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A comprehensive understanding of fish populations and their interactions is the cornerstone of modern fishery management and the basis for Fish Community Goals and Objectives for Lake Erie (Ryan et al. 2003). This report is responsive to U.S. Geological Survey (USGS) obligations via Memorandum of Understanding (MOU) with the Great Lakes Council of Lake Committees (CLC) to provide scientific information in support of fishery management. Goals for the USGS Great Lakes Deepwater Fish Assessment and Ecological Studies in 2019 were to monitor long-term changes in the fish community and population dynamics of key fishes of interest to management agencies. Specific to Lake Erie, expectations of this agreement were sustained investigations of native percids, forage (prey) fish populations, and Lake Trout.

Our 2019 deepwater program operations began in April and concluded in December, and utilized trawl, gillnet, hydroacoustic, lower trophic sampling, and telemetry methods. This work resulted in 88 bottom trawls covering 65 ha of lake-bottom and catching 24,140 fish totaling 3,622 kg during three separate trawl surveys in the West and Central basins of Lake Erie. Overnight gillnet sets (n=44) for cold water species were performed at 42 unique locations in the West and East basins of Lake Erie. A total of 8.0 km of gillnet was deployed during these surveys, which caught 286 fish, 114 of which were native coldwater species: Lake Trout, Burbot, and Lake Whitefish. USGS hydroacoustic surveys in 2019 produced 240 km of transects, and lower trophic sampling provided data from zooplankton samples (n=21) and water quality profiles (n=21) to populate a database maintained by the Ontario Ministry of Natural Resources and Forestry (OMNRF), Ohio Division of Natural Resources (ODNR), Michigan Division of Natural Resources (MDNR), Pennsylvania Fish and Boat Commission (PFBC), and New York State Department of Environmental Conservation (NYSDEC). USGS also assisted CLC member agencies with deployment and maintenance of the Great Lakes Acoustic Telemetry Observation System (GLATOS) throughout all three Lake Erie sub-basins, supporting multiple coordinated telemetry investigations.

In 2019, Lake Trout investigations included annual gill net surveys and acoustic telemetry of spawning migration and habitat use in coordination with OMNRF, NYSDEC, and PFBC. Results from Lake Trout investigations were reported in the Coldwater Task Group annual report to the Great Lakes Fishery Commission (GLFC) and the CLC (Coldwater Task Group 2020). Likewise, interagency forage fish assessments conducted with hydroacoustics were summarized and reported in the Forage Task Group annual report (Forage Task Group 2020).

This report presents biomass-based summaries of fish communities in western Lake Erie derived from USGS bottom trawl surveys conducted from 2013 to 2019 during June and September. The survey design provided temporal and spatial coverage that did not exist in the historic interagency trawl database, and thus complemented the August ODNR-OMNRF effort to reinforce stock assessments with more robust data. Analyses herein evaluated trends in: total biomass, abundance of dominant predator and forage species, non-native species composition, biodiversity and community structure. Data from this effort can be explored interactively online (https://lebs.shinyapps.io/western-basin/), and are accessible for download (https://doi.org/10.5066/P9LL6YOR, Keretz et al. 2020). Annual survey data are added to these sources as the data become available.

","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Keretz, K.R., Kocovsky, P., Kraus, R., and Schmitt, J., 2020, Fisheries research and monitoring activities of the Lake Erie Biological Station, 2019: Lake Erie Biological Station Annual Report, 12 p.","productDescription":"12 p.","ipdsId":"IP-116726","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385155,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/lake-erie-committee.php"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.4796142578125,\n 41.701627343789205\n ],\n [\n -83.056640625,\n 41.58668835697237\n ],\n [\n -82.9742431640625,\n 41.52091689636249\n ],\n [\n -83.0401611328125,\n 41.422134246213616\n ],\n [\n -82.9083251953125,\n 41.40153558289846\n ],\n [\n -82.79296874999999,\n 41.463311976686235\n ],\n [\n -82.4908447265625,\n 41.380930388318\n ],\n [\n -81.990966796875,\n 41.50446357504803\n ],\n [\n -81.7108154296875,\n 41.48800607185427\n ],\n [\n -81.3372802734375,\n 41.72623044860004\n ],\n [\n -80.48583984375,\n 41.983994270935625\n ],\n [\n -79.46411132812499,\n 42.407234661551875\n ],\n [\n -79.200439453125,\n 42.54093947168063\n ],\n [\n -78.848876953125,\n 42.79943131987838\n ],\n [\n -78.9202880859375,\n 42.879989517714826\n ],\n [\n -79.332275390625,\n 42.871938424448466\n ],\n [\n -79.4696044921875,\n 42.871938424448466\n ],\n [\n -79.7222900390625,\n 42.84375132629021\n ],\n [\n -80.22766113281249,\n 42.783307077249624\n ],\n [\n -80.46936035156249,\n 42.577354839557856\n ],\n [\n -80.85937499999999,\n 42.65012181368022\n ],\n [\n -81.30432128906249,\n 42.66628070564928\n ],\n [\n -81.5185546875,\n 42.5733097370664\n ],\n [\n -81.8096923828125,\n 42.382894009614034\n ],\n [\n -81.9140625,\n 42.285437007491545\n ],\n [\n -81.9305419921875,\n 42.256983603767466\n ],\n [\n -82.02392578125,\n 42.256983603767466\n ],\n [\n -82.430419921875,\n 42.11859868281563\n ],\n [\n -82.50732421875,\n 42.004407212963585\n ],\n [\n -82.5238037109375,\n 41.92271616673922\n ],\n [\n -82.60620117187499,\n 42.02481360781777\n ],\n [\n -82.72705078125,\n 42.02889410108475\n ],\n [\n -82.9248046875,\n 41.98807738309159\n ],\n [\n -83.045654296875,\n 42.049292638686836\n ],\n [\n -83.12805175781249,\n 42.1104489601222\n ],\n [\n -83.155517578125,\n 42.15118709351198\n ],\n [\n -83.22143554687499,\n 42.05337156043361\n ],\n [\n -83.177490234375,\n 42.004407212963585\n ],\n [\n -83.25439453125,\n 41.95540515378059\n ],\n [\n -83.4356689453125,\n 41.828642001860544\n ],\n [\n -83.4906005859375,\n 41.72623044860004\n ],\n [\n -83.4796142578125,\n 41.701627343789205\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keretz, Kevin R. 0000-0002-4808-8350 kkeretz@usgs.gov","orcid":"https://orcid.org/0000-0002-4808-8350","contributorId":5859,"corporation":false,"usgs":true,"family":"Keretz","given":"Kevin","email":"kkeretz@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":814367,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kocovsky, Patrick 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814369,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224928,"text":"70224928 - 2020 - Research in the refuge constraints to restoring diverse forest ecosystems at Hakalau","interactions":[],"lastModifiedDate":"2021-10-05T13:09:48.371083","indexId":"70224928","displayToPublicDate":"2020-04-30T08:08:00","publicationYear":"2020","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"title":"Research in the refuge constraints to restoring diverse forest ecosystems at Hakalau","docAbstract":"

No abstract available. 

","largerWorkType":{"id":25,"text":"Newsletter"},"largerWorkTitle":"Friends of Hakalau Forst National Wildlife Refuge Newsletter","largerWorkSubtype":{"id":30,"text":"Newsletter"},"language":"English","publisher":"Friends of Hakalau Forst National Wildlife Refuge","usgsCitation":"Yelenik, S.G., Rose, E., Paxton, E.H., Rehm, E.M., and D'Antonio, C., 2020, Research in the refuge constraints to restoring diverse forest ecosystems at Hakalau.","ipdsId":"IP-134024","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":390238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":390229,"type":{"id":15,"text":"Index Page"},"url":"https://myemail.constantcontact.com/Spring-2020-Newsletter---Friends-of-Hakalau-Forest-NWR.html?soid=1131173118925&aid=kgVVesY7TaQ"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hakalau Forest National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.40023803710938,\n 19.694314241825747\n ],\n [\n -155.07888793945312,\n 19.694314241825747\n ],\n [\n -155.07888793945312,\n 20.019806765982878\n ],\n [\n -155.40023803710938,\n 20.019806765982878\n ],\n [\n -155.40023803710938,\n 19.694314241825747\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":256836,"corporation":false,"usgs":false,"family":"Yelenik","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":51875,"text":"formerly U.S. Geological Survey; currently Rocky Mountain Research Station, U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":824665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rose, Eli T.","contributorId":145699,"corporation":false,"usgs":false,"family":"Rose","given":"Eli T.","affiliations":[],"preferred":false,"id":824666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":824667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rehm, Evan M","contributorId":216487,"corporation":false,"usgs":false,"family":"Rehm","given":"Evan","email":"","middleInitial":"M","affiliations":[{"id":39457,"text":"University of California at Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":824668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"D'Antonio, Carla M.","contributorId":27992,"corporation":false,"usgs":false,"family":"D'Antonio","given":"Carla M.","affiliations":[],"preferred":false,"id":824669,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210390,"text":"70210390 - 2020 - Mineralogy and lithology of the Upper Cretaceous Niobrara Formation determined by hyperspectral core imaging","interactions":[],"lastModifiedDate":"2020-06-02T13:08:40.384665","indexId":"70210390","displayToPublicDate":"2020-04-30T08:03:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2789,"text":"Mountain Geologist","active":true,"publicationSubtype":{"id":10}},"title":"Mineralogy and lithology of the Upper Cretaceous Niobrara Formation determined by hyperspectral core imaging","docAbstract":"Sections of the Upper Cretaceous (Coniacian to Campanian) Niobrara Formation in two cores from Kansas and Colorado, the Amoco Rebecca Bounds and USGS Portland 1, respectively, were examined by hyperspectral core imaging and analysis. A spectral imaging system combining high-resolution photography (50 μm), 3D laser profiling (20 μm), and near-visible + short-wave infrared reflectance spectroscopy (wavelengths from 450 to 2500 nm, 500 μm pixel size) was applied to these cores to provide spectral and textural data facilitating creation of continuous mineral and lithology class maps. In addition, compositing of pixel-based results to group pixels to create mineralogical and lithological logs (0.5 ft resolution) was performed to facilitate comparisons to other geochemical datasets. The results show general correspondence in trends identified by previous geochemistry studies, with some exceptions due to instrumental limitations related to low reflectance of some rock intervals and the limited range of infrared wavelengths examined. This study provides a cursory overview of an extensive dataset meant to demonstrate the utility of hyperspectral core scanning to studies of mudrocks in petroleum systems as well as the kinds of information this technique can provide for detailed examination of stratigraphic features in sedimentary systems more generally.","language":"English","publisher":"Rocky Mountain Association of Geologists","doi":"10.31582/rmag.mg.57.2.121","usgsCitation":"Birdwell, J.E., Fontenot, L.C., and Martini, B., 2020, Mineralogy and lithology of the Upper Cretaceous Niobrara Formation determined by hyperspectral core imaging: Mountain Geologist, v. 57, no. 2, p. 121-143, https://doi.org/10.31582/rmag.mg.57.2.121.","productDescription":"23 p.","startPage":"121","endPage":"143","ipdsId":"IP-115802","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":375241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127.08984375000001,\n 46.255846818480315\n ],\n [\n -121.55273437499999,\n 32.24997445586331\n ],\n [\n -106.5234375,\n 30.29701788337205\n ],\n [\n -99.931640625,\n 25.3241665257384\n ],\n [\n -94.833984375,\n 25.958044673317843\n ],\n [\n -94.833984375,\n 54.826007999094955\n ],\n [\n -109.6875,\n 58.6769376725869\n ],\n [\n -121.728515625,\n 60.19615576604439\n ],\n [\n -139.658203125,\n 61.312451574838214\n ],\n [\n -135.791015625,\n 54.826007999094955\n ],\n [\n -127.08984375000001,\n 46.255846818480315\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":790140,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fontenot, Lionel C.","contributorId":225058,"corporation":false,"usgs":false,"family":"Fontenot","given":"Lionel","email":"","middleInitial":"C.","affiliations":[{"id":41029,"text":"Corescan Pty. Ltd., Ascot, WA Australia","active":true,"usgs":false}],"preferred":false,"id":790141,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martini, Brigette","contributorId":225059,"corporation":false,"usgs":false,"family":"Martini","given":"Brigette","email":"","affiliations":[{"id":41030,"text":"North Shore Consulting","active":true,"usgs":false}],"preferred":false,"id":790142,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210026,"text":"70210026 - 2020 - Parsing complex terrain controls on mountain glacier response to climate forcing","interactions":[],"lastModifiedDate":"2020-08-06T19:14:26.872296","indexId":"70210026","displayToPublicDate":"2020-04-30T07:41:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Parsing complex terrain controls on mountain glacier response to climate forcing","docAbstract":"Glaciers are a key indicator of changing climate in the high mountain landscape.\nGlacier variations across a mountain range are ultimately driven by regional climate\nforcing. However, changes also reflect local, topographically driven processes such as\nsnow avalanching, snow wind-drifting, and radiation shading as well as the initial\nglacier conditions such as hypsometry and ice thickness. Here we assess the role of\nthese various terrain influences on change to Little Ice Age (LIA) glaciers in Glacier\nNational Park, U.S.A . With available data for LIA and modern glacier areas, we\nestimate glacier volumes using simple ice flow assumptions, and topographically\ndriven processes using terrain proxies. At the LIA glacial maxima there were 82\nglaciers larger than 0.1 km 2 ranging from 0.11 to 4.97 km 2 . Over the course of the\n20 th century, every single LIA glacier decreased in area and 60% (49 glaciers)\ndiminished to below the 0.1 km 2 threshold. Glaciers with large initial area (>1.5 km\n2 ) at the end of LIA persisted. Within the intermediate size class (0.5 km 2 < area <\n1.5 km 2 ), LIA glacier persistence is poorly explained by initial glacier volume, ice\nthickness, or elevation. Instead, wind exposure is an important explanatory factor.\nOur analysis demonstrates the complex response of cirque glaciers to post-LIA climate\nchange in this region: individual glaciers have not necessarily undergone equivalent\nand synchronous change. Nevertheless, that all glaciers in this mountain range\nexperienced retreat demonstrates that local processes mediated adjustments of some\nglaciers, but completely decoupled none from the regional climate forcing.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloplacha.2020.103209","usgsCitation":"Florentine, C., Harper, J.T., and Fagre, D., 2020, Parsing complex terrain controls on mountain glacier response to climate forcing: Global and Planetary Change, v. 191, 103209, 13 p., https://doi.org/10.1016/j.gloplacha.2020.103209.","productDescription":"103209, 13 p.","ipdsId":"IP-112133","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":456906,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gloplacha.2020.103209","text":"Publisher Index Page"},{"id":374649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.620361328125,\n 48.28319289548349\n ],\n [\n -112.96142578125,\n 48.28319289548349\n ],\n [\n -112.96142578125,\n 49.005447494058096\n ],\n [\n -114.620361328125,\n 49.005447494058096\n ],\n [\n -114.620361328125,\n 48.28319289548349\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"191","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Florentine, Caitlyn Elizabeth 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":224631,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn Elizabeth","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":788858,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harper, Joel T.","contributorId":173392,"corporation":false,"usgs":false,"family":"Harper","given":"Joel","email":"","middleInitial":"T.","affiliations":[{"id":16951,"text":"Department of Geosciences, University of Montana, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":788859,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagre, Daniel B. 0000-0001-8552-9461","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":224632,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":788860,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70219005,"text":"70219005 - 2020 - Automated location correction and spot height generation for named summits in the coterminous United States","interactions":[],"lastModifiedDate":"2021-03-19T12:31:02.918089","indexId":"70219005","displayToPublicDate":"2020-04-30T07:27:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2035,"text":"International Journal of Digital Earth","active":true,"publicationSubtype":{"id":10}},"title":"Automated location correction and spot height generation for named summits in the coterminous United States","docAbstract":"

Spot elevations published on historical U.S. Geological Survey topographic maps were established as needed to enhance information imparted by the quadrangle’s contours. In addition to other features, labels were routinely placed on mountain summits. While some elevations were established through field survey triangulation, many were computed during photogrammetric stereo-compilation. Today, Global Navigation Satellite System (GNSS) receivers have replaced expensive triangulation methods. However, since GNSS measurements require visiting the feature location, a national dataset containing high-accuracy spot elevations has not yet been created. Consequently, modern U.S. Topo maps are devoid of mountain peak or other spot elevations. Still, topographic map users continue to demand the display of spot heights. Therefore, a pilot study was conducted to evaluate the feasibility of automatically generating elevation values at named U.S. summits using available elevation data. The devised method uses an uphill stepping technique to find the most likely highest point in subsequently higher-resolution elevation models. Resulting elevation values are compared to other published sources. Results from 196 summits indicate that values derived from lidar are generally higher, whereas those populated from the one-third arc-second USGS Seamless 3DEP elevation dataset are generally lower. A thorough understanding of these relationships require the evaluation of more points.

","language":"English","publisher":"Taylor & Francis","doi":"10.1080/17538947.2020.1754936","usgsCitation":"Arundel, S., and Sinha, G., 2020, Automated location correction and spot height generation for named summits in the coterminous United States: International Journal of Digital Earth, v. 13, no. 12, p. 1570-1584, https://doi.org/10.1080/17538947.2020.1754936.","productDescription":"15 p.","startPage":"1570","endPage":"1584","ipdsId":"IP-112848","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":499919,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/ef9864c7c44e489185483ba722a1b09b","text":"External Repository"},{"id":384500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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(CEGIS)","active":true,"usgs":true}],"preferred":true,"id":812444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sinha, Gaurav","contributorId":220051,"corporation":false,"usgs":false,"family":"Sinha","given":"Gaurav","email":"","affiliations":[{"id":12807,"text":"Ohio University","active":true,"usgs":false}],"preferred":false,"id":812445,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209818,"text":"70209818 - 2020 - Green turtle mitochondrial microsatellites indicate finer-scale natal homing to isolated islands than to continental nesting sites","interactions":[],"lastModifiedDate":"2020-06-12T17:45:25.808564","indexId":"70209818","displayToPublicDate":"2020-04-29T12:40:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2636,"text":"MEPS","active":true,"publicationSubtype":{"id":10}},"title":"Green turtle mitochondrial microsatellites indicate finer-scale natal homing to isolated islands than to continental nesting sites","docAbstract":"

 In highly mobile philopatric species, defining the scale of natal homing is fundamental to characterizing population dynamics and effectively managing distinct populations. Genetic tools have provided evidence of regional natal philopatry in marine turtles, but extensive sharing of maternally inherited mitochondrial control region (CR) haplotypes within regions (<500 km) often impedes identification of population boundaries. Previous CR-based analyses of Florida (USA) green turtle Chelonia mydas nesting sites detected at least 2 populations, but the ubiquity of haplotype CM-A3.1 among southern rookeries decreased the power to detect differentiation. We reassessed population structure by sequencing the mitochondrial microsatellite (short tandem repeat, mtSTR) in 786 samples from 11 nesting sites spanning 700 km from Canaveral National Seashore through Dry Tortugas National Park. The mtSTR marker subdivided CM-A3.1 into 12 haplotypes that were structured among rookeries, demonstrating independent female recruitment into the Dry Tortugas and Marquesas Keys nesting populations. Combined haplotypes provided support for recognition of at least 4 management units in Florida: (1) central eastern Florida, (2) southeastern Florida, (3) Key West National Wildlife Refuge, and (4) Dry Tortugas National Park. Recapture data indicated female nesting dispersal between islands <15 km apart, but haplotype frequencies demonstrated discrete natal homing to island groups separated by 70 km. These isolated insular rookeries may be more vulnerable to climate change-mediated nesting habitat instability than those along continental coasts and should be monitored more consistently to characterize population status. Broader application of the mtSTR markers holds great promise in improving resolution of stock structure and migratory connectivity for green turtles globally.

","language":"English","publisher":"Inter-Research Science Press","doi":"10.3354/meps13348","usgsCitation":"Shamblin, B.M., Hart, K., Martin, K.J., Ceriani, S.A., Bagley, D.A., Mansfield, K.L., Ehrhart, L.M., and Nairn, C.J., 2020, Green turtle mitochondrial microsatellites indicate finer-scale natal homing to isolated islands than to continental nesting sites: MEPS, v. 643, p. 159-171, https://doi.org/10.3354/meps13348.","productDescription":"13 p.","startPage":"159","endPage":"171","ipdsId":"IP-112808","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":375563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Dry Tortugas National Park, Key West National Wildlife Refuge, Marquesas Keys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.10333251953125,\n 24.35960758535081\n ],\n [\n -82.64190673828125,\n 24.35960758535081\n ],\n [\n -82.64190673828125,\n 24.79670834894575\n ],\n [\n -83.10333251953125,\n 24.79670834894575\n ],\n [\n -83.10333251953125,\n 24.35960758535081\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.9415283203125,\n 24.171813716251364\n ],\n [\n -80.04638671875,\n 24.171813716251364\n ],\n [\n -80.04638671875,\n 26.377106813670053\n ],\n [\n -81.9415283203125,\n 26.377106813670053\n ],\n [\n -81.9415283203125,\n 24.171813716251364\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.28759765625,\n 24.404636766948936\n ],\n [\n -81.93603515625,\n 24.404636766948936\n ],\n [\n -81.93603515625,\n 24.65076163520743\n ],\n [\n -82.28759765625,\n 24.65076163520743\n ],\n [\n -82.28759765625,\n 24.404636766948936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"643","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shamblin, Brian M.","contributorId":138897,"corporation":false,"usgs":false,"family":"Shamblin","given":"Brian","email":"","middleInitial":"M.","affiliations":[{"id":12573,"text":"Daniel B. Warnell School of Forestry and Natural Resource, Athens Georiga","active":true,"usgs":false}],"preferred":false,"id":788149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":214952,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":788150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Kelly J.","contributorId":168557,"corporation":false,"usgs":false,"family":"Martin","given":"Kelly","email":"","middleInitial":"J.","affiliations":[{"id":25334,"text":"Loggerhead Marinelife Center, 14200 U.S. Highway 1, Juno Beach, Florida, 33408, USA","active":true,"usgs":false}],"preferred":false,"id":788151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ceriani, Simona A.","contributorId":224398,"corporation":false,"usgs":false,"family":"Ceriani","given":"Simona","email":"","middleInitial":"A.","affiliations":[{"id":40873,"text":"Florida Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":788152,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bagley, Dean A.","contributorId":138898,"corporation":false,"usgs":false,"family":"Bagley","given":"Dean","email":"","middleInitial":"A.","affiliations":[{"id":12574,"text":"Department of Biology , University of Central Florida, Orlando, Florida","active":true,"usgs":false}],"preferred":false,"id":788153,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mansfield, Katherine L.","contributorId":138887,"corporation":false,"usgs":false,"family":"Mansfield","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":12564,"text":"Department of Biology, University of Central Florida","active":true,"usgs":false}],"preferred":false,"id":788154,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ehrhart, Llewellyn M.","contributorId":138899,"corporation":false,"usgs":false,"family":"Ehrhart","given":"Llewellyn","email":"","middleInitial":"M.","affiliations":[{"id":12574,"text":"Department of Biology , University of Central Florida, Orlando, Florida","active":true,"usgs":false}],"preferred":false,"id":788155,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nairn, Campbell J.","contributorId":138908,"corporation":false,"usgs":false,"family":"Nairn","given":"Campbell","email":"","middleInitial":"J.","affiliations":[{"id":12573,"text":"Daniel B. Warnell School of Forestry and Natural Resource, Athens Georiga","active":true,"usgs":false}],"preferred":false,"id":788156,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70212746,"text":"70212746 - 2020 - Use of strong habitat–abundance relationships in assessing population status of cryptic fishes: An example using the Harlequin Darter","interactions":[],"lastModifiedDate":"2020-08-27T17:09:00.380248","indexId":"70212746","displayToPublicDate":"2020-04-29T12:00:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Use of strong habitat–abundance relationships in assessing population status of cryptic fishes: An example using the Harlequin Darter","docAbstract":"

Understanding trends in abundance is important to fisheries conservation, but techniques for estimating streamwide abundance of cryptic fishes with strong habitat–abundance relationships are not well established and need further development. We developed techniques for addressing this need using the Harlequin Darter Etheostoma histrio, a small, cryptic freshwater fish associated with submerged wood in streams. Our objectives were to (1) determine how Harlequin Darter abundance and the amount of submerged wood were related at sampled sites and (2) use this relationship to estimate Harlequin Darter abundance at unsampled sites and extrapolate Harlequin Darter abundance estimates and associated uncertainty streamwide. We conducted a mark–recapture study to estimate abundance of Harlequin Darters in 25‐m stream reaches at 24 sites in Big Escambia Creek (BEC) and 18 sites in Pine Barren Creek (PBC) (Escambia River tributaries in northwestern Florida). The number of wood pieces (submerged wood ≥1.5 m long and ≥0.25 m in circumference) in both creeks was counted and mapped using side‐scan sonar and a geographic information system. Harlequin Darter and wood data were used in a Bayesian multinomial mixture model to estimate site abundance of Harlequin Darters, to determine the relationship between wood and Harlequin Darter abundance, and to extrapolate Harlequin Darter abundance streamwide. We found a positive relationship between wood and Harlequin Darter abundance in both creeks, and there were more wood pieces in PBC than in BEC. Streamwide abundance of Harlequin Darters was greater in PBC than in BEC. The extrapolated streamwide abundance estimates were 9,369 Harlequin Darters (95% credible interval = 6,668–13,402) in PBC and 7,439 Harlequin Darters (95% credible interval = 4,493–11,226) in BEC. Our methods effectively estimated abundance of a small, cryptic fish that uses complex wood habitat. In addition, our findings may assist in the conservation of the Harlequin Darter.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10231","usgsCitation":"Holcomb, K.M., Schueller, P., Jelks, H.L., Knight, J.R., and Allen, M., 2020, Use of strong habitat–abundance relationships in assessing population status of cryptic fishes: An example using the Harlequin Darter: Transactions of the American Fisheries Society, v. 149, no. 3, p. 320-334, https://doi.org/10.1002/tafs.10231.","productDescription":"15 p.","startPage":"320","endPage":"334","ipdsId":"IP-107850","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":377944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Big Escambia Creek, Escambia River, Pine Barren Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.37220764160156,\n 30.537425073997134\n ],\n [\n -87.09548950195312,\n 30.537425073997134\n ],\n [\n -87.09548950195312,\n 30.994680105042487\n ],\n [\n -87.37220764160156,\n 30.994680105042487\n ],\n [\n -87.37220764160156,\n 30.537425073997134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Holcomb, Kathryn M","contributorId":239617,"corporation":false,"usgs":false,"family":"Holcomb","given":"Kathryn","email":"","middleInitial":"M","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":797405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schueller, Paul","contributorId":181829,"corporation":false,"usgs":false,"family":"Schueller","given":"Paul","email":"","affiliations":[],"preferred":false,"id":797406,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jelks, Howard L. 0000-0002-0672-6297 hjelks@usgs.gov","orcid":"https://orcid.org/0000-0002-0672-6297","contributorId":168997,"corporation":false,"usgs":true,"family":"Jelks","given":"Howard","email":"hjelks@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":797407,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, John R","contributorId":239619,"corporation":false,"usgs":false,"family":"Knight","given":"John","email":"","middleInitial":"R","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":797408,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allen, Micheal S","contributorId":239622,"corporation":false,"usgs":false,"family":"Allen","given":"Micheal S","affiliations":[{"id":47938,"text":"Fisheries and Aquatic Sciences Program, University of Florida","active":true,"usgs":false}],"preferred":false,"id":797409,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211883,"text":"70211883 - 2020 - Comparison of underwater video with electrofishing and dive‐counts for stream fish abundance estimation","interactions":[],"lastModifiedDate":"2021-02-03T23:17:27.387819","indexId":"70211883","displayToPublicDate":"2020-04-29T09:35:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of underwater video with electrofishing and dive‐counts for stream fish abundance estimation","docAbstract":"

Advances in video technology enable new strategies for stream fish research. We compared juvenile (age‐0) and adult (age 1+) Brook Trout Salvelinus fontinalis abundance estimates from underwater video with backpack electrofishing and dive‐count methods across a series of stream pools in Shenandoah National Park, Virginia (n = 41). Video methods estimated greater mean abundance of adult trout than 1‐pass electrofishing but were not different than 3‐pass electrofishing or dive‐count methods in this regard. In contrast, videos underestimated abundance of juvenile trout, and we suggest this is because predator avoidance‐behaviors by juvenile trout limit their use of microhabitat locations visible to cameras. Integrated abundance estimates from 2 cameras increased correspondence to comparison methods relative to single cameras, demonstrating the importance of an expanded field of view for video sampling in streams. Geomorphic features helped explain method‐wise differences: more adult trout were estimated with video than 3‐pass electrofishing as riffle crest depth and boulder composition increased, indicating habitat associations with trout escapement from electrofishing. Our results demonstrated that video techniques can provide a robust alternative or supplement to traditional methods for estimating adult trout abundance in stream pools.

","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10245","usgsCitation":"Hitt, N.P., Rogers, K.M., Snyder, C.D., and Dolloff, C.A., 2020, Comparison of underwater video with electrofishing and dive‐counts for stream fish abundance estimation: Transactions of the American Fisheries Society, v. 150, no. 1, p. 24-37, https://doi.org/10.1002/tafs.10245.","productDescription":"14 p.","startPage":"24","endPage":"37","ipdsId":"IP-114418","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":456915,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10245","text":"Publisher Index Page"},{"id":377329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virgiinia","otherGeospatial":"Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.78570556640624,\n 38.09674050228651\n ],\n [\n -78.6785888671875,\n 38.19610083395667\n ],\n [\n -78.56597900390625,\n 38.26945406815749\n ],\n [\n -78.45062255859374,\n 38.370732250376854\n ],\n [\n -78.34762573242188,\n 38.4428334985915\n ],\n [\n -78.24600219726562,\n 38.591113776147445\n ],\n [\n -78.23226928710936,\n 38.732661120482334\n ],\n [\n -78.15399169921874,\n 38.7283759182398\n ],\n [\n -78.09219360351561,\n 38.80332983969899\n ],\n [\n -78.189697265625,\n 38.91881851059804\n ],\n [\n -78.24188232421875,\n 38.85575072276977\n ],\n [\n -78.22677612304688,\n 38.79476766282312\n ],\n [\n -78.33114624023438,\n 38.82901019751963\n ],\n [\n -78.37646484375,\n 38.78406349514289\n ],\n [\n -78.36685180664062,\n 38.753012320665185\n ],\n [\n -78.41766357421875,\n 38.713375686254714\n ],\n [\n -78.38333129882812,\n 38.65441572712308\n ],\n [\n -78.47946166992188,\n 38.5299046000139\n ],\n [\n -78.58383178710938,\n 38.53527591154413\n ],\n [\n -78.563232421875,\n 38.45573955865588\n ],\n [\n -78.58657836914062,\n 38.407329932912106\n ],\n [\n -78.67446899414062,\n 38.37503882134334\n ],\n [\n -78.69094848632812,\n 38.34165619279595\n ],\n [\n -78.75686645507812,\n 38.31041334882078\n ],\n [\n -78.83651733398438,\n 38.25112269630296\n ],\n [\n -78.85025024414062,\n 38.098901948321256\n ],\n [\n -78.78570556640624,\n 38.09674050228651\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"150","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-12-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Karli M. 0000-0002-6188-7405","orcid":"https://orcid.org/0000-0002-6188-7405","contributorId":237955,"corporation":false,"usgs":true,"family":"Rogers","given":"Karli","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795653,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snyder, Craig D. 0000-0002-3448-597X csnyder@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-597X","contributorId":2568,"corporation":false,"usgs":true,"family":"Snyder","given":"Craig","email":"csnyder@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795654,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dolloff, C. Andrew","contributorId":97405,"corporation":false,"usgs":true,"family":"Dolloff","given":"C.","email":"","middleInitial":"Andrew","affiliations":[],"preferred":false,"id":795655,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70214673,"text":"70214673 - 2020 - Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing","interactions":[],"lastModifiedDate":"2020-10-02T13:24:58.347144","indexId":"70214673","displayToPublicDate":"2020-04-29T08:21:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing","docAbstract":"Californias Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 20072017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n=68, R2 = 0.53, normalized RMSE = 10.5%). The Central Valley-wide estimated area for ST in 2017 was 32,369 ha 2,524 ha (95% C.I.), and 13,012 ha 1,384 ha for WGSW. Mean ST seed yield ranged from 577 kg/ha in the Delta Basin to 365 kg/ha in the San Joaquin Basin. WGSW area and ST seed yield decreased while ST area increased in critical drought years compared to normal water years (Scheffes test, p<0.05). Greatest ST area increases occurred in the Sacramento Valley (~75%). Voluntary water deliveries increased in normal water years, and ST seed yield increased with water supply. Z-scores of ST seed yield can be used to evaluate wetland performance and aid resource allocation decisions. Updated maps will support habitat monitoring, conservation planning and water management in future years, which are likely to face greater uncertainty in water availability with climate change.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2153","usgsCitation":"Byrd, K.B., Lorenz, A., Anderson, J., Wallace, C., Kara Moore-O'Leary, Isola, J., Ortega, R., and Reiter, M., 2020, Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing: Ecological Applications, v. 30, no. 7, e02153, 20 p., https://doi.org/10.1002/eap.2153.","productDescription":"e02153, 20 p.","ipdsId":"IP-112842","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":378986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.16796875,\n 40.48038142908172\n ],\n [\n -122.431640625,\n 40.713955826286046\n ],\n [\n -123.00292968749999,\n 40.34654412118006\n ],\n [\n -122.958984375,\n 39.26628442213066\n ],\n [\n -122.431640625,\n 38.58252615935333\n ],\n [\n -121.9482421875,\n 37.33522435930639\n ],\n [\n -120.5419921875,\n 36.06686213257888\n ],\n [\n -119.4873046875,\n 35.02999636902566\n ],\n [\n -119.00390625,\n 34.994003757575776\n ],\n [\n -118.564453125,\n 35.209721645221386\n ],\n [\n -118.95996093749999,\n 36.35052700542763\n ],\n [\n -120.0146484375,\n 37.055177106660814\n ],\n [\n -121.201171875,\n 38.89103282648846\n ],\n [\n -122.16796875,\n 40.48038142908172\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lorenz, Austen 0000-0003-3657-5941","orcid":"https://orcid.org/0000-0003-3657-5941","contributorId":222610,"corporation":false,"usgs":true,"family":"Lorenz","given":"Austen","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, James","contributorId":242025,"corporation":false,"usgs":false,"family":"Anderson","given":"James","affiliations":[{"id":40562,"text":"Golder Associates","active":true,"usgs":false}],"preferred":false,"id":800395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wallace, Cynthia 0000-0003-0001-8828 cwallace@usgs.gov","orcid":"https://orcid.org/0000-0003-0001-8828","contributorId":149179,"corporation":false,"usgs":true,"family":"Wallace","given":"Cynthia","email":"cwallace@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kara Moore-O'Leary","contributorId":242031,"corporation":false,"usgs":false,"family":"Kara Moore-O'Leary","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":800397,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isola, Jennifer","contributorId":242027,"corporation":false,"usgs":false,"family":"Isola","given":"Jennifer","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":800398,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ortega, Ricardo","contributorId":242028,"corporation":false,"usgs":false,"family":"Ortega","given":"Ricardo","email":"","affiliations":[{"id":48476,"text":"Grassland Water District","active":true,"usgs":false}],"preferred":false,"id":800399,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reiter, Matt","contributorId":242029,"corporation":false,"usgs":false,"family":"Reiter","given":"Matt","email":"","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":800400,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70206720,"text":"tm7C24 - 2020 - Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences","interactions":[],"lastModifiedDate":"2020-04-29T12:04:07.712559","indexId":"tm7C24","displayToPublicDate":"2020-04-28T15:10:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C24","displayTitle":"Bayesian Modeling of Non-Stationary, Univariate, Spatial Data for the Earth Sciences","title":"Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences","docAbstract":"

Some Earth science data, such as geochemical measurements of element concentrations, are non-stationary—the mean and the standard deviation vary spatially. It is important to estimate the spatial variations in both statistics because such information is indicative of geological and other Earth processes. To this end, an estimation method is formulated as a Bayesian hierarchical model. The method represents the spatially varying mean and the spatially varying standard deviation with basis functions; this formulation implicitly accounts for a spatially varying covariance function. A unique advantage of this method is that it can map the mean, the standard deviation, quantiles, and exceedance probabilities. The method is demonstrated by mapping titanium concentrations, which are measured in the coastal plain of the southeastern United States. Various checks demonstrate that the model fits the data and that the estimated statistics are geologically plausible.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C24","usgsCitation":"Ellefsen, K.J., and Van Gosen, B.S., 2020, Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences: U.S. Geological Survey Techniques and Methods, book 7, chap. C24, 20 p., https://doi.org/10.3133/tm7C24.","productDescription":"Report: iii, 20 p.; Companion File","onlineOnly":"Y","ipdsId":"IP-098004","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":374242,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c24/tm7c24.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 7 C-24"},{"id":374257,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c24/supplementary_materials.zip","text":"Supplementary Materials","size":"12.0 kB","linkFileType":{"id":6,"text":"zip"},"description":"T and M 7 C-24 Supplementary Materials"},{"id":374241,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c24/coverthb.jpg"},{"id":374243,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm7C20","text":"Techniques and Methods 7-C20—","linkHelpText":"User Guide to Bayesian Modeling of Non-Stationary, Univariate, Spatial Data Using R-Language Package BMNUS"}],"contact":"

Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-04-28","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ellefsen, Karl J. 0000-0003-3075-4703 ellefsen@usgs.gov","orcid":"https://orcid.org/0000-0003-3075-4703","contributorId":789,"corporation":false,"usgs":true,"family":"Ellefsen","given":"Karl","email":"ellefsen@usgs.gov","middleInitial":"J.","affiliations":[{"id":82803,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":false}],"preferred":true,"id":775546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Gosen, Bradley S. 0000-0003-4214-3811 bvangose@usgs.gov","orcid":"https://orcid.org/0000-0003-4214-3811","contributorId":1174,"corporation":false,"usgs":true,"family":"Van Gosen","given":"Bradley","email":"bvangose@usgs.gov","middleInitial":"S.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":775547,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70200739,"text":"tm7C20 - 2020 - User guide to the bayesian modeling of non-stationary, univariate, spatial data using R language package BMNUS","interactions":[],"lastModifiedDate":"2020-04-29T11:59:05.544535","indexId":"tm7C20","displayToPublicDate":"2020-04-28T15:10:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C20","displayTitle":"User Guide to Bayesian Modeling of Non-Stationary, Univariate, Spatial Data Using R-Language Package BMNUS","title":"User guide to the bayesian modeling of non-stationary, univariate, spatial data using R language package BMNUS","docAbstract":"

Bayesian modeling of non-stationary, univariate, spatial data is performed using the R-language package BMNUS. A unique advantage of this package is that it can map the mean, standard deviation, quantiles, and probability of exceeding a specified value. The package includes several R-language classes that prepare the data for the modeling, help select suitable model parameters, and help analyze the results. This user guide describes the BMNUS package and presents step-by-step instructions to model data that accompany the package.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C20","collaboration":"","usgsCitation":"Ellefsen, K.J, Goldman, M.A., and Van Gosen, B.S., 2020, User guide to the bayesian modeling of non-stationary, univariate, spatial data using R language package BMNUS: U.S. Geological Survey Techniques and Methods, book 7, chap. 20, 27 p., https://doi.org/10.3133/tm7C20.","productDescription":"Report: iv, 27 p.; 6 Companion Files","onlineOnly":"Y","ipdsId":"IP-096956","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":374236,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c20/coverthb.jpg"},{"id":374237,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c20/tm7c20.pdf","text":"Report","size":"1.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 7 C-20"},{"id":374281,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/ScriptsInUsersGuide.R","text":"Scripts in Users Guide","size":"24.0 kB","description":"T & M 7-C20 Scripts in Users Guide"},{"id":374238,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm7C24","text":"Techniques and Methods 7-C24—","linkHelpText":"Bayesian Modeling of Non-Stationary, Univariate, Spatial Data for the Earth Sciences"},{"id":374282,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/BMNUS_1.0.0.tar.gz","text":"BMNUS Software Package","size":"308.kB","description":"T & M 7-C20 BMNUS Software Package"},{"id":374286,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/RepeatedMeasurements_1.0.0.tar.gz","text":"RepeatedMeasurements Software Package","size":"28.0 kB","description":"T & M 7-C20 RepeatedMeasurements Software Package"},{"id":374283,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/BasicCodaFunctions_1.0.0.tar.gz","text":"BasicCodaFunctions Software Package","size":"16.0 kB","description":"T & M 7-C20 BasicCodaFunctions Software Package"},{"id":374285,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/PairedMeasurements_1.0.0.tar.gz","text":"PairedMeasurements Software Package","size":"16.0 kB","description":"T & M 7-C20 PairedMeasurements Software Package"},{"id":374284,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/MappingUtilities_1.0.0.tar.gz","text":"MapUtilities Software Package","size":"8.0 kB","description":"T & M 7-C20 MapUtilities Software Package"}],"contact":"

Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-04-28","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ellefsen, Karl J. 0000-0003-3075-4703 ellefsen@usgs.gov","orcid":"https://orcid.org/0000-0003-3075-4703","contributorId":789,"corporation":false,"usgs":true,"family":"Ellefsen","given":"Karl","email":"ellefsen@usgs.gov","middleInitial":"J.","affiliations":[{"id":82803,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":false}],"preferred":true,"id":756803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldman, Margaret A. 0000-0003-2232-6362 mgoldman@usgs.gov","orcid":"https://orcid.org/0000-0003-2232-6362","contributorId":176468,"corporation":false,"usgs":true,"family":"Goldman","given":"Margaret","email":"mgoldman@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":787832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Gosen, Bradley S. 0000-0003-4214-3811 bvangose@usgs.gov","orcid":"https://orcid.org/0000-0003-4214-3811","contributorId":1174,"corporation":false,"usgs":true,"family":"Van Gosen","given":"Bradley","email":"bvangose@usgs.gov","middleInitial":"S.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":756804,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209784,"text":"fs20203023 - 2020 - Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2016–17","interactions":[],"lastModifiedDate":"2020-04-30T13:12:13.705126","indexId":"fs20203023","displayToPublicDate":"2020-04-28T14:42:14","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3023","displayTitle":"Continuous Water-Quality and Suspended-Sediment Transport Monitoring in the San Francisco Bay, California, Water Years 2016–17","title":"Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2016–17","docAbstract":"

The U.S. Geological Survey (USGS) monitors water quality and suspended-sediment transport in the San Francisco Bay (Bay) as part of a multi-agency effort to address estuary management, water supply, and ecological concerns. The San Francisco Bay area is home to millions of people, and the Bay teems with plants and both resident and migratory wildlife, and fish. Freshwater mixes with salt water in the Bay and is subject to riverine influences (floods, droughts, managed reservoir releases, and freshwater diversions) and marine influences (tides, waves, and effects of salt water). To understand this environment, the USGS along with its cooperators (see “Acknowledgments”), has been monitoring the Bay’s waters continuously since 1988.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203023","usgsCitation":"Einhell, D.C., Downing-Kunz, M.A., and Livsey, D.N., 2020, Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2016–17: U.S. Geological Survey Fact Sheet 2020–3023, 4 p., https://doi.org/10.3133/fs20203023.","productDescription":"4 p. ","numberOfPages":"4","ipdsId":"IP-113711","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":374332,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3023/fs20203023.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374331,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3023/coverthb.jpg"}],"country":"United States","state":"California","city":"","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.947998046875,\n 37.391981943533544\n ],\n [\n -121.89056396484375,\n 37.391981943533544\n ],\n [\n -121.89056396484375,\n 38.171273439283084\n ],\n [\n -122.947998046875,\n 38.171273439283084\n ],\n [\n -122.947998046875,\n 37.391981943533544\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819

","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-04-28","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Einhell, Darin C. 0000-0002-3190-7727 deinhell@usgs.gov","orcid":"https://orcid.org/0000-0002-3190-7727","contributorId":220042,"corporation":false,"usgs":true,"family":"Einhell","given":"Darin","email":"deinhell@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":787999,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Downing-Kunz, Maureen A. 0000-0002-4879-0318 mdowning-kunz@usgs.gov","orcid":"https://orcid.org/0000-0002-4879-0318","contributorId":3690,"corporation":false,"usgs":true,"family":"Downing-Kunz","given":"Maureen","email":"mdowning-kunz@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788000,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Livsey, Daniel N. 0000-0002-2028-6128 dlivsey@usgs.gov","orcid":"https://orcid.org/0000-0002-2028-6128","contributorId":181870,"corporation":false,"usgs":true,"family":"Livsey","given":"Daniel","email":"dlivsey@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788001,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70206310,"text":"sim2932C - 2020 - Geologic map of the southern flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii","interactions":[],"lastModifiedDate":"2024-05-23T22:03:38.745463","indexId":"sim2932C","displayToPublicDate":"2020-04-28T11:59:26","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2932","chapter":"C","displayTitle":"Geologic Map of the Southern Flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii","title":"Geologic map of the southern flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii","docAbstract":"

On the Island of Hawaiʻi, Mauna Loa, the largest volcano on Earth, has erupted 33 times since written descriptions became available in 1832. Some eruptions began with only brief seismic unrest, whereas others followed several months to a year of increased seismicity. Once underway, its eruptions can produce lava flows that may reach the sea in less than 24 hours, severing roads and utilities. In terms of eruption frequency, pre-eruption warning, and rapid flow emplacement, Mauna Loa has great volcanic-hazard potential for the Island of Hawai‘i. Volcanic hazards on Mauna Loa may be anticipated, and risk substantially mitigated, by documenting the past activity to refine our knowledge of the hazards and by alerting the public and local government officials of our findings and their implications for hazards assessments and risk.

Although most Mauna Loa eruptions begin in the summit area at 12,000 feet (ft) elevation, the Southwest Rift Zone (SWRZ) was the source of at least 10 flank eruptions since 1843. The SWRZ extends from the summit towards Kalae (South Point) at sea level. The lowermost part of this rift zone, marked by Pu‘uʻoke‘oke‘o to the north at 6,874 ft elevation and extending to the sea, makes up the lower SWRZ. The community of Hawaiian Ocean View Estates, with a population of about 2,500, is the largest in the region. The subdivision is built entirely on flows erupted from southern Mauna Loa, and some source vents are located within the subdivision. Approximately 25 percent of the subdivision is within Hazard Zone 1.

From east to west, the map covers the area from Punalu‘u to Miloli‘i and, from north to south, extends from north of Pu‘uʻoke‘oke‘o to Kalae (South Point). The map encompasses 1,163 square kilometers of the southwest flank of Mauna Loa, from 7,325 ft elevation to sea level. It shows the distribution of eruptive units (flows), which are separated into 16 age groups, ranging from more than 100,000 years before present to A.D. 1950.

Lava erupted from the SWRZ typically flows to the west, east, or south (depending upon vent location relative to the rift crest) and generally produces narrow flow lobes. Both morphologic lava flow types—‘a‘ā and pāhoehoe—are present. In general, the northern part of the mapped area is dominated by flows from the middle SWRZ, whereas the southern part contains flows from the lower SWRZ and includes areas adjacent to, and downslope of, the rift zone. The exceptions are flows that originated from the upper SWRZ in the northeastern part of the Punaluu quadrangle.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim2932C","usgsCitation":"Trusdell, F.A., and Lockwood, J.P., 2020, Geologic map of the southern flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii: U.S. Geological Survey Scientific Investigations Map 2932–C, pamphlet 28 p., 2 sheets, scale 1:50,000, https://doi.org/10.3133/sim2932C.","productDescription":"Pamphlet: iv, 28 p.; 2 Sheets: 51.88 x 39.18 inches and 38.20 x 38.05 inches; Read Me; Metadata; Database; 1 Appendix","numberOfPages":"28","additionalOnlineFiles":"Y","ipdsId":"IP-054346","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":429221,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2932E","text":"Scientific Investigations Map 2932-E","linkHelpText":"- Geologic Map of the Northwest Flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii"},{"id":374326,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2932B","text":"Scientific Investigations Map 2932-B","linkHelpText":"- Geologic Map of the Central-Southeast Flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii"},{"id":374327,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2932A","text":"Scientific Investigations Map 2932-A","linkHelpText":"- Geologic Map of the Northeast Flank of Mauna Loa Volcano, Island of Hawai'i, Hawaii"},{"id":374325,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sim/2932/c/sim2932c_appendix2.xlsx","text":"Appendix 2","size":"30 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":374324,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/2932/c/sim2932c_database.zip","size":"11.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":374323,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/2932/c/sim2932c_metadata.zip","size":"500 KB","linkFileType":{"id":6,"text":"zip"}},{"id":374318,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/2932/c/coverthb.jpg"},{"id":374319,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/2932/c/sim2932c_pamphlet.pdf","text":"Pamphlet","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374320,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/2932/c/sim2932c_sheet1.pdf","text":"Sheet 1","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374321,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/2932/c/sim2932c_sheet2.pdf","text":"Sheet 2","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374322,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/2932/c/sim2932c_readme.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Southern flank of Mauna Loa Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.950927734375,\n 18.869904894964883\n ],\n [\n -155.4400634765625,\n 18.869904894964883\n ],\n [\n -155.4400634765625,\n 19.267072569005542\n ],\n [\n -155.950927734375,\n 19.267072569005542\n ],\n [\n -155.950927734375,\n 18.869904894964883\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Contact HVO
Volcano Science Center, Hawaiian Volcano Observatory
U.S. Geological Survey

","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2020-04-28","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Trusdell, Frank A. 0000-0002-0681-0528 trusdell@usgs.gov","orcid":"https://orcid.org/0000-0002-0681-0528","contributorId":754,"corporation":false,"usgs":true,"family":"Trusdell","given":"Frank A.","email":"trusdell@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":774137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lockwood, John P. 0000-0002-6562-0222","orcid":"https://orcid.org/0000-0002-6562-0222","contributorId":30976,"corporation":false,"usgs":true,"family":"Lockwood","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":774138,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210513,"text":"70210513 - 2020 - Stormwater control impacts on runoff volume and peak flow: A meta-analysis of watershed modelling studies","interactions":[],"lastModifiedDate":"2020-07-09T15:05:48.672194","indexId":"70210513","displayToPublicDate":"2020-04-28T10:01:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Stormwater control impacts on runoff volume and peak flow: A meta-analysis of watershed modelling studies","docAbstract":"

Decades of research has concluded that the percent of impervious surface cover in a watershed is strongly linked to negative impacts on urban stream health. Recently, there has been a push by municipalities to offset these effects by installing structural stormwater control measures (SCMs), which are landscape features designed to retain and reduce runoff to mitigate the effects of urbanisation on event hydrology. The goal of this study is to build generalisable relationships between the level of SCM implementation in urban watersheds and resulting changes to hydrology. A literature review of 185 peer‐reviewed studies of watershed‐scale SCM implementation across the globe was used to identify 52 modelling studies suitable for a meta‐analysis to build statistical relationships between SCM implementation and hydrologic change. Hydrologic change is quantified as the percent reduction in storm event runoff volume and peak flow between a watershed with SCMs relative to a (near) identical control watershed without SCMs. Results show that for each additional 1% of SCM‐mitigated impervious area in a watershed, there is an additional 0.43% reduction in runoff and a 0.60% reduction in peak flow. Values of SCM implementation required to produce a change in water quantity metrics were identified at varying levels of probability. For example, there is a 90% probability (high confidence) of at least a 1% reduction in peak flow with mitigation of 33% of impervious surfaces. However, as the reduction target increases or mitigated impervious surface decreases, the probability of reaching the reduction target also decreases. These relationships can be used by managers to plan SCM implementation at the watershed scale.

","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13784","usgsCitation":"Bell, C.D., Wolfand, J.M., Panos, C.L., Bhaskar, A.S., Gilliom, R.L., Hogue, T.S., Hopkins, K.G., and Jefferson, A.J., 2020, Stormwater control impacts on runoff volume and peak flow: A meta-analysis of watershed modelling studies: Hydrological Processes, v. 34, no. 14, p. 3134-3152, https://doi.org/10.1002/hyp.13784.","productDescription":"19 p.","startPage":"3134","endPage":"3152","ipdsId":"IP-114115","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":456920,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13784","text":"Publisher Index Page"},{"id":375409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"14","noUsgsAuthors":false,"publicationDate":"2020-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Bell, Colin D.","contributorId":215502,"corporation":false,"usgs":false,"family":"Bell","given":"Colin","email":"","middleInitial":"D.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolfand, Jordyn M.","contributorId":225130,"corporation":false,"usgs":false,"family":"Wolfand","given":"Jordyn","email":"","middleInitial":"M.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Panos, Chelsea L.","contributorId":225131,"corporation":false,"usgs":false,"family":"Panos","given":"Chelsea","email":"","middleInitial":"L.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790476,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bhaskar, Aditi S.","contributorId":199824,"corporation":false,"usgs":false,"family":"Bhaskar","given":"Aditi","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":790477,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gilliom, Ryan L.","contributorId":225132,"corporation":false,"usgs":false,"family":"Gilliom","given":"Ryan","email":"","middleInitial":"L.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790478,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790479,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790480,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jefferson, Anne J.","contributorId":199823,"corporation":false,"usgs":false,"family":"Jefferson","given":"Anne","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":790481,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209753,"text":"70209753 - 2020 - InFRM Flood Decision Support Toolbox user guide","interactions":[],"lastModifiedDate":"2020-04-28T16:15:23.256547","indexId":"70209753","displayToPublicDate":"2020-04-28T08:31:17","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"InFRM Flood Decision Support Toolbox user guide","docAbstract":"Digital geospatial flood inundation mapping can be a powerful tool for flood risk management. Flood preparedness, communication, warning, response and mitigation can be enhanced by flood inundation mapping that shows floodwater extent and depth over the land surface. Flood inundation maps that accurately reflect observed and forecasted hydrodynamic conditions enable officials to make timely operational and public safety decisions before and during flood events. Real-time inundation maps, based on U.S. Geological Survey (USGS) real-time streamgage observations, National Weather Service (NWS) forecasts and US Army Corps of Engineers (USACE) flood operations, can significantly enhance a community’s flood warning and response operations and systems. These maps enable local officials to make more informed flood risk management decisions and enhance the communication of these decisions to the public, thereby reducing loss of life and property. In addition, flood inundation maps and scenario analysis can inform all parties of the potential risk associated with various flood management options, prior to an actual flood event.","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"U.S. Army Corps of Engineers, Federal Emergency Management Agency, National Weather Service","usgsCitation":"Interagency Flood Risk Management (InFRM), 2020, InFRM Flood Decision Support Toolbox user guide, 34 p.","productDescription":"34 p.","ipdsId":"IP-117331","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":374317,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":374313,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://webapps.usgs.gov/infrm/pubs/FDST_UserGuide_vApr2020.pdf"},{"id":374247,"type":{"id":15,"text":"Index Page"},"url":"https://infrm.us"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Interagency Flood Risk Management (InFRM)","contributorId":224366,"corporation":true,"usgs":false,"organization":"Interagency Flood Risk Management (InFRM)","id":787998,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70210271,"text":"70210271 - 2020 - Fostering real-time climate adaptation: Analyzing past, current, and forecast temperature to understand the dynamic risk to Hawaiian honeycreepers from avian malaria","interactions":[],"lastModifiedDate":"2020-05-27T13:28:31.587697","indexId":"70210271","displayToPublicDate":"2020-04-28T08:25:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Fostering real-time climate adaptation: Analyzing past, current, and forecast temperature to understand the dynamic risk to Hawaiian honeycreepers from avian malaria","docAbstract":"Various vector control options are increasingly being considered to safeguard forest birds in their natural habitats from avian malaria transmission. However, vector control options require localized deployment that is not logistically, ethically, ecologically, nor economically viable everywhere and all the time. Based on thermal tolerances of the sporogonic stages of avian malaria (Plasmodium relictum) parasite and its vector, the southern house mosquito (Culex quinquefasciatus), we examined the long-term weather trends for three high value, forest bird refuges (Alakai Wilderness Preserve on Kaua’i, Hanawi Natural Area Reserve on Maui, and Hakalau Forest National Wildlife Refuge on Hawai’i Island) to understand the temporal and site-specific differences of temperature-driven suitability for localized avian malaria transmission.\n\nOn average, Alakai had mean ambient temperatures suitable for both the vector’s immature stage development and parasite sporogonic development most of the time (85.3%), indicating that observed variability in vector abundance or disease transmission may be driven by other factors. At higher elevation sites like Hakalau and Hanawi, current mean ambient temperatures suitable only for vector development prevail (91.7% and 96.6%, respectively), while mean ambient temperatures for both vector and parasite sporogonic development seldom occur (4.4% and 0% respectively). Our results not only show differences in the temperature suitability for transmission across elevation, but also different levels of vulnerability to avian malaria transmission with any additional projected increase in temperature. For instance, under a conservative warming scenario of 1.0 °C, the joint temperature suitability of parasite and vector development increases at higher elevation sites such as Hakalau (+35.8%) and Hanawi (+15.4%). While mean ambient temperatures suitable for both vector and parasite development already occur most of the time at Alakai, the occurrence also increases (+8.4%) as well under this conservative warming scenario.\n\nBy linking current site-specific weather data to real-time weather forecasts, we developed a real-time avian malaria warning system to assist managers in identifying conditions when vector control is most needed at these three selected study sites. This online tool determines when conditions are likely to be suitable for local development of P. relictum and C. quinquefasciatus at Alakai, Hanawi, and Hakalau. This tool illustrates how managers can incorporate climate and current weather patterns into decision making without having to consider the uncertainties of long-term climatic and ecological projections.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2020.e01069","usgsCitation":"Fortini, L., Kaiser, L.R., and Lapointe, D., 2020, Fostering real-time climate adaptation: Analyzing past, current, and forecast temperature to understand the dynamic risk to Hawaiian honeycreepers from avian malaria: Global Ecology and Conservation, v. 23, e01069, 10 p., https://doi.org/10.1016/j.gecco.2020.e01069.","productDescription":"e01069, 10 p.","ipdsId":"IP-108934","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":456923,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2020.e01069","text":"Publisher Index 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\"}}]}","volume":"23","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fortini, Lucas B. 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":202074,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas B.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":789875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaiser, Lauren R.","contributorId":200422,"corporation":false,"usgs":false,"family":"Kaiser","given":"Lauren","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":789876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaPointe, Dennis A. 0000-0002-6323-263X dlapointe@usgs.gov","orcid":"https://orcid.org/0000-0002-6323-263X","contributorId":150365,"corporation":false,"usgs":true,"family":"LaPointe","given":"Dennis","email":"dlapointe@usgs.gov","middleInitial":"A.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":789877,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210775,"text":"70210775 - 2020 - Polymeric nanofiber-carbon nanotube composite mats as fast-equilibrium passive samplers for polar organic pollutants","interactions":[],"lastModifiedDate":"2020-06-24T13:26:07.192535","indexId":"70210775","displayToPublicDate":"2020-04-28T08:21:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Polymeric nanofiber-carbon nanotube composite mats as fast-equilibrium passive samplers for polar organic pollutants","docAbstract":"

To improve the performance of polymeric electrospun nanofiber mats (ENMs) for equilibrium passive sampling applications in water, we integrated two types of multiwalled carbon nanotubes (CNTs; with and without surface carboxyl groups) into polyacrylonitrile (PAN) and polystyrene (PS) ENMs. For 11 polar and moderately hydrophobic compounds (−0.07 ≤ logKOW ≤ 3.13), 90% of equilibrium uptake was achieved in under 0.8 days (t90% values) in nonmixed ENM-CNT systems. Sorption capacity of ENM-CNTs was between 2- and 50-fold greater than pure polymer ENMs, with equilibrium partition coefficients (KENM-W values) ranging from 1.4 to 3.1 log units (L/kg) depending on polymer type (hydrophilic PAN or hydrophobic PS), CNT loading (i.e., values increased with weight percent (wt %) of CNTs), and CNT type (i.e., greater uptake with carboxylated CNTs composites). During field deployment at Muddy Creek in North Liberty, Iowa, optimal ENM-CNTs (PAN with 20 wt % carboxylated CNTs) yielded atrazine concentrations in surface water with a 40% difference relative to analysis of a same-day grab sample. We also observed a mean percent difference of 30 (±20)% when comparing ENM-CNT sampler results to grab sample data collected within 1 week of deployment. With their rapid, high capacity uptake and small material footprint, ENM-CNT equilibrium passive samplers represent a promising alternative to complement traditional integrative passive samplers while offering convenience over large volume grab sampling.

","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c00609","usgsCitation":"Qian, J., Martinez, A., Marek, R.F., Nagorzanski, M.R., Zhi, H., Furlong, E., Kolpin, D., LeFevre, G.H., and Cwiertny, D.M., 2020, Polymeric nanofiber-carbon nanotube composite mats as fast-equilibrium passive samplers for polar organic pollutants: Environmental Science & Technology, v. 54, no. 11, p. 6703-6712, https://doi.org/10.1021/acs.est.0c00609.","productDescription":"10 p.","startPage":"6703","endPage":"6712","ipdsId":"IP-114860","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":456924,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7665838","text":"External Repository"},{"id":375847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Qian, Jiajie","contributorId":225499,"corporation":false,"usgs":false,"family":"Qian","given":"Jiajie","email":"","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":791354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinez, Andres","contributorId":225500,"corporation":false,"usgs":false,"family":"Martinez","given":"Andres","email":"","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":791355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marek, Rachel F","contributorId":225501,"corporation":false,"usgs":false,"family":"Marek","given":"Rachel","email":"","middleInitial":"F","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":791356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagorzanski, Matthew R.","contributorId":211881,"corporation":false,"usgs":false,"family":"Nagorzanski","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":791357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhi, Hui","contributorId":225502,"corporation":false,"usgs":false,"family":"Zhi","given":"Hui","email":"","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":791358,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Furlong, Edward 0000-0002-7305-4603","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":213730,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":791359,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":205652,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791360,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"LeFevre, Gregory H.","contributorId":211880,"corporation":false,"usgs":false,"family":"LeFevre","given":"Gregory","email":"","middleInitial":"H.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":true,"id":791361,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cwiertny, David M.","contributorId":190557,"corporation":false,"usgs":false,"family":"Cwiertny","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":791362,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70209877,"text":"70209877 - 2020 - Economic, land use, and ecosystem services impacts of Rwanda's Green Growth Strategy: An application of the IEEM+ESM platform","interactions":[],"lastModifiedDate":"2020-05-05T13:12:46.436522","indexId":"70209877","displayToPublicDate":"2020-04-28T08:08:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Economic, land use, and ecosystem services impacts of Rwanda's Green Growth Strategy: An application of the IEEM+ESM platform","docAbstract":"We develop and link the Integrated Economic-Environmental Modeling (IEEM) Platform to ecosystem services modeling (ESM). The IEEM+ESM Platform is an innovative decision-making framework for exploring complex public policy goals and elucidating synergies and trade-offs between alternative policy portfolios. The IEEM+ESM approach is powerful in its ability to shed light on (i) change in land use and ecosystem services driven by public policy and the supply and demand responses of businesses and households; and (ii) impacts on standard economic indicators of concern to Ministries of Finance such as gross domestic product and employment, as well as changes in wealth and ecosystem services. The IEEM+ESM approach is being adopted rapidly and by the end of 2020, IEEM+ESM Platforms will be implemented for about 25 countries. To demonstrate the insights generated by the IEEM+ESM approach, we apply it to the analysis of alternative green growth strategies in Rwanda, a country that has made strong progress in reducing poverty and enhancing economic growth in the last 15 years. The case of Rwanda is particularly compelling as it faces intense pressure on its natural capital base and ecosystem services, already with the highest population density in Africa, which is projected to double by 2050. In applying IEEM+ESM and comparing the outcomes of Rwanda’s green growth policies, increasing fertilization of agricultural crops shows the largest economic gains but also trade-offs in environmental quality reflected through higher nutrient export and reduced water quality. Combining crop fertilization with forest plantations better balances critical ecosystem services and their role in underpinning economic development as Rwanda progresses toward its target of middle-income status by 2035. This application to Rwanda’s green growth strategy demonstrates the value-added of the IEEM+ESM approach in generating results that speak to both economic outcomes and impacts on market and non-market ecosystem services.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.138779","collaboration":"","usgsCitation":"Banerjee, O., Bagstad, K.J., Cicowiecz, M., Dudek, S., Horridge, M., Alavalapati, J., Masozera, M.K., Rukundo, E., and Rutebuka, E., 2020, Economic, land use, and ecosystem services impacts of Rwanda's Green Growth Strategy: An application of the IEEM+ESM platform: Science of the Total Environment, v. 729, no. , https://doi.org/10.1016/j.scitotenv.2020.138779.","productDescription":"138779, 21 p.","startPage":"","ipdsId":"IP-110054","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":456930,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.138779","text":"Publisher Index Page"},{"id":374453,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Rwanda","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[30.4191,-1.13466],[30.81613,-1.69891],[30.75831,-2.28725],[30.4697,-2.41386],[29.93836,-2.34849],[29.63218,-2.91786],[29.02493,-2.83926],[29.11748,-2.29221],[29.25483,-2.21511],[29.29189,-1.62006],[29.57947,-1.34131],[29.82152,-1.44332],[30.4191,-1.13466]]]},\"properties\":{\"name\":\"Rwanda\"}}]}","volume":"729","issue":"","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Banerjee, Onil","contributorId":224437,"corporation":false,"usgs":false,"family":"Banerjee","given":"Onil","email":"","affiliations":[{"id":40887,"text":"Inter-American Development Bank","active":true,"usgs":false}],"preferred":false,"id":788365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":788366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cicowiecz, Martin","contributorId":224438,"corporation":false,"usgs":false,"family":"Cicowiecz","given":"Martin","email":"","affiliations":[{"id":40888,"text":"Universidad Nacional de la Plata","active":true,"usgs":false}],"preferred":false,"id":788367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dudek, Sebastian","contributorId":224439,"corporation":false,"usgs":false,"family":"Dudek","given":"Sebastian","email":"","affiliations":[{"id":34928,"text":"Independent Researcher","active":true,"usgs":false}],"preferred":false,"id":788368,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horridge, Mark 0000-0002-1070-5763","orcid":"https://orcid.org/0000-0002-1070-5763","contributorId":224440,"corporation":false,"usgs":false,"family":"Horridge","given":"Mark","email":"","affiliations":[{"id":27874,"text":"Victoria University","active":true,"usgs":false}],"preferred":false,"id":788369,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alavalapati, Janaki","contributorId":224441,"corporation":false,"usgs":false,"family":"Alavalapati","given":"Janaki","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":788370,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Masozera, Michel K.","contributorId":201300,"corporation":false,"usgs":false,"family":"Masozera","given":"Michel","email":"","middleInitial":"K.","affiliations":[{"id":35968,"text":"Wildlife Conservation Society, Rwanda Program","active":true,"usgs":false}],"preferred":false,"id":788371,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rukundo, Emmanuel 0000-0002-3220-3422","orcid":"https://orcid.org/0000-0002-3220-3422","contributorId":222903,"corporation":false,"usgs":false,"family":"Rukundo","given":"Emmanuel","email":"","affiliations":[{"id":16866,"text":"Beijing Normal University","active":true,"usgs":false}],"preferred":false,"id":788372,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rutebuka, Evariste 0000-0001-9267-3349","orcid":"https://orcid.org/0000-0001-9267-3349","contributorId":222904,"corporation":false,"usgs":false,"family":"Rutebuka","given":"Evariste","email":"","affiliations":[{"id":40626,"text":"University of Ibadan","active":true,"usgs":false}],"preferred":false,"id":788373,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70209785,"text":"70209785 - 2020 - Longitudinal, lateral, vertical, and temporal thermal heterogeneity in a large impounded river: Implications for cold-water refuges","interactions":[],"lastModifiedDate":"2020-04-29T13:06:52.904537","indexId":"70209785","displayToPublicDate":"2020-04-28T08:04:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Longitudinal, lateral, vertical, and temporal thermal heterogeneity in a large impounded river: Implications for cold-water refuges","docAbstract":"Dam operations can affect mixing of the water column, thereby influencing thermal heterogeneity spatially and temporally. This occurs by restricting or eliminating connectivity in longitudinal, lateral, vertical, and temporal dimensions. We examined thermal heterogeneity across space and time and identified potential cold-water refuges for salmonids in a large impounded river in inland northwestern USA. To describe these patterns, we used thermal infrared (TIR) imagery, in situ thermographs, and high-resolution, 3-D hydraulic mapping. We explained the median water temperature and probability of occurrence of cool-water areas using generalized additive models (GAMs) at reach and subcatchment scales, and we evaluated potential cold-water refuge occurrence in relation to these patterns. We demonstrated that (1) lateral contributions from tributaries dominated thermal heterogeneity, (2) thermal variability at confluences was approximately an order of magnitude greater than of the main stem, (3) potential cold-water refuges were mostly found at confluences, and (4) the probability of occurrence of cool areas and median water temperature were associated with channel geomorphology and distance from dam. These findings highlight the importance of using multiple approaches to describe thermal heterogeneity in large, impounded rivers and the need to incorporate these types of rivers in the understanding of thermal riverscapes because of their limited representation in the literature.","language":"English","publisher":"MDPI","doi":"10.3390/rs12091386","collaboration":"","usgsCitation":"Mejia, F.H., Torgersen, C.E., Berntsen, E.K., Maroney, J.R., Connor, J., Fullerton, A.H., Ebersole, J.L., and Lorang, M.L., 2020, Longitudinal, lateral, vertical, and temporal thermal heterogeneity in a large impounded river: Implications for cold-water refuges: Remote Sensing, v. 12, no. 9, https://doi.org/10.3390/rs12091386.","productDescription":"1386, 29 p.","startPage":"","ipdsId":"IP-116596","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":456932,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12091386","text":"Publisher Index Page"},{"id":374347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Mejia, Francine H. 0000-0003-4447-231X","orcid":"https://orcid.org/0000-0003-4447-231X","contributorId":214345,"corporation":false,"usgs":true,"family":"Mejia","given":"Francine","email":"","middleInitial":"H.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":788002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":788003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berntsen, Eric K","contributorId":214885,"corporation":false,"usgs":false,"family":"Berntsen","given":"Eric","email":"","middleInitial":"K","affiliations":[{"id":39131,"text":"Kalispel Tribe of Indians","active":true,"usgs":false}],"preferred":false,"id":788004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maroney, Joseph R","contributorId":224367,"corporation":false,"usgs":false,"family":"Maroney","given":"Joseph","email":"","middleInitial":"R","affiliations":[{"id":40867,"text":"Kalispel Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":788005,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Connor, J M","contributorId":224368,"corporation":false,"usgs":false,"family":"Connor","given":"J M","affiliations":[{"id":40867,"text":"Kalispel Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":788006,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fullerton, Aimee H.","contributorId":146936,"corporation":false,"usgs":false,"family":"Fullerton","given":"Aimee","email":"","middleInitial":"H.","affiliations":[{"id":12641,"text":"NOAA NMFS","active":true,"usgs":false}],"preferred":false,"id":788007,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebersole, Joseph L.","contributorId":146938,"corporation":false,"usgs":false,"family":"Ebersole","given":"Joseph","email":"","middleInitial":"L.","affiliations":[{"id":12657,"text":"EPA NEIC","active":true,"usgs":false}],"preferred":false,"id":788008,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lorang, Mark L","contributorId":224369,"corporation":false,"usgs":false,"family":"Lorang","given":"Mark","email":"","middleInitial":"L","affiliations":[{"id":40868,"text":"FreshwaterMap","active":true,"usgs":false}],"preferred":false,"id":788009,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211590,"text":"70211590 - 2020 - Time-dependent accumulation of Cd, Co, Cu, Ni, and Zn in mayfly and caddisfly larvae in experimental streams: Metal sensitivity, uptake pathways, and mixture toxicity","interactions":[],"lastModifiedDate":"2020-08-04T13:01:40.624742","indexId":"70211590","displayToPublicDate":"2020-04-28T07:56:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Time-dependent accumulation of Cd, Co, Cu, Ni, and Zn in mayfly and caddisfly larvae in experimental streams: Metal sensitivity, uptake pathways, and mixture toxicity","docAbstract":"Conceptual and quantitative models were developed to assess time-dependent processes in four sequential experimental stream studies that determined abundances of natural communities of mayfly and caddisfly larvae dosed with single metals (Cd, Co, Cu, Ni, Zn) or multiple metals (Cd+Zn, Co+Cu, Cu+Ni, Cu+Zn, Ni+Zn, Cd+Cu+Zn, Co+Cu+Ni, Cu+Ni+Zn). Metal mixtures contained environmentally relevant metal ratios found in mine drainage. Free metal ion concentrations, accumulation of metals by periphyton, and metal uptake by four families of aquatic insect larvae were either measured (Brachycentridae) or predicted (Ephemerellidae, Heptageniidae, Hydropsychidae) using equilibrium and biodynamic models. Toxicity functions, which included metal accumulations by larvae and metal potencies, were linked to abundances of the insect families. Model results indicated that mayflies accumulated more metal than caddisflies and the relative importance of metal uptake by larvae via dissolved or dietary pathways highly depended on metal uptake rate constants for each insect family and concentrations of metals in food and water. For solution compositions in the experimental streams, accumulations of Cd, Cu, and Zn in larvae occurred primarily through dietary uptake, whereas uptake of dissolved metal was more important for Co and Ni accumulations. Cd, Cu, and Ni were major contributors to toxicity in metal mixtures and for metal ratios examined. Our conceptual approach and quantitative results should aid in designing laboratory experiments and field studies that evaluate metal uptake pathways and metal mixture toxicity to aquatic biota.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.139011","usgsCitation":"Balistrieri, L.S., Mebane, C.A., and Schmidt, T., 2020, Time-dependent accumulation of Cd, Co, Cu, Ni, and Zn in mayfly and caddisfly larvae in experimental streams: Metal sensitivity, uptake pathways, and mixture toxicity: Science of the Total Environment, v. 732, 139011, 16 p., https://doi.org/10.1016/j.scitotenv.2020.139011.","productDescription":"139011, 16 p.","ipdsId":"IP-112332","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":456935,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.139011","text":"Publisher Index Page"},{"id":377004,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"732","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Balistrieri, Laurie S. 0000-0002-6359-3849 balistri@usgs.gov","orcid":"https://orcid.org/0000-0002-6359-3849","contributorId":1406,"corporation":false,"usgs":true,"family":"Balistrieri","given":"Laurie","email":"balistri@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":794740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":794742,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228436,"text":"70228436 - 2020 - Trophodynamics of per- and polyfluoroalkyl substances in the food web of a large Atlantic slope river","interactions":[],"lastModifiedDate":"2022-02-10T13:21:14.411448","indexId":"70228436","displayToPublicDate":"2020-04-28T07:17:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Trophodynamics of per- and polyfluoroalkyl substances in the food web of a large Atlantic slope river","docAbstract":"

Per- and polyfluoroalkyl substances (PFASs) have attracted scientific and regulatory attention due to their persistence, bioaccumulative potential, toxicity, and global distribution. We determined the accumulation and trophic transfer of 14 PFASs (5 short-chain and 9 long-chain) within the food web of the Yadkin-Pee Dee River of North Carolina and South Carolina, US. Food web components and pathways were determined by stable isotope analyses of producers, consumers, and organic matter. Analyses of water, sediment, organic matter, and aquatic biota revealed that PFASs were prevalent in all food web compartments. Biofilm, an aggregation of bacteria, fungi, algae, and protozoans and a basal resource for the aquatic food web, showed high PFAS accumulation (in 10 of 14 compounds), particularly for perfluorooctanoic acid, with the greatest mean concentration of 463.73 ng/g. The food web compartment with the most detections and greatest concentrations of PFASs was aquatic insects; all 14 PFASs were detected in individual aquatic insect samples (range of <limit of detection [<LOD] to 1670.10 ng/g of wet weight [WW]). These findings may suggest a trophic link between biofilm PFASs and aquatic insect PFASs. Individual fish tissue samples ranged from <LOD to 797.00 ng/g of WW, where perfluorooctanesulfonate (PFOS) was the dominant PFAS among all samples (64%). The ova of an imperiled fish, the robust redhorse (Moxostoma robustum), had concentrations of 10 PFASs (range of <LOD to 482.88 ng/g of WW) and the highest PFOS concentration (482.88 ng/g of WW), indicating a likely maternal transfer. The trophic magnification factors (TMFs) calculated in this study showed that various taxa accumulated PFAS compounds differently. PFBS, a short-chain PFAS compound that would presumably exhibit lesser TMFs, had nine values among our compartments and organisms >1.0 (range of 0.57 to 2.33); it is possible that an unmeasured PFBS precursor may be accumulating in biota and metabolizing to PFBS, leading to a higher than expected TMFs for this compound. Our findings demonstrate the prevalence of PFASs in a freshwater food web with potential implications for ecological and human health.

","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.9b05007","usgsCitation":"Penland, T.N., Cope, W., Kwak, T.J., Strynar, M., Grieshaber, C.A., Heise, R., and Sessions, F., 2020, Trophodynamics of per- and polyfluoroalkyl substances in the food web of a large Atlantic slope river: Environmental Science and Technology, v. 54, no. 11, p. 6800-6811, https://doi.org/10.1021/acs.est.9b05007.","productDescription":"12 p.","startPage":"6800","endPage":"6811","ipdsId":"IP-111358","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456938,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8190818","text":"External Repository"},{"id":395762,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.52978515625,\n 36.527294814546245\n ],\n [\n -80.771484375,\n 36.63316209558658\n ],\n [\n -81.40869140625,\n 36.77409249464195\n ],\n [\n -81.73828125,\n 36.474306755095235\n ],\n [\n -81.5625,\n 36.10237644873644\n ],\n [\n -81.2548828125,\n 35.33529320309328\n ],\n [\n -80.4638671875,\n 34.30714385628804\n ],\n [\n -79.65087890624999,\n 33.22949814144951\n ],\n [\n -79.07958984375,\n 32.879587173066305\n ],\n [\n -78.50830078125,\n 33.8339199536547\n ],\n [\n -79.07958984375,\n 34.903952965590065\n ],\n [\n -80.52978515625,\n 36.527294814546245\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Penland, T. N.","contributorId":275792,"corporation":false,"usgs":false,"family":"Penland","given":"T.","email":"","middleInitial":"N.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":834290,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cope, W. G.","contributorId":275793,"corporation":false,"usgs":false,"family":"Cope","given":"W. G.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":834291,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834292,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strynar, M.J.","contributorId":275795,"corporation":false,"usgs":false,"family":"Strynar","given":"M.J.","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":834293,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grieshaber, C. A.","contributorId":275797,"corporation":false,"usgs":false,"family":"Grieshaber","given":"C.","email":"","middleInitial":"A.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":834294,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heise, R. J.","contributorId":275798,"corporation":false,"usgs":false,"family":"Heise","given":"R. J.","affiliations":[{"id":48960,"text":"Duke Energy","active":true,"usgs":false}],"preferred":false,"id":834295,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sessions, F.W.","contributorId":275801,"corporation":false,"usgs":false,"family":"Sessions","given":"F.W.","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":834296,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70210186,"text":"70210186 - 2020 - Discovery of a reproducing wild population of the swamp eel Amphipnous cuchia (Hamilton, 1822) in North America","interactions":[],"lastModifiedDate":"2020-05-19T23:13:14.268525","indexId":"70210186","displayToPublicDate":"2020-04-27T18:07:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":994,"text":"BioInvasions Records","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Discovery of a reproducing wild population of the swamp eel Amphipnous cuchia (Hamilton, 1822) in North America","title":"Discovery of a reproducing wild population of the swamp eel Amphipnous cuchia (Hamilton, 1822) in North America","docAbstract":"

We report discovery of an established population of the Asian swamp eel Amphipnous cuchia (Hamilton, 1822) in Bayou St. John, an urban waterway in New Orleans, Louisiana, USA. This fish, commonly referred to as cuchia (kuchia), is a member of the family Synbranchidae and is native to southern and southeastern Asia. Recently-used synonyms include Monopterus cuchia and Ophichthys cuchia. We collected both adult and young-of-year cuchia from dense mats of littoral vegetation at several locations in Bayou St. John. Presence of multiple age and size classes is the first documented evidence of reproduction of this species outside of its native range. Establishment of this air-breathing, burrowing, salt-tolerant, opportunistic predator is of concern given that Bayou St. John is a tributary of Lake Pontchartrain, which provides a direct pathway for dispersal into the Mississippi River basin and coastal wetlands of the Gulf of Mexico.

","language":"English","publisher":"REABIC","usgsCitation":"Jordan, F., Nico, L., Huggins, K., Martinat, P.J., Martinez, D.A., and Rodrigues, V.L., 2020, Discovery of a reproducing wild population of the swamp eel Amphipnous cuchia (Hamilton, 1822) in North America: BioInvasions Records, v. 9, no. 2, p. 367-374.","productDescription":"8 p.","startPage":"367","endPage":"374","ipdsId":"IP-111259","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":374939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":374938,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.reabic.net/journals/bir/2020/Issue2.aspx"}],"country":"United States","state":"Louisiana","city":"New Orleans","otherGeospatial":"Bayou St John","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.09407043457031,\n 29.972111446986677\n ],\n [\n -90.07870674133301,\n 29.972111446986677\n ],\n [\n -90.07870674133301,\n 30.02080035280506\n ],\n [\n -90.09407043457031,\n 30.02080035280506\n ],\n [\n -90.09407043457031,\n 29.972111446986677\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jordan, Frank","contributorId":181811,"corporation":false,"usgs":false,"family":"Jordan","given":"Frank","email":"","affiliations":[],"preferred":false,"id":789470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nico, Leo 0000-0002-4488-7737","orcid":"https://orcid.org/0000-0002-4488-7737","contributorId":219326,"corporation":false,"usgs":true,"family":"Nico","given":"Leo","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":789471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huggins, Krystal","contributorId":224778,"corporation":false,"usgs":false,"family":"Huggins","given":"Krystal","email":"","affiliations":[{"id":40937,"text":"3Department of Biology, Xavier University, New Orleans, LA 70125, USA","active":true,"usgs":false}],"preferred":false,"id":789472,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martinat, Peter J.","contributorId":224779,"corporation":false,"usgs":false,"family":"Martinat","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":40937,"text":"3Department of Biology, Xavier University, New Orleans, LA 70125, USA","active":true,"usgs":false}],"preferred":false,"id":789473,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martinez, Dahlia A.","contributorId":224780,"corporation":false,"usgs":false,"family":"Martinez","given":"Dahlia","email":"","middleInitial":"A.","affiliations":[{"id":40938,"text":"Department of Biological Sciences, Loyola University New Orleans, New Orleans, LA 70118, USA","active":true,"usgs":false}],"preferred":false,"id":789474,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rodrigues, Victoria L.","contributorId":224781,"corporation":false,"usgs":false,"family":"Rodrigues","given":"Victoria","email":"","middleInitial":"L.","affiliations":[{"id":40939,"text":"Environment Program, Loyola University New Orleans, New Orleans, LA 70118, USA","active":true,"usgs":false}],"preferred":false,"id":789475,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209233,"text":"sir20205032 - 2020 - Magnitude and frequency of floods in Alabama, 2015","interactions":[],"lastModifiedDate":"2020-04-28T12:17:24.386512","indexId":"sir20205032","displayToPublicDate":"2020-04-27T14:21:30","publicationYear":"2020","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":"2020-5032","displayTitle":"Magnitude and Frequency of Floods in Alabama, 2015","title":"Magnitude and frequency of floods in Alabama, 2015","docAbstract":"

To improve flood-frequency estimates at rural streams in Alabama, annual exceedance probability flows at gaged locations and regional regression equations used to estimate annual exceedance probability flows at ungaged locations were developed by using current geospatial data, new analytical methods, and annual peak-flow data through September 2015 at 242 streamgages in Alabama and surrounding States. The regional regression equations were derived from statistical analyses of annual peak-flow data and basin characteristics for a subset of 217 streamgages. Four flood regions were identified based on residuals from the regional regression analyses and contain sites with similar basin characteristics. A separate set of equations was derived for estimating flood frequency and magnitude for small rural streams using a subset of 40 small basin streamgages. A large river analysis was also completed for 14 selected large-river streamgages in Alabama. Annual exceedance probability flows presented in this report reflect additional streamflow data collected since the previous study of flood magnitude and frequency in Alabama, which included streamflow through September 2003.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205032","collaboration":"Alabama Department of Transportation","usgsCitation":"Anderson, B.T., 2020, Magnitude and frequency of floods in Alabama, 2015: U.S. Geological Survey Scientific Investigations Report 2020–5032, 148 p., https://doi.org/10.3133/sir20205032.","productDescription":"Report: vii, 148 p.; 1 Plate: 20.00 x 30.00 inches; Data Release","numberOfPages":"160","onlineOnly":"N","ipdsId":"IP-104043","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":374279,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5032/sir20205032_plate01.pdf","text":"Plate 1","size":"1.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5032 plate 1","linkHelpText":"—Locations of Flood Regions and Streamgages in Alabama"},{"id":374278,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5032/sir20205032.pdf","text":"Report","size":"6.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5032"},{"id":374277,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5032/coverthb.jpg"},{"id":374280,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TYSZLL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood regions and annual exceedance probability flows for Alabama streams, data through 2015"}],"country":"United States","state":"Alabama","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.39599609375,\n 30.41078179084589\n ],\n [\n -87.36328125,\n 30.391830328088137\n ],\n [\n -87.5390625,\n 30.789036751261136\n ],\n [\n -87.47314453125,\n 31.034108344903512\n ],\n [\n -85.078125,\n 31.071755902820133\n ],\n [\n -84.96826171874999,\n 32.26855544621476\n ],\n [\n -85.62744140625,\n 34.95799531086792\n ],\n [\n -88.04443359375,\n 34.994003757575776\n ],\n [\n -88.48388671874999,\n 32.02670629333614\n ],\n [\n -88.39599609375,\n 30.41078179084589\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-04-27","noUsgsAuthors":false,"publicationDate":"2020-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Brandon T. 0000-0001-6698-0791","orcid":"https://orcid.org/0000-0001-6698-0791","contributorId":209976,"corporation":false,"usgs":true,"family":"Anderson","given":"Brandon","email":"","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785489,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70215047,"text":"70215047 - 2020 - Disturbances drive changes in coral community assemblages and coral calcification capacity","interactions":[],"lastModifiedDate":"2020-10-07T12:10:52.037108","indexId":"70215047","displayToPublicDate":"2020-04-27T14:20:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Disturbances drive changes in coral community assemblages and coral calcification capacity","docAbstract":"Anthropogenic environmental change has increased coral reef disturbance regimes in recent decades, altering the structure and function of many coral reefs globally. In this study, we used coral community survey data collected from 1996 to 2015 to evaluate coral calcification capacity (CCC) dynamics with respect to recorded pulse disturbances for 121 reef sites in the Main Hawaiian Islands and Mo'orea (French Polynesia) in the Pacific and the Florida Keys Reef Tract and St. John (U.S. Virgin Islands) in the Western Atlantic. CCC remained relatively high in the Main Hawaiian Islands in the absence of recorded widespread disturbances; declined and subsequently recovered in Mo'orea following a crown-of-thorns sea star outbreak, coral bleaching, and major cyclone; decreased and remained low following coral bleaching in the Florida Keys Reef Tract; and decreased following coral bleaching and disease in St. John. Coral taxa have diverse calcification rates and susceptibility to disturbances characterized by their life history strategies. As a result, changes in CCC over the time series in this study were driven by a combination of shifts in both overall coral cover and in the contributions of calcification by the dominant calcifying coral taxa to CCC. Analysis of coral life history strategies showed that ‘weedy’ corals increased their contributions to CCC over time while ‘competitive’ corals decreased. Conversely, shifts in contributions by ‘stress-tolerant’ and ‘generalist’ corals to CCC varied by taxa across the regions. The increasing frequency and intensity of disturbances under 21st century global change is therefore predicted to affect CCC for many coral reefs with potentially lower and more variable CCC sustained under increased disturbance regimes by the increasing dominance of ‘weedy’ and some ‘stress-tolerant’ corals.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3066","usgsCitation":"Courtney, T.A., Barnes, B.B., Chollett, I., Elahi, R., Gross, K., Guest, J.R., Kuffner, I.B., Lenz, E.A., Nelson, H.R., Rogers, C., Toth, L., and Andersson, A.J., 2020, Disturbances drive changes in coral community assemblages and coral calcification capacity: Ecosphere, v. 11, no. 4, e03066, 16 p., https://doi.org/10.1002/ecs2.3066.","productDescription":"e03066, 16 p.","ipdsId":"IP-108622","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":456941,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3066","text":"Publisher Index Page"},{"id":379104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Courtney, Travis A.","contributorId":218225,"corporation":false,"usgs":false,"family":"Courtney","given":"Travis","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":800620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, Brian B.","contributorId":218223,"corporation":false,"usgs":false,"family":"Barnes","given":"Brian","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":800621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chollett, Iliana","contributorId":218224,"corporation":false,"usgs":false,"family":"Chollett","given":"Iliana","email":"","affiliations":[],"preferred":false,"id":800622,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elahi, Robin","contributorId":218226,"corporation":false,"usgs":false,"family":"Elahi","given":"Robin","email":"","affiliations":[],"preferred":false,"id":800623,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gross, Kevin","contributorId":242664,"corporation":false,"usgs":false,"family":"Gross","given":"Kevin","affiliations":[{"id":25510,"text":"NC State University","active":true,"usgs":false}],"preferred":false,"id":800624,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Guest, James 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Super gages are an important tool providing real-time, continuous water-quality data at streamgages or groundwater wells. They are designed to address specific water-resource threats such as water-related human health issues including harmful algal blooms, floods, droughts, and hazardous substance spills. In addition, super gages improve our understanding of the effects land-use practices have on critical water resources. Super gage data allow the development of surrogates, a continuous in-stream sensor measurement used to estimate something of greater interest to environmental managers, to be modeled and reported in near real-time concentrations and loads. This fact sheet presents some of the ways water-quality data from a USGS super gage network benefits all of us.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203019","collaboration":"Prepared in cooperation with the Kentucky Governor's Office of Agricultural Policy","usgsCitation":"Crain, A.S., 2020, The importance of U.S. Geological Survey water-quality super gages: U.S. Geological Survey Fact Sheet 2020–3019, 2 p., https://doi.org/10.3133/fs20203019.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-113930","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":374216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3019/coverthb.jpg"},{"id":374217,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3019/fs20203019.pdf","text":"Report","size":"2.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3019"}],"contact":"

Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299

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