The Scaling Climate Change Adaptation in the Northern Great Plains through Regional Climate Summaries and Local Qualitative-Quantitative Scenario Planning Workshops project synthesizes climate data into 3-5 distinct but plausible climate summaries for the northern Great Plains region; crafts quantitative summaries of these climate futures for two focal areas; and applies these local summaries by developing climate-resource-management scenarios through participatory workshops and, where possible, simulation models. The two focal areas are central North Dakota and southwest South Dakota (Figure 1). The primary objective of this project is to help resource managers and scientists in a focal area use scenario planning to make management and planning decisions based on assessments of critical future uncertainties.
This report summarizes project work for public and tribal lands in the southwest South Dakota grasslands focal area, with an emphasis on Badlands National Park and Buffalo Gap National Grassland. The report explains scenario planning as an adaptation tool in general, then describes how it was applied to the focal area in three phases. Priority resource management and climate uncertainties were identified in the orientation phase. Local climate summaries for relevant, divergent, and challenging climate scenarios were developed in the second phase. In the final phase, a two-day scenario planning workshop held January 20-21, 2016 in Rapid City, South Dakota, featured scenario development and implications, testing management decisions, and methods for operationalizing scenario planning outcomes.
","language":"English","publisher":"National Park Service","publisherLocation":"Fort Collins, Colorado","usgsCitation":"Fisichelli, N.A., Schuurman, G.W., Symstad, A.J., Ray, A., Miller, B., Cross, M., and Rowland, E., 2016, Resource management and operations in southwest South Dakota: Climate change scenario planning workshop summary January 20-21, 2016, Rapid City, SD: Natural Resource Report NPS/NRSS/NRR—2016/1289, ix, 61 p.","productDescription":"ix, 61 p.","numberOfPages":"76","ipdsId":"IP-075140","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":328475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":328423,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2233058"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57d3dd3ce4b0571647d19ac3","contributors":{"authors":[{"text":"Fisichelli, Nicholas A.","contributorId":174508,"corporation":false,"usgs":false,"family":"Fisichelli","given":"Nicholas","email":"","middleInitial":"A.","affiliations":[{"id":27461,"text":"NPS, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":648451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schuurman, Gregor W. 0000-0002-9304-7742","orcid":"https://orcid.org/0000-0002-9304-7742","contributorId":147698,"corporation":false,"usgs":false,"family":"Schuurman","given":"Gregor","email":"","middleInitial":"W.","affiliations":[{"id":16909,"text":"U.S. National Park Service, Natural Resource Stewardship and Science, Fort Collins, CO, 80525, USA","active":true,"usgs":false}],"preferred":false,"id":648452,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Symstad, Amy J. 0000-0003-4231-2873 asymstad@usgs.gov","orcid":"https://orcid.org/0000-0003-4231-2873","contributorId":147543,"corporation":false,"usgs":true,"family":"Symstad","given":"Amy","email":"asymstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":648450,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ray, Andrea","contributorId":71869,"corporation":false,"usgs":true,"family":"Ray","given":"Andrea","affiliations":[],"preferred":false,"id":648453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Brian","contributorId":100753,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","affiliations":[],"preferred":false,"id":648454,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cross, Molly","contributorId":73455,"corporation":false,"usgs":true,"family":"Cross","given":"Molly","affiliations":[],"preferred":false,"id":648455,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rowland, Erika","contributorId":146177,"corporation":false,"usgs":false,"family":"Rowland","given":"Erika","email":"","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":648456,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70192912,"text":"70192912 - 2016 - Landsat-7 ETM+ radiometric calibration status","interactions":[],"lastModifiedDate":"2017-12-20T10:56:58","indexId":"70192912","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat-7 ETM+ radiometric calibration status","docAbstract":"Now in its 17th year of operation, the Enhanced Thematic Mapper + (ETM+), on board the Landsat-7 satellite, continues to systematically acquire imagery of the Earth to add to the 40+ year archive of Landsat data. Characterization of the ETM+ on-orbit radiometric performance has been on-going since its launch in 1999. The radiometric calibration of the reflective bands is still monitored using on-board calibration devices, though the Pseudo-Invariant Calibration Sites (PICS) method has proven to be an effective tool as well. The calibration gains were updated in April 2013 based primarily on PICS results, which corrected for a change of as much as -0.2%/year degradation in the worst case bands. A new comparison with the SADE database of PICS results indicates no additional degradation in the updated calibration. PICS data are still being tracked though the recent trends are not well understood. The thermal band calibration was updated last in October 2013 based on a continued calibration effort by NASA/Jet Propulsion Lab and Rochester Institute of Technology. The update accounted for a 0.036 W/m2 sr μm or 0.26K at 300K bias error. The updated lifetime trend is now stable to within +/- 0.4K.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings Volume 9972, Earth Observing Systems XXI","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"SPIE","doi":"10.1117/12.2238625","usgsCitation":"Barsi, J.A., Markham, B.L., Czapla-Myers, J.S., Helder, D.L., Hook, S., Schott, J.R., and Haque, O., 2016, Landsat-7 ETM+ radiometric calibration status, in Proceedings Volume 9972, Earth Observing Systems XXI, v. 9972, https://doi.org/10.1117/12.2238625.","ipdsId":"IP-079294","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470629,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://doi.org/10.1117/12.2238625","text":"External Repository"},{"id":350125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9972","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fcd4e4b06e28e9c24390","contributors":{"authors":[{"text":"Barsi, Julia A.","contributorId":71822,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","middleInitial":"A.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":725247,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Markham, Brian L.","contributorId":90482,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","email":"","middleInitial":"L.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":725248,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Czapla-Myers, J. S.","contributorId":101968,"corporation":false,"usgs":true,"family":"Czapla-Myers","given":"J.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":725249,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Helder, Dennis L.","contributorId":105613,"corporation":false,"usgs":true,"family":"Helder","given":"Dennis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":725250,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hook, Simon","contributorId":150339,"corporation":false,"usgs":false,"family":"Hook","given":"Simon","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":725251,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schott, John R.","contributorId":199175,"corporation":false,"usgs":false,"family":"Schott","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":725252,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":717349,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70192911,"text":"70192911 - 2016 - Radiometric calibration updates to the Landsat collection","interactions":[],"lastModifiedDate":"2018-04-23T09:09:51","indexId":"70192911","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Radiometric calibration updates to the Landsat collection","docAbstract":"The Landsat Project is planning to implement a new collection management strategy for Landsat products generated at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The goal of the initiative is to identify a collection of consistently geolocated and radiometrically calibrated images across the entire Landsat archive that is readily suitable for time-series analyses. In order to perform an accurate land change analysis, the data from all Landsat sensors must be on the same radiometric scale. Landsat 7 Enhanced Thematic Mapper Plus (ETM+) is calibrated to a radiance standard and all previous sensors are cross-calibrated to its radiometric scale. Landsat 8 Operational Land Imager (OLI) is calibrated to both radiance and reflectance standards independently. The Landsat 8 OLI reflectance calibration is considered to be most accurate. To improve radiometric calibration accuracy of historical data, Landsat 1-7 sensors also need to be cross-calibrated to the OLI reflectance scale. Results of that effort, as well as other calibration updates including the absolute and relative radiometric calibration and saturated pixel replacement for Landsat 8 OLI and absolute calibration for Landsat 4 and 5 Thematic Mappers (TM), will be implemented into Landsat products during the archive reprocessing campaign planned within the new collection management strategy. This paper reports on the planned radiometric calibration updates to the solar reflective bands of the new Landsat collection.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings Volume 9972, Earth Observing Systems XXI","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Society of Photo-Optical Instrumentation Engineers","doi":"10.1117/12.2239426","usgsCitation":"Micijevic, E., Haque, O., and Mishra, N., 2016, Radiometric calibration updates to the Landsat collection, in Proceedings Volume 9972, Earth Observing Systems XXI, v. 9972, 12 p., https://doi.org/10.1117/12.2239426.","productDescription":"12 p.","ipdsId":"IP-079592","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":350127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9972","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fcd5e4b06e28e9c24393","contributors":{"authors":[{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":717347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mishra, Nischal nischal.mishra.ctr@usgs.gov","contributorId":198842,"corporation":false,"usgs":true,"family":"Mishra","given":"Nischal","email":"nischal.mishra.ctr@usgs.gov","affiliations":[],"preferred":false,"id":717348,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195835,"text":"70195835 - 2016 - Estimating microcystin levels at recreational sites in western Lake Erie and Ohio","interactions":[],"lastModifiedDate":"2018-03-07T10:40:01","indexId":"70195835","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Estimating microcystin levels at recreational sites in western Lake Erie and Ohio","docAbstract":"Cyanobacterial harmful algal blooms (cyanoHABs) and associated toxins, such as microcystin, are a major global water-quality issue. Water-resource managers need tools to quickly predict when and where toxin-producing cyanoHABs will occur. This could be done by using site-specific models that estimate the potential for elevated toxin concentrations that cause public health concerns. With this study, samples were collected at three Ohio lakes to identify environmental and water-quality factors to develop linear-regression models to estimate microcystin levels. Measures of the algal community (phycocyanin, cyanobacterial biovolume, and cyanobacterial gene concentrations) and pH were most strongly correlated with microcystin concentrations. Cyanobacterial genes were quantified for general cyanobacteria, general Microcystis and Dolichospermum, and for microcystin synthetase (mcyE) for Microcystis, Dolichospermum, and Planktothrix. For phycocyanin, the relations were different between sites and were different between hand-held measurements on-site and nearby continuous monitor measurements for the same site. Continuous measurements of parameters such as phycocyanin, pH, and temperature over multiple days showed the highest correlations to microcystin concentrations. The development of models with high R2values (0.81–0.90), sensitivities (92%), and specificities (100%) for estimating microcystin concentrations above or below the Ohio Recreational Public Health Advisory level of 6 μg L−1 was demonstrated for one site; these statistics may change as more data are collected in subsequent years. This study showed that models could be developed for estimates of exceeding a microcystin threshold concentration at a recreational freshwater lake site, with potential to expand their use to provide relevant public health information to water resource managers and the public for both recreational and drinking waters.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2016.07.003","usgsCitation":"Francy, D.S., Brady, A.M., Ecker, C.D., Graham, J.L., Stelzer, E.A., Struffolino, P., and Loftin, K.A., 2016, Estimating microcystin levels at recreational sites in western Lake Erie and Ohio: Harmful Algae, v. 58, p. 23-34, https://doi.org/10.1016/j.hal.2016.07.003.","productDescription":"12 p.","startPage":"23","endPage":"34","ipdsId":"IP-068433","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":352264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Lake Erie","volume":"58","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee98be4b0da30c1bfc568","contributors":{"authors":[{"text":"Francy, Donna S. 0000-0001-9229-3557 dsfrancy@usgs.gov","orcid":"https://orcid.org/0000-0001-9229-3557","contributorId":1853,"corporation":false,"usgs":true,"family":"Francy","given":"Donna","email":"dsfrancy@usgs.gov","middleInitial":"S.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brady, Amie M.G. 0000-0002-7414-0992 amgbrady@usgs.gov","orcid":"https://orcid.org/0000-0002-7414-0992","contributorId":2544,"corporation":false,"usgs":true,"family":"Brady","given":"Amie","email":"amgbrady@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ecker, Christopher D. 0000-0003-0353-5855 cdecker@usgs.gov","orcid":"https://orcid.org/0000-0003-0353-5855","contributorId":149530,"corporation":false,"usgs":true,"family":"Ecker","given":"Christopher","email":"cdecker@usgs.gov","middleInitial":"D.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":false,"id":730221,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stelzer, Erin A. 0000-0001-7645-7603 eastelzer@usgs.gov","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":1933,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin","email":"eastelzer@usgs.gov","middleInitial":"A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730224,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Struffolino, Pamela","contributorId":202922,"corporation":false,"usgs":false,"family":"Struffolino","given":"Pamela","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":730219,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":730223,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70170460,"text":"ds987 - 2016 - Groundwater-quality data in the Monterey–Salinas shallow aquifer study unit, 2013: Results from the California GAMA Program","interactions":[],"lastModifiedDate":"2017-01-18T09:45:02","indexId":"ds987","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"987","title":"Groundwater-quality data in the Monterey–Salinas shallow aquifer study unit, 2013: Results from the California GAMA Program","docAbstract":"Groundwater quality in the 3,016-square-mile Monterey–Salinas Shallow Aquifer study unit was investigated by the U.S. Geological Survey (USGS) from October 2012 to May 2013 as part of the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment (GAMA) Program’s Priority Basin Project. The GAMA Monterey–Salinas Shallow Aquifer study was designed to provide a spatially unbiased assessment of untreated-groundwater quality in the shallow-aquifer systems in parts of Monterey and San Luis Obispo Counties and to facilitate statistically consistent comparisons of untreated-groundwater quality throughout California. The shallow-aquifer system in the Monterey–Salinas Shallow Aquifer study unit was defined as those parts of the aquifer system shallower than the perforated depth intervals of public-supply wells, which generally corresponds to the part of the aquifer system used by domestic wells. Groundwater quality in the shallow aquifers can differ from the quality in the deeper water-bearing zones; shallow groundwater can be more vulnerable to surficial contamination.
Samples were collected from 170 sites that were selected by using a spatially distributed, randomized grid-based method. The study unit was divided into 4 study areas, each study area was divided into grid cells, and 1 well was sampled in each of the 100 grid cells (grid wells). The grid wells were domestic wells or wells with screen depths similar to those in nearby domestic wells. A greater spatial density of data was achieved in 2 of the study areas by dividing grid cells in those study areas into subcells, and in 70 subcells, samples were collected from exterior faucets at sites where there were domestic wells or wells with screen depths similar to those in nearby domestic wells (shallow-well tap sites).
Field water-quality indicators (dissolved oxygen, water temperature, pH, and specific conductance) were measured, and samples for analysis of inorganic constituents (trace elements, nutrients, major and minor ions, silica, total dissolved solids, and alkalinity) were collected at all 170 sites. In addition to these constituents, the samples from grid wells were analyzed for organic constituents (volatile organic compounds, pesticides and pesticide degradates), constituents of special interest (perchlorate and N-nitrosodimethylamine, or NDMA), radioactive constituents (radon-222 and gross-alpha and gross-beta radioactivity), and geochemical and age-dating tracers (stable isotopes of carbon in dissolved inorganic carbon, carbon-14 abundances, stable isotopes of hydrogen and oxygen in water, and tritium activities).
Three types of quality-control samples (blanks, replicates, and matrix spikes) were collected at up to 11 percent of the wells in the Monterey–Salinas Shallow Aquifer study unit, and the results for these samples were used to evaluate the quality of the data from the groundwater samples. With the exception of trace elements, blanks rarely contained detectable concentrations of any constituent, indicating that contamination from sample-collection procedures was not a significant source of bias in the data for the groundwater samples. Low concentrations of some trace elements were detected in blanks; therefore, the data were re-censored at higher reporting levels. Replicate samples generally were within the limits of acceptable analytical reproducibility. The median values of matrix-spike recoveries were within the acceptable range (70 to 130 percent) for the volatile organic compounds (VOCs) and N-nitrosodimethylamine (NDMA), but were only approximately 64 percent for pesticides and pesticide degradates.
The sample-collection protocols used in this study were designed to obtain representative samples of groundwater. The quality of groundwater can differ from the quality of drinking water because water chemistry can change as a result of contact with plumbing systems or the atmosphere; because of treatment, disinfection, or blending with water from other sources; or some combination of these. Water quality in domestic wells is not regulated in California, however, to provide context for the water-quality data presented in this report, results were compared to benchmarks established for drinking-water quality. The primary comparison benchmarks were maximum contaminant levels established by the U.S. Environmental Protection Agency and the State of California (MCL-US and MCL-CA, respectively). Non-regulatory benchmarks were used for constituents without maximum contaminant levels (MCLs), including Health
Based Screening Levels (HBSLs) developed by the USGS and State of California secondary maximum contaminant levels (SMCL-CA) and notification levels. Most constituents detected in samples from the Monterey–Salinas Shallow Aquifer study unit had concentrations less than their respective benchmarks.
Of the 148 organic constituents analyzed in the 100 grid-well samples, 38 were detected, and all concentrations were less than the benchmarks. Volatile organic compounds were detected in 26 of the grid wells, and pesticides and pesticide degradates were detected in 28 grid wells. The special-interest constituent NDMA was detected above the HBSL in three samples, one of which also had a perchlorate concentration greater than the MCL-CA.
Of the inorganic constituents, 6 were detected at concentrations above their respective MCL benchmarks in grid-well samples: arsenic (5 grid wells above the MCL of 10 micrograms per liter, μg/L), selenium (3 grid wells, MCL of 50 μg/L), uranium (4 grid wells, MCL of 30 μg/L), nitrate (16 grid wells, MCL of 10 milligrams per liter, mg/L), adjusted gross alpha particle activity (10 grid wells, MCL of 15 picocuries per liter, pCi/L), and gross beta particle activity (1 grid well, MCL of 50 pCi/L). An additional 4 inorganic constituents were detected at concentrations above their respective HBSL benchmarks in grid-well samples: boron (1 grid well above the HBSL of 6,000 μg/L), manganese (8 grid wells, HBSL of 300 μg/L), molybdenum (6 grid wells, HBSL of 40 μg/L), and strontium (6 grid wells, HBSL of 4,000 μg/L). Of the inorganic constituents, 4 were detected at concentrations above their non-health based SMCL benchmarks in grid-well samples: iron (9 grid wells above the SMCL of 300 μg/L), chloride (7 grid wells, SMCL of 500 mg/L), sulfate (14 grid wells, SMCL of 500 mg/L), and total dissolved solids (27 grid wells, SMCL of 1,000 mg/L).
Of the inorganic constituents analyzed in the 70 shallow-well tap sites, 10 were detected at concentrations above the benchmarks. Of the inorganic constituents, 3 were detected at concentrations above their respective MCL benchmarks in shallow-well tap sites: arsenic (2 shallow-well tap sites above the MCL of 10 μg/L), uranium (2 shallow-well tap sites, MCL of 30 μg/L), and nitrate (24 shallow-well tap sites, MCL of 10 mg/L). An additional 3 inorganic constituents were detected above their respective HBSL benchmarks in shallow-well tap sites: manganese (4 shallow-well tap sites above the HBSL of 300 μg/L), molybdenum (4 shallow-well tap sites, HBSL of 40 μg/L), and zinc (2 shallow-well tap sites, HBSL of 2,000 μg/L). Of the inorganic constituents, 4 were detected at concentrations above their non-health based SMCL benchmarks in shallow-well tap sites: iron (6 shallow-well tap sites above the SMCL of 300 μg/L), chloride (1 shallow-well tap site, SMCL of 500 mg/L), sulfate (9 shallow-well tap sites, SMCL of 500 mg/L), and total dissolved solids (15 shallow-well tap sites, SMCL of 1,000 mg/L).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds987","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Goldrath, D.A., Kulongoski, J.T., and Davis, T.A., 2015, Groundwater-quality data in the Monterey–Salinas shallow aquifer study unit, (ver. 1.1, January 2017): Results from the California GAMA Program: U.S. Geological Survey Data Series 987, 132 p., https://dx.doi.org/10.3133/ds987.","productDescription":"ix, 132 p. ","numberOfPages":"146","onlineOnly":"Y","ipdsId":"IP-049716","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":333267,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/0987/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":328193,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0987/coverthb2.jpg"},{"id":328194,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0987/ds0987.pdf","text":"Report","size":"17.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 987"}],"country":"United States","state":"California ","otherGeospatial":"Monterey–Salinas Shallow Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.08282470703124,\n 36.96086580957587\n ],\n [\n -122.05261230468751,\n 36.9367208722872\n ],\n [\n -122.01690673828124,\n 36.94769679250732\n ],\n [\n -121.981201171875,\n 36.954281585675965\n ],\n [\n -121.9317626953125,\n 36.95647639022989\n ],\n [\n -121.88507080078125,\n 36.94111143010772\n ],\n [\n -121.85760498046875,\n 36.88181755936464\n ],\n [\n -121.827392578125,\n 36.82687474287728\n ],\n [\n -121.80816650390625,\n 36.78949107451841\n ],\n [\n -121.827392578125,\n 36.71687068791304\n ],\n [\n -121.85211181640625,\n 36.65079252503471\n ],\n [\n -121.871337890625,\n 36.62214102821768\n ],\n [\n -121.91802978515625,\n 36.64858894203172\n ],\n [\n -121.95648193359374,\n 36.64638529597495\n ],\n [\n -121.8878173828125,\n 36.59347887826919\n ],\n [\n -121.80816650390625,\n 36.619936625629215\n ],\n [\n -121.75872802734375,\n 36.65079252503471\n ],\n [\n -121.58843994140625,\n 36.578041001498704\n ],\n [\n -121.30004882812499,\n 36.295204533693536\n ],\n [\n -121.11328124999999,\n 36.140092827322654\n ],\n [\n -120.84136962890625,\n 35.89572525865906\n ],\n [\n -120.80291748046874,\n 35.811131416437966\n ],\n [\n -120.794677734375,\n 35.708607653285505\n ],\n [\n -120.46234130859376,\n 35.5188142812306\n ],\n [\n -120.22338867187499,\n 35.42486791930558\n ],\n [\n -120.00640869140624,\n 35.33529320309331\n ],\n [\n -119.86633300781249,\n 35.290468565908775\n ],\n [\n -119.76470947265625,\n 35.3285710912542\n ],\n [\n -119.674072265625,\n 35.250105158539355\n ],\n [\n -119.5806884765625,\n 35.191766965947394\n ],\n [\n -119.4873046875,\n 35.10193405724606\n ],\n [\n -119.43237304687499,\n 35.05922870088872\n ],\n [\n -119.29229736328126,\n 35.02099970111467\n ],\n [\n -119.07531738281251,\n 35.007502842952896\n ],\n [\n -119.02587890624999,\n 35.0502352513963\n ],\n [\n -119.02862548828125,\n 35.11766197360177\n ],\n [\n -119.05334472656249,\n 35.18952235197259\n ],\n [\n -119.25933837890624,\n 35.28374272801905\n ],\n [\n -119.43237304687499,\n 35.411438052435464\n ],\n [\n -119.49005126953124,\n 35.49198366469642\n ],\n [\n -119.65484619140625,\n 35.54116627999815\n ],\n [\n -119.91577148437499,\n 35.69522525087309\n ],\n [\n -120.09979248046874,\n 35.70414710206052\n ],\n [\n -120.24261474609374,\n 35.72421761691415\n ],\n [\n -120.28930664062499,\n 35.86456960744962\n ],\n [\n -120.9320068359375,\n 36.20660692859011\n ],\n [\n -121.13525390625,\n 36.575835338491764\n ],\n [\n -121.4813232421875,\n 36.85325222344018\n ],\n [\n -121.63238525390626,\n 36.901587303978474\n ],\n [\n -121.76422119140625,\n 36.97183825093165\n ],\n [\n -121.82464599609375,\n 37.00035919622158\n ],\n [\n -121.92626953124999,\n 37.00913272027146\n ],\n [\n -122.04986572265624,\n 37.01351910258053\n ],\n [\n -122.08282470703124,\n 36.96086580957587\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: September 1, 2016; Version 1.1: January 7, 2017","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
http://ca.water.usgs.gov
Using geostatistical models of density variations in the subsurface, constrained by geologic data, forward models of gravity anomalies can be generated by discretizing the subsurface and calculating the cumulative effect of each cell (pixel). The results of such stochastically generated forward gravity anomalies can be compared with the observed gravity anomalies to find density models that match the observed data. These models have an advantage over forward gravity anomalies generated using polygonal bodies of homogeneous density because generating numerous realizations explores a larger region of the solution space. The stochastic modeling can be thought of as dividing the forward model into two components: that due to the shape of each geologic unit and that due to the heterogeneous distribution of density within each geologic unit. The modeling demonstrates that the internally heterogeneous distribution of density within each geologic unit can contribute significantly to the resulting calculated forward gravity anomaly. Furthermore, the stochastic models match observed statistical properties of geologic units, the solution space is more broadly explored by producing a suite of successful models, and the likelihood of a particular conceptual geologic model can be compared. The Vaca Fault near Travis Air Force Base, California, can be successfully modeled as a normal or strike-slip fault, with the normal fault model being slightly more probable. It can also be modeled as a reverse fault, although this structural geologic configuration is highly unlikely given the realizations we explored.
Regions of complex topography and remote wilderness terrain have spatially varying patterns of temperature and streamflow, but due to inherent difficulties of access, are often very poorly sampled. Here we present a data set of distributed stream stage, streamflow, stream temperature, barometric pressure, and air temperature from the Tuolumne River Watershed in Yosemite National Park, Sierra Nevada, California, USA, for water years 2002–2015, as well as a quality-controlled hourly meteorological forcing time series for use in hydrologic modeling. We also provide snow data and daily inflow to the Hetch Hetchy Reservoir for 1970–2015. This paper describes data collected using low-visibility and low-impact installations for wilderness locations and can be used alone or as a critical supplement to ancillary data sets collected by cooperating agencies, referenced herein. This data set provides a unique opportunity to understand spatial patterns and scaling of hydroclimatic processes in complex terrain and can be used to evaluate downscaling techniques or distributed modeling. The paper also provides an example methodology and lessons learned in conducting hydroclimatic monitoring in remote wilderness.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016WR019261","usgsCitation":"Lundquist, J., Roche, J.W., Forrester, H., Moore, C., Keenan, E., Perry, G., Cristea, N., Henn, B., Lapo, K., McGurk, B., Cayan, D.R., and Dettinger, M., 2016, Yosemite Hydroclimate Network: Distributed stream and atmospheric data for the Tuolumne River watershed and surroundings: Water Resources Research, v. 52, no. 9, p. 7478-7489, https://doi.org/10.1002/2016WR019261.","productDescription":"12 p.","startPage":"7478","endPage":"7489","ipdsId":"IP-077662","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":334061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Tuolumne River Watershed, Yosemite National Park","volume":"52","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-22","publicationStatus":"PW","scienceBaseUri":"588b1977e4b0ad67323f97e6","contributors":{"authors":[{"text":"Lundquist, Jessica D.","contributorId":12792,"corporation":false,"usgs":true,"family":"Lundquist","given":"Jessica D.","affiliations":[],"preferred":false,"id":660936,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roche, James W.","contributorId":178800,"corporation":false,"usgs":false,"family":"Roche","given":"James","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":660937,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forrester, Harrison","contributorId":178773,"corporation":false,"usgs":false,"family":"Forrester","given":"Harrison","email":"","affiliations":[],"preferred":false,"id":660938,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Courtney","contributorId":178775,"corporation":false,"usgs":false,"family":"Moore","given":"Courtney","email":"","affiliations":[],"preferred":false,"id":660940,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keenan, Eric","contributorId":178776,"corporation":false,"usgs":false,"family":"Keenan","given":"Eric","email":"","affiliations":[],"preferred":false,"id":660941,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perry, Gwyneth","contributorId":178777,"corporation":false,"usgs":false,"family":"Perry","given":"Gwyneth","email":"","affiliations":[],"preferred":false,"id":660942,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cristea, Nicoleta","contributorId":178778,"corporation":false,"usgs":false,"family":"Cristea","given":"Nicoleta","email":"","affiliations":[],"preferred":false,"id":660943,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henn, Brian","contributorId":139777,"corporation":false,"usgs":false,"family":"Henn","given":"Brian","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":660944,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lapo, Karl","contributorId":178779,"corporation":false,"usgs":false,"family":"Lapo","given":"Karl","email":"","affiliations":[],"preferred":false,"id":660945,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McGurk, Bruce","contributorId":178780,"corporation":false,"usgs":false,"family":"McGurk","given":"Bruce","email":"","affiliations":[],"preferred":false,"id":660946,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cayan, Daniel R. 0000-0002-2719-6811 drcayan@usgs.gov","orcid":"https://orcid.org/0000-0002-2719-6811","contributorId":1494,"corporation":false,"usgs":true,"family":"Cayan","given":"Daniel","email":"drcayan@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":false,"id":660934,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Dettinger, Michael D. 0000-0002-7509-7332 mddettin@usgs.gov","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":146383,"corporation":false,"usgs":true,"family":"Dettinger","given":"Michael D.","email":"mddettin@usgs.gov","affiliations":[],"preferred":false,"id":660935,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70175152,"text":"ofr20161126 - 2016 - Evaluating integration of inland bathymetry in the U.S. Geological Survey 3D Elevation Program, 2014","interactions":[],"lastModifiedDate":"2016-09-01T15:31:00","indexId":"ofr20161126","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1126","title":"Evaluating integration of inland bathymetry in the U.S. Geological Survey 3D Elevation Program, 2014","docAbstract":"Inland bathymetry survey collections, survey data types, features, sources, availability, and the effort required to integrate inland bathymetric data into the U.S. Geological Survey 3D Elevation Program are assessed to help determine the feasibility of integrating three-dimensional water feature elevation data into The National Map. Available data from wading, acoustic, light detection and ranging, and combined technique surveys are provided by the U.S. Geological Survey, National Oceanic and Atmospheric Administration, U.S. Army Corps of Engineers, and other sources. Inland bathymetric data accessed through Web-hosted resources or contacts provide useful baseline parameters for evaluating survey types and techniques used for collection and processing, and serve as a basis for comparing survey methods and the quality of results. Historically, boat-mounted acoustic surveys have provided most inland bathymetry data. Light detection and ranging techniques that are beneficial in areas hard to reach by boat, that can collect dense data in shallow water to provide comprehensive coverage, and that can be cost effective for surveying large areas with good water clarity are becoming more common; however, optimal conditions and techniques for collecting and processing light detection and ranging inland bathymetry surveys are not yet well defined.
Assessment of site condition parameters important for understanding inland bathymetry survey issues and results, and an evaluation of existing inland bathymetry survey coverage are proposed as steps to develop criteria for implementing a useful and successful inland bathymetry survey plan in the 3D Elevation Program. These survey parameters would also serve as input for an inland bathymetry survey data baseline. Integration and interpolation techniques are important factors to consider in developing a robust plan; however, available survey data are usually in a triangulated irregular network format or other format compatible with the 3D Elevation Program so that data can be integrated with a minimal level of effort. Geomorphic site conditions are known to affect the success and accuracy of light detection and ranging and other bathymetric surveys, and a baseline that includes geomorphic data is recommended to help in evaluation of limitations imposed by geomorphology for surveys completed in the variable physiographic provinces across the United States. The geographic distribution for existing surveys identifies regions where inland bathymetry data have been collected and, conversely, where little or no survey data seem to be available to provide hydrologic and hydraulic information. This distribution, in conjunction with local to regional data needs to characterize and monitor river and lake resources, provides another important set of criteria to propose and guide acquisition of new bathymetry data for the 3D Elevation Program. An initial evaluation of needs can be based on the importance of water resources that provide primary water supplies for communities, agriculture, energy, and ecological systems; the importance of flood plain analyses; and projected population growth across the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161126","usgsCitation":"Miller-Corbett, Cynthia, 2016, Evaluating integration of inland bathymetry in the U.S. Geological Survey 3D Elevation Program, 2014: U.S. Geological Survey Open-File Report 2016–1126, 44 p., https://dx.doi.org/10.3133/ofr20161126.\n","productDescription":"vi, 44 p.","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-065698","costCenters":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"links":[{"id":328148,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1126/coverthb.jpg"},{"id":328149,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1126/ofr20161126.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1126"}],"contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Natural resource managers are seeking appropriate chemical eradication and control protocols for infestations of zebra mussels, Dreissena polymorpha (Pallas, 1769), and quagga mussels. D. rostiformis bugensis (Andrusov, 1897) that have limited effect on non-target species. Applications of low concentrations of potassium salt (as potash) have shown promise for use where the infestation and treatment can be contained or isolated. To further our understanding of such applications and obtain data that could support a pesticide registration, we conducted studies of the acute and chronic toxicity of potassium chloride to dreissenid mussels in four different water sources from infested and non-infested locations (ground water from northern Idaho, surface water from the Snake River, Idaho, USA, surface water from Lake Ontario, Ontario, Canada, and surface water from the Colorado River, Arizona, USA). We found short term exposure of veligers (< 24 h) to concentrations of 960 mg/L KCl produced rapid mortality in water from three locations, but veligers tested in Colorado River water were resistant. We used probit models to compare the mortality responses, predicted median lethal times and 95% confidence intervals. In separate experiments, we explored the sensitivity of byssal stage mussels in chronic exposures (>29 d) at concentrations of 100 and 200 mg/L KCl. Rapid mortality occurred within 10 d of exposure to concentrations of 200 mg/L KCl, regardless of water source. Kaplan-Meier estimates of mean survival of byssal mussels in 100 mg/L KCl prepared in surface water from Idaho and Lake Ontario were 4.9 or 6.9 d, respectively; however, mean survival of mussels tested in the Colorado River water was > 23 d. The sodium content of the Colorado River water was nearly three times that measured in waters from the other locations, and we hypothesized sodium concentrations may affect mussel survival. To test our hypothesis, we supplemented Snake River and Lake Ontario water with NaCl to equivalent conductivity as the Colorado River, and found mussel survival increased to levels observed in tests of veliger and byssal mussels in Colorado River water. We recommend KCl disinfection and eradication protocols must be developed to carefully consider the water quality characteristics of treatment locations.
","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2016.7.3.05","usgsCitation":"Moffitt, C.M., Stockton-Fiti, K.A., and Claudi, R., 2016, Toxicity of potassium chloride to veliger and byssal stage dreissenid mussels related to water quality: Management of Biological Invasions, v. 7, no. 3, p. 257-268, https://doi.org/10.3391/mbi.2016.7.3.05.","productDescription":"12 p.","startPage":"257","endPage":"268","ipdsId":"IP-073121","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470626,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2016.7.3.05","text":"Publisher Index Page"},{"id":348406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07e9dbe4b09af898c8cc5c","contributors":{"authors":[{"text":"Moffitt, Christine M. 0000-0001-6020-9728 cmoffitt@usgs.gov","orcid":"https://orcid.org/0000-0001-6020-9728","contributorId":2583,"corporation":false,"usgs":true,"family":"Moffitt","given":"Christine","email":"cmoffitt@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stockton-Fiti, Kelly A.","contributorId":200103,"corporation":false,"usgs":false,"family":"Stockton-Fiti","given":"Kelly","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":721003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Claudi, Renata","contributorId":171420,"corporation":false,"usgs":false,"family":"Claudi","given":"Renata","email":"","affiliations":[{"id":26908,"text":"RNT Consulting Inc., Canada","active":true,"usgs":false}],"preferred":false,"id":721004,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178354,"text":"70178354 - 2016 - Biochemical and clinical responses of Common Eiders to implanted satellite transmitters","interactions":[],"lastModifiedDate":"2016-11-15T12:02:59","indexId":"70178354","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Biochemical and clinical responses of Common Eiders to implanted satellite transmitters","docAbstract":"Implanted biologging devices, such as satellite-linked platform transmitter terminals (PTTs), have been used widely to delineate populations and identify movement patterns of sea ducks. Although in some cases these ecological studies could reveal transmitter effects on behavior and mortality, experiments conducted under controlled conditions can provide valuable information to understand the influence of implanted tags on health and physiology. We report the clinical, mass, biochemical, and histological responses of captive Common Eiders (Somateria mollissima) implanted with PTTs with percutaneous antennas. We trained 6 individuals to dive 4.9 m for their food, allowed them to acclimate to this dive depth, and implanted them with PTTs. We collected data before surgery to establish baselines, and for 3.5 mo after surgery. The first feeding dive took place 22 hr after surgery, with 5 of 6 birds diving to the bottom within 35 hr of surgery. Plumage waterproofing around surgical sites was reduced ≤21 days after surgery. Mass; albumin; albumin:globulin ratio; aspartate aminotransferase; β1-, β2-, and γ-globulins; creatine kinase; fecal glucocorticoid metabolites; heterophil:lymphocyte ratio; and packed cell volume changed from baseline on one or more of the postsurgery sampling dates, and some changes were still evident 3.5 mo after surgery. Our findings show that Common Eiders physiologically responded for up to 3.5 mo after surgical implantation of a PTT, with the greatest response occurring within the first few weeks of implantation. These responses support the need for postsurgery censor periods for satellite telemetry data and should be considered when designing studies and analyzing information from PTTs in sea ducks.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-16-7.1","usgsCitation":"Latty, C.J., Hollmen, T.E., Petersen, M.R., Powell, A., and Andrews, R.D., 2016, Biochemical and clinical responses of Common Eiders to implanted satellite transmitters: The Condor, v. 118, no. 3, p. 489-501, https://doi.org/10.1650/CONDOR-16-7.1.","productDescription":"13 p.","startPage":"489","endPage":"501","ipdsId":"IP-076365","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":462099,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-16-7.1","text":"Publisher Index Page"},{"id":438557,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MG7MR0","text":"USGS data release","linkHelpText":"Common Eider Blood Chemistry Data, Alaska, 2005"},{"id":331010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"118","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582c2ce5e4b0c253be072c06","contributors":{"authors":[{"text":"Latty, Christopher J.","contributorId":146588,"corporation":false,"usgs":false,"family":"Latty","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":653820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hollmen, Tuula E.","contributorId":106077,"corporation":false,"usgs":true,"family":"Hollmen","given":"Tuula","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":653821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petersen, Margaret R. 0000-0001-6082-3189 mrpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-6082-3189","contributorId":167729,"corporation":false,"usgs":true,"family":"Petersen","given":"Margaret","email":"mrpetersen@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":653752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Powell, Abby 0000-0002-9783-134X abby_powell@usgs.gov","orcid":"https://orcid.org/0000-0002-9783-134X","contributorId":176843,"corporation":false,"usgs":true,"family":"Powell","given":"Abby","email":"abby_powell@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":653751,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrews, Russel D.","contributorId":146589,"corporation":false,"usgs":false,"family":"Andrews","given":"Russel","email":"","middleInitial":"D.","affiliations":[{"id":16211,"text":"Alaska SeaLife Center","active":true,"usgs":false}],"preferred":false,"id":653822,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188438,"text":"70188438 - 2016 - Holocene climate changes in eastern Beringia (NW North America) – A systematic review of multi-proxy evidence","interactions":[],"lastModifiedDate":"2017-06-09T14:10:37","indexId":"70188438","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Holocene climate changes in eastern Beringia (NW North America) – A systematic review of multi-proxy evidence","docAbstract":"Reconstructing climates of the past relies on a variety of evidence from a large number of sites to capture the varied features of climate and the spatial heterogeneity of climate change. This review summarizes available information from diverse Holocene paleoenvironmental records across eastern Beringia (Alaska, westernmost Canada and adjacent seas), and it quantifies the primary trends of temperature- and moisture-sensitive records based in part on midges, pollen, and biogeochemical indicators (compiled in the recently published Arctic Holocene database, and updated here to v2.1). The composite time series from these proxy records are compared with new summaries of mountain-glacier and lake-level fluctuations, terrestrial water-isotope records, sea-ice and sea-surface-temperature analyses, and peatland and thaw-lake initiation frequencies to clarify multi-centennial- to millennial-scale trends in Holocene climate change. To focus the synthesis, the paleo data are used to frame specific questions that can be addressed with simulations by Earth system models to investigate the causes and dynamics of past and future climate change. This systematic review shows that, during the early Holocene (11.7–8.2 ka; 1 ka = 1000 cal yr BP), rather than a prominent thermal maximum as suggested previously, temperatures were highly variable, at times both higher and lower than present (approximate mid-20th-century average), with no clear spatial pattern. Composited pollen, midge and other proxy records average out the variability and show the overall lowest summer and mean-annual temperatures across the study region during the earliest Holocene, followed by warming over the early Holocene. The sparse data available on early Holocene glaciation show that glaciers in southern Alaska were as extensive then as they were during the late Holocene. Early Holocene lake levels were low in interior Alaska, but moisture indicators show pronounced differences across the region. The highest frequency of both peatland and thaw-lake initiation ages also occurred during the early Holocene. During the middle Holocene (8.2–4.2 ka), glaciers retreated as the regional average temperature increased to a maximum between 7 and 5 ka, as reflected in most proxy types. Following the middle Holocene thermal maximum, temperatures decreased starting between 4 and 3 ka, signaling the onset of Neoglacial cooling. Glaciers in the Brooks and Alaska Ranges advanced to their maximum Holocene extent as lakes generally rose to modern levels. Temperature differences for averaged 500-year time steps typically ranged by 1–2 °C for individual records in the Arctic Holocene database, with a transition to a cooler late Holocene that was neither abrupt nor spatially coherent. The longest and highest-resolution terrestrial water isotope records previously interpreted to represent changes in the Aleutian low-pressure system around this time are here shown to be largely contradictory. Furthermore, there are too few records with sufficient resolution to identify sub-centennial-scale climate anomalies, such as the 8.2 ka event. The review concludes by suggesting some priorities for future paleoclimate research in the region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2015.10.021","usgsCitation":"Kaufman, D.S., Axford, Y.L., Henderson, A.C., McKay, N.P., Oswald, W., Saenger, C., Anderson, R., Bailey, H.L., Clegg, B., Gajewski, K., Hu, F.S., Jones, M.C., Massa, C., Routson, C.C., Werner, A., Wooller, M.J., and Yu, Z., 2016, Holocene climate changes in eastern Beringia (NW North America) – A systematic review of multi-proxy evidence: Quaternary Science Reviews, v. 147, p. 312-339, https://doi.org/10.1016/j.quascirev.2015.10.021.","productDescription":"28 p.","startPage":"312","endPage":"339","ipdsId":"IP-068458","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":470606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2015.10.021","text":"Publisher Index Page"},{"id":342340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"147","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593bb3a0e4b0764e6c60e7b4","contributors":{"authors":[{"text":"Kaufman, Darrell S.","contributorId":192787,"corporation":false,"usgs":false,"family":"Kaufman","given":"Darrell","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":697736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Axford, Yarrow L.","contributorId":192788,"corporation":false,"usgs":false,"family":"Axford","given":"Yarrow","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":697737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henderson, Andrew C.G.","contributorId":192789,"corporation":false,"usgs":false,"family":"Henderson","given":"Andrew","email":"","middleInitial":"C.G.","affiliations":[],"preferred":false,"id":697738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKay, Nicolas P.","contributorId":192790,"corporation":false,"usgs":false,"family":"McKay","given":"Nicolas","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":697739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oswald, W. Wyatt","contributorId":192791,"corporation":false,"usgs":false,"family":"Oswald","given":"W. Wyatt","affiliations":[],"preferred":false,"id":697740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saenger, Casey","contributorId":192792,"corporation":false,"usgs":false,"family":"Saenger","given":"Casey","email":"","affiliations":[],"preferred":false,"id":697741,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Anderson, R. Scott","contributorId":6983,"corporation":false,"usgs":false,"family":"Anderson","given":"R. Scott","affiliations":[{"id":7034,"text":"School of Earth Sciences and Environmental Sustainability at Northern Arizona University, in Flagstaff","active":true,"usgs":false}],"preferred":false,"id":697742,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bailey, Hannah L.","contributorId":192793,"corporation":false,"usgs":false,"family":"Bailey","given":"Hannah","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":697743,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Clegg, Benjamin","contributorId":192794,"corporation":false,"usgs":false,"family":"Clegg","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":697744,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gajewski, Konrad","contributorId":192795,"corporation":false,"usgs":false,"family":"Gajewski","given":"Konrad","email":"","affiliations":[],"preferred":false,"id":697745,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hu, Feng Sheng","contributorId":192796,"corporation":false,"usgs":false,"family":"Hu","given":"Feng","email":"","middleInitial":"Sheng","affiliations":[],"preferred":false,"id":697746,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jones, Miriam C. 0000-0002-6650-7619 miriamjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":4056,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"miriamjones@usgs.gov","middleInitial":"C.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":697735,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Massa, Charly","contributorId":192797,"corporation":false,"usgs":false,"family":"Massa","given":"Charly","email":"","affiliations":[],"preferred":false,"id":697747,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Routson, Cody C. 0000-0001-8694-7809","orcid":"https://orcid.org/0000-0001-8694-7809","contributorId":187600,"corporation":false,"usgs":false,"family":"Routson","given":"Cody","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":697748,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Werner, Al","contributorId":192798,"corporation":false,"usgs":false,"family":"Werner","given":"Al","email":"","affiliations":[],"preferred":false,"id":697749,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wooller, Matthew J.","contributorId":192799,"corporation":false,"usgs":false,"family":"Wooller","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":697750,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Yu, Zicheng 0000-0003-2358-2712","orcid":"https://orcid.org/0000-0003-2358-2712","contributorId":147521,"corporation":false,"usgs":false,"family":"Yu","given":"Zicheng","email":"","affiliations":[{"id":16857,"text":"Lehigh Univ.","active":true,"usgs":false}],"preferred":false,"id":697751,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70184317,"text":"70184317 - 2016 - Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy","interactions":[],"lastModifiedDate":"2017-03-07T16:15:12","indexId":"70184317","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy","docAbstract":"The April 2010 Deepwater Horizon (DWH) oil spill was the largest coastal spill in U.S. history. Monitoring subsequent change in marsh plant community distributions is critical to assess ecosystem impacts and to establish future coastal management priorities. Strategically deployed airborne imaging spectrometers, like the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), offer the spectral and spatial resolution needed to differentiate plant species. However, obtaining satisfactory and consistent classification accuracies over time is a major challenge, particularly in dynamic intertidal landscapes.
Here, we develop and evaluate an image classification system for a time series of AVIRIS data for mapping dominant species in a heavily oiled salt marsh ecosystem. Using field-referenced image endmembers and canonical discriminant analysis (CDA), we classified 21 AVIRIS images acquired during the fall of 2010, 2011 and 2012. Classification results were evaluated using ground surveys that were conducted contemporaneously to AVIRIS collection dates. We analyzed changes in dominant species cover from 2010 to 2012 for oiled and non-oiled shorelines.
CDA discriminated dominant species with a high level of accuracy (overall accuracy = 82%, kappa = 0.78) and consistency over three imaging dates (overall2010 = 82%, overall2011 = 82%, overall2012 = 88%). Marshes dominated by Spartina alterniflora were the most spatially abundant in shoreline zones (≤ 28 m from shore) for all three dates (2010 = 79%, 2011 = 61%, 2012 = 63%), followed by Juncus roemerianus (2010 = 11%, 2011 = 19%, 2012 = 17%) and Distichlis spicata (2010 = 4%, 2011 = 10%, 2012 = 7%).
Marshes that were heavily contaminated with oil exhibited variable responses from 2010 to 2012. Marsh vegetation classes converted to a subtidal, open water class along oiled and non-oiled shorelines that were similarly situated in the landscape. However, marsh loss along oil-contaminated shorelines doubled that of non-oiled shorelines. Only S. alterniflora dominated marshes were extensively degraded, losing 15% (354,604 m2) cover in oiled shoreline zones, suggesting that S. alterniflora marshes may be more vulnerable to shoreline erosion following hydrocarbon stress, due to their landscape position.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.04.024","usgsCitation":"Beland, M., Roberts, D.A., Peterson, S.H., Biggs, T.W., Kokaly, R., Piazza, S., Roth, K.L., Khanna, S., and Ustin, S.L., 2016, Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy: Remote Sensing of Environment, v. 182, p. 192-207, https://doi.org/10.1016/j.rse.2016.04.024.","productDescription":"16 p.","startPage":"192","endPage":"207","ipdsId":"IP-069176","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":470617,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://escholarship.org/uc/item/81m5219m","text":"Publisher Index Page"},{"id":336983,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","volume":"182","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4f3e4b014cc3a3ba4a1","contributors":{"authors":[{"text":"Beland, Michael","contributorId":139569,"corporation":false,"usgs":false,"family":"Beland","given":"Michael","email":"","affiliations":[{"id":12805,"text":"Univ. of California at San Diego","active":true,"usgs":false}],"preferred":false,"id":680982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, Dar A.","contributorId":100503,"corporation":false,"usgs":false,"family":"Roberts","given":"Dar","email":"","middleInitial":"A.","affiliations":[{"id":12804,"text":"Univ. of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":680983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Seth H.","contributorId":139568,"corporation":false,"usgs":false,"family":"Peterson","given":"Seth","email":"","middleInitial":"H.","affiliations":[{"id":12804,"text":"Univ. of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":680984,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Biggs, Trent W.","contributorId":187592,"corporation":false,"usgs":false,"family":"Biggs","given":"Trent","email":"","middleInitial":"W.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":680985,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101 raymond@usgs.gov","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":1785,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","email":"raymond@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":680981,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Piazza, Sarai 0000-0001-6962-9008 piazzas@usgs.gov","orcid":"https://orcid.org/0000-0001-6962-9008","contributorId":169024,"corporation":false,"usgs":true,"family":"Piazza","given":"Sarai","email":"piazzas@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":680986,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Roth, Keely L.","contributorId":187593,"corporation":false,"usgs":false,"family":"Roth","given":"Keely","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":680987,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Khanna, Shruti","contributorId":74287,"corporation":false,"usgs":true,"family":"Khanna","given":"Shruti","affiliations":[],"preferred":false,"id":680988,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ustin, Susan L.","contributorId":52878,"corporation":false,"usgs":false,"family":"Ustin","given":"Susan","email":"","middleInitial":"L.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":680989,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70175342,"text":"sir20165104 - 2016 - Geomorphic responses of Duluth-area streams to the June 2012 flood, Minnesota","interactions":[],"lastModifiedDate":"2022-03-09T20:41:51.530149","indexId":"sir20165104","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5104","title":"Geomorphic responses of Duluth-area streams to the June 2012 flood, Minnesota","docAbstract":"In 2013, the U.S. Geological Survey, in cooperation with the Minnesota Pollution Control Agency, completed a geomorphic assessment of 51 Duluth-area stream sites in 20 basins to describe and document the stream geomorphic changes associated with the June 2012 flood. Heavy rainfall caused flood peaks with annual exceedance probabilities of less than 0.002 (flood recurrence interval of greater than 500 years) on large and small streams in and surrounding the Duluth area. A geomorphic segment-scale classification previously developed in 2003–4 by the U.S. Geological Survey for Duluth-area streams was used as a framework to characterize the observed flood-related responses along a longitudinal continuum from headwaters to rivermouths at Lake Superior related to drainage network position, slope, geologic setting, and valley type. Field assessments in 2013 followed and expanded on techniques used in 2003–4 at intensive and rapid sites. A third level of assessment was added in 2013 to increase the amount of quantitative data at a subset of 2003–4 rapid sites. Characteristics of channel morphology, channel bed substrate, exposed bars and soft sediment deposition, large wood, pools, and bank erosion were measured; and repeat photographs were taken. Additional measurements in 2013 included identification of Rosgen Level II stream types. The comparative analyses of field data collected in 2003–4 and again in 2013 indicated notable geomorphic changes, some of them expected and others not. As expected, in headwaters with gently sloping wetland segments, geomorphic changes were negligible (little measured or observed change). Downstream, middle main stems generally had bank and bluff erosion and bar formation as expected. Steep bedrock sites along middle and lower main stems had localized bank and bluff erosion in short sections with intermittent bedrock. Lower main stem and alluvial sites had bank erosion, widening, gravel bar deposition, and aggradation. Bar formation and accumulation of gravel was more widespread than expected among all main stems, especially for sites upstream and downstream from channel constrictions from road crossings, or even steep sites with localized, more gently sloping sections. Decreases in large wood and pools also were observed throughout the longitudinal continuum of main-stem sites, with immediate implications for fish and benthic invertebrate aquatic habitat. Whether or not the geomorphic conditions will return to their preflood condition depends on the location along the longitudinal continuum. The amount of large wood and pools may return after more moderate floods, whereas bars with coarse material may remain in place, locally altering flow direction and causing continued bank erosion. Results from this study can be used by local managers in postflood reconstruction efforts and provide baseline information for continued monitoring of geomorphic responses to the June 2012 flood.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165104","collaboration":"Prepared in cooperation with the Minnesota Pollution Control Agency","usgsCitation":"Fitzpatrick, F.A., Ellison, C.A., Czuba, C.R., Young, B.M., McCool, M.M., and Groten, J.T., 2016, Geomorphic responses of Duluth-area streams to the June 2012 flood, Minnesota: U.S. Geological Survey Scientific Investigations Report 2016–5104, 53 p. with appendixes, https://dx.doi.org/10.3133/sir20165104.","productDescription":"Report: vi, 53 p.; Appendixes: 1–4","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065922","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":328169,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5104/sir20165104_appendix4.xlsx","text":"Appendix 4","size":"990 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5104 Appendix 4"},{"id":328168,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5104/sir20165104_appendix3.zip","text":"Appendix 3","size":"2.36 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016–5104 Appendix 3"},{"id":328167,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5104/sir20165104_appendix2.pdf","text":"Appendix 2","size":"83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5104 Appendix 2"},{"id":328166,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5104/sir20165104_appendix1.xlsx","text":"Appendix 1","size":"30.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5104 Appendix 1"},{"id":328164,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5104/coverthb.jpg"},{"id":328165,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5104/sir20165104.pdf","text":"Report","size":"5.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5104"}],"country":"United States","state":"Minnesota","city":"Duluth","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.92741394042969,\n 46.87849898215226\n ],\n [\n -92.01805114746094,\n 46.924007100770275\n ],\n [\n -92.0328140258789,\n 46.981891954654735\n ],\n [\n -92.07744598388672,\n 47.003202171774475\n ],\n [\n -92.13890075683594,\n 46.96666516842388\n ],\n [\n -92.14302062988281,\n 46.90806019832023\n ],\n [\n -92.19657897949219,\n 46.81039934792954\n ],\n [\n -92.20756530761719,\n 46.785956378641224\n ],\n [\n -92.35382080078125,\n 46.69301892051677\n ],\n [\n -92.31021881103516,\n 46.66758028334327\n ],\n [\n -92.24292755126953,\n 46.65438516352555\n ],\n [\n -92.20378875732422,\n 46.65532777888051\n ],\n [\n -92.20172882080078,\n 46.703614817813545\n ],\n [\n -92.1866226196289,\n 46.71915170604123\n ],\n [\n -92.1488571166992,\n 46.71891633201399\n ],\n [\n -92.13924407958984,\n 46.739390031317825\n ],\n [\n -92.02869415283203,\n 46.822616668804926\n ],\n [\n -91.92741394042969,\n 46.87849898215226\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Factors affecting feeding and growth of early life stages of the federally endangered pallid sturgeon (Scaphirhynchus albus) are not fully understood, owing to their scarcity in the wild. In this study was we evaluated the performance of a combined foraging-bioenergetics model as a tool for assessing growth of age-0 pallid sturgeon in the Missouri River. In the laboratory, three size classes of sturgeon larvae (18–44 mm; 0.027–0.329 g) were grown for 7 to 14 days under differing temperature (14–24 °C) and prey density (0–9 Chironomidae larvae/d) regimes. After accounting for effects of water temperature and prey density on fish activity, we compared observed final weight, final length, and number of prey consumed to values generated from the foraging-bioenergetics model. When confronted with an independent dataset, the combined model provided reliable estimates (within 13% of observations) of fish growth and prey consumption, underscoring the usefulness of the modeling approach for evaluating growth dynamics of larval fish when empirical data are lacking.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2016.05.017","usgsCitation":"Deslauriers, D., Heironimus, L.B., and Chipps, S.R., 2016, Test of a foraging-bioenergetics model to evaluate growth dynamics of endangered pallid sturgeon (Scaphirhynchus albus): Ecological Modelling, v. 336, p. 1-12, https://doi.org/10.1016/j.ecolmodel.2016.05.017.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-077141","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":336944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"336","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4f3e4b014cc3a3ba4aa","contributors":{"authors":[{"text":"Deslauriers, David","contributorId":187586,"corporation":false,"usgs":false,"family":"Deslauriers","given":"David","email":"","affiliations":[],"preferred":false,"id":680969,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heironimus, Laura B.","contributorId":187587,"corporation":false,"usgs":false,"family":"Heironimus","given":"Laura","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":680970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":680946,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176107,"text":"70176107 - 2016 - National protocol framework for the inventory and monitoring of bees","interactions":[],"lastModifiedDate":"2018-08-10T16:16:48","indexId":"70176107","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"National protocol framework for the inventory and monitoring of bees","docAbstract":"This national protocol framework is a standardized tool for the inventory and monitoring of the approximately 4,200 species of native and non-native bee species that may be found within the National Wildlife Refuge System (NWRS) administered by the U.S. Fish and Wildlife Service (USFWS). However, this protocol framework may also be used by other organizations and individuals to monitor bees in any given habitat or location. Our goal is to provide USFWS stations within the NWRS (NWRS stations are land units managed by the USFWS such as national wildlife refuges, national fish hatcheries, wetland management districts, conservation areas, leased lands, etc.) with techniques for developing an initial baseline inventory of what bee species are present on their lands and to provide an inexpensive, simple technique for monitoring bees continuously and for monitoring and evaluating long-term population trends and management impacts. The latter long-term monitoring technique requires a minimal time burden for the individual station, yet can provide a good statistical sample of changing populations that can be investigated at the station, regional, and national levels within the USFWS’ jurisdiction, and compared to other sites within the United States and Canada. This protocol framework was developed in cooperation with the United States Geological Survey (USGS), the USFWS, and a worldwide network of bee researchers who have investigated the techniques and methods for capturing bees and tracking population changes. The protocol framework evolved from field and lab-based investigations at the USGS Bee Inventory and Monitoring Laboratory at the Patuxent Wildlife Research Center in Beltsville, Maryland starting in 2002 and was refined by a large number of USFWS, academic, and state groups. It includes a Protocol Introduction and a set of 8 Standard Operating Procedures or SOPs and adheres to national standards of protocol content and organization. The Protocol Narrative describes the history and need for the protocol framework and summarizes the basic elements of objectives, sampling design, field methods, training, data management, analysis, and reporting. The SOPs provide more detail and specific instructions for implementing the protocol framework. A central database, for managing all the resulting data is under development. We welcome use of this protocol framework by our partners, as appropriate for their bee inventory and monitoring objectives.
","language":"English","publisher":"U.S. Fish and Wildlife Service","publisherLocation":"Fort Collins, CO","usgsCitation":"Droege, S., Engler, J.D., Sellers, E.A., and O’Brien, L., 2016, National protocol framework for the inventory and monitoring of bees, v, 79 p.","productDescription":"v, 79 p.","numberOfPages":"97","ipdsId":"IP-054936","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":328772,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":327877,"type":{"id":11,"text":"Document"},"url":"https://ecos.fws.gov/ServCatFiles/reference/holding/47682"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c66ce4b0bc0bec09c978","contributors":{"authors":[{"text":"Droege, Sam sdroege@usgs.gov","contributorId":3464,"corporation":false,"usgs":true,"family":"Droege","given":"Sam","email":"sdroege@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":647131,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engler, Joseph D.","contributorId":69943,"corporation":false,"usgs":false,"family":"Engler","given":"Joseph","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":647132,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sellers, Elizabeth A. 0000-0003-4676-2994 esellers@usgs.gov","orcid":"https://orcid.org/0000-0003-4676-2994","contributorId":4704,"corporation":false,"usgs":true,"family":"Sellers","given":"Elizabeth","email":"esellers@usgs.gov","middleInitial":"A.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":647130,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Brien, Lee","contributorId":174067,"corporation":false,"usgs":false,"family":"O’Brien","given":"Lee","email":"","affiliations":[{"id":5128,"text":"U.S. Fish and Wildlife Service, University of Montana, Missoula, MT 59812","active":true,"usgs":false}],"preferred":false,"id":647133,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70185062,"text":"70185062 - 2016 - SNP discovery in candidate adaptive genes using exon capture in a free-ranging alpine ungulate","interactions":[],"lastModifiedDate":"2017-03-13T17:00:12","indexId":"70185062","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"SNP discovery in candidate adaptive genes using exon capture in a free-ranging alpine ungulate","docAbstract":"Identification of genes underlying genomic signatures of natural selection is key to understanding adaptation to local conditions. We used targeted resequencing to identify SNP markers in 5321 candidate adaptive genes associated with known immunological, metabolic and growth functions in ovids and other ungulates. We selectively targeted 8161 exons in protein-coding and nearby 5′ and 3′ untranslated regions of chosen candidate genes. Targeted sequences were taken from bighorn sheep (Ovis canadensis) exon capture data and directly from the domestic sheep genome (Ovis aries v. 3; oviAri3). The bighorn sheep sequences used in the Dall's sheep (Ovis dalli dalli) exon capture aligned to 2350 genes on the oviAri3 genome with an average of 2 exons each. We developed a microfluidic qPCR-based SNP chip to genotype 476 Dall's sheep from locations across their range and test for patterns of selection. Using multiple corroborating approaches (lositan and bayescan), we detected 28 SNP loci potentially under selection. We additionally identified candidate loci significantly associated with latitude, longitude, precipitation and temperature, suggesting local environmental adaptation. The three methods demonstrated consistent support for natural selection on nine genes with immune and disease-regulating functions (e.g. Ovar-DRA, APC, BATF2, MAGEB18), cell regulation signalling pathways (e.g. KRIT1, PI3K, ORRC3), and respiratory health (CYSLTR1). Characterizing adaptive allele distributions from novel genetic techniques will facilitate investigation of the influence of environmental variation on local adaptation of a northern alpine ungulate throughout its range. This research demonstrated the utility of exon capture for gene-targeted SNP discovery and subsequent SNP chip genotyping using low-quality samples in a nonmodel species.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.12560","usgsCitation":"Roffler, G.H., Amish, S.J., Smith, S., Cosart, T.F., Kardos, M., Schwartz, M.K., and Luikart, G., 2016, SNP discovery in candidate adaptive genes using exon capture in a free-ranging alpine ungulate: Molecular Ecology Resources, v. 16, no. 5, p. 1147-1164, https://doi.org/10.1111/1755-0998.12560.","productDescription":"18 p.","startPage":"1147","endPage":"1164","ipdsId":"IP-077118","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":470614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1755-0998.12560","text":"Publisher Index Page"},{"id":337477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-18","publicationStatus":"PW","scienceBaseUri":"58c7afa0e4b0849ce9795e9c","chorus":{"doi":"10.1111/1755-0998.12560","url":"http://dx.doi.org/10.1111/1755-0998.12560","publisher":"Wiley-Blackwell","authors":"Roffler Gretchen H., Amish Stephen J., Smith Seth, Cosart Ted, Kardos Marty, Schwartz Michael K., Luikart Gordon","journalName":"Molecular Ecology Resources","publicationDate":"7/18/2016"},"contributors":{"authors":[{"text":"Roffler, Gretchen H. groffler@usgs.gov","contributorId":1946,"corporation":false,"usgs":true,"family":"Roffler","given":"Gretchen","email":"groffler@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":684124,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amish, Stephen J.","contributorId":104799,"corporation":false,"usgs":false,"family":"Amish","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":5097,"text":"University of Montana, Division of Biological Sciences","active":true,"usgs":false}],"preferred":false,"id":684170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Seth","contributorId":189234,"corporation":false,"usgs":false,"family":"Smith","given":"Seth","email":"","affiliations":[],"preferred":false,"id":684171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cosart, Ted F.","contributorId":177052,"corporation":false,"usgs":false,"family":"Cosart","given":"Ted","email":"","middleInitial":"F.","affiliations":[{"id":5091,"text":"Flathead Lake Biological Station, Fish and Wildlife Genomics Group, Division of Biological Sciences, University of Montana, Polson, MT 59860, USA","active":true,"usgs":false}],"preferred":false,"id":684172,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kardos, Marty","contributorId":189235,"corporation":false,"usgs":false,"family":"Kardos","given":"Marty","affiliations":[],"preferred":false,"id":684173,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwartz, Michael K.","contributorId":102326,"corporation":false,"usgs":true,"family":"Schwartz","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":684174,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Luikart, Gordon","contributorId":145746,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","email":"","affiliations":[{"id":16220,"text":"Flathead Lake Biological Station, Div. Biological Science, UM","active":true,"usgs":false}],"preferred":false,"id":684175,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70187173,"text":"70187173 - 2016 - Environmental covariates associated with Cambarus veteranus (Decapoda: Cambaridae), an imperiled Appalachian crayfish endemic to West Virginia, USA","interactions":[],"lastModifiedDate":"2018-03-16T15:31:45","indexId":"70187173","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2235,"text":"Journal of Crustacean Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Environmental covariates associated with Cambarus veteranus (Decapoda: Cambaridae), an imperiled Appalachian crayfish endemic to West Virginia, USA","title":"Environmental covariates associated with Cambarus veteranus (Decapoda: Cambaridae), an imperiled Appalachian crayfish endemic to West Virginia, USA","docAbstract":"Cambarus veteranus Faxon, 1914, a narrow endemic crayfish native to the Upper Guyandotte River Basin (UGB) in West Virginia, USA, was petitioned in 2014 by the United States Fish and Wildlife Service to be listed as endangered, but a status survey was recommended to determine if listing was warranted. During May and June 2015, surveys were undertaken across the UGB to determine the current distribution of the species. A total of 71 sites were sampled, including all streams where the species was previously recorded, as well as semi-randomly selected streams, with 1-9 125 m long sites sampled per wadeable stream. Physiochemical and physical habitat data (based on the Qualitative Habitat Evaluation Index, QHEI) were obtained at each site to determine abiotic factors that were associated with the presence of C. veteranus. Site detection or non-detection of C. veteranus and associated site covariates were modeled using logistic regression to determine covariates associated with the presence of the species. Cambarus veteranus was present in both the Pinnacle Creek and Clear Fork/Laurel Fork watersheds at 10 sites, but it was not observed in the remaining 61 sites. An additive effects model with conductivity and QHEI was selected as the best approximating model. Cambarusveteranus was associated with lower than average UGB conductivity (379 µS) and high (>80) QHEI score. All sites where C. veteranus was not detected had higher conductivity and/or lower QHEI scores.
","language":"English","publisher":"Oxford Academic","doi":"10.1163/1937240x-00002456","usgsCitation":"Loughman, Z.J., Welsh, S., Sadecky, N., Dillard, Z.W., and Scott, R.K., 2016, Environmental covariates associated with Cambarus veteranus (Decapoda: Cambaridae), an imperiled Appalachian crayfish endemic to West Virginia, USA: Journal of Crustacean Biology, v. 36, no. 5, p. 642-648, https://doi.org/10.1163/1937240x-00002456.","productDescription":"7 p.","startPage":"642","endPage":"648","ipdsId":"IP-078754","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470619,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1163/1937240x-00002456","text":"Publisher Index Page"},{"id":340355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Upper Guyandotte River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.14501953125,\n 38.37611542403604\n ],\n [\n -80.83740234375,\n 38.315801006824984\n ],\n [\n -80.5517578125,\n 38.22091976683121\n ],\n [\n -80.26611328125,\n 38.08268954483802\n ],\n [\n -80.22216796875,\n 37.93553306183642\n ],\n [\n -80.343017578125,\n 37.75334401310656\n ],\n [\n -80.66162109375,\n 37.61423141542417\n ],\n [\n -81.01318359375,\n 37.501010429493284\n ],\n [\n -81.76025390625,\n 37.50972584293751\n ],\n [\n -81.968994140625,\n 37.58811876638322\n ],\n [\n -82.276611328125,\n 37.735969208590504\n ],\n [\n -82.37548828125,\n 37.95286091815649\n ],\n [\n -82.496337890625,\n 38.14319750166766\n ],\n [\n -82.4853515625,\n 38.28993659801203\n ],\n [\n -82.30957031249999,\n 38.41055825094609\n ],\n [\n -82.0458984375,\n 38.57393751557591\n ],\n [\n -81.82617187499999,\n 38.57393751557591\n ],\n [\n -81.507568359375,\n 38.53957267203905\n ],\n [\n -81.287841796875,\n 38.46219172306828\n ],\n [\n -81.14501953125,\n 38.37611542403604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59006063e4b0e85db3a5ddd7","contributors":{"authors":[{"text":"Loughman, Zachary J.","contributorId":76157,"corporation":false,"usgs":false,"family":"Loughman","given":"Zachary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":692929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":692923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadecky, Nicole M.","contributorId":179375,"corporation":false,"usgs":false,"family":"Sadecky","given":"Nicole M.","affiliations":[],"preferred":false,"id":692930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dillard, Zachary W.","contributorId":179376,"corporation":false,"usgs":false,"family":"Dillard","given":"Zachary","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":692931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scott, R. Katie","contributorId":179377,"corporation":false,"usgs":false,"family":"Scott","given":"R.","email":"","middleInitial":"Katie","affiliations":[],"preferred":false,"id":692932,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70176178,"text":"70176178 - 2016 - Safety of the molluscicide Zequanox (R) to nontarget macroinvertebrates Gammarus lacustris (Amphipoda: Gammaridae) and Hexagenia spp. (Ephemeroptera: Ephemeridae)","interactions":[],"lastModifiedDate":"2016-08-31T16:05:18","indexId":"70176178","displayToPublicDate":"2016-08-31T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Safety of the molluscicide Zequanox (R) to nontarget macroinvertebrates Gammarus lacustris (Amphipoda: Gammaridae) and Hexagenia spp. (Ephemeroptera: Ephemeridae)","docAbstract":"Zequanox® is a commercial formulation of the killed bacterium, Pseudomonas fluorescens (strain CL145A), that was developed to control dreissenid mussels. In 2014, Zequanox became the second product registered by the United States Environmental Protection Agency (USEPA) for use in open water environments as a molluscicide. Previous nontarget studies demonstrated the safety and selectivity of P. fluorescens CL154A, but the database on the toxicity of the formulation (Zequanox) is limited for macroinvertebrate taxa and exposure conditions. We evaluated the safety of Zequanox to the amphipod Gammarus lacustris lacustris, and nymphs of the burrowing mayfly, Hexagenia spp. at the maximum approved concentration (100 mg/L active ingredient, A.I.) and exposure duration (8 h). Survival of animals was assessed after 8 h of exposure and again at 24 and 96 h post-exposure. Histopathology of the digestive tract of control and treated animals was compared at 96 h post-exposure. The results showed no significant effect of Zequanox on survival of either species. Survival of G. lacustris exceeded 85% in all concentrations at all three sampling time points. Survival of Hexagenia spp. ranged from 71% (control) to 91% at 8 h, 89–93% at 24 h post-exposure, and 70–73% at 96 h post-exposure across all treatments. We saw no evidence of pathology in the visceral organs of treated animals. Our results indicate that application of Zequanox at the maximum approved concentration and exposure duration did not cause significant mortality or treatment-related histopathological changes to G. lacustris and Hexagenia spp.
","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre - REABIC","doi":"10.3391/mbi.2016.7.3.06","usgsCitation":"Waller, D.L., Luoma, J.A., and Erickson, R.A., 2016, Safety of the molluscicide Zequanox (R) to nontarget macroinvertebrates Gammarus lacustris (Amphipoda: Gammaridae) and Hexagenia spp. (Ephemeroptera: Ephemeridae): Management of Biological Invasions, v. 7, no. 3, p. 269-280, https://doi.org/10.3391/mbi.2016.7.3.06.","productDescription":"12 p.","startPage":"269","endPage":"280","ipdsId":"IP-071502","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":470630,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2016.7.3.06","text":"Publisher Index Page"},{"id":328153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57c7f1ade4b0f2f0cebf11b3","contributors":{"authors":[{"text":"Waller, Diane L. 0000-0002-6104-810X dwaller@usgs.gov","orcid":"https://orcid.org/0000-0002-6104-810X","contributorId":5272,"corporation":false,"usgs":true,"family":"Waller","given":"Diane","email":"dwaller@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":647609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":647610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":647611,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175411,"text":"sir20165034 - 2016 - Regional chloride distribution in the Northern Atlantic Coastal Plain aquifer system from Long Island, New York, to North Carolina","interactions":[],"lastModifiedDate":"2017-01-18T13:24:36","indexId":"sir20165034","displayToPublicDate":"2016-08-31T14:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5034","title":"Regional chloride distribution in the Northern Atlantic Coastal Plain aquifer system from Long Island, New York, to North Carolina","docAbstract":"The aquifers of the Northern Atlantic Coastal Plain are the principal source of water supply for the region’s nearly 20 million residents. Water quality and water levels in the aquifers, and maintenance of streamflow, are of concern because of the use of this natural resource for water supply and because of the possible effects of climate change and changes in land use on groundwater. The long-term sustainability of this natural resource is a concern at the local community scale, as well as at a regional scale, across state boundaries. In 2010, the U.S. Geological Survey (USGS) began a regional assessment of the Northern Atlantic Coastal Plain aquifers. An important part of this assessment is a regional interpretation of the extent of saltwater and the proximity of saltwater to fresh-groundwater resources and includes samples and published interpretations of chloride concentrations newly available since the last regional chloride assessment in 1989. This updated assessment also includes consideration of chloride samples and refined interpretations that stem from the 1994 discovery of the buried 35 million year old Chesapeake Bay impact structure that has substantially altered the understanding of the hydrogeologic framework and saltwater distribution in eastern Virginia.
\nIn this study, the regional area of concern for the chloride samples and interpretations extends from the Fall Line in the west to the outer edge of the Continental Shelf in the east and from the eastern tip of Long Island in the north to about halfway down the North Carolina coast in the south. Discussions of chloride distribution are presented for each of the 10 regional aquifer layers of the Northern Atlantic Coastal Plain, including the offshore extents. Maps of interpreted lines of equal concentration or isochlors were manually prepared for nine of the regional aquifers; a map was not prepared for the surficial regional aquifer. The isochlor interpretations include the offshore extent of the nine regional aquifers and are presented on a 1:2,000,000 scale base map. Vertically, the chloride samples and interpretations range from deepest (oldest) to shallowest (youngest)—Potomac-Patuxent, Potomac-Patapsco, Magothy, Matawan, Monmouth-Mount Laurel, Aquia, Piney Point, Lower Chesapeake, and Upper Chesapeake regional aquifers.
\nThe approach of this study maximizes the overall density of chloride information and data by assessing relevant published interpretations, all USGS chloride samples, and all relevant offshore samples in one comprehensive interpretation. Published isochlors, where they were interpreted by regional aquifer, were used as much as possible for this regional isochlor assessment. Publication dates for the isochlors used range from 1982 to 2015, and the scales for the isochlors range from local (county or municipality) to state (sub-regional) to regional. The USGS National Water Information System database provided well sample data for the parts of aquifers that are mainly beneath the land areas and yielded 37,517 water-quality records for 1903 through 2011. Published data reports from four phases of research-related offshore coring (1976, 1993, 1997, 2009) were the main source of water-quality data for the parts of aquifers from the shoreline to the outer edge of the Continental Shelf and yielded samples from multiple depths of each of 13 cores. This study also used interpretations and offshore core data from the last regional chloride assessment (1989) which, in addition to 7 offshore cores, included water-quality data from about 500 wells, and borehole geophysics interpretations from a subset of 11 wells. All published information and data that were used in this study were considered time independent and did not assess the published interpretations or data for temporal trends. The approach used here examined only published interpretations and available chloride data, and did not directly use supplemental techniques that can provide insight into the distribution of saltwater, such as geochemical characterization, borehole geophysical information, and geochronology.
\nIsochlor maps for this study are limited to manual interpretations of the 250-milligram per liter (mg/L) and 10,000-mg/L boundaries developed for 9 of the 10 regional aquifers that constitute the regional hydrogeologic framework of the Northern Atlantic Coastal Plain. For a given aquifer, the approach was to initially consider published isochlor interpretations, where available, then to modify the published interpretations, if necessary, to the extent indicated by the well and core samples. The final step was to interpolate isochlors to the full extent of each aquifer layer in areas with sufficient samples or cited interpretations, or to extrapolate isochlors in areas with no samples or where samples were sparse.
\nThe principal limitation of this study is that, because of its regional extent, data and information density can vary greatly, and thus confidence in interpretations can vary widely for onshore and offshore areas across the study area. In areas of sparse data, some samples of elevated chloride could be misinterpreted as being part of a regional elevated chloride trend, and in other cases, an elevated concentration could be misinterpreted as being of only local importance. The interpretive work of this study was applied to a 1:2,000,000 scale base map. Locations of isochlors, wells, cores, political boundaries, and shorelines are meant to be considered approximate.
\nThe isochlors presented in this study were manually interpreted for each aquifer unit as a conceptual representation of an equal concentration line approximately in the middle of an aquifer’s thickness. Differences in chloride concentration lines between the top and bottom of an aquifer could be substantial, especially for the thick parts of aquifers, but that information is not presented in this regional assessment.
\nAlthough additional offshore chloride data are available compared to 27 years ago (1989), the offshore information remains sparse, resulting in less confidence in the offshore interpretations than in the onshore interpretations. Regionally, the 250- and 10,000-mg/L isochlors tend to map progressively eastward from the deepest to the shallowest aquifers across the Northern Atlantic Coastal Plain aquifer system but with some exceptions. The additional data, conceptual understanding, and interpretations in the vicinity of the buried Chesapeake Bay impact structure in eastern Virginia resulted in substantial refinement of isochlors in that area. Overall, the interpretations in this study are updates of the previous regional study from 1989 but do not comprise major differences in interpretation and do not indicate regional movement of the freshwater-saltwater interface since then.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165034","usgsCitation":"Charles, E.G., 2016, Regional chloride distribution in the Northern Atlantic Coastal Plain aquifer system from Long Island, New York, to North Carolina: U.S. Geological Survey Scientific Investigations Report 2016–5034, 37 p., appendixes, https://dx.doi.org/10.3133/sir20165034.","productDescription":"Report: v, 35 p.; Appendixes: 1 and 2; Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-068551","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":326322,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/pp1829","text":"Professional Paper 1829 - ","description":"SIR 2016-5034","linkHelpText":"Assessment of Groundwater Availability in the Northern Atlantic Coastal Plain Aquifer System From Long Island, New York, to North Carolina"},{"id":326324,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20163046","text":"Fact Sheet 2016–3046 - ","description":"SIR 2016-5034","linkHelpText":"Sustainability of Groundwater Supplies in the Northern Atlantic Coastal Plain Aquifer System "},{"id":326323,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ds996","text":"Data Series 996 - ","description":"SIR 2016-5034","linkHelpText":"Digital Elevations and Extents of Regional Hydrogeologic Units in the Northern Atlantic Coastal Plain Aquifer System From Long Island, New York, to North Carolina"},{"id":326321,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20165076","text":"Scientific Investigations Report 2016–5076 -","description":"SIR 2016-5034","linkHelpText":"Documentation of a Groundwater Flow Model Developed To Assess Groundwater Availability in the Northern Atlantic Coastal Plain Aquifer System From Long Island, New York, to North Carolina"},{"id":327881,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5034/sir20165034_appendix1.zip","text":"Appendix 1 - ","size":"1.26 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5034","linkHelpText":"Offshore chloride concentrations [data]"},{"id":327882,"rank":13,"type":{"id":18,"text":"Project Site"},"url":"https://water.usgs.gov/wausp/","text":"USGS Water Availability and Use Science Program","description":"Project Site"},{"id":327102,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F70V89WN","text":"USGS data release","description":"USGS data release","linkHelpText":"Digital elevations and extents of hydrogeologic units"},{"id":326319,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5034/coverthb.jpg"},{"id":326320,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5034/sir20165034.pdf","text":"Report","size":"6.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5034"},{"id":328112,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5034/sir20165034_appendix1_metadata.xml","text":"Appendix 1 -","size":"23.3 KB xml","description":"SIR 2016-5034","linkHelpText":"Offshore chloride concentrations [metadata]"},{"id":328113,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5034/sir20165034_appendix2_metadata.xml","text":"Appendix 2 -","size":"23.6 KB xml","description":"SIR 2016-5034","linkHelpText":"Isochlors for 250- and 10,000-mg/L concentrations [metadata]"},{"id":328023,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5034/sir20165034_appendix2.zip","text":"Appendix 2 - ","size":"1.46 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5034","linkHelpText":"Isochlors for 250- and 10,000-mg/L concentrations [data]"},{"id":327101,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7MG7MKR","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-NWT model"}],"country":"United States","state":"Delaware, Maryland, New Jersey, New York, North Carolina, Virginia","otherGeospatial":"Northern Atlantic Coastal Plain aquifer system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {\n \"stroke\": \"#555555\",\n \"stroke-width\": 2,\n \"stroke-opacity\": 1,\n \"fill\": \"#555555\",\n \"fill-opacity\": 0.5\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.57568359375,\n 41.32732632036622\n ],\n [\n -71.630859375,\n 41.343824581185686\n ],\n [\n -71.21337890625,\n 41.261291493919856\n ],\n [\n -71.015625,\n 41.0130657870063\n ],\n [\n -71.30126953124999,\n 40.88029480552824\n ],\n [\n -71.78466796874999,\n 40.04443758460859\n ],\n [\n -72.6416015625,\n 38.37611542403604\n ],\n [\n -73.32275390625,\n 37.317751851636906\n ],\n [\n -73.564453125,\n 36.4566360115962\n ],\n [\n -74.06982421875,\n 35.15584570226544\n ],\n [\n -75.16845703124999,\n 34.939985151560435\n ],\n [\n -76.92626953125,\n 35.585851593232356\n ],\n [\n -77.2998046875,\n 36.26199220445664\n ],\n [\n -77.27783203125,\n 37.37015718405753\n ],\n [\n -76.81640625,\n 38.75408327579141\n ],\n [\n -75.7177734375,\n 39.757879992021756\n ],\n [\n -75.21240234375,\n 40.26276066437183\n ],\n [\n -74.8828125,\n 40.613952441166596\n ],\n [\n -74.6630859375,\n 40.6639728763869\n ],\n [\n -74.35546875,\n 40.78054143186031\n ],\n [\n -74.1357421875,\n 40.79717741518769\n ],\n [\n -73.89404296875,\n 40.830436877649255\n ],\n [\n -73.54248046875,\n 40.9964840143779\n ],\n [\n -73.19091796875,\n 41.1455697310095\n ],\n [\n -72.57568359375,\n 41.32732632036622\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Water Availability and Use Science Program
U.S. Geological Survey
150 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://water.usgs.gov/wausp/
The U.S. Geological Survey developed a groundwater flow model for the Northern Atlantic Coastal Plain aquifer system from Long Island, New York, to northeastern North Carolina as part of a detailed assessment of the groundwater availability of the area and included an evaluation of how these resources have changed over time from stresses related to human uses and climate trends. The assessment was necessary because of the substantial dependency on groundwater for agricultural, industrial, and municipal needs in this area.
The three-dimensional, groundwater flow model developed for this investigation used the numerical code MODFLOW–NWT to represent changes in groundwater pumping and aquifer recharge from predevelopment (before 1900) to future conditions, from 1900 to 2058. The model was constructed using existing hydrogeologic and geospatial information to represent the aquifer system geometry, boundaries, and hydraulic properties of the 19 separate regional aquifers and confining units within the Northern Atlantic Coastal Plain aquifer system and was calibrated using an inverse modeling parameter-estimation (PEST) technique.
The parameter estimation process was achieved through history matching, using observations of heads and flows for both steady-state and transient conditions. A total of 8,868 annual water-level observations from 644 wells from 1986 to 2008 were combined into 29 water-level observation groups that were chosen to focus the history matching on specific hydrogeologic units in geographic areas in which distinct geologic and hydrologic conditions were observed. In addition to absolute water-level elevations, the water-level differences between individual measurements were also included in the parameter estimation process to remove the systematic bias caused by missing hydrologic stresses prior to 1986. The total average residual of –1.7 feet was normally distributed for all head groups, indicating minimal bias. The average absolute residual value of 12.3 feet is about 3 percent of the total observed water-level range throughout the aquifer system.
Streamflow observation data of base flow conditions were derived for 153 sites from the U.S. Geological Survey National Hydrography Dataset Plus and National Water Information System. An average residual of about –8 cubic feet per second and an average absolute residual of about 21 cubic feet per second for a range of computed base flows of about 417 cubic feet per second were calculated for the 122 sites from the National Hydrography Dataset Plus. An average residual of about 10 cubic feet per second and an average absolute residual of about 34 cubic feet per second were calculated for the 568 flow measurements in the 31 sites obtained from the National Water Information System for a range in computed base flows of about 1,141 cubic feet per second.
The numerical representation of the hydrogeologic information used in the development of this regional flow model was dependent upon how the aquifer system and simulated hydrologic stresses were discretized in space and time. Lumping hydraulic parameters in space and hydrologic stresses and time-varying observational data in time can limit the capabilities of this tool to simulate how the groundwater flow system responds to changes in hydrologic stresses, particularly at the local scale.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165076","usgsCitation":"Masterson, J.P., Pope, J.P., Fienen, M.N., Monti, Jack Jr., Nardi, M.R., and Finkelstein, J.S., 2016, Documentation of a groundwater flow model developed to assess groundwater availability in the Northern Atlantic Coastal Plain aquifer system from Long Island, New York, to North Carolina (ver. 1.1, December 2016): U.S. Geological Survey Scientific Investigations Report 2016–5076, 70 p., https://dx.doi.org/10.3133/sir20165076.","productDescription":"Report: vi, 70 p.; Data Releases","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070585","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":326329,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20163046","text":"Fact Sheet 2016–3046 - ","description":"SIR 2016-5076","linkHelpText":"Sustainability of Groundwater Supplies in the Northern Atlantic Coastal Plain Aquifer System "},{"id":326328,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ds996","text":"Data Series 996 -","description":"SIR 2016-5076","linkHelpText":"Digital Elevations and Extents of Regional Hydrogeologic Units in the Northern Atlantic Coastal Plain Aquifer System From Long Island, New York, to North Carolina"},{"id":326327,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20165034","text":"Scientific Investigations Report 2016–5034 - ","description":"SIR 2016-5076","linkHelpText":"Regional Chloride Distribution in the Northern Atlantic Coastal Plain Aquifer System From Long Island, New York, to North Carolina"},{"id":326330,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/pp1829","text":"Professional Paper 1829 - ","description":"SIR 2016-5076","linkHelpText":"Assessment of Groundwater Availability in the Northern Atlantic Coastal Plain Aquifer System From Long Island, New York, to North Carolina"},{"id":332513,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5076/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":326839,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7MG7MKR","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-NWT model"},{"id":326325,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5076/coverthb2.jpg"},{"id":327889,"rank":9,"type":{"id":18,"text":"Project Site"},"url":"https://water.usgs.gov/wausp/","text":"USGS Water Availability and Use Science Program","description":"Project Site"},{"id":326840,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F70V89WN","text":"USGS data release","description":"USGS data release","linkHelpText":"Digital elevations and extents of hydrogeologic units"},{"id":326326,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5076/sir20165076.pdf","text":"Report","size":"26.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5076"}],"country":"United States","state":"Delaware, Maryland, New Jersey, New York, North Carolina, Virginia","otherGeospatial":"Northern Atlantic Coastal Plain aquifer system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.71875,\n 41.244772343082104\n ],\n [\n -72.861328125,\n 41.22824901518532\n ],\n [\n -73.93798828125,\n 40.830436877649255\n ],\n [\n -75.78369140625,\n 39.707186656826565\n ],\n [\n -77.080078125,\n 38.94232097947902\n ],\n [\n -77.62939453125,\n 38.39333888832238\n ],\n [\n -77.62939453125,\n 37.56199695314352\n ],\n [\n -77.5634765625,\n 36.82687474287728\n ],\n [\n -78.02490234375,\n 35.88905007936091\n ],\n [\n -75.6298828125,\n 34.63320791137959\n ],\n [\n -74.4873046875,\n 36.06686213257888\n ],\n [\n -71.103515625,\n 40.64730356252251\n ],\n [\n -71.71875,\n 41.244772343082104\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted August 31, 2016; Version 1.1: December 23, 2016","publicComments":"Version 1.1","contact":"Water Availability and Use Science Program
U.S. Geological Survey
150 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://water.usgs.gov/wausp/
Estimating streamwater solute loads is a central objective of many water-quality monitoring and research studies, as loads are used to compare with atmospheric inputs, to infer biogeochemical processes, and to assess whether water quality is improving or degrading. In this study, we evaluate loads and associated errors to determine the best load estimation technique among three methods (a period-weighted approach, the regression-model method, and the composite method) based on a solute's concentration dynamics and sampling frequency. We evaluated a broad range of varying concentration dynamics with stream flow and season using four dissolved solutes (sulfate, silica, nitrate, and dissolved organic carbon) at five diverse small watersheds (Sleepers River Research Watershed, VT; Hubbard Brook Experimental Forest, NH; Biscuit Brook Watershed, NY; Panola Mountain Research Watershed, GA; and Río Mameyes Watershed, PR) with fairly high-frequency sampling during a 10- to 11-yr period. Data sets with three different sampling frequencies were derived from the full data set at each site (weekly plus storm/snowmelt events, weekly, and monthly) and errors in loads were assessed for the study period, annually, and monthly. For solutes that had a moderate to strong concentration–discharge relation, the composite method performed best, unless the autocorrelation of the model residuals was <0.2, in which case the regression-model method was most appropriate. For solutes that had a nonexistent or weak concentration–discharge relation (modelR2 < about 0.3), the period-weighted approach was most appropriate. The lowest errors in loads were achieved for solutes with the strongest concentration–discharge relations. Sample and regression model diagnostics could be used to approximate overall accuracies and annual precisions. For the period-weighed approach, errors were lower when the variance in concentrations was lower, the degree of autocorrelation in the concentrations was higher, and sampling frequency was higher. The period-weighted approach was most sensitive to sampling frequency. For the regression-model and composite methods, errors were lower when the variance in model residuals was lower. For the composite method, errors were lower when the autocorrelation in the residuals was higher. Guidelines to determine the best load estimation method based on solute concentration–discharge dynamics and diagnostics are presented, and should be applicable to other studies.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1298","usgsCitation":"Aulenbach, B.T., Burns, D.A., Shanley, J.B., Yanai, R.D., Bae, K., Wild, A., Yang, Y., and Yi, D., 2016, Approaches to stream solute load estimation for solutes with varying dynamics from five diverse small watershed: Ecosphere, v. 7, no. 6, e01298; 22 p., https://doi.org/10.1002/ecs2.1298.","productDescription":"e01298; 22 p.","ipdsId":"IP-065579","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":470632,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1298","text":"Publisher Index Page"},{"id":328145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"6","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-17","publicationStatus":"PW","scienceBaseUri":"57c7f1a3e4b0f2f0cebf119f","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":647649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":647650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":647651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yanai, Ruth D.","contributorId":59720,"corporation":false,"usgs":true,"family":"Yanai","given":"Ruth","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":647652,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bae, Kikang","contributorId":174183,"corporation":false,"usgs":false,"family":"Bae","given":"Kikang","email":"","affiliations":[{"id":27381,"text":"State University of New York, College of Environmental Science and Forestry, Syracuse, NY","active":true,"usgs":false}],"preferred":false,"id":647653,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wild, Adam","contributorId":174184,"corporation":false,"usgs":false,"family":"Wild","given":"Adam","email":"","affiliations":[{"id":27381,"text":"State University of New York, College of Environmental Science and Forestry, Syracuse, NY","active":true,"usgs":false}],"preferred":false,"id":647654,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yang, Yang","contributorId":174185,"corporation":false,"usgs":false,"family":"Yang","given":"Yang","email":"","affiliations":[{"id":27381,"text":"State University of New York, College of Environmental Science and Forestry, Syracuse, NY","active":true,"usgs":false}],"preferred":false,"id":647655,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yi, Dong","contributorId":174186,"corporation":false,"usgs":false,"family":"Yi","given":"Dong","email":"","affiliations":[{"id":27381,"text":"State University of New York, College of Environmental Science and Forestry, Syracuse, NY","active":true,"usgs":false}],"preferred":false,"id":647656,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70175054,"text":"fs20163055 - 2016 - Streamflow of 2015—Water year national summary","interactions":[],"lastModifiedDate":"2016-09-12T09:41:28","indexId":"fs20163055","displayToPublicDate":"2016-08-30T11:15:00","publicationYear":"2016","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":"2016-3055","title":"Streamflow of 2015—Water year national summary","docAbstract":"The maps and graphs in this summary describe national streamflow conditions for water year 2015 (October 1, 2014, to September 30, 2015) in the context of the 86-year period 1930–2015, unless otherwise noted. The illustrations are based on observed data from the U.S. Geological Survey’s (USGS) National Streamflow Information Program http://water.usgs.gov/nsip). The period 1930–2015 was used because prior to 1930, the number of streamgages was too small to provide representative data for computing statistics for most regions of the country.
In the summary, reference is made to the term “runoff,” which is the depth to which a river basin, State, or other geographic area would be covered with water if all the streamflow within the area during a specified time period was uniformly distributed upon it. Runoff quantifies the magnitude of water flowing through the Nation's rivers and streams in measurement units that can be compared from one area to another.
Each of the maps and graphs can be expanded to a larger view by clicking on the image. In all of the graphics, a rank of 1 indicates the highest flow of all years analyzed. Rankings of streamflow are grouped into much-below normal, below normal, normal, above normal, and much-above normal, based on percentiles of flow (greater than 90 percent, 76–90 percent, 25–75 percent, 10–24 percent, and less than 10 percent, respectively) (http://waterwatch.usgs.gov/?id=ww_current). Some data used to produce maps and graphs are provisional and subject to change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163055","usgsCitation":"Jian, Xiaodong, Wolock, D.M., Lins, H.F., and Brady, S.J., 2016, Streamflow of 2015—Water year national summary: U.S. Geological Survey Fact Sheet 2016–3055, 6 p., https://dx.doi.org/10.3133/fs20163055.","productDescription":"6 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-075689","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":326773,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3055/fs20163055.pdf","text":"Report","size":"609 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3055"},{"id":326772,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3055/coverthb1.jpg"}],"country":"United States","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-66.28243,18.51476],[-65.7713,18.42668],[-65.591,18.22803],[-65.84716,17.97591],[-66.59993,17.98182],[-67.18416,17.94655],[-67.24243,18.37446],[-67.10068,18.5206],[-66.28243,18.51476]]],[[[-155.54211,19.08348],[-155.68817,18.91619],[-155.93665,19.05939],[-155.90806,19.33888],[-156.07347,19.70294],[-156.02368,19.81422],[-155.85008,19.97729],[-155.91907,20.17395],[-155.86108,20.26721],[-155.78505,20.2487],[-155.40214,20.07975],[-155.22452,19.99302],[-155.06226,19.8591],[-154.80741,19.50871],[-154.83147,19.45328],[-155.22217,19.23972],[-155.54211,19.08348]]],[[[-156.07926,20.64397],[-156.41445,20.57241],[-156.58673,20.783],[-156.70167,20.8643],[-156.71055,20.92676],[-156.61258,21.01249],[-156.25711,20.91745],[-155.99566,20.76404],[-156.07926,20.64397]]],[[[-156.75824,21.17684],[-156.78933,21.06873],[-157.32521,21.09777],[-157.25027,21.21958],[-156.75824,21.17684]]],[[[-157.65283,21.32217],[-157.70703,21.26442],[-157.7786,21.27729],[-158.12667,21.31244],[-158.2538,21.53919],[-158.29265,21.57912],[-158.0252,21.71696],[-157.94161,21.65272],[-157.65283,21.32217]]],[[[-159.34512,21.982],[-159.46372,21.88299],[-159.80051,22.06533],[-159.74877,22.1382],[-159.5962,22.23618],[-159.36569,22.21494],[-159.34512,21.982]]],[[[-94.81758,49.38905],[-94.64,48.84],[-94.32914,48.67074],[-93.63087,48.60926],[-92.61,48.45],[-91.64,48.14],[-90.83,48.27],[-89.6,48.01],[-89.27292,48.01981],[-88.37811,48.30292],[-87.43979,47.94],[-86.46199,47.55334],[-85.65236,47.22022],[-84.87608,46.90008],[-84.77924,46.6371],[-84.54375,46.53868],[-84.6049,46.4396],[-84.3367,46.40877],[-84.14212,46.51223],[-84.09185,46.27542],[-83.89077,46.11693],[-83.61613,46.11693],[-83.46955,45.99469],[-83.59285,45.81689],[-82.55092,45.34752],[-82.33776,44.44],[-82.13764,43.57109],[-82.43,42.98],[-82.9,42.43],[-83.12,42.08],[-83.142,41.97568],[-83.02981,41.8328],[-82.69009,41.67511],[-82.43928,41.67511],[-81.27775,42.20903],[-80.24745,42.3662],[-78.93936,42.86361],[-78.92,42.965],[-79.01,43.27],[-79.17167,43.46634],[-78.72028,43.62509],[-77.73789,43.62906],[-76.82003,43.62878],[-76.5,44.01846],[-76.375,44.09631],[-75.31821,44.81645],[-74.867,45.00048],[-73.34783,45.00738],[-71.50506,45.0082],[-71.405,45.255],[-71.08482,45.30524],[-70.66,45.46],[-70.305,45.915],[-69.99997,46.69307],[-69.23722,47.44778],[-68.905,47.185],[-68.23444,47.35486],[-67.79046,47.06636],[-67.79134,45.70281],[-67.13741,45.13753],[-66.96466,44.8097],[-68.03252,44.3252],[-69.06,43.98],[-70.11617,43.68405],[-70.64548,43.09024],[-70.81489,42.8653],[-70.825,42.335],[-70.495,41.805],[-70.08,41.78],[-70.185,42.145],[-69.88497,41.92283],[-69.96503,41.63717],[-70.64,41.475],[-71.12039,41.49445],[-71.86,41.32],[-72.295,41.27],[-72.87643,41.22065],[-73.71,40.9311],[-72.24126,41.11948],[-71.945,40.93],[-73.345,40.63],[-73.982,40.628],[-73.95232,40.75075],[-74.25671,40.47351],[-73.96244,40.42763],[-74.17838,39.70926],[-74.90604,38.93954],[-74.98041,39.1964],[-75.20002,39.24845],[-75.52805,39.4985],[-75.32,38.96],[-75.07183,38.78203],[-75.05673,38.40412],[-75.37747,38.01551],[-75.94023,37.21689],[-76.03127,37.2566],[-75.72205,37.93705],[-76.23287,38.31921],[-76.35,39.15],[-76.54272,38.71762],[-76.32933,38.08326],[-76.99,38.23999],[-76.30162,37.91794],[-76.25874,36.9664],[-75.9718,36.89726],[-75.86804,36.55125],[-75.72749,35.55074],[-76.36318,34.80854],[-77.39763,34.51201],[-78.05496,33.92547],[-78.55435,33.86133],[-79.06067,33.49395],[-79.20357,33.15839],[-80.30132,32.50935],[-80.86498,32.0333],[-81.33629,31.44049],[-81.49042,30.72999],[-81.31371,30.03552],[-80.98,29.18],[-80.53558,28.47213],[-80.53,28.04],[-80.05654,26.88],[-80.08801,26.20576],[-80.13156,25.81677],[-80.38103,25.20616],[-80.68,25.08],[-81.17213,25.20126],[-81.33,25.64],[-81.71,25.87],[-82.24,26.73],[-82.70515,27.49504],[-82.85526,27.88624],[-82.65,28.55],[-82.93,29.1],[-83.70959,29.93656],[-84.1,30.09],[-85.10882,29.63615],[-85.28784,29.68612],[-85.7731,30.15261],[-86.4,30.4],[-87.53036,30.27433],[-88.41782,30.3849],[-89.18049,30.31598],[-89.59383,30.15999],[-89.41373,29.89419],[-89.43,29.48864],[-89.21767,29.29108],[-89.40823,29.15961],[-89.77928,29.30714],[-90.15463,29.11743],[-90.88022,29.14854],[-91.62678,29.677],[-92.49906,29.5523],[-93.22637,29.78375],[-93.84842,29.71363],[-94.69,29.48],[-95.60026,28.73863],[-96.59404,28.30748],[-97.14,27.83],[-97.37,27.38],[-97.38,26.69],[-97.33,26.21],[-97.14,25.87],[-97.53,25.84],[-98.24,26.06],[-99.02,26.37],[-99.3,26.84],[-99.52,27.54],[-100.11,28.11],[-100.45584,28.69612],[-100.9576,29.38071],[-101.6624,29.7793],[-102.48,29.76],[-103.11,28.97],[-103.94,29.27],[-104.45697,29.57196],[-104.70575,30.12173],[-105.03737,30.64402],[-105.63159,31.08383],[-106.1429,31.39995],[-106.50759,31.75452],[-108.24,31.75485],[-108.24194,31.34222],[-109.035,31.34194],[-111.02361,31.33472],[-113.30498,32.03914],[-114.815,32.52528],[-114.72139,32.72083],[-115.99135,32.61239],[-117.12776,32.53534],[-117.29594,33.04622],[-117.944,33.62124],[-118.4106,33.74091],[-118.51989,34.02778],[-119.081,34.078],[-119.43884,34.34848],[-120.36778,34.44711],[-120.62286,34.60855],[-120.74433,35.15686],[-121.71457,36.16153],[-122.54747,37.55176],[-122.51201,37.78339],[-122.95319,38.11371],[-123.7272,38.95166],[-123.86517,39.76699],[-124.39807,40.3132],[-124.17886,41.14202],[-124.2137,41.99964],[-124.53284,42.76599],[-124.14214,43.70838],[-124.02053,44.6159],[-123.89893,45.52341],[-124.07963,46.86475],[-124.39567,47.72017],[-124.68721,48.18443],[-124.5661,48.37971],[-123.12,48.04],[-122.58736,47.096],[-122.34,47.36],[-122.5,48.18],[-122.84,49],[-120,49],[-117.03121,49],[-116.04818,49],[-113,49],[-110.05,49],[-107.05,49],[-104.04826,48.99986],[-100.65,49],[-97.22872,49.0007],[-95.15907,49],[-95.15609,49.38425],[-94.81758,49.38905]]],[[[-153.00631,57.11584],[-154.00509,56.73468],[-154.5164,56.99275],[-154.67099,57.4612],[-153.76278,57.81657],[-153.22873,57.96897],[-152.56479,57.90143],[-152.14115,57.59106],[-153.00631,57.11584]]],[[[-165.57916,59.90999],[-166.19277,59.75444],[-166.84834,59.94141],[-167.45528,60.21307],[-166.46779,60.38417],[-165.67443,60.29361],[-165.57916,59.90999]]],[[[-171.73166,63.78252],[-171.11443,63.59219],[-170.49111,63.69498],[-169.68251,63.43112],[-168.68944,63.29751],[-168.77194,63.1886],[-169.52944,62.97693],[-170.29056,63.19444],[-170.67139,63.37582],[-171.55306,63.31779],[-171.79111,63.40585],[-171.73166,63.78252]]],[[[-155.06779,71.14778],[-154.34417,70.69641],[-153.90001,70.88999],[-152.21001,70.82999],[-152.27,70.60001],[-150.73999,70.43002],[-149.72,70.53001],[-147.61336,70.21403],[-145.68999,70.12001],[-144.92001,69.98999],[-143.58945,70.15251],[-142.07251,69.85194],[-140.98599,69.712],[-140.9925,66.00003],[-140.99777,60.3064],[-140.013,60.27684],[-139.039,60.00001],[-138.34089,59.56211],[-137.4525,58.905],[-136.47972,59.46389],[-135.47583,59.78778],[-134.945,59.27056],[-134.27111,58.86111],[-133.35555,58.41029],[-132.73042,57.69289],[-131.70781,56.55212],[-130.00778,55.91583],[-129.97999,55.285],[-130.53611,54.80275],[-131.08582,55.17891],[-131.96721,55.49778],[-132.25001,56.37],[-133.53918,57.17889],[-134.07806,58.12307],[-135.03821,58.18771],[-136.62806,58.21221],[-137.80001,58.5],[-139.86779,59.53776],[-140.82527,59.72752],[-142.57444,60.08445],[-143.95888,59.99918],[-145.92556,60.45861],[-147.11437,60.88466],[-148.22431,60.67299],[-148.01807,59.97833],[-148.57082,59.91417],[-149.72786,59.70566],[-150.60824,59.36821],[-151.71639,59.15582],[-151.85943,59.74498],[-151.40972,60.7258],[-150.34694,61.03359],[-150.62111,61.28442],[-151.89584,60.7272],[-152.57833,60.06166],[-154.01917,59.35028],[-153.28751,58.86473],[-154.23249,58.14637],[-155.30749,57.72779],[-156.30833,57.42277],[-156.5561,56.97998],[-158.11722,56.46361],[-158.43332,55.99415],[-159.60333,55.56669],[-160.28972,55.64358],[-161.22305,55.36473],[-162.23777,55.02419],[-163.06945,54.68974],[-164.78557,54.40417],[-164.94223,54.57222],[-163.84834,55.03943],[-162.87,55.34804],[-161.80417,55.89499],[-160.5636,56.00805],[-160.07056,56.41806],[-158.68444,57.01668],[-158.4611,57.21692],[-157.72277,57.57],[-157.55027,58.32833],[-157.04167,58.91888],[-158.19473,58.6158],[-158.51722,58.78778],[-159.05861,58.42419],[-159.71167,58.93139],[-159.98129,58.57255],[-160.35527,59.07112],[-161.355,58.67084],[-161.96889,58.67166],[-162.05499,59.26693],[-161.87417,59.63362],[-162.51806,59.98972],[-163.81834,59.79806],[-164.66222,60.26748],[-165.34639,60.5075],[-165.35083,61.0739],[-166.12138,61.50002],[-165.73445,62.075],[-164.91918,62.63308],[-164.56251,63.14638],[-163.75333,63.21945],[-163.06722,63.05946],[-162.26056,63.54194],[-161.53445,63.45582],[-160.77251,63.76611],[-160.95834,64.2228],[-161.51807,64.40279],[-160.77778,64.7886],[-161.39193,64.77724],[-162.45305,64.55944],[-162.75779,64.33861],[-163.54639,64.55916],[-164.96083,64.44695],[-166.42529,64.68667],[-166.845,65.0889],[-168.11056,65.67],[-166.70527,66.08832],[-164.47471,66.57666],[-163.65251,66.57666],[-163.7886,66.07721],[-161.67777,66.11612],[-162.48971,66.73557],[-163.71972,67.11639],[-164.43099,67.61634],[-165.39029,68.04277],[-166.76444,68.35888],[-166.20471,68.88303],[-164.43081,68.91554],[-163.16861,69.37111],[-162.93057,69.85806],[-161.9089,70.33333],[-160.9348,70.44769],[-159.03918,70.89164],[-158.11972,70.82472],[-156.58082,71.35776],[-155.06779,71.14778]]]]},\"properties\":{\"name\":\"United States\"}}]}","contact":"Office of Surface Water
U.S. Geological Survey
415 National Center
Reston, VA 20192
http://water.usgs.gov/osw/
Advances in understanding the factors that limit a species’ range, particularly in the context of climate change, have come disproportionately through investigations at range edges or margins. The margins of a species’ range might often correspond with anomalous microclimates that confer habitat suitability where the species would otherwise fail to persist. We addressed this hypothesis using data from an interior, climatic range margin of the American pika (Ochotona princeps), an indicator of relatively cool, mesic climates in rocky habitats of western North America. Pikas in Lava Beds National Monument, northeastern California, USA, occur at elevations much lower than predicted by latitude and longitude. We hypothesized that pika occurrence within Lava Beds would be associated primarily with features such as “ice caves” in which sub-surface ice persists outside the winter months. We used data loggers to monitor sub-surface temperatures at cave entrances and at non-cave sites, confirming that temperatures were cooler and more stable at cave entrances. We surveyed habitat characteristics and evidence of pika occupancy across a random sample of cave and non-cave sites over a 2-yr period. Pika detection probability was high (~0.97), and the combined occupancy of cave and non-cave sites varied across the 2 yr from 27% to 69%. Contrary to our hypothesis, occupancy was not higher at cave sites. Vegetation metrics were the best predictors of site use by pikas, followed by an edge effect and elevation. The importance of vegetation as a predictor of pika distribution at this interior range margin is congruent with recent studies from other portions of the species’ range. However, we caution that vegetation composition depends on microclimate, which might be the proximal driver of pika distribution. The microclimates available in non-cave crevices accessible to small animals have not been characterized adequately for lava landscapes. We advocate innovation in the acquisition and use of microclimatic data for understanding the distributions of many taxa. Appropriately scaled microclimatic data are increasingly available but rarely used in studies of range dynamics.
","language":"English","publisher":"John Wiley & Sons","doi":"10.1002/ecs2.1379","usgsCitation":"Ray, C., Beever, E., and Rodhouse, T., 2016, Distribution of a climate-sensitive species at an interior range margin: Ecosphere, v. 7, no. 6, e01379; 22 p., https://doi.org/10.1002/ecs2.1379.","productDescription":"e01379; 22 p.","ipdsId":"IP-067004","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":470638,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1379","text":"Publisher Index Page"},{"id":327980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-29","publicationStatus":"PW","scienceBaseUri":"57c54e9ee4b0f2f0cebc9864","contributors":{"authors":[{"text":"Ray, Chris","contributorId":150148,"corporation":false,"usgs":false,"family":"Ray","given":"Chris","email":"","affiliations":[{"id":17921,"text":"Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado","active":true,"usgs":false}],"preferred":false,"id":647256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beever, Erik A. 0000-0002-9369-486X ebeever@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-486X","contributorId":147685,"corporation":false,"usgs":true,"family":"Beever","given":"Erik A.","email":"ebeever@usgs.gov","affiliations":[{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":647255,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodhouse, Thomas J.","contributorId":127378,"corporation":false,"usgs":false,"family":"Rodhouse","given":"Thomas J.","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":647257,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176124,"text":"70176124 - 2016 - Toward an integrated understanding of perceived biodiversity values and environmental conditions in a national park","interactions":[],"lastModifiedDate":"2016-08-31T11:11:52","indexId":"70176124","displayToPublicDate":"2016-08-29T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Toward an integrated understanding of perceived biodiversity values and environmental conditions in a national park","docAbstract":"In spatial planning and management of protected areas, increased priority is being given to research that integrates social and ecological data. However, public viewpoints of the benefits provided by ecosystems are not easily quantified and often implicitly folded into natural resource management decisions. Drawing on a spatially explicit participatory mapping exercise and a Social Values for Ecosystem Services (SolVES) analysis tool, the present study empirically examined and integrated social values for ecosystem services and environmental conditions within Channel Islands National Park, California. Specifically, a social value indicator of perceived biodiversity was examined using on-site survey data collected from a sample of people who visited the park. This information was modeled alongside eight environmental conditions including faunal species richness for six taxa, vegetation density, categories of marine and terrestrial land cover, and distance to features relevant for decision-makers. Results showed that biodiversity value points assigned to places by the pooled sample of respondents were widely and unevenly mapped, which reflected the belief that biodiversity was embodied to varying degrees by multiple locations in the park. Models generated for two survey subgroups defined by their self-reported knowledge of the Channels Islands revealed distinct spatial patterns of these perceived values. Specifically, respondents with high knowledge valued large spaces that were publicly inaccessible and unlikely to contain on-ground biodiversity, whereas respondents with low knowledge valued places that were experienced first-hand. Accessibility and infrastructure were also important considerations for anticipating how and where people valued the protected land and seascapes of Channel Islands National Park.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2016.07.029","usgsCitation":"van Riper, C.J., Kyle, G.T., Sherrouse, B.C., Bagstad, K.J., and Sutton, S., 2016, Toward an integrated understanding of perceived biodiversity values and environmental conditions in a national park: Ecological Indicators, v. 72, p. 278-287, https://doi.org/10.1016/j.ecolind.2016.07.029.","productDescription":"10 p.","startPage":"278","endPage":"287","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058049","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":327978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Channel Islands National Park, Santa Cruz Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.65209960937501,\n 34.02648590051866\n ],\n [\n -119.62875366210938,\n 34.02990029603909\n ],\n [\n -119.61914062499999,\n 34.04014265821752\n ],\n [\n -119.61090087890625,\n 34.057210513510306\n ],\n [\n -119.59716796875,\n 34.064036693555465\n ],\n [\n -119.5751953125,\n 34.06517433677496\n ],\n [\n -119.54910278320312,\n 34.06517433677496\n ],\n [\n -119.52301025390624,\n 34.04924594193161\n ],\n [\n -119.5037841796875,\n 34.03672867489511\n ],\n [\n -119.52438354492186,\n 34.01738017414996\n ],\n [\n -119.53536987304688,\n 34.00144280255186\n ],\n [\n -119.55459594726561,\n 33.98322500128355\n ],\n [\n -119.58755493164061,\n 33.98208625901939\n ],\n [\n -119.62051391601564,\n 33.98208625901939\n ],\n [\n -119.65209960937501,\n 33.97866994069442\n ],\n [\n -119.68231201171876,\n 33.96158628979907\n ],\n [\n -119.74411010742186,\n 33.95361274499209\n ],\n [\n -119.77981567382812,\n 33.95361274499209\n ],\n [\n -119.80590820312499,\n 33.95475186857191\n ],\n [\n -119.83612060546874,\n 33.96044725772099\n ],\n [\n -119.85946655273438,\n 33.96500329452543\n ],\n [\n -119.8773193359375,\n 33.97183689217048\n ],\n [\n -119.893798828125,\n 33.99119576995599\n ],\n [\n -119.89929199218749,\n 34.01168859910852\n ],\n [\n -119.89242553710938,\n 34.033314554166765\n ],\n [\n -119.92813110351561,\n 34.048108084909835\n ],\n [\n -119.93911743164062,\n 34.067449577393454\n ],\n [\n -119.92675781249999,\n 34.08792399342335\n ],\n [\n -119.90478515625,\n 34.08906131584996\n ],\n [\n -119.8773193359375,\n 34.0833745509365\n ],\n [\n -119.85671997070312,\n 34.07996230865873\n ],\n [\n -119.82650756835938,\n 34.068587174791965\n ],\n [\n -119.80316162109374,\n 34.066311964721045\n ],\n [\n -119.7784423828125,\n 34.066311964721045\n ],\n [\n -119.74960327148438,\n 34.064036693555465\n ],\n [\n -119.72076416015625,\n 34.05493499798558\n ],\n [\n -119.69467163085936,\n 34.04469442222683\n ],\n [\n -119.66995239257812,\n 34.03445260967645\n ],\n [\n -119.65209960937501,\n 34.02648590051866\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"72","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57c54ea1e4b0f2f0cebc9872","contributors":{"authors":[{"text":"van Riper, Carena J.","contributorId":42827,"corporation":false,"usgs":false,"family":"van Riper","given":"Carena","email":"","middleInitial":"J.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":647266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kyle, Gerard T.","contributorId":69405,"corporation":false,"usgs":true,"family":"Kyle","given":"Gerard","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":647267,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherrouse, Benson C. 0000-0002-5102-5895 bcsherrouse@usgs.gov","orcid":"https://orcid.org/0000-0002-5102-5895","contributorId":2445,"corporation":false,"usgs":true,"family":"Sherrouse","given":"Benson","email":"bcsherrouse@usgs.gov","middleInitial":"C.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":647265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":647269,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sutton, Stephen G.","contributorId":14685,"corporation":false,"usgs":true,"family":"Sutton","given":"Stephen G.","affiliations":[],"preferred":false,"id":647268,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}