While many studies on tribal water resources of individual tribal lands in the United States (US) have been conducted, the importance of tribal water resources at a national scale has largely gone unrecognized because their combined totals have not been quantified. Thus, we sought to provide a numerical estimate of major water budget components on tribal lands within the conterminous US and on USGS hydrologic unit codes (HUC2) regions. Using existing national-scale data and models, we estimated mean annual precipitation, evapotranspiration, excess precipitation, streamflow, and water use for the period 1971–2000. Tribal lands represent about 3.4 percent of the total land area of the conterminous US and on average account for 1.9 percent of precipitation, 2.4 percent of actual evapotranspiration, 0.95 percent of excess precipitation, 1.6 percent of water use, and 0.43 percent of streamflow origination. Additionally, approximately 9.5 and 11.3 percent of US streamflow flows through or adjacent as boundaries to tribal lands, respectively. Streamflow through or adjacent to tribal lands accounts for 42 and 48 percent of streamflow in the Missouri region, respectively; and for 86 and 88 percent in the Lower Colorado region, respectively. On average, 5,600 million cubic meters of streamflow per year was produced on tribal lands in the Pacific Northwest region, nearly five times greater than tribal lands in any other region. Tribal lands in the Great Lakes, Missouri, Arkansas-White-Red, and California regions all produced between 1,000 and 1,400 million cubic meters per year.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0203872","usgsCitation":"Blasch, K.W., Hundt, S., Wurster, P., Sando, R., and Berthelote, A., 2018, Streamflow contributions from tribal lands to major river basins of the United States: PLoS ONE, v. 13, no. 9, p. 1-16, https://doi.org/10.1371/journal.pone.0203872.","productDescription":"e0203872; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-089346","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":468411,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0203872","text":"Publisher Index Page"},{"id":357234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"13","issue":"9","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-11","publicationStatus":"PW","scienceBaseUri":"5b98a25fe4b0702d0e842e3e","contributors":{"authors":[{"text":"Blasch, Kyle W. 0000-0002-0590-0724","orcid":"https://orcid.org/0000-0002-0590-0724","contributorId":203415,"corporation":false,"usgs":true,"family":"Blasch","given":"Kyle","email":"","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":744764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hundt, Stephen A. 0000-0002-6484-0637","orcid":"https://orcid.org/0000-0002-6484-0637","contributorId":204678,"corporation":false,"usgs":true,"family":"Hundt","given":"Stephen","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":744765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wurster, Patrick 0000-0003-2668-2014","orcid":"https://orcid.org/0000-0003-2668-2014","contributorId":207806,"corporation":false,"usgs":false,"family":"Wurster","given":"Patrick","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":false,"id":744766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sando, Roy 0000-0003-0704-6258","orcid":"https://orcid.org/0000-0003-0704-6258","contributorId":3874,"corporation":false,"usgs":true,"family":"Sando","given":"Roy","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":744767,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berthelote, Antony","contributorId":207807,"corporation":false,"usgs":false,"family":"Berthelote","given":"Antony","email":"","affiliations":[{"id":37636,"text":"Salish Kootenai College","active":true,"usgs":false}],"preferred":false,"id":744768,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201102,"text":"70201102 - 2018 - Chronic wasting disease detection and mortality sources in semi-protected deer population","interactions":[],"lastModifiedDate":"2019-11-13T13:16:32","indexId":"70201102","displayToPublicDate":"2018-09-11T11:03:04","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Chronic wasting disease detection and mortality sources in semi-protected deer population","docAbstract":"Surveillance for wildlife diseases is essential for assessing population dynamics of ungulates, especially in free-ranging populations where infected animals are difficult to sample. Chronic wasting disease (CWD) is an emerging infectious disease of concern because of the potential for substantial negative effects on populations of cervids. Variability in the likelihood that CWD is detected could invalidate traditional estimators for prevalence. In some instances, deer located after death cannot be tested for infectious diseases, including CWD, because of lack of availability or condition of appropriate tissues. We used various methods to detect infectious diseases that could cause mortality for deer Odocoileus spp. residing in Wind Cave National Park, South Dakota, USA, and we report survival estimates for animals in this population. We included 34 monthly encounters of deer resightings and 67 mortalities. We tested live deer by tonsillar biopsy for CWD and estimated pooled prevalence (mean ± SE) at 5.6 ± 3.0% over the three-year study. Live deer potentially had exposure to several infectious diseases, including bluetongue, epizootic hemorrhagic disease, bovine viral diarrhea, West Nile virus, and malignant catarrhal fever, but no apparent morbidity or mortality from those diseases. We tested survival and influence of covariates, including age and sex, using known-fate analysis in Program MARK. Those data best supported a model with time-invariant encounter probability and an annual survival of 72.8%. Even without direct pressure from hunting within the park, average life expectancy in this population was 3.2 years. Only 68% of mortalities contained sufficient material for CWD sampling (because of predation and scavenger activity) and >42% of these were CWD-positive. These findings underscore the possible biases in postmortem surveillance estimates of disease prevalence because of potential for subclinical infected animals to be removed by predators and not tested.
","language":"English","publisher":"Nordic Board for Wildlife Research","doi":"10.2981/wlb.00437","usgsCitation":"Schuler, K.L., Jenks, J.A., Klaver, R.W., Jennelle, C.S., and Bowyer, R.T., 2018, Chronic wasting disease detection and mortality sources in semi-protected deer population: Wildlife Biology, v. 2018, no. 1, wlb.00437, 7 p., https://doi.org/10.2981/wlb.00437.","productDescription":"wlb.00437, 7 p.","ipdsId":"IP-064498","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468413,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00437","text":"Publisher Index Page"},{"id":359778,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Wind Cave National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.6669921875,\n 43.48680489735277\n ],\n [\n -103.304443359375,\n 43.48680489735277\n ],\n [\n -103.304443359375,\n 43.67581809328341\n ],\n [\n -103.6669921875,\n 43.67581809328341\n ],\n [\n -103.6669921875,\n 43.48680489735277\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2018","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-11","publicationStatus":"PW","scienceBaseUri":"5c0108d5e4b0815414cc2dfb","contributors":{"authors":[{"text":"Schuler, Krysten L.","contributorId":210886,"corporation":false,"usgs":false,"family":"Schuler","given":"Krysten","email":"","middleInitial":"L.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":752680,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenks, Jonathan A.","contributorId":210887,"corporation":false,"usgs":false,"family":"Jenks","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":752681,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":752679,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennelle, Christopher S.","contributorId":210888,"corporation":false,"usgs":false,"family":"Jennelle","given":"Christopher","email":"","middleInitial":"S.","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":752682,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowyer, R. Terry","contributorId":210889,"corporation":false,"usgs":false,"family":"Bowyer","given":"R.","email":"","middleInitial":"Terry","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":752683,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263652,"text":"70263652 - 2018 - The 1952 Kern County, California earthquake: A case study of issues in the analysis of historical intensity data for estimation of source parameters","interactions":[],"lastModifiedDate":"2025-02-19T14:24:12.731569","indexId":"70263652","displayToPublicDate":"2018-09-11T10:06:37","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3071,"text":"Physics of the Earth and Planetary Interiors","active":true,"publicationSubtype":{"id":10}},"title":"The 1952 Kern County, California earthquake: A case study of issues in the analysis of historical intensity data for estimation of source parameters","docAbstract":"Seismic intensity data based on first-hand accounts of shaking give valuable insight into historical and early instrumental earthquakes. Comparing an observed intensity distribution to intensity-prediction models based on modern calibration events allows the magnitude to be estimated for many historic earthquakes. Magnitude estimates can also potentially be refined for earthquakes for which limited instrumental data are available. However, the complicated nature of macroseismic data and the methods used to collect and interpret the data introduce significant uncertainties. In this paper, we illustrate these challenges and possible solutions using the 1952 Kern County, California, earthquake as a case study. Published estimates of its magnitude vary from MW 7.2–7.5, making it possibly the second largest in California during the 20th century. We considered over 1100 first-hand reports of shaking, supplemented with other data, and inferred the magnitude in several ways using intensity prediction equations, yielding a preferred intensity magnitude MI 7.2 ± 0.2, where the uncertainty reflects our judgement. The revised intensity distribution reveals stronger shaking on the hanging wall, south of the surface expression of the White Wolf fault, than on the footwall. Characterizing the magnitude and shaking distribution of this early instrumental earthquake can help improve estimation of the seismic hazard of the region. Such reinterpreted intensities for historic earthquakes, combined with U.S. Geological Survey (USGS) Did You Feel It? data for more recent events, can be used to produce a uniform shaking dataset with which earthquake hazard map performance can be assessed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.pepi.2018.08.007","usgsCitation":"Salditch, L., Hough, S.E., Stein, S., Spencer, B., Brooks, E., Neely, J.S., and Lucas, M.C., 2018, The 1952 Kern County, California earthquake: A case study of issues in the analysis of historical intensity data for estimation of source parameters: Physics of the Earth and Planetary Interiors, v. 283, p. 140-151, https://doi.org/10.1016/j.pepi.2018.08.007.","productDescription":"12 p.","startPage":"140","endPage":"151","ipdsId":"IP-101209","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482166,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.84752123561667,\n 35.795851992110386\n ],\n [\n -119.84752123561667,\n 34.87128790332352\n ],\n [\n -118.27725907195563,\n 34.87128790332352\n ],\n [\n -118.27725907195563,\n 35.795851992110386\n ],\n [\n -119.84752123561667,\n 35.795851992110386\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"283","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Salditch, Leah","contributorId":263445,"corporation":false,"usgs":false,"family":"Salditch","given":"Leah","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hough, Susan E. 0000-0002-5980-2986 hough@usgs.gov","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":587,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"hough@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stein, Seth","contributorId":263457,"corporation":false,"usgs":false,"family":"Stein","given":"Seth","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spencer, Bruce","contributorId":350997,"corporation":false,"usgs":false,"family":"Spencer","given":"Bruce","affiliations":[],"preferred":false,"id":927671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brooks, Edward","contributorId":350999,"corporation":false,"usgs":false,"family":"Brooks","given":"Edward","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927672,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Neely, James S.","contributorId":263454,"corporation":false,"usgs":false,"family":"Neely","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927673,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lucas, Madeleine C.","contributorId":336741,"corporation":false,"usgs":false,"family":"Lucas","given":"Madeleine","middleInitial":"C.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":927674,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70199221,"text":"fs20183032 - 2018 - Changes in Earth’s gravity reveal changes in groundwater storage","interactions":[],"lastModifiedDate":"2018-09-12T10:07:10","indexId":"fs20183032","displayToPublicDate":"2018-09-11T10:01:46","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-3032","title":"Changes in Earth’s gravity reveal changes in groundwater storage","docAbstract":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
A study was conducted by the U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, to reconstruct streamflows for use in the RiverWare model. Methods and data used to estimate daily reconstructed streamflows at 53 sites in selected subbasins in northern New Jersey and southeastern New York are presented in the report. These subbasins contain one or more surface-water diversions that are operated or have been operated in the past by water purveyors or the New Jersey Department of Environmental Protection. Reconstructed streamflows are estimates of those streamflows that would have occurred without the effects of changes in reservoir storage or surface-water diversions by water purveyors.
Reconstructed flows at 47 sites were determined from monthly observed streamflows, changes in reservoir storage, and surface-water diversions. Monthly reconstructed streamflows were calculated directly for those months with sufficient data. Missing monthly reconstructed flows were estimated from relations between selected calculated values of monthly reconstructed flows and monthly observed flows at selected index gages. Daily reconstructed flows were determined from the disaggregation of monthly reconstructed flows on the basis of daily observed flows at selected streamgages. At six sites, reconstructed flows were determined from relations between discrete measurements of observed streamflows and daily observed streamflows at selected index gages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185068","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Hickman, R.E., and McHugh, A.R., 2018, Methods used to reconstruct historical daily streamflows in northern New Jersey and southeastern New York, water years 1922–2010: U.S. Geological Survey Scientific Investigations Report 2018–5068, 75 p., https://doi.org/10.3133/sir20185068.","productDescription":"viii, 75 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070147","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":437761,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J965CZ","text":"USGS data release","linkHelpText":"Data and equations used to reconstruct historical daily streamflows in northern New Jersey and southeastern New York, water years 1922-2010"},{"id":357153,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5068/coverthb2.jpg"},{"id":357154,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5068/sir20185068.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5068"},{"id":357155,"rank":3,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/F7J965CZ","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and equations used to reconstruct historical daily streamflows in northern New Jersey and southeastern New York, water years 1922–2010"}],"country":"United States","state":"New Jersey, New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.049,\n 40.402\n ],\n [\n -73.906,\n 40.402\n ],\n [\n -73.906,\n 41.396\n ],\n [\n -75.049,\n 41.396\n ],\n [\n -75.049,\n 40.402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
USGS scientists provide scientific information and options that land and resource managers and private industries can use to make decisions regarding the development of energy resources while protecting the health of ecosystems. Studies focus on delivering information to avoid, minimize, or mitigate the impacts of energy infrastructure on fish and wildlife. USGS scientists are currently developing mapping tools and models that identify areas of biological strengths and weaknesses or high- and low-quality habitat and can identify opportunities for conservation—areas of high-quality habitat where energy-generating potential is low—and areas of potential risk—areas of high-quality habitat where energy-generating potential is high. These tools can assist resource managers and the industry concerning siting of energy development and selection of off-site mitigation areas. Scientific efforts, such as these, further the understanding of impacts related to energy development and create workable solutions. The three goals guiding USGS activities related to the interactions between wildlife and energy development are to understand risks by identifying when, where, and how fish and wildlife share space with energy facilities, measure direct and indirect impacts to species, and inform feasible and cost-effective solutions to minimize impacts through technological fixes, management, and mitigation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1447","isbn":"978-1-4113-4257-6","collaboration":" ","usgsCitation":"Khalil, Mona, ed., 2018, U.S. Geological Survey energy and wildlife research annual report for 2018 (ver. 1.1, October 2018): U.S. Geological Survey Circular 1447, 102 p., https://doi.org/10.3133/cir1447.","productDescription":"v, 101 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-099243","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":357117,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1447/cir1447.pdf","text":"Report","size":"20.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIRC 1447"},{"id":357956,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1447/versionHist.txt","size":"1.32 KB","linkFileType":{"id":2,"text":"txt"}},{"id":357116,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1447/coverthb4.jpg"}],"edition":"Version 1.1: October 2018; Version 1.0: September 2018","contact":"Energy and Wildlife Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
With growing concern for the depletion of soil resources, conventional soil maps need to be updated and provided at finer and finer resolutions to be able to support spatially explicit human–landscape models. Three US soil point datasets—the National Cooperative Soil Survey Characterization Database, the National Soil Information System, and the Rapid Carbon Assessment dataset—were combined with a stack of over 200 environmental datasets and gSSURGO polygon maps to generate complete coverage gridded predictions at 100-m spatial resolution of six soil properties (percentage of organic C, total N, bulk density, pH, and percentage of sand and clay) and two US soil taxonomic classes (291 great groups [GGs] and 78 modified particle size classes [mPSCs]) for the conterminous United States. Models were built using parallelized random forest and gradient boosting algorithms as implemented in the ranger and xgboost packages for R. Soil property predictions were generated at seven standard soil depths (0, 5, 15, 30, 60, 100, and 200 cm). Prediction probability maps for US soil taxonomic classifications were also generated. Cross validation results indicated an out-of-bag classification accuracy of 60% for GGs and 66% for mPSCs; for soil properties, RMSE for leave-location-out cross-validation was 0.74 (R2 = 0.68), 17.8 wt% (R2 = 0.57), 12 wt% (R2 = 0.46), 3.63 wt% (R2 = 0.41), 0.2 g cm−3 (R2 = 0.42), and 0.27 wt% (R2 = 0.39) for pH, percent sand and clay, weight percentage of organic C, bulk density, and weight percentage of total N, respectively. Nine independent validation datasets were used to assess prediction accuracies for soil class models, and results ranged between 24 and 58% and between 24 and 93% for GG and mPSC prediction accuracies, respectively. Although mapping accuracies were variable and likely lower than gSSURGO in some areas, this modeling approach can enable easier integration of soil information with spatially explicit models compared with multicomponent map units.
","language":"English","publisher":"Soil Science Society of America","doi":"10.2136/sssaj2017.04.0122","usgsCitation":"Ramcharan, A., Hengl, T., Nauman, T.W., Brungard, C.W., Waltman, S., Wills, S.A., and Thompson, J., 2018, Soil property and class maps of the conterminous United States at 100-meter spatial resolution: Soil Science Society of America Journal, v. 82, no. 1, p. 186-201, https://doi.org/10.2136/sssaj2017.04.0122.","productDescription":"16 p.","startPage":"186","endPage":"201","ipdsId":"IP-086724","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468422,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2136/sssaj2017.04.0122","text":"Publisher Index Page"},{"id":357147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Such droughts have been linked with massive tree mortality, and data suggest that warming interacts with drought to aggravate plant performance. Yet how forests will respond to hotter droughts remains unclear, as does the suite of mechanisms trees use to deal with hot droughts. We used an ecosystem‐scale manipulation of precipitation and temperature on piñon pine (Pinus edulis) and juniper (Juniperus monosperma) trees to investigate nitrogen (N) cycling‐induced mitigation processes related to hotter droughts. We found that while negative impacts on plant carbon and water balance are manifest after prolonged drought, performance reductions were not amplified by warmer temperatures. Rather, increased temperatures for 5 years stimulated soil N cycling under piñon trees and modified tree N allocation for both species, resulting in mitigation of hotter drought impacts on tree water and carbon functions. These findings suggest that adjustments in N cycling are likely after multi‐year warming conditions and that such changes may buffer reductions in tree performance during hotter droughts. The results highlight our incomplete understanding of trees' ability to acclimate to climate change, raising fundamental questions about the resistance potential of forests to long‐term, compound climatic stresses.
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In addition to the traditional sources of errors that affect more permeable formations (e.g. sample selection, non-representative specimens, disturbance introduced during sample acquisition and preparation), tight rocks can be particularly prone to solid–fluid interactions and thus more sensitive to the methods, procedures and techniques used to measure permeability. To address this problem, it is desirable to collect, for a single material, measurements obtained by different methods and pore-fluids. For that purpose a collaborative benchmarking exercise involving 24 laboratories was organized for measuring the permeability of a single low permeability material, the Grimsel granodiorite, at a common effective confining pressure (5 MPa). The objectives of the benchmark were: (i) to compare the results for a given method, (ii) to compare the results between different methods, (iii) to analyze the accuracy of each method, (iv) to study the influence of experimental conditions (especially the nature of pore fluid), (v) to discuss the relevance of indirect methods and models and finally (vi) to suggest good practice for low permeability measurements. In total 39 measurements were collected that allowed us to discuss the influence of (i) pore-fluid, (ii) measurement method, (iii) sample size and (iv) pressure sensitivity. Discarding some outliers from the bulk data set (4 out of 39) an average permeability of 1.11 × 10−18 m² with a standard deviation of 0.57 × 10−18 m² was obtained. The most striking result was the large difference in permeability for gas measurements compared to liquid measurements. Regardless of the method used, gas permeability was higher than liquid permeability by a factor approximately 2 (kgas = 1.28 × 10−18 m² compared to kliquid = 0.65 × 10−18 m²). Possible explanations are that (i) liquid permeability was underestimated due to fluid-rock interactions (ii) gas permeability was overestimated due to insufficient correction for gas slippage and/or (iii) gases and liquids do not probe exactly the same porous networks. The analysis of Knudsen numbers shows that the gas permeability measurements were performed in conditions for which the Klinkenberg correction is sufficient. Smaller samples had a larger scatter of permeability values, suggesting that their volume were below the Representative Elementary Volume. The pressure dependence of permeability was studied by some of the participating teams in the range 1–30 MPa and could be fitted to an exponential law k = ko.exp(–γPeff) with γ = 0.093 MPa−1. Good practice rules for measuring permeability in tight materials are also provided.
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conditions","interactions":[],"lastModifiedDate":"2022-04-22T16:59:01.163613","indexId":"70199260","displayToPublicDate":"2018-09-07T16:03:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions","docAbstract":"Long-term, interdisciplinary studies of relations between climate and ecological conditions on wetland-upland landscapes have been lacking, especially studies integrated across scales meaningful for adaptive resource management. We collected data in situ at individual wetlands, and via satellite for surrounding 4-km2 landscape blocks, to assess relations between annual weather dynamics, snow duration, phenology, wetland surface-water availability, amphibian presence and calling activity, greenness, and evapotranspiration in four U.S. conservation areas from 2008 to 2012. Amid recent decades of relatively warm growing seasons, 2012 and 2010 were the first and second warmest seasons, respectively, dating back to 1895. Accordingly, we observed the earliest starts of springtime biological activity during those two years. In all years, early-season amphibians first called soon after daily mean air temperatures were ≥ 0°C and snow had mostly melted. Similarly, satellite-based indicators suggested seasonal leaf-out happened soon after snowmelt and temperature thresholds for plant growth had occurred. Daily fluctuations in weather and water levels were related to amphibian calling activity, including decoupling the timing of the onset of calling at the start of season from the onset of calling events later in the season. Within-season variation in temperature and precipitation also was related to vegetation greenness and evapotranspiration, but more at monthly and seasonal scales. Wetland water levels were moderately to strongly associated with precipitation and early or intermittent wetland drying likely reduced amphibian reproduction success in some years, even though Pseudacris crucifer occupied sites at consistently high levels. Notably, satellite-based indicators of landscape water availability did not suggest such consequential, intra-seasonal variability in wetland surface-water availability. Our cross-disciplinary data show how temperature and precipitation interacted to affect key ecological relations and outcomes on our study landscapes. These results demonstrate the value of multi-year studies and the importance of scale for understanding actual climate-related effects in these areas.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0201951","usgsCitation":"Sadinski, W., Gallant, A.L., Roth, M., Brown, J.F., Senay, G., Brininger, W.L., Jones, P.M., and Stoker, J.M., 2018, Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions: PLoS ONE, v. 13, no. 9, e0201951, 50 p., https://doi.org/10.1371/journal.pone.0201951.","productDescription":"e0201951, 50 p.","ipdsId":"IP-089216","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468426,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0201951","text":"Publisher Index Page"},{"id":437763,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CR5SBH","text":"USGS data release","linkHelpText":"Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate's effects on wetland surface water, amphibians, and landscape conditions: Data"},{"id":357291,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"North Temperate Lake LTER area, St. Croix National Scenic Riverway, Tamarac National Wildlife Refuge, Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.68817138671874,\n 46.897739085507\n ],\n [\n -95.54534912109375,\n 46.897739085507\n ],\n [\n -95.54534912109375,\n 47.07386310181414\n ],\n [\n -95.68817138671874,\n 47.07386310181414\n ],\n [\n 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senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":744881,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brininger, Wayne L.","contributorId":191467,"corporation":false,"usgs":false,"family":"Brininger","given":"Wayne","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":744882,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Perry M. 0000-0002-6569-5144 pmjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6569-5144","contributorId":2231,"corporation":false,"usgs":true,"family":"Jones","given":"Perry","email":"pmjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":744908,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":744883,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70201103,"text":"70201103 - 2018 - Impacts of tidal road-stream crossings on aquatic organism passage","interactions":[],"lastModifiedDate":"2018-11-29T15:11:27","indexId":"70201103","displayToPublicDate":"2018-09-07T15:11:21","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"131-2018","title":"Impacts of tidal road-stream crossings on aquatic organism passage","docAbstract":"ivers and streams are highly vulnerable to fragmentation from roads due to their prevalence in the landscape. Road-stream crossings are far more numerous than other anthropogenic barriers such as dams; these crossing structures (culverts, bridges, fords, and tide gates) have been demonstrated to impede the passage of aquatic organisms. However, road-stream crossings vary widely in the extent to which they serve as a barrier. It is important to identify barrier severity to facilitate prioritization of restoration activities, since proactively addressing all structures is not feasible. In 2015 the North Atlantic Landscape Conservation Collaborative (LCC) funded a project managed by the North Atlantic Aquatic Connectivity Cooperative (NAACC) to develop a unified protocol for assessing aquatic road-stream crossings focusing on aquatic connectivity. The NAACC relied on rapid field-based assessments, which have been shown to be a useful tool for gathering information necessary for prioritization. However, the rapid assessment protocol developed from the NAACC initiative is not applicable to tidal crossings as it does not address two-directional flow, daily water depth fluctuations, or many of the species likely present in coastal habitats.
The goal of this report is to provide the background necessary to create guidelines and rapid assessment tools for assessing risk posed to aquatic organism passage at tidal crossings. To accomplish these goals, this report identifies species present in tidally influenced coastal wetlands, the unique traits they may display that puts them at risk for detrimental impact from impeded passage, and passage threats unique to tidal crossings that are not addressed by protocols designed for non-tidal systems. Species lists were compiled through literature reviews and discussions with regional researchers and managers familiar with coastal ecosystems or fish passage concerns. Life history traits, environmental sensitivities, and movement patterns for each species were compiled to build a database that can be queried to identify species that are highly vulnerable to impeded passage at tidal crossings (Available at: https://umass.box.com/s/w5mhokxjxshyxmr7si2v0gzcypcitu9d). These risk factors for species, combined with passage threats associated with specific crossing characteristics are discussed in this report. The species list is thorough enough to provide a baseline summary of the types of threats experienced by aquatic organisms at tidal road-stream crossings, but it is not exhaustive. Unique ecosystems, species assemblages, management goals, and prioritization models may require different approaches and solutions. Thus, care must be taken to ensure that assessment tools are appropriate to a project’s target species, habitats, and scale.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Becker, S., Jackson, S., Jordaan, A., and Roy, A.H., 2018, Impacts of tidal road-stream crossings on aquatic organism passage: Cooperator Science Series 131-2018, ii, 57 p.","productDescription":"ii, 57 p.","ipdsId":"IP-090405","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":359809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":359808,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalmedia.fws.gov/digital/collection/document/id/2238/"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c0108d5e4b0815414cc2dfd","contributors":{"authors":[{"text":"Becker, Sarah","contributorId":210890,"corporation":false,"usgs":false,"family":"Becker","given":"Sarah","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":752685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, Scott","contributorId":210891,"corporation":false,"usgs":false,"family":"Jackson","given":"Scott","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":752686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jordaan, Adrian","contributorId":210892,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":752687,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":752684,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201135,"text":"70201135 - 2018 - Patterns of host-associated fecal indicators driven by hydrology, precipitation, and land use attributes in Great Lakes watersheds","interactions":[],"lastModifiedDate":"2018-12-03T10:40:21","indexId":"70201135","displayToPublicDate":"2018-09-07T10:40:14","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Patterns of host-associated fecal indicators driven by hydrology, precipitation, and land use attributes in Great Lakes watersheds","docAbstract":"Fecal contamination from sewage and agricultural runoff is a pervasive problem in Great Lakes watersheds. Most work examining fecal pollution loads relies on discrete samples of fecal indicators and modeling land use. In this study, we made empirical measurements of human and ruminant-associated fecal indicator bacteria and combined these with hydrological measurements in eight watersheds ranging from predominantly forested to highly urbanized. Flow composited river samples were collected over low-flow (n = 89) and rainfall or snowmelt runoff events (n = 130). Approximately 90% of samples had evidence of human fecal pollution, with highest loads from urban watersheds. Ruminant indicators were found in ∼60–100% of runoff-event samples in agricultural watersheds, with concentrations and loads related to cattle density. Rain depth, season, agricultural tile drainage, and human or cattle density explained variability in daily flux of human or ruminant indicators. Mapping host-associated indicator loads to watershed discharge points sheds light on the type, level, and possible health risk from fecal pollution entering the Great Lakes and can inform total maximum daily load implementation and other management practices to target specific fecal pollution sources.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.8b01945","usgsCitation":"Dila, D.K., Corsi, S., Lenaker, P.L., Baldwin, A.K., Bootsma, M.J., and McLellan, S.L., 2018, Patterns of host-associated fecal indicators driven by hydrology, precipitation, and land use attributes in Great Lakes watersheds: Environmental Science & Technology, v. 52, no. 20, p. 11500-11509, https://doi.org/10.1021/acs.est.8b01945.","productDescription":"10 p.","startPage":"11500","endPage":"11509","ipdsId":"IP-084279","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":468430,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6437017","text":"External Repository"},{"id":437765,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VX0DRH","text":"USGS data release","linkHelpText":"Regression models and associated data for describing variability of host specific bacteria fluxes in eight Great Lakes tributaries, 2011-2013"},{"id":359866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"20","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-27","publicationStatus":"PW","scienceBaseUri":"5c064ee3e4b0815414cecb0e","contributors":{"authors":[{"text":"Dila, Deborah K.","contributorId":210966,"corporation":false,"usgs":false,"family":"Dila","given":"Deborah","email":"","middleInitial":"K.","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":752863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lenaker, Peter L. 0000-0002-9469-6285 plenaker@usgs.gov","orcid":"https://orcid.org/0000-0002-9469-6285","contributorId":5572,"corporation":false,"usgs":true,"family":"Lenaker","given":"Peter","email":"plenaker@usgs.gov","middleInitial":"L.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752864,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bootsma, Melinda J.","contributorId":210967,"corporation":false,"usgs":false,"family":"Bootsma","given":"Melinda","email":"","middleInitial":"J.","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":752865,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McLellan, Sandra L. 0000-0003-3283-1151","orcid":"https://orcid.org/0000-0003-3283-1151","contributorId":210968,"corporation":false,"usgs":false,"family":"McLellan","given":"Sandra","email":"","middleInitial":"L.","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":752866,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201057,"text":"70201057 - 2018 - Drought and land-cover conditions in the Great Plains","interactions":[],"lastModifiedDate":"2018-11-27T10:14:50","indexId":"70201057","displayToPublicDate":"2018-09-07T10:14:45","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Drought and land-cover conditions in the Great Plains","docAbstract":"Land–atmosphere interactions play a critical role in the Earth system, and a better understanding of these interactions could improve weather and climate models. The interaction among drought, vegetation productivity, and land cover is of particular significance. In a semiarid environment, such as the U.S. Great Plains, droughts can have a large influence on the productivity of agriculture and grasslands, with serious environmental and economic impacts. Here, we used the vegetation drought response index (VegDRI) drought indicator to investigate the response of vegetation to weather and climate for land-cover types in the Great Plains in the United States from 1989 to 2012. We found that analysis that focused on land-cover types within ecoregion divisions provided substantially more and land-cover-based detail on the timing and intensity of drought than did summarizing across the entire Great Plains region. In the northern Great Plains, VegDRI measured more frequent drought impacts on vegetation in the western ecoregions than in the eastern ecoregions. Across the ecoregions of the Great Plains, drought impacts on vegetation were more commonly found in grassland than in cropland. For example, in the “Northwestern Great Plains” ecoregion (which encompasses areas of Montana, Wyoming, North Dakota, South Dakota, and Nebraska), grassland and nonirrigated cropland were observed in VegDRI to have historical fractional drought coverages in the growing season of 17% and 11%, respectively.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/EI-D-17-0025.1","usgsCitation":"Tollerud, H.J., Brown, J.F., Loveland, T., Mahmood, R., and Bliss, N.B., 2018, Drought and land-cover conditions in the Great Plains: Earth Interactions, v. 22, p. 1-25, https://doi.org/10.1175/EI-D-17-0025.1.","productDescription":"Paper no. 17; 25 p.","startPage":"1","endPage":"25","ipdsId":"IP-096098","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468431,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/ei-d-17-0025.1","text":"Publisher Index Page"},{"id":359700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great 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\"}}]}","volume":"22","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-17","publicationStatus":"PW","scienceBaseUri":"5bfe65e2e4b0815414ca60f8","contributors":{"authors":[{"text":"Tollerud, Heather J. 0000-0001-9507-4456","orcid":"https://orcid.org/0000-0001-9507-4456","contributorId":210820,"corporation":false,"usgs":true,"family":"Tollerud","given":"Heather","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":752112,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":752113,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loveland, Thomas 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":202518,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":752114,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahmood, Rezaul","contributorId":210821,"corporation":false,"usgs":false,"family":"Mahmood","given":"Rezaul","email":"","affiliations":[{"id":38153,"text":"Department of Geography and Geology and Kentucky Climate Center","active":true,"usgs":false}],"preferred":false,"id":752115,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bliss, Norman B. 0000-0003-2409-5211 bliss@usgs.gov","orcid":"https://orcid.org/0000-0003-2409-5211","contributorId":1921,"corporation":false,"usgs":true,"family":"Bliss","given":"Norman","email":"bliss@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":752116,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241566,"text":"70241566 - 2018 - Spatial sampling bias in the Neotoma paleoecological archives affects species paleo-distribution models","interactions":[],"lastModifiedDate":"2023-03-23T14:10:11.687985","indexId":"70241566","displayToPublicDate":"2018-09-07T09:08:31","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13627,"text":"Journal of Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Spatial sampling bias in the Neotoma paleoecological archives affects species paleo-distribution models","title":"Spatial sampling bias in the Neotoma paleoecological archives affects species paleo-distribution models","docAbstract":"The ability to infer paleo-distributions with limited knowledge of absence makes species distribution modeling (SDM) a useful tool for exploring paleobiogeographic questions. Spatial sampling bias is a known issue when modeling extant species. Here we quantify the spatial sampling bias in a North American packrat midden archive and explore its impact on estimating paleo-distributions. We test whether (1) spatial sampling bias inherent in this macrofossil record can influence estimates of paleo-distributions, (2) this bias can alter the ability to measure shifts in distributions and climatic niche breadth from the Northgrippian subdivision of the Holocene (8.3 ka – 4.2 ka) to present day (1950–2000 yr), and (3) bias correction methods can improve estimates of paleo-distributions and analyses of range shifts and niche breadth. We estimate spatial sampling bias for the mid-Holocene period with a three-stage statistical model, each representing a hypothesized source of bias: fossil site availability, preservation and accessibility. This approach enables the use of SDM to evaluate three separate paleo-distributions calibrated on the packrat midden archive: those without bias correction (σ-naïve), those created with a standard method (σ-standard), and those created with a novel alternative (σ-modeled) incorporating the three-stage model of bias. We find that paleo-distributions modeled for the mid-Holocene without bias correction (σ-naïve) provided poor estimates of hindcast paleo-distributions, and that the σ-modeled correction method improved paleo-distributions for our six species with, on average, 50% higher overlap to hindcast distributions than σ-naïve paleo-distributions (σ-standard results fell between σ-naïve and σ-modeled).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2018.08.015","usgsCitation":"Inman, R.D., Franklin, J., Esque, T., and Nussear, K.E., 2018, Spatial sampling bias in the Neotoma paleoecological archives affects species paleo-distribution models: Journal of Quaternary Science Reviews, v. 198, p. 115-125, https://doi.org/10.1016/j.quascirev.2018.08.015.","productDescription":"11 p.","startPage":"115","endPage":"125","ipdsId":"IP-100974","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468432,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2018.08.015","text":"Publisher Index Page"},{"id":437766,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9843JFT","text":"USGS data release","linkHelpText":"Spatial predictions of habitat suitability for present-day (1950 - 2000 yr) and mid-Holocene (8.3 ka - 4.2 ka) time intervals"},{"id":414613,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"198","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Inman, Richard D. 0000-0002-1982-7791 rdinman@usgs.gov","orcid":"https://orcid.org/0000-0002-1982-7791","contributorId":187754,"corporation":false,"usgs":true,"family":"Inman","given":"Richard","email":"rdinman@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867342,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Franklin, Janet","contributorId":192373,"corporation":false,"usgs":false,"family":"Franklin","given":"Janet","affiliations":[],"preferred":false,"id":867343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nussear, Kenneth E.","contributorId":117361,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":867345,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198107,"text":"sir20185087 - 2018 - Tidal flushing of mercury from the Bremerton Naval Complex through the PSNS015 stormwater drain system to Sinclair Inlet, Kitsap County, Washington, 2011 -12","interactions":[],"lastModifiedDate":"2018-09-07T16:38:05","indexId":"sir20185087","displayToPublicDate":"2018-09-07T08:27:21","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5087","title":"Tidal flushing of mercury from the Bremerton Naval Complex through the PSNS015 stormwater drain system to Sinclair Inlet, Kitsap County, Washington, 2011 -12","docAbstract":"The sediments of Sinclair Inlet, in Puget Sound, Washington, have elevated levels of contaminants including mercury. The Bremerton Naval Complex is adjacent to Sinclair Inlet, and has known areas of historical soil mercury contamination. The U.S. Geological Survey, in cooperation with the U.S. Navy, has been investigating the potential for mercury sources on the Bremerton Naval Complex to recontaminate recently remediated marine sediment. In 2011–12, the U.S. Geological Survey conducted three tidal- related sampling campaigns to characterize mercury dynamics in the largest stormwater drain system on the Bremerton Naval Complex, which passes through the soils of an area known as Site 2 that has elevated soil mercury concentrations. The sampling campaigns confirmed that the stormwater drain system, PSNS015, serves as a conduit for seawater transport more than 250 m landward of the contaminated soils that subsequently facilitates mercury transport to Sinclair Inlet.
During the December 2011 reconnaissance sampling campaign, no freshwater source of mercury to PSNS015 was identified. There was heavy precipitation preceding and stormwater runoff generated during the reconnaissance survey, which suggests that the primary source of mercury in PSNS015 is not precipitation-induced. During the May 2012 spring-tide sampling campaign, the water in PSNS015 drained to Sinclair Inlet during a negative low tide, and the highest filtered total mercury concentration in the stormwater drain system (60 ng/L) was measured during the lower-low tide in the freshwater flowing into the seaward-most stormwater drain vault from either up-pipe or local groundwater intrusion. Similar conditions were not observed during the June 2012 companion neap-tide sampling campaign, when the water-level elevation of the positive low tide in Sinclair Inlet dropped only slightly below the stormwater drain vault elevation, the water in the seaward-most stormwater vault was brackish rather than fresh, and the filtered total mercury concentration never exceeded 24 ng/L. Particulate total mercury concentrations and dynamics during the spring- and neap-tide sampling campaigns were variable, with higher concentrations (as much as 133 ng/L) measured throughout the neap-tide study compared to those measured during the spring-tide study (as much as 4.34 ng/L). The highest filtered total mercury concentration of all sampling campaigns (1,140 ng/L) was measured during ebb tide in a nearshore monitoring well that represents groundwater discharging from the contaminated soils directly to Sinclair Inlet along an unwalled part of the shoreline.
The results suggest that mercury extracted from Site 2 soils can be carried to Sinclair Inlet during ebb tides by at least two mechanisms: (1) through groundwater directly to Sinclair Inlet along an unwalled part of the shoreline or (2) through the stormwater drain system when the water level in Sinclair Inlet drops below the water level in the stormwater drain system. The data can be used to guide future modifications to the seawall and stormwater drain system that aim to hydraulically disconnect the stormwater drain system from the surrounding contaminated soils.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185087","collaboration":"Prepared in cooperation with U.S. Department of the Navy","usgsCitation":"Conn, K.E., Paulson, A.J., Dinicola, R.S., and DeWild, J.F., 2018, Tidal flushing of mercury from the Bremerton Naval Complex through the PSNS015 stormwater drain system to Sinclair Inlet, Kitsap County, Washington, 2011 -12: U.S. Geological Survey Scientific Investigations Report 2018-5087, 23 p., https://doi.org/10.3133/sir20185087.","productDescription":"vi, 23 p.","onlineOnly":"Y","ipdsId":"IP-097597","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":357094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5087/coverthb.jpg"},{"id":357095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5087/sir20185087.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5087"}],"country":"United States","state":"Washington","county":"Kitsap County","otherGeospatial":"Sinclair Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.86285400390624,\n 47.43737696208075\n ],\n [\n -122.178955078125,\n 47.43737696208075\n ],\n [\n -122.178955078125,\n 48.21003212234042\n ],\n [\n -122.86285400390624,\n 48.21003212234042\n ],\n [\n -122.86285400390624,\n 47.43737696208075\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Studying habitat overlap between sympatric species is one of the best ways to identify interspecies relationships and to direct conservation efforts so that multiple species can benefit. However, studies exploring interspecies relationships are very limited in Nepal, making it difficult for the government of Nepal and conservation partners to manage wildlife in their habitats, especially in Himalayan protected areas. In this study, we identified habitat overlap between Asiatic black bear (Ursus thibetanus) and red panda (Ailurus fulgens) as well as important habitat types for both species in the Makalu Barun National Park, Nepal using Maximum Entropy (MaxEnt) modeling. GPS points of species occurrence were collected from the field, and environmental variables were extracted from freely available sources. We found that the study area contained 647 km2 of Asiatic black bear habitat and 443 km2 of the red panda habitat. 368 km2 supported both species, which constituted 57% of the Asiatic black bear habitat and 83% of the red panda habitat. We found that conifer forest was the most important habitat type for both species. Because the largest portions of both species’ habitat were located inside the buffer zone, a peripheral zone of national park, conservation efforts for these sympatric species should be focused inside the buffer zone to be most effective.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0203697","usgsCitation":"Bista, M., Panthi, S., and Weiskopf, S.R., 2018, Habitat overlap between Asiatic black bear Ursus thibetanus and red panda Ailurus fulgens in Himalaya: PLoS ONE, v. 13, no. 9, e0203697; 12 p., https://doi.org/10.1371/journal.pone.0203697.","productDescription":"e0203697; 12 p.","ipdsId":"IP-098076","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":468433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0203697","text":"Publisher Index Page"},{"id":357120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 86.66015624999999,\n 27.408347455871425\n ],\n [\n 87.42095947265625,\n 27.408347455871425\n ],\n [\n 87.42095947265625,\n 27.982274659104082\n ],\n [\n 86.66015624999999,\n 27.982274659104082\n ],\n [\n 86.66015624999999,\n 27.408347455871425\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-06","publicationStatus":"PW","scienceBaseUri":"5b98a267e4b0702d0e842e6e","contributors":{"authors":[{"text":"Bista, Manjit","contributorId":207700,"corporation":false,"usgs":false,"family":"Bista","given":"Manjit","email":"","affiliations":[{"id":37609,"text":"Ministry of Forests and Environment, Department of National Parks and Wildlife Conservation, Babarmahal, Kathmandu, Nepal","active":true,"usgs":false}],"preferred":false,"id":744460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Panthi, Saroj","contributorId":207701,"corporation":false,"usgs":false,"family":"Panthi","given":"Saroj","email":"","affiliations":[{"id":37610,"text":"Ministry of Forests and Environment, Department of Forests, Babarmahal, Kathmandu, Nepal","active":true,"usgs":false}],"preferred":false,"id":744461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weiskopf, Sarah R. 0000-0002-5933-8191","orcid":"https://orcid.org/0000-0002-5933-8191","contributorId":207699,"corporation":false,"usgs":true,"family":"Weiskopf","given":"Sarah","email":"","middleInitial":"R.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":744459,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199147,"text":"70199147 - 2018 - Experimental infection of Tadarida brasiliensis with Pseudogymnoascus destructans, the fungus that causes white-nose syndrome","interactions":[],"lastModifiedDate":"2023-06-29T16:53:02.854016","indexId":"70199147","displayToPublicDate":"2018-09-06T13:29:13","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5160,"text":"mSphere","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Experimental infection of Tadarida brasiliensis with Pseudogymnoascus destructans, the fungus that causes white-nose syndrome","title":"Experimental infection of Tadarida brasiliensis with Pseudogymnoascus destructans, the fungus that causes white-nose syndrome","docAbstract":"White-nose syndrome (WNS) is causing significant declines in populations of North American hibernating bats, and recent western and southern expansions of the disease have placed additional species at risk. Understanding differences in species susceptibility and identifying management actions to reduce mortality of bats from WNS are top research priorities. However, the use of wild-caught susceptible bats, such as Myotis lucifugus, as model species for WNS research is problematic and places additional pressure on remnant populations. We investigated the feasibility of using Tadarida brasiliensis, a highly abundant species of bat that tolerates captivity, as the basis for an experimental animal model for WNS. Using methods previously established to confirm the etiology of WNS in M. lucifugus, we experimentally infected 11 T. brasiliensis bats with Pseudogymnoascus destructans in the laboratory under conditions that induced hibernation. We detected P. destructans on all 11 experimentally infected bats, 7 of which exhibited localized proliferation of hyphae within the epidermis, dermis, and subcutaneous tissue, similar to invasive cutaneous ascomycosis observed in M. lucifugusbats with WNS. However, the distribution of lesions across wing membranes of T. brasiliensisbats was limited, and only one discrete “cupping erosion,” diagnostic for WNS, was identified. Thus, the rarity of lesions definitive for WNS suggests that T. brasiliensis does not likely represent an appropriate model for studying the pathophysiology of this disease. Nonetheless, the results of this study prompt questions concerning the potential for free-ranging, migratory T. brasiliensis bats to become infected with P. destructans and move the fungal pathogen between roost sites used by species susceptible to WNS.
","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/mSphere.00250-18","usgsCitation":"Verant, M., Meteyer, C.U., Stading, B., and Blehert, D.S., 2018, Experimental infection of Tadarida brasiliensis with Pseudogymnoascus destructans, the fungus that causes white-nose syndrome: mSphere, v. 3, no. 4, e00250-18; 10 p.; Data Release, https://doi.org/10.1128/mSphere.00250-18.","productDescription":"e00250-18; 10 p.; Data Release","ipdsId":"IP-097559","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":468434,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1128/msphere.00250-18","text":"Publisher Index Page"},{"id":357102,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418633,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93WAKH3","linkFileType":{"id":5,"text":"html"}}],"volume":"3","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a267e4b0702d0e842e70","contributors":{"authors":[{"text":"Verant, Michelle 0000-0001-6994-6257","orcid":"https://orcid.org/0000-0001-6994-6257","contributorId":204269,"corporation":false,"usgs":false,"family":"Verant","given":"Michelle","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":744324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meteyer, Carol U. 0000-0002-4007-3410 cmeteyer@usgs.gov","orcid":"https://orcid.org/0000-0002-4007-3410","contributorId":127748,"corporation":false,"usgs":true,"family":"Meteyer","given":"Carol","email":"cmeteyer@usgs.gov","middleInitial":"U.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true}],"preferred":true,"id":744325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stading, Benjamin","contributorId":207602,"corporation":false,"usgs":false,"family":"Stading","given":"Benjamin","affiliations":[{"id":37569,"text":"University of Wisconsin-Madison, School of Veterinary Medicine, Department of Pathobiological Sciences, 2015 Linden Dr., Madison, Wisconsin 53706","active":true,"usgs":false}],"preferred":false,"id":744326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blehert, David S. 0000-0002-1065-9760 dblehert@usgs.gov","orcid":"https://orcid.org/0000-0002-1065-9760","contributorId":140397,"corporation":false,"usgs":true,"family":"Blehert","given":"David","email":"dblehert@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":744323,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217632,"text":"70217632 - 2018 - The 2015 landslide and tsunami in Taan Fiord, Alaska","interactions":[],"lastModifiedDate":"2023-11-09T17:21:13.228908","indexId":"70217632","displayToPublicDate":"2018-09-06T07:25:31","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"The 2015 landslide and tsunami in Taan Fiord, Alaska","docAbstract":"Glacial retreat in recent decades has exposed unstable slopes and allowed deep water to extend beneath some of those slopes. Slope failure at the terminus of Tyndall Glacier on 17 October 2015 sent 180 million tons of rock into Taan Fiord, Alaska. The resulting tsunami reached elevations as high as 193 m, one of the highest tsunami runups ever documented worldwide. Precursory deformation began decades before failure, and the event left a distinct sedimentary record, showing that geologic evidence can help understand past occurrences of similar events, and might provide forewarning. The event was detected within hours through automated seismological techniques, which also estimated the mass and direction of the slide - all of which were later confirmed by remote sensing. Our field observations provide a benchmark for modeling landslide and tsunami hazards. Inverse and forward modeling can provide the framework of a detailed understanding of the geologic and hazards implications of similar events. Our results call attention to an indirect effect of climate change that is increasing the frequency and magnitude of natural hazards near glaciated mountains.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s41598-018-30475-w","usgsCitation":"Higman, B., Shugar, D., Stark, C.P., Ekstrom, G., Koppes, M.N., Lynett, P., Dufresne, A., Haeussler, P., Geertsema, M., Gulick, S., Mattox, A., Venditti, J., Walton, M.A., McCall, N., Mckittrick, E., MacInnes, B., Bilderback, E.L., Tang, H., Willis, M., Richmond, B., Reece, B., Larsen, C.F., Olson, B., Capra, J., Ayca, A., Bloom, C.K., Williams, H., Bonno, D., Weiss, R., Keen, A., Skanavis, V., and Loso, M., 2018, The 2015 landslide and tsunami in Taan Fiord, Alaska: Scientific Reports, v. 8, 12993, 12 p., https://doi.org/10.1038/s41598-018-30475-w.","productDescription":"12993, 12 p.","ipdsId":"IP-086124","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":468435,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-018-30475-w","text":"Publisher Index 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Wrangell-St. Elias National Park & Preserve","active":true,"usgs":false}],"preferred":false,"id":809026,"contributorType":{"id":1,"text":"Authors"},"rank":32}]}} ,{"id":70208991,"text":"70208991 - 2018 - A 42 year inference of cloud base height trends in the Luquillo Mountains of northeastern Puerto Rico","interactions":[],"lastModifiedDate":"2020-03-10T14:23:52","indexId":"70208991","displayToPublicDate":"2018-09-05T14:21:56","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1249,"text":"Climate Research","active":true,"publicationSubtype":{"id":10}},"title":"A 42 year inference of cloud base height trends in the Luquillo Mountains of northeastern Puerto Rico","docAbstract":"The Luquillo Mountains of eastern Puerto Rico are home to the only tropical rainforest\nmanaged by the United States Forest Service, with cloud-immersed forests historically occupying the highest elevations. However, within the past 50 yr, studies of the Luquillo cloud forest have suggested an increase in cloud base heights (CBH), although the CBH in the area was not quantified until recently. The present work uses radiosonde observations from nearby San Juan, Puerto Rico, to contextualize the present-day CBH within a 42 yr (1975−2016) proxy record and determine evidence for rising cloud base. Two key questions are addressed: (1) Can theoretical CBH calculations from San Juan provide a reasonable proxy for CBHs in the Luquillo Mountains? (2) Does a significant trend accompany the CBH lifting inferred from recent work in the region? The mean-layer lifted condensation level (MLLCL), a thermodynamic parameter expressing the altitude at which a rising air parcel reaches 100% relative humidity, serves as the proxy. The 42 yr MLLCL time series corroborates both the low CBHs claimed in the 1980s and the higher CBHs documented by recent work. When considering all available radiosonde data, statistically significant increasing CBH trends are detected for all seasons. However, when the record is standardized to correct for progressive vertical resolution improvements to radiosonde observations, recent CBH increases are more modest than initially indicated, and statistically significant increases are only apparent in the late rainfall season.","language":"English","publisher":"Inter-Research Science Center","doi":"10.3354/cr01529","usgsCitation":"Miller, P.W., Mote, T.L., Ramseyer, C., Van Beusekom, A.E., Scholl, M.A., and Gonzalez, G., 2018, A 42 year inference of cloud base height trends in the Luquillo Mountains of northeastern Puerto Rico: Climate Research, v. 76, no. 1, p. 87-94, https://doi.org/10.3354/cr01529.","productDescription":"8 p.","startPage":"87","endPage":"94","ipdsId":"IP-094568","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":373074,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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The approach allows dispersion computations for a large suite of discretizations, model dimensions (1-, 2-, or 3-dimensional), spatial and temporal discretization, and turbulence closures. As the model is based on a discrete non-interacting particle approach, parallelization is straightforward, such that simulations with large numbers of particles are tractable. Results from the approach are compared to dispersion measurements made with conventional Rhodamine WT dye experiment in which typical at-a-point sensors are employed to determine concentration. The model performs well, but spatial resolution for experiments over large and or complex river flows was inadequate for model testing. To address this issue, we explored the idea of measuring spatial concentrations in river flows using hyperspectral remote sensing. Experiments both for idealized channels and real rivers show that this technique is viable and can provide high levels of spatial detail in concentration measurements with quantitatively accurate concentrations.
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We found that the choice of model had an impact on the estimability of unknown model parameters and affected the bias and variance of corrected counts. Finite mixture models provided better fit than conventional removal models and better adjusted for count duration. However, reliably estimating parameters and minimizing variance using mixture models required at least 200–1,000 detections. Mixture models with time-varying proportions of infrequent singers were best supported across species, indicating that accounting for date- and time-related heterogeneity is important when combining data across studies over large spatial scales, multiple sampling time frames, or variable survey protocols. Our flexible and continuous time-removal modeling framework can be used to account for such heterogeneity through the incorporation of easily obtainable covariates, such as methods, date, time, and location. 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Morphologic characteristics of channels—e.g., width, depth, slope, and the relationships among them—can constrain interpretations of channel-forming processes. This work uses morphometric scaling relationships extracted from high-resolution seafloor bathymetry to infer connections between morphology and process in submarine channels. Analysis of 36 modern channels in five geographic regions shows that channel widths vary regionally (from <100 m to >10 km wide) but occupy the same range of aspect ratios (~10:1–100:1). This suggests an autogenic control on aspect ratio, perhaps resulting from feedback processes in levee growth and/or bank erosion, and allogenic (e.g., sediment supply, grain size) controls on channel width. Submarine channel aspect ratios tend to decrease with increasing dimensions, while the opposite relationship has been observed for fluvial channels, likely due to opposing relationships between flow discharge and channel distance. Additionally, observation of an apparent lag between channel thalweg and levee responses to gradient changes suggests that thalweg and levee deposition and erosion may be partially decoupled due to the vertical structure of turbidity currents, with thalweg evolution driven by the basal, higher-shear-stress portion of the flow and levee evolution by the dilute upper portion. The data presented here provide a basis for predicting channel metrics in exploration scenarios, in which data coverage may be sparse. This documentation of a diverse suite of channels also captures the range of scales and variability exhibited globally by submarine channel systems, providing context for local studies.
","language":"English","publisher":"Geologic Society of America","doi":"10.1130/GES01674.1","usgsCitation":"Shumaker, L.E., Jobe, Z.R., Johnstone, S., Pettinga, L.A., Cai, D., and Moody, J.D., 2018, Controls on submarine channel-modifying processes identified through morphometric scaling relationships: Geosphere, v. 14, no. 5, p. 2171-2187, https://doi.org/10.1130/GES01674.1.","productDescription":"17 p.","startPage":"2171","endPage":"2187","ipdsId":"IP-094567","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":468439,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01674.1","text":"Publisher Index Page"},{"id":357077,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-10","publicationStatus":"PW","scienceBaseUri":"5b98a268e4b0702d0e842e76","contributors":{"authors":[{"text":"Shumaker, Lauren E.","contributorId":207546,"corporation":false,"usgs":false,"family":"Shumaker","given":"Lauren","email":"","middleInitial":"E.","affiliations":[{"id":37560,"text":"Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado 80401, USA","active":true,"usgs":false}],"preferred":false,"id":744136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jobe, Zane R.","contributorId":207547,"corporation":false,"usgs":false,"family":"Jobe","given":"Zane","email":"","middleInitial":"R.","affiliations":[{"id":37560,"text":"Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado 80401, USA","active":true,"usgs":false}],"preferred":false,"id":744137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnstone, Samuel 0000-0002-3945-2499","orcid":"https://orcid.org/0000-0002-3945-2499","contributorId":207545,"corporation":false,"usgs":true,"family":"Johnstone","given":"Samuel","email":"","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":744135,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pettinga, Luke A.","contributorId":207548,"corporation":false,"usgs":false,"family":"Pettinga","given":"Luke","email":"","middleInitial":"A.","affiliations":[{"id":37560,"text":"Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado 80401, USA","active":true,"usgs":false}],"preferred":false,"id":744138,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cai, Dingxin","contributorId":207549,"corporation":false,"usgs":false,"family":"Cai","given":"Dingxin","email":"","affiliations":[{"id":37560,"text":"Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado 80401, USA","active":true,"usgs":false}],"preferred":false,"id":744139,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moody, Jeremiah D.","contributorId":207550,"corporation":false,"usgs":false,"family":"Moody","given":"Jeremiah","email":"","middleInitial":"D.","affiliations":[{"id":37561,"text":"Chevron Energy Technology Company, Houston, TX 77002, USA","active":true,"usgs":false}],"preferred":false,"id":744140,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199125,"text":"70199125 - 2018 - Leveraging big data towards functionally-based, catchment scale restoration prioritization","interactions":[],"lastModifiedDate":"2018-11-21T15:06:36","indexId":"70199125","displayToPublicDate":"2018-09-05T10:19:29","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Leveraging big data towards functionally-based, catchment scale restoration prioritization","docAbstract":"The persistence of freshwater degradation has necessitated the growth of an expansive stream and wetland restoration industry, yet restoration prioritization at broad spatial extents is still limited and ad-hoc restoration prevails. The River Basin Restoration Prioritization tool has been developed to incorporate vetted, distributed data models into a catchment scale restoration prioritization framework. Catchment baseline condition and potential improvement with restoration activity is calculated for all National Hydrography Dataset stream reaches and catchments in North Carolina and compared to other catchments within the river subbasin to assess where restoration efforts may best be focused. Hydrologic, water quality, and aquatic habitat quality conditions are assessed with peak flood flow, nitrogen and phosphorus loading, and aquatic species distribution models. The modular nature of the tool leaves ample opportunity for future incorporation of novel and improved datasets to better represent the holistic health of a watershed, and the nature of the datasets used herein allow this framework to be applied at much broader scales than North Carolina.
","language":"English","publisher":"Springer","doi":"10.1007/s00267-018-1100-z","usgsCitation":"Lovette, J.P., Duncan, J.M., Smart, L.S., Fay, J.P., Urban, D.L., Daly, N., Blackwell, J., Hoos, A.B., Garcia, A.M., and Band, L.E., 2018, Leveraging big data towards functionally-based, catchment scale restoration prioritization: Environmental Management, v. 62, no. 6, p. 1007-1024, https://doi.org/10.1007/s00267-018-1100-z.","productDescription":"18 p.","startPage":"1007","endPage":"1024","ipdsId":"IP-094881","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":357074,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-31","publicationStatus":"PW","scienceBaseUri":"5b98a268e4b0702d0e842e78","contributors":{"authors":[{"text":"Lovette, John P.","contributorId":207568,"corporation":false,"usgs":false,"family":"Lovette","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":37566,"text":"UNC","active":true,"usgs":false}],"preferred":false,"id":744230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duncan, Jonathan M.","contributorId":207569,"corporation":false,"usgs":false,"family":"Duncan","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":744231,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smart, Lindsey S.","contributorId":207570,"corporation":false,"usgs":false,"family":"Smart","given":"Lindsey","email":"","middleInitial":"S.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":744232,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fay, John P.","contributorId":207571,"corporation":false,"usgs":false,"family":"Fay","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":744233,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Urban, Dean L.","contributorId":207572,"corporation":false,"usgs":false,"family":"Urban","given":"Dean","email":"","middleInitial":"L.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":744234,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Daly, Nancy","contributorId":207573,"corporation":false,"usgs":false,"family":"Daly","given":"Nancy","email":"","affiliations":[{"id":37567,"text":"Wake County Department of Environmental Services","active":true,"usgs":false}],"preferred":false,"id":744235,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blackwell, Jamie","contributorId":207574,"corporation":false,"usgs":false,"family":"Blackwell","given":"Jamie","email":"","affiliations":[{"id":24615,"text":"North Carolina Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":744236,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoos, Anne B. 0000-0001-9845-7831","orcid":"https://orcid.org/0000-0001-9845-7831","contributorId":207575,"corporation":false,"usgs":true,"family":"Hoos","given":"Anne","email":"","middleInitial":"B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":744237,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Garcia, Ana M. 0000-0002-5388-1281 agarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-1281","contributorId":207567,"corporation":false,"usgs":true,"family":"Garcia","given":"Ana","email":"agarcia@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - 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However, the extent of spatial overlap between high-use foraging areas (where multiple marine species congregate) and anthropogenic threats is not well known. Our modeling approach incorporates data on sea turtle spatial ecology and a suite of threats in the Gulf of Mexico to identify and map “hot spots” of threats to two imperiled turtle species. Of all 820 “high” threats grid cells, our tracked turtles foraged at least one day in 77% of them. Although threat data were not available outside the U.S. Exclusive Economic Zone, our map of turtle and threat “hotspots” can be incorporated in future more comprehensive threat analyses for the region. Knowledge of these shared foraging- and threat-areas can assist managers charged with designing effective conservation and population recovery strategies, in future habitat modeling efforts, and in designations of Gulf of Mexico habitat with high conservation value.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2018.00336","usgsCitation":"Hart, K.M., Iverson, A., Fujisaki, I., Lamont, M.M., Bucklin, D.N., and Shaver, D.J., 2018, Marine threats overlap key foraging habitat for two imperiled sea turtle species in the Gulf of Mexico: Frontiers in Marine Science, v. 5, 336, 9 p., https://doi.org/10.3389/fmars.2018.00336.","productDescription":"336, 9 p.","ipdsId":"IP-091381","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468440,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2018.00336","text":"Publisher Index Page"},{"id":357068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.404296875,\n 18.06231230454674\n ],\n [\n -81.123046875,\n 18.06231230454674\n ],\n [\n -81.123046875,\n 31.27855085894653\n ],\n [\n -99.404296875,\n 31.27855085894653\n ],\n [\n -99.404296875,\n 18.06231230454674\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-24","publicationStatus":"PW","scienceBaseUri":"5b98a268e4b0702d0e842e7c","contributors":{"authors":[{"text":"Hart, Kristen M. 0000-0002-5257-7974 kristen_hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":1966,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","email":"kristen_hart@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":744196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iverson, Autumn R. 0000-0002-8353-6745","orcid":"https://orcid.org/0000-0002-8353-6745","contributorId":173555,"corporation":false,"usgs":false,"family":"Iverson","given":"Autumn R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":744197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fujisaki, Ikuko","contributorId":38359,"corporation":false,"usgs":false,"family":"Fujisaki","given":"Ikuko","affiliations":[],"preferred":false,"id":744198,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lamont, Margaret M. 0000-0001-7520-6669 mlamont@usgs.gov","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":4525,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","email":"mlamont@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":744199,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bucklin, David N.","contributorId":175273,"corporation":false,"usgs":false,"family":"Bucklin","given":"David","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":744200,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shaver, Donna J.","contributorId":191186,"corporation":false,"usgs":false,"family":"Shaver","given":"Donna","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":744201,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}