Note to All Banders was a special extra communication with more urgent information relevant to banders. This Note to All Banders was sent to U.S. bird banders on December 20, 2022. This note includes holiday greetings and a review of the 2022 successes at the Bird Banding Laboratory. Throughout 2022, the BBL increased communication, engagement, and collaboration, within the Eastern Ecological Science Center, U.S. Geological Survey, and with organization partners and local communities. This Note to Banders highlights these efforts in more detail.
Ungulates (hooved mammals) have a broad distribution across the western United States and play an important role in maintaining predator-prey dynamics, affecting vegetation communities, and providing economic benefits to regional communities through tourism and hunting. Throughout the diverse landscapes they occupy, many ungulate populations undertake seasonal migrations to exploit spatially and temporally variable resources and to avoid predation or other threats. As the human footprint continues to expand across the western United States, ungulates increasingly face more obstacles on their migratory journeys. These obstacles threaten the long-term persistence of existing migrations. As a result, wildlife management agencies across the western United States have worked to identify and protect (or enhance) ungulate migration corridors and seasonal ranges identified from global positioning system (GPS) collar data. These efforts garnered additional support through the U.S. Department of the Interior Secretarial Order (SO) 3362, which was initiated in 2018 and provided Federal support for enhancing habitat quality of big-game winter ranges and migration corridors across the western states.
Further, SO 3362 prompted the U.S. Geological Survey (USGS) to establish the Corridor Mapping Team (CMT): a collaboration between USGS and participating State and Federal wildlife management agencies, as well as numerous Tribal Nations. The CMT works collaboratively to map ungulate migrations and seasonal ranges throughout the western United States within the Ungulate Migrations of the Western United States report series. Volume 1 of the series was published in 2020 and contained migrations and winter ranges from 42 herds across 5 states. Volume 2 was published in 2022 and contained migrations and seasonal ranges from an additional 65 herds. This report, Volume 3 in the series, details migrations and seasonal ranges from an additional 45 herds throughout most western states. In aggregate, the report series has detailed and mapped the migrations and seasonal ranges of 152 ungulate herds and serves as a map-based inventory of the documented ungulate migrations across the western United States. The data layers for most of the herds included in the report series are also available to the public by the USGS. In addition to the included herd maps, this volume provides an overview of the many ways the mapping efforts associated with the CMT are being integrated into local conservation, management, and policy throughout the western United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20225088","usgsCitation":"Kauffman, M., Lowrey, B., Berg, J., Bergen, S., Brimeyer, D., Burke, P., Cufaude, T., Cain, J.W., III, Cole, J., Courtemanch, A., Cowardin, M., Cunningham, J., DeVivo, M., Diamond, J., Duvuvuei, O., Fattebert, J., Ennis, J., Finley, D., Fort, J., Fralick, G., Freeman, E., Gagnon, J., Garcia, J., Gelzer, E., Graham, M., Gray, J., Greenspan, E., Hall, L.E., Hendricks, C., Holland, A., Holmes, B., Huggler, K., Hurley, M., Jeffreys, E., Johnson, A., Knox, L., Krasnow, K., Lockyer, Z., Manninen, H., McDonald, M., McKee, J.L., Meacham, J., Merkle, J., Moore, B., Mong, T.W., Nielsen, C., Oates, B., Olsen, K., Olson, D., Olson, L., Pieron, M., Powell, J., Prince, A., Proffitt, K., Reddell, C., Riginos, C., Ritson, R., Robatcek, S., Roberts, S., Sawyer, H., Schroeder, C., Shapiro, J., Simpson, N., Sprague, S., Steingisser, A., Tatman, N., Turnock, B., Wallace, C., and Wolf, L., 2022, Ungulate migrations of the western United States, Volume 3: U.S. Geological Survey Scientific Investigations Report 2022–5088, 114 p., https://doi.org/10.3133/sir20225088.","productDescription":"Report: xvi, 114 p.; Data Release","ipdsId":"IP-140325","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482396,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205101","text":"Ungulate Migrations of the Western United States, Volume 1"},{"id":410869,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225088/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5088"},{"id":410109,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LSKEZQ","text":"USGS data release","linkHelpText":"Ungulate migrations of the western United States—Volume 3"},{"id":410108,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5088/sir20225088.pdf","text":"Report","size":"46.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5088"},{"id":410590,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5088/sir20225088.xml"},{"id":482399,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245111","text":"Ungulate Migrations of the Western United States, Volume 5"},{"id":482398,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245006","text":"Ungulate Migrations of the Western United States, Volume 4"},{"id":482397,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225008","text":"Ungulate Migrations of the Western United States, Volume 2"},{"id":410589,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5088/images"},{"id":410107,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5088/coverthb.jpg"}],"country":"United States","state":"Arizona, California, Idaho, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.42035048637896,\n 31.96511270775069\n ],\n [\n -102.95879379353022,\n 32.09619220535299\n ],\n [\n -103.13494353105585,\n 37.18138856203808\n ],\n [\n -109.23595720566402,\n 36.94073809885069\n ],\n [\n -108.97760183013798,\n 41.07505691031014\n ],\n [\n -104.04885860727893,\n 41.037575887820424\n ],\n [\n -103.99337420317454,\n 45.12768947014476\n ],\n [\n -110.97964185421287,\n 44.941345247282584\n ],\n [\n -111.40161785171546,\n 44.544500805297844\n ],\n [\n -113.19445602509825,\n 44.46638294566654\n ],\n [\n -114.40947007708905,\n 45.686187837725555\n ],\n [\n -114.52887129856197,\n 46.79485489705277\n ],\n [\n -115.91818301512166,\n 47.62622801365191\n ],\n [\n -115.83639053795412,\n 49.05632926968573\n ],\n [\n -123.29267815383338,\n 48.983622439405565\n ],\n [\n -123.07800303736917,\n 47.953965988507235\n ],\n [\n -124.96057854880837,\n 48.33165246547705\n ],\n [\n -125.30525778678984,\n 41.07649272781754\n ],\n [\n -121.70178467265586,\n 34.62189088197306\n ],\n [\n -118.19264180429954,\n 32.88345216344581\n ],\n [\n -117.0106337464697,\n 32.50945638640991\n ],\n [\n -114.31442509866966,\n 32.635073285847525\n ],\n [\n -111.07671729769325,\n 31.381047387792464\n ],\n [\n -108.08998878966005,\n 31.351258024309203\n ],\n [\n -108.22418391489185,\n 31.845660224062968\n ],\n [\n -106.51293927385098,\n 31.96835429085307\n ],\n [\n -106.37491785144061,\n 31.922584970375112\n ],\n [\n -106.42035048637896,\n 31.96511270775069\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Associate Director, Ecosystems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 300
Reston, VA 20192
Misinformation carries the potential for immense damage to public understanding of science and for evidence-based decision making at an individual and policy level. Our research explores the following questions within seismology: which claims can be considered misinformation, which are supported by a consensus, and which are still under scientific debate? Consensus and debate are important to quantify, because where levels of scientific consensus on an issue are high, communication of this fact may itself serve as a useful tool in combating misinformation. This is a challenge for earthquake science, where certain theories and facts in seismology are still being established. The present study collates a list of common public statements about earthquakes and provides–to the best of our knowledge–the first elicitation of the opinions of 164 earth scientists on the degree of verity of these statements. The results provide important insights for the state of knowledge in the field, helping identify those areas where consensus messaging may aid in the fight against earthquake related misinformation and areas where there is currently lack of consensus opinion. We highlight the necessity of using clear, accessible, jargon-free statements with specified parameters and precise wording when communicating with the public about earthquakes, as well as of transparency about the uncertainties around some issues in seismology.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2022.937055","usgsCitation":"Dryhurst, S., Mulder, F., Dallo, I., Kerr, J., McBride, S., Fallou, L., and Becker, J., 2022, Fighting misinformation in seismology: Expert opinion on earthquake facts vs fiction: Frontiers in Earth Science, v. 10, 937055, 9 p., https://doi.org/10.3389/feart.2022.937055.","productDescription":"937055, 9 p.","ipdsId":"IP-140840","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487710,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.937055","text":"Publisher Index Page"},{"id":482563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Dryhurst, Sarah","contributorId":351570,"corporation":false,"usgs":false,"family":"Dryhurst","given":"Sarah","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":928949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mulder, Femke","contributorId":351571,"corporation":false,"usgs":false,"family":"Mulder","given":"Femke","affiliations":[{"id":84010,"text":"Anglia Ruskin University, Chelmsford, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":928950,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dallo, Irina","contributorId":351572,"corporation":false,"usgs":false,"family":"Dallo","given":"Irina","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":928951,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kerr, John","contributorId":351573,"corporation":false,"usgs":false,"family":"Kerr","given":"John","affiliations":[{"id":84011,"text":"Department of Psychology, University of Cambridge, Cambridge, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":928952,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928953,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fallou, Laure","contributorId":351574,"corporation":false,"usgs":false,"family":"Fallou","given":"Laure","affiliations":[{"id":35318,"text":"European-Mediterranean Seismological Centre","active":true,"usgs":false}],"preferred":false,"id":928954,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Becker, Julia S.","contributorId":217541,"corporation":false,"usgs":false,"family":"Becker","given":"Julia S.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":928955,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70238949,"text":"70238949 - 2022 - Insights from the Alabama Hills into Mesozoic magmatism and tectonics in eastern California","interactions":[],"lastModifiedDate":"2022-12-19T15:24:47.46453","indexId":"70238949","displayToPublicDate":"2022-12-15T09:24:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2626,"text":"Lithosphere","active":true,"publicationSubtype":{"id":10}},"title":"Insights from the Alabama Hills into Mesozoic magmatism and tectonics in eastern California","docAbstract":"New zircon U-Pb ages for the Alabama Hills Granite in Owens Valley, eastern California, range from 103 to 102 Ma, nearly 20 Ma older than previously published zircon ages. The data preclude previously implied links between the pluton and the adjacent Late Cretaceous Mount Whitney Intrusive Suite. Geochronologic and isotopic data indicate a connection between the Alabama Hills Granite and leucogranites to the northwest on the Sierra Nevada crest, as well as a pluton to the southeast in the Coso Range. We refer to these units as the Kearsarge plutons. The suite was intruded from 103 to 100.5 Ma with
Green Lake, located in central Wisconsin USA within a watershed with land use dominated by agriculture, is listed as impaired under Sect. 303(d) of the Clean Water Act. The primary tributary, Silver Creek, is also impaired because of high total phosphorus (TP) concentrations. Silver Creek flows through a shallow marsh before reaching the lake. Prior to 2006, the marsh was turbid and free of macrophytes. Efforts to restrict carp (Cyprinus carpio) in the marsh and reduce the primary upstream phosphorus point source, resulted in the marsh becoming a clear-water, macrophyte-dominated system.
The point source reduction and marsh phytoplankton-to-macrophyte shift reduced the export of TP and suspended sediment (SS). These measured reductions at the marsh outlet exceeded the documented reductions in the upstream point source suggesting that the shift to a macrophyte-dominated system drove part of the TP reductions. TP loads at the marsh outlet significantly decreased in all seasons; however, SS loads significantly decreased in all seasons except winter, suggesting the vegetation shift was an important driver for these reductions. During 2012–2017, the marsh served as an overall sink for TP and SS, retaining on average 1.59 kg/day and 0.95 MT/day, respectively. Overall, this study documents benefits of a multi-stakeholder, collaborative ecological effort to restore a marsh from a turbid system to a macrophyte-dominated system, which resulted in significant reductions in downstream TP and SS loading to a major inland lake. This effort may serve as a model for similar restorations in other watersheds with land use dominated by agriculture.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-022-01649-0","usgsCitation":"Fuller, S., Boswell, E.P., Thompson, A., and Robertson, D., 2022, Water-quality improvement of an agricultural watershed marsh after macrophyte establishment and point-source reduction: Wetlands, v. 42, 129, 13 p., https://doi.org/10.1007/s13157-022-01649-0.","productDescription":"129, 13 p.","ipdsId":"IP-139219","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":413530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.08144298728385,\n 43.755719784168264\n ],\n [\n -88.8933819839438,\n 43.755719784168264\n ],\n [\n -88.8933819839438,\n 43.85676732584028\n ],\n [\n -89.08144298728385,\n 43.85676732584028\n ],\n [\n -89.08144298728385,\n 43.755719784168264\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2022-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Fuller, Sarah","contributorId":302730,"corporation":false,"usgs":false,"family":"Fuller","given":"Sarah","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":865263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boswell, Edward P 0000-0002-2644-4043","orcid":"https://orcid.org/0000-0002-2644-4043","contributorId":302732,"corporation":false,"usgs":false,"family":"Boswell","given":"Edward","email":"","middleInitial":"P","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":865264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Anita M.","contributorId":200233,"corporation":false,"usgs":false,"family":"Thompson","given":"Anita M.","affiliations":[{"id":16128,"text":"Department of Biological System Engineering, University of Wisconsin—Madison, Madison, WI, USA","active":true,"usgs":false}],"preferred":false,"id":865265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865266,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270408,"text":"70270408 - 2022 - Examining landowners’ preferences for a chronic wasting disease management program","interactions":[],"lastModifiedDate":"2025-08-19T15:18:27.733358","indexId":"70270408","displayToPublicDate":"2022-12-15T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Examining landowners’ preferences for a chronic wasting disease management program","docAbstract":"Private landowners are key partners in chronic wasting disease (CWD) management, especially in landscapes where there is limited public ownership. In this study, we evaluated landowners' preferences for alternative hypothetical CWD management programs using a stated choice experiment. We were particularly interested in understanding preferences for the use of financial incentives to motivate white-tailed deer harvest and facilitate hunter access to private lands as potential CWD management tools. We used latent class analysis to characterize preference heterogeneity among landowners stemming from patterns of choice. We compared means and distributions of auxiliary variables related to landowners' perceived risks, trust, attitudes toward management, and sociodemographics across latent classes stemming from choice model results. The pooled model demonstrated that reducing deer population density, providing payments to landowners for CWD-positive deer taken from their property, the form of incentives for public access, and banning recreational deer feeding had a small positive effect on respondents' choice of CWD management program. However, providing financial payments to hunters for harvesting CWD-positive deer and the use of targeted culling had the opposite effect on choice. Latent class models revealed that a majority of respondents exhibited a pattern of preference where all forms of incentives exerted a negative effect on choice, but smaller subsets of landowners positively evaluate the use of some incentives. Post-hoc contrasts revealed relationships between patterns of preferences and trust, risk, and attitudes toward CWD management with small to medium effects. Results demonstrated limited support for the use of financial incentives as a tool to manage access and harvest in the southeast Minnesota CWD management zone
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1401","usgsCitation":"Landon, A., Smith, K., Cornicelli, L., Fulton, D.C., McInenly, L.E., and Schroeder, S.A., 2022, Examining landowners’ preferences for a chronic wasting disease management program: Wildlife Society Bulletin, v. 47, no. 1, e1401, 19 p., https://doi.org/10.1002/wsb.1401.","productDescription":"e1401, 19 p.","ipdsId":"IP-122867","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1401","text":"Publisher Index Page"},{"id":494314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","county":"Fillmore County, Houston County, Wisconsin County","otherGeospatial":"southeastern Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.13879824080021,\n 44.7131470360637\n ],\n [\n -93.13879824080021,\n 43.4796764343609\n ],\n [\n -91.23563813665416,\n 43.4796764343609\n ],\n [\n -91.23137485428805,\n 43.89405306400184\n ],\n [\n -92.58709230231764,\n 44.71116401915873\n ],\n [\n -93.13879824080021,\n 44.7131470360637\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Landon, Adam","contributorId":279439,"corporation":false,"usgs":false,"family":"Landon","given":"Adam","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":946336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Kyle","contributorId":359833,"corporation":false,"usgs":false,"family":"Smith","given":"Kyle","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":946340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornicelli, Louis","contributorId":359827,"corporation":false,"usgs":false,"family":"Cornicelli","given":"Louis","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":946337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fulton, David C. 0000-0001-5763-7887","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":333043,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":79716,"text":"Minnesota Cooperative Unit","active":true,"usgs":false}],"preferred":true,"id":946335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McInenly, Leslie E.","contributorId":359829,"corporation":false,"usgs":false,"family":"McInenly","given":"Leslie","middleInitial":"E.","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":946338,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schroeder, Susan A.","contributorId":359831,"corporation":false,"usgs":false,"family":"Schroeder","given":"Susan","middleInitial":"A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":946339,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238872,"text":"sir20225100 - 2022 - Analysis of groundwater and surface water in areas of isoxaflutole application, Tuscola and Kalamazoo Counties, Michigan","interactions":[],"lastModifiedDate":"2022-12-14T23:20:00.981699","indexId":"sir20225100","displayToPublicDate":"2022-12-14T17:45:00","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":"2022-5100","displayTitle":"Analysis of Groundwater and Surface Water in Areas of Isoxaflutole Application, Tuscola and Kalamazoo Counties, Michigan","title":"Analysis of groundwater and surface water in areas of isoxaflutole application, Tuscola and Kalamazoo Counties, Michigan","docAbstract":"The herbicide 5-cyclopropyl-4-(2-methylsulfonyl-4-trifluoromethylbenzoyl) isoxazole, also known as isoxaflutole (IXF), was conditionally approved for use on corn in Michigan in 2015. The fate of IXF and its degradates in different environmental settings and the processes by which these compounds move to groundwater or to surface-water bodies have been previously studied, but little information about possible persistence and buildup of this herbicide and its degradates in Michigan’s groundwater and surface water is available. Therefore, from 2015 to 2020, the U.S. Geological Survey, in cooperation with the Michigan Department of Agriculture and Rural Development, studied IXF and two of its degradates in two locations where IXF was applied.
IXF and its degradates were rarely detected in shallow groundwater downgradient from IXF applications. In contrast, one or more of the three target IXF compounds were detected in 40 percent of surface-water samples. The degradates 1-(2-methylsulfonyl-4-trifluoromethylphenyl)-2-cyano-3-cyclopropyl propan-1-dione), also known as diketonitrile isoxaflutole (DKN), and 2-methylsulfonyl-4-(trifluoromethyl) benzoic acid (BAA), the benzoic acid analogue of IXF, were detected more frequently than the parent compound. At surface-water sites, DKN and BAA reached maximum concentrations within about the first 5 weeks after IXF application after rainfall-runoff events. Concentrations subsequent to post-application maxima decreased through time approximately following first-order, exponential loss kinetics. Carryover of DKN and BAA, from an application year to the following spring, occurred at several surface-water sites, and springtime concentrations were typically 1–5 percent of maximum, post-application concentrations. Virtually no detections were recorded later in the growing season during non-application years. Results indicate rapid loss of IXF and very few detections of the parent compound from the study areas. The degradates DKN and BAA were more frequently detected in surface runoff up to about 1 year after IXF application, but no evidence of longer-term accumulation was found.
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Box 25046, MS-980
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The United States is an Arctic nation because of Alaska and thus maintains tremendous interests and stewardship responsibilities in the region, especially as the region undergoes substantial environmental transformation. This Arctic Science Strategy is intended to support those interests and responsibilities by expressing the core values, mission, vision, and the broad research goals and priority objectives of the U.S. Geological Survey (USGS) for science coordination in Arctic Alaska. It synthesizes strategic planning activities across the USGS in the Arctic over the next 3-year planning horizon, identifies some major networks of collaboration, and aligns with current research priorities of the Department of the Interior Climate Action Plan, released October 7, 2021 (U.S. Department of the Interior, 2021). Also, in recognition of the rapid pace of Arctic environmental changes and the corresponding need to move with urgency beyond routine patterns of operation, this strategy extends an implementation challenge to staff and colleagues in hopes of reaching for breakthrough results in some target areas of performance.
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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The U.S. Geological Survey, in cooperation with the city of Harrisonville, Missouri, assessed flooding of Muddy Creek resulting from varying precipitation magnitudes and durations, antecedent runoff conditions, and channel modifications (cleaned culverts and added detention storage). The precipitation scenarios were used to develop a library of flood-inundation maps that included a 3.8-mile reach of Muddy Creek and tributaries within and adjacent to the city.
Hydrologic and hydraulic models of the upper Muddy Creek study basin were used to assess streamflow magnitudes associated with simulated precipitation amounts and the resulting flood-inundation conditions. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS; version 4.4.1) was used to simulate the amount of streamflow produced from a range of precipitation events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS; version 5.0.7) was then used to route streamflows and map resulting areas of flood inundation.
The hydrologic and hydraulic models were calibrated to the September 28, 2019; May 27, 2021; and June 25, 2021, runoff events representing a range of antecedent runoff conditions and hydrologic responses. The calibrated HEC–HMS model was used to simulate streamflows from design rainfall events of 30-minute to 24-hour durations and ranging from a 100- to 0.1-percent annual exceedance probability. Flood-inundation maps were produced for reference stages of 1.0 foot (ft), or near bankfull, to 4.0 ft, or a stage exceeding the 0.1-percent annual exceedance probability interval precipitation, using the HEC–RAS model. The results of each precipitation duration-frequency value were represented by a 0.5-ft increment inundation map based on the generated peak streamflow from that rainfall event and the corresponding stage at the Muddy Creek reference location.
Seven scenarios were developed with the HEC–HMS hydrologic model with resulting streamflows routed in a HEC–RAS hydraulic model, and these scenarios varied by antecedent runoff condition and potential channel modifications. The same precipitation scenarios were used in each of the seven antecedent runoff and channel conditions, and the simulation results were assigned to a flood-inundation map condition based on the generated peak flow and corresponding stage at the Muddy Creek reference location.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225084","collaboration":"Prepared in cooperation with the city of Harrisonville, Missouri","usgsCitation":"Heimann, D.C., and Rydlund, P.H., 2022, Precipitation-driven flood-inundation mapping of Muddy Creek at Harrisonville, Missouri: U.S. Geological Survey Scientific Investigations Report 2022–5084, 18 p., https://doi.org/10.3133/sir20225084.","productDescription":"Report: viii, 18 p.; Data Release; Dataset; Application Site","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-135285","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":410386,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5084/sir20225084.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5084"},{"id":410482,"rank":7,"type":{"id":4,"text":"Application Site"},"url":"https://ci.harrisonville.mo.us/1052/Stormwater-Management","text":"City of Harrisonville web page","linkHelpText":"—Flood-inundation mapping and model of Muddy Creek"},{"id":410385,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5084/coverthb.jpg"},{"id":410388,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5084/sir20225084.XML"},{"id":410389,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5084/images"},{"id":410390,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P969ZOLB","text":"USGS data release","linkHelpText":"Geospatial data and model archives associated with precipitation-driven flood-inundation mapping of Muddy Creek at Harrisonville, Missouri (ver. 2.0, December 2022)"},{"id":410391,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Missouri","county":"Cass County","city":"Harrisonville","otherGeospatial":"Muddy Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.38510524959862,\n 38.66691333285476\n ],\n [\n -94.38510524959862,\n 38.60174153214416\n ],\n [\n -94.30827923799478,\n 38.60174153214416\n ],\n [\n -94.30827923799478,\n 38.66691333285476\n ],\n [\n -94.38510524959862,\n 38.66691333285476\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Field-based transplant gardens, including common and reciprocal garden experiments, are a powerful tool for studying genetic variation and gene-by-environment interactions. These experiments assume that individuals within the garden represent independent replicates growing in a homogenous environment. Plant neighborhood interactions are pervasive across plant populations and could violate assumptions of transplant garden experiments. We demonstrate how spatially explicit models for plant–plant interactions can provide novel insights on genotypes' performance in field-transplant garden designs. We used individual-based models, based on data from a sagebrush (Artemisia spp.) common garden, to simulate the impact of spatial plant–plant interactions on between-group differences in plant growth. We found that planting densities within the range of those used in many common gardens can bias experimental outcomes. Our results demonstrate that higher planting densities can lead to inflated group differences and may confound genotypes' competitive ability and genetically underpinned variation. Synthesis. We propose that spatially explicit models can help avoid biased results by informing the design and analysis of field-based transplant garden experiments. Alternately, including neighborhood effects in post hoc analyses of transplant garden experiments is likely to provide novel insights into the roles of biotic factors and density dependence in genetic differentiation.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9630","usgsCitation":"Zaiats, A., Requena-Mullor, J.M., Germino, M., Forbey, J.S., Richardson, B.A., and Caughlin, T., 2022, Spatial models can improve the experimental design of field-based transplant gardens by preventing bias due to neighborhood crowding: Ecology and Evolution, v. 12, no. 12, e9630, 9 p., https://doi.org/10.1002/ece3.9630.","productDescription":"e9630, 9 p.","ipdsId":"IP-139927","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":445664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9630","text":"Publisher Index Page"},{"id":410630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii 0000-0001-8978-4152","orcid":"https://orcid.org/0000-0001-8978-4152","contributorId":257072,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":859166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Requena-Mullor, Juan M.","contributorId":218132,"corporation":false,"usgs":false,"family":"Requena-Mullor","given":"Juan","email":"","middleInitial":"M.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":859167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":859168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Forbey, Jennifer S.","contributorId":194442,"corporation":false,"usgs":false,"family":"Forbey","given":"Jennifer","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":859169,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richardson, Bryce A.","contributorId":207820,"corporation":false,"usgs":false,"family":"Richardson","given":"Bryce","email":"","middleInitial":"A.","affiliations":[{"id":37640,"text":"U.S.D.A. Forest Service Rocky Mountain Research Station, Provo, UT, 84606 USA","active":true,"usgs":false}],"preferred":false,"id":859170,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caughlin, T. Trevor","contributorId":257076,"corporation":false,"usgs":false,"family":"Caughlin","given":"T. Trevor","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":859171,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238856,"text":"70238856 - 2022 - Acetylenotrophic and diazotrophic Bradyrhizobium sp. strain I71 from TCE-contaminated soils","interactions":[],"lastModifiedDate":"2022-12-14T15:30:15.135816","indexId":"70238856","displayToPublicDate":"2022-12-14T09:10:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":850,"text":"Applied and Environmental Microbiology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Acetylenotrophic and diazotrophic Bradyrhizobium sp. strain I71 from TCE-contaminated soils","title":"Acetylenotrophic and diazotrophic Bradyrhizobium sp. strain I71 from TCE-contaminated soils","docAbstract":"Rare plant species that are constrained by shading may be threatened by a lack of natural disturbance that removes overhanging vegetation. The original distribution of the study species Physostegia correllii (Lundell) Shinners included freshwater floodplains of large rivers in the southcentral U.S. (Colorado, Rio Grande, and Mississippi rivers). A second species, Trillium texanum Buckley was found in seep spring baygalls in east-central Texas and extreme northwestern Louisiana. Experiments to determine the effects of shading on P. correllii and T. texanum were conducted using short-term shade cloth treatments (full sunlight vs. 30% shading for 2–3 weeks), and a dryness treatment for T. texanum (moist vs. less moist). Mean height and cover responses of individuals for both species were determined in conservation gardens located in Lafayette, Louisiana. Physostegia correllii grown in shaded environments for 2.5 weeks had shorter mean height than if grown in full sunlight. Half of the shaded plants in shaded plots had died by the mid-summer. For T. texanum, shading reduced the mean height and cover of plants. Therefore, management to remove overhanging ground vegetation to mimic natural disturbance might revive P. correllii and/or T. texanum populations where overhanging vegetation is increasing due to lack of natural disturbance (e.g., flood pulsing, grazing, burning).
","language":"English","publisher":"Botanical Research Institute of Texas","doi":"10.17348/jbrit.v16.i2.1270","usgsCitation":"Middleton, B., Williams, C.R., Doffitt, C., and Johnson, D., 2022, Effects of shading on the rare plant species, Physostegia correllii (Lamiaceae) and Trillium texanum (Melanthiaceae): Journal of the Botanical Research Institute of Texas, v. 16, no. 2, p. 591-603, https://doi.org/10.17348/jbrit.v16.i2.1270.","productDescription":"13 p.; 2 Data Releases","startPage":"591","endPage":"603","ipdsId":"IP-133104","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":445669,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.17348/jbrit.v16.i2.1270","text":"Publisher Index Page"},{"id":410472,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":414828,"rank":3,"type":{"id":30,"text":"Data 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USA","interactions":[],"lastModifiedDate":"2022-12-14T14:19:16.067841","indexId":"70238870","displayToPublicDate":"2022-12-14T08:09:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2653,"text":"Mammalian Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Tooth wear and the apparent consumption of human foods among American black bears (Ursus americanus) in Great Smoky Mountains National Park, USA","title":"Tooth wear and the apparent consumption of human foods among American black bears (Ursus americanus) in Great Smoky Mountains National Park, USA","docAbstract":"Stable isotope analyses of hair have been used to estimate the consumption of human foods by American black bears (Ursus americanus). Consumption of human foods influences body mass and reproductive success of bears. However, the underlying factors that cause some bears to become conflict bears and resort to consuming human foods as a portion of their diet are not fully understood. We collected hair samples for stable isotope analysis from 51 black bears in Great Smoky Mountains National Park, Tennessee, USA in 2006. We used δ13C values of hairs to determine if the bears were consuming C3-based (natural foods) or C4-based (human foods) diets, and δ15N values, which increase with more meat in the diet, as a further indication of the consumption of human foods. Male bears with the heaviest tooth wear had a combination of higher δ15N and δ13C values, suggesting that they consume human foods to a greater extent than do other black bears. Based on our results, we hypothesize that tooth wear, and thus dental health, may play a role in the consumption of human foods by larger, male bears.
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Post","active":true,"usgs":false}],"preferred":false,"id":859003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loveless, Amanda","contributorId":299911,"corporation":false,"usgs":false,"family":"Loveless","given":"Amanda","email":"","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":859004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roeder, Beverly L.","contributorId":299912,"corporation":false,"usgs":false,"family":"Roeder","given":"Beverly","email":"","middleInitial":"L.","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":859005,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van 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structure of freshwater networks, but is essential for promoting ecological resilience under climate change. Although the importance of stream network connectivity has been recognized, lake-stream network connectivity has largely been ignored. Furthermore, protected areas are generally not designed to maintain or encompass entire freshwater networks. We applied a coarse-filter approach to identify potential freshwater corridors for diverse taxa by calculating connectivity scores for 385 lake-stream networks across the conterminous US based on network size, structure, resistance to fragmentation, and dam prevalence. We also identified 2080 disproportionately important lakes for maintaining intact networks (i.e., “hubs”; 2% of all network lakes) and analyzed the protection status of hubs and potential freshwater corridors. Just 3% of networks received high connectivity scores based on their large size and structure (medians of 1303 lakes, 498.6 km north-south stream distance), but these also contained a median of 454 dams. In contrast, undammed networks (17% of networks) were considerably smaller (medians of 6 lakes, 7.2 km north-south stream distance), indicating that the functional connectivity of the largest potential freshwater corridors in the conterminous US currently may be diminished compared to smaller, undammed networks. Network lakes and hubs were protected at similar rates nationally across different levels of protection (8-18% and 6-20%, respectively), but were generally more protected in the western US. Our results indicate that conterminous US protection of major freshwater corridors and the hubs that maintain them generally fell short of the international conservation goal of protecting an ecologically representative, well-connected set of fresh waters (≥ 17%) by 2020 (Aichi Target 11). Conservation planning efforts might consider focusing on restoring natural hydrologic connectivity at or near hubs, particularly in larger networks, less protected, or biodiverse regions, to support freshwater biodiversity conservation under climate change.
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University","active":true,"usgs":false}],"preferred":false,"id":922933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"King, Katelyn B.S.","contributorId":348095,"corporation":false,"usgs":false,"family":"King","given":"Katelyn B.S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922931,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238833,"text":"ofr20221107 - 2022 - Black Abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2021, annual report","interactions":[],"lastModifiedDate":"2023-09-18T20:00:12.711841","indexId":"ofr20221107","displayToPublicDate":"2022-12-13T13:23:14","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":"2022-1107","displayTitle":"Black Abalone Surveys at Naval Base Ventura County, San Nicolas Island, California: 2021, Annual Report","title":"Black Abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2021, annual report","docAbstract":"The U.S. Geological Survey monitors a suite of intertidal black abalone sites at San Nicolas Island, California, in cooperation with the U.S. Navy, which owns the island. The nine rocky intertidal sites were established in 1980 to study the potential effect of translocated sea otters on the intertidal black abalone population at the island. The sites were monitored from 1981 to 1997, usually annually or biennially. Monitoring resumed in 2001 and has been completed annually since then. Since 2018, the work has been carried out by the U.S. Geological Survey Western Ecological Research Center. The study sites became particularly important, from a management perspective, after a virulent disease decimated black abalone populations throughout southern California beginning in the mid-1980s. The disease, withering syndrome, was first observed on San Nicolas Island in 1992 and during the next few years, it reduced the population there by more than 99 percent. The species was subsequently listed as endangered under the Endangered Species Act in 2009.
The subject of this report is the 2021 survey of the sites and how the measured population status compares to the long-term data (collected over several decades) at San Nicolas Island. During the last two decades, the total monitored black abalone population at the island has grown approximately ten-fold after the disease related decline, from about 200 to more than 2,000 abalone. Since it was first consistently measured in 2005, the mean distance between adjacent black abalone has decreased substantially from approximately 50 centimeters to less than 15 centimeters, indicating that abalone are close enough together at several of the sites to reproduce successfully. The 2021 counts were the first since 2016 to indicate a possible decline in the monitored population at San Nicolas Island. Although still more than ten times the population counted in 2001, counts on the transects dropped by 13.6 percent from the survey count in 2020. The most significant decline was the loss of 341 abalone, from the previous count of 547, on a transect that since 2002 had the highest count of all 44 transects. Between 2020 and 2021, there were increases and decreases at the sites and the transects at each site. Although the 2021 count was lower than the 2020 count, it was the second highest since 1997. Based on the number of small abalone counted, recruitment rates were similar to most years since 2008 and higher than the rates observed before the population declines resulting from withering syndrome.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221107","collaboration":"Prepared in cooperation with the U.S. Navy","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Kenner, M.C., and Yee, J.L., 2022, Black Abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2021, annual report: U.S. Geological Survey Open-File Report 2022–1107, 34 p., https://doi.org/10.3133/ofr20221107.","productDescription":"vii, 34 p.","onlineOnly":"Y","ipdsId":"IP-144353","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":410409,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1107/ofr20221107.XML"},{"id":410407,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221107/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1107"},{"id":410408,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1107/images"},{"id":410406,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1107/ofr20221107.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1107"},{"id":410405,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1107/coverthb.jpg"}],"country":"United States","state":"California","county":"Ventura County","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.59425743862556,\n 33.2937160305206\n ],\n [\n -119.59425743862556,\n 33.20294676390226\n ],\n [\n -119.4170269925379,\n 33.20294676390226\n ],\n [\n -119.4170269925379,\n 33.2937160305206\n ],\n [\n -119.59425743862556,\n 33.2937160305206\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
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This study evaluated carbon dioxide (CO2) injected into water as a possible behavioral stimulant to enhance capture and removal of invasive red swamp crayfish (RSC, Procambarus clarkii [Girard, 1852]) from a retention pond in southeastern Michigan. Objectives of this study were (1) to determine if target CO2 concentrations were attainable within the infested pond and (2) to determine if CO2 treatment was effective to push RSC either towards shorelines or onto dry land, where they could be collected and removed. Carbon dioxide was applied directly into one treatment pond (about [~]2,500 cubic meters) in Novi, Michigan. Two nearby ponds in Livonia, Mich., were used as untreated control ponds. Crayfish removal efficiency was evaluated in all ponds using baited traps and shoreline surveys. Results showed that the CO2 treatment pond reached its target concentration of greater than (>) 200 milligrams per liter (mg/L) of CO2, a benchmark determined from previous laboratory studies, approximately 11 hours after injection started, and maintained concentrations between 200 and 351 mg/L of CO2 for about 2.5 days. During treatment, some emergent crayfish were observed near influent culverts around the pond, which possibly brought about a behavioral response. However, the number of individuals and crayfish observations were minimal and infrequent. Crayfish continued to be removed throughout CO2 treatment with baited traps and perimeter surveys, but differences in catch rates between the treatment and control ponds were not apparent and confounded by a temporal decline in catch rates across all ponds. Overall, this study demonstrated that open-water treatment applications with CO2 are possible, but its effectiveness to enhance RSC removal was unclear because of the limited crayfish observations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221105","collaboration":"Prepared in cooperation with Michigan Department of Natural Resources, Michigan State University, and Auburn University","programNote":"Biological Threats and Invasive Species Research Program","usgsCitation":"Smerud, J., Rivera, J., Johnson, T., Tix, J., Fredricks, K., Barbour, M., Herbst, S., Thomas, S., Nathan, L., Roth, B., Smith, K., Allert, A., Stoeckel, J., and Cupp, A., 2022, Field application of carbon dioxide as a behavioral control method for invasive red swamp crayfish (Procambarus clarkii) in southeastern Michigan water retention ponds: U.S. Geological Survey Open-File Report 2022–1105, 12 p., https://doi.org/10.3133/ofr20221105.","productDescription":"Report: vii, 12 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-138063","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":501841,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113941.htm","linkFileType":{"id":5,"text":"html"}},{"id":410370,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221105/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":410273,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99OUHMV","text":"USGS data release","linkHelpText":"Water quality and atmospheric carbon dioxide data for field application of carbon dioxide during summer 2018 as a behavioral control method for invasive red swamp crayfish (Procambarus clarkii) in southeastern Michigan water retention ponds"},{"id":410272,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1105/images"},{"id":410271,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1105/ofr20221105.XML"},{"id":410270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1105/ofr20221105.pdf","text":"Report","size":"1.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1105"},{"id":410269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1105/coverthb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.44162797914937,\n 42.44723847720684\n ],\n [\n -83.44162797914937,\n 42.42580674596837\n ],\n [\n -83.3843952063379,\n 42.42580674596837\n ],\n [\n -83.3843952063379,\n 42.44723847720684\n ],\n [\n -83.44162797914937,\n 42.44723847720684\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
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No abstract available.
","language":"English","publisher":"San Diego Association of Governments","usgsCitation":"Preston, K.L., Perkins, E., Brown, C., McCutcheon, S.J., Bernabe, A.E., Luciani, E., Kus, B., and Wynn, S., 2022, State of the Regional Preserve System in western San Diego County, 425 p.","productDescription":"425 p.","ipdsId":"IP-147124","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":414619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":414597,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sdmmp.com/view_article.php?cid=SDMMP_CID_71_63979fd98137e#"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.12389525448718,\n 32.55187919418343\n ],\n [\n -116.55244310819583,\n 32.5815668626354\n ],\n [\n -116.71291939585285,\n 33.12737230744057\n ],\n [\n -117.19043420302773,\n 33.33363414773244\n ],\n [\n -117.38222293705692,\n 33.206005600396665\n ],\n [\n -117.26088720736499,\n 32.96989434633531\n ],\n [\n -117.28437154214421,\n 32.835159168666536\n ],\n [\n -117.2726293747546,\n 32.69033753274033\n ],\n [\n -117.12389525448718,\n 32.5452806004228\n ],\n [\n -117.12389525448718,\n 32.55187919418343\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Preston, Kristine L. 0000-0002-6958-1128 kpreston@usgs.gov","orcid":"https://orcid.org/0000-0002-6958-1128","contributorId":207765,"corporation":false,"usgs":true,"family":"Preston","given":"Kristine","email":"kpreston@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perkins, Emily E. 0000-0002-6286-3480","orcid":"https://orcid.org/0000-0002-6286-3480","contributorId":225022,"corporation":false,"usgs":true,"family":"Perkins","given":"Emily E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Christopher W. 0000-0002-2545-9171","orcid":"https://orcid.org/0000-0002-2545-9171","contributorId":240860,"corporation":false,"usgs":true,"family":"Brown","given":"Christopher W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCutcheon, Sarah Joelle 0000-0002-9377-0527","orcid":"https://orcid.org/0000-0002-9377-0527","contributorId":303334,"corporation":false,"usgs":true,"family":"McCutcheon","given":"Sarah","email":"","middleInitial":"Joelle","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bernabe, Annabelle E 0000-0001-7453-8805","orcid":"https://orcid.org/0000-0001-7453-8805","contributorId":303335,"corporation":false,"usgs":true,"family":"Bernabe","given":"Annabelle","email":"","middleInitial":"E","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Luciani, Emilie","contributorId":303336,"corporation":false,"usgs":false,"family":"Luciani","given":"Emilie","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":867275,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867276,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wynn, Susan","contributorId":303337,"corporation":false,"usgs":false,"family":"Wynn","given":"Susan","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":867277,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70238829,"text":"ofr20221095 - 2022 - Assessment of significant sand resources in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California","interactions":[],"lastModifiedDate":"2026-03-30T20:46:36.091721","indexId":"ofr20221095","displayToPublicDate":"2022-12-13T09:16:00","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":"2022-1095","displayTitle":"Assessment of Significant Sand Resources in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand Littoral Cell Study Areas along the Continental Shelf of California","title":"Assessment of significant sand resources in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California","docAbstract":"The Sand Resources Project was established through collaborative agreements between the U.S. Geological Survey (USGS), the Bureau of Ocean Energy Management (BOEM), and the California Ocean Protection Council (OPC) with the purpose of evaluating sand and gravel resources in Federal and California State Waters for potential use in future beach-nourishment projects. Project partners worked in collaboration with California Coastal Sediment Management Workgroup (CSMW) members to define priority study areas for this work based on the potential for finding sand within the broader region and the needs for this sand as shown by beach erosion areas of concern in the adjacent littoral cells. The final study areas were defined to be (1) the San Francisco Littoral Cell, (2) the Oceanside Littoral Cell, and (3) the Silver Strand Littoral Cell.
A two-stage approach was used to assess the study areas. The initial stage was a synthesis of the existing geophysical and sediment-sampling data in each area. This allowed for evaluations of the data availability, data gaps, and general patterns of sediment thickness and grain size. This synthesis was published in a separate USGS open-file report (Warrick and others, 2022). The findings from this assessment were used to refine study area boundaries and develop sampling plans for stage two of the project.
Stage two of the project is the collection, processing, and synthesis of new data, including high-resolution geophysical surveys and sediment cores—this report addresses the second stage. The work focuses on two of the study areas—the San Francisco and the Oceanside Littoral Cells, where several research cruises have been conducted. A more limited, exploratory approach was used for the Silver Strand Littoral Cell, owing to the lack of existing high-resolution bathymetric data for this study area. The data collected provide new information about the three study areas, including sediment thickness, grain-size distributions, and total organic carbon.
Sediment in all three study areas of the Sand Resources Study was suitable for beach nourishment, as reflected by their grain-size distributions and sediment thicknesses. For example, sandy sediment in the San Francisco Littoral Cell study area was on and immediately outside of the ebb-tidal bar of the San Francisco Bay, a landform that has a strong influence on grain-size patterns of the region. The presence of thick sediment deposits in this area was interpreted to be a function of tectonics, which has caused physical features that include a graben north of the Golden Gate whose deposits were thicker and siltier than the remaining area. Sandy sediment on the inner and outer parts of the continental shelf in the Oceanside Littoral Cell may be useful for nourishment, whereas the midshelf between these areas was dominated by silty sediment. Sediment in the Silver Strand Littoral Cell, which was only sampled selectively, had the greatest potential for beach nourishment because of the greater prevalence of beach-comparable grain sizes, especially in the more distal and deeper areas where medium sands were found.
The Sand Resources Project did identify several sandy regions of the continental shelf that are deeper than dredging technologies currently (2022) available in the United States, which are generally limited to 30 meters (m) water depth or less. Although sandy sediment exists in all three study areas at water depths of 30 m or less, additional sediment supplies—most of which are in Federal Waters—are present in deeper settings, especially for the Oceanside and Silver Strand Littoral Cell study areas. Although the Silver Strand Littoral Cell study area was found to be considerably replete in sand resources, these conclusions are based on a limited sampling exercise across that study area. Thus, it may be beneficial to complete a more thorough characterization of the sediment resources in the Silver Strand Littoral Cell study area if it is determined that a need for sandy coastal sediment exists in this region.
As a result of the Sand Resources Project, several areas of sand resources in Federal and California State Waters were found where they were previously unknown. As such, this project may provide important data for future coastal-management decisions in California, and it should provide a model for future investigations of sediment resources in other regions of the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221095","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management and the State of California Ocean Protection Council","usgsCitation":"Warrick, J.A., Conrad, J.E., Papesh, A., Lorenson, T., and Sliter, R.W., 2022, Assessment of significant sand resources in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California: U.S. Geological Survey Open-File Report 2022–1095, 104 p., https://doi.org/10.3133/ofr20221095.","productDescription":"Report: viii, 104 p.; 3 Data Releases","onlineOnly":"Y","ipdsId":"IP-130530","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501837,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113943.htm","linkFileType":{"id":5,"text":"html"}},{"id":410366,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UELSBU","text":"USGS data release","description":"USGS data release","linkHelpText":"Geophysical and sampling data collected offshore Oceanside, southern California during field activity 2017-686-FA from 2017-10-23 to 2017-10-31"},{"id":410365,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9690BEK","text":"USGS data release","description":"USGS data release","linkHelpText":"Geophysical and core sample data collected offshore Oceanside to San Diego, southern California, during field activity 2018-638-FA from 2018-05-21 to 2018-05-26"},{"id":410364,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LBG9H5","text":"USGS data release","description":"USGS data release","linkHelpText":"Geophysical and core sample data collected offshore San Francisco, California, during field activity 2019-649-FA from 2019-10-11 to 2019-10-18"},{"id":410367,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221094","text":"OFR 2022-1094 —","linkHelpText":"Compilation of existing data for sand resource studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California—Strategy for field studies and sand resource assessment"},{"id":410363,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1095/ofr20221095.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1095"},{"id":410362,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1095/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco, Oceanside, and Silver Strand littoral cell study areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.32845828309286,\n 37.818674364541195\n ],\n [\n -123.23587958971336,\n 37.818674364541195\n ],\n [\n -123.23587958971336,\n 36.79251013661299\n ],\n [\n -122.32845828309286,\n 36.79251013661299\n ],\n [\n -122.32845828309286,\n 37.818674364541195\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.13365815618306,\n 32.493660702226535\n ],\n [\n -117.13365815618306,\n 32.93175821350262\n ],\n [\n -117.80008333663935,\n 32.93175821350262\n ],\n [\n -117.80008333663935,\n 32.493660702226535\n ],\n [\n -117.13365815618306,\n 32.493660702226535\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.26754558635474,\n 33.52924818029179\n ],\n [\n -118.26754558635474,\n 33.1318551432673\n ],\n [\n -117.66427888472828,\n 33.1318551432673\n ],\n [\n -117.66427888472828,\n 33.52924818029179\n ],\n [\n -118.26754558635474,\n 33.52924818029179\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St
Santa Cruz, CA 95060
In North America, native unionid mussels are imperiled due to factors such as habitat degradation, pollution, and invasive species. One of the most substantial threats is that posed by dreissenid mussels, which are invasive mussels that attach to hard substrates including unionid shells and can restrict movement and feeding of unionids. This dreissenid mussel biofouling of unionids varies spatially in large ecosystems, such as the Great Lakes, with some areas having low enough biofouling to form effective refugia where unionid mussels might persist. Here, we measured biofouling on mussels suspended in cages over the growing season (generally first week in June to last week of August) over 3 years in nearshore areas in Lake Erie (2014–2016), Lake Michigan (Grand Traverse Bay, 2015 and Green Bay, 2016), and Lake Huron (2015). Biofouling varied substantially by years within Lake Erie, with increasingly higher biofouling rates each year. Although dreissenid mussels are present throughout these lakes, we observed very low biofouling in Grand Traverse Bay (Lake Michigan) and Saginaw Bay (Lake Huron), with no dreissenid mussels in 8 of 9 sites across these two bays. Sampling in the rivermouth of the Fox River (Wisconsin) and the Maumee River (Ohio) both showed very high biofouling in areas adjacent to the outlet of these tributaries into Green Bay and Maumee Bay (Lake Erie), respectively. These watersheds are dominated by agriculture, and we would expect high growth of primary producers (i.e., mussel food) and primary consumers (unionids and zebra mussels) in these areas compared to the other sampled bays or the open waters of the Great Lakes.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9557","usgsCitation":"Larson, J.H., Bailey, S., and Evans, M.A., 2022, Biofouling of a unionid mussel by dreissenid mussels in nearshore zones of the Great Lakes: Ecology and Evolution, v. 12, no. 12, e9557, 11 p., https://doi.org/10.1002/ece3.9557.","productDescription":"e9557, 11 p.","ipdsId":"IP-119013","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":445674,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9557","text":"Publisher Index Page"},{"id":435596,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RL5BU4","text":"USGS data release","linkHelpText":"Biofouling and mussel growth from mussels deployed in Great Lakes embayments (2013-2016)"},{"id":424273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","otherGeospatial":"Grand Traverse Bay, Green Bay, Saginaw Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.61003738442467,\n 44.756931452092545\n ],\n [\n -85.52803993650261,\n 44.86389372309026\n ],\n [\n -85.48307359409397,\n 44.976271285175784\n ],\n [\n -85.60210214752885,\n 44.98188439355215\n ],\n [\n -85.66029388476409,\n 44.84139183490089\n ],\n [\n -85.61003738442467,\n 44.756931452092545\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.62607211384494,\n 44.13681310024381\n ],\n [\n -83.62607211384494,\n 43.89732876661685\n ],\n [\n -83.25259294281665,\n 43.89732876661685\n ],\n [\n -83.25259294281665,\n 44.13681310024381\n ],\n [\n -83.62607211384494,\n 44.13681310024381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.15455872752736,\n 44.31587096210228\n ],\n [\n -87.73632981104318,\n 44.66243758388063\n ],\n [\n -87.97406621030899,\n 44.83198507088326\n ],\n [\n -88.27876424634985,\n 44.388255504467054\n ],\n [\n -88.15455872752736,\n 44.31587096210228\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":891820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Sean 0000-0003-0361-7914 sbailey@usgs.gov","orcid":"https://orcid.org/0000-0003-0361-7914","contributorId":198515,"corporation":false,"usgs":true,"family":"Bailey","given":"Sean","email":"sbailey@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":891821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":149358,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":891822,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238830,"text":"ofr20221094 - 2022 - Compilation of existing data for sand resource studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California—Strategy for field studies and sand resource assessment","interactions":[],"lastModifiedDate":"2026-03-30T20:45:03.699515","indexId":"ofr20221094","displayToPublicDate":"2022-12-13T08:57:20","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":"2022-1094","displayTitle":"Compilation of Existing Data for Sand Resource Studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand Littoral Cell Study Areas along the Continental Shelf of California—Strategy for Field Studies and Sand Resource Assessment","title":"Compilation of existing data for sand resource studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California—Strategy for field studies and sand resource assessment","docAbstract":"The Sand Resources Project was established through collaborative agreements between the U.S. Geological Survey (USGS), the Bureau of Ocean Energy Management (BOEM), and the California Ocean Protection Council (OPC) with the purpose of evaluating sand and gravel resources in Federal and California State Waters for potential use in future beach-nourishment projects. Project partners worked in collaboration with California Coastal Sediment Management Workgroup (CSMW) members to define priority study areas for this work based on the potential for finding sand within the broader region and the needs for this sand as shown by beach erosion areas of concern in the adjacent littoral cells. The final study areas were defined to be (1) the San Francisco Littoral Cell, (2) the Oceanside Littoral Cell, and (3) the Silver Strand Littoral Cell.
A two-stage approach was used to assess the study areas. This report addresses the initial stage, which is a synthesis of the existing geophysical and sediment-sampling data in each area. This allowed for evaluations of the data availability, data gaps, and general patterns of sediment thickness and grain size. This report provides a description of the methods and results of this synthesis. The findings from this work were used to refine study area boundaries and develop sampling plans for stage two of the project.
Stage two of the project, the results of which will be published separately, will be the collection, processing, and synthesis of new data, including high-resolution geophysical surveys and sediment cores within the three study areas. The data collected will provide new information about the three study areas including sediment thickness, grain-size distributions, and total organic carbon. The description and results of stage two of the work is included in another USGS report (Warrick and others, 2022).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221094","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management and the State of California Ocean Protection Council","usgsCitation":"Warrick, J.A., Conrad, J.E., Papesh, A., Lorenson, T., and Sliter, R.W., 2022, Compilation of existing data for sand resource studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California—Strategy for field studies and sand resource assessment: U.S. Geological Survey Open-File Report 2022–1094, 21 p., https://doi.org/10.3133/ofr20221094.","productDescription":"vii, 21 p.","onlineOnly":"Y","ipdsId":"IP-142746","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":410369,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1094/ofr20221094.pdf","text":"Report","size":"6.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1094"},{"id":410368,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1094/coverthb.jpg"},{"id":501836,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113942.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"San Francisco, Oceanside, and Silver Strand littoral cell study areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.32845828309286,\n 37.818674364541195\n ],\n [\n -123.23587958971336,\n 37.818674364541195\n ],\n [\n -123.23587958971336,\n 36.79251013661299\n ],\n [\n -122.32845828309286,\n 36.79251013661299\n ],\n [\n -122.32845828309286,\n 37.818674364541195\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.13365815618306,\n 32.493660702226535\n ],\n [\n -117.13365815618306,\n 32.93175821350262\n ],\n [\n -117.80008333663935,\n 32.93175821350262\n ],\n [\n -117.80008333663935,\n 32.493660702226535\n ],\n [\n -117.13365815618306,\n 32.493660702226535\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.26754558635474,\n 33.52924818029179\n ],\n [\n -118.26754558635474,\n 33.1318551432673\n ],\n [\n -117.66427888472828,\n 33.1318551432673\n ],\n [\n -117.66427888472828,\n 33.52924818029179\n ],\n [\n -118.26754558635474,\n 33.52924818029179\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St
Santa Cruz, CA 95060
Accurate and complete data on fire locations and burned areas are needed to quantify trends and patterns of fire occurrence, characterize drivers of fire, project future fire pattern behavior, and help with assessments of fire effects on natural and social systems. The Landsat Collection 2 Level-3 Burned Area science product is designed to identify burned areas across all ecosystems (for example, forests, shrublands, and grasslands) for Landsat 4–9 data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223083","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Burned Area science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3083, 2 p., https://doi.org/10.3133/fs20223083.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139624","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":418240,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3083/coverthb2.jpg"},{"id":410296,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418243,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3083/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":418241,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3083/fs20223083.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3038"}],"edition":"Version 1.0: December 13, 2022; Version 1.1: June 20, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product provides raster data that represent surface water inundation per pixel in Landsat 4–9 imagery. The Collection 2 Dynamic Surface Water Extent science product contains six acquisition-based raster products relating to surface water. Surface water extent is modulated by weather and climate, stream network hydrology, and geological processes such as isostatic rebound. Land use, ecosystem and service management, and overall water management also are affected by changes in surface water extent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223084","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3084, 2 p., https://doi.org/10.3133/fs20223084.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139625","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":410308,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418247,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3084/coverthb2.jpg"},{"id":418249,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3084/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":418248,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3084/fs20223084.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3084"}],"edition":"Version 1.0: December 13, 2022; Version 1.1: June 21, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Many organizations in the Delaware River Basin (DRB) monitor surface-water quality for regulatory, scientific, and decision-making purposes. In support of these purposes, over 260,000 water-quality records provided by 8 different organizations were compiled, screened, and used to generate water-quality trends in the DRB. These trends, for periods of record that end in 2018, were generated for 124 sites and up to 16 constituents using 2 trend methods: the Seasonal Kendall Test and the Weighted Regressions on Time, Discharge, and Season model. Seasonal Kendall Tests were performed on all water-quality records to detect monotonic trends in concentration over the period of record and for as many as four additional trend periods (1978–2018, 1998–2018, 2003–18, and 2008–18). The Weighted Regressions on Time, Discharge, and Season model was applied to water-quality records that passed more stringent screening criteria and was used to detect monontonic and nonmonotonic trends, account for variations in streamflow, and estimate annual concentrations. These two trend methods produced different trend directions less than 1 percent of the time, illustrating general agreement between the methods despite the different approaches and data input requirements. Overall, the changes in concentration for salinity constituents (specific conductance and total dissolved solids), chloride, and sodium were increases; those increases were some of the largest changes observed in the basin, and they occurred at faster rates over time. Total dissolved solids concentration trends at 4 of the 60 sites increased from below to above the level of concern threshold (a secondary drinking water threshold) over the period of record, indicating potentially meaningful degradation in water quality. Nutrient constituent (ammonia, nitrate, orthophosphate, total nitrogen, and total phosphorus) concentrations tended to decrease over the period of record, although fewer sites had significant trends and the changes in concentration were smaller compared to the salinity constituents. Total nitrogen and total phosphorus were the only nutrient constituents to have decreasing concentration trends that crossed from above to below the level of concern threshold, U.S. Environmental Protection Agency (EPA) ecoregional nutrient criteria, (EPA, undated c). This finding indicates water-quality improvement at sites with these trends (nine sites with total nitrogen trends and one site with a total phosphorus trend), although many sites were still in exceedance of the level of concern. Trends for total suspended solids and some major ions (calcium, magnesium, potassium) were largely nonsignificant or variable between sites, with no prevalent patterns across the DRB; however, sulfate concentrations decreased at most sites. Cumulative land-surface change within each watershed had a strong positive relation with changes in water-quality concentrations for the salinity constituents and most major ions, but not for the other constituents, indicating that land-surface changes are related to the sources and transport of these constituents. Investigating long-term trends (a decade or longer) in water quality can help the DRB water management community quantify the success of management practices and identify potential threats to water availability.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225097","programNote":"Water Availability and Use Science Program, National Water Quality Program","usgsCitation":"Shoda, M.E., and Murphy, J.C., 2022, Water-quality trends in the Delaware River Basin calculated using multisource data and two methods for trend periods ending in 2018: U.S. Geological Survey Scientific Investigations Report 2022–5097, 60 p., https://doi.org/10.3133/sir20225097.","productDescription":"Report v, 60 p.; 2 Data Releases","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122487","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":410210,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KMWNJ5","text":"USGS data release","linkHelpText":"Water-quality trends for rivers and streams in the Delaware River Basin using Weighted Regressions on Time, Discharge, and Season (WRTDS) models, Seasonal Kendall Trend (SKT) tests, and multisource data, water years 1978–2018"},{"id":410211,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PX8LZO","text":"USGS data release","linkHelpText":"Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin"},{"id":410212,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5097/sir20225097.XML"},{"id":410208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5097/sir20225097.pdf","text":"Report","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5097"},{"id":410207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5097/coverthb.jpg"},{"id":433058,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233014","text":"USGS Fact Sheet 2023-3014","linkFileType":{"id":5,"text":"html"}},{"id":410209,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225097/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5097"},{"id":410213,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5097/images/"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.68904449140135,\n 38.5435194562952\n ],\n [\n -75.2278145853991,\n 38.47477347574127\n ],\n [\n -75.03014462568353,\n 38.95461543352394\n ],\n [\n -74.87640132368297,\n 39.566811726970116\n ],\n [\n -74.28339144453736,\n 40.70854805354401\n ],\n [\n -74.1735748002507,\n 41.56866114554592\n ],\n [\n -73.77823488082065,\n 42.41747384003048\n ],\n [\n -74.21750145796516,\n 42.64406475670245\n ],\n [\n -74.96425463911187,\n 42.44989429480822\n ],\n [\n -75.44744787397136,\n 42.043433704426945\n ],\n [\n -75.88671445111639,\n 41.486448364247764\n ],\n [\n -76.19420105511806,\n 40.79174109891525\n ],\n [\n -76.23812771283252,\n 40.37473571760768\n ],\n [\n -76.01849442425974,\n 39.702128532888\n ],\n [\n -75.75493447797302,\n 39.14224249538836\n ],\n [\n -75.68904449140135,\n 38.5435194562952\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Program Coordinator
Water Availability and Use Science Program
National Water Quality Program
U.S. Geological Survey
Geologic maps are the fundamental building blocks of surface and subsurface three-dimensional geologic framework models of the Earth’s crust. However, as the production and availability of geologic map databases continues to increase, inconsistent data models and the lack of synthesized, national geologic map data at scales appropriate for informed decision making negatively affect the functional integration of geologic map data with other national datasets. The Geologic Map Schema (GeMS) is the publication and archive database standard for geologic map data funded by the U.S. Geological Survey National Cooperative Geologic Mapping Program, and standardizes the organization and content of a single map database. However, synthesizing multiple databases into a seamless geologic map database creates a different set of challenges and database needs than GeMS was designed to accommodate. The Seamless Integrated Geologic Mapping (SIGMa) extension is designed to expand the capabilities of GeMS by enabling integration of map-based geoscience data. In particular, the SIGMa extension enables capturing a diverse and ever-changing set of map units, produced by many contributors operating independently, and by incremental and noncontiguous assembly and publication. Feature-level metadata fields allow data sources and digital compilation methods to be attributed separately and a relational structure is designed to support the link between data sources and features attributed with multiple data sources. Instead of paragraph-style map-unit descriptions that can be highly inconsistent, SIGMa parses fundamental map-unit attributes, including material, genetic process, and age, into thematically specific fields. The SIGMa extension uses a hierarchical map-unit organization to facilitate a dynamic and evolving, formation-level stratigraphic framework. The hierarchy is developed around geologic provinces that represent temporally restricted geologic events, processes, and settings. Geologic provinces can include magmatic events, depositional settings associated with tectonic processes or stable continental margins, and processes that are actively shaping the modern landscape. A geologic province hierarchy places map units into a geologic context at subregional to continental scales and provides the flexibility to support incremental assembly of the stratigraphy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225115","usgsCitation":"Turner, K.J., Workman, J.B., Colgan, J.P., Gilmer, A.K., Berry, M.E., Johnstone, S.A., Warrell, K.F., Dechesne, M., VanSistine, D.P., Thompson, R.A., Hudson, A.M., Zellman, K.L., Sweetkind, D., and Ruleman, C.A., 2022, The Seamless Integrated Geologic Mapping (SIGMa) extension to the Geologic Map Schema (GeMS): U.S. Geological Survey Scientific Investigations Report 2022–5115, 33 p., https://doi.org/10.3133/sir20225115.","productDescription":"vii, 33 p.","onlineOnly":"Y","ipdsId":"IP-125234","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":410653,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225115/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5115"},{"id":410248,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5115/sir20225115.xml"},{"id":410247,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5115/images"},{"id":410246,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5115/sir20225115.pdf","text":"Report","size":"3.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5115"},{"id":410245,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5115/coverthb.jpg"}],"contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046