real-time product generation. The final version of the algorithm successfully minimizes error for non-bloom periods when chlorophyll&nbsp;</span><span class=\"html-italic\">a</span><span>&nbsp;is typically &lt;30 mg m</span><sup>−3</sup><span>, while also capturing bloom periods of &gt;100 mg m</span><sup>−3</sup><span>&nbsp;chlorophyll&nbsp;</span><i><span class=\"html-italic\">a</span></i><span>.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs16061103","usgsCitation":"Kudela, R.M., Senn, D.B., Richardson, E.T., Bouma-Gregson, K., Bergamaschi, B.A., and Sim, L., 2024, Evaluation and refinement of chlorophyll-a algorithms for high-biomass blooms in San Francisco Bay (USA): Remote Sensing, v. 16, no. 6, 1103, 15 p., https://doi.org/10.3390/rs16061103.","productDescription":"1103, 15 p.","ipdsId":"IP-160723","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":440089,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16061103","text":"Publisher Index Page"},{"id":435018,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GXJHZ3","text":"USGS data release","linkHelpText":"Assessing spatial variability of nutrients, phytoplankton, and related water-quality constituents in the San Francisco Bay, California: 2021-2022 High-resolution mapping surveys"},{"id":427314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.07594813067364,\n              37.409521554962424\n            ],\n            [\n              -122.01459141030173,\n              37.543473010939294\n            ],\n            [\n              -122.3138751078345,\n              37.9439399615851\n            ],\n            [\n              -122.2371288097209,\n              38.076947981745235\n            ],\n            [\n              -122.4289707157553,\n              38.1493978144897\n            ],\n            [\n              -122.49802549540809,\n              38.08298835733879\n            ],\n            [\n              -122.5286656402067,\n              37.93788689033987\n            ],\n            [\n              -122.4673236665621,\n              37.792501512759955\n            ],\n            [\n              -122.35216906140471,\n              37.610380642182776\n            ],\n            [\n              -122.07594813067364,\n              37.409521554962424\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Kudela, Raphael M.","contributorId":205181,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":897798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senn, David B.","contributorId":205182,"corporation":false,"usgs":false,"family":"Senn","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":897799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richardson, Emily T. 0000-0003-2696-8266","orcid":"https://orcid.org/0000-0003-2696-8266","contributorId":304430,"corporation":false,"usgs":true,"family":"Richardson","given":"Emily","email":"","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouma-Gregson, Keith 0000-0002-0304-6034","orcid":"https://orcid.org/0000-0002-0304-6034","contributorId":311235,"corporation":false,"usgs":true,"family":"Bouma-Gregson","given":"Keith","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897801,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897802,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sim, Lawrence","contributorId":168731,"corporation":false,"usgs":false,"family":"Sim","given":"Lawrence","email":"","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":897803,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70252252,"text":"70252252 - 2024 - New diagnostic assessment of MCMC algorithm effectiveness, efficiency, reliability, and controllability","interactions":[],"lastModifiedDate":"2024-03-26T15:03:05.007788","indexId":"70252252","displayToPublicDate":"2024-03-18T06:42:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17179,"text":"IEEE Access","active":true,"publicationSubtype":{"id":10}},"title":"New diagnostic assessment of MCMC algorithm effectiveness, efficiency, reliability, and controllability","docAbstract":"<div class=\"u-mb-1\"><div>Markov Chain Monte Carlo (MCMC) is a robust statistical approach for estimating posterior distributions. However, the significant computational cost associated with MCMC presents a considerable challenge, complicating the selection of an appropriate algorithm tailored to the specific problem at hand. This study introduces a novel and comprehensive framework for evaluating the performance of MCMC algorithms, drawing inspiration from diagnostics used for multi-objective evolutionary algorithms. We employ visualizations to evaluate key algorithmic characteristics: Effectiveness (the ability to accurately find representative posterior modes, quantified by the Kullback-Leibler Divergence (KLD) andWasserstein Distance (WD)), Efficiency (the speed of posterior characterization), Reliability (consistency across different random seeds), and Controllability (insensitivity to hyperparameter variation). Evaluating three prominent MCMC algorithms—Metropolis-Hastings (MH), Adaptive Metropolis (AM), and Differential Evolution Adaptive Metropolis (DREAM)—on high-dimensional and bimodal test problems, our analysis uncovers several insights. First, across algorithms, the number of function evaluations most controls performance on the high-dimensional problem, while the number of chains most controls performance on the bimodal problem. While this suggests similar controllability across algorithms, differences emerge on the other algorithmic characteristics. For high numbers of functions evaluations, AM performs best on the high-dimensional problem, while for low (&lt;5) and high (&gt;15) chain counts, MH and AM perform best on the bimodal problem, as measured by KLD. However, outside these specific cases, DREAM consistently demonstrates superior efficiency and reliability, making it a robust choice for both high-dimensional and multimodal problems. These findings can inform MCMC algorithm selection for Bayesian inference applications, as well as hyperparameterization of the chosen algorithm...</div></div>","language":"English","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","doi":"10.1109/ACCESS.2024.3378752","usgsCitation":"KavianiHamedani, H., Quinn, J.D., and Smith, J.D., 2024, New diagnostic assessment of MCMC algorithm effectiveness, efficiency, reliability, and controllability: IEEE Access, v. 12, p. 42385-42400, https://doi.org/10.1109/ACCESS.2024.3378752.","productDescription":"16 p.","startPage":"42385","endPage":"42400","ipdsId":"IP-159625","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":440103,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/access.2024.3378752","text":"Publisher Index Page"},{"id":426884,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"KavianiHamedani, Hossein 0000-0002-9652-7418","orcid":"https://orcid.org/0000-0002-9652-7418","contributorId":334948,"corporation":false,"usgs":false,"family":"KavianiHamedani","given":"Hossein","email":"","affiliations":[{"id":25492,"text":"University of Virginia","active":true,"usgs":false}],"preferred":false,"id":897051,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quinn, Julianne D. 0000-0001-7806-4416","orcid":"https://orcid.org/0000-0001-7806-4416","contributorId":334950,"corporation":false,"usgs":false,"family":"Quinn","given":"Julianne","email":"","middleInitial":"D.","affiliations":[{"id":25492,"text":"University of Virginia","active":true,"usgs":false}],"preferred":false,"id":897052,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Jared David 0000-0003-3124-8255","orcid":"https://orcid.org/0000-0003-3124-8255","contributorId":329716,"corporation":false,"usgs":true,"family":"Smith","given":"Jared","email":"","middleInitial":"David","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":897053,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70257434,"text":"70257434 - 2024 - White-tailed deer detection rates increase when coyotes are present","interactions":[],"lastModifiedDate":"2024-09-06T16:44:01.482689","indexId":"70257434","displayToPublicDate":"2024-03-17T11:37:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"White-tailed deer detection rates increase when coyotes are present","docAbstract":"<p><span>Predator species can indirectly affect prey species through the cost of anti-predator behavior responses, which may involve shifts in occupancy, space use, or movement. Quantifying the various strategies implemented by prey species to avoid adverse interactions with predators can lead to a better understanding of potential population-level repercussions. Therefore, the purpose of this study was to examine predator–prey interactions by quantifying the effect of predator species presence on detection rates of prey species, using coyotes (</span><i>Canis latrans</i><span>) and white-tailed deer (</span><i>Odocoileus virginianus</i><span>) in Central Appalachian forests of the eastern United States as a model predator–prey system. To test two competing hypotheses related to interspecific interactions, we modeled species detections from 319 camera traps with a two-species occupancy model that incorporated a continuous-time detection process. We found that white-tailed deer occupancy was independent of coyote occupancy, but white-tailed deer were more frequently detectable and had greater detection intensity at sites where coyotes were present, regardless of vegetation-related covariates. In addition, white-tailed deer detection rates at sites with coyotes were highest when presumed forage availability was relatively low. These findings suggest that white-tailed deer may be exhibiting an active avoidance behavioral response to predators by increasing movement rates when coyotes are present in an area, perhaps due to reactive evasive maneuvers and/or proactive attempts to reduce adverse encounters with them. Concurrently, coyotes could be occupying sites with higher white-tailed deer densities. Because white-tailed deer did not exhibit significant shifts in daily activity patterns based on coyote occupancy, we further suggest that white-tailed deer in our study system generally do not use temporal partitioning as their primary strategy for avoiding encounters with coyotes. Overall, our study implements a recently developed analytical approach for modeling multi-species occupancy from camera traps and provides novel ecological insight into the complex relationships between predator and prey species.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11149","usgsCitation":"Clipp, H.L., Pesi, S.M., Miller, M.L., Gigliotti, L., Skelly, B.P., and Rota, C., 2024, White-tailed deer detection rates increase when coyotes are present: Ecology and Evolution, v. 14, no. 3, e11149, 13 pp., https://doi.org/10.1002/ece3.11149.","productDescription":"e11149, 13 pp.","ipdsId":"IP-157302","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11149","text":"Publisher Index Page"},{"id":433571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania, West Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -80.4877790212181,\n              39.94978819373756\n            ],\n            [\n              -82.01878727948417,\n              39.01317775882444\n            ],\n            [\n              -82.58830516880357,\n              38.2047845680037\n            ],\n            [\n              -79.5468825034643,\n              37.66652638635898\n            ],\n            [\n              -77.96682473836029,\n              39.5141262945331\n            ],\n            [\n              -76.37907636338417,\n              40.11338027059526\n            ],\n            [\n              -77.28462588707731,\n              41.09189702989761\n            ],\n            [\n              -80.4877790212181,\n              39.94978819373756\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"14","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Clipp, Hannah L.","contributorId":342785,"corporation":false,"usgs":false,"family":"Clipp","given":"Hannah","email":"","middleInitial":"L.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pesi, Sarah M.","contributorId":342786,"corporation":false,"usgs":false,"family":"Pesi","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Madison L.","contributorId":342787,"corporation":false,"usgs":false,"family":"Miller","given":"Madison","email":"","middleInitial":"L.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gigliotti, Laura C. 0000-0002-6390-4133","orcid":"https://orcid.org/0000-0002-6390-4133","contributorId":200327,"corporation":false,"usgs":false,"family":"Gigliotti","given":"Laura C.","affiliations":[],"preferred":false,"id":910395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skelly, Brett P.","contributorId":342789,"corporation":false,"usgs":false,"family":"Skelly","given":"Brett","email":"","middleInitial":"P.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910396,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rota, Christopher T.","contributorId":342791,"corporation":false,"usgs":false,"family":"Rota","given":"Christopher T.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910397,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70252441,"text":"70252441 - 2024 - Data-driven adjustments for combined use of NGA-East hard-rock ground motion and site amplification models","interactions":[],"lastModifiedDate":"2024-05-07T14:35:47.184916","indexId":"70252441","displayToPublicDate":"2024-03-17T08:31:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Data-driven adjustments for combined use of NGA-East hard-rock ground motion and site amplification models","docAbstract":"<p><span>Model development in the Next Generation Attenuation-East (NGA-East) project included two components developed concurrently and independently: (1) earthquake ground-motion models (GMMs) that predict the median and aleatory variability of various intensity measures conditioned on magnitude and distance, derived for a reference hard-rock site condition with an average shear-wave velocity in the upper 30 m (</span><i>V<sub>S30</sub></i><span>) = 3000 m/s; and (2) a site amplification model that modifies intensity measures for softer site conditions. We investigate whether these models, when used in tandem, are compatible with ground-motion recordings in central and eastern North America (CENA) using an expanded version of the NGA-East database that includes new events from November 2011 (end date of NGA-East data curation) to April 2022. Following this expansion, the data set has 187 events, 2096 sites, and 16,272 three-component recordings, although the magnitude range remains limited (∼4 to 5.8). We compute residuals using 17 NGA-East GMMs and three data selection criteria that reflect within-CENA regional variations in ground-motion attributes. Mixed-effects regression of the residuals reveals a persistent pattern in which ground motions are overpredicted at short periods (0.01–0.6 s, including peak ground acceleration (PGA)) and underpredicted at longer periods. These misfits are regionally variable, with the Texas–Oklahoma–Kansas region having larger absolute misfits than other parts of CENA. Two factors potentially influencing these misfits are (1) differences in the site amplification models used to adjust the data to the reference condition during NGA-East GMM development relative to CENA amplification models applied since the 2018 National Seismic Hazard Model (NSHM), and (2) potential bias in simulation-based factors used to adjust ground motions from the hard-rock reference condition to a&nbsp;</span><i>V<sub>S30</sub></i><span> = 760 m/s condition. We provide adjustment factors and their epistemic uncertainties and discuss implications for applications.</span></p>","language":"English","publisher":"Sage Journals","doi":"10.1177/87552930241231825","usgsCitation":"Ramos-Sepulveda, M.E., Stewart, J.P., Parker, G.A., Moschetti, M.P., Thompson, E.M., Brandenberg, S.J., Hashash, Y.M., and Rathje, E., 2024, Data-driven adjustments for combined use of NGA-East hard-rock ground motion and site amplification models: Earthquake Spectra, v. 40, no. 2, p. 1132-1157, https://doi.org/10.1177/87552930241231825.","productDescription":"26 p.","startPage":"1132","endPage":"1157","ipdsId":"IP-153071","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":440108,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930241231825","text":"Publisher Index 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