This study is part of the Coastal Protection and Restoration Authority (CPRA) Louisiana Barrier Island Comprehensive Monitoring (BICM) program. The goal of the BICM program is to provide long-term data on the barrier islands of Louisiana for monitoring change and assisting in coastal management. The BICM program uses historical data and acquires new data to map and monitor shoreline position, sediment properties, topography, bathymetry, and habitat. Since 2006, the U.S. Geological Survey (USGS) has collected geophysical and sedimentologic data across the Breton National Wildlife Refuge (BNWR) through the BICM program and collaborative USGS projects such as the Barrier Island Evolution Research project (under CPRA contract number 2000339324, BICM2–Chandeleurs TopoBathy DEM), which builds upon the previous BICM physical assessment of the BNWR outlined in a separate report. This project uses topographic and bathymetric data from three periods (1917–1922, 2006–2007, and 2013–2015) to develop digital elevation models (DEMs), measure elevation change, and calculate sediment budgets for the barrier island system. The sediment budget analysis, derived from the volumetric change between the three periods, is necessary for understanding sediment transport dynamics along barrier islands and providing information for effective coastal management. This report describes the methods used to acquire, process, and produce these products.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221020","collaboration":"Prepared in cooperation with the Coastal Protection and Restoration Authority of Louisiana","programNote":"Louisiana Barrier Island Comprehensive Monitoring Program 2015–2020","usgsCitation":"Flocks, J.G., Forde, A.S., and Bernier, J.C., 2022, Chandeleur Islands to Breton Island bathymetric and topographic datasets and operational sediment budget development—Methodology and analysis report: U.S. Geological Survey Open-File Report 2022–1020, 48 p., https://doi.org/10.3133/ofr20221020.","productDescription":"ix, 48 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122915","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":397352,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20231020/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397307,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1020/coverthb.jpg"},{"id":397308,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1020/ofr20221020.pdf","text":"Report","size":"47.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1020"},{"id":397309,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1020/images/"},{"id":397310,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1020/ofr20221020.XML"},{"id":501765,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112713.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton Island, Breton National Wildlife Refuge, Chandeleur Islands, Curlew Shoals, Grand Gosier Shoals, Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.22477722167967,\n 29.351656186711196\n ],\n [\n -88.83064270019531,\n 29.438999582891338\n ],\n [\n -88.61228942871094,\n 29.685070141332993\n ],\n [\n -88.59992980957031,\n 29.956124387148986\n ],\n [\n -88.72833251953125,\n 30.19439868711761\n ],\n [\n -89.09431457519531,\n 30.064934211006477\n ],\n [\n -89.00230407714844,\n 29.854341876042557\n ],\n [\n -89.14306640625,\n 29.664189403696138\n ],\n [\n -89.36073303222656,\n 29.467101009006807\n ],\n [\n -89.22477722167967,\n 29.351656186711196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Fisheries managers and ecologists use statistical models to estimate population-level relations and demographic rates (e.g., length-maturity curves, growth curves, and mortality rates). These relations and rates provide insight into populations and inputs for other models. For example, growth curves may vary across lakes showing fish populations differ due to management actions or underlying environmental conditions. A fisheries manager could use this information to set lake-specific harvest limits or an ecologist could use this information to test scientific hypotheses about fish populations. The above example also demonstrates how populations exist within hierarchical structures where sub-populations may be nested within a meta-population. More generally, these hierarchical structures may be both biological (e.g., different lakes or river pools) and statistical (e.g., correlated error structures). Currently, limited options exist for fitting these hierarchical models and people seeking to use them often must program their own implementations. Furthermore, many fisheries managers and researchers may not have Bayesian programming skills, but many can use interactive languages such as R. Additionally, programs such as JAGS often require long run times (e.g., hours if not days) to fit hierarchical models and programs such as Stan can be more difficult to program because it is a compiled language. We created fishStan to share hierarchical models for fisheries and ecology in an easy-to-use R package.
","language":"English","publisher":"Open Journals","doi":"10.21105/joss.03444","usgsCitation":"Erickson, R.A., Stich, D.S., and Hebert, J.L., 2022, FishStan: Hierarchical Bayesian models for fisheries: Journal of Open Source Software, v. 7, no. 71, 3444, 2 p., https://doi.org/10.21105/joss.03444.","productDescription":"3444, 2 p.","ipdsId":"IP-125667","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":448415,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21105/joss.03444","text":"Publisher Index Page"},{"id":397534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"71","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":838685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stich, Daniel S.","contributorId":280276,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":33660,"text":"SUNY Oneonta","active":true,"usgs":false}],"preferred":false,"id":838686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hebert, Jillian Lee 0000-0003-4893-8287","orcid":"https://orcid.org/0000-0003-4893-8287","contributorId":289197,"corporation":false,"usgs":true,"family":"Hebert","given":"Jillian","email":"","middleInitial":"Lee","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":838687,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229834,"text":"70229834 - 2022 - Using carbon, nitrogen, and mercury isotope values to distinguish mercury sources to Alaskan lake trout","interactions":[],"lastModifiedDate":"2022-04-26T12:13:45.041649","indexId":"70229834","displayToPublicDate":"2022-03-21T11:32:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7485,"text":"Environmental Science and Technology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Using carbon, nitrogen, and mercury isotope values to distinguish mercury sources to Alaskan lake trout","docAbstract":"Lake trout (Salvelinus namaycush), collected from 13 remote lakes located in southwestern Alaska, were analyzed for carbon, nitrogen, and mercury (Hg) stable isotope values to assess the importance of migrating oceanic salmon, volcanic activity, and atmospheric deposition to fish Hg burden. Methylmercury (MeHg) bioaccumulation in phytoplankton (5.0–6.9 kg L–1) was also measured to quantify the basal uptake of MeHg to these aquatic food webs. Hg isotope values in lake trout revealed that while the extent of precipitation-delivered Hg was similar across the entire study area, volcanic Hg is likely an important additional source to lake trout in proximate lakes. In contrast, migratory salmon (Oncorhynchus nerka) deliver little MeHg to lake trout directly, although indirect delivery processes via decay could exist. A high level of variability in carbon, nitrogen, and Hg isotope values indicates niche partitioning in lake trout populations within each lake and that a complex suite of ecological interactions is occurring, complicating the conceptually linear assessment of the contaminant source to the receiving organism. Without connecting energy and contaminant isotope axes, we would not have understood why lake trout from these pristine lakes have highly variable Hg burdens despite consistently low water Hg and comparable age-length dynamics.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.estlett.2c00096","usgsCitation":"Lepak, R., Ogorek, J.M., Bartz, K.K., Janssen, S., Tate, M., Runsheng, Y., Hurley, J., Young, D.B., Eagles-Smith, C., and Krabbenhoft, D.P., 2022, Using carbon, nitrogen, and mercury isotope values to distinguish mercury sources to Alaskan lake trout: Environmental Science and Technology Letters, v. 9, no. 4, p. 312-319, https://doi.org/10.1021/acs.estlett.2c00096.","productDescription":"8 p.","startPage":"312","endPage":"319","ipdsId":"IP-132699","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":448420,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9171711","text":"External Repository"},{"id":435917,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UEP9C5","text":"USGS data release","linkHelpText":"Assessment of mercury sources in Alaskan lake food webs (version 1.1, September 2023)"},{"id":397405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Katmai National Park and preserve, Lake Clark Nation Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.325439453125,\n 60.31606836555203\n ],\n [\n -152.138671875,\n 60.8663124746226\n ],\n [\n -152.75390624999997,\n 60.94644199944748\n ],\n [\n -152.874755859375,\n 61.554109444927185\n ],\n [\n -153.643798828125,\n 61.543641475549954\n ],\n [\n -154.017333984375,\n 61.26495144723964\n ],\n [\n -154.368896484375,\n 61.079544234557304\n ],\n [\n -154.53369140625,\n 61.01040072727077\n ],\n [\n -154.44580078125,\n 60.71619779357714\n ],\n [\n 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A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":838506,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":838507,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70229832,"text":"ofr20221010 - 2022 - Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019","interactions":[],"lastModifiedDate":"2026-03-27T19:46:42.747184","indexId":"ofr20221010","displayToPublicDate":"2022-03-21T10:33:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1010","displayTitle":"Documentation of Models Describing Relations Between Continuous Real-Time and Discrete Water-Quality Constituents in the Little Arkansas River, South-Central Kansas, 1998–2019","title":"Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019","docAbstract":"Data were collected at two monitoring sites along the Little Arkansas River in south-central Kansas that bracket most of the easternmost part of the Equus Beds aquifer. The data were used as part of the city of Wichita’s aquifer storage and recovery project to evaluate source water quality. The U.S. Geological Survey, in cooperation with the City of Wichita, has continued to monitor the water quality of these sites through 2019 to update previously published regression-based models using continuously measured physicochemical properties and discretely sampled water-quality constituents of interest. The purpose of this report is to provide an update of the previously published linear regression models that have been used to continuously compute estimates of water-quality constituent concentrations or densities at these two sites. Water-quality constituent model updates include those for dissolved and suspended solids, suspended-sediment concentration, hardness, alkalinity, primary ions (bicarbonate, calcium, sodium, chloride, and sulfate), nutrients (total Kjeldahl nitrogen and total phosphorus), total organic carbon, indicator bacteria (Escherichia coli and fecal coliform bacteria), a trace element (arsenic), and a pesticide (atrazine).
Regression analyses were used to develop surrogate models that related continuously measured physicochemical properties, streamflow, and seasonal components to discretely sampled water-quality constituent concentrations or densities. Specific conductance was an explanatory variable for dissolved solids, primary ions, and atrazine. Turbidity was an explanatory variable for total suspended solids and sediment, nutrients, total organic carbon, and indicator bacteria. Streamflow and water temperature were explanatory variables for dissolved arsenic. Seasonal components were included as explanatory variables for atrazine models. The amount of variance explained by most of the updated models was within 5 percent of previously published models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221010","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., and Klager, B.J., 2022, Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019: U.S. Geological Survey Open-File Report 2022–1010, 34 p., https://doi.org/10.3133/ofr20221010.","productDescription":"Report: vii, 34 p.; 2 Appendixes; Dataset","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-126572","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":397345,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221010/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397333,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397331,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010_appendix1.zip","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Model Archive Summaries for the Little Arkansas River at Highway 50 near Halstead, Kansas (Halstead Site; U.S. Geological Survey Station Number 07143672)"},{"id":501756,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112715.htm","linkFileType":{"id":5,"text":"html"}},{"id":397330,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1010/images"},{"id":397332,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010_appendix2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Model Archive Summaries for the Little Arkansas River near Sedgwick, Kansas (Sedgwick Site; U.S. Geological Survey Station Number 07144100)"},{"id":397329,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010.XML"},{"id":397328,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010.pdf","text":"Report","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1010"},{"id":397327,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1010/coverthb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.1667,\n 37.714244967649265\n ],\n [\n -97.1667,\n 37.714244967649265\n ],\n [\n -97.1667,\n 38.533333\n ],\n [\n -98.1667,\n 38.533333\n ],\n [\n -98.1667,\n 37.714244967649265\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Lake Powell is the second largest constructed water reservoir by storage capacity in the United States and represents a critical component in management of water resources in the Colorado River Basin. The reservoir provides hydroelectric power generation at Glen Canyon Dam, banks water storage for the Upper Colorado River Basin, stabilizes water commitments downstream, and buffers the Lower Colorado River Basin, including Lake Mead, against sedimentation and fluctuations in hydrological conditions. With completion of the dam in 1963, Lake Powell steadily filled with water before reaching full pool in 1980 and has become a popular destination for recreation, welcoming more than 4 million visitors per year. Since the early 2000s, severe drought and increases in water demand have resulted in a significant drop in reservoir elevation and stored water, prompting a heightened level of interest in the current state and future of Lake Powell.
Beginning in 2017, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, completed topobathymetric surveys of Lake Powell for the first update of elevation-area-capacity relationships since 1986. This report presents results of these surveys and comparisons with estimates from previous surveys. The storage volume and surface area, as of completion of the topobathymetric survey in spring 2018, are calculated at 0.33-foot (0.10-meter) increments for elevations ranging from 3,120.08 to 3,717.19 feet above the North American Vertical Datum of 1988 (NAVD 88). Between 0.33-foot increments, the storage volumes and areas were linearly interpolated at 0.01-foot intervals. Interpolation error in the 0.01-foot interval estimates was assessed at lower (3,160.00–3,161.00 feet above NAVD 88), middle (3,400.00–3,401.00 feet above NAVD 88), and upper (3,700.00–3,711.00 feet above NAVD 88) elevations. The interpolated storage capacity and area estimates are comparable to the measured values with differences ranging from 0.00 to 0.02 percent and from −0.01 to 0.03 percent, respectively.
Current storage capacity at full pool (3702.91 feet above NAVD 88) is 25,160,000 acre-feet. Compared to previously published estimates, this volume represents a 6.79 percent or 1,833,000-acre-foot decrease in storage capacity from 1963 to 2018 and a 4.00 percent or 1,049,000-acre-foot decrease from 1986 to 2018. Areal extent, as of spring 2018, at full pool is 159,200 acres, which represents a 1.33-percent decrease from 1963 to 2018 and a 0.96 percent decrease from 1986 to 2018.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225017","collaboration":"Prepared in cooperation with the Bureau of Reclamation","programNote":"Water Resources Mission Area","usgsCitation":"Root, J.C., and Jones, D.K., 2022, Elevation-area-capacity relationships of Lake Powell in 2018 and estimated loss of storage capacity since 1963: U.S. Geological Survey Scientific Investigations Report 2022–5017, 21 p., https://doi.org/10.3133/sir20225017.","productDescription":"Report: vii, 21 p.; 2 data releases","numberOfPages":"21","onlineOnly":"Y","ipdsId":"IP-120332","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":396681,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O3IPG3","text":"Elevation-area-capacity tables for Lake Powell, 2018","description":"Jones, D.K., and Root, J.C, 2022, Elevation-area-capacity tables for Lake Powell, 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9O3IPG3."},{"id":396680,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H60YCF","text":"Modified topobathymetric elevation data for Lake Powell","description":"Jones, D.K., and Root, J.C., 2021, Modified topobathymetric elevation data for Lake Powell: U.S. Geological Survey data release, https://doi.org/10.5066/P9H60YCF."},{"id":396679,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5017/images"},{"id":396678,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5017/sir20225017.xml"},{"id":396677,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5017/sir20225017.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":396676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5017/coverthb.jpg"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Colorado River, Glen Canyon Dam, Glen Canyon National Recreation Area, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.06054687499999,\n 36.39033486213649\n ],\n [\n -109.566650390625,\n 36.39033486213649\n ],\n [\n -109.566650390625,\n 38.49229419236133\n ],\n [\n -112.06054687499999,\n 38.49229419236133\n ],\n [\n -112.06054687499999,\n 36.39033486213649\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
Bennett et al. (2021, Science 373, 1528–1531) reported that ancient human footprints discovered in White Sands National Park, New Mexico date to between ∼23,000 and 21,000 years ago. Haynes (2022, PaleoAmerica, this issue) proposes two alternate hypotheses to explain the antiquity of the footprints. One is that they were made by humans crossing over older sediments sometime during the Holocene. This is incorrect as there are Pleistocene megafauna tracks interspersed with the human footprints, so they cannot be Holocene in age. The other hypothesis maintains seeds used to date the human footprints were exhumed from older sediments, transported across the Tularosa Basin, and deposited on moist ground that was traversed by Clovis people at ∼13,000 years ago. This scenario requires a series of events that are highly unlikely, if not impossible. We maintain the seeds were collected from their original depositional context and the ages of the footprints fall within the Last Glacial Maximum.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/20555563.2022.2039863","usgsCitation":"Pigati, J.S., Springer, K.B., Holliday, V.T., Bennett, M.R., Bustos, D., Urban, T.M., Reynolds, S.C., and Odess, D., 2022, Reply to “Evidence for humans at White Sands National Park during the Last Glacial Maximum could actually be for Clovis people ~13,000 years ago” by C. Vance Haynes, Jr.: PaleoAmerica, v. 8, no. 2, p. 99-101, https://doi.org/10.1080/20555563.2022.2039863.","productDescription":"13 p.","startPage":"99","endPage":"101","ipdsId":"IP-137468","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":397391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"White Sands National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.48223876953125,\n 32.676372772089834\n ],\n [\n -106.12792968749999,\n 32.676372772089834\n ],\n [\n -106.12792968749999,\n 32.88189375925038\n ],\n [\n -106.48223876953125,\n 32.88189375925038\n ],\n [\n -106.48223876953125,\n 32.676372772089834\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holliday, Vance T.","contributorId":265971,"corporation":false,"usgs":false,"family":"Holliday","given":"Vance","email":"","middleInitial":"T.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":838587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bennett, Matthew R.","contributorId":265968,"corporation":false,"usgs":false,"family":"Bennett","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":54847,"text":"Bournemouth University, U.K.","active":true,"usgs":false}],"preferred":false,"id":838588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bustos, David","contributorId":265969,"corporation":false,"usgs":false,"family":"Bustos","given":"David","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":838589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Urban, Thomas M.","contributorId":271168,"corporation":false,"usgs":false,"family":"Urban","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":838590,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reynolds, Sally C.","contributorId":265972,"corporation":false,"usgs":false,"family":"Reynolds","given":"Sally","email":"","middleInitial":"C.","affiliations":[{"id":54847,"text":"Bournemouth University, U.K.","active":true,"usgs":false}],"preferred":false,"id":838591,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Odess, Daniel","contributorId":265975,"corporation":false,"usgs":false,"family":"Odess","given":"Daniel","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":838592,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70229815,"text":"sir20225015 - 2022 - Distribution of streamflow, sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, 2016–19","interactions":[],"lastModifiedDate":"2022-04-14T15:54:47.465404","indexId":"sir20225015","displayToPublicDate":"2022-03-21T07:50:59","publicationYear":"2022","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":"2022-5015","displayTitle":"Distribution of Streamflow, Sediment, and Nutrients Entering Galveston Bay from the Trinity River, Texas, 2016–19","title":"Distribution of streamflow, sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, 2016–19","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the Texas Water Development Board, collected streamflow and water-quality data at USGS monitoring stations in the lower Trinity River Basin from January 2016 to December 2019 to characterize streamflow, nutrients, and suspended sediment entering Galveston Bay from the Trinity River. Results from previous studies indicate that water from the main channel of the Trinity River is diverted into surrounding wetlands and water bodies and is stored or discharged directly into Galveston Bay through distributary channels in the delta. This study provides an assessment of the distribution of streamflow in the various channels that form the delta of the Trinity River to evaluate the effects of streamflow diversions on the eventual supply of freshwater, nutrients, and suspended sediment to Galveston Bay.
Instantaneous streamflow data and continuous streamflow records from USGS monitoring stations in the delta of the Trinity River were used to quantify freshwater inflow into Galveston Bay and assess the distribution of streamflow in the lowermost reaches of the Trinity River Basin. In this report, periods in which releases from Lake Livingston caused a rise in streamflow farther downstream at USGS station 08067000 Trinity River at Liberty, Tex. (hereinafter referred to as the “Liberty site”) that did not exceed 20,000 cubic feet per second (ft3/s) are referred to as “low-flow events,” and periods in which streamflow at the Liberty site exceeded 20,000 ft3/s are referred to as “high-flow events.”
During this study, it was estimated that only about 55 percent of the total water volume released from Lake Livingston was accounted for at USGS station 08067252 Trinity River at Wallisville, Tex. (hereinafter referred to as the “Wallisville site”), which is approximately 8 river miles upstream from where the Trinity River enters Galveston Bay. The difference in water volumes between what is released from Lake Livingston and what is measured at the Wallisville site is consistent with findings from previous studies and indicates that a large part of the volume released from Lake Livingston does not reach Galveston Bay through the main channel of the Trinity River.
To assess the distribution of streamflow and estimate the amount of water diverted from the main channel of the Trinity River into distributary channels, instantaneous streamflow measurements were made at USGS station 08067230 Old River Lake near Wallisville, Tex. (hereinafter referred to as the “Old River Lake site”) and the Wallisville site during a range of hydrologic conditions. Results indicate that a large portion of the freshwater inflow was likely delivered to Galveston Bay through pathways other than the main channel of the Trinity River, including Old River Lake. When streamflow at the Liberty site, located upstream from the Wallisville site, exceeded approximately 40,000 ft3/s, Old River Lake and its network of hydrologically connected channels likely became the primary pathway for freshwater inflow entering Galveston Bay.
Water quality was characterized from discrete samples collected during a range of hydrologic conditions at the Old River Lake site and the Wallisville site in order to evaluate the effects of streamflow diversions on the supply of suspended sediment and nutrients into Galveston Bay. Suspended-sediment concentrations were typically higher at the Wallisville site than at the Old River Lake site, likely because of lower water velocities at the Old River Lake site than at the Wallisville site; low water velocities allow suspended sediment to settle, thus reducing concentrations. Suspended-sediment loads were also typically higher at the Wallisville site than at the Old River Lake site during high-flow events. However, when streamflows at the Liberty site exceeded approximately 60,000 ft3/s, suspended-sediment loads were higher at the Old River Lake, which likely became the primary pathway for suspended-sediment delivery into Galveston Bay.
Suspended-sediment concentrations and loads were computed at the Wallisville and Liberty sites for the duration of 11 hydrologic events representing different streamflows by using the regression equations developed for each monitoring station. Overall, approximately 25 percent of the total sediment load measured during events at the Liberty site was measured at the Wallisville site, indicating that only a portion of the suspended-sediment load from the Liberty site reached Galveston Bay through the main channel of the Trinity River during the measured events. Based on data from discrete samples, some of this sediment load was diverted into Old River Lake and associated distributary channels.
Results from analysis of nutrient samples indicate that streamflow conditions affect the nitrogen concentrations in the delta of the Trinity River. At the Old River Lake site, nitrate plus nitrite and total dissolved nitrogen concentrations were typically lower during low-flow conditions than during high-flow events; low-flow conditions represent low-flow events or tidal-flow conditions (during low-flow conditions the streamflow at the Liberty site was less than 20,000 ft3/s). Lower concentrations of nitrate plus nitrite and total dissolved nitrogen at the Old River Lake site may be associated with various physical and biogeochemical processes, including the transformation and biological uptake of nitrate, nitrite, and other species of nitrogen resulting from extended water residence times and relatively small inputs of nitrogen from the upstream reaches of the Trinity River Basin. During high-flow events, the proportions of nitrogen species were similar among sites, indicating that the travel path through wetlands and channels surrounding Old River Lake likely does not affect the relative concentrations of the various nitrogen species present in freshwater inflow to Galveston Bay.
Results from analysis of nutrient samples also indicate that the pathways for nutrient delivery from the Trinity River into Galveston Bay are dependent on event magnitude. When streamflows at the Liberty site were low (approximately 20,000 ft3/s), the main channel of the Trinity River was the primary pathway for nitrogen and phosphorus entering Galveston Bay. Once streamflow at the Liberty site exceeded 20,000 ft3/s, however, the contribution of nutrient loading through Old River Lake to Galveston Bay increased proportionally to the nutrient loading in the main channel, and when streamflow at the Liberty site exceeded approximately 50,000 ft3/s, Old River Lake likely became the primary pathway for nutrient delivery into Galveston Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225015","collaboration":"Prepared in cooperation with the Texas Water Development Board","usgsCitation":"Lucena, Z., and Lee, M.T., 2022, Distribution of streamflow, sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, 2016–19: U.S. Geological Survey Scientific Investigations Report 2022–5015, 55 p., https://doi.org/10.3133/sir20225015.","productDescription":"Report: vi, 55 p.; Dataset","numberOfPages":"66","onlineOnly":"N","ipdsId":"IP-126129","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":397262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5015/coverthb.jpg"},{"id":397263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5015/sir20225015.pdf","text":"Report","size":"4.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5015"},{"id":397265,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5015/images"},{"id":397264,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5015/sir20225015.XML"},{"id":397266,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397341,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225015/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Texas","otherGeospatial":"Galveston Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.86145019531249,\n 29.260044678228486\n ],\n [\n -94.50714111328125,\n 29.537619205973428\n ],\n [\n -94.71038818359375,\n 29.84302629154662\n ],\n [\n -95.03173828125,\n 29.752455480021393\n ],\n [\n -95.0592041015625,\n 29.59017705987947\n ],\n [\n -94.98504638671875,\n 29.489815619374962\n ],\n [\n -94.921875,\n 29.401319510041485\n ],\n [\n -94.8944091796875,\n 29.305561325527698\n ],\n [\n -94.86145019531249,\n 29.260044678228486\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Habitat loss is the most prevalent threat to biodiversity in North America. One of the most threatened landscapes in the United States is the sagebrush (Artemisia spp.) ecosystem, much of which has been fragmented or converted to non-native grasslands via the cheatgrass-fire cycle. Like many sagebrush obligates, greater sage-grouse (Centrocercus urophasianus) depend upon sagebrush for food and cover and are affected by changes to this ecosystem. We investigated habitat selection by 28 male greater sage-grouse during each of 3 years after a 113,000-ha wildfire in a sagebrush steppe ecosystem in Idaho and Oregon. During the study period, seeding and herbicide treatments were applied for habitat restoration. We evaluated sage-grouse responses to vegetation and post-fire restoration treatments. Throughout the 3 years post-fire, sage-grouse avoided areas with high exotic annual grass cover but selected strongly for recovering sagebrush and moderately strongly for perennial grasses. By the third year post-fire, they preferred high-density sagebrush, especially in winter when sagebrush is the primary component of the sage-grouse diet. Sage-grouse preferred forb habitat immediately post-fire, especially in summer, but this selection preference was less strong in later years. They also selected areas that were intensively treated with herbicide and seeded with sagebrush, grasses, and forbs, although these responses varied with time since treatment. Wildfire can have severe consequences for sagebrush-obligate species due to loss of large sagebrush plants used for food and for protection from predators and thermal extremes. Our results show that management efforts, including herbicide application and seeding of plants, directed at controlling exotic annual grasses after a wildfire can positively affect habitat selection by sage-grouse.
Per- and polyfluoroalkyl substances (PFAS) are a group of synthetic chemicals detected throughout the environment. To better understand the distribution of PFAS in an aquatic system (the Laurentian Great Lakes), stable isotope enrichment (δ13C and δ15N), fatty acid (FA) profiles, and PFAS were measured in various species from the Lake Ontario (LO) aquatic food web. Sampled organisms included top predator fish, prey fish, and benthic and pelagic macroinvertebrates. The trophic level of each species in the LO food web was determined using δ15N, and FA concentrations (range: <1–139 mg/g wet weight (ww)). The individual PFAS concentrations in the LO food web were ~1.5 to 5 times lower than previously reported. The highest PFAS concentrations were observed in deepwater sculpin (Myoxocephalus thompsonii, 150 ± 35.7 ng/g ww) suggesting a potential source of PFAS from the offshore benthic zone or sediment. The concentration of PFOS and long chain (C9-C14) perfluoroalkyl carboxylic acids (PFCAs) were significantly higher than short chain PFAS indicating the significant impact of hydrophobicity on the bioaccumulation of PFAS in organisms from the food web. However, high molecular weight PFCAs (>C8) did not exhibit increasing biomagnification factors (BMFs) and trophic magnification factors (TMFs) with log Kow, suggesting hydrophobicity does not govern the movement of PFAS from low to high trophic levels in the LO food web.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.08.013","usgsCitation":"Ren, J., Point, A., Baygi, S.F., Fernando, S., Hopke, P.K., Holsen, T., Lantry, B.F., Weidel, B., and Crimmins, B., 2022, Bioaccumulation of perfluoroalkyl substances in a Lake Ontario food web: Journal of Great Lakes Research, v. 48, no. 2, p. 315-325, https://doi.org/10.1016/j.jglr.2021.08.013.","productDescription":"11 p.","startPage":"315","endPage":"325","ipdsId":"IP-126345","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":412935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.59665775029957,\n 44.61217824875047\n ],\n [\n -80.38466344117938,\n 44.61217824875047\n ],\n [\n -80.38466344117938,\n 43.027624166656324\n ],\n [\n -75.59665775029957,\n 43.027624166656324\n ],\n [\n -75.59665775029957,\n 44.61217824875047\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ren, Junda","contributorId":302337,"corporation":false,"usgs":false,"family":"Ren","given":"Junda","email":"","affiliations":[{"id":12960,"text":"Clarkson University","active":true,"usgs":false}],"preferred":false,"id":864059,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Point, Adam","contributorId":302338,"corporation":false,"usgs":false,"family":"Point","given":"Adam","email":"","affiliations":[{"id":12960,"text":"Clarkson University","active":true,"usgs":false}],"preferred":false,"id":864060,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baygi, Sadjad Fakouri","contributorId":302339,"corporation":false,"usgs":false,"family":"Baygi","given":"Sadjad","email":"","middleInitial":"Fakouri","affiliations":[{"id":12960,"text":"Clarkson University","active":true,"usgs":false}],"preferred":false,"id":864061,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernando, Sujan","contributorId":302340,"corporation":false,"usgs":false,"family":"Fernando","given":"Sujan","affiliations":[{"id":12960,"text":"Clarkson University","active":true,"usgs":false}],"preferred":false,"id":864062,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hopke, Philip K.","contributorId":302341,"corporation":false,"usgs":false,"family":"Hopke","given":"Philip","email":"","middleInitial":"K.","affiliations":[{"id":37381,"text":"University of Rochester","active":true,"usgs":false}],"preferred":false,"id":864063,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holsen, Thomas M.","contributorId":302342,"corporation":false,"usgs":false,"family":"Holsen","given":"Thomas M.","affiliations":[{"id":12960,"text":"Clarkson University","active":true,"usgs":false}],"preferred":false,"id":864064,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lantry, Brian F. 0000-0001-8797-3910 bflantry@usgs.gov","orcid":"https://orcid.org/0000-0001-8797-3910","contributorId":3435,"corporation":false,"usgs":true,"family":"Lantry","given":"Brian","email":"bflantry@usgs.gov","middleInitial":"F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":864065,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":864066,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Crimmins, Bernard S.","contributorId":302343,"corporation":false,"usgs":false,"family":"Crimmins","given":"Bernard S.","affiliations":[{"id":12960,"text":"Clarkson University","active":true,"usgs":false}],"preferred":false,"id":864067,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262193,"text":"70262193 - 2022 - Contemporary spatial extent and environmental drivers of larval coregonine distributions across Lake Ontario","interactions":[],"lastModifiedDate":"2025-01-15T16:57:43.97835","indexId":"70262193","displayToPublicDate":"2022-03-20T10:50:47","publicationYear":"2022","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":"Contemporary spatial extent and environmental drivers of larval coregonine distributions across Lake Ontario","docAbstract":"Coregonine fishes are important to Laurentian Great Lakes food webs and fisheries and are central to basin-wide conservation initiatives. In Lake Ontario, binational management objectives include conserving and restoring spawning stocks of cisco (Coregonus artedi) and lake whitefish (C. clupeaformis), but the spatial extent of contemporary coregonine spawning habitat and the environmental factors regulating early life success are not well characterized. In Spring 2018, we conducted a binational ichthyoplankton assessment to describe the spatial extent of coregonine spawning habitat across Lake Ontario. We then quantified the relative importance of a suite of biophysical variables hypothesized to influence coregonine early life success using generalized additive mixed models and multimodel inference. Between April 10 and May 14, we conducted 1,092 ichthyoplankton tows and captured 2,350+ coregonine larvae across 17 sampling areas, predominantly within embayments. Although 95% of catches were in the eastern basin, coregonine larvae were also found in historical south shore spawning areas. Most coregonine larvae were cisco; <6% were lake whitefish. Observed catches of both species across sampling areas were strongly and similarly associated with ice cover duration, but the importance of site-specific characteristics varied, such as distance to shore and site depth for cisco and lake whitefish, respectively. These results suggest that regional-scale climatic drivers and local environmental habitat characteristics interact to regulate early life stage success. Furthermore, strong regional and cross-species variation in larval distributions emphasize the importance of lake-wide assessments for monitoring both the current eastern basin populations and potential expansions into western Lake Ontario habitats.
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Evans, Cooper, A., Reinhart, D., Cameron David, and Weidel, B., 2022, Contemporary spatial extent and environmental drivers of larval coregonine distributions across Lake Ontario: Journal of Great Lakes Research, v. 48, no. 2, p. 359-370, https://doi.org/10.1016/j.jglr.2021.07.009.","productDescription":"12 p.","startPage":"359","endPage":"370","ipdsId":"IP-126583","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466431,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.925537109375,\n 43.265206318396025\n ],\n [\n -79.8101806640625,\n 43.281204464332745\n ],\n [\n -79.5904541015625,\n 43.18515250937298\n ],\n [\n -79.3597412109375,\n 43.16512263158296\n ],\n [\n -79.1839599609375,\n 43.193162620926074\n ],\n [\n -78.9532470703125,\n 43.27320591705845\n ],\n [\n -78.6236572265625,\n 43.329173667843904\n ],\n [\n -78.4259033203125,\n 43.369119087738554\n ],\n [\n -78.189697265625,\n 43.35713822211053\n ],\n [\n -78.123779296875,\n 43.35713822211053\n ],\n [\n -77.95898437499999,\n 43.35314407444698\n ],\n [\n -77.750244140625,\n 43.32517767999296\n ],\n [\n -77.596435546875,\n 43.23719944365308\n ],\n [\n -77.51953125,\n 43.197167282501276\n ],\n [\n -77.5140380859375,\n 43.241201214257885\n ],\n [\n -77.3602294921875,\n 43.26920624914964\n ],\n [\n -77.14050292968749,\n 43.27720532212024\n ],\n [\n -77.01416015625,\n 43.26120612479979\n ],\n [\n -76.89880371093749,\n 43.213183300738876\n ],\n [\n -76.904296875,\n 43.26920624914964\n ],\n [\n -76.7340087890625,\n 43.32517767999296\n ],\n [\n -76.6461181640625,\n 43.37311218382002\n ],\n [\n -76.5966796875,\n 43.42898792344155\n ],\n [\n -76.48681640625,\n 43.48082639482503\n ],\n [\n -76.4208984375,\n 43.50872101129684\n ],\n [\n -76.365966796875,\n 43.52465500687185\n ],\n [\n -76.31103515625,\n 43.51270490464819\n ],\n [\n -76.2396240234375,\n 43.51270490464819\n ],\n [\n -76.17919921875,\n 43.624147145668076\n ],\n [\n -76.1407470703125,\n 43.6599240747891\n ],\n [\n -76.1846923828125,\n 43.691707903073805\n ],\n [\n -76.1956787109375,\n 43.78299262890581\n ],\n [\n -76.201171875,\n 43.8503744993026\n ],\n [\n -76.0968017578125,\n 43.91768033000405\n ],\n [\n -75.9979248046875,\n 44.008620115415354\n ],\n [\n -76.1297607421875,\n 44.08363928284644\n ],\n [\n -76.300048828125,\n 44.14279782818058\n ],\n [\n -76.3934326171875,\n 44.17826452922573\n ],\n [\n -76.453857421875,\n 44.25700308645885\n ],\n [\n -76.629638671875,\n 44.25700308645885\n ],\n [\n -76.8109130859375,\n 44.17038488259618\n ],\n [\n -76.97021484375,\n 44.08758502824516\n ],\n [\n -77.069091796875,\n 44.071800467511565\n ],\n [\n -77.069091796875,\n 43.96514454266273\n ],\n [\n -77.080078125,\n 43.88997537383687\n ],\n [\n -77.3162841796875,\n 43.96119063892024\n ],\n [\n -77.4151611328125,\n 43.96909818325171\n ],\n [\n -77.574462890625,\n 44.06390660801779\n ],\n [\n -77.7337646484375,\n 44.040218713142146\n ],\n [\n -78.01391601562499,\n 44.004669106432225\n ],\n [\n -78.33251953125,\n 43.95328204198018\n ],\n [\n -78.5137939453125,\n 43.90185050527358\n ],\n [\n -78.7664794921875,\n 43.88997537383687\n ],\n [\n -78.9862060546875,\n 43.862257524417934\n ],\n [\n -79.1015625,\n 43.81471121600004\n ],\n [\n -79.2279052734375,\n 43.723474896114794\n ],\n [\n -79.3377685546875,\n 43.65197548731187\n ],\n [\n -79.4805908203125,\n 43.644025847699496\n ],\n [\n -79.5684814453125,\n 43.56845179881218\n ],\n [\n -79.617919921875,\n 43.52465500687185\n ],\n [\n -79.6343994140625,\n 43.464880828929545\n ],\n [\n -79.7113037109375,\n 43.37710501700073\n ],\n [\n -79.82666015625,\n 43.329173667843904\n ],\n [\n -79.925537109375,\n 43.265206318396025\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor A. Brown","contributorId":348419,"corporation":false,"usgs":false,"family":"Taylor A. Brown","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lars G. Rudstam","contributorId":348420,"corporation":false,"usgs":false,"family":"Lars G. Rudstam","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923443,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jeremy P. Holden","contributorId":348421,"corporation":false,"usgs":false,"family":"Jeremy P. Holden","affiliations":[{"id":16762,"text":"Ontario Ministry of Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":923444,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Michael J. Connerton","contributorId":348422,"corporation":false,"usgs":false,"family":"Michael J. Connerton","affiliations":[{"id":56930,"text":"New York DEC","active":true,"usgs":false}],"preferred":false,"id":923445,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dimitry Gorsky","contributorId":348423,"corporation":false,"usgs":false,"family":"Dimitry Gorsky","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":923446,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Curtis T. Karboski","contributorId":348424,"corporation":false,"usgs":false,"family":"Curtis T. Karboski","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":923447,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chalupnicki, Marc 0000-0002-3792-9345","orcid":"https://orcid.org/0000-0002-3792-9345","contributorId":242991,"corporation":false,"usgs":true,"family":"Chalupnicki","given":"Marc","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923448,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nicholas M. Sard","contributorId":348426,"corporation":false,"usgs":false,"family":"Nicholas M. Sard","affiliations":[{"id":48660,"text":"SUNY Oswego","active":true,"usgs":false}],"preferred":false,"id":923449,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923450,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Scott E. Prindle","contributorId":348429,"corporation":false,"usgs":false,"family":"Scott E. Prindle","affiliations":[{"id":56930,"text":"New York DEC","active":true,"usgs":false}],"preferred":false,"id":923451,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Matthew J. Sanderson","contributorId":348431,"corporation":false,"usgs":false,"family":"Matthew J. Sanderson","affiliations":[{"id":56930,"text":"New York DEC","active":true,"usgs":false}],"preferred":false,"id":923452,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thomas M. Evans","contributorId":348434,"corporation":false,"usgs":false,"family":"Thomas M. Evans","affiliations":[{"id":83364,"text":"St. Mary's College","active":true,"usgs":false}],"preferred":false,"id":923453,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Cooper, Amanda","contributorId":348575,"corporation":false,"usgs":false,"family":"Cooper","given":"Amanda","affiliations":[],"preferred":false,"id":923606,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Reinhart, Daren J.","contributorId":348576,"corporation":false,"usgs":false,"family":"Reinhart","given":"Daren J.","affiliations":[],"preferred":false,"id":923607,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Cameron David","contributorId":348436,"corporation":false,"usgs":false,"family":"Cameron David","affiliations":[{"id":62863,"text":"Great Lakes Science Center","active":true,"usgs":false}],"preferred":false,"id":923455,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923454,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70229973,"text":"70229973 - 2022 - Microbial source tracking and evaluation of best management practices for restoring degraded beaches of Lake Michigan","interactions":[],"lastModifiedDate":"2022-03-21T15:08:43.6858","indexId":"70229973","displayToPublicDate":"2022-03-20T09:43:45","publicationYear":"2022","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":"Microbial source tracking and evaluation of best management practices for restoring degraded beaches of Lake Michigan","docAbstract":"Attempts to mitigate shoreline microbial contamination require a thorough understanding of pollutant sources, which often requires multiple years of data collection (e.g., point/nonpoint) and the interacting factors that influence water quality. Because restoration efforts can alter shoreline or beach morphology, revisiting source inputs is often necessary. Microbial source tracking (MST) using source-specific molecular markers, genomic community analyses, and physical modeling was used to identify contamination sources along three Lake Michigan beaches of the Laurentian Great Lakes with historically high fecal indicator bacteria (FIB, E. coli) concentrations. Genetic markers for human (Bacteroides HF183) and mixed gull species (Catellicoccus marimammalium) fecal sources were tested from water and sediment. Gene sequencing (16S rRNA) was used to identify similarities in bacterial communities in nearshore water, river inputs, sand, sediment, and groundwater. Synoptic surveys of water exchange were conducted to determine nearshore-offshore interactions of FIB. In addition to these MST studies, best management practices to mitigate FIB, including gull deterrence, slope grading, wetland establishment, and shoreline plantings, were reviewed for their effectiveness at reducing FIB concentrations over time. Using multiple tools for MST helped identify primary and secondary sources of FIB (gulls, stormwater inputs) and the physical processes that exacerbate FIB concentrations (onshore currents, limited circulation). Management actions were successful in the short-term at reducing FIB, but scope of success was temporally limited, with FIB concentrations often rebounding. Results highlight the usefulness of MST to inform best management practices and the need for a sustained adaptive approach that adjusts for changes in the coastal system.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.01.009","usgsCitation":"Nevers, M., Buszka, P.M., Byappanahalli, M., Cole, T., Corsi, S., Jackson, P.R., Kinzelman, J.L., Nakatsu, C.H., and Phanikumar, M.S., 2022, Microbial source tracking and evaluation of best management practices for restoring degraded beaches of Lake Michigan: Journal of Great Lakes Research, v. 48, no. 2, p. 441-454, https://doi.org/10.1016/j.jglr.2022.01.009.","productDescription":"14 p.","startPage":"441","endPage":"454","ipdsId":"IP-127246","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":448429,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2022.01.009","text":"Publisher Index Page"},{"id":397344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Wisconsin","city":"Chicago, Racine","otherGeospatial":"Jeorse Park Beach, Lake Michigan, North Beach, 63rd Street Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.9345703125,\n 41.60928183876483\n ],\n [\n -86.7095947265625,\n 41.60928183876483\n ],\n [\n -86.7095947265625,\n 42.84777884235988\n ],\n [\n -87.9345703125,\n 42.84777884235988\n ],\n [\n -87.9345703125,\n 41.60928183876483\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":838533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buszka, Paul M. 0000-0001-8218-826X pmbuszka@usgs.gov","orcid":"https://orcid.org/0000-0001-8218-826X","contributorId":1786,"corporation":false,"usgs":true,"family":"Buszka","given":"Paul","email":"pmbuszka@usgs.gov","middleInitial":"M.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":241924,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":838535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Travis 0000-0002-0935-381X","orcid":"https://orcid.org/0000-0002-0935-381X","contributorId":289099,"corporation":false,"usgs":false,"family":"Cole","given":"Travis","affiliations":[{"id":62047,"text":"Crane Environmental Services, LLC","active":true,"usgs":false}],"preferred":false,"id":838536,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838537,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838538,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kinzelman, Julie L.","contributorId":236944,"corporation":false,"usgs":false,"family":"Kinzelman","given":"Julie","email":"","middleInitial":"L.","affiliations":[{"id":37612,"text":"City of Racine Health Department","active":true,"usgs":false}],"preferred":false,"id":838539,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nakatsu, Cindy H 0000-0003-0663-180X","orcid":"https://orcid.org/0000-0003-0663-180X","contributorId":215593,"corporation":false,"usgs":false,"family":"Nakatsu","given":"Cindy","email":"","middleInitial":"H","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":838540,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Phanikumar, Mantha S.","contributorId":147924,"corporation":false,"usgs":false,"family":"Phanikumar","given":"Mantha","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":838541,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70233923,"text":"70233923 - 2022 - Depth drives growth dynamics of dreissenid mussels in Lake Ontario","interactions":[],"lastModifiedDate":"2022-07-28T12:05:12.320501","indexId":"70233923","displayToPublicDate":"2022-03-20T07:03:20","publicationYear":"2022","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":"Depth drives growth dynamics of dreissenid mussels in Lake Ontario","docAbstract":"Understanding dreissenid mussel population dynamics and their impacts on lake ecosystems requires quantifying individual growth across a range of habitats. Most dreissenid mussel growth rates have been estimated in nutrient rich or nearshore environments, but mussels have continued to expand into deep, cold, low-nutrient habitats of the Great Lakes. We measured annual quagga mussel (Dreissena rostriformis bugensis) growth at 15 m, 45 m, and 90 m in Lake Ontario using caged mussels near Oswego, New York, USA from June 2018 to May 2019. Quagga mussel growth (starting size 12 mm) was greatest at 15 m (mean shell length increase = 10.2 mm), and was lower at 45 m (5.9 mm) and 90 m (0.7 mm). Caged mussels were obtained from near the 90-m site and those reared at 15 and 45 m developed thicker shells than those that were caged at 90 m. We observed relatively high colonization by quagga and, to a lesser degree, zebra mussels (Dreissena polymorpha) at 15 m, very few colonizers at 45 m, and none at 90 m. Higher growth potential, but low natural mussel densities observed at 15 m and 45 m suggest factors other than growth limit dreissenid abundance at these depths. The relatively slow dreissenid growth rates observed in offshore habitats are consistent with the gradual abundance increases documented in these zones across the Great Lakes and suggest new mussels that become established in these habitats may contribute to ecosystem effects for decades.
Wild reproduction by stocked lake trout Salvelinus namaycush in Lake Ontario has yet to produce a self-sustaining population, requiring a reliance on stocking. Once released, age-1 juvenile lake trout are not typically surveyed until age-2, creating a gap in knowledge of fine-scale post-release behaviors. A method to track fine-scale movements and estimate mortality of juvenile lake trout could complement standard survey methods and benefit management decisions regarding stocking locations. We used acoustic telemetry to estimate post-stocking mortality and observe fine-scale spatial and temporal movements of 38 hatchery-reared, age-1 lake trout from an offshore stocking site in the eastern basin of Lake Ontario from 2017 to 2018. Cumulative post-stocking mortality was estimated at 5.3%, 10.5%, and 26.3% after one week, one month and one year, respectively. The majority of lake trout (68.4%) emigrated from the stocking location within two months and entered deep water (∼50 m) once warm-water incursions at the stocking site exceeded lake trout thermal preferences (15 °C). Lake trout made large movements (i.e., median 1.9 km, maximum 12.4 km straight-line distance) within the first hour post-release and had an average swimming speed of 1.64 km‧hr−1over the first day. There was no statistically significant relationship between total distance traveled and time of day, although distance traveled tended to be greater during crepuscular and dark periods compared to daylight. Our results provide a conservative estimate of post-release mortality and reveal behaviors of hatchery-reared juvenile lake trout that may be helpful when selecting stocking locations beneficial to restoration program goals.
Saginaw Bay is a shallow, nutrient-rich embayment in Lake Huron that historically had a complex network of natural rocky reefs. These reef habitats were used as spawning and nursery areas for a variety of fish species, but decades of land-use related sedimentation caused many of these reefs to be degraded. Our study objectives were to analyze abiotic and biotic conditions on degraded and remnant reefs and describe spawning patterns of walleye (Sander vitreus) and lake whitefish (Coregonus clupeaformis) at these sites to determine the potential for increased utilization following reef restoration. During fall and spring 2014–2016, we evaluated water quality and egg predation at four sites with varying levels of reef degradation. Further, we documented reproductive utilization through capture of spawning adults and quantification of egg deposition. Walleye and lake whitefish utilized multiple sites for reproduction; however, densities of spawners and deposited eggs were low, suggesting that they were not utilizing study sites as major spawning locations. Walleye and lake whitefish eggs were eaten by multiple fish species, including larger fish such as channel catfish (Ictalurus punctatus). Dissolved oxygen levels were adequate (i.e., >7 mg 02 L−1) during spring walleye egg incubation; however, bottom dissolved oxygen levels became very low at some sites during winter ice cover, coinciding with lake whitefish egg incubation. As restoration of rocky reefs proceeds in the Bay, evidence of remnant reef spawning fish bodes well for long-term success, though potential limiting factors such as low dissolved oxygen, sedimentation, and egg predation require continued monitoring.
Inland lakes face unprecedented pressures from climatic and anthropogenic stresses, causing their recession and desiccation globally. Climate change is increasingly blamed for such environmental degradation, but in many regions, direct anthropogenic pressures compound, and sometimes supersede, climatic factors. This study examined a human-environmental system – the terminal Hamun Lakes on the Iran-Afghanistan border – that embodies amplified challenges of inland waters. Satellite and climatic data from 1984 to 2019 were fused, which documented that the Hamun Lakes lost 89% of their surface area between 1999 and 2001 (3809 km2 versus 410 km2), coincident with a basin-wide, multi-year meteorological drought. The lakes continued to shrink afterwards and desiccated in 2012, despite the above-average precipitation in the upstream basin. Rapid growth in irrigated agricultural lands occurred in upstream Afghanistan in the recent decade, consuming water that otherwise would have fed the Hamun Lakes. Compounding upstream anthropogenic stressors, Iran began storing flood water that would have otherwise drained to the lakes, for urban and agricultural consumption in 2009. Results from a deep Learning model of Hamun Lakes' dynamics indicate that the average lakes' surface area from 2010 to 2019 would have been 2.5 times larger without increasing anthropogenic stresses across the basin. The Hamun Lakes' desiccation had major socio-environmental consequences, including loss of livelihood, out-migration, dust-storms, and loss of important species in the region.
Secretive marsh birds (SMBs) are important indicator species of coastal wetlands but are difficult to detect and monitor. In coastal Louisiana, an important stronghold for these species, climate and hydrological models predict that freshwater and intermediate marshes will expand in the next 50 years, while brackish marshes will shrink. We used a multi-species Bayesian hierarchical occupancy model to estimate detection and occupancy probabilities for 11 SMB species in low and high saline marshes using data from automated recording units at 33 sites in southwestern Louisiana from February–June 2012. A quadratic effect of Julian date, but not minimum daily temperature nor precipitation affected detection of SMB species. King Rail (Rallus elegans), American Bittern (Botaurus lentiginosus), Common Gallinule (Gallinula galeata), and Pied-billed Grebe (Podilymbus podiceps) occupied mainly freshwater and intermediate marshes. Clapper Rail (Rallus crepitans), Seaside Sparrow (Ammospiza maritima), and Sora (Porzana carolina) predominantly occupied brackish and salt marshes. American Coot (Fulica americana), Purple Gallinule (Porphyrio martinica), Least Bittern (Ixobrychus exilis), and Marsh Wren (Cistothorus palustris) occupied both low and high saline marshes, showing flexibility that could maintain populations of these species as marsh salinities change in the future. If the current distribution of SMB species persists as marsh availability changes under future conditions, populations of the 4 species we found in low saline marshes may increase, whereas populations of at least 2 species found primarily in high saline marshes may decrease. Our modeling indicates that automatic recording units can produce comparable detection probabilities to other studies using traditional SMB sampling methods.
Climate change is thawing and potentially mobilizing vast quantities of organic carbon (OC) previously stored for millennia in permafrost soils of northern circumpolar landscapes. Climate-driven increases in fire and thermokarst may play a key role in OC mobilization by thawing permafrost and promoting transport of OC. Yet, the extent of OC mobilization and mechanisms controlling terrestrial-aquatic transfer are unclear. We demonstrate that hydrologic transport of soil dissolved OC (DOC) from the active layer and thawing permafrost to headwater streams is extremely heterogeneous and regulated by the interactions of soils, seasonal thaw, fire, and thermokarst. Repeated sampling of streams in eight headwater catchments of interior Alaska showed that the mean age of DOC for each stream ranges widely from modern to ∼2,000 years B.P. Together, an endmember mixing model and nonlinear, generalized additive models demonstrated that Δ14C-DOC signature (and mean age) increased from spring to fall, and was proportional to hydrologic contributions from a solute-rich water source, related to presumed deeper flow paths found predominantly in silty catchments. This relationship was correlated with and mediated by catchment properties. Mean DOC ages were older in catchments with >50% burned area, indicating that fire is also an important explanatory variable. These observations underscore the high heterogeneity in aged C export and difficulty of extrapolating estimates of permafrost-derived DOC export from watersheds to larger scales. Our results provide the foundation for developing a conceptual model of permafrost DOC export necessary for advancing understanding and prediction of land-water C exchange in changing boreal landscapes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GB007242","usgsCitation":"Koch, J.C., Bogard, M., Butman, D., Finlay, K., Ebel, B., James, J., Johnston, S.E., Jorgenson, M., Pastick, N., Spencer, R., Striegl, R., Walvoord, M.A., and Wickland, K., 2022, Heterogeneous patterns of aged organic carbon export driven by hydrologic flow paths, soil texture, fire, and thaw in discontinuous permafrost headwaters: Global Biogeochemical Cycles, v. 36, no. 4, e2021GB007242, 16 p., https://doi.org/10.1029/2021GB007242.","productDescription":"e2021GB007242, 16 p.","ipdsId":"IP-134558","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448441,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gb007242","text":"Publisher Index Page"},{"id":398213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151,\n 65\n ],\n [\n -147,\n 65\n ],\n [\n -147,\n 67\n ],\n [\n -151,\n 67\n ],\n [\n -151,\n 65\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":839768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bogard, Matthew","contributorId":272635,"corporation":false,"usgs":false,"family":"Bogard","given":"Matthew","affiliations":[{"id":16962,"text":"U. 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We developed and used an environmental DNA (eDNA) assay for longnose darter paired with visual surveys to better determine the species’ range and compare detection probability between sampling approaches in an occupancy modeling framework. We detected longnose darter eDNA further upstream in the mainstem St. Francis River than previously reported and in a tributary for the first time. Our multi-scale occupancy approach compared models where detection was constant against a model that allowed detection to vary by survey method. The constant model received the most support indicating survey method was not a strong predictor and detection was estimated at 0.70 (0.45–0.86; 95% CI) across both methods. Our study produced effective longnose darter eDNA primers and demonstrated the application of eDNA for sampling small-bodied, cryptic fish. We detected longnose darter eDNA 27 km upstream of their known range and determined that snorkel surveys are the most efficient sampling method if water clarity allows. We recommend target sample sizes to achieve various detection goals for both sample methods and our results inform future design of distributional and monitoring efforts.
","language":"English","publisher":"MPDI","doi":"10.3390/fishes7020070","usgsCitation":"Westhoff, J.T., Berkman, L.K., Klymus, K.E., Thompson, N., and Richter, C.A., 2022, A comparison of eDNA and visual survey methods for detection of longnose darter Percina nasuta in Missouri: Fishes, v. 7, no. 2, 70, 16 p., https://doi.org/10.3390/fishes7020070.","productDescription":"70, 16 p.","ipdsId":"IP-121698","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":448443,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes7020070","text":"Publisher Index Page"},{"id":435919,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z597LN","text":"USGS data release","linkHelpText":"Longnose darter (Percina nasuta) eDNA survey results from the St. Francis River, Missouri 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\"}}]}","volume":"7","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Westhoff, Jacob Thomas 0000-0002-2347-5098","orcid":"https://orcid.org/0000-0002-2347-5098","contributorId":288958,"corporation":false,"usgs":true,"family":"Westhoff","given":"Jacob","email":"","middleInitial":"Thomas","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berkman, Leah K.","contributorId":275349,"corporation":false,"usgs":false,"family":"Berkman","given":"Leah","email":"","middleInitial":"K.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":838494,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":838495,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":838496,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richter, Cathy A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":1878,"corporation":false,"usgs":true,"family":"Richter","given":"Cathy","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":838497,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229838,"text":"70229838 - 2022 - Errors in aerial survey count data: Identifying pitfalls and solutions","interactions":[],"lastModifiedDate":"2022-03-21T13:41:26.57231","indexId":"70229838","displayToPublicDate":"2022-03-18T08:34:52","publicationYear":"2022","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":"Errors in aerial survey count data: Identifying pitfalls and solutions","docAbstract":"Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8733","usgsCitation":"Davis, K.L., Silverman, E., Sussman, A., Wilson, R., and Zipkin, E.F., 2022, Errors in aerial survey count data: Identifying pitfalls and solutions: Ecology and Evolution, v. 12, no. 3, e8733, 14 p., https://doi.org/10.1002/ece3.8733.","productDescription":"e8733, 14 p.","ipdsId":"IP-128816","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":448446,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8733","text":"External Repository"},{"id":397342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.0791015625,\n 25.025884063244828\n ],\n [\n -80.85937499999999,\n 25.304303764403617\n ],\n [\n -81.82617187499999,\n 26.78484736105119\n ],\n [\n -82.72705078125,\n 27.819644755099446\n ],\n [\n -82.50732421875,\n 29.017748018496047\n ],\n [\n -84.0673828125,\n 30.240086360983426\n ],\n [\n -85.14404296875,\n 29.783449456820605\n ],\n [\n -85.62744140625,\n 30.315987718557867\n ],\n [\n -86.572265625,\n 30.543338954230222\n ],\n [\n -87.8466796875,\n 30.65681556429287\n ],\n [\n -89.736328125,\n 30.391830328088137\n ],\n [\n -90.7470703125,\n 30.4297295750316\n ],\n [\n -90.54931640625,\n 29.783449456820605\n ],\n [\n -90.85693359375,\n 29.49698759653577\n ],\n [\n -91.95556640625,\n 30.088107753367257\n ],\n [\n -92.4169921875,\n 29.783449456820605\n ],\n [\n -93.7353515625,\n 29.897805610155874\n ],\n [\n -94.52636718749999,\n 29.80251790576445\n ],\n [\n -94.7900390625,\n 29.954934549656144\n ],\n [\n -95.0537109375,\n 29.783449456820605\n ],\n [\n -95.0537109375,\n 29.34387539941801\n ],\n [\n -96.13037109375,\n 28.86391842622456\n ],\n [\n -96.6796875,\n 28.748396571187406\n ],\n [\n -96.92138671875,\n 28.497660832963472\n ],\n [\n -96.9873046875,\n 28.304380682962783\n ],\n [\n -97.42675781249999,\n 28.110748760633534\n ],\n [\n -97.646484375,\n 27.547241546253268\n ],\n [\n -97.6904296875,\n 27.15692045688088\n ],\n [\n -97.3828125,\n 26.05678288577881\n ],\n [\n -95.888671875,\n 25.97779895546436\n ],\n [\n -94.46044921875,\n 27.293689224852407\n ],\n [\n -91.58203125,\n 27.780771643348196\n ],\n [\n -88.11035156249999,\n 27.46928747369202\n ],\n [\n -86.28662109375,\n 27.839076094777816\n ],\n [\n -83.64990234375,\n 26.05678288577881\n ],\n [\n -82.8369140625,\n 24.926294766395593\n ],\n [\n -82.46337890625,\n 24.56710835257599\n ],\n [\n -81.0791015625,\n 25.025884063244828\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Kayla L.","contributorId":177595,"corporation":false,"usgs":false,"family":"Davis","given":"Kayla","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":838512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Silverman, Emily D","contributorId":288964,"corporation":false,"usgs":false,"family":"Silverman","given":"Emily D","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":838513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sussman, Allison 0000-0002-6996-9982","orcid":"https://orcid.org/0000-0002-6996-9982","contributorId":211294,"corporation":false,"usgs":true,"family":"Sussman","given":"Allison","email":"","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":838511,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, R. Randy","contributorId":288965,"corporation":false,"usgs":false,"family":"Wilson","given":"R. Randy","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":838514,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zipkin, Elise F. 0000-0003-4155-6139","orcid":"https://orcid.org/0000-0003-4155-6139","contributorId":192755,"corporation":false,"usgs":false,"family":"Zipkin","given":"Elise","email":"","middleInitial":"F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":838515,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228752,"text":"sim3483 - 2022 - Geologic map of the South Boston 30' × 60' quadrangle, Virginia and North Carolina","interactions":[{"subject":{"id":17533,"text":"ofr93244 - 1993 - Preliminary geologic map of the South Boston 30 x 60 minute quadrangle, Virginia and North Carolina","indexId":"ofr93244","publicationYear":"1993","noYear":false,"title":"Preliminary geologic map of the South Boston 30 x 60 minute quadrangle, Virginia and North Carolina"},"predicate":"SUPERSEDED_BY","object":{"id":70228752,"text":"sim3483 - 2022 - Geologic map of the South Boston 30' × 60' quadrangle, Virginia and North Carolina","indexId":"sim3483","publicationYear":"2022","noYear":false,"title":"Geologic map of the South Boston 30' × 60' quadrangle, Virginia and North Carolina"},"id":1}],"lastModifiedDate":"2026-03-31T21:19:34.48763","indexId":"sim3483","displayToPublicDate":"2022-03-18T07:15:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3483","displayTitle":"Geologic Map of the South Boston 30' × 60' Quadrangle, Virginia and North Carolina","title":"Geologic map of the South Boston 30' × 60' quadrangle, Virginia and North Carolina","docAbstract":"This 1:100,000-scale geologic map of the South Boston 30’ × 60’ quadrangle, Virginia and North Carolina, provides geologic information for the Piedmont along the I–85 and U.S. Route 58 corridors and in the Roanoke River watershed, which includes the John H. Kerr Reservoir and Lake Gaston. The Raleigh terrane (located on the eastern side of the map) contains Neoproterozoic to early Paleozoic(?) polydeformed, amphibolite-facies gneisses and schists. The Carolina slate belt of the Carolina terrane (located in the central part of the map) contains Neoproterozoic metavolcanic and metasedimentary rocks at greenschist facies. Although locally complicated, the slate-belt structure mapped across the South Boston map area is generally a broad, complex anticlinorium of the Hyco Formation (here called the Chase City anticlinorium) and is flanked to the west and east by synclinoria, which are cored by the overlying Aaron and Virgilina Formations. The western flank of the Carolina terrane (located in the western-central part of the map) contains similar rocks at higher metamorphic grade. This terrane includes epidote-amphibolite-facies to amphibolite-facies gneisses of the Neoproterozoic Country Line complex, which extends north-northeastward across the map. The Milton terrane (located on the western side of the map) contains Ordovician amphibolite-facies metavolcanic and metasedimentary gneisses of the Cunningham complex.
Crosscutting relations and fabrics in mafic to felsic plutonic rocks constrain the timing of Neoproterozoic to late Paleozoic deformations across the Piedmont. In the eastern part of the map, a 5- to 9-kilometer-wide band of tectonic elements that contains two late Paleozoic mylonite zones (Nutbush Creek and Lake Gordon) and syntectonic granite (Buggs Island pluton) separates the Raleigh and Carolina terranes. Amphibolite-facies, infrastructural metaigneous and metasedimentary rocks east of the Lake Gordon mylonite zone are generally assigned to the Raleigh terrane. In the western part of the map area, a 5- to 8-kilometer-wide band of late Paleozoic tectonic elements includes the Hyco and Clover shear zones, syntectonic granitic sheets, and amphibolite-facies gneisses along the western margin of the Carolina terrane at its boundary with the Milton terrane. This band of tectonic elements is also the locus for early Mesozoic extensional faults associated with the early Mesozoic Scottsburg, Randolph, and Roanoke Creek rift basins.
The map shows fluvial terrace deposits of sand and gravel on hills and slopes near the Roanoke and Dan Rivers. The terrace deposits that are highest in altitude are the oldest. Saprolite regolith is spatially associated with geologic source units and is not shown separately on the map.
Mineral resources in the area include gneiss and granite quarried for crushed stone, tungsten-bearing vein deposits of the Hamme district, and copper and gold deposits of the Virgilina district. Surface-water resources are abundant and include rivers, tributaries, the John H. Kerr Reservoir, and Lake Gaston. Groundwater flow is concentrated in saprolite regolith, along fractures in the crystalline bedrock, and along fractures and bedding-plane partings in the Mesozoic rift basins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3483","usgsCitation":"Horton, J.W., Jr., Peper, J.D., Burton, W.C., Weems, R.E., and Sacks, P.E., 2022, Geologic map of the South Boston 30' × 60' quadrangle, Virginia and North Carolina: U.S. Geological Survey Scientific Investigations Map 3483, 1 sheet, scale 1:100,000, 46-p. pamphlet, https://doi.org/10.3133/sim3483. [Supersedes USGS Open-File Report 93–244.]","productDescription":"Pamphlet: vi, 46 p.; 1 Sheet: 62.00 x 35.00 inches; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112223","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501889,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112691.htm","linkFileType":{"id":5,"text":"html"}},{"id":396136,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3483/sim3483_map.pdf","text":"Map","size":"38.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3483 map"},{"id":396134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3483/coverthb2.jpg"},{"id":396135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3483/sim3483_pamphlet.pdf","text":"Pamphlet","size":"871 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3483 pamphlet"},{"id":396935,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98AQDR7","text":"USGS data release","linkHelpText":"Database for the Geologic Map of the South Boston 30' × 60' Quadrangle, Virginia and North Carolina"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"South Boston 30 x 60 minute quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79,\n 36.5\n ],\n [\n -78,\n 36.5\n ],\n [\n -78,\n 37\n ],\n [\n -79,\n 37\n ],\n [\n -79,\n 36.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
U.S. Geological Survey
Mail Stop 926A
12201 Sunrise Valley Drive
Reston, VA 20192
Spring viremia of carp virus (SVCV), is a lethal freshwater pathogen of cyprinid fish, and Cyprinus carpio koi is a primary host species. The virus was initially described in the 1960s after outbreaks occurred in Europe, but a global expansion of SVCV has been ongoing since the late 1990s. Genetic typing of SVCV isolates separates them into 4 genotypes that are correlated with geographic origin: Ia (Asia), Ib and Ic (Eastern Europe), and Id (Central Europe). We compared infectivity and virulence of 8 SVCV strains, including 4 uncharacterized Chinese Ia isolates and representatives of genotypes Ia-d in 2 morphologically distinct varieties of koi: long-fin semi-scaled Beni Kikokuryu koi and short-fin fully scaled Sanke koi. Mortality ranged from 4 to 82% in the Beni Kikokuryu koi and 0 to 94% in the Sanke koi following immersion challenge. Genotype Ia isolates of Asian origin had a wide range in virulence (0-94%). Single isolates representing the European genotypes Ib and Ic were moderately virulent (38-56%). Each virus strain produced similar levels of mortality in both koi breeds, with the exception of the SVCV Id strain that appeared to have both moderate and high virulence phenotypes (60% in Beni Kikokuryu koi vs. 87% in Sanke koi). Overall SVCV strain virulence appeared to be a dominant factor in determining disease outcomes, whereas intraspecies variation, based on koi variety, had less of an impact. This study is the first side-by-side comparison of Chinese SVCV isolates and genotype Ia-d strain virulence in a highly susceptible host.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/dao03650","usgsCitation":"Emmenegger, E.J., Bueren, E.K., Jia, P., Hendrix, N., and Liu, H., 2022, Comparative virulence of spring viremia of carp virus (SVCV) genotypes in two koi varieties: Disease of Aquatic Organisms, v. 148, p. 95-112, https://doi.org/10.3354/dao03650.","productDescription":"18 p.","startPage":"95","endPage":"112","ipdsId":"IP-129753","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":448448,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.3354/dao03650","text":"External Repository"},{"id":435920,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WS6Q0M","text":"USGS data release","linkHelpText":"Comparative Virulence of Spring Viremia of Carp Virus (SVCV) Genotypes in Two Koi Varieties"},{"id":398121,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"148","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Emmenegger, Eveline J. 0000-0001-5217-6030 eemmenegger@usgs.gov","orcid":"https://orcid.org/0000-0001-5217-6030","contributorId":2434,"corporation":false,"usgs":true,"family":"Emmenegger","given":"Eveline","email":"eemmenegger@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":839551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bueren, Emma K. 0000-0002-5738-3917","orcid":"https://orcid.org/0000-0002-5738-3917","contributorId":289657,"corporation":false,"usgs":false,"family":"Bueren","given":"Emma","email":"","middleInitial":"K.","affiliations":[{"id":62212,"text":"Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061","active":true,"usgs":false}],"preferred":false,"id":839552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jia, Peng","contributorId":191750,"corporation":false,"usgs":false,"family":"Jia","given":"Peng","email":"","affiliations":[],"preferred":false,"id":839553,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hendrix, Noble","contributorId":289658,"corporation":false,"usgs":false,"family":"Hendrix","given":"Noble","email":"","affiliations":[{"id":62214,"text":"QEDA Consulting, 4007 Densmore Ave N, Seattle, WA 98103, USA","active":true,"usgs":false}],"preferred":false,"id":839554,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liu, Hong","contributorId":191763,"corporation":false,"usgs":false,"family":"Liu","given":"Hong","email":"","affiliations":[],"preferred":false,"id":839555,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229768,"text":"70229768 - 2022 - Inherit the kingdom or storm the castle? Breeding strategies in a social carnivore","interactions":[],"lastModifiedDate":"2022-03-17T14:59:55.730675","indexId":"70229768","displayToPublicDate":"2022-03-17T09:54:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1589,"text":"Ethology","active":true,"publicationSubtype":{"id":10}},"title":"Inherit the kingdom or storm the castle? Breeding strategies in a social carnivore","docAbstract":"Breeding opportunities are inherently limited for animals that live and breed in groups. Turnover in breeding positions can have marked effects on groups of cooperative breeders, particularly social carnivores. We generally know little about how breeding vacancies are filled in social carnivores and what factors might influence an individual's ability to successfully fill a vacancy. I used a long-term (11 years) genetic dataset from gray wolves to ask whether breeding vacancies were filled by individuals from within groups or by adoptees (i.e., adult animals immigrating into the group) from outside the group. Males were three times more likely than females to be adopted into breeding positions outside their group. Females typically inherited breeding positions within their natal groups (80%, n = 20), while males obtained breeding positions outside their group (76%, n = 17). Group size did not influence whether a breeding vacancy was filled by an adoptee or inherited by an individual from within the group. Prior to adoption, genetic relatedness was 30% higher in groups when females were adopted into breeding positions compared to when they inherited breeding positions from within groups. Thus, genetic relatedness within groups appears to play a role in whether females are adopted into groups or not. Because of their strong reliance on dispersal to secure a breeding position, male wolves appear to be the couriers of genetic diversity in populations of gray wolves. Many states in the United States have recently implemented hunting and trapping seasons for gray wolves. If dispersing male wolves are disproportionately harvested, genetic connectivity and diversity in populations may be affected.
The sea lamprey (Petromyzon marinus) is an invasive species in the Great Lakes and the focus of a large control and assessment program. Current assessment methods provide information on the census size of spawning adult sea lamprey in a small number of streams, but information characterizing reproductive success of spawning adults is rarely available. We used RAD-capture sequencing to genotype single nucleotide polymorphism (SNP) loci for ~1600 sea lamprey larvae collected from three streams in northern Michigan (Black Mallard, Pigeon, and Ocqueoc Rivers). Larval genotypes were used to reconstruct family pedigrees, which were combined with Gaussian mixture analyses to identify larval age classes for estimation of spawning population size. Two complementary estimates of effective breeding size (Nb), as well as the extrapolated minimum number of spawners (Ns), were also generated for each cohort. Reconstructed pedigrees highlighted inaccuracies of cohort assignments from traditionally used mixture analyses. However, combining genotype-based pedigree information with length-at-age assignment of cohort membership greatly improved cohort identification accuracy. Population estimates across all three streams sampled in this study indicate a small number of successfully spawning adults when barriers were in operation, implying that barriers limited adult spawning numbers but were not completely effective at blocking access to spawning habitats. Thus, the large numbers of larvae present in sampled systems were a poor indicator of spawning adult abundance. Overall, pedigree-based Nb and Ns estimates provide a promising and rapid assessment tool for sea lamprey and other species.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.13364","usgsCitation":"Weise, E.M., Scribner, K.T., Adams, J.V., Boeberitz, O., Jubar, A.K., Bravener, G., Johnson, N.S., and Robinson, J.D., 2022, Pedigree analysis and estimates of effective breeding size characterize sea lamprey reproductive biology: Evolutionary Applications, v. 15, no. 3, p. 484-500, https://doi.org/10.1111/eva.13364.","productDescription":"17 p.","startPage":"484","endPage":"500","ipdsId":"IP-130783","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":448450,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/eva.13364","text":"External Repository"},{"id":397235,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Black Mallard River, Ocqueoc River, Pigeon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.48211669921875,\n 45.408092022812276\n ],\n [\n -84.21501159667969,\n 45.408092022812276\n ],\n [\n -84.21501159667969,\n 45.6716438522655\n ],\n [\n -84.48211669921875,\n 45.6716438522655\n ],\n [\n -84.48211669921875,\n 45.408092022812276\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Weise, Ellen M.","contributorId":288846,"corporation":false,"usgs":false,"family":"Weise","given":"Ellen","email":"","middleInitial":"M.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":838310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scribner, Kim T.","contributorId":146113,"corporation":false,"usgs":false,"family":"Scribner","given":"Kim","email":"","middleInitial":"T.","affiliations":[{"id":16582,"text":"Department of Fisheries and Wildlife and Department of Zoology, 480 Wilson Rd. 13 Natural Resources Building, Michigan State University, East Lansing, MI 48824","active":true,"usgs":false},{"id":135,"text":"Biological Resources Division","active":false,"usgs":true}],"preferred":false,"id":838311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, Jean V. 0000-0002-9101-068X jvadams@usgs.gov","orcid":"https://orcid.org/0000-0002-9101-068X","contributorId":3140,"corporation":false,"usgs":true,"family":"Adams","given":"Jean","email":"jvadams@usgs.gov","middleInitial":"V.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":838312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boeberitz, Olivia","contributorId":288848,"corporation":false,"usgs":false,"family":"Boeberitz","given":"Olivia","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":838313,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jubar, Aaron K.","contributorId":150999,"corporation":false,"usgs":false,"family":"Jubar","given":"Aaron","email":"","middleInitial":"K.","affiliations":[{"id":18161,"text":"US Fish and Wildlife Service, Lundington Biological Station","active":true,"usgs":false}],"preferred":false,"id":838314,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bravener, Gale","contributorId":150995,"corporation":false,"usgs":false,"family":"Bravener","given":"Gale","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":838315,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":838316,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Robinson, John D.","contributorId":288851,"corporation":false,"usgs":false,"family":"Robinson","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":838317,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70230003,"text":"70230003 - 2022 - Special Issue on PFAS","interactions":[],"lastModifiedDate":"2022-03-23T14:28:50.591852","indexId":"70230003","displayToPublicDate":"2022-03-17T09:24:48","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10522,"text":"GeoHEALTH–USGS Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"Special Issue on PFAS","docAbstract":"No abstract available.
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We analyzed 1-km normalized difference vegetation index (NDVI) timeseries data (1989–2016) derived from the Advanced Very High Resolution Radiometer (AVHRR) and developed growing season time-integrated NDVI (GS-TIN) for estimating seasonal vegetation activity across stable natural land cover in the conterminous United States (CONUS). After removing areas from analysis that had experienced land cover conversion or modification, we conducted a monotonic trend analysis on the GS-TIN timeseries and found that significant positive temporal trends occurred over 35% of the area, while significant negative trends were observed over only 3.5%. Positive trends were prevalent in the forested lands of the eastern third of CONUS and far northwest, as well as in grasslands in the north central plains. We observed negative and nonsignificant trends mainly in the shrublands and grasslands across the northwest, southwest, and west central plains. To understand the relationship of climate variability with these temporal trends, we conducted partial and multiple correlation analyses on GS-TIN, growing season temperature, and water-year precipitation timeseries. The GS-TIN trends in northern forests were positively correlated with temperature. The GS-TIN trends in the central and western shrublands and grasslands were negatively correlated with temperature and positively correlated with precipitation. 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