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":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":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","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.
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":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
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
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
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
A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 58th Annual Report of the River Master of the Delaware River. It covers the 2011 River Master report year, the period from December 1, 2010, to November 30, 2011.
During the report year, precipitation in the upper Delaware River Basin was 71.43 inches or 162 percent of the long-term average. On December 1, 2010, combined usable storage in the New York City reservoirs in the upper Delaware River Basin was 230.430 billion gallons or 85.1 percent of combined storage capacity. The reservoirs were at about 100 percent of usable capacity on May 31, 2011. Combined storage remained high (above 80 percent combined capacity) through November 2011. River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 5 days during the report year (July 24–28, 2011). Conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year.
The quality of water in the Delaware Estuary between Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at various locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201020","usgsCitation":"DiFrenna, V.J., Andrews, W.J., Russell, K.L., Norris, J.M., and Mason, R.R., Jr., 2020, Report of the River Master of the Delaware River for the period December 1, 2010–November 30, 2011: U.S. Geological Survey Open-File Report 2020–1020, 127 p., https://doi.org/10.3133/ofr20201020.","productDescription":"x, 127 p.","numberOfPages":"141","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-112762","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":374239,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1020/coverthb.jpg"},{"id":374254,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1020/ofr20201020.pdf","text":"Report","size":"4.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1020"}],"country":"United States","state":"New York, New Jersey, Pennsylvania ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.673583984375,\n 39.7240885773337\n ],\n [\n -73.828125,\n 39.7240885773337\n ],\n [\n -73.828125,\n 42.67435857693381\n ],\n [\n -76.673583984375,\n 42.67435857693381\n ],\n [\n -76.673583984375,\n 39.7240885773337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Deputy Delaware River Master
Office of the Delaware River Master
U.S. Geological Survey
120 Route 209 South
Milford, PA 18337
Salinity, selenium, and uranium pose water‐quality challenges for the Arkansas River in southeastern Colorado and other rivers that support irrigation in semiarid regions. This study used 31 years of continuous discharge and specific conductance (SC) monitoring data to assess interannual patterns in water quality using mass balance on a 120‐km reach of river. Discrete sampling data were used to link the SC records to salinity, selenium, and uranium. Several important patterns emerged. Consumptive use reduced discharge by a median value of 33% and drove corresponding increases in salinity and uranium concentrations. Increased water availability for irrigation from rainfall and upstream snowpack in 1995–1999 flushed additional salinity and uranium into the river in 1996–2000; average annual total dissolved solids (salinity) concentrations increased 25%, and loads increased 131%. Smaller flushing events have occurred, sometimes lagging an increase in water availability by about one year. The pattern indicates flushing of salts temporarily stored, evaporatively concentrated, or of geologic origin. Mobilization of selenium from the reach was minor compared to salinity and uranium, and net selenium removal from the river was suggested in some years. Several processes related to irrigation could be removing selenium. The results provide context for efforts to improve water quality in the Arkansas River and rivers in other semiarid regions.
","language":"English","publisher":"American Water Resources Association","doi":"10.1111/1752-1688.12841","usgsCitation":"Bern, C.R., Holmberg, M.J., and Kisfalusi, Z.D., 2020, Salt flushing, salt storage, and controls on selenium: A 31-year mass-balance analysis of an irrigated, semiarid valley: Journal of the American Water Resources Association, v. 56, no. 4, p. 647-668, https://doi.org/10.1111/1752-1688.12841.","productDescription":"22 p.","startPage":"647","endPage":"668","ipdsId":"IP-102689","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":456966,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12841","text":"Publisher Index Page"},{"id":377212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Arkansas River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.06298828125,\n 38.62545397209084\n ],\n [\n -103.45275878906249,\n 39.104488809440475\n ],\n [\n -104.5074462890625,\n 39.35978526869001\n ],\n [\n -105.9906005859375,\n 39.29604824402406\n ],\n [\n -106.622314453125,\n 39.78321267821705\n ],\n [\n -107.13317871093749,\n 39.65222681530652\n ],\n [\n -105.58959960937499,\n 38.12159327165922\n ],\n [\n -105.3369140625,\n 37.85316995894978\n ],\n [\n -105.4852294921875,\n 37.592471511019085\n ],\n [\n -105.2105712890625,\n 37.61858263247881\n ],\n [\n -105.018310546875,\n 37.405073750176925\n ],\n [\n -105.16113281249999,\n 37.03325468997236\n ],\n [\n -102.041015625,\n 36.99816565700228\n ],\n [\n -102.06298828125,\n 38.62545397209084\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Bern, Carleton R. 0000-0002-8980-1781 cbern@usgs.gov","orcid":"https://orcid.org/0000-0002-8980-1781","contributorId":201152,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton","email":"cbern@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holmberg, Michael J. 0000-0002-1316-0412 mholmber@usgs.gov","orcid":"https://orcid.org/0000-0002-1316-0412","contributorId":190084,"corporation":false,"usgs":true,"family":"Holmberg","given":"Michael","email":"mholmber@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795267,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kisfalusi, Zachary D. 0000-0001-6016-3213","orcid":"https://orcid.org/0000-0001-6016-3213","contributorId":222422,"corporation":false,"usgs":true,"family":"Kisfalusi","given":"Zachary","email":"","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795268,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211831,"text":"70211831 - 2020 - Model selection for the North American Breeding Bird Survey","interactions":[],"lastModifiedDate":"2020-09-10T20:29:14.489574","indexId":"70211831","displayToPublicDate":"2020-04-23T16:28:27","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":"Model selection for the North American Breeding Bird Survey","docAbstract":"The North American Breeding Bird Survey (BBS) provides data that can be used in complex, multiscale analyses of population change, while controlling for scale‐specific nuisance factors. Many alternative models can be fit to the data, but most model selection procedures are not appropriate for hierarchical models. Leave‐one‐out cross‐validation (LOOCV), in which relative model fit is assessed by omitting an observation and assessing the prediction of a model fit using the remainder of the data, provides a reasonable approach for assessing models, but is time consuming and not feasible to apply for all observations in large data sets. We report the first large‐scale formal model selection for BBS data, applying LOOCV to stratified random samples of observations from BBS data. Our results are for 548 species of North American birds, comparing the fit of four alternative models that differ in year effect structures and in descriptions of extra‐Poisson overdispersion. We use a hierarchical model among species to evaluate posterior probabilities that models are best for individual species. Models in which differences in year effects are conditionally independent (D models) were generally favored over models in which year effects are modeled by a slope parameter and a random year effect (S models), and models in which extra‐Poisson overdispersion effects are independent and t‐distributed (H models) tended to be favored over models where overdispersion was independent and normally distributed. Our conclusions lead us to recommend a change from the conventional S model to D and H models for the vast majority of species (544/548). Comparison of estimated population trends based on the favored model relative to the S model currently used for BBS summaries indicates no consistent differences in estimated trends. Of the 18 species that showed large differences in estimated trends between models, estimated trends from the default S model were more extreme, reflecting the influence of the slope parameter in that model for species that are undergoing large population changes. WAIC, a computationally simpler alternative to LOOCV, does not appear to be a reliable alternative to LOOCV.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2137","usgsCitation":"Link, W.A., Sauer, J.R., and Niven, D.K., 2020, Model selection for the North American Breeding Bird Survey: Ecological Applications, v. 30, no. 6, e2037, 10 p., https://doi.org/10.1002/eap.2137.","productDescription":"e2037, 10 p.","ipdsId":"IP-112644","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":377210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.2109375,\n 7.013667927566642\n ],\n [\n -71.015625,\n 20.3034175184893\n ],\n [\n -77.34374999999999,\n 28.92163128242129\n ],\n [\n -68.5546875,\n 40.713955826286046\n ],\n [\n -50.625,\n 49.15296965617042\n ],\n [\n -62.22656249999999,\n 68.65655498475735\n ],\n [\n -84.375,\n 76.67978490310692\n ],\n [\n -123.04687499999999,\n 77.61770905279676\n ],\n [\n -131.1328125,\n 71.52490903732816\n ],\n [\n -159.2578125,\n 71.85622888185527\n ],\n [\n -166.9921875,\n 69.03714171275197\n ],\n [\n -166.9921875,\n 62.75472592723178\n ],\n [\n -162.7734375,\n 58.07787626787517\n ],\n [\n -162.421875,\n 54.97761367069628\n ],\n [\n -148.0078125,\n 56.36525013685606\n ],\n [\n -141.328125,\n 57.326521225217064\n ],\n [\n -134.296875,\n 54.36775852406841\n ],\n [\n -127.265625,\n 47.040182144806664\n ],\n [\n -126.21093749999999,\n 37.16031654673677\n ],\n [\n -116.01562499999999,\n 26.43122806450644\n ],\n [\n -104.0625,\n 14.944784875088372\n ],\n [\n -81.2109375,\n 7.013667927566642\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niven, Daniel K 0000-0002-3253-7211 dniven@usgs.gov","orcid":"https://orcid.org/0000-0002-3253-7211","contributorId":237775,"corporation":false,"usgs":true,"family":"Niven","given":"Daniel","email":"dniven@usgs.gov","middleInitial":"K","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795279,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215561,"text":"70215561 - 2020 - Climate change causes river network contraction and disconnection in the H.J. Andrews Experimental Forest, Oregon, USA","interactions":[],"lastModifiedDate":"2020-10-23T13:58:50.631328","indexId":"70215561","displayToPublicDate":"2020-04-23T08:55:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Climate change causes river network contraction and disconnection in the H.J. Andrews Experimental Forest, Oregon, USA","docAbstract":"Headwater streams account for more than 89% of global river networks and provide numerous ecosystem services that benefit downstream ecosystems and human water uses. It has been established that changes in climate have shifted the timing and magnitude of observed precipitation, which, at specific gages, have been directly linked to long-term reductions in large river discharge. However, climate impacts on ungaged headwater streams, where ecosystem function is tightly coupled to flow permanence along the river corridor, remain unknown due to the lack of data sets and ability to model and predict flow permanence. We analyzed a network of 10 gages with 38–69 years of records across a 5th-order river basin in the U.S. Pacific Northwest, finding increasing frequency of lower low-flow conditions across the basin. Next, we simulated river network expansion and contraction for a 65-year period of record, revealing 24% and 9% declines in flowing and contiguous network length, respectively, during the driest months of the year. This study is the first to mechanistically simulate network expansion and contraction at the scale of a large river basin, informing if and how climate change is altering connectivity along river networks. While the heuristic model presented here yields basin-specific conclusions, this approach is generalizable and transferable to the study of other large river basins. Finally, we interpret our model results in the context of regulations based on flow permanence, demonstrating the complications of static regulatory definitions in the face of non-stationary climate.
We present a comprehensive relative sea-level (RSL) database for north, central, and south-central Chile (18.5°S – 43.6°S) using a consistent, systematic, and internationally comparable approach. Despite its latitudinal extent, this coastline has received little rigorous or systematic attention and details of its RSL history remain largely unexplored. To address this knowledge gap, we re-evaluate the geological context and age of previously published sea-level indicators, providing 78 index points and 84 marine or terrestrial limiting points spanning from 11 ka to the present day. Many data points were originally collected for research in other fields and have not previously been examined for the information they provide on sea-level change. Additionally, we describe new sea-level data from four sites located between the Gulf of Arauco and Valdivia. By compiling RSL histories for 11 different regions, we summarise current knowledge of Chilean RSL. These histories indicate mid Holocene sea levels above present in all regions, but at highly contrasting elevations from ∼30 m to <5 m. We compare the spatiotemporal distribution of sea-level data points with a suite of glacial isostatic adjustment models and place first-order constraints on the influence of tectonic processes over 103–104 year timescales. While seven regions indicate uplift rates <1 m ka−1, the remaining regions may experience substantially higher rates. In addition to enabling discussion of the factors driving sea-level change, our compilation provides a resource to assist attempts to understand the distribution of archaeological, palaeoclimatic, and palaeoseismic evidence in the coastal zone and highlights directions for future sea-level research in Chile.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106281","usgsCitation":"Garrett, E., Melnick, D., Dura, T., Cisternas, M., Ely, L., Wesson, R.L., Jara-Munoz, J., and Whitehouse, P.L., 2020, Holocene relative sea-level change along the tectonically active Chilean coast: Quaternary Science Reviews, v. 236, 106281, 18 p., https://doi.org/10.1016/j.quascirev.2020.106281.","productDescription":"106281, 18 p.","ipdsId":"IP-117621","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":456975,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2020.106281","text":"Publisher Index Page"},{"id":387803,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2890625,\n -41.178653972331674\n ],\n [\n -72.333984375,\n -41.442726377672116\n ],\n [\n -67.939453125,\n -21.943045533438166\n ],\n [\n -73.47656249999999,\n -22.350075806124853\n ],\n [\n -75.673828125,\n -22.350075806124853\n ],\n [\n -76.2890625,\n -41.178653972331674\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"236","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Garrett, Ed","contributorId":263491,"corporation":false,"usgs":false,"family":"Garrett","given":"Ed","email":"","affiliations":[],"preferred":false,"id":820908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Melnick, Daniel","contributorId":195525,"corporation":false,"usgs":false,"family":"Melnick","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":820909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dura, Tina","contributorId":195530,"corporation":false,"usgs":false,"family":"Dura","given":"Tina","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":820910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cisternas, Marco","contributorId":198928,"corporation":false,"usgs":false,"family":"Cisternas","given":"Marco","affiliations":[],"preferred":false,"id":820911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ely, Lisa","contributorId":195528,"corporation":false,"usgs":false,"family":"Ely","given":"Lisa","affiliations":[],"preferred":false,"id":820912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wesson, Robert L. 0000-0003-2702-0012 rwesson@usgs.gov","orcid":"https://orcid.org/0000-0003-2702-0012","contributorId":850,"corporation":false,"usgs":true,"family":"Wesson","given":"Robert","email":"rwesson@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820913,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jara-Munoz, Julius","contributorId":263474,"corporation":false,"usgs":false,"family":"Jara-Munoz","given":"Julius","affiliations":[{"id":53996,"text":"Department of Earth and Environmental Sciences, University of Potsdam, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":820914,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Whitehouse, Pippa L","contributorId":263475,"corporation":false,"usgs":false,"family":"Whitehouse","given":"Pippa","email":"","middleInitial":"L","affiliations":[{"id":53998,"text":"Department of Geography, Durham University, Durham, UK","active":true,"usgs":false}],"preferred":false,"id":820915,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209736,"text":"70209736 - 2020 - Airborne lidar and electro-optical imagery along surface ruptures of the 2019 Ridgecrest earthquake sequence, Southern California","interactions":[],"lastModifiedDate":"2020-07-09T14:51:19.4533","indexId":"70209736","displayToPublicDate":"2020-04-22T09:55:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Airborne lidar and electro-optical imagery along surface ruptures of the 2019 Ridgecrest earthquake sequence, Southern California","docAbstract":"Surface rupture from the 2019 Ridgecrest earthquake sequence, initially associated with the M 6.4 foreshock, occurred on July 4 on a ~17 km long, northeast-southwest oriented, left-lateral zone of faulting. Following the M 7.1 mainshock on July 5 (local time), extensive northwest-southeast-oriented, right-lateral faulting was then also mapped along a ~50 km long zone of faults, including sub-parallel splays in several areas. The largest slip was observed in the epicentral area, and crossing the dry lakebed of China Lake to the southeast. Surface fault rupture mapping by a large team, reported elsewhere, was used to guide the airborne data acquisition reported here. Rapid rupture mapping allowed for accurate and efficient flight line planning for the high-resolution lidar and aerial photography. Flight line planning trade-offs were considered to allocate the medium (25 pulses per square meter, or ppsm) and high resolution (80 ppsm) lidar data collection polygons. The National Center for Airborne Laser Mapping (NCALM) acquired the airborne imagery with a Titan multispectral lidar system and DiMAC aerial digital camera, and USGS acquired GPS ground control data. This effort required extensive coordination with the Navy as much of the airborne data acquisition occurred within their restricted airspace at the China Lake Ranges.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190338","usgsCitation":"Hudnut, K.W., Brooks, B.A., Scharer, K.M., Hernandez, J.L., Dawson, T.E., Oskin, M.E., Arrowsmith, J.R., Goulet, C.A., Blake, K., Boggie, M.A., Bork, S., Craig L. Glennie, Fernandez-Diaz, J., Singhania, A., Hauser, D., and Sorhus, S., 2020, Airborne lidar and electro-optical imagery along surface ruptures of the 2019 Ridgecrest earthquake sequence, Southern California: Seismological Research Letters, v. 91, no. 4, p. 2096-2107, https://doi.org/10.1785/0220190338.","productDescription":"11 p.","startPage":"2096","endPage":"2107","ipdsId":"IP-114239","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":374220,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Calilfornia","otherGeospatial":"Ridgecrest Earthquake Sequence","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.125,\n 35.1154153142536\n ],\n [\n -117.2076416015625,\n 35.1154153142536\n ],\n [\n -117.2076416015625,\n 36.27085020723902\n ],\n [\n -118.125,\n 36.27085020723902\n ],\n [\n -118.125,\n 35.1154153142536\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-22","publicationStatus":"PW","contributors":{"editors":[{"text":"Brooks, Benjamin A. 0000-0001-7954-6281 bbrooks@usgs.gov","orcid":"https://orcid.org/0000-0001-7954-6281","contributorId":5237,"corporation":false,"usgs":true,"family":"Brooks","given":"Benjamin","email":"bbrooks@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":787726,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":787727,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Hernandez, Janis","contributorId":216335,"corporation":false,"usgs":false,"family":"Hernandez","given":"Janis","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":787728,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Dawson, Timothy E.","contributorId":24429,"corporation":false,"usgs":false,"family":"Dawson","given":"Timothy","email":"","middleInitial":"E.","affiliations":[{"id":7099,"text":"Calif. 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Warmer and drier conditions, coupled with dispersal distance limitations, are impeding tree seedling establishment and survival following high-severity fire. High-severity patches are commonly dominated by non-forest vegetation, a state that can be reinforced by subsequent fire events. We sought to determine the influence of fire probability on post-fire vegetation development in a severely burned landscape in New Mexico, USA. We used LANDIS-II to simulate three fire probability scenarios—historical fire probability, contemporary fire probability, and the mean of the two—with contemporary climate. As fire probability increased, the mean size of the largest fires and the mean landscape fire severity increased. These changes in fire characteristics resulted in decreased total aboveground biomass and photosynthetic capacity on the landscape after 50 years. Additionally, the distribution of individual species biomass shifted, with early successional species, especially those that resprout after fire, increasing as a fraction of total biomass with increasing fire occurrence. Counter to empirical data, our simulations did not show a conifer establishment limitation, suggesting a source of uncertainty that will need to be addressed to improve projections of forest dynamics under future climate. Even without limited conifer regeneration, continued increases in fire frequency are likely to favor resprouting species and result in a loss of forest biomass and ecosystem productivity in this southwestern forest landscape.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-020-00498-4","usgsCitation":"Keyser, A.R., Krofchek, D.J., Remy, C.C., Allen, C.D., and Hurteau, M.D., 2020, Simulated increases in fire activity reinforce shrub conversion in a southwestern US forest: Ecosystems, v. 23, p. 1702-1713, https://doi.org/10.1007/s10021-020-00498-4.","productDescription":"12 p.","startPage":"1702","endPage":"1713","ipdsId":"IP-117785","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":488249,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-05042363","text":"External Repository"},{"id":382542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8695068359375,\n 35.706377408871774\n ],\n [\n -106.3970947265625,\n 35.706377408871774\n ],\n [\n -106.3970947265625,\n 36.10681461011844\n ],\n [\n -106.8695068359375,\n 36.10681461011844\n ],\n [\n -106.8695068359375,\n 35.706377408871774\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","noUsgsAuthors":false,"publicationDate":"2020-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Keyser, Alisa R.","contributorId":248331,"corporation":false,"usgs":false,"family":"Keyser","given":"Alisa","email":"","middleInitial":"R.","affiliations":[{"id":49860,"text":"Univ. of New Mexico","active":true,"usgs":false}],"preferred":false,"id":808892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krofchek, Dan J.","contributorId":248332,"corporation":false,"usgs":false,"family":"Krofchek","given":"Dan","email":"","middleInitial":"J.","affiliations":[{"id":35754,"text":"Univ of New Mexico","active":true,"usgs":false}],"preferred":false,"id":808893,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Remy, Cecile C.","contributorId":248333,"corporation":false,"usgs":false,"family":"Remy","given":"Cecile","email":"","middleInitial":"C.","affiliations":[{"id":35754,"text":"Univ of New Mexico","active":true,"usgs":false}],"preferred":false,"id":808894,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allen, Craig D. 0000-0002-8777-5989 craig_allen@usgs.gov","orcid":"https://orcid.org/0000-0002-8777-5989","contributorId":2597,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"craig_allen@usgs.gov","middleInitial":"D.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":808895,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hurteau, Matthew D.","contributorId":211635,"corporation":false,"usgs":false,"family":"Hurteau","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":38287,"text":"Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA","active":true,"usgs":false}],"preferred":false,"id":808896,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209671,"text":"ofr20201023 - 2020 - Design and methods of the California stream quality assessment (CSQA), 2017","interactions":[],"lastModifiedDate":"2020-04-27T12:01:20.795249","indexId":"ofr20201023","displayToPublicDate":"2020-04-21T14:01:19","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1023","displayTitle":"Design and Methods of the California Stream Quality Assessment (CSQA), 2017","title":"Design and methods of the California stream quality assessment (CSQA), 2017","docAbstract":"During 2017, as part of the National Water-Quality Assessment Project, the U.S. Geological Survey conducted the California Stream Quality Assessment to investigate the quality of streams in the Central California Foothills and Coastal Mountains ecoregion, United States. The goal of the California Stream Quality Assessment study was to assess the health of wadeable streams in the region by characterizing multiple water-quality factors that are stressors to aquatic biota and by evaluating the relation between these stressors and biological indicators of stream health. Urbanization, agriculture, and modifications to streamflow are anthropogenic changes that affect water quality in the region; consequently, the study design primarily targeted sites and specific stressors associated with these activities. For the study, 85 stream sites were selected to represent the types and intensity of land use in the watershed; categories of site types were undeveloped, urban (low, medium, high), agriculture (low, high), and mixed (urban and agriculture). Most sites (about 70 percent) represent a gradient of urbanization from undeveloped to 99-percent urbanized. At most of the sites, streamgages or pressure transducers were used to monitor stream discharge and stage, as well as temperature. Water-quality samples were collected routinely at all sites and were analyzed for major ions, organic contaminants, nutrients, and suspended sediment. Sampling frequency varied on the basis of site type and location. Discrete water samples were collected weekly and generally 6 times per site, except for 11 undeveloped sites that were sampled only 4 times (during the last 4 weeks). Water sampling began at sites in the southern part of the study on March 13, 2017, and at sites in the northern part of the study on April 3, 2017. Passive samplers were deployed at most sites for measurement of polar organic contaminants (pesticides and pharmaceuticals). In May 2017, coincident with completion of water-quality sampling, an ecological survey was conducted at each site to assess benthic algal and macroinvertebrate communities and instream habitat. During the ecological surveys, a single composite streambed-sediment sample was collected for chemical analysis and toxicity testing. In addition, a few focused studies were done at subsets of sites, namely, measuring pesticides using small-volume automated samplers, measuring pesticides in biofilms, and sampling suspended sediments using passive samplers. This report describes the various study components and methods of the California Stream Quality Assessment, including measurements of water quality, sediment chemistry, habitat assessments, and ecological surveys, as well as procedures for sample analysis, quality assurance and quality control, and data management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201023","collaboration":"National Water Quality Program","usgsCitation":"May, J.T., Nowell, L.H., Coles, J.F., Button, D.T., Bell, A.H., Qi, S.L., and Van Metre, P.C., 2020, Design and methods of the California stream quality assessment, 2017: U.S. Geological Survey Open-File Report 2020–1023, 88 p.,","productDescription":"Report: x, 88 p.; 1 Table","numberOfPages":"88","onlineOnly":"Y","ipdsId":"IP-105445","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":374128,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1023/ofr20201023.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374127,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1023/coverthb.jpg"},{"id":374197,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1023/ofr20201023_app_table_1.1.xlsx","text":"Table 1.1","size":"30 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":" - Sampling matrix for the 85 sites used in the U.S. Geological Survey California Stream Quality Assessment in 2017."}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.34423828125,\n 34.50655662164561\n ],\n [\n -119.267578125,\n 34.95799531086792\n ],\n [\n -121.4208984375,\n 37.78808138412046\n ],\n [\n -122.10205078125,\n 39.14710270770074\n ],\n [\n -122.25585937500001,\n 39.41922073655956\n ],\n [\n -123.48632812499999,\n 38.85682013474361\n ],\n [\n -122.67333984374999,\n 37.87485339352928\n ],\n [\n -122.05810546875,\n 37.055177106660814\n ],\n [\n -121.79443359375,\n 36.65079252503471\n ],\n [\n -121.9482421875,\n 36.61552763134925\n ],\n [\n -121.83837890625,\n 36.155617833818525\n ],\n [\n -121.26708984374999,\n 35.585851593232356\n ],\n [\n -120.58593749999999,\n 35.04798673426734\n ],\n [\n -120.5419921875,\n 34.488447837809304\n ],\n [\n -120.34423828125,\n 34.50655662164561\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
1) Interannual variability of seed crops (CVp) has profound consequences for plant populations and food webs, where high CVp is termed ‘masting’. Here we ask: is global variation in CVp better predicted by plant or habitat differences consistent with adaptive economies of scale, in which flower and seed benefits increase disproportionately during mast years; or to passive mechanisms, in which seed production responds to variation in resource availability associated with climate variability?
2) To address this question, we compiled a dataset for phylogenetic comparative analysis of long-term fruit/seed production for plants comprising 920 time-series spanning 311 plant species.
3) Factors associated with both adaptive benefits of CVp (wind pollination and seed dispersal) and climatic variability (variability of summer precipitation) were among the best predictors of global variation in CVp. We observed a hump-shaped relationship between CVp and latitude and intermediate phylogenetic and geographic signals in CVp.
4) CVp is patterned non-randomly across the globe and over the plant tree of life, where high CVp is associated with species benefiting from economies of scale of seed or flower production and with species that experience variable rainfall over summer months when seeds usually mature.
","language":"English","publisher":"Wiley","doi":"10.1111/nph.16617","usgsCitation":"Pearse, I., LaMontagne, J., Lordon, M., Hipp, A., and Koenig, W.D., 2020, Biogeography and phylogeny of masting: Do global patterns fit functional hypotheses?: New Phytologist, v. 227, no. 5, p. 1557-1567, https://doi.org/10.1111/nph.16617.","productDescription":"11 p.","startPage":"1557","endPage":"1567","ipdsId":"IP-106437","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":456994,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/nph.16617","text":"Publisher Index Page"},{"id":437017,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U7278U","text":"USGS data release","linkHelpText":"Data on interannual seed set variation, weather, and reproductive traits for global plants"},{"id":377406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"227","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":795874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaMontagne, Jalene M.","contributorId":223096,"corporation":false,"usgs":false,"family":"LaMontagne","given":"Jalene","middleInitial":"M.","affiliations":[{"id":36623,"text":"DePaul University","active":true,"usgs":false}],"preferred":false,"id":795875,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lordon, Michael","contributorId":228860,"corporation":false,"usgs":false,"family":"Lordon","given":"Michael","email":"","affiliations":[{"id":36623,"text":"DePaul University","active":true,"usgs":false}],"preferred":false,"id":795876,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hipp, Andrew","contributorId":219598,"corporation":false,"usgs":false,"family":"Hipp","given":"Andrew","email":"","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":795877,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koenig, Walter D.","contributorId":46255,"corporation":false,"usgs":false,"family":"Koenig","given":"Walter","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":795878,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209777,"text":"70209777 - 2020 - Mapping perceived social values to support a respondent-defined restoration economy: Case study in southeastern Arizona, USA","interactions":[],"lastModifiedDate":"2020-04-30T13:31:57.437681","indexId":"70209777","displayToPublicDate":"2020-04-21T07:47:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":686,"text":"Air, Soil and Water Research","active":true,"publicationSubtype":{"id":10}},"title":"Mapping perceived social values to support a respondent-defined restoration economy: Case study in southeastern Arizona, USA","docAbstract":"Investment in conservation and ecological restoration depends on various socioeconomic factors and the social license for these activities. Our study demonstrates a method for targeting management of ecosystem services based on social values, identified by respondents through a collection of social survey data. We applied the Social Values for Ecosystem Services (SolVES) geographic information systems (GIS)-based tool in the Sonoita Creek watershed, Arizona, to map social values across the watershed. The survey focused on how respondents engage with the landscape, including through their ranking of 12 social values (eg, recreational, economic, or aesthetic value) and their placement of points on a map to identify their associations with the landscape. Additional information was elicited regarding how respondents engaged with water and various land uses, as well as their familiarity with restoration terminology. Results show how respondents perceive benefits from the natural environment. Specifically, maps of social values on the landscape show high social value along streamlines. Life-sustaining services, biological diversity, and aesthetics were the respondents’ highest rated social values. Land surrounding National Forest and private lands had lower values than conservation-based and state-owned areas, which we associate with landscape features. Results can inform watershed management by allowing managers to consider social values when prioritizing restoration or conservation investments.","language":"English","publisher":"SAGE Journals","doi":"10.1177/1178622120913318","collaboration":"","usgsCitation":"Petrakis, R., Norman, L., Lysaght, O., Sherrouse, B.C., Semmens, D.J., Bagstad, K.J., and Pritzlaff, R., 2020, Mapping perceived social values to support a respondent-defined restoration economy: Case study in southeastern Arizona, USA: Air, Soil and Water Research, v. 13, https://doi.org/10.1177/1178622120913318.","productDescription":"16 p.","startPage":"","ipdsId":"IP-112256","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":457002,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/1178622120913318","text":"Publisher Index Page"},{"id":437018,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98B4B1X","text":"USGS data release","linkHelpText":"Perceived Social Value of the Sonoita Creek Watershed using the Social Values for Ecosystem Services (SolVES) Tool, Arizona, U.S.A."},{"id":374311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.42333984375,\n 31.287939892641734\n ],\n [\n -110.63232421875,\n 31.287939892641734\n ],\n [\n -110.63232421875,\n 31.765537409484374\n ],\n [\n -111.42333984375,\n 31.765537409484374\n ],\n [\n -111.42333984375,\n 31.287939892641734\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2020-04-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Lysaght, Oliver","contributorId":224361,"corporation":false,"usgs":false,"family":"Lysaght","given":"Oliver","email":"","affiliations":[{"id":40864,"text":"Borderlands Restoration Network and London School of Economics and Political Science","active":true,"usgs":false}],"preferred":false,"id":787965,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Sherrouse, Benson C. 0000-0002-5102-5895 bcsherrouse@usgs.gov","orcid":"https://orcid.org/0000-0002-5102-5895","contributorId":2445,"corporation":false,"usgs":true,"family":"Sherrouse","given":"Benson","email":"bcsherrouse@usgs.gov","middleInitial":"C.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":787966,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":787967,"contributorType":{"id":2,"text":"Editors"},"rank":5},{"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":787968,"contributorType":{"id":2,"text":"Editors"},"rank":6},{"text":"Pritzlaff, Richard","contributorId":224362,"corporation":false,"usgs":false,"family":"Pritzlaff","given":"Richard","email":"","affiliations":[{"id":40865,"text":"The Biophilia Foundation","active":true,"usgs":false}],"preferred":false,"id":787969,"contributorType":{"id":2,"text":"Editors"},"rank":7}],"authors":[{"text":"Petrakis, Roy E. 0000-0001-8932-077X rpetrakis@usgs.gov","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":174623,"corporation":false,"usgs":true,"family":"Petrakis","given":"Roy","email":"rpetrakis@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":787963,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":787964,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lysaght, Oliver","contributorId":224361,"corporation":false,"usgs":false,"family":"Lysaght","given":"Oliver","email":"","affiliations":[{"id":40864,"text":"Borderlands Restoration Network and London School of Economics and Political Science","active":true,"usgs":false}],"preferred":false,"id":787993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherrouse, Benson C. 0000-0002-5102-5895 bcsherrouse@usgs.gov","orcid":"https://orcid.org/0000-0002-5102-5895","contributorId":2445,"corporation":false,"usgs":true,"family":"Sherrouse","given":"Benson","email":"bcsherrouse@usgs.gov","middleInitial":"C.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":787994,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Semmens, Darius J. 0000-0001-7924-6529 dsemmens@usgs.gov","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":1714,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius","email":"dsemmens@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":787995,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":787996,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pritzlaff, Richard","contributorId":224362,"corporation":false,"usgs":false,"family":"Pritzlaff","given":"Richard","email":"","affiliations":[{"id":40865,"text":"The Biophilia Foundation","active":true,"usgs":false}],"preferred":false,"id":787997,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223193,"text":"70223193 - 2020 - Understanding sportsperson retention and reactivation through license purchasing behavior","interactions":[],"lastModifiedDate":"2021-08-17T12:38:14.859094","indexId":"70223193","displayToPublicDate":"2020-04-21T07:36:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Understanding sportsperson retention and reactivation through license purchasing behavior","docAbstract":"Most state and provincial fish and wildlife agencies have access to important information about patterns in sportsperson participation through their license databases. Using transaction data from Nebraska Game and Parks Commission's electronic hunting and fishing license system, we tracked license purchases of Nebraska, USA, resident license holders in 2010 through 2017. We categorized sportspersons by gender and yearly purchases as hunting only (Hunter), fishing only (Angler), a combination of hunting and fishing (Hunter–Angler), or no purchases (Inactive). The probability of movement among active sportsperson groups was limited and varied little based on initial group participation. The Angler group had the greatest probability of an individual transitioning to the Inactive group (females = 0.39; males = 0.33). The Hunter–Angler group had the greatest probability of an individual remaining within the same group (females = 0.65; males = 0.76). There was a relatively low probability of an individual in the Hunter group moving to the Angler group and vice versa (≤0.02). The sportsperson population is dynamic and understanding patterns of sportsperson participation is important for the future of fish and wildlife management in North America. Using data readily available to most fish and wildlife agencies has the potential to significantly improve our understanding of hunter and angler participation and aid management agencies and conservation organizations in the development of more effective strategies for managing sportspersons. © 2020 The Wildlife Society.
Background
Biological controls with predators of larval mosquito vectors have historically focused almost exclusively on insectivorous animals, with few studies examining predatory plants as potential larvacidal agents. In this study, we experimentally evaluate a generalist plant predator of North America, Utricularia macrorhiza, the common bladderwort, and evaluate its larvacidal efficiency for the mosquito vectors Aedes aegypti and Aedes albopictus in no-choice, laboratory experiments. We sought to determine first, whether U. macrorhiza is a competent predator of container-breeding mosquitoes, and second, its predation efficiency for early and late instar larvae of each mosquito species.
Methods
Newly hatched, first instar Aedes albopictus and Aedes aegypti larvae were separately exposed in cohorts of 10 to field collected U. macrorhiza cuttings. Data on development time and larval survival were collected on a daily basis to ascertain the effectiveness of U. macrorhiza as a larval predator. Survival models were used to assess differences in larval survival between cohorts that were exposed to U. macrorhiza and those that were not. A permutation analysis was used to investigate whether storing U. macrorhiza in laboratory conditions for extended periods of time (1 month vs. 6 months) affected its predation efficiency.
Results
Our results indicated a 100% and 95% reduction of survival of Ae. aegypti and Ae. albopictus larvae respectively, in the presence of U. macrorhiza relative to controls within five days, with peak larvacidal efficiency in plant cuttings from ponds collected in August. Utricularia macrorhiza cuttings, which were prey-deprived, and maintained in laboratory conditions for 6 months were more effective larval predators than cuttings, which were maintained prey-free for 1 month.
Conclusions
Due to the combination of high predation efficiency and the unique biological feature of facultative predation, we suggest that U. macrorhiza warrants further development as a method for larval mosquito control.