Geoelectric fields at the Earth's surface caused by magnetic storms constitute a hazard to the operation of electric power grids and related infrastructure. The ability to estimate these geoelectric fields in close to real time and provide local predictions would better equip the industry to mitigate negative impacts on their operations. Here we report progress toward this goal: development of robust algorithms that convolve a magnetic storm time series with a frequency domain impedance for a realistic three-dimensional (3-D) Earth, to estimate the local, storm time geoelectric field. Both frequency domain and time domain approaches are presented and validated against storm time geoelectric field data measured in Japan. The methods are then compared in the context of a real-time application.
","language":"English","publisher":"AGU","doi":"10.1002/2017SW001594","usgsCitation":"Kelbert, A., Balch, C., Pulkkinen, A., Egbert, G.D., Love, J.J., Rigler, E.J., and Fujii, I., 2017, Methodology for time-domain estimation of storm time geoelectric fields using the 3-D magnetotelluric response tensors: Space Weather, v. 15, no. 7, p. 874-894, https://doi.org/10.1002/2017SW001594.","productDescription":"21 p.","startPage":"874","endPage":"894","ipdsId":"IP-086748","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":469635,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/53630","text":"External Repository"},{"id":346474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-19","publicationStatus":"PW","scienceBaseUri":"59dddc0be4b05fe04ccd05d1","contributors":{"authors":[{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":712133,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Balch, Christopher","contributorId":156386,"corporation":false,"usgs":false,"family":"Balch","given":"Christopher","affiliations":[{"id":20337,"text":"NOAA Space Weather Prediciton Center","active":true,"usgs":false}],"preferred":false,"id":712134,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pulkkinen, Antti","contributorId":196970,"corporation":false,"usgs":false,"family":"Pulkkinen","given":"Antti","email":"","affiliations":[],"preferred":false,"id":712135,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Egbert, Gary D.","contributorId":187462,"corporation":false,"usgs":false,"family":"Egbert","given":"Gary","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":712136,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":712137,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rigler, E. Joshua 0000-0003-4850-3953 erigler@usgs.gov","orcid":"https://orcid.org/0000-0003-4850-3953","contributorId":4367,"corporation":false,"usgs":true,"family":"Rigler","given":"E.","email":"erigler@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":712138,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fujii, Ikuko","contributorId":196971,"corporation":false,"usgs":false,"family":"Fujii","given":"Ikuko","email":"","affiliations":[],"preferred":false,"id":712139,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193245,"text":"70193245 - 2017 - A multi-species synthesis of physiological mechanisms in drought-induced tree mortality","interactions":[],"lastModifiedDate":"2018-01-23T09:27:51","indexId":"70193245","displayToPublicDate":"2017-08-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5263,"text":"Nature Ecology & Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A multi-species synthesis of physiological mechanisms in drought-induced tree mortality","docAbstract":"Widespread tree mortality associated with drought has been observed on all forested continents and global change is expected to exacerbate vegetation vulnerability. Forest mortality has implications for future biosphere–atmosphere interactions of carbon, water and energy balance, and is poorly represented in dynamic vegetation models. Reducing uncertainty requires improved mortality projections founded on robust physiological processes. However, the proposed mechanisms of drought-induced mortality, including hydraulic failure and carbon starvation, are unresolved. A growing number of empirical studies have investigated these mechanisms, but data have not been consistently analysed across species and biomes using a standardized physiological framework. Here, we show that xylem hydraulic failure was ubiquitous across multiple tree taxa at drought-induced mortality. All species assessed had 60% or higher loss of xylem hydraulic conductivity, consistent with proposed theoretical and modelled survival thresholds. We found diverse responses in non-structural carbohydrate reserves at mortality, indicating that evidence supporting carbon starvation was not universal. Reduced non-structural carbohydrates were more common for gymnosperms than angiosperms, associated with xylem hydraulic vulnerability, and may have a role in reducing hydraulic function. Our finding that hydraulic failure at drought-induced mortality was persistent across species indicates that substantial improvement in vegetation modelling can be achieved using thresholds in hydraulic function.
","language":"English","publisher":"Nature","doi":"10.1038/s41559-017-0248-x","usgsCitation":"Adams, H., Zeppel, M., Anderegg, W.R., Hartmann, H., Landhausser, S.M., Tissue, D.T., Huxman, T.E., Hudson, P.J., Franz, T.E., Allen, C.D., Anderegg, L., Barron-Gafford, G.A., Beerling, D., Breshears, D.D., Brodribb, T.J., Bugmann, H., Cobb, R.C., Collins, A.D., Dickman, L.T., Duan, H., Ewers, B.E., Galiano, L., Galvez, D.A., Garcia-Forner, N., Gaylord, M.L., Germino, M.J., Gessler, A., Hacke, U.G., Hakamada, R., Hector, A., Jenkins, M., Kane, J.M., Kolb, T.E., Law, D., Lewis, J.D., Limousin, J., Love, D., Macalady, A.K., Martínez-Vilalta, J., Mencuccini, M., Mitchell, P.J., Muss, J.D., O’Brien, M.J., O’Grady, A.P., Pangle, R.E., Pinkard, E.A., Piper, F.I., Plaut, J., Pockman, W.T., Quirk, J., Reinhardt, K., Ripullone, F., Ryan, M., Sala, A., Sevanto, S., Sperry, J.S., Vargas, R., Vennetier, M., Way, D.A., Wu, C., Yepez, E.A., and McDowell, N.G., 2017, A multi-species synthesis of physiological mechanisms in drought-induced tree mortality: Nature Ecology & Evolution, v. 1, p. 1285-1291, https://doi.org/10.1038/s41559-017-0248-x.","productDescription":"7 p.","startPage":"1285","endPage":"1291","ipdsId":"IP-072990","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469637,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1038/s41559-017-0248-x","text":"External Repository"},{"id":348043,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-07","publicationStatus":"PW","scienceBaseUri":"59fadd21e4b0531197b13c84","contributors":{"authors":[{"text":"Adams, Henry D.","contributorId":105619,"corporation":false,"usgs":true,"family":"Adams","given":"Henry D.","affiliations":[],"preferred":false,"id":719021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zeppel, Melanie","contributorId":147096,"corporation":false,"usgs":false,"family":"Zeppel","given":"Melanie","email":"","affiliations":[{"id":16788,"text":"Macquarie University","active":true,"usgs":false}],"preferred":false,"id":719022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderegg, William R.L.","contributorId":147089,"corporation":false,"usgs":false,"family":"Anderegg","given":"William","email":"","middleInitial":"R.L.","affiliations":[{"id":16784,"text":"Princeton U.","active":true,"usgs":false}],"preferred":false,"id":719023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartmann, Henrik","contributorId":181974,"corporation":false,"usgs":false,"family":"Hartmann","given":"Henrik","email":"","affiliations":[],"preferred":false,"id":719024,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landhausser, Simon M.","contributorId":199409,"corporation":false,"usgs":false,"family":"Landhausser","given":"Simon","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":719025,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tissue, David T.","contributorId":199410,"corporation":false,"usgs":false,"family":"Tissue","given":"David","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":719026,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huxman, Travis E.","contributorId":53898,"corporation":false,"usgs":false,"family":"Huxman","given":"Travis","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":719027,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hudson, Patrick J.","contributorId":199411,"corporation":false,"usgs":false,"family":"Hudson","given":"Patrick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":719028,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Franz, Trenton E.","contributorId":199412,"corporation":false,"usgs":false,"family":"Franz","given":"Trenton","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":719029,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Allen, Craig D. 0000-0002-8777-5989 craig_allen@usgs.gov","orcid":"https://orcid.org/0000-0002-8777-5989","contributorId":2597,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"craig_allen@usgs.gov","middleInitial":"D.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":719155,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Anderegg, Leander D. 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monitoring","docAbstract":"Whitebark pine and foxtail pine serve foundational roles in the subalpine zone of the Sierra Nevada. They provide the dominant structure in tree-line forests and regulate key ecosystem processes and community dynamics. Climate change models suggest that there will be changes in temperature regimes and in the timing and magnitude of precipitation within the current distribution of these species, and these changes may alter the species’ distributional limits. Other stressors include the non-native pathogen white pine blister rust and mountain pine beetle, which have played a role in the decline of whitebark pine throughout much of its range. The National Park Service is monitoring status and trends of these species. This report provides complementary information in the form of habitat suitability models to predict climate change impacts on the future distribution of these species within Sierra Nevada national parks.
We used maximum entropy modeling to build habitat suitability models by relating species occurrence to environmental variables. Species occurrence was available from 328 locations for whitebark pine and 244 for foxtail pine across the species’ distributions within the parks. We constructed current climate surfaces for modeling by interpolating data from weather stations. Climate surfaces included mean, minimum, and maximum temperature and total precipitation for January, April, July, and October. We downscaled five general circulation models for the 2050s and the 2090s from ~125 km2 to 1 km2 under both an optimistic and an extreme climate scenario to bracket potential climatic change and its influence on projected suitable habitat.
To describe anticipated changes in the distribution of suitable habitat, we compared, for each species, climate scenario, and time period, the current models with future models in terms of proportional change in habitat size, elevation distribution, model center points, and where habitat is predicted to expand or contract.
Overall, models indicated that suitable habitats for whitebark and foxtail pine are more likely to shift geographically within the parks by 2100 rather than decline precipitously. This implies park managers might focus conservation efforts on stressors other than climate change, working toward species resilience in the face of threats from introduced disease and elevated native insect damage. More specifically, further understanding of the incidence and severity of white pine blister rust and other stressors in high elevation white pines would help assess vulnerability from threats other than climate change.
A geochemical study was conducted on 37 springs discharging from the Toroweap Formation, Coconino Sandstone, Hermit Formation, Supai Group, and Redwall Limestone north of the Grand Canyon near areas of breccia-pipe uranium mining. Baseline concentrations were established for the elements As, B, Li, Se, SiO2, Sr, Tl, U, and V. Three springs exceeded U.S. Environmental Protection Agency drinking water standards: Fence Spring for arsenic, Pigeon Spring for selenium and uranium, and Willow (Hack) Spring for selenium. The majority of the spring sites had uranium values of less than 10 micrograms per liter (μg/L), but six springs discharging from all of the geologic units studied that are located stratigraphically above the Redwall Limestone had uranium values greater than 10 μg/L (Cottonwood [Tuckup], Grama, Pigeon, Rock, and Willow [Hack and Snake Gulch] Springs). The geochemical characteristics of these six springs with elevated uranium include Ca-Mg-SO4 water type, circumneutral pH, high specific conductance, correlation and multivariate associations between U, Mo, Sr, Se, Li, and Zn, low 87Sr/86Sr, low 234U/238U activity ratios (1.34–2.31), detectable tritium, and carbon isotopic interpretation indicating they may be a mixture of modern and pre-modern waters. Similar geochemical compositions of spring waters having elevated uranium concentrations are observed at sites located both near and away from sites of uranium-mining activities in the present study. Therefore, mining does not appear to explain the presence of elevated uranium concentrations in groundwater at the six springs noted above. The elevated uranium at the six previously mentioned springs may be influenced by iron mineralization associated with mineralized breccia pipe deposits. Six springs discharging from the Coconino Sandstone (Upper Jumpup, Little, Horse, and Slide Springs) and Redwall Limestone (Kanab and Side Canyon Springs) contained water with corrected radiocarbon ages as much as 9,300 years old. Of the springs discharging water with radiocarbon age, Kanab and Side Canyon Springs contain tritium of more than 1.3 picocuries per liter (pCi/L), indicating they may contain a component of modern water recharged after 1952. Springs containing high values of tritium (greater than 5.1 pCi/L), which may suggest a significant component of modern water, include Willow (Hack), Saddle Horse, Cottonwood (Tuckup), Hotel, Bitter, Unknown, Hole in the Wall, and Hanging Springs. Fence and Rider Springs, located on the eastern end of the study area near the Colorado River, have distinctly different geochemical compositions compared to the other springs of the study. Additionally, water from Fence Spring has the highest 87Sr/86Sr for samples analyzed from this study with a value greater than those known in sedimentary rocks from the region. Strontium isotope data likely indicate that water discharging at Fence Spring has interacted with Precambrian basement rocks. Rider Spring had the most depleted values of stable O and H isotopes indicating that recharge, if recent, occurred at higher elevations or was recharged during earlier, cooler-climate conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175068","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Beisner, K.R., Tillman, F.D., Anderson, J.R., Antweiler, R.C., and Bills, D.J., 2017, Geochemical characterization of groundwater discharging from springs north of the Grand Canyon, Arizona, 2009–2016: U.S. Geological Survey Scientific Investigations Report 2017–5068, 58 p., https://doi.org/10.3133/sir20175068.","productDescription":"Report: vi, 58 p.; 6 Appendixes","onlineOnly":"Y","ipdsId":"IP-084230","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":344518,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5068/sir20175068_appendixes.xlsx","text":"Appendixes 1–6","size":"85 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5068"},{"id":344517,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5068/sir20175068_.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5068"},{"id":344516,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5068/coverthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.4,\n 35.6\n ],\n [\n -111.6,\n 35.6\n ],\n [\n -111.6,\n 37\n ],\n [\n -113.4,\n 37\n ],\n [\n -113.4,\n 35.6\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
(520) 670-6671
The plague bacterium Yersinia pestis was introduced to California in 1900 and spread rapidly as a sylvatic disease of mammalian hosts and flea vectors, invading the Great Plains in the United States by the 1930s to 1940s. In grassland ecosystems, plague causes periodic, devastating epizootics in colonies of black-tailed prairie dogs (Cynomys ludovicianus), sciurid rodents that create and maintain subterranean burrows. In doing so, plague inhibits prairie dogs from functioning as keystone species of grassland communities. The rate at which fleas transmit Y. pestis is thought to increase when fleas are abundant. Flea densities can increase during droughts when vegetative production is reduced and herbivorous prairie dogs are malnourished and have weakened defenses against fleas. Epizootics of plague have erupted frequently in prairie dogs during years in which precipitation was plentiful, and the accompanying cool temperatures might have facilitated the rate at which fleas transmitted Y. pestis. Together these observations evoke the hypothesis that transitions from dry-to-wet years provide conditions for plague epizootics in prairie dogs. Using generalized linear models, we analyzed a 24-year dataset on the occurrence of plague epizootics in 42 colonies of prairie dogs from Colorado, USA, 1982–2005. Of the 33 epizootics observed, 52% erupted during years with increased precipitation in summer. For the years with increased summer precipitation, if precipitation in the prior growing season declined from the maximum of 502 mm to the minimum of 200 mm, the prevalence of plague epizootics was predicted to increase 3-fold. Thus, reduced precipitation may have predisposed prairie dogs to plague epizootics when moisture returned. Biologists sometimes assume dry conditions are detrimental for plague. However, 48% of epizootics occurred during years in which precipitation was scarce in summer. In some cases, an increased abundance of fleas during dry years might compensate for other conditions that become less favorable for plague transmission. Global warming is forecasted to amplify the hydrological cycle in the Great Plains, causing an increased occurrence of prolonged droughts interceded by brief periods of intense precipitation. Results herein suggest these changes might affect plague cycles in prairie dogs. Both negative and positive consequences of dry conditions should be considered when managing plague.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21281","usgsCitation":"Eads, D., and Biggins, D.E., 2017, Paltry past-precipitation: Predisposing prairie dogs to plague?: Journal of Wildlife Management, v. 81, no. 6, p. 990-998, https://doi.org/10.1002/jwmg.21281.","productDescription":"9 p.","startPage":"990","endPage":"998","ipdsId":"IP-086521","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":438257,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71G0K17","text":"USGS data release","linkHelpText":"Occurrence of plague epizootics in colonies of black-tailed prairie dogs, Pawnee National Grassland, Colorado, 1982-2005"},{"id":349075,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"81","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-04","publicationStatus":"PW","scienceBaseUri":"5a60fb74e4b06e28e9c230b6","contributors":{"authors":[{"text":"Eads, David deads@usgs.gov","contributorId":200549,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","affiliations":[],"preferred":true,"id":722638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":722639,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70189908,"text":"70189908 - 2017 - Uncertainty, variability, and earthquake physics in ground‐motion prediction equations","interactions":[],"lastModifiedDate":"2017-09-25T13:49:05","indexId":"70189908","displayToPublicDate":"2017-08-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty, variability, and earthquake physics in ground‐motion prediction equations","docAbstract":"Residuals between ground‐motion data and ground‐motion prediction equations (GMPEs) can be decomposed into terms representing earthquake source, path, and site effects. These terms can be cast in terms of repeatable (epistemic) residuals and the random (aleatory) components. Identifying the repeatable residuals leads to a GMPE with reduced uncertainty for a specific source, site, or path location, which in turn can yield a lower hazard level at small probabilities of exceedance. We illustrate a schematic framework for this residual partitioning with a dataset from the ANZA network, which straddles the central San Jacinto fault in southern California. The dataset consists of more than 3200 1.15≤M≤3 earthquakes and their peak ground accelerations (PGAs), recorded at close distances (R≤20 km). We construct a small‐magnitude GMPE for these PGA data, incorporating VS30 site conditions and geometrical spreading. Identification and removal of the repeatable source, path, and site terms yield an overall reduction in the standard deviation from 0.97 (in ln units) to 0.44, for a nonergodic assumption, that is, for a single‐source location, single site, and single path. We give examples of relationships between independent seismological observables and the repeatable terms. We find a correlation between location‐based source terms and stress drops in the San Jacinto fault zone region; an explanation of the site term as a function of kappa, the near‐site attenuation parameter; and a suggestion that the path component can be related directly to elastic structure. These correlations allow the repeatable source location, site, and path terms to be determined a priori using independent geophysical relationships. Those terms could be incorporated into location‐specific GMPEs for more accurate and precise ground‐motion prediction.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160164","usgsCitation":"Baltay Sundstrom, A.S., Hanks, T.C., and Abrahamson, N., 2017, Uncertainty, variability, and earthquake physics in ground‐motion prediction equations: Bulletin of the Seismological Society of America, v. 107, no. 4, p. 1754-1772, https://doi.org/10.1785/0120160164.","productDescription":"19 p.","startPage":"1754","endPage":"1772","ipdsId":"IP-074959","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":344489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-30","publicationStatus":"PW","scienceBaseUri":"59819313e4b0e2f5d463b78f","contributors":{"authors":[{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":706729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanks, Thomas C. 0000-0003-0928-0056 thanks@usgs.gov","orcid":"https://orcid.org/0000-0003-0928-0056","contributorId":3065,"corporation":false,"usgs":true,"family":"Hanks","given":"Thomas","email":"thanks@usgs.gov","middleInitial":"C.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":706730,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abrahamson, Norm A.","contributorId":56337,"corporation":false,"usgs":true,"family":"Abrahamson","given":"Norm A.","affiliations":[],"preferred":false,"id":706731,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192610,"text":"70192610 - 2017 - Empirical estimation of recreational exploitation of burbot, Lota lota, in the Wind River drainage of Wyoming using a multistate capture–recapture model","interactions":[],"lastModifiedDate":"2017-11-10T11:32:56","indexId":"70192610","displayToPublicDate":"2017-08-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Empirical estimation of recreational exploitation of burbot, Lota lota, in the Wind River drainage of Wyoming using a multistate capture–recapture model","title":"Empirical estimation of recreational exploitation of burbot, Lota lota, in the Wind River drainage of Wyoming using a multistate capture–recapture model","docAbstract":"Burbot, Lota lota (Linnaeus), is a regionally popular sportfish in the Wind River drainage of Wyoming, USA, at the southern boundary of the range of the species. Recent declines in burbot abundances were hypothesised to be caused by overexploitation, entrainment in irrigation canals and habitat loss. This study addressed the overexploitation hypothesis using tagging data to generate reliable exploitation, abundance and density estimates from a multistate capture–recapture model that accounted for incomplete angler reporting and tag loss. Exploitation rate μ was variable among the study lakes and inversely correlated with density. Exploitation thresholds μ40 associated with population densities remaining above 40% of carrying capacity were generated to characterise risk of overharvest using exploitation and density estimates from tagging data and a logistic surplus-production model parameterised with data from other burbot populations. Bull Lake (μ = 0.06, 95% CI: 0.03–0.11; μ40 = 0.18) and Torrey Lake (μ = 0.02, 95% CI: 0.00–0.11; μ40 = 0.18) had a low risk of overfishing, Upper Dinwoody Lake had intermediate risk (μ = 0.08, 95% CI: 0.02–0.32; μ40 = 0.18) and Lower Dinwoody Lake had high risk (μ = 0.32, 95% CI: 0.10–0.67; μ40 = 0.08). These exploitation and density estimates can be used to guide sustainable management of the Wind River drainage recreational burbot fishery and inform management of other burbot fisheries elsewhere.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12226","usgsCitation":"Lewandoski, S., Guy, C.S., Zale, A.V., Gerrity, P.C., Deromedi, J.W., Johnson, K.M., and Skates, D.L., 2017, Empirical estimation of recreational exploitation of burbot, Lota lota, in the Wind River drainage of Wyoming using a multistate capture–recapture model: Fisheries Management and Ecology, v. 24, no. 4, p. 298-307, https://doi.org/10.1111/fme.12226.","productDescription":"10 p.","startPage":"298","endPage":"307","ipdsId":"IP-076704","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":348576,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","volume":"24","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-03","publicationStatus":"PW","scienceBaseUri":"5a06c8cae4b09af898c86108","contributors":{"authors":[{"text":"Lewandoski, S. A.","contributorId":200246,"corporation":false,"usgs":false,"family":"Lewandoski","given":"S. A.","affiliations":[],"preferred":false,"id":721592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716544,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zale, Alexander V. 0000-0003-1703-885X zale@usgs.gov","orcid":"https://orcid.org/0000-0003-1703-885X","contributorId":3010,"corporation":false,"usgs":true,"family":"Zale","given":"Alexander","email":"zale@usgs.gov","middleInitial":"V.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716545,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerrity, Paul C.","contributorId":104198,"corporation":false,"usgs":true,"family":"Gerrity","given":"Paul","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":721593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deromedi, J. W.","contributorId":200247,"corporation":false,"usgs":false,"family":"Deromedi","given":"J.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":721594,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, K. M.","contributorId":23513,"corporation":false,"usgs":true,"family":"Johnson","given":"K.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721595,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Skates, D. L.","contributorId":200248,"corporation":false,"usgs":false,"family":"Skates","given":"D.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":721596,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193043,"text":"70193043 - 2017 - Automated quantification of surface water inundation in wetlands using optical satellite imagery","interactions":[],"lastModifiedDate":"2017-11-12T11:13:09","indexId":"70193043","displayToPublicDate":"2017-08-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Automated quantification of surface water inundation in wetlands using optical satellite imagery","docAbstract":"We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.
","language":"English","publisher":"MDPI","doi":"10.3390/rs9080807","usgsCitation":"DeVries, B., Huang, C., Lang, M.W., Jones, J., Huang, W., Creed, I., and Carroll, M.L., 2017, Automated quantification of surface water inundation in wetlands using optical satellite imagery: Remote Sensing, v. 9, no. 8, Article 807; 22 p., https://doi.org/10.3390/rs9080807.","productDescription":"Article 807; 22 p.","ipdsId":"IP-087428","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469631,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9080807","text":"Publisher Index Page"},{"id":348619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"8","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-07","publicationStatus":"PW","scienceBaseUri":"5a096bb1e4b09af898c94143","contributors":{"authors":[{"text":"DeVries, Ben 0000-0003-2136-3401","orcid":"https://orcid.org/0000-0003-2136-3401","contributorId":198971,"corporation":false,"usgs":false,"family":"DeVries","given":"Ben","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":717737,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huang, Chengquan 0000-0003-0055-9798","orcid":"https://orcid.org/0000-0003-0055-9798","contributorId":198972,"corporation":false,"usgs":false,"family":"Huang","given":"Chengquan","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":717738,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lang, Megan W.","contributorId":196284,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":717739,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, John 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":717736,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huang, Wenli 0000-0001-9608-1690","orcid":"https://orcid.org/0000-0001-9608-1690","contributorId":198973,"corporation":false,"usgs":false,"family":"Huang","given":"Wenli","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":717740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Creed, Irena F.","contributorId":81209,"corporation":false,"usgs":false,"family":"Creed","given":"Irena F.","affiliations":[{"id":27655,"text":"Department of Biology, University of Western Ontario, London, ON Canada","active":true,"usgs":false}],"preferred":false,"id":717741,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carroll, Mark L.","contributorId":145826,"corporation":false,"usgs":false,"family":"Carroll","given":"Mark","email":"","middleInitial":"L.","affiliations":[{"id":16246,"text":"Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA","active":true,"usgs":false},{"id":7239,"text":"Science Systems and Applications, Inc.","active":true,"usgs":false},{"id":16247,"text":"Sigma Space Corp, NASA Goddard Space Flight Center, Greenbelt, MD, USA","active":true,"usgs":false}],"preferred":false,"id":721689,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70189156,"text":"ofr20171079 - 2017 - Compilation of geospatial data for the mineral industries and related infrastructure of Latin America and the Caribbean","interactions":[],"lastModifiedDate":"2017-08-28T13:20:56","indexId":"ofr20171079","displayToPublicDate":"2017-07-31T17:35:00","publicationYear":"2017","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":"2017-1079","title":"Compilation of geospatial data for the mineral industries and related infrastructure of Latin America and the Caribbean","docAbstract":"This report describes the U.S. Geological Survey’s (USGS) ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports in Latin America and the Caribbean. The report includes an overview of data sources and an explanation of the geospatial PDF map format.
The geodatabase and geospatial data layers described in this report create a new geographic information product in the form of a geospatial portable document format (PDF) map. The geodatabase contains additional data layers from USGS, foreign governmental, and open-source sources as follows: (1) coal occurrence areas, (2) electric power generating facilities, (3) electric power transmission lines, (4) hydrocarbon resource cumulative production data, (5) liquefied natural gas terminals, (6) oil and gas concession leasing areas, (7) oil and gas field center points, (8) oil and gas pipelines, (9) USGS petroleum provinces, (10) railroads, (11) recoverable proven plus probable hydrocarbon resources, (12) major cities, (13) major rivers, and (14) undiscovered porphyry copper tracts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171079","collaboration":"Prepared in cooperation with the Inter-American Development Bank","usgsCitation":"Baker, M.S., Buteyn, S.D., Freeman, P.A., Trippi, M.H., and Trimmer, L.M., III, 2017, Compilation of geospatial data for the mineral industries and related infrastructure of Latin America and the Caribbean: U.S. Geological Survey Open-File Report 2017–1079, 87 p., 1 geodatabase and 1 geospatial PDF map, https://doi.org/10.3133/ofr20171079. ","productDescription":"Report: xi, 87 p; 3 Data Releases; Geodatabase and Metadata; Map: 8.5 x 11.0 inches","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078672","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":438258,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BZ6460","text":"USGS data release","linkHelpText":"Mineral commodity exporting ports of Latin America and the Caribbean"},{"id":344263,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/58093603e4b0f497e78f3f31","text":"USGS data release","description":"USGS data release","linkHelpText":"Mineral commodity exporting ports of Latin America and the Caribbean"},{"id":344257,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1079/coverthb.jpg"},{"id":344258,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1079/ofr20171079.pdf","text":"Report","size":"4.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1079"},{"id":344259,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2017/1079/ofr20171079_lac-indust-infra.pdf","text":"Geospatial Map","size":"29 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Compilation of Geospatial Data for the Mineral Industries and Related Infrastructure of Latin America and the Caribbean"},{"id":344260,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2017/1079/ofr20171079_lac-indust-infra.zip","text":"Geodatabase and metadata","size":"28.7 MB","linkFileType":{"id":6,"text":"zip"}},{"id":344261,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MG7MM6","text":"USGS data release","description":"USGS data release","linkHelpText":"Mineral facilities of Latin America and the Caribbean"},{"id":344262,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GQ6VWG","text":"USGS data release","description":"USGS data release","linkHelpText":"Mineral exploration sites of Latin America and the Caribbean"}],"otherGeospatial":"Caribbean, Latin America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.828125,\n -57.51582286553883\n ],\n [\n -29.179687499999996,\n -57.51582286553883\n ],\n [\n -29.179687499999996,\n 34.30714385628804\n ],\n [\n -118.828125,\n 34.30714385628804\n ],\n [\n -118.828125,\n -57.51582286553883\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Elevated concentrations of nitrogen in groundwater that discharges to surface-water bodies can degrade surface-water quality and habitats in the New Jersey Coastal Plain. An analysis of groundwater flow in the Kirkwood-Cohansey aquifer system and deeper confined aquifers that underlie the Barnegat Bay–Little Egg Harbor (BB-LEH) watershed and estuary was conducted by using groundwater-flow simulation, in conjunction with a particle-tracking routine, to provide estimates of groundwater flow paths and travel times to streams and the BB-LEH estuary.
Water-quality data from the Ambient Groundwater Quality Monitoring Network, a long-term monitoring network of wells distributed throughout New Jersey, were used to estimate the initial nitrogen concentration in recharge for five different land-use classes—agricultural cropland or pasture, agricultural orchard or vineyard, urban non-residential, urban residential, and undeveloped. Land use at the point of recharge within the watershed was determined using a geographic information system (GIS). Flow path starting locations were plotted on land-use maps for 1930, 1973, 1986, 1997, and 2002. Information on the land use at the time and location of recharge, time of travel to the discharge location, and the point of discharge were determined for each simulated flow path. Particle-tracking analysis provided the link from the point of recharge, along the particle flow path, to the point of discharge, and the particle travel time. The travel time of each simulated particle established the recharge year. Land use during the year of recharge was used to define the nitrogen concentration associated with each flow path. The recharge-weighted average nitrogen concentration for all flow paths that discharge to the Toms River upstream from streamflow-gaging station 01408500 or to the BB-LEH estuary was calculated.
Groundwater input into the Barnegat Bay–Little Egg Harbor estuary from two main sources— indirect discharge from base flow to streams that eventually flow into the bay and groundwater discharge directly into the estuary and adjoining coastal wetlands— is summarized by quantity, travel time, and estimated nitrogen concentration. Simulated average groundwater discharge to streams in the watershed that flow into the BB-LEH estuary is approximately 400 million gallons per day. Particle-tracking results indicate that the travel time of 56 percent of this discharge is less than 7 years. Fourteen percent of the groundwater discharge to the streams in the BB-LEH watershed has a travel time of less than 7 years and originates in urban land. Analysis of flow-path simulations indicate that approximately 13 percent of the total groundwater flow through the study area discharges directly to the estuary and adjoining coastal wetlands (approximately 64 million gallons per day). The travel time of 19 percent of this discharge is less than 7 years. Ten percent of this discharge (1 percent of the total groundwater flow through the study area) originates in urban areas and has a travel time of less than 7 years. Groundwater that discharges to the streams that flow into the BB-LEH, in general, has shorter travel times, and a higher percentage of it originates in urban areas than does direct groundwater discharge to the Barnegat Bay–Little Egg Harbor estuary.
The simulated average nitrogen concentration in groundwater that discharges to the Toms River, upstream from streamflow-gaging station 01408500 was computed and compared to summary concentrations determined from analysis of multiple surface-water samples. The nitrogen concentration in groundwater that discharges directly to the estuary and adjoining coastal wetlands is a current data gap. The particle tracking methodology used in this study provides an estimate of this concentration.\"
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165169","collaboration":"Prepared in cooperation with the Barnegat Bay Partnership","usgsCitation":"Voronin, L.M., and Cauller, S.J., 2017, Simulated groundwater flow paths, travel time, and advective transport of nitrogen in the Kirkwood-Cohansey aquifer system, Barnegat Bay–Little Egg Harbor Watershed, New Jersey: U.S. Geological Survey Scientific Investigations Report 2016–5169, 17 p., https://doi.org/10.3133/sir20165169.","productDescription":"v, 17 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077222","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":344342,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5169/sir20165169.pdf","text":"Report","size":"24.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5169"},{"id":346055,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55M0W","text":"USGS data release","description":"USGS data release","linkHelpText":"MODPATH particle-tracking analysis of groundwater flow and travel times to the Barnegat Bay-Little Egg Harbor estuary and streams within the Barnegat Bay-Little Egg Harbor watershed, New Jersey"},{"id":344341,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5169/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Kirkwood-Cohansey Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.5,\n 40.25\n ],\n [\n -73.75,\n 40.25\n ],\n [\n -73.75,\n 39.5\n ],\n [\n -74.5,\n 39.5\n ],\n [\n -74.5,\n 40.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
The North American Breeding Bird Survey (BBS) provides data for >420 bird species at multiple geographic scales over 5 decades. Modern computational methods have facilitated the fitting of complex hierarchical models to these data. It is easy to propose and fit new models, but little attention has been given to model selection. Here, we discuss and illustrate model selection using leave-one-out cross validation, and the Bayesian Predictive Information Criterion (BPIC). Cross-validation is enormously computationally intensive; we thus evaluate the performance of the Watanabe-Akaike Information Criterion (WAIC) as a computationally efficient approximation to the BPIC. Our evaluation is based on analyses of 4 models as applied to 20 species covered by the BBS. Model selection based on BPIC provided no strong evidence of one model being consistently superior to the others; for 14/20 species, none of the models emerged as superior. For the remaining 6 species, a first-difference model of population trajectory was always among the best fitting. Our results show that WAIC is not reliable as a surrogate for BPIC. Development of appropriate model sets and their evaluation using BPIC is an important innovation for the analysis of BBS data.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-17-1.1","usgsCitation":"Link, W.A., Sauer, J.R., and Niven, D., 2017, Model selection for the North American Breeding Bird Survey: A comparison of methods: Condor, v. 119, no. 3, p. 546-556, https://doi.org/10.1650/CONDOR-17-1.1.","productDescription":"11 p.","startPage":"546","endPage":"556","ipdsId":"IP-082007","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469652,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-17-1.1","text":"Publisher Index Page"},{"id":344469,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59804198e4b0a38ca278932a","contributors":{"authors":[{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":706959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":706960,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niven, Daniel 0000-0002-9527-0577 dniven@usgs.gov","orcid":"https://orcid.org/0000-0002-9527-0577","contributorId":179148,"corporation":false,"usgs":true,"family":"Niven","given":"Daniel","email":"dniven@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":706961,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189895,"text":"70189895 - 2017 - CO2 time series patterns in contrasting headwater streams of North America","interactions":[],"lastModifiedDate":"2022-11-02T13:59:04.643794","indexId":"70189895","displayToPublicDate":"2017-07-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"displayTitle":"CO2 time series patterns in contrasting headwater streams of North America","title":"CO2 time series patterns in contrasting headwater streams of North America","docAbstract":"We explored the underlying patterns of temporal stream CO2 partial pressure (pCO2) variability using highfrequency sensors in seven disparate headwater streams distributed across the northern hemisphere. We also compared this dataset of [40,000 pCO2 records with other published records from lotic systems. Individual stream sites exhibited relatively distinct pCO2 patterns over time with few consistent traits across sites. Some sites showed strong diel variability, some exhibited increasing pCO2 with increasing discharge, whereas other streams had reduced pCO2 with increasing discharge or no clear response to changes in flow. The only ‘‘universal’’ signature observed in headwater streams was a late summer pCO2 maxima that was likely driven by greatest rates of organic matter respiration due to highest annual temperatures. However, we did not observe this seasonal pattern in a southern hardwood forest site, likely because the region was transitioning from a severe drought. This work clearly illustrates the heterogeneous nature of headwater streams, and highlights the idiosyncratic nature of a non-conservative solute that is jointly influenced by physics, hydrology, and biology. We suggest that future researchers carefully select sensor locations (within and among streams) and provide additional contextual information when attempting to explain pCO2 patterns.
","language":"English","publisher":"Springer","doi":"10.1007/s00027-016-0511-2","usgsCitation":"Crawford, J.T., Stanley, E.H., Dornblaser, M.M., and Striegl, R.G., 2017, CO2 time series patterns in contrasting headwater streams of North America: Aquatic Sciences, v. 79, no. 3, p. 473-486, https://doi.org/10.1007/s00027-016-0511-2.","productDescription":"14 p.","startPage":"473","endPage":"486","ipdsId":"IP-075321","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Colorado, Georgia, Puerto Rico, Vermont, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.75875345162515,\n 40.41826699675971\n ],\n [\n -105.75875345162515,\n 40.26772177168533\n ],\n [\n -105.54604103183873,\n 40.26772177168533\n ],\n [\n -105.54604103183873,\n 40.41826699675971\n ],\n [\n -105.75875345162515,\n 40.41826699675971\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.2335731672356,\n 33.68198344862023\n ],\n [\n -84.2335731672356,\n 33.60505958304719\n ],\n [\n -84.04073633747926,\n 33.60505958304719\n ],\n [\n -84.04073633747926,\n 33.68198344862023\n ],\n [\n -84.2335731672356,\n 33.68198344862023\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.69511006355633,\n 46.098665913281394\n ],\n [\n -89.69511006355633,\n 45.985605857218644\n ],\n [\n -89.50896090902005,\n 45.985605857218644\n ],\n [\n -89.50896090902005,\n 46.098665913281394\n ],\n [\n -89.69511006355633,\n 46.098665913281394\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -145.88153479471083,\n 65.7139553812934\n ],\n [\n -145.88153479471083,\n 65.6132521487456\n ],\n [\n -145.48928986621956,\n 65.6132521487456\n ],\n [\n -145.48928986621956,\n 65.7139553812934\n ],\n [\n -145.88153479471083,\n 65.7139553812934\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.16368555616823,\n 44.487264061945865\n ],\n [\n -72.16368555616823,\n 44.386327502883944\n ],\n [\n -71.96577709121281,\n 44.386327502883944\n ],\n [\n -71.96577709121281,\n 44.487264061945865\n ],\n [\n -72.16368555616823,\n 44.487264061945865\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -65.76509199333995,\n 18.412962038648402\n ],\n [\n -65.76509199333995,\n 18.28241342108312\n ],\n [\n -65.61976597390735,\n 18.28241342108312\n ],\n [\n -65.61976597390735,\n 18.412962038648402\n ],\n [\n -65.76509199333995,\n 18.412962038648402\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"79","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-04","publicationStatus":"PW","scienceBaseUri":"59804199e4b0a38ca2789332","contributors":{"authors":[{"text":"Crawford, John T. 0000-0003-4440-6945 jtcrawford@usgs.gov","orcid":"https://orcid.org/0000-0003-4440-6945","contributorId":4081,"corporation":false,"usgs":true,"family":"Crawford","given":"John","email":"jtcrawford@usgs.gov","middleInitial":"T.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":706646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":706647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dornblaser, Mark M. 0000-0002-6298-3757 mmdornbl@usgs.gov","orcid":"https://orcid.org/0000-0002-6298-3757","contributorId":1636,"corporation":false,"usgs":true,"family":"Dornblaser","given":"Mark","email":"mmdornbl@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":706648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":706645,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189822,"text":"70189822 - 2017 - Historical and projected trends in landscape drivers affecting carbon dynamics in Alaska","interactions":[],"lastModifiedDate":"2017-07-27T13:59:27","indexId":"70189822","displayToPublicDate":"2017-07-27T00:00:00","publicationYear":"2017","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":"Historical and projected trends in landscape drivers affecting carbon dynamics in Alaska","docAbstract":"Modern climate change in Alaska has resulted in widespread thawing of permafrost, increased fire activity, and extensive changes in vegetation characteristics that have significant consequences for socioecological systems. Despite observations of the heightened sensitivity of these systems to change, there has not been a comprehensive assessment of factors that drive ecosystem changes throughout Alaska. Here we present research that improves our understanding of the main drivers of the spatiotemporal patterns of carbon dynamics using in situ observations, remote sensing data, and an array of modeling techniques. In the last 60 yr, Alaska has seen a large increase in mean annual air temperature (1.7°C), with the greatest warming occurring over winter and spring. Warming trends are projected to continue throughout the 21st century and will likely result in landscape-level changes to ecosystem structure and function. Wetlands, mainly bogs and fens, which are currently estimated to cover 12.5% of the landscape, strongly influence exchange of methane between Alaska's ecosystems and the atmosphere and are expected to be affected by thawing permafrost and shifts in hydrology. Simulations suggest the current proportion of near-surface (within 1 m) and deep (within 5 m) permafrost extent will be reduced by 9–74% and 33–55% by the end of the 21st century, respectively. Since 2000, an average of 678 595 ha/yr was burned, more than twice the annual average during 1950–1999. The largest increase in fire activity is projected for the boreal forest, which could result in a reduction in late-successional spruce forest (8–44%) and an increase in early-successional deciduous forest (25–113%) that would mediate future fire activity and weaken permafrost stability in the region. Climate warming will also affect vegetation communities across arctic regions, where the coverage of deciduous forest could increase (223–620%), shrub tundra may increase (4–21%), and graminoid tundra might decrease (10–24%). This study sheds light on the sensitivity of Alaska's ecosystems to change that has the potential to significantly affect local and regional carbon balance, but more research is needed to improve estimates of land-surface and subsurface properties, and to better account for ecosystem dynamics affected by a myriad of biophysical factors and interactions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1538","usgsCitation":"Pastick, N.J., Duffy, P.A., Genet, H., Rupp, T.S., Wylie, B.K., Johnson, K., Jorgenson, M., Bliss, N.B., McGuire, A.D., Jafarov, E., and Knight, J.F., 2017, Historical and projected trends in landscape drivers affecting carbon dynamics in Alaska: Ecological Applications, v. 27, no. 5, p. 1383-1402, https://doi.org/10.1002/eap.1538.","productDescription":"20 p.","startPage":"1383","endPage":"1402","ipdsId":"IP-076738","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469655,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1356157","text":"External Repository"},{"id":344395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Scott","contributorId":195180,"corporation":false,"usgs":false,"family":"Rupp","given":"T.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":706471,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":706472,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Kristofer","contributorId":195181,"corporation":false,"usgs":false,"family":"Johnson","given":"Kristofer","affiliations":[],"preferred":false,"id":706473,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jorgenson, M. Torre","contributorId":140457,"corporation":false,"usgs":false,"family":"Jorgenson","given":"M. Torre","affiliations":[{"id":13506,"text":"Alaska Ecoscience","active":true,"usgs":false}],"preferred":false,"id":706474,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bliss, Norman B. 0000-0003-2409-5211 bliss@usgs.gov","orcid":"https://orcid.org/0000-0003-2409-5211","contributorId":1921,"corporation":false,"usgs":true,"family":"Bliss","given":"Norman","email":"bliss@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":706475,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McGuire, Anthony D. 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":2493,"corporation":false,"usgs":true,"family":"McGuire","given":"Anthony","email":"ffadm@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":false,"id":706476,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jafarov, Elchin","contributorId":195182,"corporation":false,"usgs":false,"family":"Jafarov","given":"Elchin","affiliations":[],"preferred":false,"id":706477,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Knight, Joseph F.","contributorId":55311,"corporation":false,"usgs":true,"family":"Knight","given":"Joseph","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":706478,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70189857,"text":"70189857 - 2017 - Integrating Breeding Bird Survey and demographic data to estimate Wood Duck population size in the Atlantic Flyway","interactions":[],"lastModifiedDate":"2017-07-27T13:54:42","indexId":"70189857","displayToPublicDate":"2017-07-27T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Integrating Breeding Bird Survey and demographic data to estimate Wood Duck population size in the Atlantic Flyway","docAbstract":"The U.S. Fish and Wildlife Service (USFWS) uses data from the North American Breeding Bird Survey (BBS) to assist in monitoring and management of some migratory birds. However, BBS analyses provide indices of population change rather than estimates of population size, precluding their use in developing abundance-based objectives and limiting applicability to harvest management. Wood Ducks (Aix sponsa) are important harvested birds in the Atlantic Flyway (AF) that are difficult to detect during aerial surveys because they prefer forested habitat. We integrated Wood Duck count data from a ground-plot survey in the northeastern U.S. with AF-wide BBS, banding, parts collection, and harvest data to derive estimates of population size for the AF. Overlapping results between the smaller-scale intensive ground-plot survey and the BBS in the northeastern U.S. provided a means for scaling BBS indices to the breeding population size estimates. We applied these scaling factors to BBS results for portions of the AF lacking intensive surveys. Banding data provided estimates of annual survival and harvest rates; the latter, when combined with parts-collection data, provided estimates of recruitment. We used the harvest data to estimate fall population size. Our estimates of breeding population size and variability from the integrated population model (N̄ = 0.99 million, SD = 0.04) were similar to estimates of breeding population size based solely on data from the AF ground-plot surveys and the BBS (N̄ = 1.01 million, SD = 0.04) from 1998 to 2015. Integrating BBS data with other data provided reliable population size estimates for Wood Ducks at a scale useful for harvest and habitat management in the AF, and allowed us to derive estimates of important demographic parameters (e.g., seasonal survival rates, sex ratio) that were not directly informed by data.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-17-7.1","usgsCitation":"Zimmerman, G.S., Sauer, J.R., Boomer, G., Devers, P.K., and Garrettson, P., 2017, Integrating Breeding Bird Survey and demographic data to estimate Wood Duck population size in the Atlantic Flyway: The Condor, v. 119, no. 3, p. 616-628, https://doi.org/10.1650/CONDOR-17-7.1.","productDescription":"13 p.","startPage":"616","endPage":"628","ipdsId":"IP-087327","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469661,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-17-7.1","text":"Publisher Index Page"},{"id":344393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"597afba3e4b0a38ca2750b3a","contributors":{"authors":[{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":706574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":706570,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boomer, G. Scott","contributorId":84603,"corporation":false,"usgs":true,"family":"Boomer","given":"G. Scott","affiliations":[],"preferred":false,"id":706571,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Devers, Patrick K.","contributorId":167173,"corporation":false,"usgs":false,"family":"Devers","given":"Patrick","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":706572,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garrettson, Pamela R.","contributorId":146531,"corporation":false,"usgs":false,"family":"Garrettson","given":"Pamela R.","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":706573,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189852,"text":"70189852 - 2017 - Use of North American Breeding Bird Survey data in avian conservation assessments","interactions":[],"lastModifiedDate":"2017-07-27T13:56:17","indexId":"70189852","displayToPublicDate":"2017-07-27T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Use of North American Breeding Bird Survey data in avian conservation assessments","docAbstract":"Conservation resources are limited, and prioritizing species based on their relative vulnerability and risk of extinction is a fundamental component of conservation planning. In North America, the conservation consortium Partners in Flight (PIF) has developed and implemented a data-driven species assessment process, at global and regional scales, based on quantitative vulnerability criteria. This species assessment process has formed the biological basis for PIF's continental and regional planning and has informed the ranking and legal listing of bird species for conservation protection by state, provincial, and national agencies in Canada, the U.S., and Mexico. Because of its long time series, extensive geographic and species coverage, standardized survey methods, and prompt availability of results, the North American Breeding Bird Survey (BBS) has been an invaluable source of data, allowing PIF to assign objective vulnerability scores calibrated across more than 460 landbird species. BBS data have been most valuable for assessing long-term population trends (PT score). PIF has also developed methods for estimating population size by extrapolating from BBS abundance indices, allowing the assignment of categorical population size (PS) scores for landbird species. At regional scales, BBS relative abundance indices have allowed PIF to assess the area importance (i.e. stewardship responsibility) of each Bird Conservation Region (BCR) for each species, using measures of both relative density and percent of total population in each BCR. Besides direct applicability to assessment scores, PIF has recently used BBS trend data to create new metrics of conservation urgency (e.g., ‘half-life'), as well as for setting population objectives for tracking progress toward meeting conservation goals. Future directions include integrating BBS data with other sources (e.g., eBird) to assess additional species and nonbreeding season measures, working closely with BBS coordinators to expand surveys into Mexico, and providing assessment scores at implementation-relevant scales, such as for migratory bird joint ventures.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-17-57.1","usgsCitation":"Rosenberg, K.V., Blancher, P.J., Stanton, J.C., and Panjabi, A.O., 2017, Use of North American Breeding Bird Survey data in avian conservation assessments: The Condor, v. 119, no. 3, p. 594-606, https://doi.org/10.1650/CONDOR-17-57.1.","productDescription":"13 p.","startPage":"594","endPage":"606","ipdsId":"IP-080515","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":469659,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1650/condor-17-57.1","text":"External Repository"},{"id":344394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"597afba4e4b0a38ca2750b43","contributors":{"authors":[{"text":"Rosenberg, Kenneth V.","contributorId":171463,"corporation":false,"usgs":false,"family":"Rosenberg","given":"Kenneth","email":"","middleInitial":"V.","affiliations":[{"id":27615,"text":"Cornell Lab of Ornithology, Conservation Science Program","active":true,"usgs":false}],"preferred":false,"id":706551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blancher, Peter J.","contributorId":175182,"corporation":false,"usgs":false,"family":"Blancher","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":706552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":706550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Panjabi, Arvind O.","contributorId":169967,"corporation":false,"usgs":false,"family":"Panjabi","given":"Arvind","email":"","middleInitial":"O.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":706553,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189870,"text":"70189870 - 2017 - Greenup and evapotranspiration following the Minute 319 pulse flow to Mexico: An analysis using Landsat 8 Normalized Difference Vegetation Index (NDVI) data","interactions":[],"lastModifiedDate":"2017-08-27T18:10:36","indexId":"70189870","displayToPublicDate":"2017-07-27T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Greenup and evapotranspiration following the Minute 319 pulse flow to Mexico: An analysis using Landsat 8 Normalized Difference Vegetation Index (NDVI) data","docAbstract":"In the southwestern U.S., many riparian ecosystems have been altered by dams, water diversions, and other anthropogenic activities. This is particularly true of the Colorado River, where numerous dams and agricultural diversions have affected this water course, especially south of the U.S.–Mexico border. In the spring of 2014, 130 million cubic meters of water was released to the lower Colorado River Delta in Mexico. To understand the impact of this pulse flow release on vegetation in the delta’s riparian corridor, we analyzed a modified form of Landsat 8 Operational Land Imager (OLI) Normalized Difference Vegetation Index (NDVI*) data. We assessed greenup during the growing period and estimated actual evapotranspiration (ETa) for the period prior to (yr. 2013) and following (i.e., yr. 2014 and 2015) the pulse flow. We found a significant increase in NDVI* from 2013 to 2014 (P < 0.05) and a decrease from 2014 to 2015; however, 2015 levels were still significantly higher than in 2013. ETa was also higher in 2014 vs. 2013, with an estimated 74.5 million cubic meters in 2013 and 88.9 in 2014. The most intense greening occurred in the zone of inundation but also extended into the non-flooded part of the riparian zone, indicating replenishment of groundwater. These findings suggest the peak response by vegetation to the flow lasted about one year, followed by a decrease in NDVI*. As a long term solution to the declining condition of vegetation, additional pulse releases are likely needed for restoration and survival of riparian plant communities in the Colorado River Delta.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2016.08.007","usgsCitation":"Jarchow, C.J., Nagler, P.L., and Glenn, E., 2017, Greenup and evapotranspiration following the Minute 319 pulse flow to Mexico: An analysis using Landsat 8 Normalized Difference Vegetation Index (NDVI) data: Ecological Engineering, v. 106, no. B, p. 776-783, https://doi.org/10.1016/j.ecoleng.2016.08.007.","productDescription":"8 p.","startPage":"776","endPage":"783","ipdsId":"IP-074636","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469656,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoleng.2016.08.007","text":"Publisher Index Page"},{"id":344415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.11886596679688,\n 32.132594234149906\n ],\n [\n -114.67941284179688,\n 32.132594234149906\n ],\n [\n -114.67941284179688,\n 32.72375394304274\n ],\n [\n -115.11886596679688,\n 32.72375394304274\n ],\n [\n -115.11886596679688,\n 32.132594234149906\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"B","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"597afba2e4b0a38ca2750b36","contributors":{"authors":[{"text":"Jarchow, Christopher J. 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":5813,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":706599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":706600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, Edward P.","contributorId":56542,"corporation":false,"usgs":false,"family":"Glenn","given":"Edward P.","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":706601,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189854,"text":"70189854 - 2017 - High-resolution seismic profiling reveals faulting associated with the 1934 Ms 6.6 Hansel Valley earthquake (Utah, USA)","interactions":[],"lastModifiedDate":"2017-09-25T13:50:17","indexId":"70189854","displayToPublicDate":"2017-07-27T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"High-resolution seismic profiling reveals faulting associated with the 1934 Ms 6.6 Hansel Valley earthquake (Utah, USA)","docAbstract":"The 1934 Ms 6.6 Hansel Valley, Utah, earthquake produced an 8-km-long by 3-km-wide zone of north-south−trending surface deformation in an extensional basin within the easternmost Basin and Range Province. Less than 0.5 m of purely vertical displacement was measured at the surface, although seismologic data suggest mostly strike-slip faulting at depth. Characterization of the origin and kinematics of faulting in the Hansel Valley earthquake is important to understand how complex fault ruptures accommodate regions of continental extension and transtension. Here, we address three questions: (1) How does the 1934 surface rupture compare with faults in the subsurface? (2) Are the 1934 fault scarps tectonic or secondary features? (3) Did the 1934 earthquake have components of both strike-slip and dip-slip motion? To address these questions, we acquired a 6.6-km-long, high-resolution seismic profile across Hansel Valley, including the 1934 ruptures. We observed numerous east- and west-dipping normal faults that dip 40°−70° and offset late Quaternary strata from within a few tens of meters of the surface down to a depth of ∼1 km. Spatial correspondence between the 1934 surface ruptures and subsurface faults suggests that ruptures associated with the earthquake are of tectonic origin. Our data clearly show complex basin faulting that is most consistent with transtensional tectonics. Although the kinematics of the 1934 earthquake remain underconstrained, we interpret the disagreement between surface (normal) and subsurface (strike-slip) kinematics as due to slip partitioning during fault propagation and to the effect of preexisting structural complexities. We infer that the 1934 earthquake occurred along an ∼3-km wide, off-fault damage zone characterized by distributed deformation along small-displacement faults that may be alternatively activated during different earthquake episodes.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B31516.1","usgsCitation":"Bruno, P.P., DuRoss, C., and Kokkalas, S., 2017, High-resolution seismic profiling reveals faulting associated with the 1934 Ms 6.6 Hansel Valley earthquake (Utah, USA): GSA Bulletin, v. 129, no. 9-10, p. 1227-1240, https://doi.org/10.1130/B31516.1.","productDescription":"14 p.","startPage":"1227","endPage":"1240","ipdsId":"IP-080664","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":344385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Hansel Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.06854248046875,\n 41.00477542222947\n ],\n [\n -111.98638916015625,\n 41.00477542222947\n ],\n [\n -111.98638916015625,\n 41.99828401778616\n ],\n [\n -113.06854248046875,\n 41.99828401778616\n ],\n [\n -113.06854248046875,\n 41.00477542222947\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"129","issue":"9-10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-16","publicationStatus":"PW","scienceBaseUri":"597afba3e4b0a38ca2750b3c","contributors":{"authors":[{"text":"Bruno, Pier Paolo G.","contributorId":195227,"corporation":false,"usgs":false,"family":"Bruno","given":"Pier","email":"","middleInitial":"Paolo G.","affiliations":[],"preferred":false,"id":706560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":706561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kokkalas, Sotirios","contributorId":195228,"corporation":false,"usgs":false,"family":"Kokkalas","given":"Sotirios","email":"","affiliations":[],"preferred":false,"id":706562,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189208,"text":"ds1058 - 2017 - Drilling, construction, geophysical log data, and lithologic log for boreholes USGS 142 and USGS 142A, Idaho National Laboratory, Idaho","interactions":[],"lastModifiedDate":"2017-08-28T13:23:25","indexId":"ds1058","displayToPublicDate":"2017-07-27T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1058","title":"Drilling, construction, geophysical log data, and lithologic log for boreholes USGS 142 and USGS 142A, Idaho National Laboratory, Idaho","docAbstract":"Starting in 2014, the U.S. Geological Survey in cooperation with the U.S. Department of Energy, drilled and constructed boreholes USGS 142 and USGS 142A for stratigraphic framework analyses and long-term groundwater monitoring of the eastern Snake River Plain aquifer at the Idaho National Laboratory in southeast Idaho. Borehole USGS 142 initially was cored to collect rock and sediment core, then re-drilled to complete construction as a screened water-level monitoring well. Borehole USGS 142A was drilled and constructed as a monitoring well after construction problems with borehole USGS 142 prevented access to upper 100 feet (ft) of the aquifer. Boreholes USGS 142 and USGS 142A are separated by about 30 ft and have similar geology and hydrologic characteristics. Groundwater was first measured near 530 feet below land surface (ft BLS) at both borehole locations. Water levels measured through piezometers, separated by almost 1,200 ft, in borehole USGS 142 indicate upward hydraulic gradients at this location. Following construction and data collection, screened water-level access lines were placed in boreholes USGS 142 and USGS 142A to allow for recurring water level measurements.
Borehole USGS 142 was cored continuously, starting at the first basalt contact (about 4.9 ft BLS) to a depth of 1,880 ft BLS. Excluding surface sediment, recovery of basalt, rhyolite, and sediment core at borehole USGS 142 was approximately 89 percent or 1,666 ft of total core recovered. Based on visual inspection of core and geophysical data, material examined from 4.9 to 1,880 ft BLS in borehole USGS 142 consists of approximately 45 basalt flows, 16 significant sediment and (or) sedimentary rock layers, and rhyolite welded tuff. Rhyolite was encountered at approximately 1,396 ft BLS. Sediment layers comprise a large percentage of the borehole between 739 and 1,396 ft BLS with grain sizes ranging from clay and silt to cobble size. Sedimentary rock layers had calcite cement. Basalt flows ranged in thickness from about 2 to 100 ft and varied from highly fractured to dense, and ranged from massive to diktytaxitic to scoriaceous, in texture.
Geophysical logs were collected on completion of drilling at boreholes USGS 142 and USGS 142A. Geophysical logs were examined with available core material to describe basalt, sediment and sedimentary rock layers, and rhyolite. Natural gamma logs were used to confirm sediment layer thickness and location; neutron logs were used to examine basalt flow units and changes in hydrogen content; gamma-gamma density logs were used to describe general changes in rock properties; and temperature logs were used to understand hydraulic gradients for deeper sections of borehole USGS 142. Gyroscopic deviation was measured to record deviation from true vertical at all depths in boreholes USGS 142 and USGS 142A.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1058","collaboration":"Prepared in cooperation with the U.S. Department of Energy DOE/ID-22243","usgsCitation":"Twining, B.V., Hodges, M.K.V., Schusler, Kyle, and Mudge, Christopher, 2017, Drilling, construction, geophysical log data, and lithologic log for boreholes USGS 142 and USGS 142A, Idaho National Laboratory, Idaho: U.S. Geological Survey Data Series 1058 (DOE/ID-22243), 21 p., plus appendixes, https://doi.org/10.3133/ds1058.","productDescription":"Report: v, 21 p.; Appendices A-C","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079458","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":344347,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1058/ds1058.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1058"},{"id":344346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1058/coverthb.jpg"},{"id":344348,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1058/ds1058_appendix.A.pdf","text":"Appendix A","size":"350 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1058 Appendix A"},{"id":344349,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1058/ds1058_appendix.B.pdf","text":"Appendix B","size":"130 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1058 Appendix B"},{"id":344350,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1058/ds1058_appendix.C.pdf","text":"Appendix C","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1058 Appendix C"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.75,\n 44.25\n ],\n [\n -112.25,\n 44.25\n ],\n [\n -112.25,\n 43.3\n ],\n [\n -113.75,\n 43.3\n ],\n [\n -113.75,\n 44.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
The He, Ne, and Ar isotopic composition of fluid inclusions in ore and gangue minerals were analyzed to determine the source of volatiles in the high-grade Goldfield and Tonopah epithermal Au-Ag deposits in southwestern Nevada, USA. Ar and Ne are mainly atmospheric, whereas He has only a minor atmospheric component. Corrected 3He/4He ratios (with atmospheric He removed) range widely from 0.05 to 35.8 times the air 3He/4He ratio (RA), with a median of 1.43 RA. Forty-one percent of measured 3He/4He ratios are ≥4 RA, corresponding to ≥50% mantle He assuming a mantle ratio of 8 RA. These results suggest that mafic magmas were part of the magmatic-hydrothermal system underlying Goldfield and Tonopah, and that associated mantle-sourced volatiles may have played a role in ore formation. The three highest corrected 3He/4He ratios of 17.0, 23.7, and 35.8 RAindicate a primitive mantle He source and are the highest yet reported for any epithermal-porphyry system and for the Cascades arc region. Compiled 3He/4He measurements from epithermal-porphyry systems in subduction-related magmatic arcs around the world (n = 209) display a statistically significant correlation between 3He/4He and Au-Ag grade. The correlation suggests that conditions which promote higher fluid inclusion 3He/4He ratios (abundance of mantle volatiles and focused upward volatile transport) have some relation to conditions that promote higher Au-Ag grades (focused flow of metal-bearing fluids and efficient chemical traps). Results of this and previous investigations of He isotopes in epithermal-porphyry systems are consistent with the hypothesis posed in recent studies that mafic magmas serve an important function in the formation of these deposits.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2017.06.023","usgsCitation":"Manning, A.H., and Hofstra, A.H., 2017, Noble gas data from Goldfield and Tonopah epithermal Au-Ag deposits, ancestral Cascades Arc, USA: Evidence for a primitive mantle volatile source: Ore Geology Reviews, v. 89, p. 683-700, https://doi.org/10.1016/j.oregeorev.2017.06.023.","productDescription":"18 p.","startPage":"683","endPage":"700","ipdsId":"IP-079179","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":469663,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2017.06.023","text":"Publisher Index Page"},{"id":344344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Cascades Arc","volume":"89","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5979aa4fe4b0ec1a488b8bcf","contributors":{"authors":[{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":706437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hofstra, Albert H. 0000-0002-2450-1593 ahofstra@usgs.gov","orcid":"https://orcid.org/0000-0002-2450-1593","contributorId":1302,"corporation":false,"usgs":true,"family":"Hofstra","given":"Albert","email":"ahofstra@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":706438,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70189774,"text":"70189774 - 2017 - Detect and exploit hidden structure in fatty acid signature data","interactions":[],"lastModifiedDate":"2018-08-03T16:07:06","indexId":"70189774","displayToPublicDate":"2017-07-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Detect and exploit hidden structure in fatty acid signature data","docAbstract":"Estimates of predator diet composition are essential to our understanding of their ecology. Although several methods of estimating diet are practiced, methods based on biomarkers have become increasingly common. Quantitative fatty acid signature analysis (QFASA) is a popular method that continues to be refined and extended. Quantitative fatty acid signature analysis is based on differences in the signatures of prey types, often species, which are recognized and designated by investigators. Similarly, predator signatures may be structured by known factors such as sex or age class, and the season or region of sample collection. The recognized structure in signature data inherently influences QFASA results in important and typically beneficial ways. However, predator and prey signatures may contain additional, hidden structure that investigators either choose not to incorporate into an analysis or of which they are unaware, being caused by unknown ecological mechanisms. Hidden structure also influences QFASA\r\nresults, most often negatively. We developed a new method to explore signature data for hidden structure, called divisive magnetic clustering (DIMAC). Our DIMAC approach is based on the same distance measure used in diet estimation, closely linking methods of data exploration and parameter estimation, and it does not require data transformation or distributional assumptions, as do many multivariate ordination methods in common use. We investigated the potential benefits of the DIMAC method to detect and subsequently exploit hidden structure in signature data using two prey signature libraries with quite different characteristics. We found that the existence of hidden structure in prey signatures can increase the\r\nconfusion between prey types and thereby reduce the accuracy and precision of QFASA diet estimates. Conversely, the detection and exploitation of hidden structure represent a potential opportunity to improve predator diet estimates and may lead to new insights into the ecology of either predator or prey.\r\nThe DIMAC algorithm is implemented in the R diet estimation package qfasar.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1896","usgsCitation":"Bromaghin, J.F., Budge, S.M., and Thiemann, G.W., 2017, Detect and exploit hidden structure in fatty acid signature data: Ecosphere, v. 8, no. 7, e01896; 13 p., https://doi.org/10.1002/ecs2.1896.","productDescription":"e01896; 13 p.","ipdsId":"IP-085747","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":469665,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1896","text":"Publisher Index Page"},{"id":344326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"8","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-24","publicationStatus":"PW","scienceBaseUri":"5979aa53e4b0ec1a488b8beb","contributors":{"authors":[{"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":706309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budge, Suzanne M.","contributorId":92168,"corporation":false,"usgs":false,"family":"Budge","given":"Suzanne","email":"","middleInitial":"M.","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":741240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thiemann, Gregory W.","contributorId":83023,"corporation":false,"usgs":false,"family":"Thiemann","given":"Gregory","email":"","middleInitial":"W.","affiliations":[{"id":27291,"text":"York University, Toronto, ON","active":true,"usgs":false}],"preferred":false,"id":741241,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189790,"text":"70189790 - 2017 - Competition amplifies drought stress in forests across broad climatic and compositional gradients","interactions":[],"lastModifiedDate":"2017-07-26T10:50:52","indexId":"70189790","displayToPublicDate":"2017-07-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Competition amplifies drought stress in forests across broad climatic and compositional gradients","docAbstract":"Forests around the world are experiencing increasingly severe droughts and elevated competitive intensity due to increased tree density. However, the influence of interactions between drought and competition on forest growth remains poorly understood. Using a unique dataset of stand-scale dendrochronology sampled from 6405 trees, we quantified how annual growth of entire tree populations responds to drought and competition in eight, long-term (multi-decadal), experiments with replicated levels of density (e.g., competitive intensity) arrayed across a broad climatic and compositional gradient. Forest growth (cumulative individual tree growth within a stand) declined during drought, especially during more severe drought in drier climates. Forest growth declines were exacerbated by high density at all sites but one, particularly during periods of more severe drought. Surprisingly, the influence of forest density was persistent overall, but these density impacts were greater in the humid sites than in more arid sites. Significant density impacts occurred during periods of more extreme drought, and during warmer temperatures in the semi-arid sites but during periods of cooler temperatures in the humid sites. Because competition has a consistent influence over growth response to drought, maintaining forests at lower density may enhance resilience to drought in all climates.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1849","usgsCitation":"Gleason, K., Bradford, J.B., Bottero, A., D’Amato, T., Fraver, S., Palik, B.J., Battaglia, M., Iverson, L.R., Kenefic, L., and Kern, C.C., 2017, Competition amplifies drought stress in forests across broad climatic and compositional gradients: Ecosphere, v. 8, no. 7, p. 1-16, https://doi.org/10.1002/ecs2.1849.","productDescription":"e01849; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-081501","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469662,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1849","text":"Publisher Index Page"},{"id":438260,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7125RW8","text":"USGS data release","linkHelpText":"Long-term Experimental Forest Growth and Drought Data"},{"id":344318,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-14","publicationStatus":"PW","scienceBaseUri":"5979aa4fe4b0ec1a488b8bd3","contributors":{"authors":[{"text":"Gleason, Kelly kgleason@usgs.gov","contributorId":195150,"corporation":false,"usgs":true,"family":"Gleason","given":"Kelly","email":"kgleason@usgs.gov","affiliations":[],"preferred":true,"id":706378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":706377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bottero, Alessandra 0000-0002-0410-2675","orcid":"https://orcid.org/0000-0002-0410-2675","contributorId":190300,"corporation":false,"usgs":false,"family":"Bottero","given":"Alessandra","email":"","affiliations":[],"preferred":false,"id":706379,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"D’Amato, Tony","contributorId":195151,"corporation":false,"usgs":false,"family":"D’Amato","given":"Tony","email":"","affiliations":[],"preferred":false,"id":706380,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fraver, Shawn","contributorId":91379,"corporation":false,"usgs":false,"family":"Fraver","given":"Shawn","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":706381,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Palik, Brian J.","contributorId":190301,"corporation":false,"usgs":false,"family":"Palik","given":"Brian","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":706382,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Battaglia, Michael","contributorId":30529,"corporation":false,"usgs":true,"family":"Battaglia","given":"Michael","affiliations":[],"preferred":false,"id":706383,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Iverson, Louis R.","contributorId":149884,"corporation":false,"usgs":false,"family":"Iverson","given":"Louis","email":"","middleInitial":"R.","affiliations":[{"id":13259,"text":"USDA Forest Service Northern Research Station","active":true,"usgs":false}],"preferred":false,"id":706384,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kenefic, Laura","contributorId":195152,"corporation":false,"usgs":false,"family":"Kenefic","given":"Laura","email":"","affiliations":[],"preferred":false,"id":706385,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kern, Christel C.","contributorId":191240,"corporation":false,"usgs":false,"family":"Kern","given":"Christel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":706386,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70189792,"text":"70189792 - 2017 - Dating of river terraces along Lefthand Creek, western High Plains, Colorado, reveals punctuated incision","interactions":[],"lastModifiedDate":"2017-07-25T17:54:54","indexId":"70189792","displayToPublicDate":"2017-07-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Dating of river terraces along Lefthand Creek, western High Plains, Colorado, reveals punctuated incision","docAbstract":"The response of erosional landscapes to Quaternary climate oscillations is recorded in fluvial terraces whose quantitative interpretation requires numerical ages. We investigate gravel-capped strath terraces along the western edge of Colorado's High Plains to constrain the incision history of this shale-dominated landscape. We use ¹⁰Be and ²⁶Al cosmogenic radionuclides (CRNs), optically stimulated luminescence (OSL), and thermally transferred OSL (TT-OSL) to date three strath terraces, all beveled in shale bedrock and then deposited upon by Lefthand Creek, which drains the crystalline core of the Front Range. Our study reveals: (i) a long history (hundreds of thousands of years) of fluvial occupation of the second highest terrace, T2 (Table Mountain), with fluvial abandonment at 92 ± 3 ka; (ii) a brief occupation of a narrow and spatially confined terrace, T3, at 98 ± 7 ka; and (iii) a 10–25 thousand year period of cutting and fluvial occupation of a lower terrace, T4, marked by the deposition of a lower alluvial unit between 59 and 68 ka, followed by deposition of an upper alluvial package at 40 ± 3 ka. In conjunction with other recent CRN studies of strath terraces along the Colorado Front Range (Riihimaki et al., 2006; Dühnforth et al., 2012), our data reveal that long periods of lateral planation and fluvial occupation of strath terraces, sometimes lasting several glacial-interglacial cycles, are punctuated by brief episodes of rapid vertical bedrock incision. These data call into question what a singular terrace age represents, as the strath may be cut at one time (its cutting-age) and the terrace surface may be abandoned at a much later time (its abandonment age), and challenge models of strath terraces that appeal to simple pacing by the glacial-interglacial cycles.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2017.04.044","usgsCitation":"Foster, M.A., Anderson, R.S., Gray, H.J., and Mahan, S.A., 2017, Dating of river terraces along Lefthand Creek, western High Plains, Colorado, reveals punctuated incision: Geomorphology, v. 295, p. 176-190, https://doi.org/10.1016/j.geomorph.2017.04.044.","productDescription":"15 p.","startPage":"176","endPage":"190","ipdsId":"IP-066143","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":344314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Boulder","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.67886352539062,\n 39.65539876418111\n ],\n [\n -104.886474609375,\n 39.65539876418111\n ],\n [\n -104.886474609375,\n 40.24179856487036\n ],\n [\n -105.67886352539062,\n 40.24179856487036\n ],\n [\n -105.67886352539062,\n 39.65539876418111\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"295","edition":"295","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"597858b4e4b0ec1a488a0906","contributors":{"authors":[{"text":"Foster, Melissa A.","contributorId":195153,"corporation":false,"usgs":false,"family":"Foster","given":"Melissa","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":706398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Robert S.","contributorId":195154,"corporation":false,"usgs":false,"family":"Anderson","given":"Robert","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":706399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Harrison J. 0000-0002-4555-7473 hgray@usgs.gov","orcid":"https://orcid.org/0000-0002-4555-7473","contributorId":4991,"corporation":false,"usgs":true,"family":"Gray","given":"Harrison","email":"hgray@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":706400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":706397,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191907,"text":"70191907 - 2017 - A synthesis of thresholds for focal species along the U.S. Atlantic and Gulf Coasts: A review of research and applications","interactions":[],"lastModifiedDate":"2020-07-28T15:17:43.789695","indexId":"70191907","displayToPublicDate":"2017-07-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2926,"text":"Ocean and Coastal Management","active":true,"publicationSubtype":{"id":10}},"title":"A synthesis of thresholds for focal species along the U.S. Atlantic and Gulf Coasts: A review of research and applications","docAbstract":"The impacts from climate change are increasing the possibility of vulnerable coastal species and habitats crossing critical thresholds that could spur rapid and possibly irreversible changes. For species of high conservation concern, improved knowledge of quantitative thresholds could greatly improve management. To meet this need, we synthesized information pertaining to biological responses as tipping points to sea level rise (SLR) and coastal storms for 45 fish, wildlife, and plant species along the U.S. Atlantic and Gulf Coasts and Caribbean through a literature review and expert elicitation. Although these species were selected based on their ecological, economic, and cultural importance, just over half (56%, n = 25) have quantitative threshold data currently available that can be used to assess the effects of SLR and storms during some aspect of their life history. Birds, reptiles, and plants represent the best studied coastal species. Thirteen of the species (29%) are projected to lose at least 50% of their population or habitat (e.g., foraging, nesting, spawning, or resting habitat) in some areas with a 0.5 m or greater rise in sea levels by 2100. Two species (a bird and reptile) may gain habitat from projected SLR and be resilient to future impacts. Numeric thresholds were not available for the remaining 20 species we searched for. Coastal fishes, mammals, and amphibians were among the groups representing a major information gap in this field of research. In addition, quantitative threshold responses to coastal storms were scarce for all taxa. While vulnerability assessments and qualitative research related to the impacts of SLR and storms on coastal species and habitats are increasing, work that incorporates quantitative thresholds as response and impact metrics remains limited. Additional monitoring, modeling, and research that provides multiple quantitative thresholds across species' life stages and/or latitudinal gradients is ideal to support robust coastal management and decision-making across spatio-temporal scales in the face of climate change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ocecoaman.2017.07.012","usgsCitation":"Powell, E.J., Tyrrell, M.C., Milliken, A., Tirpak, J.M., and Staudinger, M., 2017, A synthesis of thresholds for focal species along the U.S. Atlantic and Gulf Coasts: A review of research and applications: Ocean and Coastal Management, v. 148, p. 75-88, https://doi.org/10.1016/j.ocecoaman.2017.07.012.","productDescription":"14 p.","startPage":"75","endPage":"88","ipdsId":"IP-080659","costCenters":[{"id":41705,"text":"Northeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":469667,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ocecoaman.2017.07.012","text":"Publisher Index Page"},{"id":346930,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"148","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e86835e4b05fe04cd4d1ee","contributors":{"authors":[{"text":"Powell, Emily J.","contributorId":197493,"corporation":false,"usgs":false,"family":"Powell","given":"Emily","email":"","middleInitial":"J.","affiliations":[{"id":34949,"text":"DOI North Atlantic Landscape Conservation Cooperative","active":true,"usgs":false}],"preferred":false,"id":713622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tyrrell, Megan C.","contributorId":197494,"corporation":false,"usgs":false,"family":"Tyrrell","given":"Megan","email":"","middleInitial":"C.","affiliations":[{"id":34949,"text":"DOI North Atlantic Landscape Conservation Cooperative","active":true,"usgs":false}],"preferred":false,"id":713623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milliken, Andrew","contributorId":174078,"corporation":false,"usgs":false,"family":"Milliken","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":713624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tirpak, John M.","contributorId":85704,"corporation":false,"usgs":true,"family":"Tirpak","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":713625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staudinger, Michelle D. 0000-0002-4535-2005","orcid":"https://orcid.org/0000-0002-4535-2005","contributorId":207908,"corporation":false,"usgs":true,"family":"Staudinger","given":"Michelle D.","affiliations":[{"id":484,"text":"Northwest Climate Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":713621,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189748,"text":"70189748 - 2017 - Knowing requires data","interactions":[],"lastModifiedDate":"2017-09-25T13:51:18","indexId":"70189748","displayToPublicDate":"2017-07-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Knowing requires data","docAbstract":"Groundwater-flow models are often calibrated using a limited number of observations relative to the unknown inputs required for the model. This is especially true for models that simulate groundwater surface-water interactions. In this case, subsurface temperature sensors can be an efficient means for collecting long-term data that capture the transient nature of physical processes such as seepage losses. Continuous and spatially dense network of diverse observation data can be used to improve knowledge of important physical drivers, conceptualize and calibrate variably saturated groundwater flow models. An example is presented for which the results of such analysis were used to help guide irrigation districts and water management decisions on costly upgrades to conveyance systems to improve water usage, farm productivity and restoration efforts to improve downstream water quality and ecosystems.","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12553","usgsCitation":"Naranjo, R.C., 2017, Knowing requires data: Groundwater, v. 55, no. 5, p. 674-677, https://doi.org/10.1111/gwat.12553.","productDescription":"4 p.","startPage":"674","endPage":"677","ipdsId":"IP-087078","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":344272,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"5","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-11","publicationStatus":"PW","scienceBaseUri":"59770748e4b0ec1a48889f2a","contributors":{"authors":[{"text":"Naranjo, Ramon C. 0000-0003-4469-6831 rnaranjo@usgs.gov","orcid":"https://orcid.org/0000-0003-4469-6831","contributorId":3391,"corporation":false,"usgs":true,"family":"Naranjo","given":"Ramon","email":"rnaranjo@usgs.gov","middleInitial":"C.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":706181,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70189747,"text":"70189747 - 2017 - Volcano geodesy in the Cascade arc, USA","interactions":[],"lastModifiedDate":"2018-10-25T15:58:17","indexId":"70189747","displayToPublicDate":"2017-07-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Volcano geodesy in the Cascade arc, USA","docAbstract":"Experience during historical time throughout the Cascade arc and the lack of deep-seated deformation prior to the two most recent eruptions of Mount St. Helens might lead one to infer that Cascade volcanoes are generally quiescent and, specifically, show no signs of geodetic change until they are about to erupt. Several decades of geodetic data, however, tell a different story. Ground- and space-based deformation studies have identified surface displacements at five of the 13 major Cascade arc volcanoes that lie in the USA (Mount Baker, Mount St. Helens, South Sister, Medicine Lake, and Lassen volcanic center). No deformation has been detected at five volcanoes (Mount Rainier, Mount Hood, Newberry Volcano, Crater Lake, and Mount Shasta), and there are not sufficient data at the remaining three (Glacier Peak, Mount Adams, and Mount Jefferson) for a rigorous assessment. In addition, gravity change has been measured at two of the three locations where surveys have been repeated (Mount St. Helens and Mount Baker show changes, while South Sister does not). Broad deformation patterns associated with heavily forested and ice-clad Cascade volcanoes are generally characterized by low displacement rates, in the range of millimeters to a few centimeters per year, and are overprinted by larger tectonic motions of several centimeters per year. Continuous GPS is therefore the best means of tracking temporal changes in deformation of Cascade volcanoes and also for characterizing tectonic signals so that they may be distinguished from volcanic sources. Better spatial resolution of volcano deformation can be obtained through the use of campaign GPS, semipermanent GPS, and interferometric synthetic aperture radar observations, which leverage the accumulation of displacements over time to improve signal to noise. Deformation source mechanisms in the Cascades are diverse and include magma accumulation and withdrawal, post-emplacement cooling of recent volcanic deposits, magmatic-tectonic interactions, and loss of volatiles plus densification of magma. The Cascade Range thus offers an outstanding opportunity for investigating a wide range of volcanic processes. Indeed, there may be areas of geodetic change that have yet to be discovered, and there is good potential for addressing a number of important questions about how arc volcanoes work before, during, and after eruptions by continuing geodetic research in the Cascade Range.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-017-1140-x","usgsCitation":"Poland, M.P., Lisowski, M., Dzurisin, D., Kramer, R., McLay, M., and Pauk, B., 2017, Volcano geodesy in the Cascade arc, USA: Bulletin of Volcanology, v. 79, p. 1-33, https://doi.org/10.1007/s00445-017-1140-x.","productDescription":"Article 59; 33 p.","startPage":"1","endPage":"33","ipdsId":"IP-084861","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":344270,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Cascade Arc","volume":"79","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-12","publicationStatus":"PW","scienceBaseUri":"59770749e4b0ec1a48889f2e","contributors":{"authors":[{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":706175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lisowski, Michael 0000-0003-4818-2504 mlisowski@usgs.gov","orcid":"https://orcid.org/0000-0003-4818-2504","contributorId":637,"corporation":false,"usgs":true,"family":"Lisowski","given":"Michael","email":"mlisowski@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":706179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dzurisin, Daniel 0000-0002-0138-5067 dzurisin@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-5067","contributorId":538,"corporation":false,"usgs":true,"family":"Dzurisin","given":"Daniel","email":"dzurisin@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":706178,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kramer, Rebecca 0000-0002-4873-1983 rkramer@usgs.gov","orcid":"https://orcid.org/0000-0002-4873-1983","contributorId":195070,"corporation":false,"usgs":true,"family":"Kramer","given":"Rebecca","email":"rkramer@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":706180,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLay, Megan 0000-0002-7527-1820 mmclay@usgs.gov","orcid":"https://orcid.org/0000-0002-7527-1820","contributorId":5095,"corporation":false,"usgs":true,"family":"McLay","given":"Megan","email":"mmclay@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":706176,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pauk, Benjamin 0000-0003-3036-5927 bpauk@usgs.gov","orcid":"https://orcid.org/0000-0003-3036-5927","contributorId":195069,"corporation":false,"usgs":true,"family":"Pauk","given":"Benjamin","email":"bpauk@usgs.gov","affiliations":[],"preferred":true,"id":706177,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}