Hawaiian volcanoes are highly accessible and well monitored by ground instruments. Nevertheless, observational gaps remain and thermal satellite imagery has proven useful in Hawai‘i for providing synoptic views of activity during intervals between field visits. Here we describe the beginning of a thermal remote sensing programme at the US Geological Survey Hawaiian Volcano Observatory (HVO). Whereas expensive receiving stations have been traditionally required to achieve rapid downloading of satellite data, we exploit free, low-latency data sources on the internet for timely access to GOES, MODIS, ASTER and EO-1 ALI imagery. Automated scripts at the observatory download these data and provide a basic display of the images. Satellite data have been extremely useful for monitoring the ongoing lava flow activity on Kīlauea's East Rift Zone at Pu‘u ‘Ō‘ō over the past few years. A recent lava flow, named Kahauale‘a 2, was upslope from residential subdivisions for over a year. Satellite data helped track the slow advance of the flow and contributed to hazard assessments. Ongoing improvement to thermal remote sensing at HVO incorporates automated hotspot detection, effusion rate estimation and lava flow forecasting, as has been done in Italy. These improvements should be useful for monitoring future activity on Mauna Loa.
","language":"English","publisher":"Geological Society of London","doi":"10.1144/SP426.17","usgsCitation":"Patrick, M.R., Kauahikaua, J.P., Orr, T., Davies, A., and Ramsey, M.S., 2016, Operational thermal remote sensing and lava flow monitoring at the Hawaiian Volcano Observatory: Geological Society of London Special Publications, v. 426, no. 1, p. 489-503, https://doi.org/10.1144/SP426.17.","productDescription":"15 p.","startPage":"489","endPage":"503","ipdsId":"IP-058010","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":341307,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"426","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-06","publicationStatus":"PW","scienceBaseUri":"591abe35e4b0a7fdb43c8bf3","contributors":{"authors":[{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":548039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":548040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orr, Tim R. torr@usgs.gov","contributorId":140376,"corporation":false,"usgs":true,"family":"Orr","given":"Tim R.","email":"torr@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":548041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davies, Ashley G.","contributorId":36827,"corporation":false,"usgs":true,"family":"Davies","given":"Ashley G.","affiliations":[],"preferred":false,"id":548042,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ramsey, Michael S.","contributorId":58514,"corporation":false,"usgs":true,"family":"Ramsey","given":"Michael","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":548043,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70174570,"text":"70174570 - 2016 - Can you hear me now? Range-testing a submerged passive acoustic receiver array in a Caribbean coral reef habitat","interactions":[],"lastModifiedDate":"2016-07-28T10:26:07","indexId":"70174570","displayToPublicDate":"2016-07-13T17:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Can you hear me now? Range-testing a submerged passive acoustic receiver array in a Caribbean coral reef habitat","docAbstract":"Submerged passive acoustic technology allows researchers to investigate spatial and temporal movement patterns of many marine and freshwater species. The technology uses receivers to detect and record acoustic transmissions emitted from tags attached to an individual. Acoustic signal strength naturally attenuates over distance, but numerous environmental variables also affect the probability a tag is detected. Knowledge of receiver range is crucial for designing acoustic arrays and analyzing telemetry data. Here, we present a method for testing a relatively large-scale receiver array in a dynamic Caribbean coastal environment intended for long-term monitoring of multiple species. The U.S. Geological Survey and several academic institutions in collaboration with resource management at Buck Island Reef National Monument (BIRNM), off the coast of St. Croix, recently deployed a 52 passive acoustic receiver array. We targeted 19 array-representative receivers for range-testing by submersing fixed delay interval range-testing tags at various distance intervals in each cardinal direction from a receiver for a minimum of an hour. Using a generalized linear mixed model (GLMM), we estimated the probability of detection across the array and assessed the effect of water depth, habitat, wind, temperature, and time of day on the probability of detection. The predicted probability of detection across the entire array at 100 m distance from a receiver was 58.2% (95% CI: 44.0–73.0%) and dropped to 26.0% (95% CI: 11.4–39.3%) 200 m from a receiver indicating a somewhat constrained effective detection range. Detection probability varied across habitat classes with the greatest effective detection range occurring in homogenous sand substrate and the smallest in high rugosity reef. Predicted probability of detection across BIRNM highlights potential gaps in coverage using the current array as well as limitations of passive acoustic technology within a complex coral reef environment.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.2228","usgsCitation":"Selby, T.H., Hart, K.M., Fujisaki, I., Smith, B.J., Pollock, C.J., Hillis-Star, Z.M., Lundgren, I., and Oli, M.K., 2016, Can you hear me now? Range-testing a submerged passive acoustic receiver array in a Caribbean coral reef habitat: Ecology and Evolution, v. 6, no. 14, p. 4823-4835, https://doi.org/10.1002/ece3.2228.","productDescription":"13 p.","startPage":"4823","endPage":"4835","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070624","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470751,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.2228","text":"Publisher Index Page"},{"id":325231,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Buck Island Reef National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.65763092041016,\n 17.771573896194067\n ],\n [\n -64.65763092041016,\n 17.833027985030018\n ],\n [\n -64.57592010498047,\n 17.833027985030018\n ],\n [\n -64.57592010498047,\n 17.771573896194067\n ],\n [\n -64.65763092041016,\n 17.771573896194067\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"14","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-17","publicationStatus":"PW","scienceBaseUri":"57875827e4b0d27deb364f54","contributors":{"authors":[{"text":"Selby, Thomas H. 0000-0003-2116-0807 tselby@usgs.gov","orcid":"https://orcid.org/0000-0003-2116-0807","contributorId":5685,"corporation":false,"usgs":true,"family":"Selby","given":"Thomas","email":"tselby@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":642332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Kristen M. 0000-0002-5257-7974 kristen_hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":1966,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","email":"kristen_hart@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":642331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fujisaki, Ikuko","contributorId":38359,"corporation":false,"usgs":false,"family":"Fujisaki","given":"Ikuko","affiliations":[],"preferred":false,"id":642333,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Brian J. 0000-0002-0531-0492 bjsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":899,"corporation":false,"usgs":true,"family":"Smith","given":"Brian","email":"bjsmith@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":642334,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pollock, Clayton J","contributorId":172870,"corporation":false,"usgs":false,"family":"Pollock","given":"Clayton","email":"","middleInitial":"J","affiliations":[{"id":5106,"text":"National Park Service, Yellowstone National Park, Mammoth, Wyoming 82190","active":true,"usgs":false}],"preferred":false,"id":642335,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hillis-Star, Zandy M","contributorId":106418,"corporation":false,"usgs":true,"family":"Hillis-Star","given":"Zandy","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":642336,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lundgren, Ian","contributorId":29727,"corporation":false,"usgs":true,"family":"Lundgren","given":"Ian","affiliations":[],"preferred":false,"id":642337,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oli, Madan K.","contributorId":86089,"corporation":false,"usgs":true,"family":"Oli","given":"Madan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":642338,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70174573,"text":"sir20105090Z - 2016 - Spatial database for a global assessment of undiscovered copper resources: Chapter Z in Global mineral resource assessment","interactions":[{"subject":{"id":70174573,"text":"sir20105090Z - 2016 - Spatial database for a global assessment of undiscovered copper resources: Chapter Z in Global mineral resource assessment","indexId":"sir20105090Z","publicationYear":"2016","noYear":false,"chapter":"Z","title":"Spatial database for a global assessment of undiscovered copper resources: Chapter Z in Global mineral resource assessment"},"predicate":"IS_PART_OF","object":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"id":1}],"isPartOf":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"lastModifiedDate":"2018-10-29T08:57:57","indexId":"sir20105090Z","displayToPublicDate":"2016-07-13T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5090","chapter":"Z","title":"Spatial database for a global assessment of undiscovered copper resources: Chapter Z in Global mineral resource assessment","docAbstract":"As part of the first-ever U.S. Geological Survey global assessment of undiscovered copper resources, data common to several regional spatial databases published by the U.S. Geological Survey, including one report from Finland and one from Greenland, were standardized, updated, and compiled into a global copper resource database. This integrated collection of spatial databases provides location, geologic and mineral resource data, and source references for deposits, significant prospects, and areas permissive for undiscovered deposits of both porphyry copper and sediment-hosted copper. The copper resource database allows for efficient modeling on a global scale in a geographic information system (GIS) and is provided in an Esri ArcGIS file geodatabase format.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Global mineral resource assessment (Scientific Investigations Report 2010-5090)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105090Z","usgsCitation":"Dicken, C.L., Dunlap, Pamela, Parks, H.L., Hammarstrom, J.M., and Zientek, M.L., 2016, Spatial database for a global assessment of undiscovered copper resources: U.S. Geological Survey Scientific Investigations Report 2010–5090–Z, 29 p., and GIS data, available at https://dx.doi.org/10.3133/sir20105090Z.","productDescription":"Report: v, 29 p.; GIS Data","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066779","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":325183,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5090/z/sir20105090z.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090-Z Report PDF"},{"id":325184,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2010/5090/z/sir20105090z_gis.zip","text":"GIS Data","size":"66 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2010-5090-Z GIS Data"},{"id":325182,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2010/5090/z/coverthb.jpg"}],"contact":"Contact Information, Mineral Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
913 National Center
Reston, VA 20192
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The mid-Piacenzian is known as a period of relative warmth when compared to the present day. A comprehensive understanding of conditions during the Piacenzian serves as both a conceptual model and a source for boundary conditions as well as means of verification of global climate model experiments. In this paper we present the PRISM4 reconstruction, a paleoenvironmental reconstruction of the mid-Piacenzian ( ∼ 3 Ma) containing data for paleogeography, land and sea ice, sea-surface temperature, vegetation, soils, and lakes. Our retrodicted paleogeography takes into account glacial isostatic adjustments and changes in dynamic topography. Soils and lakes, both significant as land surface features, are introduced to the PRISM reconstruction for the first time. Sea-surface temperature and vegetation reconstructions are unchanged but now have confidence assessments. The PRISM4 reconstruction is being used as boundary condition data for the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) experiments.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/cp-12-1519-2016","usgsCitation":"Dowsett, H.J., Dolan, A.M., Rowley, D., Moucha, R., Forte, A., Mitrovica, J.X., Pound, M., Salzmann, U., Robinson, M.M., Chandler, M., Foley, K.M., and Haywood, A.M., 2016, The PRISM4 (mid-Piacenzian) paleoenvironmental reconstruction: Climate of the Past, v. 12, no. 7, p. 1519-1538, https://doi.org/10.5194/cp-12-1519-2016.","productDescription":"20 p.","startPage":"1519","endPage":"1538","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074298","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":470753,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-12-1519-2016","text":"Publisher Index Page"},{"id":438588,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NF43WW","text":"USGS data release","linkHelpText":"PRISM4 (mid-Piacenzian) Paleoenvironmental Reconstruction Data"},{"id":325569,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-13","publicationStatus":"PW","scienceBaseUri":"5793444de4b0eb1ce79e8c1b","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":643308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dolan, Aisling M.","contributorId":30117,"corporation":false,"usgs":true,"family":"Dolan","given":"Aisling","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":643309,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rowley, David","contributorId":173099,"corporation":false,"usgs":false,"family":"Rowley","given":"David","email":"","affiliations":[{"id":12621,"text":"University of Chicago and University of South Florida","active":true,"usgs":false}],"preferred":false,"id":643310,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moucha, Robert","contributorId":173102,"corporation":false,"usgs":false,"family":"Moucha","given":"Robert","email":"","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":643317,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Forte, Alessandro","contributorId":173103,"corporation":false,"usgs":false,"family":"Forte","given":"Alessandro","email":"","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":643318,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mitrovica, Jerry X.","contributorId":86200,"corporation":false,"usgs":true,"family":"Mitrovica","given":"Jerry","email":"","middleInitial":"X.","affiliations":[],"preferred":false,"id":643319,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pound, Matthew","contributorId":173100,"corporation":false,"usgs":false,"family":"Pound","given":"Matthew","email":"","affiliations":[{"id":18103,"text":"Northumbria University, Newcastle Upon Tyne, UK","active":true,"usgs":false}],"preferred":false,"id":643311,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Salzmann, Ulrich","contributorId":173101,"corporation":false,"usgs":false,"family":"Salzmann","given":"Ulrich","email":"","affiliations":[{"id":18103,"text":"Northumbria University, Newcastle Upon Tyne, UK","active":true,"usgs":false}],"preferred":false,"id":643312,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Robinson, Marci M. 0000-0002-9200-4097 mmrobinson@usgs.gov","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":2082,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci","email":"mmrobinson@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":643313,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Chandler, Mark","contributorId":17320,"corporation":false,"usgs":true,"family":"Chandler","given":"Mark","affiliations":[],"preferred":false,"id":643314,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Foley, Kevin M. 0000-0003-1013-462X kfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-1013-462X","contributorId":2543,"corporation":false,"usgs":true,"family":"Foley","given":"Kevin","email":"kfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":643315,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Haywood, Alan M.","contributorId":86663,"corporation":false,"usgs":true,"family":"Haywood","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":643316,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70173803,"text":"pp1812 - 2016 - Eruptive history of Mammoth Mountain and its mafic periphery, California","interactions":[],"lastModifiedDate":"2017-01-05T10:07:20","indexId":"pp1812","displayToPublicDate":"2016-07-13T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1812","title":"Eruptive history of Mammoth Mountain and its mafic periphery, California","docAbstract":"This report and accompanying geologic map portray the eruptive history of Mammoth Mountain and a surrounding array of contemporaneous volcanic units that erupted in its near periphery. The moderately alkaline Mammoth eruptive suite, basaltic to rhyodacitic, represents a discrete new magmatic system, less than 250,000 years old, that followed decline of the subalkaline rhyolitic system active beneath adjacent Long Valley Caldera since 2.2 Ma (Hildreth, 2004). The scattered vent array of the Mammoth system, 10 by 20 km wide, is unrelated to the rangefront fault zone, and its broad nonlinear footprint ignores both Long Valley Caldera and the younger Mono-Inyo rangefront vent alignment.
\nThe Mammoth Lakes area of Mono County, owing to its spectacular alpine landscape, has become one of California’s busiest recreational playgrounds and a regional center of real estate development. The name applies to the town of Mammoth Lakes as well as to the cluster of lakes in a large cirque southwest of town that is now locally called the Lakes Basin. The town has spread around the eastern base of Mammoth Mountain, a late Pleistocene pile of silicic lava domes, and has locally expanded onto lower slopes of the mountain itself (fig. 1). Looming nearly 1,000 m above the downtown area, much of the 5-km-wide volcanic edifice has been laced with chair lifts, gondolas, ski runs, and bike paths by the Mammoth Mountain Ski Area, a corporate entity under permit from Inyo National Forest. In addition to skiing, longestablished, snowboarding and summertime mountain biking have recently become major activities. Tourism to Mammoth Lakes is estimated to be 1,300,000 visitors per winter and 1,500,000 per summer. Some of America’s top long-distance runners also live and train in Mammoth Lakes, attracted by its elevation and its variety of challenging trails.
\nAt the western base of Mammoth Mountain, along the canyon of the Middle Fork San Joaquin River, lies the Devils Postpile National Monument, a National Park Service enclave surrounded by extensive wilderness areas administered by the U.S. Forest Service. As many as 2,000 visitors per day enter the monument during the summer season. The area also contains several of the busiest trailheads in the Sierra Nevada, providing wilderness access for hikers, pack animals, mountaineers, and fishermen.
\nMany geographic names that appear in this report are informal despite having been in local use for decades. Most appear on maps distributed by the Town of Mammoth Lakes or the Mammoth Mountain Ski Area and can be found here on map figures 2–5, on several photo figures, and on the geologic map.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1812","usgsCitation":"Hildreth, Wes, and Fierstein, Judy, 2016, Eruptive history of Mammoth Mountain and its mafic periphery, California: U.S. Geological Survey Professional Paper 1812, 128 p., 2 plates, scale 1:24,000, https://www.dx.doi.org/10.3133/pp1812. ","productDescription":"Pamphlet: vi, 128 p.; 2 Plates: 52.28 x 50.79 inches and 46.27 x 38.77 inches; Appendix; Metadata; Read Me; Spatial Data","numberOfPages":"138","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-046206","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":325069,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/pp/1812/pp1812_plate1.pdf","text":"Plate 1","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1812 Plate 1"},{"id":325068,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1812/pp1812_pamphlet.pdf","text":"Pamphlet","size":"27.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1812 Pamphlet"},{"id":325070,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/pp/1812/pp1812_plate2.pdf","text":"Plate 2","size":"4.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1812 Plate 2"},{"id":325071,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1812/pp1812_appendix.xls","text":"Appendix","size":"474 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1812 Appendix"},{"id":325067,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1812/coverthb.jpg"},{"id":329500,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/pp/1812/pp1812_mammothmountain.zip","text":"Database","size":"213.7 MB","linkFileType":{"id":6,"text":"zip"},"description":"PP 1812 Spatial Data"},{"id":329501,"rank":7,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/pp/1812/pp1812_readme.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"PP 1812 Read Me"},{"id":329502,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/pp/1812/pp1812_mammothmountain.txt","text":"Data","size":"21 KB","linkFileType":{"id":2,"text":"txt"},"description":"PP 1812 Metadata"},{"id":329503,"rank":9,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/pp/1812/pp1812_topo_base_NAD83_FGDC.txt","text":"Base Map","size":"11 KB","linkFileType":{"id":2,"text":"txt"},"description":"PP 1812 Base Map Metadata"}],"country":"United States","state":"California","otherGeospatial":"Mammoth Mountain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.27307128906249,\n 37.53368798315969\n ],\n [\n -119.27307128906249,\n 37.96477144899956\n ],\n [\n -118.52462768554686,\n 37.96477144899956\n ],\n [\n -118.52462768554686,\n 37.53368798315969\n ],\n [\n -119.27307128906249,\n 37.53368798315969\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Volcano Science Center - Menlo Park
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345 Middlefield Road, MS 910
Menlo Park, CA 94025
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Play fairway analysis in geothermal exploration derives from a systematic methodology originally developed within the petroleum industry and is based on a geologic and hydrologic framework of identified geothermal systems. We are tailoring this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region. This project has contributed to the success of this approach by cataloging the critical elements controlling exploitable hydrothermal systems, establishing risk matrices that evaluate these elements in terms of both probability of success and level of knowledge, and building automated tools to process results. ArcGIS was used to compile a range of different data types, which we refer to as ‘elements’ (e.g., faults, vents, heatflow…), with distinct characteristics and confidence values.
Raw data for each element were transformed into data layers with a common format. Because different data types have different uncertainties, each evidence layer had an accompanying confidence layer, which reflects spatial variations in these uncertainties. Risk maps represent the product of evidence and confidence layers, and are the basic building blocks used to construct Common Risk Segment (CRS) maps for heat, permeability, and seal. CRS maps quantify the variable risk associated with each of these critical components. In a final step, the three CRS maps were combined into a Composite Common Risk Segment (CCRS) map for analysis that reveals favorable areas for geothermal exploration.
Python scripts were developed to automate data processing and to enhance the flexibility of the data analysis. Python scripting provided the structure that makes a custom workflow possible. Nearly every tool available in the ArcGIS ArcToolbox can be executed using commands in the Python programming language. This enabled the construction of a group of tools that could automate most of the processing for the project. Currently, our tools are repeatable, scalable, modifiable, and transferrable, allowing us to automate the task of data analysis and the production of CRS and CCRS maps. Our ultimate goal is to produce a toolkit that can be imported into ArcGIS and applied to any geothermal play type, with fully tunable parameters that will allow for the production of multiple versions of the CRS and CCRS maps in order to better test for sensitivity and to validate results.
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Two indicators of potential corrosivity—the Langelier Saturation Index (LSI) and the Potential to Promote Galvanic Corrosion (PPGC)—were used to identify which areas in the United States might be more susceptible to elevated concentrations of metals in household drinking water and which areas might be less susceptible. On the basis of the LSI, about one-third of the samples collected from about 21,000 groundwater sites are classified as potentially corrosive. On the basis of the PPGC, about two-thirds of the samples collected from about 27,000 groundwater sites are classified as moderate PPGC, and about one-tenth as high PPGC. Potentially corrosive groundwater occurs in all 50 states and the District of Columbia.
National maps have been prepared to identify the occurrence of potentially corrosive groundwater in the 50 states and the District of Columbia. Eleven states and the District of Columbia were classified as having a very high prevalence of potentially corrosive groundwater, 14 states as having a high prevalence of potentially corrosive groundwater, 19 states as having a moderate prevalence of potentially corrosive groundwater, and 6 states as having a low prevalence of potentially corrosive groundwater. These findings have the greatest implication for people dependent on untreated groundwater for drinking water, such as the 44 million people that are self-supplied and depend on domestic wells or springs for their water supply.
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U.S. Geological Survey
Denver Federal Center
West 6th Avenue and Kipling Street
Lakewood, CO 80225
http://water.usgs.gov/nawqa/
Suspended-sediment characteristics can be computed using acoustic indices derived from acoustic Doppler velocity meter (ADVM) backscatter data. The sediment acoustic index method applied in these types of studies can be used to more accurately and cost-effectively provide time-series estimates of suspended-sediment concentration and load, which is essential for informed solutions to many sediment-related environmental, engineering, and agricultural concerns. Advantages of this approach over other sediment surrogate methods include: (1) better representation of cross-sectional conditions from large measurement volumes, compared to other surrogate instruments that measure data at a single point; (2) high temporal resolution of collected data; (3) data integrity when biofouling is present; and (4) less rating curve hysteresis compared to streamflow as a surrogate. An additional advantage of this technique is the potential expansion of monitoring suspended-sediment concentrations at sites with existing ADVMs used in streamflow velocity monitoring. This report provides much-needed standard techniques for sediment acoustic index methods to help ensure accurate and comparable documented results.
\nA sediment acoustic index gage is used to collect continuous acoustic backscatter data, using an ADVM deployed in a fixed location, which are related to results from discrete suspended-sediment samples. The raw ADVM backscatter data are adjusted for variables affecting backscatter other than the sediment concentration to compute the sediment-corrected backscatter (SCB) and sediment attenuation coefficient (SAC). The sediment acoustic index rating (rating) is then developed by relating the sediment characteristics from the periodic samples to the SCB and (or) SAC and other explanatory variables in a site-specific, instrument-specific, simple or multiple linear regression model. The rating is reviewed and checked to ensure the technique has been applied appropriately. This review includes an assessment of the theoretical soundness, the adequacy of the model calibration dataset, and the quality of the regression model and regression diagnostics. The rating can then be applied to the acoustic surrogates and other explanatory variables to obtain continuous records of computed suspended-sediment concentration. The estimates of suspended-sediment concentration can then be paired with streamflow data, if available, to compute continuous records of suspended-sediment load.
\nOnce developed, sediment acoustic index ratings must be validated with additional suspended-sediment samples, beyond the period of record used in the rating development, to verify that the regression model continues to adequately represent sediment conditions within the stream. Changes in ADVM configuration or installation, or replacement with another ADVM, may require development of a new rating. The best practices described in this report can be used to develop continuous estimates of suspended-sediment concentration and load using sediment acoustic surrogates to enable more informed and accurate responses to diverse sedimentation issues.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Sediment and erosion techniques in Book 3: Applications of Hydraulics","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm3C5","usgsCitation":"Landers, M.N., Straub, T.D., Wood, M.S., and Domanski, M.M., 2016, Sediment acoustic index method for computing continuous suspended-sediment concentrations: U.S. Geological Survey Techniques and Methods, book 3, chap. C5, 63 p., https://dx.doi.org/10.3133/tm3C5.","productDescription":"vii, 63 p.","endPage":"83","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062080","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":324847,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/03/c05/coverthb.jpg"},{"id":324848,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/03/c05/tm3c5.pdf","text":"Report","size":"9.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 3C-05"}],"publicComments":"This report is Chapter 5 of Section C: Sediment and erosion techniques in Book 3: Applications of Hydraulics.","contact":"Chief, Office of Surface Water
U.S. Geological Survey
415 National Center
12201 Sunrise Valley Drive
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(703) 648-5301
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","tableOfContents":"In response to the recent removal of international sanctions on Iran, including the lifting of “secondary” sanctions by the United States on investment into and trade with Iran, the U.S. Geological Survey National Minerals Information Center compiled and analyzed available information on the current state of Iran’s nonfuel minerals industry. This Circular features a new map and table that identify existing mines and mineral-processing facilities and provide information on location, ownership, and capacity for metals and industrial minerals whose output levels may substantially change in the near future. Additionally, the report covers Iran’s mineral resources and reserves, official mineral production targets for 2025, and current output and share of global mineral production. Recent trends and developments in individual mineral commodities are discussed, including mineral exploration and partnerships with foreign investors.
\nThe U.S. Geological Survey estimated that Iran held globally significant reserves of feldspar (2d largest in the world), barite (5th largest), gypsum (5th largest), fluorspar (8th largest), and iron ore (10th largest). The Government of Iran claimed to also have significant reserves of chromium, copper, gold, manganese, phosphate rock, and zinc. In 2014, Iran was the second-leading producer of gypsum and the sixth-leading producer of barite, with 6.1 percent and 3.6 percent of world output, respectively. Iran was also the world’s 7th-leading producer of cement, feldspar, and fluorspar; 8th-leading producer of bentonite; 9th-leading producer of molybdenum; 11th-leading producer of iron ore; and 14th-leading producer of crude steel. The Government of Iran plans to quadruple the output of aluminum, copper cathode, direct-reduced iron, and iron ore pellets; triple that of crude steel and gold; and double that of cement, pig iron, and zinc by 2025. It also plans to double the contribution of mining and to quadruple that of mineral processing to the national economy in the next decade. In order to achieve these major goals, the construction and expansion of several mines and mineral facilities are planned or under development. Whether Iran’s annual mineral production increases as rapidly as envisioned by the Government will depend largely on the amount of foreign investment into the minerals industry; integration of modern technology into mineral facilities; and availability of energy to aluminum, copper, and steel plants at competitive prices to international investors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1421","isbn":"978-1-4113-4066-4","usgsCitation":"Hastorun, Sinan, Renaud, K.M., and Lederer, G.W., 2016, Recent trends in the nonfuel minerals industry of Iran:\nU.S. Geological Survey Circular 1421, 18 p., https://dx.doi.org/10.3133/cir1421.","productDescription":"v, 18 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075384","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":324839,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1421/circ1421.pdf","text":"Report","size":"1.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIRC 1421"},{"id":324838,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1421/coverthb.jpg"}],"country":"Iran","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": 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U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Or visit the USGS Minerals Information Web site at http://minerals.usgs.gov/minerals/
","tableOfContents":"The crater lake of Kawah Ijen volcano, East Java, Indonesia, has displayed large and rapid changes in temperature at point locations during periods of unrest, but measurement techniques employed to-date have not resolved how the lake’s thermal regime has evolved over both space and time. We applied a novel approach for mapping and monitoring variations in crater-lake apparent surface (“skin”) temperatures at high spatial (~32 cm) and temporal (every two minutes) resolution at Kawah Ijen on 18 September 2014. We used a ground-based FLIR T650sc camera with digital and thermal infrared (TIR) sensors from the crater rim to collect (1) a set of visible imagery around the crater during the daytime and (2) a time series of co-located visible and TIR imagery at one location from pre-dawn to daytime. We processed daytime visible imagery with the Structure-from-Motion photogrammetric method to create a digital elevation model onto which the time series of TIR imagery was orthorectified and georeferenced. Lake apparent skin temperatures typically ranged from ~21 to 33oC. At two locations, apparent skin temperatures were ~ 4 and 7 oC less than in-situ lake temperature measurements at 1.5 and 5 m depth, respectively. These differences, as well as the large spatio-temporal variations observed in skin temperatures, were likely largely associated with atmospheric effects such as evaporative cooling of the lake surface and infrared absorption by water vapor and SO2. Calculations based on orthorectified TIR imagery thus yielded underestimates of volcanic heat fluxes into the lake, whereas volcanic heat fluxes estimated based on in-situ temperature measurements (68 to 111 MW) were likely more representative of Kawah Ijen in a quiescent state. The ground-based imaging technique should provide a valuable tool to continuously monitor crater-lake temperatures and contribute insight into the spatio-temporal evolution of these temperatures associated with volcanic activity.
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Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-08","publicationStatus":"PW","scienceBaseUri":"5790a181e4b030378fb47431","contributors":{"authors":[{"text":"Lewicki, Jennifer L. 0000-0003-1994-9104 jlewicki@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-9104","contributorId":5071,"corporation":false,"usgs":true,"family":"Lewicki","given":"Jennifer","email":"jlewicki@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":642918,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corentin Caudron","contributorId":172993,"corporation":false,"usgs":false,"family":"Corentin Caudron","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":642919,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Hinsberg, Vincent","contributorId":172994,"corporation":false,"usgs":false,"family":"van Hinsberg","given":"Vincent","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":642920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hilley, George","contributorId":147793,"corporation":false,"usgs":false,"family":"Hilley","given":"George","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":642921,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170126,"text":"70170126 - 2016 - Using Cape Sable seaside sparrow distribution data for water management decision support","interactions":[],"lastModifiedDate":"2016-07-11T15:35:32","indexId":"70170126","displayToPublicDate":"2016-07-08T09:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Using Cape Sable seaside sparrow distribution data for water management decision support","docAbstract":"The Cape Sable Seaside Sparrow (Ammodramus maritimus mirabilis; hereafter sparrow) is endemic to south Florida and a key indicator species of marl prairie, the most diverse freshwater community in the Florida Everglades. Marl prairie habitat is shaped by intermediate levels of disturbances such as flooding, drying, and fire, which maintain periphyton production (Gaiser et al. 2011), vegetation composition (Sah et al. 2011), and habitat structure for wildlife (Lockwood et al. 2003). Historically, patches of marl prairie shifted in response to changing climatic conditions,; however, habitat loss and hydrologic alteration have restricted the sparrow’s range and increased their sensitivity to changing hydropatterns. As a result, sparrow numbers have declined as much as 60% range-wide since 1992 (Curnutt et al. 1998, Nott et al. 1998). Currently, the sparrow is restricted to the freshwater prairies of the Everglades National Park (ENP) and Big Cypress Preserve (Lockwood et al. 1997). Because this non-migratory bird is restricted in its range it was among the first species to be listed as endangered by the US Fish and Wildlife Service on March 11, 1967 (Pimm et al. 2000). Now protected by the Endangered Species Act of 1973, the sparrow is listed as an endangered species, and the marl prairies that it resides in are listed as critical habitat. Since its designation as an endangered species, federal agencies have a statutory obligation to not jeopardize the survival of the species or modify its critical habitat. However, there are still uncertainties in how to increase suitable habitat within and surrounding the six existing sparrow subpopulations (Fig. 1) which are vulnerable to environmental stochasticity because of their small population size and restricted range. Since Because maintenance and creation of suitable habitat is seen as the most important pathway to the persistence of sparrow subpopulations (Sustainable Ecosystems Institute 2007), emphasis should be on identifying factors affecting sparrow habitat suitability and expanding the total area of suitable habitat over a gradient of environmental conditions. Our objective is to improve the definition of suitable sparrow habitat based on the relationship between daily sparrow distributions from 1992-present and hydrologic and habitat variables. Further, these models can provide an estimate of habitat quality when linked with estimates of reproductive responses.
","largerWorkTitle":"Report to the U.S. Fish and Wildlife Service","language":"English","usgsCitation":"Beerens, J.M., and Romanach, S.S., 2016, Using Cape Sable seaside sparrow distribution data for water management decision support, 20 p.","productDescription":"20 p.","startPage":"1","endPage":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073857","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":325061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5784c347e4b0e02680be59fa","contributors":{"authors":[{"text":"Beerens, James M. 0000-0001-8143-916X jbeerens@usgs.gov","orcid":"https://orcid.org/0000-0001-8143-916X","contributorId":143722,"corporation":false,"usgs":true,"family":"Beerens","given":"James","email":"jbeerens@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":626225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Romanach, Stephanie S. 0000-0003-0271-7825 sromanach@usgs.gov","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":140419,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","email":"sromanach@usgs.gov","middleInitial":"S.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":626226,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70174292,"text":"70174292 - 2016 - M≥7 Earthquake rupture forecast and time-dependent probability for the Sea of Marmara region, Turkey","interactions":[],"lastModifiedDate":"2016-07-07T12:07:38","indexId":"70174292","displayToPublicDate":"2016-07-07T13:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"M≥7 Earthquake rupture forecast and time-dependent probability for the Sea of Marmara region, Turkey","docAbstract":"We forecast time-independent and time-dependent earthquake ruptures in the Marmara region of Turkey for the next 30 years using a new fault-segmentation model. We also augment time-dependent Brownian Passage Time (BPT) probability with static Coulomb stress changes (ΔCFF) from interacting faults. We calculate Mw > 6.5 probability from 26 individual fault sources in the Marmara region. We also consider a multisegment rupture model that allows higher-magnitude ruptures over some segments of the Northern branch of the North Anatolian Fault Zone (NNAF) beneath the Marmara Sea. A total of 10 different Mw=7.0 to Mw=8.0 multisegment ruptures are combined with the other regional faults at rates that balance the overall moment accumulation. We use Gaussian random distributions to treat parameter uncertainties (e.g., aperiodicity, maximum expected magnitude, slip rate, and consequently mean recurrence time) of the statistical distributions associated with each fault source. We then estimate uncertainties of the 30-year probability values for the next characteristic event obtained from three different models (Poisson, BPT, and BPT+ΔCFF) using a Monte Carlo procedure. The Gerede fault segment located at the eastern end of the Marmara region shows the highest 30-yr probability, with a Poisson value of 29%, and a time-dependent interaction probability of 48%. We find an aggregated 30-yr Poisson probability of M >7.3 earthquakes at Istanbul of 35%, which increases to 47% if time dependence and stress transfer are considered. We calculate a 2-fold probability gain (ratio time-dependent to time-independent) on the southern strands of the North Anatolian Fault Zone.
","language":"English","publisher":"AGU","doi":"10.1002/2015JB012595","usgsCitation":"Murru, M., Akinci, A., Falcone, G., Pucci, S., Console, R., and Parsons, T.E., 2016, M≥7 Earthquake rupture forecast and time-dependent probability for the Sea of Marmara region, Turkey: Journal of Geophysical Research, v. 121, no. 4, p. 2679-2707, https://doi.org/10.1002/2015JB012595.","productDescription":"29 p.","startPage":"2679","endPage":"2707","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074489","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488316,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012595","text":"Publisher Index Page"},{"id":324812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":324806,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1002/2015JB012595/full"}],"country":"Turkey","otherGeospatial":"Sea if Marmara","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 25.9716796875,\n 40.75557964275591\n ],\n [\n 27.9931640625,\n 41.9921602333763\n ],\n [\n 31.92626953125,\n 41.57436130598913\n ],\n [\n 30.278320312499996,\n 39.740986355883564\n ],\n [\n 27.454833984375,\n 39.93501296038254\n ],\n [\n 26.411132812499996,\n 39.977120098439634\n ],\n [\n 25.9716796875,\n 40.75557964275591\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-18","publicationStatus":"PW","scienceBaseUri":"577f6f1be4b0ef4d2f45d436","contributors":{"authors":[{"text":"Murru, Maura","contributorId":172714,"corporation":false,"usgs":false,"family":"Murru","given":"Maura","email":"","affiliations":[{"id":27088,"text":"Istituto Nazionale di Geofisica e Vulcanologia (INGV)","active":true,"usgs":false}],"preferred":false,"id":641702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Akinci, Aybige","contributorId":172715,"corporation":false,"usgs":false,"family":"Akinci","given":"Aybige","email":"","affiliations":[{"id":27088,"text":"Istituto Nazionale di Geofisica e Vulcanologia (INGV)","active":true,"usgs":false}],"preferred":false,"id":641703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Falcone, Guiseppe","contributorId":172716,"corporation":false,"usgs":false,"family":"Falcone","given":"Guiseppe","email":"","affiliations":[{"id":27088,"text":"Istituto Nazionale di Geofisica e Vulcanologia (INGV)","active":true,"usgs":false}],"preferred":false,"id":641704,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pucci, Stefano","contributorId":172717,"corporation":false,"usgs":false,"family":"Pucci","given":"Stefano","email":"","affiliations":[{"id":27088,"text":"Istituto Nazionale di Geofisica e Vulcanologia (INGV)","active":true,"usgs":false}],"preferred":false,"id":641705,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Console, Rodolfo","contributorId":172718,"corporation":false,"usgs":false,"family":"Console","given":"Rodolfo","email":"","affiliations":[{"id":27089,"text":"Center of Integrated Geomorphology for the Mediterranean Area, Potenza, Italy","active":true,"usgs":false}],"preferred":false,"id":641706,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":641701,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70174286,"text":"70174286 - 2016 - Demography of an apex predator at the edge of its range: impacts of changing sea ice on polar bears in Hudson Bay","interactions":[],"lastModifiedDate":"2016-07-15T15:10:32","indexId":"70174286","displayToPublicDate":"2016-07-07T13:00:00","publicationYear":"2016","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":"Demography of an apex predator at the edge of its range: impacts of changing sea ice on polar bears in Hudson Bay","docAbstract":"Changes in the abundance and distribution of wildlife populations are common consequences of historic and contemporary climate change. Some Arctic marine mammals, such as the polar bear (Ursus maritimus), may be particularly vulnerable to such changes due to the loss of Arctic sea ice. We evaluated the impacts of environmental variation on demographic rates for the Western Hudson Bay (WH), polar bear subpopulation from 1984 to 2011 using live-recapture and dead-recovery data in a Bayesian implementation of multistate capture–recapture models. We found that survival of female polar bears was related to the annual timing of sea ice break-up and formation. Using estimated vital rates (e.g., survival and reproduction) in matrix projection models, we calculated the growth rate of the WH subpopulation and projected population responses under different environmental scenarios while accounting for parametric uncertainty, temporal variation, and demographic stochasticity. Our analysis suggested a long-term decline in the number of bears from 1185 (95% Bayesian credible interval [BCI] = 993–1411) in 1987 to 806 (95% BCI = 653–984) in 2011. In the last 10 yr of the study, the number of bears appeared stable due to temporary stability in sea ice conditions (mean population growth rate for the period 2001–2010 = 1.02, 95% BCI = 0.98–1.06). Looking forward, we estimated long-term growth rates for the WH subpopulation of ~1.02 (95% BCI = 1.00–1.05) and 0.97 (95% BCI = 0.92–1.01) under hypothetical high and low sea ice conditions, respectively. Our findings support previous evidence for a demographic linkage between sea ice conditions and polar bear population dynamics. Furthermore, we present a robust framework for sensitivity analysis with respect to continued climate change (e.g., to inform scenario planning) and for evaluating the combined effects of climate change and management actions on the status of wildlife populations.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-1256","usgsCitation":"Lunn, N., Servanty, S., Regehr, E.V., Converse, S.J., Richardson, E.S., and Stirling, I., 2016, Demography of an apex predator at the edge of its range: impacts of changing sea ice on polar bears in Hudson Bay: Ecological Applications, v. 26, no. 5, p. 1302-1320, https://doi.org/10.1890/15-1256.","productDescription":"19 p.","startPage":"1302","endPage":"1320","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070782","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":324810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","otherGeospatial":"Hudson Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.0986328125,\n 64.88626540914477\n ],\n [\n -84.462890625,\n 65.03506043658815\n ],\n [\n -76.81640625,\n 61.79390039913458\n ],\n [\n -77.16796875,\n 58.81374171570782\n ],\n [\n -75.498046875,\n 56.022948079627454\n ],\n [\n -78.3984375,\n 54.34214886448341\n ],\n [\n -77.87109375,\n 51.944264879028765\n ],\n [\n -79.8046875,\n 50.819818262156545\n ],\n [\n -82.4853515625,\n 52.3755991766591\n ],\n [\n -83.49609375,\n 54.57206165565852\n ],\n [\n -90.65917968749999,\n 56.68037378950137\n ],\n [\n -93.779296875,\n 56.8249328650072\n ],\n [\n -95.4052734375,\n 59.31076795603884\n ],\n [\n -94.833984375,\n 61.68987220045999\n ],\n [\n -91.0986328125,\n 64.88626540914477\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-05","publicationStatus":"PW","scienceBaseUri":"577f6f1ae4b0ef4d2f45d428","contributors":{"authors":[{"text":"Lunn, Nicholas J.","contributorId":78421,"corporation":false,"usgs":true,"family":"Lunn","given":"Nicholas J.","affiliations":[],"preferred":false,"id":641683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Servanty, Sabrina","contributorId":53296,"corporation":false,"usgs":true,"family":"Servanty","given":"Sabrina","affiliations":[],"preferred":false,"id":641684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":641685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":3513,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":641682,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richardson, Evan S.","contributorId":139901,"corporation":false,"usgs":false,"family":"Richardson","given":"Evan","email":"","middleInitial":"S.","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":641686,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stirling, Ian","contributorId":72079,"corporation":false,"usgs":false,"family":"Stirling","given":"Ian","email":"","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":641687,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70174288,"text":"70174288 - 2016 - Density-dependent home-range size revealed by spatially explicit capture–recapture","interactions":[],"lastModifiedDate":"2016-07-12T19:11:20","indexId":"70174288","displayToPublicDate":"2016-07-07T12:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Density-dependent home-range size revealed by spatially explicit capture–recapture","docAbstract":"The size of animal home ranges often varies inversely with population density among populations of a species. This fact has implications for population monitoring using spatially explicit capture–recapture (SECR) models, in which both the scale of home-range movements σ and population density D usually appear as parameters, and both may vary among populations. It will often be appropriate to model a structural relationship between population-specific values of these parameters, rather than to assume independence. We suggest re-parameterizing the SECR model using kp = σp √Dp, where kp relates to the degree of overlap between home ranges and the subscript p distinguishes populations. We observe that kp is often nearly constant for populations spanning a range of densities. This justifies fitting a model in which the separate kp are replaced by the single parameter k and σp is a density-dependent derived parameter. Continuous density-dependent spatial variation in σ may also be modelled, using a scaled non-Euclidean distance between detectors and the locations of animals. We illustrate these methods with data from automatic photography of tigers (Panthera tigris) across India, in which the variation is among populations, from mist-netting of ovenbirds (Seiurus aurocapilla) in Maryland, USA, in which the variation is within a single population over time, and from live-trapping of brushtail possums (Trichosurus vulpecula) in New Zealand, modelling spatial variation within one population. Possible applications and limitations of the methods are discussed. A model in which kp is constant, while density varies, provides a parsimonious null model for SECR. The parameter k of the null model is a concise summary of the empirical relationship between home-range size and density that is useful in comparative studies. We expect deviations from this model, particularly the dependence of kp on covariates, to be biologically interesting.
","language":"English","publisher":"Blackwell Publishers","publisherLocation":"Oxford","doi":"10.1111/ecog.01511","usgsCitation":"Efford, M., Dawson, D.K., Jhala, Y., and Qureshi, Q., 2016, Density-dependent home-range size revealed by spatially explicit capture–recapture: Ecography, v. 39, no. 7, p. 676-688, https://doi.org/10.1111/ecog.01511.","productDescription":"13 p.","startPage":"676","endPage":"688","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065283","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":324803,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"7","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-07","publicationStatus":"PW","scienceBaseUri":"577f6f1ae4b0ef4d2f45d42c","contributors":{"authors":[{"text":"Efford, M.G.","contributorId":13352,"corporation":false,"usgs":true,"family":"Efford","given":"M.G.","affiliations":[],"preferred":false,"id":641693,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dawson, Deanna K. ddawson@usgs.gov","contributorId":1257,"corporation":false,"usgs":true,"family":"Dawson","given":"Deanna","email":"ddawson@usgs.gov","middleInitial":"K.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":641690,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jhala, Y.V.","contributorId":96889,"corporation":false,"usgs":true,"family":"Jhala","given":"Y.V.","email":"","affiliations":[],"preferred":false,"id":641694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qureshi, Q.","contributorId":172713,"corporation":false,"usgs":false,"family":"Qureshi","given":"Q.","email":"","affiliations":[],"preferred":false,"id":641695,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70174237,"text":"70174237 - 2016 - Composition and structure of the shallow subsurface of Ceres revealed by crater morphology","interactions":[],"lastModifiedDate":"2016-07-07T11:12:34","indexId":"70174237","displayToPublicDate":"2016-07-07T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Composition and structure of the shallow subsurface of Ceres revealed by crater morphology","docAbstract":"Before NASA’s Dawn mission, the dwarf planet Ceres was widely believed to contain a substantial ice-rich layer below its rocky surface. The existence of such a layer has significant implications for Ceres’s formation, evolution, and astrobiological potential. Ceres is warmer than icy worlds in the outer Solar System and, if its shallow subsurface is ice-rich, large impact craters are expected to be erased by viscous flow on short geologic timescales. Here we use digital terrain models derived from Dawn Framing Camera images to show that most of Ceres’s largest craters are several kilometres deep, and are therefore inconsistent with the existence of an ice-rich subsurface. We further show from numerical simulations that the absence of viscous relaxation over billion-year timescales implies a subsurface viscosity that is at least one thousand times greater than that of pure water ice. We conclude that Ceres’s shallow subsurface is no more than 30% to 40% ice by volume, with a mixture of rock, salts and/or clathrates accounting for the other 60% to 70%. However, several anomalously shallow craters are consistent with limited viscous relaxation and may indicate spatial variations in subsurface ice content.
","language":"English","publisher":"Nature Pub. Group","publisherLocation":"New York","doi":"10.1038/NGEO2743","usgsCitation":"Bland, M.T., Carol A. Raymond, Schenk, P.M., Fu, R.R., Kneisl, T., Hendrick Pasckert, J., Hiesinger, H., Frank Preusker, Park, R.S., Marchi, S., King, S., Castillo-Rogez, J., and Christopher T. Russell, 2016, Composition and structure of the shallow subsurface of Ceres revealed by crater morphology: Nature Geoscience, v. 9, p. 538-542, https://doi.org/10.1038/NGEO2743.","productDescription":"5 p.","startPage":"538","endPage":"542","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074012","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":470759,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1038/ngeo2743","text":"External Repository"},{"id":324801,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-29","publicationStatus":"PW","scienceBaseUri":"577f6f19e4b0ef4d2f45d41d","contributors":{"authors":[{"text":"Bland, Michael T. 0000-0001-5543-1519 mbland@usgs.gov","orcid":"https://orcid.org/0000-0001-5543-1519","contributorId":146287,"corporation":false,"usgs":true,"family":"Bland","given":"Michael","email":"mbland@usgs.gov","middleInitial":"T.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":641553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carol A. Raymond","contributorId":172681,"corporation":false,"usgs":false,"family":"Carol A. Raymond","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":641554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schenk, Paul M.","contributorId":172682,"corporation":false,"usgs":false,"family":"Schenk","given":"Paul","email":"","middleInitial":"M.","affiliations":[{"id":27077,"text":"Lunar and Planetary Inst.","active":true,"usgs":false}],"preferred":false,"id":641555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fu, Roger R.","contributorId":172683,"corporation":false,"usgs":false,"family":"Fu","given":"Roger","email":"","middleInitial":"R.","affiliations":[{"id":27078,"text":"Columbia University, New York","active":true,"usgs":false}],"preferred":false,"id":641556,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kneisl, Thomas","contributorId":172684,"corporation":false,"usgs":false,"family":"Kneisl","given":"Thomas","email":"","affiliations":[{"id":27079,"text":"Institute of Geological Sciences, Freie Universität Berlin","active":true,"usgs":false}],"preferred":false,"id":641557,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hendrick Pasckert, Jan","contributorId":172685,"corporation":false,"usgs":false,"family":"Hendrick Pasckert","given":"Jan","email":"","affiliations":[{"id":27080,"text":"Institut für Planetologie, Westfälische Wilhelms-Universität, Münster","active":true,"usgs":false}],"preferred":false,"id":641558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hiesinger, Harald","contributorId":172686,"corporation":false,"usgs":false,"family":"Hiesinger","given":"Harald","email":"","affiliations":[{"id":27080,"text":"Institut für Planetologie, Westfälische Wilhelms-Universität, Münster","active":true,"usgs":false}],"preferred":false,"id":641559,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frank Preusker","contributorId":172687,"corporation":false,"usgs":false,"family":"Frank Preusker","affiliations":[{"id":27020,"text":"German Aerospace Center (DLR), Institut für Optische Sensorsysteme","active":true,"usgs":false}],"preferred":false,"id":641560,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Park, Ryan S.","contributorId":172688,"corporation":false,"usgs":false,"family":"Park","given":"Ryan","email":"","middleInitial":"S.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":641561,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marchi, Simone","contributorId":172689,"corporation":false,"usgs":false,"family":"Marchi","given":"Simone","email":"","affiliations":[{"id":27081,"text":"Southwest Research Inst.","active":true,"usgs":false}],"preferred":false,"id":641562,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"King, Scott","contributorId":172690,"corporation":false,"usgs":false,"family":"King","given":"Scott","email":"","affiliations":[{"id":27082,"text":"Virginia Inst. of Tech","active":true,"usgs":false}],"preferred":false,"id":641563,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Castillo-Rogez, Julie C.","contributorId":172691,"corporation":false,"usgs":false,"family":"Castillo-Rogez","given":"Julie C.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":641564,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Christopher T. Russell","contributorId":172692,"corporation":false,"usgs":false,"family":"Christopher T. Russell","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":641565,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70179390,"text":"70179390 - 2016 - Detection, prevalence, and transmission of avian hematozoa in waterfowl at the Arctic/sub-Arctic interface: co-infections, viral interactions, and sources of variation.","interactions":[],"lastModifiedDate":"2016-12-30T10:37:46","indexId":"70179390","displayToPublicDate":"2016-07-07T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3010,"text":"Parasites & Vectors","printIssn":"1756-3305","active":true,"publicationSubtype":{"id":10}},"title":"Detection, prevalence, and transmission of avian hematozoa in waterfowl at the Arctic/sub-Arctic interface: co-infections, viral interactions, and sources of variation.","docAbstract":"Background: The epidemiology of avian hematozoa at high latitudes is still not well understood, particularly in sub-Arctic and Arctic habitats, where information is limited regarding seasonality and range of transmission, co-infection dynamics with parasitic and viral agents, and possible fitness consequences of infection. Such information is important as climate warming may lead to northward expansion of hematozoa with unknown consequences to northern-breeding avian taxa, particularly populations that may be previously unexposed to blood parasites.\nMethods: We used molecular methods to screen blood samples and cloacal/oropharyngeal swabs collected from 1347 ducks of five species during May-August 2010, in interior Alaska, for the presence of hematozoa, Influenza A Virus (IAV), and IAV antibodies. Using models to account for imperfect detection of parasites, we estimated seasonal variation in prevalence of three parasite genera (Haemoproteus, Plasmodium, Leucocytozoon) and investigated how co-infection with parasites and viruses were related to the probability of infection.\n\nResults: We detected parasites from each hematozoan genus in adult and juvenile ducks of all species sampled. Seasonal patterns in detection and prevalence varied by parasite genus and species, age, and sex of duck hosts. The probabilities of infection for Haemoproteus and Leucocytozoon parasites were strongly positively correlated, but hematozoa infection was not correlated with IAV infection or serostatus. The probability of Haemoproteus infection was negatively related to body condition in juvenile ducks; relationships between Leucocytozoon infection and body condition varied among host species.\n\nConclusions: We present prevalence estimates for Haemoproteus, Leucocytozoon, and Plasmodium infections in waterfowl at the interface of the sub-Arctic and Arctic and provide evidence for local transmission of all three parasite genera. Variation in prevalence and molecular detection of hematozoa parasites in wild ducks is influenced by seasonal timing and a number of host traits. A positive correlation in co-infection of Leucocytozoon and Haemoproteus suggests that infection probability by parasites in one or both genera is enhanced by infection with the other, or that encounter rates of hosts and genus-specific vectors are correlated. Using size-adjusted mass as an index of host condition, we did not find evidence for strong deleterious consequences of hematozoa infection in wild ducks.","language":"English","publisher":"BioMed Central","doi":"10.1186/s13071-016-1666-3","usgsCitation":"Meixell, B.W., Arnold, T.W., Lindberg, M.S., Smith, M.M., Ramey, A.M., and Runstadler, J.A., 2016, Detection, prevalence, and transmission of avian hematozoa in waterfowl at the Arctic/sub-Arctic interface: co-infections, viral interactions, and sources of variation.: Parasites & Vectors, v. 9, no. 390, 18 p., https://doi.org/10.1186/s13071-016-1666-3.","productDescription":"18 p.","ipdsId":"IP-074072","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":470767,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13071-016-1666-3","text":"Publisher Index Page"},{"id":438594,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QJ7FD2","text":"USGS data release","linkHelpText":"Morphology and Disease Information from Waterfowl, Interior Alaska, 2010"},{"id":332677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":332668,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/ 10.1186/s13071-016-1666-3"}],"volume":"9","issue":"390","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-07","publicationStatus":"PW","scienceBaseUri":"586781f8e4b0cd2dabe7c719","chorus":{"doi":"10.1186/s13071-016-1666-3","url":"http://dx.doi.org/10.1186/s13071-016-1666-3","publisher":"Springer Nature","authors":"Meixell Brandt W., Arnold Todd W., Lindberg Mark S., Smith Matthew M., Runstadler Jonathan A., Ramey Andrew M.","journalName":"Parasites & Vectors","publicationDate":"7/7/2016"},"contributors":{"authors":[{"text":"Meixell, Brandt W. 0000-0002-6738-0349 bmeixell@usgs.gov","orcid":"https://orcid.org/0000-0002-6738-0349","contributorId":138716,"corporation":false,"usgs":true,"family":"Meixell","given":"Brandt","email":"bmeixell@usgs.gov","middleInitial":"W.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":657031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arnold, Todd W.","contributorId":36058,"corporation":false,"usgs":false,"family":"Arnold","given":"Todd","email":"","middleInitial":"W.","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":657046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindberg, Mark S.","contributorId":63292,"corporation":false,"usgs":false,"family":"Lindberg","given":"Mark","email":"","middleInitial":"S.","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":657047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Matthew M. 0000-0002-2259-5135 mmsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-2259-5135","contributorId":5115,"corporation":false,"usgs":true,"family":"Smith","given":"Matthew","email":"mmsmith@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":657032,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":657033,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runstadler, Jonathan A.","contributorId":24706,"corporation":false,"usgs":false,"family":"Runstadler","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":657048,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70174063,"text":"sim3360 - 2016 - Geologic map of the Murray Quadrangle, Newton County, Arkansas","interactions":[],"lastModifiedDate":"2016-07-06T16:36:17","indexId":"sim3360","displayToPublicDate":"2016-07-06T16:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3360","title":"Geologic map of the Murray Quadrangle, Newton County, Arkansas","docAbstract":"This map summarizes the geology of the Murray quadrangle in the Ozark Plateaus region of northern Arkansas. Geologically, the area is on the southern flank of the Ozark dome, an uplift that has the oldest rocks exposed at its center, in Missouri. Physiographically, the Murray quadrangle is within the Boston Mountains, a high plateau region underlain by Pennsylvanian sandstones and shales. Valleys of the Buffalo River and Little Buffalo River and their tributaries expose an approximately 1,600-ft-thick (488-meter-thick) sequence of Ordovician, Mississippian, and Pennsylvanian carbonate and clastic sedimentary rocks that have been mildly deformed by a series of faults and folds. The Buffalo National River, a park that encompasses the Buffalo River and adjacent land that is administered by the National Park Service is present at the northwestern edge of the quadrangle.
Mapping for this study was carried out by field inspection of numerous sites and was compiled as a 1:24,000 geographic information system (GIS) database. Locations and elevation of sites were determined with the aid of a global positioning satellite receiver and a hand-held barometric altimeter that was frequently recalibrated at points of known elevation. Hill-shade relief and slope maps derived from a U.S. Geological Survey 10-meter digital elevation model as well as orthophotographs were used to help trace ledge-forming units between field traverses within the Upper Mississippian and Pennsylvanian part of the stratigraphic sequence. Strike and dip of beds were typically measured along stream drainages or at well-exposed ledges. Structure contours, constructed on the top of the Boone Formation and the base of a prominent sandstone unit within the Bloyd Formation, were drawn based on the elevations of field sites on these contacts well as other limiting information for their minimum elevations above hilltops or their maximum elevations below valley bottoms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3360","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Hudson, M.R., and Turner, K.J., 2016, Geologic map of the Murray quadrangle, Newton County, Arkansas: U.S. Geological Survey Scientific Investigations Map 3360, 1 sheet, scale 1:24,000, https://dx.doi.org/10.3133/sim3360.","productDescription":"Sheet: 51.07 x 36.00 inches; Metadata; Read Me; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-062555","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":324754,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_georeferenced.pdf","text":"Georeferenced geologic map","size":"127.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3360 Geologic Georeferenced Map"},{"id":324755,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_GIS.gdb.zip","text":"Geodatabase","size":"9.16 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3360 Geodatabase"},{"id":324756,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_shapefiles.zip","text":"Shapefiles","size":"1.25 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3360 Shapefiles"},{"id":324751,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3360/coverthb.jpg"},{"id":324758,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_metadata.zip","text":"Metadata","size":"16.0 kB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3360 Metadata"},{"id":324752,"rank":2,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_readme_version2.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3360 ReadMe"},{"id":324757,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_basemaps.zip","text":"Base maps","size":"17.0 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3360 Base maps"},{"id":324753,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3360/sim3360.pdf","text":"Geologic map","size":"34.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3360 Geologic Map"}],"country":"United States","state":"Arkansas","county":"Newton County","otherGeospatial":"Murray Quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.375,\n 35.875\n ],\n [\n -93.375,\n 36\n ],\n [\n -93.25,\n 36\n ],\n [\n -93.25,\n 35.875\n ],\n [\n -93.375,\n 35.875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"U.S. Geological Survey
Center Director, USGS Geosciences and Environmental Change Science Center
Box 25046, MS-980
Denver Federal Center
Denver, CO 80225-0046
http://gec.cr.usgs.gov
Effective conservation planning requires understanding and ranking threats to wildlife populations. We developed a Bayesian network model to evaluate the relative influence of environmental and anthropogenic stressors, and their mitigation, on the persistence of polar bears (Ursus maritimus). Overall sea ice conditions, affected by rising global temperatures, were the most influential determinant of population outcomes. Accordingly, unabated rise in atmospheric greenhouse gas (GHG) concentrations was the dominant influence leading to worsened population outcomes, with polar bears in three of four ecoregions reaching a dominant probability of decreased or greatly decreased by the latter part of this century. Stabilization of atmospheric GHG concentrations by mid-century delayed the greatly reduced state by ≈25 yr in two ecoregions. Prompt and aggressive mitigation of emissions reduced the probability of any regional population becoming greatly reduced by up to 25%. Marine prey availability, linked closely to sea ice trend, had slightly less influence on outcome state than sea ice availability itself. Reduced mortality from hunting and defense of life and property interactions resulted in modest declines in the probability of a decreased or greatly decreased population outcome. Minimizing other stressors such as trans-Arctic shipping, oil and gas exploration, and contaminants had a negligible effect on polar bear outcomes, although the model was not well-informed with respect to the potential influence of these stressors. Adverse consequences of loss of sea ice habitat became more pronounced as the summer ice-free period lengthened beyond four months, which could occur in most of the Arctic basin after mid-century if GHG emissions are not promptly reduced. Long-term conservation of polar bears would be best supported by holding global mean temperature to ≤ 2°C above preindustrial levels. Until further sea ice loss is stopped, management of other stressors may serve to slow the transition of populations to progressively worsened outcomes, and improve the prospects for their long-term persistence.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1370","usgsCitation":"Atwood, T.C., Marcot, B.G., Douglas, D.C., Amstrup, S.C., Rode, K.D., Durner, G.M., and Bromaghin, J.F., 2016, Forecasting the relative influence of environmental and anthropogenic stressors on polar bears: Ecosphere, v. 7, no. 6, Article e01370; 22 p., https://doi.org/10.1002/ecs2.1370.","productDescription":"Article e01370; 22 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066721","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":470768,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1370","text":"Publisher Index Page"},{"id":324786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-29","publicationStatus":"PW","scienceBaseUri":"577e1d9de4b0ef4d2f43e6bd","contributors":{"authors":[{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":641661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marcot, Bruce G.","contributorId":152612,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":18944,"text":"Pacific Northwest Research Station, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":641662,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":641663,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amstrup, Steven C.","contributorId":67034,"corporation":false,"usgs":false,"family":"Amstrup","given":"Steven","email":"","middleInitial":"C.","affiliations":[{"id":13182,"text":"Polar Bears International","active":true,"usgs":false}],"preferred":false,"id":641664,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":641665,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":641666,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":641667,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70171113,"text":"ofr20161085 - 2016 - Identifying bird and reptile vulnerabilities to climate change in the southwestern United States","interactions":[],"lastModifiedDate":"2017-11-25T13:39:32","indexId":"ofr20161085","displayToPublicDate":"2016-07-06T16:00:00","publicationYear":"2016","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":"2016-1085","title":"Identifying bird and reptile vulnerabilities to climate change in the southwestern United States","docAbstract":"Current and future breeding ranges of 15 bird and 16 reptile species were modeled in the Southwestern United States. Rather than taking a broad-scale, vulnerability-assessment approach, we created a species distribution model (SDM) for each focal species incorporating climatic, landscape, and plant variables. Baseline climate (1940–2009) was characterized with Parameter-elevation Regressions on Independent Slopes Model (PRISM) data and future climate with global-circulation-model data under an A1B emission scenario. Climatic variables included monthly and seasonal temperature and precipitation; landscape variables included terrain ruggedness, soil type, and insolation; and plant variables included trees and shrubs commonly associated with a focal species. Not all species-distribution models contained a plant, but if they did, we included a built-in annual migration rate for more accurate plant-range projections in 2039 or 2099. We conducted a group meta-analysis to (1) determine how influential each variable class was when averaged across all species distribution models (birds or reptiles), and (2) identify the correlation among contemporary (2009) habitat fragmentation and biological attributes and future range projections (2039 or 2099). Projected changes in bird and reptile ranges varied widely among species, with one-third of the ranges predicted to expand and two-thirds predicted to contract. A group meta-analysis indicated that climatic variables were the most influential variable class when averaged across all models for both groups, followed by landscape and plant variables (birds), or plant and landscape variables (reptiles), respectively. The second part of the meta-analysis indicated that numerous contemporary habitat-fragmentation (for example, patch isolation) and biological-attribute (for example, clutch size, longevity) variables were significantly correlated with the magnitude of projected range changes for birds and reptiles. Patch isolation was a significant trans-specific driver of projected bird and reptile ranges, suggesting that strategic actions should focus on restoration and enhancement of habitat at local and regional scales to promote landscape connectivity and conservation of core areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161085","usgsCitation":"Hatten, J.R., Giermakowski, J.T., Holmes, J.A., Nowak, E.M., Johnson, M.J., Ironside, K.E., van Riper, Charles, III, Peters, Michael, Truettner, Charles, and Cole, K.L., 2016, Identifying bird and reptile vulnerabilities to climate change in the Southwestern United States: U.S. Geological Survey Open-File Report 2016-1085, 76 p., https://dx.doi.org/10.3133/ofr20161085.","productDescription":"vi, 76 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070152","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":324760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1085/coverthb.jpg"},{"id":324761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1085/ofr20161085.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1085 Report PDF"}],"country":"United States","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.053249,41.001406],[-102.124972,41.002338],[-102.051292,40.749591],[-102.04192,37.035083],[-102.979613,36.998549],[-103.002247,36.911587],[-103.064423,32.000518],[-106.565142,32.000736],[-106.577244,31.810406],[-106.750547,31.783706],[-108.208394,31.783599],[-108.208573,31.333395],[-111.000643,31.332177],[-114.813613,32.494277],[-114.722746,32.713071],[-117.118868,32.534706],[-117.50565,33.334063],[-118.088896,33.729817],[-118.428407,33.774715],[-118.519514,34.027509],[-119.159554,34.119653],[-119.616862,34.420995],[-120.441975,34.451512],[-120.608355,34.556656],[-120.644311,35.139616],[-120.873046,35.225688],[-120.884757,35.430196],[-121.851967,36.277831],[-121.932508,36.559935],[-121.788278,36.803994],[-121.880167,36.950151],[-122.140578,36.97495],[-122.419113,37.24147],[-122.511983,37.77113],[-122.425942,37.810979],[-122.168449,37.504143],[-122.144396,37.581866],[-122.385908,37.908136],[-122.301804,38.105142],[-122.484411,38.11496],[-122.492474,37.82484],[-122.972378,38.020247],[-123.103706,38.415541],[-123.725367,38.917438],[-123.851714,39.832041],[-124.373599,40.392923],[-124.063076,41.439579],[-124.536073,42.814175],[-124.150267,43.91085],[-123.962887,45.280218],[-123.996766,46.20399],[-123.548194,46.248245],[-124.029924,46.308312],[-124.06842,46.601397],[-123.97083,46.47537],[-123.84621,46.716795],[-124.022413,46.708973],[-124.108078,46.836388],[-123.86018,46.948556],[-124.138035,46.970959],[-124.425195,47.738434],[-124.672427,47.964414],[-124.727022,48.371101],[-123.981032,48.164761],[-122.748911,48.117026],[-122.637425,47.889945],[-123.15598,47.355745],[-122.527593,47.905882],[-122.578211,47.254804],[-122.725738,47.33047],[-122.691771,47.141958],[-122.796646,47.341654],[-122.863732,47.270221],[-122.67813,47.103866],[-122.364168,47.335953],[-122.429841,47.658919],[-122.230046,47.970917],[-122.425572,48.232887],[-122.358375,48.056133],[-122.512031,48.133931],[-122.424102,48.334346],[-122.689121,48.476849],[-122.425271,48.599522],[-122.796887,48.975026],[-104.048736,48.999877],[-104.053249,41.001406]]],[[[-119.789798,34.05726],[-119.5667,34.053452],[-119.795938,33.962929],[-119.916216,34.058351],[-119.789798,34.05726]]],[[[-118.524531,32.895488],[-118.573522,32.969183],[-118.369984,32.839273],[-118.524531,32.895488]]],[[[-118.500212,33.449592],[-118.32446,33.348782],[-118.593969,33.467198],[-118.500212,33.449592]]],[[[-122.519535,48.288314],[-122.66921,48.240614],[-122.400628,48.036563],[-122.419274,47.912125],[-122.744612,48.20965],[-122.664928,48.374823],[-122.519535,48.288314]]],[[[-122.800217,48.60169],[-122.883759,48.418793],[-123.173061,48.579086],[-122.949116,48.693398],[-122.743049,48.661991],[-122.800217,48.60169]]]]},\"properties\":{\"name\":\"Arizona\",\"nation\":\"USA \"}}]}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
The Kittlitz's Murrelet (Brachyramphus brevirostris) is a small, non-colonial seabird endemic to marine waters of Alaska and eastern Russia that may have experienced significant population decline in recent decades, in part because of low reproductive success and terrestrial threats. Although recent studies have shed new light on Kittlitz's Murrelet nesting habitat in a few discrete areas, the location and extent of suitable nesting habitat throughout most of its range remains unclear. Here, we have compiled all existing nest records and locations to identify landscape-scale parameters (distance to coast, elevation, slope, and land cover) that provide potential nesting habitat in four regions: northern Alaska, Aleutian Islands, Alaska Peninsula Mountains and Kodiak Island, and Pacific Coastal Mountains (including nearshore interior Canada). We produced a final map classifying 12% (70,411 km2) of the lands assessed as potential Kittlitz's Murrelet nesting habitat, with dense but distinct patches in northern Alaska and a more uninterrupted, narrow band extending across the Pacific Coastal Mountains, Alaska Peninsula Mountains, and Aleutian Islands. The extent of habitat-capable parameter values varied regionally, indicating that the Kittlitz's Murrelet may be able to use a variety of habitats for nesting, depending on availability. Future nesting habitat studies could employ spatially random sampling designs to allow for quantitatively robust modeling of nesting habitat and predictive extrapolation to areas where nests have not been located but likely exist.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/112015-JFWM-116","usgsCitation":"Felis, J.J., Kissling, M.L., Kaler, R., Kenney, L., and Lawonn, M.J., 2016, Identifying Kittlitz's Murrelet nesting habitat in North America at the landscape scale: Journal of Fish and Wildlife Management, v. 7, no. 2, p. 323-333, https://doi.org/10.3996/112015-JFWM-116.","productDescription":"11 p.","startPage":"323","endPage":"333","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075507","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":488466,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/112015-jfwm-116","text":"Publisher Index Page"},{"id":438595,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71V5C2W","text":"USGS data release","linkHelpText":"Identifying Kittlitz's Murrelet nesting habitat in North America at the 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freshwaters","docAbstract":"To gain insights into the risks associated with uranium (U) mining and processing, we investigated the biogeochemical controls of U bioavailability in the model freshwater speciesLymnaea stagnalis (Gastropoda). Bioavailability of dissolved U(VI) was characterized in controlled laboratory experiments over a range of water hardness, pH, and in the presence of complexing ligands in the form of dissolved natural organic matter (DOM). Results show that dissolved U is bioavailable under all the geochemical conditions tested. Uranium uptake rates follow first order kinetics over a range encompassing most environmental concentrations. Uranium uptake rates in L. stagnalis ultimately demonstrate saturation uptake kinetics when exposure concentrations exceed 100 nM, suggesting uptake via a finite number of carriers or ion channels. The lack of a relationship between U uptake rate constants and Ca uptake rates suggest that U does not exclusively use Ca membrane transporters. In general, U bioavailability decreases with increasing pH, increasing Ca and Mg concentrations, and when DOM is present. Competing ions did not affect U uptake rates. Speciation modeling that includes formation constants for U ternary complexes reveals that the aqueous concentration of dicarbonato U species (UO2(CO3)2–2) best predicts U bioavailability to L. stagnalis, challenging the free-ion activity model postulate.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.6b02406","usgsCitation":"Croteau, M.N., Fuller, C.C., Cain, D.J., Campbell, K.M., and Aiken, G.R., 2016, Biogeochemical controls of uranium bioavailability from the dissolved phase in natural freshwaters: Environmental Science & Technology, v. 50, no. 15, p. 8120-8127, https://doi.org/10.1021/acs.est.6b02406.","productDescription":"8 p.","startPage":"8120","endPage":"8127","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075146","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":325712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"15","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-21","publicationStatus":"PW","scienceBaseUri":"5799db3be4b0589fa1c7e732","chorus":{"doi":"10.1021/acs.est.6b02406","url":"http://dx.doi.org/10.1021/acs.est.6b02406","publisher":"American Chemical Society (ACS)","authors":"Croteau Marie-Noële, Fuller Christopher C., Cain Daniel J., Campbell Kate M., Aiken George","journalName":"Environmental Science & Technology","publicationDate":"8/2/2016"},"contributors":{"authors":[{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":438,"text":"National Research Program - 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2016 - External quality assurance project report for the National Atmospheric Deposition Program’s National Trends Network and Mercury Deposition Network, 2013–14","interactions":[],"lastModifiedDate":"2016-07-06T16:25:29","indexId":"sir20165069","displayToPublicDate":"2016-07-05T17:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5069","title":"External quality assurance project report for the National Atmospheric Deposition Program’s National Trends Network and Mercury Deposition Network, 2013–14","docAbstract":"The U.S. Geological Survey Branch of Quality Systems operated five distinct programs to provide external quality assurance monitoring for the National Atmospheric Deposition Program’s (NADP) National Trends Network and Mercury Deposition Network during 2013–14. The National Trends Network programs include (1) a field audit program to evaluate sample contamination and stability, (2) an interlaboratory comparison program to evaluate analytical laboratory performance, and (3) a colocated sampler program to evaluate bias from precipitation sampler upgrades. The Mercury Deposition Network programs include the (4) system blank program and (5) an interlaboratory comparison program. The results indicate that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends for chemical constituents in wet deposition.
\nThe field audit program results indicate that sample contamination levels for calcium, nitrate, and sulfate continued to increase during the study period while sodium and chloride contamination decreased and magnesium, potassium, ammonium, and hydrogen-ion contamination have remained relatively constant. Analyte losses due to potential sample instability were negligible. The NADP Central Analytical Laboratory produced interlaboratory comparison results with low bias and variability compared to other domestic and international laboratories that support atmospheric deposition monitoring.
\nColocated sampler program results from dissimilar colocated collectors suggest that the retrofit of the National Trends Network with N-CON Systems precipitation collectors could cause shifts in NADP annual deposition (concentration multiplied by depth) values from +6.2 to +51 percent for ammonium, from +8.1 to +61 percent for nitrate, from 3.8 to 71 percent for sulfate, from –24 to +15 percent for hydrogenion deposition, and larger shifts (from –14 to +102 percent) for calcium, magnesium, sodium, potassium, and chloride. The N-CON Systems collector typically catches more precipitation than the NADP-approved Aerochem Metrics Model 301 collector, but it typically caught slightly less precipitation than the Aerochem Metrics collector at a wind-swept, high-altitude site during water year 2013.
\nPaired, identical OTT Pluvio-2 and ETI Noah IV rain gages were operated at the same sites. Results of the colocated rain gages indicate from 0 to 3.7 percent median absolute percent difference for weekly precipitation-depth measurements and from 0.05 to 5.6 absolute percent difference for annual total precipitation depth.
\nThe Mercury Deposition Network programs include the system blank program and an interlaboratory comparison program. System blank results indicated that maximum total mercury contamination concentrations in samples were less than the third percentile of all Mercury Deposition Network sample concentrations. The Mercury Analytical Laboratory produced chemical concentration results with low bias and variability compared with other domestic and international laboratories that support atmospheric-deposition monitoring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165069","usgsCitation":"Wetherbee, G.A., and Martin, RoseAnn, 2016, External quality assurance project report for the National Atmospheric Deposition Program’s National Trends Network and Mercury Deposition Network, 2013–14: U.S. Geological Survey Scientific Investigations Report 2016–5069, 22 p., https://dx.doi.org/10.3133/sir20165069.","productDescription":"vi, 22 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070529","costCenters":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"links":[{"id":324394,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5069/coverthb.jpg"},{"id":324395,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5069/sir20165069.pdf","text":"Report","size":"4.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5069"}],"contact":"Chief, USGS Branch of Quality Systems
Box 25046, Mail Stop 401
Denver, CO 80225
Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.2292","usgsCitation":"Wright, W., Irvine, K.M., and Rodhouse, T., 2016, A goodness-of-fit test for occupancy models with correlated within-season revisits: Ecology and Evolution, v. 6, no. 15, p. 5404-5415, https://doi.org/10.1002/ece3.2292.","productDescription":"12 p.","startPage":"5404","endPage":"5415","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072344","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":470769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.2292","text":"Publisher Index Page"},{"id":324896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":324890,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1002/ece3.2292/full"}],"volume":"6","issue":"15","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-05","publicationStatus":"PW","scienceBaseUri":"5780ceaee4b0811616822292","chorus":{"doi":"10.1002/ece3.2292","url":"http://dx.doi.org/10.1002/ece3.2292","publisher":"Wiley-Blackwell","authors":"Wright Wilson J., Irvine Kathryn M., Rodhouse Thomas J.","journalName":"Ecology and Evolution","publicationDate":"7/5/2016"},"contributors":{"authors":[{"text":"Wright, Wilson","contributorId":172748,"corporation":false,"usgs":false,"family":"Wright","given":"Wilson","affiliations":[{"id":5120,"text":"Montana State University, Department of Mathematical Sciences, Bozeman, MT 59717","active":true,"usgs":false}],"preferred":false,"id":641895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":641894,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodhouse, Thomas J.","contributorId":127378,"corporation":false,"usgs":false,"family":"Rodhouse","given":"Thomas J.","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":641896,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175393,"text":"70175393 - 2016 - Implications of climate change for wetland-dependent birds in the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2017-01-03T16:15:45","indexId":"70175393","displayToPublicDate":"2016-07-04T09:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Implications of climate change for wetland-dependent birds in the Prairie Pothole Region","docAbstract":"The habitats and food resources required to support breeding and migrant birds dependent on North American prairie wetlands are threatened by impending climate change. The North American Prairie Pothole Region (PPR) hosts nearly 120 species of wetland-dependent birds representing 21 families. Strategic management requires knowledge of avian habitat requirements and assessment of species most vulnerable to future threats. We applied bioclimatic species distribution models (SDMs) to project range changes of 29 wetland-dependent bird species using ensemble modeling techniques, a large number of General Circulation Models (GCMs), and hydrological climate covariates. For the U.S. PPR, mean projected range change, expressed as a proportion of currently occupied range, was −0.31 (± 0.22 SD; range − 0.75 to 0.16), and all but two species were projected to lose habitat. Species associated with deeper water were expected to experience smaller negative impacts of climate change. The magnitude of climate change impacts was somewhat lower in this study than earlier efforts most likely due to use of different focal species, varying methodologies, different modeling decisions, or alternative GCMs. Quantification of the projected species-specific impacts of climate change using species distribution modeling offers valuable information for vulnerability assessments within the conservation planning process.
","language":"English","publisher":"Society of Wetland Scientists","publisherLocation":"McClean, VA","doi":"10.1007/s13157-016-0791-2","usgsCitation":"Steen, V., Skagen, S., and Melcher, C.P., 2016, Implications of climate change for wetland-dependent birds in the Prairie Pothole Region: Wetlands, v. 36, no. s2, p. 445-459, https://doi.org/10.1007/s13157-016-0791-2.","productDescription":"15 p.","startPage":"445","endPage":"459","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073432","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":326285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Prairie Pothole Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.73046875,\n 48.83579746243093\n ],\n [\n -112.3681640625,\n 48.40003249610685\n ],\n [\n -108.984375,\n 48.63290858589532\n ],\n [\n -105.6884765625,\n 48.574789910928864\n ],\n [\n -104.150390625,\n 48.45835188280866\n ],\n [\n -103.0517578125,\n 48.22467264956519\n ],\n [\n -102.48046875,\n 47.84265762816538\n ],\n [\n -101.953125,\n 47.54687159892238\n ],\n [\n -100.5029296875,\n 47.100044694025215\n ],\n [\n -100.32714843749999,\n 46.46813299215554\n ],\n [\n -100.2392578125,\n 45.82879925192134\n ],\n [\n -99.97558593749999,\n 45.02695045318546\n ],\n [\n -99.5361328125,\n 44.11914151643737\n ],\n [\n -98.26171875,\n 43.929549935614595\n ],\n [\n -97.03125,\n 43.89789239125797\n ],\n [\n -96.45996093749999,\n 44.276671273775186\n ],\n [\n -95.712890625,\n 43.866218006556394\n ],\n [\n -95.4052734375,\n 43.229195113965005\n ],\n [\n -94.921875,\n 42.68243539838623\n ],\n [\n -94.1748046875,\n 42.00032514831621\n ],\n [\n -93.6474609375,\n 41.77131167976407\n ],\n [\n -93.0322265625,\n 42.032974332441405\n ],\n [\n -92.6806640625,\n 42.94033923363181\n ],\n [\n -93.251953125,\n 43.89789239125797\n ],\n [\n -94.2626953125,\n 44.77793589631623\n ],\n [\n -95.2294921875,\n 45.521743896993634\n ],\n [\n -95.625,\n 45.67548217560647\n ],\n [\n -96.15234375,\n 47.100044694025215\n ],\n [\n -96.94335937499999,\n 48.45835188280866\n ],\n [\n -97.3828125,\n 49.095452162534826\n ],\n [\n -99.404296875,\n 50.875311142200765\n ],\n [\n -100.7666015625,\n 52.214338608258224\n ],\n [\n -102.4365234375,\n 53.35710874569601\n ],\n [\n -105.46875,\n 54.80068486732233\n ],\n [\n -108.3251953125,\n 56.24334992410525\n ],\n [\n -110.61035156249999,\n 57.16007826737998\n ],\n [\n -111.97265625,\n 57.77451753559619\n ],\n [\n -115.400390625,\n 57.088515327886505\n ],\n [\n -114.6533203125,\n 54.316523240258284\n ],\n [\n -114.5654296875,\n 51.37178037591739\n ],\n [\n -113.73046875,\n 48.83579746243093\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"s2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-04","publicationStatus":"PW","scienceBaseUri":"57aaff4ce4b05e859be0f5bf","contributors":{"authors":[{"text":"Steen, Valerie vsteen@usgs.gov","contributorId":5598,"corporation":false,"usgs":true,"family":"Steen","given":"Valerie","email":"vsteen@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":645033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Skagen, Susan K. 0000-0002-6744-1244 skagens@usgs.gov","orcid":"https://orcid.org/0000-0002-6744-1244","contributorId":167829,"corporation":false,"usgs":true,"family":"Skagen","given":"Susan K.","email":"skagens@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":645032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Melcher, Cynthia P. 0000-0002-8044-9689 melcherc@usgs.gov","orcid":"https://orcid.org/0000-0002-8044-9689","contributorId":5094,"corporation":false,"usgs":true,"family":"Melcher","given":"Cynthia","email":"melcherc@usgs.gov","middleInitial":"P.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":645034,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}