An analytical method for the in situ measurement of “common” Pb isotope ratios in silicate glasses and minerals using a 193-nm excimer laser ablation (LA) system with a double-focusing single-collector (SC)-ICPMS is presented and evaluated as a possible alternative to multiple-collector (MC)-ICPMS. This LA-SC-ICPMS technique employs fast-scanning ion deflectors to sequentially place a series of flat-topped isotope peaks into a single ion-counting detector at a fixed accelerating voltage and magnetic field strength. Reference materials (including NIST, MPI-DING, and USGS glasses) are used to identify two analytical artifacts on the Pb isotope ratios (expressed here as heavier/lighter isotopes) when corrected for mass bias relative to NIST SRM610. The first artifact is characterized by anomalously low Pb isotope ratios (~0.1%/AMU) when SRM610 is analyzed in raster mode as an unknown at small spot sizes (<25 µm), which may indicate that (1) SRM610 is isotopically heterogeneous on a small length scale and/or (2) there is a non-spectral matrix effect on the Pb isotope ratios related to differences in spot size. The second artifact is characterized by anomalously high Pb isotope ratios (<0.1%/AMU) for NIST SRM612 (in raster mode) and some Fe-rich glass reference materials (BCR-2G, GOR132-G, and T1-G). These offsets are thought to be caused by one or more non-spectral matrix effects related to differences in the ablation behavior, composition, or physical properties of these reference materials compared to the bracketing SRM610 standard. The precision (±2SD) of our LA-SC-ICPMS Pb isotopic measurements is similar to (207Pb/206Pb and 208Pb/206Pb, or 20XPb/206Pb) or better than (206Pb/204Pb,207Pb/204Pb, and 208Pb/204Pb, or 20XPb/204Pb) a series of published studies that used a different type of SC-ICPMS and obtained a factor of ~3-4 higher sensitivity for Pb. An increase in the sensitivity of our LA-SC-ICPMS would likely improve the precision of the 20XPb/206Pb and 20XPb/204Pb ratios for low-Pb materials (<5 ppm), possibly making the technique broadly similar to LA-MC-ICPMS (particularly compared to methods that rely upon at least one ion-counting detector). Further improvement in the precision of the 20XPb/206Pb and 20XPb/204Pb ratios for high-Pb materials (>5 ppm) by LA-SC-ICPMS is unlikely, and in this case, LA-MC-ICPMS remains the preferable analytical technique.
","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/c7ja00005g","usgsCitation":"Pietruszka, A.J., and Neymark, L., 2017, Evaluation of laser ablation double-focusing SC-ICPMS for “common” lead isotopic measurements in silicate glasses and mineral: Journal of Analytical Atomic Spectrometry, v. 32, no. 6, p. 1135-1154, https://doi.org/10.1039/c7ja00005g.","productDescription":"20 p.","startPage":"1135","endPage":"1154","ipdsId":"IP-082736","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":340845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"590c3dc8e4b0e541a038dd1f","contributors":{"authors":[{"text":"Pietruszka, Aaron J. 0000-0002-2826-9509 apietruszka@usgs.gov","orcid":"https://orcid.org/0000-0002-2826-9509","contributorId":4552,"corporation":false,"usgs":true,"family":"Pietruszka","given":"Aaron","email":"apietruszka@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":694220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neymark, Leonid A. 0000-0003-4190-0278 lneymark@usgs.gov","orcid":"https://orcid.org/0000-0003-4190-0278","contributorId":140338,"corporation":false,"usgs":true,"family":"Neymark","given":"Leonid A.","email":"lneymark@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":694221,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187355,"text":"ofr20171049 - 2017 - Eastern Denali Fault surface trace map, eastern Alaska and Yukon, Canada","interactions":[],"lastModifiedDate":"2023-11-03T16:52:08.991249","indexId":"ofr20171049","displayToPublicDate":"2017-05-04T00:00:00","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":"2017-1049","title":"Eastern Denali Fault surface trace map, eastern Alaska and Yukon, Canada","docAbstract":"We map the 385-kilometer (km) long surface trace of the right-lateral, strike-slip Denali Fault between the Totschunda-Denali Fault intersection in Alaska, United States and the village of Haines Junction, Yukon, Canada. In Alaska, digital elevation models based on light detection and ranging and interferometric synthetic aperture radar data enabled our fault mapping at scales of 1:2,000 and 1:10,000, respectively. Lacking such resources in Yukon, we developed new structure-from-motion digital photogrammetry products from legacy aerial photos to map the fault surface trace at a scale of 1:10,000 east of the international border. The section of the fault that we map, referred to as the Eastern Denali Fault, did not rupture during the 2002 Denali Fault earthquake (moment magnitude 7.9). Seismologic, geodetic, and geomorphic evidence, along with a paleoseismic record of past ground-rupturing earthquakes, demonstrate Holocene and contemporary activity on the fault, however. This map of the Eastern Denali Fault surface trace complements other data sets by providing an openly accessible digital interpretation of the location, length, and continuity of the fault’s surface trace based on the accompanying digital topography dataset. Additionally, the digitized fault trace may provide geometric constraints useful for modeling earthquake scenarios and related seismic hazard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171049","usgsCitation":"Bender, A.M., and Haeussler, P.J., 2017, Eastern Denali Fault surface trace map, eastern Alaska and Yukon, Canada: U.S. Geological Survey Open-File Report 2017–1049, 10 p., https://doi.org/10.3133/ofr20171049.","productDescription":"iii, 10 p.","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084514","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":438353,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T151WC","text":"USGS data release","linkHelpText":"Eastern Denali Fault Surface Trace Map, Eastern Alaska and Adjacent Canada, 1978-2008"},{"id":422373,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_105646.htm","linkFileType":{"id":5,"text":"html"}},{"id":340824,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1049/coverthb.jpg"},{"id":340825,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1049/ofr20171049.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1049"}],"country":"Canada, United States","state":"Alaska, Yukon","otherGeospatial":"Denali Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -147,\n 60\n ],\n [\n -135,\n 60\n ],\n [\n -135,\n 64\n ],\n [\n -147,\n 64\n ],\n [\n -147,\n 60\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Science Center
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
The U.S. Geological Survey Monthly Water Balance Model Futures Portal (https://my.usgs.gov/mows/) is a user-friendly interface that summarizes monthly historical and simulated future conditions for seven hydrologic and meteorological variables (actual evapotranspiration, potential evapotranspiration, precipitation, runoff, snow water equivalent, atmospheric temperature, and streamflow) at locations across the conterminous United States (CONUS).
The estimates of these hydrologic and meteorological variables were derived using a Monthly Water Balance Model (MWBM), a modular system that simulates monthly estimates of components of the hydrologic cycle using monthly precipitation and atmospheric temperature inputs. Precipitation and atmospheric temperature from 222 climate datasets spanning historical conditions (1952 through 2005) and simulated future conditions (2020 through 2099) were summarized for hydrographic features and used to drive the MWBM for the CONUS. The MWBM input and output variables were organized into an open-access database. An Open Geospatial Consortium, Inc., Web Feature Service allows the querying and identification of hydrographic features across the CONUS. To connect the Web Feature Service to the open-access database, a user interface—the Monthly Water Balance Model Futures Portal—was developed to allow the dynamic generation of summary files and plots based on plot type, geographic location, specific climate datasets, period of record, MWBM variable, and other options. Both the plots and the data files are made available to the user for download
Director, USGS Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.
Agassiz’s desert tortoise (Gopherus agassizii) is a conservation-reliant species with populations north and west of the Colorado River protected as threatened under the Endangered Species Act (Averill-Murray et al. 2012). Since it was listed under this category in 1990, a great deal has been learned about the natural history of the species, and it is now one of the best-studied turtles in the United States (Lovich and Ennen 2013). However, the accumulated body of scientific data available for the species has not yet been translated into recovery or delisting of the species. Successful conservation of any species requires knowledge of their natural history and how vital rates affect their ability to maintain stable populations in the face of natural and anthropogenic stresses.
Agassiz’s desert tortoises occur from southwestern Utah to near the Mexican border in California – a distance of over 450 km – but population densities vary greatly across this immense landscape (U.S. Fish and Wildlife Service 2015). Tortoises occur in the Sonoran Desert of California, including the eastern and western ends of the Coachella Valley, where it is one of 27 species covered under the Coachella Valley Multiple Species Habitat Conservation Plan and Natural Community Conservation Plan (CVMSHCP/NCCP). The southern portion of Joshua Tree National Park (JTNP) lies within this 1.1 million acre planning area, and was predicted to be an area of low-density tortoise populations using habitat suitability modeling (Barrows 2011). JTNP is near the southern distributional limit of G. agassizii, yet very little has been published regarding the ecology of tortoises in the Sonoran Desert of California.
Reproductive output is an important gross measure of the ability of a population to persist. When integrated with data on fertility and survivorship, this information forms a foundation for assessing population status and formulating effective management strategies (e.g., Congdon et al. 1993, 1994), especially for imperiled species. One aspect of the biology of G. agassizii that has been particularly well-studied is reproductive output. However, most of what we know about this topic comes from research in the Mojave Desert portion of the species’ range (Ernst and Lovich 2009). Comparatively little has been published on the reproductive ecology of populations living in the Sonoran Desert ecosystem of California. Publications by Lovich et al. (1999, 2011, 2012, 2014, 2015) constitute the main body of literature on desert tortoise reproductive ecology in the Sonoran Desert of California, with one study population located at the western end of the CVMSHCP/NCCP area. Collecting data on Agassiz’s desert tortoise ecology in the Sonoran Desert ecosystem is important due to significant differences between the two adjacent desert ecosystems, especially the timing and amounts of annual precipitation, and their potential effects on reproductive output (e.g., Lovich et al. 5 2015). There are also differences in the vulnerability of tortoises to the effects of a warming, drying climate between the two deserts (Barrows 2011; Zylstra et al. 2012).
The overall goal of this study was to collect data on demography, reproductive output, and genetic affinities at a study site in the Sonoran Desert portion of JTNP in the eastern end of the CVMSHCP/NCCP area. Specific objectives included: 1) Collect data to establish baselines on tortoise populations and/or their habitat suitability in core habitat within the CVNCCP area, including biotic and abiotic variables affecting persistence of tortoise populations; 2) Compare and contrast with data collected on desert tortoises at USGS/BLM study site near Palm Springs over 16 years; 3) Support long-term modeling efforts needed to determine tortoise population viability; 4) Refine modeled relationships with identified threats such as fire, invasive species and climate change; and 5) Prioritize adaptive management needs for the desert tortoise in and beyond the CVNCCP area. The data from this study will aid in determining baseline estimates of the desert tortoise population size within the planning area as well as establish a marked population of Agassiz’s desert tortoises for future monitoring. Data will be integrated with habitat modeling in order to refine model output. Genetic data will be collected on both the north and south sides of Interstate 10 to determine the potential effects of habitat fragmentation and genetic mixing. Analyses are ongoing and results beyond those presented in this report will be published in peer-reviewed scientific journals following inclusion of additional data collected on the south side of Shavers Valley in 2017-2018.
","language":"English","publisher":"Coachella Valley Conservation Commission","usgsCitation":"Lovich, J.E., and Puffer, S., 2017, Developing an effective Agassiz's Desert Tortoise monitoring program: Final report to the Coachella Valley Conservation Commission, 26 p.","productDescription":"26 p.","ipdsId":"IP-088374","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":358690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":358644,"type":{"id":11,"text":"Document"},"url":"https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=152890&inline"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10ac2ce4b034bf6a7e6966","contributors":{"authors":[{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":749275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Puffer, Shellie R. 0000-0003-4957-0963","orcid":"https://orcid.org/0000-0003-4957-0963","contributorId":193099,"corporation":false,"usgs":true,"family":"Puffer","given":"Shellie R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":749276,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193697,"text":"70193697 - 2017 - Microbial formation of labile organic carbon in Antarctic glacial environments","interactions":[],"lastModifiedDate":"2017-11-20T12:17:44","indexId":"70193697","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Microbial formation of labile organic carbon in Antarctic glacial environments","docAbstract":"Roughly six petagrams of organic carbon are stored within ice worldwide. This organic carbon is thought to be of old age and highly bioavailable. Along with storage of ancient and new atmospherically deposited organic carbon, microorganisms may contribute substantially to the glacial organic carbon pool. Models of glacial microbial carbon cycling vary from net respiration to net carbon fixation. Supraglacial streams have not been considered in models although they are amongst the largest ecosystems on most glaciers and are inhabited by diverse microbial communities. Here we investigate the biogeochemical sequence of organic carbon production and uptake in an Antarctic supraglacial stream in the McMurdo Dry Valleys using nanometre-scale secondary ion mass spectrometry, fluorescence spectroscopy, stable isotope analysis and incubation experiments. We find that heterotrophic production relies on highly labile organic carbon freshly derived from photosynthetic bacteria rather than legacy organic carbon. Exudates from primary production were utilized by heterotrophs within 24 h, and supported bacterial growth demands. The tight coupling of microbially released organic carbon and rapid uptake by heterotrophs suggests a dynamic local carbon cycle. Moreover, as temperatures increase there is the potential for positive feedback between glacial melt and microbial transformations of organic carbon.
","language":"English","publisher":"Nature","doi":"10.1038/ngeo2925","usgsCitation":"Smith, H., Foster, R., McKnight, D., Lisle, J.T., Littmann, S., Kuypers, M., and Foreman, C., 2017, Microbial formation of labile organic carbon in Antarctic glacial environments: Nature Geoscience, v. 10, p. 356-359, https://doi.org/10.1038/ngeo2925.","productDescription":"4 p.","startPage":"356","endPage":"359","ipdsId":"IP-084212","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469882,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarworks.montana.edu/xmlui/handle/1/13063","text":"External Repository"},{"id":349135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"McMurdo Dry Valleys","volume":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-03","publicationStatus":"PW","scienceBaseUri":"5a60fbd6e4b06e28e9c236ce","contributors":{"authors":[{"text":"Smith, H.J.","contributorId":199755,"corporation":false,"usgs":false,"family":"Smith","given":"H.J.","email":"","affiliations":[],"preferred":false,"id":719951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, R.","contributorId":199756,"corporation":false,"usgs":false,"family":"Foster","given":"R.","email":"","affiliations":[],"preferred":false,"id":719952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKnight, D.M.","contributorId":189736,"corporation":false,"usgs":false,"family":"McKnight","given":"D.M.","email":"","affiliations":[],"preferred":false,"id":719953,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lisle, John T. 0000-0002-5447-2092 jlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-5447-2092","contributorId":2944,"corporation":false,"usgs":true,"family":"Lisle","given":"John","email":"jlisle@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":719950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Littmann, S.","contributorId":199757,"corporation":false,"usgs":false,"family":"Littmann","given":"S.","email":"","affiliations":[],"preferred":false,"id":719954,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kuypers, M.M.M.","contributorId":199758,"corporation":false,"usgs":false,"family":"Kuypers","given":"M.M.M.","affiliations":[],"preferred":false,"id":719955,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Foreman, C.M.","contributorId":199759,"corporation":false,"usgs":false,"family":"Foreman","given":"C.M.","email":"","affiliations":[],"preferred":false,"id":719956,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193454,"text":"70193454 - 2017 - Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method","interactions":[],"lastModifiedDate":"2017-11-10T15:02:08","indexId":"70193454","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method","docAbstract":"Understanding broad-scale ecological patterns and processes often involves accounting for regional-scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question—How well do these regions capture regional-scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km2); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft-ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation—approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.2884","usgsCitation":"Cheruvelil, K.S., Yuan, S., Webster, K.E., Tan, P., Lapierre, J., Collins, S.M., Fergus, C.E., Scott, C.E., Norton Henry, E., Soranno, P.A., Filstrup, C.T., and Wagner, T., 2017, Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method: Ecology and Evolution, v. 7, no. 9, p. 3046-3058, https://doi.org/10.1002/ece3.2884.","productDescription":"13 p.","startPage":"3046","endPage":"3058","ipdsId":"IP-078752","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":469896,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.2884","text":"Publisher Index Page"},{"id":348589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.734375,\n 35.96022296929667\n ],\n [\n -66.62109375,\n 35.96022296929667\n ],\n [\n -66.62109375,\n 49.03786794532644\n ],\n [\n -97.734375,\n 49.03786794532644\n ],\n [\n -97.734375,\n 35.96022296929667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-26","publicationStatus":"PW","scienceBaseUri":"5a06c8cee4b09af898c8612a","contributors":{"authors":[{"text":"Cheruvelil, Kendra Spence","contributorId":150607,"corporation":false,"usgs":false,"family":"Cheruvelil","given":"Kendra","email":"","middleInitial":"Spence","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":721616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yuan, Shuai","contributorId":172187,"corporation":false,"usgs":false,"family":"Yuan","given":"Shuai","affiliations":[],"preferred":false,"id":721617,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Webster, Katherine E.","contributorId":147903,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":721618,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tan, Pang-Ning","contributorId":172193,"corporation":false,"usgs":false,"family":"Tan","given":"Pang-Ning","affiliations":[],"preferred":false,"id":721619,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lapierre, Jean-Francois","contributorId":172182,"corporation":false,"usgs":false,"family":"Lapierre","given":"Jean-Francois","email":"","affiliations":[],"preferred":false,"id":721620,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collins, Sarah M.","contributorId":172181,"corporation":false,"usgs":false,"family":"Collins","given":"Sarah","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721621,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fergus, C. 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White-cheeked Pintail (Anas bahamensis) breeding in Puerto Rico encounter a variety of wetland types that may influence duckling survival. We monitored fates of 92 radio-tagged ducklings in 31 broods in 5 wetland habitat types at Humacao Nature Reserve in southeastern Puerto Rico from 2000 to 2002. Wetlands included 2 separate coastal lagoon complexes, mangrove forest, and managed and unmanaged wetland impoundments containing herbaceous vegetation. We used known-fate models to estimate daily and interval survival rates of ducklings and broods. We conducted conservative and liberal analyses of survival because of uncertain fates of 36 ducklings. In the conservative analysis, the most parsimonious model for duckling survival contained wetland type and a positive influence of daily precipitation. In the liberal analysis, duckling survival also varied among wetlands, was positively influenced by daily precipitation, but negatively influenced by hatch date. Brood survival was also positively influenced by precipitation and female body mass. Managed wetland impoundments and shallowly flooded lagoon habitats containing ferns, interspersed cattail (Typha dominguensis), and other herbaceous cover promoted up to 3 times higher survival of ducklings over the course of a 30-day duckling period than we found in mangroves, more deeply flooded lagoons with predominately restricted shoreline cover, or unmanaged impoundments overgrown with vegetation. Broad confidence intervals for survival estimates among wetlands preclude unequivocal interpretation, but our results suggest that White-cheeked Pintail ducklings survive poorly in mangroves but benefit from appropriate management.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-16-169.1","usgsCitation":"Davis, J.B., Vilella, F., Lancaster, J.D., Lopez-Flores, M., Kaminski, R.M., and Cruz-Burgos, J.A., 2017, White-cheeked Pintail duckling and brood survival across wetland types at Humacao Nature Reserve, Puerto Rico: Condor, v. 119, no. 2, p. 308-320, https://doi.org/10.1650/CONDOR-16-169.1.","productDescription":"13 p.","startPage":"308","endPage":"320","ipdsId":"IP-079748","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":469874,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-16-169.1","text":"Publisher Index Page"},{"id":348250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"Humacao Nature Reserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.78699111938477,\n 18.131822535661165\n ],\n [\n -65.72742462158203,\n 18.131822535661165\n ],\n [\n -65.72742462158203,\n 18.19559753948241\n ],\n [\n -65.78699111938477,\n 18.19559753948241\n ],\n [\n -65.78699111938477,\n 18.131822535661165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"119","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07e8f6e4b09af898c8cbd5","contributors":{"authors":[{"text":"Davis, J. Brian hdavis@usgs.gov","contributorId":199997,"corporation":false,"usgs":false,"family":"Davis","given":"J.","email":"hdavis@usgs.gov","middleInitial":"Brian","affiliations":[],"preferred":false,"id":720581,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vilella, Francisco 0000-0003-1552-9989 fvilella@usgs.gov","orcid":"https://orcid.org/0000-0003-1552-9989","contributorId":171363,"corporation":false,"usgs":true,"family":"Vilella","given":"Francisco","email":"fvilella@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720580,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lancaster, Joseph D.","contributorId":199998,"corporation":false,"usgs":false,"family":"Lancaster","given":"Joseph","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":720582,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lopez-Flores, Marisel","contributorId":199999,"corporation":false,"usgs":false,"family":"Lopez-Flores","given":"Marisel","email":"","affiliations":[],"preferred":false,"id":720583,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kaminski, Richard M.","contributorId":78205,"corporation":false,"usgs":false,"family":"Kaminski","given":"Richard","email":"","middleInitial":"M.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":720584,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cruz-Burgos, Jose A.","contributorId":200001,"corporation":false,"usgs":false,"family":"Cruz-Burgos","given":"Jose","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":720585,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70193456,"text":"70193456 - 2017 - Fall and winter survival of brook trout and brown trout in a north-central Pennsylvania watershed","interactions":[],"lastModifiedDate":"2017-11-10T11:15:39","indexId":"70193456","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Fall and winter survival of brook trout and brown trout in a north-central Pennsylvania watershed","docAbstract":"Stream-dwelling salmonids that spawn in the fall generally experience their lowest survival during the fall and winter due to behavioral changes associated with spawning and energetic deficiencies during this time of year. We used data from Brook Trout Salvelinus fontinalis and Brown Trout Salmo trutta implanted with radio transmitters in tributaries of the Hunts Run watershed of north-central Pennsylvania to estimate survival from the fall into the winter seasons (September 2012–February 2013). We examined the effects that individual-level covariates (trout species, size, and movement rates) and stream-level covariates (individual stream and cumulative drainage area of a stream) have on survival. Brook Trout experienced significantly lower survival than Brown Trout, especially in the early fall during their peak spawning period. Besides a significant species effect, none of the other covariates examined influenced survival for either species. A difference in life history between these species, with Brook Trout having a shorter life expectancy than Brown Trout, is likely the primary reason for the lower survival of Brook Trout. However, Brook Trout also spawn earlier in the fall than Brown Trout and low flows during Brook Trout spawning may have resulted in a greater risk of predation for Brook Trout compared with Brown Trout, thereby also contributing to the observed differences in survival between these species. Our estimates of survival can aid parameterization of future population models for Brook Trout and Brown Trout through the spawning season and into winter.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2017.1305987","usgsCitation":"Sweka, J.A., Davis, L.A., and Wagner, T., 2017, Fall and winter survival of brook trout and brown trout in a north-central Pennsylvania watershed: Transactions of the American Fisheries Society, v. 146, no. 4, p. 744-752, https://doi.org/10.1080/00028487.2017.1305987.","productDescription":"9 p.","startPage":"744","endPage":"752","ipdsId":"IP-079502","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Hunts Run watershed","volume":"146","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-26","publicationStatus":"PW","scienceBaseUri":"5a06c8cde4b09af898c86123","contributors":{"authors":[{"text":"Sweka, John A.","contributorId":198858,"corporation":false,"usgs":false,"family":"Sweka","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":719128,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Lori A.","contributorId":187762,"corporation":false,"usgs":false,"family":"Davis","given":"Lori","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":721572,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":721573,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195117,"text":"70195117 - 2017 - Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System","interactions":[],"lastModifiedDate":"2021-10-26T15:52:22.76388","indexId":"70195117","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5621,"text":"Journal of Operational Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System","docAbstract":"This paper outlines strategies that would advance coastal ocean modelling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the US Integrated Ocean Observing System (IOOS®) and the Global Ocean Observing System (GOOS). The views presented are the consensus of a group of US-based researchers with a cross-section of coastal oceanography and ocean modelling expertise and community representation drawn from Regional and US Federal partners in IOOS. Priorities for research and development are suggested that would enhance the value of IOOS observations through model-based synthesis, deliver better model-based information products, and assist the design, evaluation, and operation of the observing system itself. The proposed priorities are: model coupling, data assimilation, nearshore processes, cyberinfrastructure and model skill assessment, modelling for observing system design, evaluation and operation, ensemble prediction, and fast predictors. Approaches are suggested to accomplish substantial progress in a 3–8-year timeframe. In addition, the group proposes steps to promote collaboration between research and operations groups in Regional Associations, US Federal Agencies, and the international ocean research community in general that would foster coordination on scientific and technical issues, and strengthen federal–academic partnerships benefiting IOOS stakeholders and end users.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/1755876X.2017.1322026","usgsCitation":"Wilkin, J.L., Rosenfeld, L., Allen, A., Baltes, R., Baptista, A., He, R., Hogan, P., Kurapov, A., Mehra, A., Quintrell, J., Schwab, D., Signell, R.P., and Smith, J., 2017, Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System: Journal of Operational Oceanography, v. 10, no. 2, p. 115-126, https://doi.org/10.1080/1755876X.2017.1322026.","productDescription":"12 p.","startPage":"115","endPage":"126","ipdsId":"IP-086129","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469897,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/1755876x.2017.1322026","text":"Publisher Index Page"},{"id":351300,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-20","publicationStatus":"PW","scienceBaseUri":"5a7c1e7ce4b00f54eb229351","contributors":{"authors":[{"text":"Wilkin, John L. 0000-0002-5444-9466","orcid":"https://orcid.org/0000-0002-5444-9466","contributorId":28872,"corporation":false,"usgs":true,"family":"Wilkin","given":"John","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":727018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenfeld, Leslie 0000-0002-0768-819X","orcid":"https://orcid.org/0000-0002-0768-819X","contributorId":140915,"corporation":false,"usgs":false,"family":"Rosenfeld","given":"Leslie","email":"","affiliations":[{"id":13614,"text":"Naval Postgraduate School, Monterey, CA","active":true,"usgs":false}],"preferred":false,"id":727019,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Arthur 0000-0002-6061-9396","orcid":"https://orcid.org/0000-0002-6061-9396","contributorId":70870,"corporation":false,"usgs":true,"family":"Allen","given":"Arthur","email":"","affiliations":[],"preferred":false,"id":727020,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baltes, Rebecca 0000-0003-3121-1495","orcid":"https://orcid.org/0000-0003-3121-1495","contributorId":201818,"corporation":false,"usgs":false,"family":"Baltes","given":"Rebecca","email":"","affiliations":[{"id":36259,"text":"U.S. IOOS Program Office","active":true,"usgs":false}],"preferred":false,"id":727021,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baptista, Antonio 0000-0002-7641-5937","orcid":"https://orcid.org/0000-0002-7641-5937","contributorId":202188,"corporation":false,"usgs":false,"family":"Baptista","given":"Antonio","email":"","affiliations":[],"preferred":false,"id":727022,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"He, Ruoying","contributorId":68029,"corporation":false,"usgs":true,"family":"He","given":"Ruoying","affiliations":[],"preferred":false,"id":727773,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hogan, Patrick 0000-0001-5931-3675","orcid":"https://orcid.org/0000-0001-5931-3675","contributorId":201819,"corporation":false,"usgs":false,"family":"Hogan","given":"Patrick","email":"","affiliations":[{"id":36260,"text":"U.S. Naval Research Laboratory and GCOOS","active":true,"usgs":false}],"preferred":false,"id":727024,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kurapov, Alexander","contributorId":201820,"corporation":false,"usgs":false,"family":"Kurapov","given":"Alexander","email":"","affiliations":[{"id":36261,"text":"Oregon State University and NANOOS","active":true,"usgs":false}],"preferred":false,"id":727025,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mehra, Avichal","contributorId":201821,"corporation":false,"usgs":false,"family":"Mehra","given":"Avichal","email":"","affiliations":[{"id":36262,"text":"NOAA National Centers for Environmental Prediction","active":true,"usgs":false}],"preferred":false,"id":727026,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Quintrell, Josie","contributorId":201822,"corporation":false,"usgs":false,"family":"Quintrell","given":"Josie","email":"","affiliations":[{"id":36263,"text":"IOOS Association","active":true,"usgs":false}],"preferred":false,"id":727027,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schwab, David","contributorId":202190,"corporation":false,"usgs":false,"family":"Schwab","given":"David","affiliations":[],"preferred":false,"id":727028,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Signell, Richard P. 0000-0003-0682-9613 rsignell@usgs.gov","orcid":"https://orcid.org/0000-0003-0682-9613","contributorId":140906,"corporation":false,"usgs":true,"family":"Signell","given":"Richard","email":"rsignell@usgs.gov","middleInitial":"P.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":727017,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Smith, Jane","contributorId":202191,"corporation":false,"usgs":false,"family":"Smith","given":"Jane","affiliations":[],"preferred":false,"id":727029,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70193740,"text":"70193740 - 2017 - Automatic mapping of the base of aquifer — A case study from Morrill, Nebraska","interactions":[],"lastModifiedDate":"2017-11-06T11:01:35","indexId":"70193740","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3906,"text":"Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Automatic mapping of the base of aquifer — A case study from Morrill, Nebraska","docAbstract":"When a geologist sets up a geologic model, various types of disparate information may be available, such as exposures, boreholes, and (or) geophysical data. In recent years, the amount of geophysical data available has been increasing, a trend that is only expected to continue. It is nontrivial (and often, in practice, impossible) for the geologist to take all the details of the geophysical data into account when setting up a geologic model. We have developed an approach that allows for the objective quantification of information from geophysical data and borehole observations in a way that is easy to integrate in the geologic modeling process. This will allow the geologist to make a geologic interpretation that is consistent with the geophysical information at hand. We have determined that automated interpretation of geologic layer boundaries using information from boreholes and geophysical data alone can provide a good geologic layer model, even before manual interpretation has begun. The workflow is implemented on a set of boreholes and airborne electromagnetic (AEM) data from Morrill, Nebraska. From the borehole logs, information about the depth to the base of aquifer (BOA) is extracted and used together with the AEM data to map a surface that represents this geologic contact. Finally, a comparison between our automated approach and a previous manual mapping of the BOA in the region validates the quality of the proposed method and suggests that this workflow will allow a much faster and objective geologic modeling process that is consistent with the available data.
We present a case study evaluation of gill-net catches of Walleye Sander vitreus to assess potential effects of large-scale changes in Oneida Lake, New York, including the disruption of trophic interactions by double-crested cormorants Phalacrocorax auritus and invasive dreissenid mussels. We used the empirical long-term gill-net time series and a negative binomial linear mixed model to partition the variability in catches into spatial and coherent temporal variance components, hypothesizing that variance partitioning can help quantify spatiotemporal variability and determine whether variance structure differs before and after large-scale perturbations. We found that the mean catch and the total variability of catches decreased following perturbation but that not all sampling locations responded in a consistent manner. There was also evidence of some spatial homogenization concurrent with a restructuring of the relative productivity of individual sites. Specifically, offshore sites generally became more productive following the estimated break point in the gill-net time series. These results provide support for the idea that variance structure is responsive to large-scale perturbations; therefore, variance components have potential utility as statistical indicators of response to a changing environment more broadly. The modeling approach described herein is flexible and would be transferable to other systems and metrics. For example, variance partitioning could be used to examine responses to alternative management regimes, to compare variability across physiographic regions, and to describe differences among climate zones. Understanding how individual variance components respond to perturbation may yield finer-scale insights into ecological shifts than focusing on patterns in the mean responses or total variability alone.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2017.1301992","usgsCitation":"Vidal, T.E., Irwin, B.J., Wagner, T., Rudstam, L.G., Jackson, J.R., and Bence, J., 2017, Using variance structure to quantify responses to perturbation in fish catches: Transactions of the American Fisheries Society, v. 146, no. 4, p. 584-593, https://doi.org/10.1080/00028487.2017.1301992.","productDescription":"10 p.","startPage":"584","endPage":"593","ipdsId":"IP-068926","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"146","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-25","publicationStatus":"PW","scienceBaseUri":"59fedfb3e4b0531197b573c0","contributors":{"authors":[{"text":"Vidal, Tiffany E.","contributorId":169096,"corporation":false,"usgs":false,"family":"Vidal","given":"Tiffany","email":"","middleInitial":"E.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":720353,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irwin, Brian J. 0000-0002-0666-2641 bjirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":4037,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian","email":"bjirwin@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720354,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":720355,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rudstam, Lars G.","contributorId":56609,"corporation":false,"usgs":false,"family":"Rudstam","given":"Lars","email":"","middleInitial":"G.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":720356,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackson, James R.","contributorId":55709,"corporation":false,"usgs":false,"family":"Jackson","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":720357,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bence, James R.","contributorId":95026,"corporation":false,"usgs":false,"family":"Bence","given":"James R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":720358,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70193168,"text":"70193168 - 2017 - Achieving full connectivity of sites in the multiperiod reserve network design problem","interactions":[],"lastModifiedDate":"2017-11-20T15:32:16","indexId":"70193168","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5556,"text":"Computers & Operations Research","active":true,"publicationSubtype":{"id":10}},"title":"Achieving full connectivity of sites in the multiperiod reserve network design problem","docAbstract":"The conservation reserve design problem is a challenge to solve because of the spatial and temporal nature of the problem, uncertainties in the decision process, and the possibility of alternative conservation actions for any given land parcel. Conservation agencies tasked with reserve design may benefit from a dynamic decision system that provides tactical guidance for short-term decision opportunities while maintaining focus on a long-term objective of assembling the best set of protected areas possible. To plan cost-effective conservation over time under time-varying action costs and budget, we propose a multi-period mixed integer programming model for the budget-constrained selection of fully connected sites. The objective is to maximize a summed conservation value over all network parcels at the end of the planning horizon. The originality of this work is in achieving full spatial connectivity of the selected sites during the schedule of conservation actions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.cor.2016.12.017","usgsCitation":"Jafari, N., Nuse, B.L., Moore, C.T., Dilkina, B., and Hepinstall-Cymerman, J., 2017, Achieving full connectivity of sites in the multiperiod reserve network design problem: Computers & Operations Research, v. 81, p. 119-127, https://doi.org/10.1016/j.cor.2016.12.017.","productDescription":"9 p.","startPage":"119","endPage":"127","ipdsId":"IP-064927","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":349156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"81","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fbd6e4b06e28e9c236d0","contributors":{"authors":[{"text":"Jafari, Nahid","contributorId":200626,"corporation":false,"usgs":false,"family":"Jafari","given":"Nahid","email":"","affiliations":[],"preferred":false,"id":722920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nuse, Bryan L.","contributorId":200627,"corporation":false,"usgs":false,"family":"Nuse","given":"Bryan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":722921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":718116,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dilkina, Bistra","contributorId":177110,"corporation":false,"usgs":false,"family":"Dilkina","given":"Bistra","affiliations":[],"preferred":false,"id":722922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hepinstall-Cymerman, Jeffrey","contributorId":51998,"corporation":false,"usgs":true,"family":"Hepinstall-Cymerman","given":"Jeffrey","email":"","affiliations":[],"preferred":false,"id":722923,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191275,"text":"70191275 - 2017 - National Park Service Vegetation Mapping Inventory Program: Appalachian National Scenic Trail vegetation mapping project","interactions":[],"lastModifiedDate":"2017-10-03T11:47:48","indexId":"70191275","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NETN/NRR—2017/1437","title":"National Park Service Vegetation Mapping Inventory Program: Appalachian National Scenic Trail vegetation mapping project","docAbstract":"The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program classifies, describes, and maps existing vegetation of national park units for the NPS Natural Resource Inventory and Monitoring (I&M) Program. The NPS VMI Program is managed by the NPS I&M Division and provides baseline vegetation information to the NPS Natural Resource I&M Program. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, NPS Northeast Temperate Network, and NPS Appalachian National Scenic Trail (APPA) have completed vegetation classification and mapping of APPA for the NPS VMI Program.
Mappers, ecologists, and botanists collaborated to affirm vegetation types within the U.S. National Vegetation Classification (USNVC) of APPA and to determine how best to map the vegetation types by using aerial imagery. Analyses of data from 1,618 vegetation plots were used to describe USNVC associations of APPA. Data from 289 verification sites were collected to test the field key to vegetation associations and the application of vegetation associations to a sample set of map polygons. Data from 269 validation sites were collected to assess vegetation mapping prior to submitting the vegetation map for accuracy assessment (AA). Data from 3,265 AA sites were collected, of which 3,204 were used to test accuracy of the vegetation map layer. The collective of these datasets affirmed 280 USNVC associations for the APPA vegetation mapping project.
To map the vegetation and land cover of APPA, 169 map classes were developed. The 169 map classes consist of 150 that represent natural (including ruderal) vegetation types in the USNVC, 11 that represent cultural (agricultural and developed) vegetation types in the USNVC, 5 that represent natural landscapes with catastrophic disturbance or some other modification to natural vegetation preventing accurate classification in the USNVC, and 3 that represent nonvegetated water (non-USNVC). Features were interpreted from viewing 4-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems (GIS). (Digital aerial imagery was collected each fall during 2009–11 to capture leaf-phenology change of hardwood trees across the latitudinal range of APPA.) The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in GIS. Polygon units were mapped to either a 0.5-hectare (ha) or 0.25-ha minimum mapping unit, depending on vegetation type or scenario; however, polygon units were mapped to 0.1 ha for alpine vegetation.
A geodatabase containing various feature-class layers and tables provide locations and support data to USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, validation sites, AA sites, project boundary extent and zones, and aerial image centers and flight lines. The feature-class layer and related tables of the vegetation map layer provide 30,395 polygons of detailed attribute data covering 110,919.7 ha, with an average polygon size of 3.6 ha; the vegetation map coincides closely with the administrative boundary for APPA.
Summary reports generated from the vegetation map layer of the map classes representing USNVC natural (including ruderal) vegetation types apply to 28,242 polygons (92.9% of polygons) and cover 106,413.0 ha (95.9%) of the map extent for APPA. The map layer indicates APPA to be 92.4% forest and woodland (102,480.8 ha), 1.7% shrubland (1866.3 ha), and 1.8% herbaceous cover (2,065.9 ha). Map classes representing park-special vegetation (undefined in the USNVC) apply to 58 polygons (0.2% of polygons) and cover 404.3 ha (0.4%) of the map extent. Map classes representing USNVC cultural types apply to 1,777 polygons (5.8% of polygons) and cover 2,516.3 ha (2.3%) of the map extent. Map classes representing nonvegetated water (non-USNVC) apply to 332 polygons (1.1% of polygons) and cover 1,586.2 ha (1.4%) of the map extent.
The U.S. Geological Survey Earthquake Science Center collects and processes Global Positioning System (GPS) data throughout the western United States to measure crustal deformation related to earthquakes and tectonic processes as part of a long‐term program of research and monitoring. Here, we outline data collection procedures and present the GPS dataset built through repeated temporary deployments since 1992. This dataset consists of observations at ∼1950 locations. In addition, this article details our data processing and analysis procedures, which consist of the following. We process the raw data collected through temporary deployments, in addition to data from continuously operating western U.S. GPS stations operated by multiple agencies, using the GIPSY software package to obtain position time series. Subsequently, we align the positions to a common reference frame, determine the optimal parameters for a temporally correlated noise model, and apply this noise model when carrying out time‐series analysis to derive deformation measures, including constant interseismic velocities, coseismic offsets, and transient postseismic motion.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160204","usgsCitation":"Murray, J.R., and Svarc, J.L., 2017, Global Positioning System data collection, processing, and analysis conducted by the U.S. Geological Survey Earthquake Hazards Program: Seismological Research Letters, v. 88, no. 3, p. 916-925, https://doi.org/10.1785/0220160204.","productDescription":"10 p.","startPage":"916","endPage":"925","ipdsId":"IP-081230","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":347479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-01","publicationStatus":"PW","scienceBaseUri":"5a07e8f7e4b09af898c8cbdd","contributors":{"authors":[{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Svarc, Jerry L. 0000-0002-2802-4528 jsvarc@usgs.gov","orcid":"https://orcid.org/0000-0002-2802-4528","contributorId":2413,"corporation":false,"usgs":true,"family":"Svarc","given":"Jerry","email":"jsvarc@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715956,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192469,"text":"70192469 - 2017 - A probabilistic approach to remote compositional analysis of planetary surfaces","interactions":[],"lastModifiedDate":"2017-10-31T14:21:28","indexId":"70192469","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2317,"text":"Journal of Geophysical Research E: Planets","active":true,"publicationSubtype":{"id":10}},"title":"A probabilistic approach to remote compositional analysis of planetary surfaces","docAbstract":"Reflected light from planetary surfaces provides information, including mineral/ice compositions and grain sizes, by study of albedo and absorption features as a function of wavelength. However, deconvolving the compositional signal in spectra is complicated by the nonuniqueness of the inverse problem. Trade-offs between mineral abundances and grain sizes in setting reflectance, instrument noise, and systematic errors in the forward model are potential sources of uncertainty, which are often unquantified. Here we adopt a Bayesian implementation of the Hapke model to determine sets of acceptable-fit mineral assemblages, as opposed to single best fit solutions. We quantify errors and uncertainties in mineral abundances and grain sizes that arise from instrument noise, compositional end members, optical constants, and systematic forward model errors for two suites of ternary mixtures (olivine-enstatite-anorthite and olivine-nontronite-basaltic glass) in a series of six experiments in the visible-shortwave infrared (VSWIR) wavelength range. We show that grain sizes are generally poorly constrained from VSWIR spectroscopy. Abundance and grain size trade-offs lead to typical abundance errors of ≤1 wt % (occasionally up to ~5 wt %), while ~3% noise in the data increases errors by up to ~2 wt %. Systematic errors further increase inaccuracies by a factor of 4. Finally, phases with low spectral contrast or inaccurate optical constants can further increase errors. Overall, typical errors in abundance are <10%, but sometimes significantly increase for specific mixtures, prone to abundance/grain-size trade-offs that lead to high unmixing uncertainties. These results highlight the need for probabilistic approaches to remote determination of planetary surface composition.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016JE005248","usgsCitation":"Lapotre, M.G., Ehlmann, B.L., and Minson, S.E., 2017, A probabilistic approach to remote compositional analysis of planetary surfaces: Journal of Geophysical Research E: Planets, v. 122, no. 5, p. 983-1009, https://doi.org/10.1002/2016JE005248.","productDescription":"17 p.","startPage":"983","endPage":"1009","ipdsId":"IP-084590","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469884,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016je005248","text":"Publisher Index Page"},{"id":347891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-26","publicationStatus":"PW","scienceBaseUri":"59f98bb8e4b0531197af9ff5","contributors":{"authors":[{"text":"Lapotre, Mathieu G.A.","contributorId":198421,"corporation":false,"usgs":false,"family":"Lapotre","given":"Mathieu","email":"","middleInitial":"G.A.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":716006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ehlmann, Bethany L. 0000-0002-2745-3240","orcid":"https://orcid.org/0000-0002-2745-3240","contributorId":147154,"corporation":false,"usgs":false,"family":"Ehlmann","given":"Bethany","email":"","middleInitial":"L.","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":716007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":716005,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192471,"text":"70192471 - 2017 - The California Earthquake Advisory Plan: A history","interactions":[],"lastModifiedDate":"2017-10-30T11:05:09","indexId":"70192471","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"The California Earthquake Advisory Plan: A history","docAbstract":"Since 1985, the California Office of Emergency Services (Cal OES) has issued advisory statements to local jurisdictions and the public following seismic activity that scientists on the California Earthquake Prediction Evaluation Council view as indicating elevated probability of a larger earthquake in the same area during the next several days. These advisory statements are motivated by statistical studies showing that about 5% of moderate earthquakes in California are followed by larger events within a 10-km, five-day space-time window (Jones, 1985; Agnew and Jones, 1991; Reasenberg and Jones, 1994). Cal OES issued four earthquake advisories from 1985 to 1989. In October, 1990, the California Earthquake Advisory Plan formalized this practice, and six Cal OES Advisories have been issued since then. This article describes that protocol’s scientific basis and evolution.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160183","usgsCitation":"Roeloffs, E.A., and Goltz, J.D., 2017, The California Earthquake Advisory Plan: A history: Seismological Research Letters, v. 88, no. 3, p. 784-797, https://doi.org/10.1785/0220160183.","productDescription":"14 p.","startPage":"784","endPage":"797","ipdsId":"IP-069239","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":347482,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"88","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-08","publicationStatus":"PW","scienceBaseUri":"59f83a37e4b063d5d30980e7","contributors":{"authors":[{"text":"Roeloffs, Evelyn A. 0000-0002-4761-0469 evelynr@usgs.gov","orcid":"https://orcid.org/0000-0002-4761-0469","contributorId":2680,"corporation":false,"usgs":true,"family":"Roeloffs","given":"Evelyn","email":"evelynr@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":716011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goltz, James D.","contributorId":198432,"corporation":false,"usgs":false,"family":"Goltz","given":"James","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":716012,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192035,"text":"70192035 - 2017 - Low stress drops observed for aftershocks of the 2011 Mw 5.7 Prague, Oklahoma, earthquake","interactions":[],"lastModifiedDate":"2017-10-24T14:13:06","indexId":"70192035","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Low stress drops observed for aftershocks of the 2011 Mw 5.7 Prague, Oklahoma, earthquake","docAbstract":"In November 2011, three Mw ≥ 4.8 earthquakes and thousands of aftershocks occurred along the structurally complex Wilzetta fault system near Prague, Oklahoma. Previous studies suggest that wastewater injection induced a Mw 4.8 foreshock, which subsequently triggered a Mw 5.7 mainshock. We examine source properties of aftershocks with a standard Brune-type spectral model and jointly solve for seismic moment (M0), corner frequency (f0), and kappa (κ) with an iterative Gauss-Newton global downhill optimization method. We examine 934 earthquakes with initial moment magnitudes (Mw) between 0.33 and 4.99 based on the pseudospectral acceleration and recover reasonable M0, f0, and κ for 87 earthquakes with Mw 1.83–3.51 determined by spectral fit. We use M0 and f0 to estimate the Brune-type stress drop, assuming a circular fault and shear-wave velocity at the hypocentral depth of the event. Our observations suggest that stress drops range between 0.005 and 4.8 MPa with a median of 0.2 MPa (0.03–26.4 MPa with a median of 1.1 MPa for Madariaga-type), which is significantly lower than typical eastern United States intraplate events (>10 MPa). We find that stress drops correlate weakly with hypocentral depth and magnitude. Additionally, we find the stress drops increase with time after the mainshock, although temporal variation in stress drop is difficult to separate from spatial heterogeneity and changing event locations. The overall low median stress drop suggests that the fault segments may have been primed to fail as a result of high pore fluid pressures, likely related to nearby wastewater injection.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016JB013153","usgsCitation":"Sumy, D.F., Neighbors, C.J., Cochran, E.S., and Keranen, K.M., 2017, Low stress drops observed for aftershocks of the 2011 Mw 5.7 Prague, Oklahoma, earthquake: Journal of Geophysical Research B: Solid Earth, v. 122, no. 5, p. 3813-3834, https://doi.org/10.1002/2016JB013153.","productDescription":"22 p.","startPage":"3813","endPage":"3834","ipdsId":"IP-075342","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469876,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jb013153","text":"Publisher Index Page"},{"id":347249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","city":"Prague","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.5,\n 34.5\n ],\n [\n -95.5,\n 34.5\n ],\n [\n -95.5,\n 36.5\n ],\n [\n -97.5,\n 36.5\n ],\n [\n -97.5,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-21","publicationStatus":"PW","scienceBaseUri":"59f05122e4b0220bbd9a1d9a","contributors":{"authors":[{"text":"Sumy, Danielle F.","contributorId":108025,"corporation":false,"usgs":true,"family":"Sumy","given":"Danielle","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":713942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neighbors, Corrie J.","contributorId":197629,"corporation":false,"usgs":false,"family":"Neighbors","given":"Corrie","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":713943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":713941,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keranen, Katie M.","contributorId":197630,"corporation":false,"usgs":false,"family":"Keranen","given":"Katie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":713944,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187141,"text":"ofr20171029 - 2017 - Guidelines for preparation of State water-use estimates for 2015","interactions":[],"lastModifiedDate":"2017-05-02T08:59:14","indexId":"ofr20171029","displayToPublicDate":"2017-05-01T00:00:00","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":"2017-1029","title":"Guidelines for preparation of State water-use estimates for 2015","docAbstract":"The U.S. Geological Survey (USGS) has estimated the use of water in the United States at 5-year intervals since 1950. This report describes the water-use categories and data elements used for the national water-use compilation conducted as part of the USGS National Water-Use Science Project. The report identifies sources of water-use information, provides standard methods and techniques for estimating water use at the county level, and outlines steps for preparing documentation for the United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands.
As part of this USGS program to document water use on a national scale, estimates of water withdrawals for the categories of public supply, self-supplied domestic, industrial, irrigation, and thermoelectric power are prepared for each county in each State, District, or territory by using the guidelines in this report. County estimates of water withdrawals for aquaculture, livestock, and mining are prepared for each State by using a county-based national model, although water-use programs in each State or Water Science Center have the option of producing independent county estimates of water withdrawals for these categories. Estimates of water withdrawals and consumptive use for thermoelectric power will be aggregated to the county level for each State by the national project; additionally, irrigation consumptive use at the county level will also be provided, although study chiefs in each State have the option of producing independent county estimates of water withdrawals and consumptive use for these categories.
Estimates of deliveries of water from public supplies for domestic use by county also will be prepared for each State. As a result, total domestic water use can be determined for each State by combining self-supplied domestic withdrawals and public-supplied domestic deliveries. Fresh groundwater and surface-water estimates will be prepared for all categories of use, and saline groundwater and surface-water estimates by county will be prepared for the categories of public supply, industrial, mining, and thermoelectric power. Power production for thermoelectric power and irrigated acres by irrigation system type will be compiled. If data are available, reclaimed-wastewater use will be compiled for the public-supply, industrial, mining, thermoelectric-power, and irrigation categories.
Optional water-use categories are commercial, hydroelectric power, and wastewater treatment. Optional data elements are public-supply deliveries to commercial, industrial, and thermoelectric-power users; consumptive use (for categories other than thermoelectric power and irrigation); irrigation conveyance loss; and number of facilities. Aggregation of water-use data by stream basin (eight-digit hydrologic unit code) and principal aquifers also is optional.
Water-use data compiled by the States will be stored in the USGS Aggregate Water-Use Data System (AWUDS). This database is a comprehensive aggregated database designed to store mandatory and optional data elements. AWUDS contains several routines that can be used for quality assurance and quality control of the data, and AWUDS produces tables of water-use data from the previous compilations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171029","collaboration":"National Water-Use Science Project","usgsCitation":"Bradley, M.W., comp., 2017, Guidelines for preparation of State water-use estimates for 2015: U.S. Geological Survey Open-File Report 2017–1029, 54 p., https://doi.org/10.3133/ofr20171029.","productDescription":"viii, 54 p.","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-078880","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":340450,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1029/coverthb2.jpg"},{"id":340259,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1029/ofr20171029.pdf","text":"Report","size":"719 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1029"},{"id":340258,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1029/coverthb.jpg"}],"contact":"Director, Lower Mississippi-Gulf Water Science Center—Tennessee
640 Grassmere Park
Suite 100
Nashville, TN 37211
A probabilistic mineral resource assessment of metal resources in undiscovered porphyry copper deposits of the Ural Mountains in Russia and Kazakhstan was done using a quantitative form of mineral resource assessment. Permissive tracts were delineated on the basis of mapped and inferred subsurface distributions of igneous rocks assigned to tectonic zones that include magmatic arcs where the occurrence of porphyry copper deposits within 1 km of the Earth's surface are possible. These permissive tracts outline four north-south trending volcano-plutonic belts in major structural zones of the Urals. From west to east, these include permissive lithologies for porphyry copper deposits associated with Paleozoic subduction-related island-arc complexes preserved in the Tagil and Magnitogorsk arcs, Paleozoic island-arc fragments and associated tonalite-granodiorite intrusions in the East Uralian zone, and Carboniferous continental-margin arcs developed on the Kazakh craton in the Transuralian zone. The tracts range from about 50,000 to 130,000 km2 in area. The Urals host 8 known porphyry copper deposits with total identified resources of about 6.4 million metric tons of copper, at least 20 additional porphyry copper prospect areas, and numerous copper-bearing skarns and copper occurrences.
Probabilistic estimates predict a mean of 22 undiscovered porphyry copper deposits within the four permissive tracts delineated in the Urals. Combining estimates with established grade and tonnage models predicts a mean of 82 million metric tons of undiscovered copper. Application of an economic filter suggests that about half of that amount could be economically recoverable based on assumed depth distributions, availability of infrastructure, recovery rates, current metals prices, and investment environment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2016.09.007","usgsCitation":"Hammarstrom, J.M., Mihalasky, M.J., Ludington, S., Phillips, J., Berger, B.R., Denning, P., Dicken, C., Mars, J.C., Zientek, M.L., Herrington, R.J., and Seltmann, R., 2017, Undiscovered porphyry copper resources in the Urals—A probabilistic mineral resource assessment: Ore Geology Reviews, v. 85, p. 181-203, https://doi.org/10.1016/j.oregeorev.2016.09.007.","productDescription":"23 p.","startPage":"181","endPage":"203","ipdsId":"IP-068679","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":461619,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2016.09.007","text":"Publisher Index Page"},{"id":346315,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kazakhstan, Russia","otherGeospatial":"Urals","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 56,\n 50\n ],\n [\n 68,\n 50\n ],\n [\n 68,\n 70\n ],\n [\n 56,\n 70\n ],\n [\n 56,\n 50\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59d35026e4b05fe04cc34d54","contributors":{"authors":[{"text":"Hammarstrom, Jane M. 0000-0003-2742-3460 jhammars@usgs.gov","orcid":"https://orcid.org/0000-0003-2742-3460","contributorId":1226,"corporation":false,"usgs":true,"family":"Hammarstrom","given":"Jane","email":"jhammars@usgs.gov","middleInitial":"M.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":711714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mihalasky, Mark J. 0000-0002-0082-3029 mjm@usgs.gov","orcid":"https://orcid.org/0000-0002-0082-3029","contributorId":3692,"corporation":false,"usgs":true,"family":"Mihalasky","given":"Mark","email":"mjm@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":false,"id":711715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ludington, Stephen 0000-0002-6265-4996 slud@usgs.gov","orcid":"https://orcid.org/0000-0002-6265-4996","contributorId":172672,"corporation":false,"usgs":true,"family":"Ludington","given":"Stephen","email":"slud@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":711716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phillips, Jeffrey 0000-0002-6459-2821 jeff@usgs.gov","orcid":"https://orcid.org/0000-0002-6459-2821","contributorId":127453,"corporation":false,"usgs":true,"family":"Phillips","given":"Jeffrey","email":"jeff@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":711717,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berger, Byron R. bberger@usgs.gov","contributorId":1490,"corporation":false,"usgs":true,"family":"Berger","given":"Byron","email":"bberger@usgs.gov","middleInitial":"R.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":711718,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Denning, Paul pdenning@usgs.gov","contributorId":168842,"corporation":false,"usgs":true,"family":"Denning","given":"Paul","email":"pdenning@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":711719,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dicken, Connie cdicken@usgs.gov","contributorId":172878,"corporation":false,"usgs":true,"family":"Dicken","given":"Connie","email":"cdicken@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":711720,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mars, John C. 0000-0002-0421-1388 jmars@usgs.gov","orcid":"https://orcid.org/0000-0002-0421-1388","contributorId":178265,"corporation":false,"usgs":true,"family":"Mars","given":"John","email":"jmars@usgs.gov","middleInitial":"C.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":711721,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zientek, Michael L. 0000-0002-8522-9626 mzientek@usgs.gov","orcid":"https://orcid.org/0000-0002-8522-9626","contributorId":2420,"corporation":false,"usgs":true,"family":"Zientek","given":"Michael","email":"mzientek@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":711722,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Herrington, Richard J.","contributorId":70688,"corporation":false,"usgs":true,"family":"Herrington","given":"Richard","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":711723,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Seltmann, Reimar","contributorId":73450,"corporation":false,"usgs":true,"family":"Seltmann","given":"Reimar","email":"","affiliations":[],"preferred":false,"id":711724,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70192902,"text":"70192902 - 2017 - Response of fish population dynamics to mitigation activities in a large regulated river","interactions":[],"lastModifiedDate":"2017-11-08T10:13:07","indexId":"70192902","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Response of fish population dynamics to mitigation activities in a large regulated river","docAbstract":"Extensive water development in large rivers has precipitated many negative ecological effects on native fish populations. Mitigation for such development often focuses on restoring biological integrity through remediation of the physical and chemical properties of regulated rivers. However, evaluating and defining the success of those programs can be difficult. We modeled the influence of mitigation-related environmental factors on growth and recruitment of two ecologically important native fish species (Largescale Sucker Catostomus macrocheilus and Mountain Whitefish Prosopium williamsoni) in the Kootenai River, Idaho. Artificial nutrient (phosphorus) addition best predicted the variability in annual growth of both species. Nutrient addition was positively related to Largescale Sucker growth but negatively related to Mountain Whitefish growth. The best model explained 82% of the annual variability in incremental growth for Largescale Suckers and 61% of the annual variability for Mountain Whitefish. Year-class strength of Largescale Suckers was not closely related to any of the environmental variables evaluated; however, year-class strength of Mountain Whitefish was closely associated with nutrient addition, discharge, and temperature. Most research has focused on biotic assemblages to evaluate the effects of mitigation activities on fishes, but there is an increased need to identify the influence of rehabilitation activities on fish population dynamics within those assemblages. Here, we demonstrate how fish growth can serve as an indicator of rehabilitation success in a highly regulated large river. Future fish restoration projects can likely benefit from a change in scope and from consideration of an evaluation framework involving the response of population rate functions to mitigation.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2017.1308882","usgsCitation":"Watkins, C.J., Ross, T.J., Quist, M.C., and Hardy, R.S., 2017, Response of fish population dynamics to mitigation activities in a large regulated river: Transactions of the American Fisheries Society, v. 146, no. 4, p. 703-715, https://doi.org/10.1080/00028487.2017.1308882.","productDescription":"13 p.","startPage":"703","endPage":"715","ipdsId":"IP-079245","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":348398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Kootenai River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.36581420898438,\n 48.59386747325061\n ],\n [\n -116.04583740234374,\n 48.59386747325061\n ],\n [\n -116.04583740234374,\n 48.73717255965176\n ],\n [\n -116.36581420898438,\n 48.73717255965176\n ],\n [\n -116.36581420898438,\n 48.59386747325061\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"146","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-11","publicationStatus":"PW","scienceBaseUri":"5a0425b8e4b0dc0b45b4537c","contributors":{"authors":[{"text":"Watkins, Carson J.","contributorId":171708,"corporation":false,"usgs":false,"family":"Watkins","given":"Carson","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":720984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ross, Tyler J.","contributorId":171777,"corporation":false,"usgs":false,"family":"Ross","given":"Tyler","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":720985,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Quist, Michael C. 0000-0001-8268-1839 mquist@usgs.gov","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":171392,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","email":"mquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":717331,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hardy, Ryan S.","contributorId":167032,"corporation":false,"usgs":false,"family":"Hardy","given":"Ryan","email":"","middleInitial":"S.","affiliations":[{"id":6764,"text":"Idaho Department of Fish and Game, Nampa, Idaho","active":true,"usgs":false}],"preferred":false,"id":720986,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193115,"text":"70193115 - 2017 - Experimental evaluation of four ground-motion scaling methods for dynamic response-history analysis of nonlinear structures","interactions":[],"lastModifiedDate":"2017-10-31T10:24:59","indexId":"70193115","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1101,"text":"Bulletin of Earthquake Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Experimental evaluation of four ground-motion scaling methods for dynamic response-history analysis of nonlinear structures","docAbstract":"This paper experimentally evaluates four methods to scale earthquake ground-motions within an ensemble of records to minimize the statistical dispersion and maximize the accuracy in the dynamic peak roof drift demand and peak inter-story drift demand estimates from response-history analyses of nonlinear building structures. The scaling methods that are investigated are based on: (1) ASCE/SEI 7–10 guidelines; (2) spectral acceleration at the fundamental (first mode) period of the structure, Sa(T1); (3) maximum incremental velocity, MIV; and (4) modal pushover analysis. A total of 720 shake-table tests of four small-scale nonlinear building frame specimens with different static and dynamic characteristics are conducted. The peak displacement demands from full suites of 36 near-fault ground-motion records as well as from smaller “unbiased” and “biased” design subsets (bins) of ground-motions are included. Out of the four scaling methods, ground-motions scaled to the median MIV of the ensemble resulted in the smallest dispersion in the peak roof and inter-story drift demands. Scaling based on MIValso provided the most accurate median demands as compared with the “benchmark” demands for structures with greater nonlinearity; however, this accuracy was reduced for structures exhibiting reduced nonlinearity. The modal pushover-based scaling (MPS) procedure was the only method to conservatively overestimate the median drift demands.
","language":"English","publisher":"Springer","doi":"10.1007/s10518-016-0052-z","usgsCitation":"O’Donnell, A.P., Kurama, Y.C., Kalkan, E., and Taflanidis, A.A., 2017, Experimental evaluation of four ground-motion scaling methods for dynamic response-history analysis of nonlinear structures: Bulletin of Earthquake Engineering, v. 15, no. 5, p. 1899-1924, https://doi.org/10.1007/s10518-016-0052-z.","productDescription":"26 p.","startPage":"1899","endPage":"1924","ipdsId":"IP-069003","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":347808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-02","publicationStatus":"PW","scienceBaseUri":"59f98bb6e4b0531197af9fea","contributors":{"authors":[{"text":"O’Donnell, Andrew P.","contributorId":199049,"corporation":false,"usgs":false,"family":"O’Donnell","given":"Andrew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":718026,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kurama, Yahya C.","contributorId":199050,"corporation":false,"usgs":false,"family":"Kurama","given":"Yahya","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":718027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":718025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taflanidis, Alexandros A.","contributorId":199051,"corporation":false,"usgs":false,"family":"Taflanidis","given":"Alexandros","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":718028,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187357,"text":"70187357 - 2017 - Spatiotemporal variability of snow depletion curves derived from SNODAS for the conterminous United States, 2004-2013","interactions":[],"lastModifiedDate":"2017-06-07T10:16:29","indexId":"70187357","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal variability of snow depletion curves derived from SNODAS for the conterminous United States, 2004-2013","docAbstract":"Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental-scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental-extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1-km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed-scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed-scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national-scale categorization of snowmelt processes.
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\"}}]}\n","volume":"53","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-13","publicationStatus":"PW","scienceBaseUri":"59084922e4b0fc4e448ffd3e","contributors":{"authors":[{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":693604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":693605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":693606,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192603,"text":"70192603 - 2017 - Magmatic degassing, lava dome extrusion, and explosions from Mount Cleveland volcano, Alaska, 2011–2015: Insight into the continuous nature of volcanic activity over multi-year timescales","interactions":[],"lastModifiedDate":"2017-10-31T16:46:52","indexId":"70192603","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Magmatic degassing, lava dome extrusion, and explosions from Mount Cleveland volcano, Alaska, 2011–2015: Insight into the continuous nature of volcanic activity over multi-year timescales","docAbstract":"Mount Cleveland volcano (1730 m) is one of the most active volcanoes in the Aleutian arc, Alaska, but heightened activity is rarely accompanied by geophysical signals, which makes interpretation of the activity difficult. In this study, we combine volcanic gas emissions measured for the first time in August 2015 with longer-term measurements of thermal output and lava extrusion rates between 2011 and 2015 calculated from MODIS satellite data with the aim to develop a better understanding of the nature of volcanic activity at Mount Cleveland. Degassing measurements were made in the month following two explosive events (21 July and 7 August 2015) and during a period of new dome growth in the summit crater. SO2 emission rates ranged from 400 to 860 t d− 1 and CO2/SO2 ratios were < 3, consistent with the presence of shallow magma in the conduit and the observed growth of a new lava dome. Thermal anomalies derived from MODIS data from 2011 to 2015 had an average repose time of only 4 days, pointing to the continuous nature of volcanic activity at this volcano. Rapid increases in the cumulative thermal output were often coincident with visual confirmation of dome growth or accumulations of tephra in the crater. The average rate of lava extrusion calculated for 9 periods of rapid increase in thermal output was 0.28 m3 s− 1, and the total volume extruded from 2011 to 2015 was 1.9–5.8 Mm3. The thermal output from the lava extrusion events only accounts for roughly half of the thermal budget, suggesting a continued presence of shallow magma in the upper conduit, likely driven by convection. Axisymmetric dome morphology and occasional drain back of lava into the conduit suggests low-viscosity magmas drive volcanism at Mount Cleveland. It follows also that only small overpressures can be maintained given the small domes and fluid magmas, which is consistent with the low explosivity of most of Mount Cleveland's eruptions. Changes between phases of dome growth and explosive activity are somewhat unpredictable and likely result from plugs that are related to the dome obtaining a critical dimension, or from small variations in the magma ascent rate that lead to crystallization-induced blockages in the upper conduit, thereby reducing the ability of magma to degas. We suggest the small magma volumes, slow ascent rates, and low magma viscosity lead to the overall lack of anomalous geophysical signals prior to eruptions, and that more continuous volcanic degassing measurements might lead to more successful eruption forecasting at this continuously-active open-vent volcano.
","language":"English","publisher":"Elsever","doi":"10.1016/j.jvolgeores.2017.03.001","usgsCitation":"Werner, C., Kern, C., Coppola, D., Lyons, J.J., Kelly, P.J., Wallace, K.L., Schneider, D.J., and Wessels, R., 2017, Magmatic degassing, lava dome extrusion, and explosions from Mount Cleveland volcano, Alaska, 2011–2015: Insight into the continuous nature of volcanic activity over multi-year timescales: Journal of Volcanology and Geothermal Research, v. 337, p. 98-110, https://doi.org/10.1016/j.jvolgeores.2017.03.001.","productDescription":"13 p.","startPage":"98","endPage":"110","ipdsId":"IP-081346","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":469894,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://hdl.handle.net/2318/1652262","text":"Publisher Index Page"},{"id":347945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Mount Cleveland Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -171.650390625,\n 52.22443459871999\n ],\n [\n -166.57470703125,\n 52.22443459871999\n ],\n [\n -166.57470703125,\n 54.04971418210692\n ],\n [\n -171.650390625,\n 54.04971418210692\n ],\n [\n -171.650390625,\n 52.22443459871999\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"337","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f98bb7e4b0531197af9ff0","contributors":{"authors":[{"text":"Werner, Cynthia","contributorId":198599,"corporation":false,"usgs":false,"family":"Werner","given":"Cynthia","affiliations":[],"preferred":false,"id":716519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":716518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coppola, Diego","contributorId":190919,"corporation":false,"usgs":false,"family":"Coppola","given":"Diego","email":"","affiliations":[],"preferred":false,"id":716520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":716521,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":716522,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wallace, Kristi L. 0000-0002-0962-048X kwallace@usgs.gov","orcid":"https://orcid.org/0000-0002-0962-048X","contributorId":3454,"corporation":false,"usgs":true,"family":"Wallace","given":"Kristi","email":"kwallace@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":716523,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schneider, David J. 0000-0001-9092-1054 djschneider@usgs.gov","orcid":"https://orcid.org/0000-0001-9092-1054","contributorId":198601,"corporation":false,"usgs":true,"family":"Schneider","given":"David","email":"djschneider@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":716524,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wessels, Rick 0000-0001-9711-6402 rwessels@usgs.gov","orcid":"https://orcid.org/0000-0001-9711-6402","contributorId":198602,"corporation":false,"usgs":true,"family":"Wessels","given":"Rick","email":"rwessels@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":716525,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70190747,"text":"70190747 - 2017 - Temporal variability of foliar nutrients: responses to nitrogen deposition and prescribed fire in a temperate steppe","interactions":[],"lastModifiedDate":"2017-09-13T15:34:18","indexId":"70190747","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Temporal variability of foliar nutrients: responses to nitrogen deposition and prescribed fire in a temperate steppe","docAbstract":"Plant nutrient concentrations and stoichiometry drive fundamental ecosystem processes, with important implications for primary production, diversity, and ecosystem sustainability. While a range of evidence exists regarding how plant nutrients vary across spatial scales, our understanding of their temporal variation remains less well understood. Nevertheless, we know nutrients regulate plant function across time, and that important temporal controls could strongly interact with environmental change. Here, we report results from a 3-year assessment of inter-annual changes of foliar nitrogen (N) and phosphorus (P) concentrations and stoichiometry in three dominant grasses in response to N deposition and prescribed fire in a temperate steppe of northern China. Foliar N and P concentrations and their ratios varied greatly among years, with this temporal variation strongly related to inter-annual variation in precipitation. Nitrogen deposition significantly increased foliar N concentrations and N:P ratios in all species, while fire significantly altered foliar N and P concentrations but had no significant impacts on N:P ratios. Generally, N addition enhanced the temporal stability of foliar N and decreased that of foliar P and of N:P ratios. Our results indicate that plant nutrient status and response to environmental change are temporally dynamic and that there are differential effects on the interactions between environmental change drivers and timing for different nutrients. These responses have important implications for consideration of global change effects on plant community structure and function, management strategies, and the modeling of biogeochemical cycles under global change scenarios.
","language":"English","publisher":"Springer","doi":"10.1007/s10533-017-0333-x","usgsCitation":"Lu, X., Reed, S.C., Hou, S., Hu, Y., Wei, H., Lu, F., Cui, Q., and Han, X., 2017, Temporal variability of foliar nutrients: responses to nitrogen deposition and prescribed fire in a temperate steppe: Biogeochemistry, v. 133, no. 3, p. 295-305, https://doi.org/10.1007/s10533-017-0333-x.","productDescription":"11 p.","startPage":"295","endPage":"305","ipdsId":"IP-086239","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":345703,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"133","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-18","publicationStatus":"PW","scienceBaseUri":"59ba43b9e4b091459a5629bd","contributors":{"authors":[{"text":"Lu, Xiao-Tao","contributorId":196421,"corporation":false,"usgs":false,"family":"Lu","given":"Xiao-Tao","email":"","affiliations":[],"preferred":false,"id":710307,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":710306,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hou, Shuang-Li","contributorId":196422,"corporation":false,"usgs":false,"family":"Hou","given":"Shuang-Li","email":"","affiliations":[],"preferred":false,"id":710308,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hu, Yan-Yu","contributorId":196423,"corporation":false,"usgs":false,"family":"Hu","given":"Yan-Yu","email":"","affiliations":[],"preferred":false,"id":710309,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wei, Hai-Wei","contributorId":196424,"corporation":false,"usgs":false,"family":"Wei","given":"Hai-Wei","email":"","affiliations":[],"preferred":false,"id":710310,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lu, Fu-Mei","contributorId":196425,"corporation":false,"usgs":false,"family":"Lu","given":"Fu-Mei","email":"","affiliations":[],"preferred":false,"id":710311,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cui, Qiang","contributorId":196426,"corporation":false,"usgs":false,"family":"Cui","given":"Qiang","email":"","affiliations":[],"preferred":false,"id":710312,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Han, Xing Guo","contributorId":196427,"corporation":false,"usgs":false,"family":"Han","given":"Xing Guo","affiliations":[],"preferred":false,"id":710313,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}