Water quality in groundwater resources used for public drinking-water supply in the Western San Joaquin Valley (WSJV) was investigated by the USGS in cooperation with the California State Water Resources Control Board (SWRCB) as part of its Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project. The WSJV includes two study areas: the Delta–Mendota and Westside subbasins of the San Joaquin Valley groundwater basin. Study objectives for the WSJV study unit included two assessment types: (1) a status assessment yielding quantitative estimates of the current (2010) status of groundwater quality in the groundwater resources used for public drinking water, and (2) an evaluation of natural and anthropogenic factors that could be affecting the groundwater quality. The assessments characterized the quality of untreated groundwater, not the quality of treated drinking water delivered to consumers by water distributors.
The status assessment was based on data collected from 43 wells sampled by the U.S. Geological Survey for the GAMA Priority Basin Project (USGS-GAMA) in 2010 and data compiled in the SWRCB Division of Drinking Water (SWRCB-DDW) database for 74 additional public-supply wells sampled for regulatory compliance purposes between 2007 and 2010. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency (EPA) and SWRCB-DDW regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a spatially weighted, grid-based method to estimate the proportion of the groundwater resources used for public drinking water that has concentrations for particular constituents or class of constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale within the WSJV study unit, and permits comparison of the two study areas to other areas assessed by the GAMA Priority Basin Project statewide.
Groundwater resources used for public drinking water in the WSJV study unit are among the most saline and most affected by high concentrations of inorganic constituents of all groundwater resources used for public drinking water that have been assessed by the GAMA Priority Basin Project statewide. Among the 82 GAMA Priority Basin Project study areas statewide, the Delta–Mendota study area ranked above the 90th percentile for aquifer-scale proportions of groundwater resources having concentrations of total dissolved solids (TDS), sulfate, chloride, manganese, boron, chromium(VI), selenium, and strontium above benchmarks, and the Westside study area ranked above the 90th percentile for TDS, sulfate, manganese, and boron.
In the WSJV study unit as a whole, one or more inorganic constituents with regulatory or non-regulatory, health-based benchmarks were present at concentrations above benchmarks in about 53 percent of the groundwater resources used for public drinking water, and one or more organic constituents with regulatory health-based benchmarks were detected at concentrations above benchmarks in about 3 percent of the resource. Individual constituents present at concentrations greater than health-based benchmarks in greater than 2 percent of groundwater resources used for public drinking water included: boron (51 percent, SWRCB-DDW notification level), chromium(VI) (25 percent, SWRCB-DDW maximum contaminant level (MCL)), arsenic (10 percent, EPA MCL), strontium (5.1 percent, EPA Lifetime health advisory level (HAL)), nitrate (3.9 percent, EPA MCL), molybdenum (3.8 percent, EPA HAL), selenium (2.6 percent, EPA MCL), and benzene (2.6 percent, SWRCB-DDW MCL). In addition, 50 percent of the resource had TDS concentrations greater than non-regulatory, aesthetic-based SWRCB-DDW upper secondary maximum contaminant level (SMCL), and 44 percent had manganese concentrations greater than the SWRCB-DDW SMCL.
Natural and anthropogenic factors that could affect the groundwater quality were evaluated by using results from statistical testing of associations between constituent concentrations and values of potential explanatory factors, inferences from geochemical and age-dating tracer results, and by considering the water-quality results in the context of the hydrogeologic setting of the WSJV study unit.
Natural factors, particularly the lithologies of the source areas for groundwater recharge and of the aquifers, were the dominant factors affecting groundwater quality in most of the WSJV study unit. However, where groundwater resources used for public supply included groundwater recharged in the modern era, mobilization of constituents by recharge of water used for irrigation also affected groundwater quality. Public-supply wells in the Westside study area had a median depth of 305 m and primarily tapped groundwater recharged hundreds to thousands of years ago, whereas public-supply wells in the Delta–Mendota study area had a median depth of 85 m and primarily tapped either groundwater recharged within the last 60 years or groundwater consisting of mixtures of this modern recharge and older recharge.
Public-supply wells in the WSJV study unit are screened in the Tulare Formation and zones above and below the Corcoran Clay Member are used. The Tulare Formation primarily consists of alluvial sediments derived from the Coast Ranges to the west, except along the valley trough at the eastern margin of the WSJV study unit where the Tulare Formation consists of fluvial sands derived from the Sierra Nevada to the east. Groundwater from wells screened in the Sierra Nevada sands had manganese-reducing or manganese- and iron-reducing oxidation-reduction (redox) conditions. These redox conditions commonly were associated with elevated arsenic or molybdenum concentrations, and the dominance of arsenic(III) in the dissolved arsenic supports reductive dissolution of iron and manganese oxyhydroxides as the mechanism. In addition, groundwater from many wells screened in Sierra Nevada sands contained low concentrations of nitrite or ammonium, indicating reduction of nitrate by denitrification or dissimilatory processes, respectively.
Geology of the Coast Ranges westward of the study unit strongly affects groundwater quality in the WSJV. Elevated concentrations of TDS, sulfate, boron, selenium and strontium in groundwater were primarily associated with aquifer sediments and recharge derived from areas of the Coast Ranges dominated by Cretaceous-to-Miocene age, organic-rich, reduced marine shales, known as the source of selenium in WSJV soils, surface water, and groundwater. Low sulfur-isotopic values (δ34S) of dissolved sulfate indicate that the sulfate was largely derived from oxidation of biogenic pyrite from the shales, and correlations with trace element concentrations, geologic setting, and groundwater geochemical modeling indicated that distributions of sulfate, strontium, and selenium in groundwater were controlled by dissolution of secondary sulfate minerals in soils and sediments.
Elevated concentrations of chromium(VI) were primarily associated with aquifer sediments and recharge derived from areas of the Coast Ranges dominated by the Franciscan Complex and ultramafic rocks. The Franciscan Complex also has boron-rich, sodium-chloride dominated hydrothermal fluids that contribute to elevated concentrations of boron and TDS.
Groundwater from wells screened in Coast Ranges alluvium was primarily oxic and relatively alkaline (median pH value of 7.55) in the Delta–Mendota study area, and primarily nitrate-reducing or suboxic and alkaline (median pH value of 8.4) in the Westside study area. Many groundwater samples from those wells have elevated concentrations of arsenic(V), molybdenum, selenium, or chromium(VI), consistent with desorption of metal oxyanions from mineral surfaces under those geochemical conditions.
High concentrations of benzene were associated with deep wells located in the vicinity of petroleum deposits at the southern end of the Westside study area. Groundwater from these wells had premodern age and anoxic geochemical conditions, and the ratios among concentrations of hydrocarbon constituents were different from ratios found in fuels and combustion products, which is consistent with a geogenic source for the benzene rather than contamination from anthropogenic sources.
Water stable-isotope compositions, groundwater recharge temperatures, and groundwater ages were used to infer four types of groundwater: (1) groundwater derived from natural recharge of water from major rivers draining the Sierra Nevada; (2) groundwater primarily derived from natural recharge of water from Coast Ranges runoff; (3) groundwater derived from recharge of pumped groundwater applied to the land surface for irrigation; and (4) groundwater derived from recharge during a period of much cooler paleoclimate. Water previously used for irrigation was found both above and below the Corcoran Clay, supporting earlier inferences that this clay member is no longer a robust confining unit.
Recharge of water used for irrigation has direct and indirect effects on groundwater quality. Elevated nitrate concentrations and detections of herbicides and fumigants in the Delta–Mendota study area generally were associated with greater agricultural land use near the well and with water recharged during the last 60 years. However, the extent of the groundwater resource affected by agricultural sources of nitrate was limited by groundwater redox conditions sufficient to reduce nitrate. The detection frequency of perchlorate in Delta–Mendota groundwater was greater than expected for natural conditions. Perchlorate, nitrate, selenium, and strontium concentrations were correlated with one another and were greater in groundwater inferred to be recharge of previously pumped groundwater used for irrigation. The source of the perchlorate, selenium, and strontium appears to be salts deposited in the soils and sediments of the arid WSJV that are dissolved and flushed into groundwater by the increased amount of recharge caused by irrigation. In the Delta–Mendota study area, the groundwater with elevated concentrations of selenium was found deeper in the aquifer system than it was reported by a previous study 25 years earlier, suggesting that this transient front of groundwater with elevated concentrations of constituents derived from dissolution of soil salts by irrigation recharge is moving down through the aquifer system and is now reaching the depth zone used for public drinking water supply.
California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
We know economic and social policy has implications for ecosystems at large, but the consequences for a given geographic area or specific wildlife population are more difficult to conceptualize and communicate. Species distribution models, which extrapolate species-habitat relationships across ecological scales, are capable of predicting population changes in distribution and abundance in response to management and policy, and thus, are an ideal means for facilitating proactive management within a larger policy framework. To illustrate the capabilities of species distribution modeling in scenario planning for wildlife populations, we projected an existing distribution model for ring-necked pheasants (Phasianus colchicus) onto a series of alternative future landscape scenarios for Nebraska, USA. Based on our scenarios, we qualitatively and quantitatively estimated the effects of agricultural policy decisions on pheasant populations across Nebraska, in specific management regions, and at wildlife management areas.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/wsb.763","usgsCitation":"Fontaine, J.J., Jorgensen, C., Stuber, E.F., Gruber, L.F., Bishop, A.A., Lusk, J.J., Zach, E.S., and Decker, K.L., 2017, Species distributions models in wildlife planning: agricultural policy and wildlife management in the great plains: Wildlife Society Bulletin, v. 41, no. 2, p. 194-204, https://doi.org/10.1002/wsb.763.","productDescription":"11 p.","startPage":"194","endPage":"204","ipdsId":"IP-074173","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500009,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/96c855f789ed430aa033cce5d08fd393","text":"External Repository"},{"id":347513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","volume":"41","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-09","publicationStatus":"PW","scienceBaseUri":"59f83a35e4b063d5d30980d6","contributors":{"authors":[{"text":"Fontaine, Joseph J. 0000-0002-7639-9156 jfontaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-9156","contributorId":3820,"corporation":false,"usgs":true,"family":"Fontaine","given":"Joseph","email":"jfontaine@usgs.gov","middleInitial":"J.","affiliations":[{"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":716477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgensen, Christopher","contributorId":198580,"corporation":false,"usgs":false,"family":"Jorgensen","given":"Christopher","affiliations":[],"preferred":false,"id":716478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stuber, Erica F.","contributorId":198581,"corporation":false,"usgs":false,"family":"Stuber","given":"Erica","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":716479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gruber, Lutz F.","contributorId":198582,"corporation":false,"usgs":false,"family":"Gruber","given":"Lutz","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":716480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bishop, Andrew A.","contributorId":93323,"corporation":false,"usgs":true,"family":"Bishop","given":"Andrew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":716481,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lusk, Jeffrey J.","contributorId":198584,"corporation":false,"usgs":false,"family":"Lusk","given":"Jeffrey","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":716482,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zach, Eric S.","contributorId":198585,"corporation":false,"usgs":false,"family":"Zach","given":"Eric","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":716483,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Decker, Karie L.","contributorId":51094,"corporation":false,"usgs":true,"family":"Decker","given":"Karie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":716484,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70188420,"text":"70188420 - 2017 - Marine ferromanganese encrustations: Archives of changing oceans","interactions":[],"lastModifiedDate":"2017-06-09T09:50:09","indexId":"70188420","displayToPublicDate":"2017-06-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1490,"text":"Elements","active":true,"publicationSubtype":{"id":10}},"title":"Marine ferromanganese encrustations: Archives of changing oceans","docAbstract":"Marine iron–manganese oxide coatings occur in many shallow and deep-water areas of the global ocean and can form in three ways: 1) Fe–Mn crusts can precipitate from seawater onto rocks on seamounts; 2) Fe–Mn nodules can form on the sediment surface around a nucleus by diagenetic processes in sediment pore water; 3) encrustations can precipitate from hydrothermal fluids. These oxide coatings have been growing for thousands to tens of millions of years. They represent a vast archive of how oceans have changed, including variations of climate, ocean currents, geological activity, erosion processes on land, and even anthropogenic impact. A growing toolbox of age-dating methods and element and isotopic signatures are being used to exploit these archives.
","language":"English","publisher":"Mineralogical Society of America","doi":"10.2113/gselements.13.3.177","usgsCitation":"Koschinsky, A., and Hein, J.R., 2017, Marine ferromanganese encrustations: Archives of changing oceans: Elements, v. 13, no. 3, p. 177-182, https://doi.org/10.2113/gselements.13.3.177.","productDescription":"6 p.","startPage":"177","endPage":"182","ipdsId":"IP-081254","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":342316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-01","publicationStatus":"PW","scienceBaseUri":"593ad6e0e4b0764e6c602143","contributors":{"authors":[{"text":"Koschinsky, Andrea","contributorId":83813,"corporation":false,"usgs":true,"family":"Koschinsky","given":"Andrea","affiliations":[],"preferred":false,"id":697668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hein, James R. 0000-0002-5321-899X jhein@usgs.gov","orcid":"https://orcid.org/0000-0002-5321-899X","contributorId":140835,"corporation":false,"usgs":true,"family":"Hein","given":"James","email":"jhein@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":697667,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188400,"text":"70188400 - 2017 - An updated geospatial liquefaction model for global application","interactions":[],"lastModifiedDate":"2017-06-08T10:30:00","indexId":"70188400","displayToPublicDate":"2017-06-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"An updated geospatial liquefaction model for global application","docAbstract":"We present an updated geospatial approach to estimation of earthquake-induced liquefaction from globally available geospatial proxies. Our previous iteration of the geospatial liquefaction model was based on mapped liquefaction surface effects from four earthquakes in Christchurch, New Zealand, and Kobe, Japan, paired with geospatial explanatory variables including slope-derived VS30, compound topographic index, and magnitude-adjusted peak ground acceleration from ShakeMap. The updated geospatial liquefaction model presented herein improves the performance and the generality of the model. The updates include (1) expanding the liquefaction database to 27 earthquake events across 6 countries, (2) addressing the sampling of nonliquefaction for incomplete liquefaction inventories, (3) testing interaction effects between explanatory variables, and (4) overall improving model performance. While we test 14 geospatial proxies for soil density and soil saturation, the most promising geospatial parameters are slope-derived VS30, modeled water table depth, distance to coast, distance to river, distance to closest water body, and precipitation. We found that peak ground velocity (PGV) performs better than peak ground acceleration (PGA) as the shaking intensity parameter. We present two models which offer improved performance over prior models. We evaluate model performance using the area under the curve under the Receiver Operating Characteristic (ROC) curve (AUC) and the Brier score. The best-performing model in a coastal setting uses distance to coast but is problematic for regions away from the coast. The second best model, using PGV, VS30, water table depth, distance to closest water body, and precipitation, performs better in noncoastal regions and thus is the model we recommend for global implementation.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160198","usgsCitation":"Zhu, J., Baise, L.G., and Thompson, E.M., 2017, An updated geospatial liquefaction model for global application: Bulletin of the Seismological Society of America, v. 107, no. 3, p. 1365-1385, https://doi.org/10.1785/0120160198.","productDescription":"21 p. 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The U.S. Geological Survey (USGS) developed a fully automated Depth-Integrated Sample Arm (DISA) as a way to reduce bias and improve accuracy in water-quality concentration data. The DISA was designed to integrate with existing autosampler configurations commonly used for the collection of water-quality samples in vertical profile thereby providing a better representation of average suspended sediment and sediment-associated pollutant concentrations and distributions than traditional fixed-point samplers. In controlled laboratory experiments, known concentrations of suspended sediment ranging from 596 to 1,189 mg/L were injected into a 3 foot diameter closed channel (circular pipe) with regulated flows ranging from 1.4 to 27.8 ft3 /s. Median suspended sediment concentrations in water-quality samples collected using the DISA were within 7 percent of the known, injected value compared to 96 percent for traditional fixed-point samplers. Field evaluation of this technology in open channel fluvial systems showed median differences between paired DISA and fixed-point samples to be within 3 percent. The range of particle size measured in the open channel was generally that of clay and silt. Differences between the concentration and distribution measured between the two sampler configurations could potentially be much larger in open channels that transport larger particles, such as sand.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 5th Federal Interagency Hydrologic Modeling Conference and the 10th Federal Interagency Sedimentation Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Joint Federal Interagency Conference 2015","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","publisher":"Department of Interior","publisherLocation":"Reston, VA","usgsCitation":"Selbig, W.R., 2017, Collecting a better water-quality sample: Reducing vertical stratification bias in open and closed channels, in Proceedings of the 5th Federal Interagency Hydrologic Modeling Conference and the 10th Federal Interagency Sedimentation Conference, Reno, NV, April 19-23, 2015, 11 p.","productDescription":"11 p.","ipdsId":"IP-060694","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":342312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":342311,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://acwi.gov/sos/pubs/3rdJFIC/"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593ad6e1e4b0764e6c602147","contributors":{"authors":[{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697626,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70187278,"text":"fs20173032 - 2017 - Assessment of undiscovered continuous oil and gas resources in the Heath Formation, central Montana and western North Dakota, 2016","interactions":[],"lastModifiedDate":"2017-06-08T09:30:39","indexId":"fs20173032","displayToPublicDate":"2017-06-07T17:00:00","publicationYear":"2017","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":"2017-3032","title":" Assessment of undiscovered continuous oil and gas resources in the Heath Formation, central Montana and western North Dakota, 2016","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean resources of 884 million barrels of oil and 106 billion cubic feet of gas in the North-Central Montana and Williston Basin Provinces of central Montana and western North Dakota.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173032","usgsCitation":"Drake, R.M., II, Schenk, C.J., Klett, T.R., Le, P.A., Leathers-Miller, H.M., Brownfield, M.E., Finn, T.M., Gaswirth, S.B., Marra, K.R., and Tennyson, M.E., 2017, Assessment of undiscovered continuous oil and gas resources in the Heath Formation, central Montana and western North Dakota, 2016: U.S. Geological Survey Fact Sheet 2017–3032, 2 p., https://doi.org/10.3133/fs20173032.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-082665","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":438302,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75D8QRW","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project - North-Central Montana and Williston Basin Provinces, Heath Formation Assessment Unit Boundaries"},{"id":342017,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2013/3013","text":"Fact Sheet 2013–3013 : Assessment of undiscovered oil resources in the Bakken and Three Forks Formations, Williston Basin Province, Montana, North Dakota, and South Dakota, 2013"},{"id":342015,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3032/fs20173032.pdf","text":"Report","size":"468 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3032"},{"id":342016,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/of/2011/1167","text":"Open-File Report 2011–1167: USGS methodology for assessing continuous petroleum resources"},{"id":342014,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3032/coverthb.jpg"}],"country":"United States","state":"Montana, North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.6669921875,\n 47.65058757118734\n ],\n [\n -104.13940429687499,\n 47.78363463526376\n ],\n [\n -104.87548828125,\n 47.71715357016648\n ],\n [\n -105.44677734375,\n 47.58393661978134\n ],\n [\n -106.44653320312499,\n 47.53203824675999\n ],\n [\n -111.785888671875,\n 47.57652571374621\n ],\n [\n -112.69775390625,\n 47.42065432071318\n ],\n [\n -112.78564453124999,\n 47.204642388766935\n ],\n [\n -112.554931640625,\n 46.830133640447386\n ],\n [\n -111.59912109375,\n 46.057985244793024\n ],\n [\n -110.028076171875,\n 46.06560846138691\n ],\n [\n -106.534423828125,\n 46.03510927947334\n ],\n [\n -104.34814453125,\n 45.97406038956237\n ],\n [\n -103.546142578125,\n 46.42271253466717\n ],\n [\n -103.348388671875,\n 46.912750956378915\n ],\n [\n -103.348388671875,\n 47.331377157798244\n ],\n [\n -103.6669921875,\n 47.65058757118734\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The Piney Point aquifer in Virginia is newly described and delineated as being composed of six geologic units, in a study conducted by the U.S. Geological Survey in cooperation with the Virginia Department of Environmental Quality (VA DEQ). The eastward-dipping geologic units include, in stratigraphically ascending order, the
Identification of geologic units is based on typical sediment lithologies of geologic formations. Fine-grained sediments that compose confining units positioned immediately above and below the Piney Point aquifer are also described.
The Piney Point aquifer is one of several confined aquifers within the Virginia Coastal Plain and includes a highly porous and solution-channeled indurated limestone within the Piney Point Formation from which withdrawals are made. The limestone is relatively continuous laterally across central parts of the Northern Neck, Middle Peninsula, and York-James Peninsula. Other geologic units are of variable extent. The configurations of most of the geologic units are further affected by newly identified faults that are aligned radially from the Chesapeake Bay impact crater and create constrictions or barriers to groundwater flow. Some geologic units are also truncated beneath the lower Rappahannock River by a resurge channel associated with the impact crater.
Groundwater withdrawals from the Piney Point aquifer increased from approximately 1 million gallons per day (Mgal/d) during 1900 to 7.35 Mgal/d during 2004. As a result, a water-level cone of depression in James City and northern York Counties was estimated to be as low as 70 feet (ft) below the National Geodetic Vertical Datum of 1929 (NGVD 29) by 2005. Withdrawals decreased to 5.01 Mgal/d by 2009 as withdrawals were shifted toward other sources, and by 2015 water levels had recovered to approximately 50 ft below NGVD 29.
The mean estimated transmissivity of the Piney Point aquifer in York and James City Counties is 16,300 feet squared per day (ft2/d), but farther north it is only 925 ft2/d. The mean well specific capacity in York and James City Counties is 11.4 gallons per minute per foot (gal/min/ft). Farther north in Virginia, mean specific capacity is only 2.26 gal/min/ft, and in Maryland it is 0.99 gal/min/ft. The northward decrease in specific capacity probably reflects the northward decrease in transmissivity, which results from poor development of the solution-channeled limestone.
An aquifer test in northern York County induced vertical leakage to the solution-channeled limestone from overlying silty sand and a change in response of the aquifer to pumping from a single layer to two layers. Transmissivity of the limestone of approximately 19,800 ft2/d was distinguished from the silty sand of approximately 2,500 ft2/d.
Most of the water in the Piney Point aquifer is slightly alkaline with moderate concentrations primarily of sodium and bicarbonate that are slightly undersaturated with respect to calcite. Iron concentrations are generally less than 0.3 milligrams per liter (mg/L). Mixing of freshwater with seawater has elevated chloride concentrations to the southeast to as much as 7,120 mg/L.
Information on the Piney Point aquifer can benefit water-resource management in siting production wells, predicting likely well yield, and anticipating water-level response to withdrawals. Models that vertically discretize individual geologic units can potentially be used to evaluate groundwater flow in greater detail by representing lateral flow and vertical leakage among the geologic units.
Because groundwater withdrawals are made primarily from the limestone and sand of the Piney Point Formation, the VA DEQ has considered regarding the limestone and sand singly as a regulated aquifer apart from the other geologic units. Under current policy in Virginia, if only the limestone and sand were regarded as a regulated aquifer, a greater amount of drawdown would be allowed than is allowed for the Piney Point aquifer consisting of six geologic units. Some production wells intercept multiple geologic units, and the units can undergo water-level decline and vertical leakage induced by pumping from the limestone and sand. Whether the other geologic units are to be regarded as regulated aquifers is an additional consideration for the VA DEQ.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175041","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"McFarland, E.R., 2017, Hydrogeologic framework and hydrologic conditions of the Piney Point aquifer in Virginia: U.S. Geological Survey Scientific Investigations Report 2017–5041, 63 p., 2 pl., and CD-ROM, https://doi.org/10.3133/sir20175041.","productDescription":"Report: vii, 62 p.; 2 Plates: 24 x 36 inches and 36 x 24 inches; Appendixes 1-2; Data Release; Read Me","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-075864","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":342072,"rank":7,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2017/5041/readme.txt","size":"1.27 KB","linkFileType":{"id":2,"text":"txt"}},{"id":342068,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5041/sir20175041_appendix1.xlsx","text":"Appendix 1","size":"36.2 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Borehole Geologic-Unit Top-Surface Altitudes, Piney Point Aquifer, Virginia"},{"id":342066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5041/coverthb.jpg"},{"id":342069,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5041/sir20175041_appendix2.xlsx","text":"Appendix 2 ","size":"23.1 MB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Aquifer-Component Test data, Piney Point Aquifer, Virginia"},{"id":342071,"rank":6,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5041/sir20175041_plate2.pdf","text":"Plate 2 ","size":"397 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Hydrogeologic Sections A–A’, B–B’, and C–C’ of the Piney Point Aquifer in Virginia"},{"id":342076,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BV7DV5","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydrogeologic Framework and Hydrologic Conditions of the Piney Point Aquifer in Virginia"},{"id":342067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5041/sir20175041.pdf","text":"Report","size":"8.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5041"},{"id":342070,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5041/sir20175041_plate1.pdf","text":"Plate 1 ","size":"444 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Locations of Boreholes and Extent of Productive Limestone in the Piney Point Aquifer in Virginia"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.291667,\n 38.291667\n ],\n [\n -76.208333,\n 38.291667\n ],\n [\n -76.208333,\n 37.125\n ],\n [\n -77.291667,\n 37.125\n ],\n [\n -77.291667,\n 38.291667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Variability in seismic instrumentation performance plays a fundamental role in our ability to carry out experiments in observational seismology. Many such experiments rely on the assumed performance of various seismic sensors as well as on methods to isolate the sensors from nonseismic noise sources. We look at the repeatability of estimating the self‐noise, midband sensitivity, and the relative orientation by comparing three collocated Nanometrics Trillium Compact sensors. To estimate the repeatability, we conduct a total of 15 trials in which one sensor is repeatedly reinstalled, alongside two undisturbed sensors. We find that we are able to estimate the midband sensitivity with an error of no greater than 0.04% with a 99th percentile confidence, assuming a standard normal distribution. We also find that we are able to estimate mean sensor self‐noise to within ±5.6 dB with a 99th percentile confidence in the 30–100‐s‐period band. Finally, we find our relative orientation errors have a mean difference in orientation of 0.0171° from the reference, but our trials have a standard deviation of 0.78°.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120170006","usgsCitation":"Ringler, A.T., Holland, A., and Wilson, D.C., 2017, Repeatability of testing a small broadband sensor in the Albuquerque Seismological Laboratory Underground Vault: Bulletin of the Seismological Society of America, v. 107, no. 3, p. 1557-1563, https://doi.org/10.1785/0120170006.","productDescription":"7 p.","startPage":"1557","endPage":"1563","ipdsId":"IP-081437","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-25","publicationStatus":"PW","scienceBaseUri":"593910a8e4b0764e6c5e8842","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":145576,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holland, Austin 0000-0002-7843-1981 aaholland@usgs.gov","orcid":"https://orcid.org/0000-0002-7843-1981","contributorId":173969,"corporation":false,"usgs":true,"family":"Holland","given":"Austin","email":"aaholland@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697460,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697461,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188354,"text":"70188354 - 2017 - Duckling survival of mallards in Southland, New Zealand","interactions":[],"lastModifiedDate":"2019-12-17T09:40:09","indexId":"70188354","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Duckling survival of mallards in Southland, New Zealand","docAbstract":"The southern portion of New Zealand's South Island is a productive area for mallards (Anas platyrhynchos) despite a notable lack of permanent or semi-permanent wetlands. Most broods are reared in pastures that may or may not be flooded with ephemeral water. In recent years, there has been an increased conversion from continuous to sporadic grazing that has resulted in a functional change in the emergent and upland vegetation available for broods. In 2014, we investigated mallard duckling survival on different pastures relative to a suite of characteristics pertaining to the adult female, clutch, brood, weather, and habitat. We monitored 438 ducklings from 50 radio-marked females to 30 days post-hatch. Duckling survival was unaffected by pasture type but increased with duckling age, the presence of ephemeral water, and with greater distance from the nearest anthropogenic structure. Survival was lower for broods of second year (SY) females than for broods of after-second year (ASY) females, in areas with more dense cover, and when ducklings moved, on average, greater daily distances. Cumulative 30-day duckling survival ranged from 0.11 for ducklings of SY females without ephemeral water present to 0.46 for ducklings of ASY females with ephemeral water present. Therefore, increasing available seasonal water sources may increase duckling survival. Further, narrow, linear patches of dense cover present in our study could support a greater abundance of predators or increase their foraging efficiency. As such, managers could consider increasing patch sizes of dense cover to reduce predator efficiency, and employing predator removal in these areas to improve duckling survival.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.21256","usgsCitation":"Garrick, E., Amundson, C.L., and Seddon, P.J., 2017, Duckling survival of mallards in Southland, New Zealand: Journal of Wildlife Management, v. 81, no. 5, p. 858-867, https://doi.org/10.1002/jwmg.21256.","productDescription":"10 p.","startPage":"858","endPage":"867","ipdsId":"IP-079621","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":342193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"Southland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 167.552490234375,\n -46.86019101567025\n ],\n [\n 169.87060546875,\n -46.86019101567025\n ],\n [\n 169.87060546875,\n -46.30140615437331\n ],\n [\n 167.552490234375,\n -46.30140615437331\n ],\n [\n 167.552490234375,\n -46.86019101567025\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-29","publicationStatus":"PW","scienceBaseUri":"593910aae4b0764e6c5e884a","contributors":{"authors":[{"text":"Garrick, Erin","contributorId":192685,"corporation":false,"usgs":false,"family":"Garrick","given":"Erin","email":"","affiliations":[],"preferred":false,"id":697364,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amundson, Courtney L. 0000-0002-0166-7224 camundson@usgs.gov","orcid":"https://orcid.org/0000-0002-0166-7224","contributorId":4833,"corporation":false,"usgs":true,"family":"Amundson","given":"Courtney","email":"camundson@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":697363,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seddon, Phillip J.","contributorId":147258,"corporation":false,"usgs":false,"family":"Seddon","given":"Phillip","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":697365,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188355,"text":"70188355 - 2017 - Perturbational and nonperturbational inversion of Rayleigh-wave velocities","interactions":[],"lastModifiedDate":"2017-06-07T08:33:59","indexId":"70188355","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1808,"text":"Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Perturbational and nonperturbational inversion of Rayleigh-wave velocities","docAbstract":"The inversion of Rayleigh-wave dispersion curves is a classic geophysical inverse problem. We have developed a set of MATLAB codes that performs forward modeling and inversion of Rayleigh-wave phase or group velocity measurements. We describe two different methods of inversion: a perturbational method based on finite elements and a nonperturbational method based on the recently developed Dix-type relation for Rayleigh waves. In practice, the nonperturbational method can be used to provide a good starting model that can be iteratively improved with the perturbational method. Although the perturbational method is well-known, we solve the forward problem using an eigenvalue/eigenvector solver instead of the conventional approach of root finding. Features of the codes include the ability to handle any mix of phase or group velocity measurements, combinations of modes of any order, the presence of a surface water layer, computation of partial derivatives due to changes in material properties and layer boundaries, and the implementation of an automatic grid of layers that is optimally suited for the depth sensitivity of Rayleigh waves.
Despite changes in shrub cover and weather patterns associated with climate change in the Arctic, little is known about the breeding requirements of most passerines tied to northern regions. We investigated the nesting biology and nest habitat characteristics of Smith's Longspurs (Calcarius pictus) in 2 study areas in the Brooks Range of Alaska, USA. First, we examined variation in nesting phenology in relation to local temperatures. We then characterized nesting habitat and analyzed nest-site selection for a subset of nests (n = 86) in comparison with paired random points. Finally, we estimated the daily survival rate of 257 nests found in 2007–2013 with respect to both habitat characteristics and weather variables. Nest initiation was delayed in years with snow events, heavy rain, and freezing temperatures early in the breeding season. Nests were typically found in open, low-shrub tundra, and never among tall shrubs (mean shrub height at nests = 26.8 ± 6.7 cm). We observed weak nest-site selection patterns. Considering the similarity between nest sites and paired random points, coupled with the unique social mating system of Smith's Longspurs, we suggest that habitat selection may occur at the neighborhood scale and not at the nest-site scale. The best approximating model explaining nest survival suggested a positive relationship with the numbers of days above 21°C that an individual nest experienced; there was little support for models containing habitat variables. The daily nest survival rate was high (0.972–0.982) compared with that of most passerines in forested or grassland habitats, but similar to that of passerines nesting on tundra. Considering their high nesting success and ability to delay nest initiation during inclement weather, Smith's Longspurs may be resilient to predicted changes in weather regimes on the breeding grounds. Thus, the greatest threat to breeding Smith's Longspurs associated with climate change may be the loss of low-shrub habitat types, which could significantly change the characteristics of breeding areas.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-16-87.1","usgsCitation":"McFarland, H.R., Kendall, S.J., and Powell, A., 2017, Nest-site selection and nest success of an Arctic-breeding passerine, Smith's Longspur, in a changing climate: The Condor, v. 119, no. 1, p. 85-97, https://doi.org/10.1650/CONDOR-16-87.1.","productDescription":"13 p.","startPage":"85","endPage":"97","ipdsId":"IP-066082","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469763,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-16-87.1","text":"Publisher Index Page"},{"id":342247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Atigun Gorge, Slope Mountain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.42779541015625,\n 68.38754917350626\n ],\n [\n -149.32411193847656,\n 68.38754917350626\n ],\n [\n -149.32411193847656,\n 68.42621140720802\n ],\n [\n -149.42779541015625,\n 68.42621140720802\n ],\n [\n -149.42779541015625,\n 68.38754917350626\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.26025390624997,\n 68.24954858146121\n ],\n [\n -149.15725708007812,\n 68.24954858146121\n ],\n [\n -149.15725708007812,\n 68.28895380229444\n ],\n [\n -149.26025390624997,\n 68.28895380229444\n ],\n [\n -149.26025390624997,\n 68.24954858146121\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"119","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593910a9e4b0764e6c5e8846","contributors":{"authors":[{"text":"McFarland, Heather R.","contributorId":192723,"corporation":false,"usgs":false,"family":"McFarland","given":"Heather","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":697503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, Steve J. 0000-0002-9290-5629","orcid":"https://orcid.org/0000-0002-9290-5629","contributorId":169663,"corporation":false,"usgs":false,"family":"Kendall","given":"Steve","email":"","middleInitial":"J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":697504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":697441,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188373,"text":"70188373 - 2017 - Efficacy of SpayVac® as a contraceptive in feral horses","interactions":[],"lastModifiedDate":"2017-06-07T14:28:54","indexId":"70188373","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Efficacy of SpayVac® as a contraceptive in feral horses","docAbstract":"ABSTRACT We tested the efficacy of 2 formulations of the immunocontraceptive SpayVac1, which packages the immunogen porcine zona pellucida (PZP) and an adjuvant in multilamellar liposomes, as a contraceptive in captive feral horses (Equus caballus) for 3 consecutive breeding seasons (Pauls Valley, OK, USA; 2012–2014) following a single inoculation. Annual fertility rates in control adult female horses (n ¼ 30 each yr) were 100%, 96.7%, and 100%. In the nonaqueous treatment group, fertility was 16.7% in the first year (n ¼ 30) and 75.9% in the second year (n ¼ 29), at which point we dropped the group from the study. Fertility rates in the aqueous group were 13.3%, 46.7%, and 43.3% (n ¼ 30 each yr). Fifteen of the females in the aqueous group were infertile in all 3 years. Across 11 sampling dates postvaccination, mean PZP antibody titers in serum were 33.7–91.9% greater in nonpregnant females than pregnant females for the aqueous treatment group and 7.8–82.8% greater for the nonaqueous group. However, the 15 consistently infertile females did not necessarily have the greatest antibody titers. Reactions at the injection site occurred in 29.8% of the 84 females that received an injection other than saline solution, but there was no evidence that the reactions were painful or affected mobility. The nonaqueous formulation produced more local reactions than did the aqueous, but presence of PZP did not increase the frequency of reactions above that seen with liposomes þ adjuvant. Uterine edema was not found at frequencies greater than would be expected in untreated females. Additional research to explore relationships between vaccine dose, adjuvant, and efficacy is warranted. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.729","usgsCitation":"Roelle, J.E., Germaine, S.S., Kane, A.J., and Cade, B.S., 2017, Efficacy of SpayVac® as a contraceptive in feral horses: Wildlife Society Bulletin, v. 41, no. 1, p. 107-115, https://doi.org/10.1002/wsb.729.","productDescription":"9 p. 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\"}}]}\n","volume":"41","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-02","publicationStatus":"PW","scienceBaseUri":"593910a9e4b0764e6c5e8844","contributors":{"authors":[{"text":"Roelle, James E. roelleb@usgs.gov","contributorId":2330,"corporation":false,"usgs":true,"family":"Roelle","given":"James","email":"roelleb@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":697442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germaine, Stephen S. 0000-0002-7614-2676 germaines@usgs.gov","orcid":"https://orcid.org/0000-0002-7614-2676","contributorId":192417,"corporation":false,"usgs":true,"family":"Germaine","given":"Stephen","email":"germaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":697443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kane, Albert J.","contributorId":192705,"corporation":false,"usgs":false,"family":"Kane","given":"Albert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":697444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cade, Brian S. 0000-0001-9623-9849 cadeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9623-9849","contributorId":1278,"corporation":false,"usgs":true,"family":"Cade","given":"Brian","email":"cadeb@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":697445,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188385,"text":"70188385 - 2017 - Denitrifying woodchip bioreactor and phosphorus filter pairing to minimize pollution swapping","interactions":[],"lastModifiedDate":"2017-06-07T15:26:26","indexId":"70188385","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Denitrifying woodchip bioreactor and phosphorus filter pairing to minimize pollution swapping","docAbstract":"Pairing denitrifying woodchip bioreactors and phosphorus-sorbing filters provides a unique, engineered approach for dual nutrient removal from waters impaired with both nitrogen (N) and phosphorus (P). This column study aimed to test placement of two P-filter media (acid mine drainage treatment residuals and steel slag) relative to a denitrifying system to maximize N and P removal and minimize pollution swapping under varying flow conditions (i.e., woodchip column hydraulic retention times (HRTs) of 7.2, 18, and 51 h; P-filter HRTs of 7.6–59 min). Woodchip denitrification columns were placed either upstream or downstream of P-filters filled with either medium. The configuration with woodchip denitrifying systems placed upstream of the P-filters generally provided optimized dissolved P removal efficiencies and removal rates. The P-filters placed upstream of the woodchip columns exhibited better P removal than downstream-placed P-filters only under overly long (i.e., N-limited) retention times when highly reduced effluent exited the woodchip bioreactors. The paired configurations using mine drainage residuals provided significantly greater P removal than the steel slag P-filters (e.g., 25–133 versus 8.8–48 g P removed m−3 filter media d−1, respectively), but there were no significant differences in N removal between treatments (removal rates: 8.0–18 g N removed m−3 woodchips d−1; N removal efficiencies: 18–95% across all HRTs). The range of HRTs tested here resulted in various undesirable pollution swapping by-products from the denitrifying bioreactors: nitrite production when nitrate removal was not complete and sulfate reduction, chemical oxygen demand production and decreased pH during overly long retention times. The downstream P-filter placement provided a polishing step for removal of chemical oxygen demand and nitrite.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2017.05.026","usgsCitation":"Christianson, L.E., Lepine, C., Sibrell, P., Penn, C.J., and Summerfelt, S.T., 2017, Denitrifying woodchip bioreactor and phosphorus filter pairing to minimize pollution swapping: Water Research, v. 121, p. 129-139, https://doi.org/10.1016/j.watres.2017.05.026.","productDescription":"11 p.","startPage":"129","endPage":"139","ipdsId":"IP-084848","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":469766,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2017.05.026","text":"Publisher Index Page"},{"id":342275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"121","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593910a7e4b0764e6c5e883c","contributors":{"authors":[{"text":"Christianson, Laura E.","contributorId":192714,"corporation":false,"usgs":false,"family":"Christianson","given":"Laura","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":697484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lepine, Christine","contributorId":192715,"corporation":false,"usgs":false,"family":"Lepine","given":"Christine","email":"","affiliations":[],"preferred":false,"id":697485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sibrell, Philip 0000-0001-5666-1228 psibrell@usgs.gov","orcid":"https://orcid.org/0000-0001-5666-1228","contributorId":168582,"corporation":false,"usgs":true,"family":"Sibrell","given":"Philip","email":"psibrell@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":697483,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Penn, Chad J.","contributorId":116060,"corporation":false,"usgs":false,"family":"Penn","given":"Chad","email":"","middleInitial":"J.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":697486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Summerfelt, Steven T.","contributorId":192709,"corporation":false,"usgs":false,"family":"Summerfelt","given":"Steven","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":697487,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188391,"text":"70188391 - 2017 - The 2008 Wells, Nevada earthquake sequence: Source constraints using calibrated multiple event relocation and InSAR","interactions":[],"lastModifiedDate":"2017-06-07T14:55:35","indexId":"70188391","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"The 2008 Wells, Nevada earthquake sequence: Source constraints using calibrated multiple event relocation and InSAR","docAbstract":"The 2008 Wells, NV earthquake represents the largest domestic event in the conterminous U.S. outside of California since the October 1983 Borah Peak earthquake in southern Idaho. We present an improved catalog, magnitude complete to 1.6, of the foreshock-aftershock sequence, supplementing the current U.S. Geological Survey (USGS) Preliminary Determination of Epicenters (PDE) catalog with 1,928 well-located events. In order to create this catalog, both subspace and kurtosis detectors are used to obtain an initial set of earthquakes and associated locations. The latter are then calibrated through the implementation of the hypocentroidal decomposition method and relocated using the BayesLoc relocation technique. We additionally perform a finite fault slip analysis of the mainshock using InSAR observations. By combining the relocated sequence with the finite fault analysis, we show that the aftershocks occur primarily updip and along the southwestern edge of the zone of maximum slip. The aftershock locations illuminate areas of post-mainshock strain increase; aftershock depths, ranging from 5 to 16 km, are consistent with InSAR imaging, which shows that the Wells earthquake was a buried source with no observable near-surface offset.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160298","usgsCitation":"Nealy, J., Benz, H.M., Hayes, G.P., Berman, E., and Barnhart, W., 2017, The 2008 Wells, Nevada earthquake sequence: Source constraints using calibrated multiple event relocation and InSAR: Bulletin of the Seismological Society of America, v. 107, no. 3, p. 1107-1117, https://doi.org/10.1785/0120160298.","productDescription":"11 p. ","startPage":"1107","endPage":"1117","ipdsId":"IP-082805","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":438303,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WS8RFK","text":"USGS data release","linkHelpText":"Earthquake Relocations for the 2008 Wells, Nevada Earthquake Sequence"},{"id":342256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","city":"Wells","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.224609375,\n 40.44694705960048\n ],\n [\n -114.0380859375,\n 40.44694705960048\n ],\n [\n -114.0380859375,\n 41.335575973123916\n ],\n [\n -115.224609375,\n 41.335575973123916\n ],\n [\n -115.224609375,\n 40.44694705960048\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-14","publicationStatus":"PW","scienceBaseUri":"593910a6e4b0764e6c5e883a","contributors":{"authors":[{"text":"Nealy, Jennifer 0000-0002-6743-2487 jnealy@usgs.gov","orcid":"https://orcid.org/0000-0002-6743-2487","contributorId":147559,"corporation":false,"usgs":true,"family":"Nealy","given":"Jennifer","email":"jnealy@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berman, Eric","contributorId":192729,"corporation":false,"usgs":false,"family":"Berman","given":"Eric","email":"","affiliations":[],"preferred":false,"id":697522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, William D. 0000-0003-0498-1697","orcid":"https://orcid.org/0000-0003-0498-1697","contributorId":192730,"corporation":false,"usgs":false,"family":"Barnhart","given":"William D.","affiliations":[],"preferred":false,"id":697523,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189162,"text":"70189162 - 2017 - Synthesis centers as critical research infrastructure","interactions":[],"lastModifiedDate":"2018-02-13T14:43:53","indexId":"70189162","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":997,"text":"BioScience","active":true,"publicationSubtype":{"id":10}},"title":"Synthesis centers as critical research infrastructure","docAbstract":"Demand for the opportunity to participate in a synthesis-center activity has increased in the years since the US National Science Foundation (NSF)–funded National Center for Ecological Analysis and Synthesis (NCEAS) opened its doors in 1995 and as more scientists across a diversity of scientific disciplines have become aware of what synthesis centers provide. The NSF has funded four synthesis centers, and more than a dozen new synthesis centers have been established around the world, some following the NSF model and others following different models suited to their national funding environment (http://synthesis-consortium.org).
Scientific synthesis integrates diverse data and knowledge to increase the scope and applicability of results and yield novel insights or explanations within and across disciplines (Pickett et al. 2007, Carpenter et al. 2009). The demand for synthesis comes from the pressing societal need to address grand challenges related to global change and other issues that cut across multiple societal sectors and disciplines and from recognition that substantial added scientific value can be achieved through the synthesis-based analysis of existing data. Demand also comes from groups of scientists who see exciting opportunities to generate new knowledge from interdisciplinary and transdisciplinary collaboration, often capitalizing on the increasingly large volume and variety of available data (Kelling et al. 2009, Bishop et al. 2014, Specht et al. 2015b). The ever-changing nature of societal challenges and the availability of data with which to address them suggest there will be an expanding need for synthesis.
However, we are now entering a phase in which government support for some existing synthesis centers has ended or will be ending soon, forcing those centers to close or develop new operational models, approaches, and funding streams. We argue here that synthesis centers play such a unique role in science that continued long-term public investment to maximize benefits to science and society is justified. In particular, we argue that synthesis centers represent community infrastructure more akin to research vessels than to term-funded centers of science and technology (e.g., NSF Science and Technology Centers). Through our experience running synthesis centers and, in some cases, developing postfederal funding models, we offer our perspective on the purpose and value of synthesis centers. We present case studies of different outcomes of transition plans and argue for a fundamental shift in the conception of synthesis science and the strategic funding of these centers by government funding agencies.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/biosci/bix053","usgsCitation":"Baron, J., Specht, A., Garnier, E., Bishop, P., Campbell, C.A., Davis, F., Fady, B., Field, D., Gross, L.J., Guru, S.M., Halpern, B., Hampton, S.E., Leavitt, P.R., Meagher, T.R., Ometto, J., Parker, J.N., Price, R., Rawson, C.H., Rodrigo, A., Sheble, L.A., and Winter, M., 2017, Synthesis centers as critical research infrastructure: BioScience, v. 67, no. 8, p. 750-759, https://doi.org/10.1093/biosci/bix053.","productDescription":"10 p. ","startPage":"750","endPage":"759","ipdsId":"IP-084553","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":469767,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/bix053","text":"Publisher Index Page"},{"id":343281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"67","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-07","publicationStatus":"PW","scienceBaseUri":"595ca913e4b0d1f9f054ca12","contributors":{"authors":[{"text":"Baron, Jill 0000-0002-5902-6251 jill_baron@usgs.gov","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":194124,"corporation":false,"usgs":true,"family":"Baron","given":"Jill","email":"jill_baron@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":703286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Specht, Alison","contributorId":178726,"corporation":false,"usgs":false,"family":"Specht","given":"Alison","email":"","affiliations":[],"preferred":false,"id":703299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garnier, Eric","contributorId":194148,"corporation":false,"usgs":false,"family":"Garnier","given":"Eric","email":"","affiliations":[],"preferred":false,"id":703300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bishop, Pamela","contributorId":178724,"corporation":false,"usgs":false,"family":"Bishop","given":"Pamela","email":"","affiliations":[],"preferred":false,"id":703301,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell, C. Andrew","contributorId":194149,"corporation":false,"usgs":false,"family":"Campbell","given":"C.","email":"","middleInitial":"Andrew","affiliations":[],"preferred":false,"id":703302,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davis, Frank W.","contributorId":70273,"corporation":false,"usgs":true,"family":"Davis","given":"Frank W.","affiliations":[],"preferred":false,"id":703303,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fady, Bruno","contributorId":194150,"corporation":false,"usgs":false,"family":"Fady","given":"Bruno","email":"","affiliations":[],"preferred":false,"id":703304,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Field, Dawn","contributorId":194151,"corporation":false,"usgs":false,"family":"Field","given":"Dawn","email":"","affiliations":[],"preferred":false,"id":703305,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gross, Louis J.","contributorId":56705,"corporation":false,"usgs":true,"family":"Gross","given":"Louis","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":703306,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Guru, Siddeswara M.","contributorId":194152,"corporation":false,"usgs":false,"family":"Guru","given":"Siddeswara","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":703307,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Halpern, Benjamin S","contributorId":178719,"corporation":false,"usgs":false,"family":"Halpern","given":"Benjamin S","affiliations":[],"preferred":false,"id":703308,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hampton, Stephanie E.","contributorId":178718,"corporation":false,"usgs":false,"family":"Hampton","given":"Stephanie","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":703309,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Leavitt, Peter R.","contributorId":173070,"corporation":false,"usgs":false,"family":"Leavitt","given":"Peter","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":703310,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Meagher, Thomas R.","contributorId":178725,"corporation":false,"usgs":false,"family":"Meagher","given":"Thomas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":703311,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Ometto, Jean","contributorId":194154,"corporation":false,"usgs":false,"family":"Ometto","given":"Jean","affiliations":[],"preferred":false,"id":703312,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Parker, John N.","contributorId":178722,"corporation":false,"usgs":false,"family":"Parker","given":"John","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":703313,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Price, Richard","contributorId":194155,"corporation":false,"usgs":false,"family":"Price","given":"Richard","email":"","affiliations":[],"preferred":false,"id":703314,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Rawson, Casey H.","contributorId":194156,"corporation":false,"usgs":false,"family":"Rawson","given":"Casey","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":703315,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Rodrigo, Allen","contributorId":194157,"corporation":false,"usgs":false,"family":"Rodrigo","given":"Allen","email":"","affiliations":[],"preferred":false,"id":703316,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Sheble, Laura A.","contributorId":194158,"corporation":false,"usgs":false,"family":"Sheble","given":"Laura","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":703317,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Winter, Marten","contributorId":178720,"corporation":false,"usgs":false,"family":"Winter","given":"Marten","email":"","affiliations":[],"preferred":false,"id":703318,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70193216,"text":"70193216 - 2017 - Paleozoic and mesozoic GIS data from the Geologic Atlas of the Rocky Mountain Region: Volume 1","interactions":[],"lastModifiedDate":"2017-11-08T10:14:29","indexId":"70193216","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":4,"text":"Book"},"title":"Paleozoic and mesozoic GIS data from the Geologic Atlas of the Rocky Mountain Region: Volume 1","docAbstract":"The Rocky Mountain Association of Geologists (RMAG) is, once again, publishing portions of the 1972 Geologic Atlas of the Rocky Mountain Region (Mallory, ed., 1972) as a geospatial map and data package. Georeferenced tiff (Geo TIFF) images of map figures from this atlas has served as the basis for these data products. Shapefiles and file geodatabase features have been generated and cartographically represented for select pages from the following chapters:
• Phanerozoic Rocks (page 56)
• Cambrian System (page 63)
• Ordovician System (pages 78 and 79)
• Silurian System (pages 87 - 89)
• Devonian System (pages 93, 94, and 96 - 98)
• Mississippian System (pages 102 and 103)
• Pennsylvanian System (pages 114 and 115)
• Permian System (pages 146 and 149 - 154)
• Triassic System (pages 168 and 169)
• Jurassic System (pages 179 and 180)
• Cretaceous System (pages 197 - 201, 207 - 210, 215, - 218, 221, 222, 224, 225, and 227).
The primary purpose of this publication is to provide regional-scale, as well as local-scale, geospatial data of the Rocky Mountain Region for use in geoscience studies. An important aspect of this interactive map product is that it does not require extensive GIS experience or highly specialized software.
","language":"English","publisher":"The Rocky Mountain Association of Geologists","usgsCitation":"2017, Paleozoic and mesozoic GIS data from the Geologic Atlas of the Rocky Mountain Region: Volume 1, e-book.","productDescription":"e-book","ipdsId":"IP-079177","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":348354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347815,"type":{"id":15,"text":"Index Page"},"url":"https://www.rmag.org/paleozoic---mesozoic-gis-data"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425b7e4b0dc0b45b45359","contributors":{"editors":[{"text":"Graeber, Aimee 0000-0001-5671-3403 agraeber@usgs.gov","orcid":"https://orcid.org/0000-0001-5671-3403","contributorId":169548,"corporation":false,"usgs":true,"family":"Graeber","given":"Aimee","email":"agraeber@usgs.gov","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":720863,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Gunther, Gregory L. 0000-0002-1761-1604 ggunther@usgs.gov","orcid":"https://orcid.org/0000-0002-1761-1604","contributorId":1581,"corporation":false,"usgs":true,"family":"Gunther","given":"Gregory","email":"ggunther@usgs.gov","middleInitial":"L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":720864,"contributorType":{"id":2,"text":"Editors"},"rank":2}]}} ,{"id":70182226,"text":"70182226 - 2017 - Climate change-induced vegetation shifts lead to more ecological droughts despite projected rainfall increases in many global temperate drylands","interactions":[],"lastModifiedDate":"2017-12-04T11:45:53","indexId":"70182226","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Climate change-induced vegetation shifts lead to more ecological droughts despite projected rainfall increases in many global temperate drylands","docAbstract":"Drylands occur world-wide and are particularly vulnerable to climate change since dryland ecosystems depend directly on soil water availability that may become increasingly limited as temperatures rise. Climate change will both directly impact soil water availability, and also change plant biomass, with resulting indirect feedbacks on soil moisture. Thus, the net impact of direct and indirect climate change effects on soil moisture requires better understanding.
We used the ecohydrological simulation model SOILWAT at sites from temperate dryland ecosystems around the globe to disentangle the contributions of direct climate change effects and of additional indirect, climate change-induced changes in vegetation on soil water availability. We simulated current and future climate conditions projected by 16 GCMs under RCP 4.5 and RCP 8.5 for the end of the century. We determined shifts in water availability due to climate change alone and due to combined changes of climate and the growth form and biomass of vegetation.
Vegetation change will mostly exacerbate low soil water availability in regions already expected to suffer from negative direct impacts of climate change (with the two RCP scenarios giving us qualitatively similar effects). By contrast, in regions that will likely experience increased water availability due to climate change alone, vegetation changes will counteract these increases due to increased water losses by interception. In only a small minority of locations, climate change induced vegetation changes may lead to a net increase in water availability. These results suggest that changes in vegetation in response to climate change may exacerbate drought conditions and may dampen the effects of increased precipitation, i.e. leading to more ecological droughts despite higher precipitation in some regions. Our results underscore the value of considering indirect effects of climate change on vegetation when assessing future soil moisture conditions in water-limited ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13598","usgsCitation":"Tietjen, B., Schlaepfer, D., Bradford, J.B., Laurenroth, W.K., Hall, S.A., Duniway, M.C., Hochstrasser, T., Jia, G., Munson, S.M., Pyke, D.A., and Wilson, S.D., 2017, Climate change-induced vegetation shifts lead to more ecological droughts despite projected rainfall increases in many global temperate drylands: Global Change Biology, v. 23, no. 7, p. 2743-2754, https://doi.org/10.1111/gcb.13598.","productDescription":"12 p.","startPage":"2743","endPage":"2754","ipdsId":"IP-079913","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":335892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"7","noUsgsAuthors":false,"publicationDate":"2017-03-06","publicationStatus":"PW","scienceBaseUri":"58ad5fc2e4b01ccd54f8b521","chorus":{"doi":"10.1111/gcb.13598","url":"http://dx.doi.org/10.1111/gcb.13598","publisher":"Wiley-Blackwell","authors":"Tietjen Britta, Schlaepfer Daniel R., Bradford John B., Lauenroth William K., Hall Sonia A., Duniway Michael C., Hochstrasser Tamara, Jia Gensuo, Munson Seth M., Pyke David A., Wilson Scott D.","journalName":"Global Change Biology","publicationDate":"3/2017","publiclyAccessibleDate":"3/6/2017"},"contributors":{"authors":[{"text":"Tietjen, Britta","contributorId":181517,"corporation":false,"usgs":false,"family":"Tietjen","given":"Britta","email":"","affiliations":[],"preferred":false,"id":670060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schlaepfer, Daniel R.","contributorId":105189,"corporation":false,"usgs":false,"family":"Schlaepfer","given":"Daniel R.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":670061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":670062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laurenroth, William K.","contributorId":175203,"corporation":false,"usgs":false,"family":"Laurenroth","given":"William","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":670063,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hall, Sonia A.","contributorId":181518,"corporation":false,"usgs":false,"family":"Hall","given":"Sonia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":670064,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":670065,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hochstrasser, Tamara","contributorId":181931,"corporation":false,"usgs":false,"family":"Hochstrasser","given":"Tamara","email":"","affiliations":[{"id":18091,"text":"University College Dublin","active":true,"usgs":false}],"preferred":false,"id":670066,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jia, Gensuo","contributorId":181520,"corporation":false,"usgs":false,"family":"Jia","given":"Gensuo","email":"","affiliations":[],"preferred":false,"id":670067,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":670068,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":670069,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wilson, Scott D.","contributorId":181519,"corporation":false,"usgs":false,"family":"Wilson","given":"Scott","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":670070,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70188156,"text":"sir20175029 - 2017 - Flood of July 2016 in northern Wisconsin and the Bad River Reservation","interactions":[],"lastModifiedDate":"2017-06-07T09:34:07","indexId":"sir20175029","displayToPublicDate":"2017-06-06T12:00:00","publicationYear":"2017","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":"2017-5029","title":"Flood of July 2016 in northern Wisconsin and the Bad River Reservation","docAbstract":"Heavy rain fell across northern Wisconsin and the Bad River Reservation on July 11, 2016, as a result of several rounds of thunderstorms. The storms caused major flooding in the Bad River Basin and nearby tributaries along the south shore of Lake Superior. Rainfall totals were 8–10 inches or more and most of the rain fell in an 8-hour period. A streamgage on the Bad River near Odanah, Wisconsin, rose from 300 cubic feet per second to a record peak streamflow of 40,000 cubic feet per second in only 15 hours. Following the storms and through September 2016, personnel from the U.S. Geological Survey and Bad River Tribe Natural Resources Department recovered and documented 108 high-water marks near the Bad River Reservation. Many of these high-water marks were used to create three flood-inundation maps for the Bad River, Beartrap Creek, and Denomie Creek for the Bad River Reservation in the vicinity of the community of Odanah.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175029","collaboration":"Prepared in cooperation with the Bad River Band of the Lake Superior Chippewa Tribe","usgsCitation":"Fitzpatrick, F.A., Dantoin, E.D., Tillison, Naomi, Watson, K.M., Waschbusch, R.J., and Blount, J.D., 2017, Flood of July 2016 in northern Wisconsin and the Bad River Reservation: U.S. Geological Survey Scientific Investigations Report 2017–5029, 21 p., 1 app., https://doi.org/10.3133/sir20175029.","productDescription":"Report: vi, 27 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-080637","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":342093,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/get/592eda03e4b092b266f13e44","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood Inundation, Flood Depth, and High-Water Marks Associated with the Flood of July 2016 in Northern Wisconsin and the Bad River Reservation "},{"id":438305,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78C9THZ","text":"USGS data release","linkHelpText":"Flood Inundation, Flood Depth, and High-Water Marks Associated with the Flood of July 2016 in Northern Wisconsin and the Bad River Reservation"},{"id":342095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5029/sir20175029.pdf","text":"Report","size":"7.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5029"},{"id":342094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5029/coverthb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.3333,\n 45.955\n ],\n [\n -90.25,\n 45.955\n ],\n [\n -90.25,\n 47.166667\n ],\n [\n -92.3333,\n 47.166667\n ],\n [\n -92.3333,\n 45.955\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
A previously developed regional groundwater flow model was used to simulate the effects of changes in pumping rates on groundwater-flow paths and extent of recharge discharging to wells for a contaminated fractured bedrock aquifer in southeastern Pennsylvania. Groundwater in the vicinity of the North Penn Area 7 Superfund site, Montgomery County, Pennsylvania, was found to be contaminated with organic compounds, such as trichloroethylene (TCE), in 1979. At the time contamination was discovered, groundwater from the underlying fractured bedrock (shale) aquifer was the main source of supply for public drinking water and industrial use. As part of technical support to the U.S. Environmental Protection Agency (EPA) during the Remedial Investigation of the North Penn Area 7 Superfund site from 2000 to 2005, the U.S. Geological Survey (USGS) developed a model of regional groundwater flow to describe changes in groundwater flow and contaminant directions as a result of changes in pumping. Subsequently, large decreases in TCE concentrations (as much as 400 micrograms per liter) were measured in groundwater samples collected by the EPA from selected wells in 2010 compared to 2005‒06 concentrations.
To provide insight on the fate of potentially contaminated groundwater during the period of generally decreasing pumping rates from 1990 to 2010, steady-state simulations were run using the previously developed groundwater-flow model for two conditions prior to extensive remediation, 1990 and 2000, two conditions subsequent to some remediation 2005 and 2010, and a No Pumping case, representing pre-development or cessation of pumping conditions. The model was used to (1) quantify the amount of recharge, including potentially contaminated recharge from sources near the land surface, that discharged to wells or streams and (2) delineate the areas contributing recharge that discharged to wells or streams for the five conditions.
In all simulations, groundwater divides differed from surface-water divides, partly because of differences in stream elevations and because of geologic structure and pumping. In the 1990 and 2000 simulations, all recharge in and near the vicinity of North Penn Area 7 discharged to wells, but in the 2005 and 2010 simulations some recharge in this area discharged to streams, indicating possible discharge of contaminated groundwater from North Penn Area 7 sources to streams. As the amount of groundwater withdrawals by wells has declined since 1990, the area contributing recharge to wells in the vicinity of North Penn Area 7 has decreased.
To determine the effect of changes in pumping on flow paths and possible flow-path-related contributions to the observed changes in spatial distribution of contaminants in groundwater from 2005 to 2010, the USGS conducted simulations using the previously developed regional groundwater-flow model using reported pumping and estimated recharge rates for 2005 and 2010. Flow paths from recharge at known contaminant source areas to discharge locations at wells or streams were simulated under steady-state conditions for the two periods. Simulated groundwater-flow paths shifted only slightly from 2005 to 2010 as a result of changes in pumping rates. These slight changes in groundwater-flow paths from known sources of contamination are not coincident with the spatial distribution of observed changes in TCE concentrations from 2005 to 2010, indicating that the decreases of TCE concentrations may be a result of other processes, such as source removal or degradation. Results of the simulations and the absence of increases in TCE-degradation-product concentrations indicate that the decreases of TCE concentrations observed in 2010 may be at least partly related to contaminant-source removal by soil excavation completed in 2005, although additional data would be needed to confirm this preliminary explanation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175014","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Senior, L.A., and Goode, D.J., 2017, Effects of changes in pumping on regional groundwater-flow paths, 2005 and 2010, and areas contributing recharge to discharging wells, 1990–2010, in the vicinity of North Penn Area 7 Superfund site, Montgomery County, Pennsylvania: U.S. Geological Survey Scientific Investigations Report 2017–5014, 36 p., https://doi.org/10.3133/sir20175014.","productDescription":"Report: vi, 36 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077142","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":341375,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7FN14BQ","text":"USGS data release","description":"USGS data release"},{"id":341337,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5014/sir20175014.pdf","text":"Report","size":"4.61 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":341336,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5014/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Montgomery County","otherGeospatial":"North Penn Area 7 Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.304167,\n 40.244444\n ],\n [\n -75.263333,\n 40.244444\n ],\n [\n -75.263333,\n 40.205556\n ],\n [\n -75.304167,\n 40.205556\n ],\n [\n -75.304167,\n 40.244444\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
During two seasonal sampling events in spring (June) and fall (August) of 2015, the U.S. Geological Survey collected benthos (benthic invertebrates) and plankton (zooplankton and phytoplankton) at three sites each in the Waukegan Harbor Area of Concern (AOC) in Illinois and in Burns Harbor-Port of Indiana, a non-AOC comparison site in Indiana. The study was done in cooperation with the U.S. Environmental Protection Agency and the Illinois Department of Natural Resources. Samples were collected concurrently for physical and chemical parameters (specific conductance, temperature, pH, dissolved oxygen, chlorophyll-a, total and volatile suspended solids in water samples; particle size and volatile-on-ignition solids of sediment in dredge samples). The purpose of the study was to assess whether or not aquatic communities at the AOC were degraded in comparison to communities at the non-AOC, which was presumed to be less impaired than the AOC. Benthos were collected by using Hester-Dendy artificial substrate samplers and a Ponar® dredge sampler to collect composited grabs of bottom sediment; zooplankton were collected by using tows from depth to the surface with a 63-micrometer mesh plankton net; phytoplankton were collected by using whole water samples composited from set depth intervals. Aquatic communities at the AOC and the non-AOC were compared by use of univariate statistical analyses with metrics such as taxa richness (number of unique taxa), diversity, and a multimetric Index of Biotic Integrity (IBI, for artificial-substrate samples only) as well as by use of multivariate statistical analyses of taxa relative abundances.
Although benthos communities at Waukegan Harbor AOC were not rated as degraded in comparison to the non-AOC, metrics for zooplankton and phytoplankton communities did show some impairment for the 2015 sampling. Across seasons, benthos richness and diversity were significantly higher and rated as less degraded at the AOC compared to the non-AOC; however, benthos IBIs were not significantly different. Multivariate comparisons revealed that the benthos communities in the AOC and non-AOC were significantly different, but these comparisons do not address current degradation in either harbor. The dominant taxa in dredge samples were oligochaete worms in both harbors, but there were differences in the relative abundances of Dreissena as well as oligochaete and midge taxa. Although zooplankton richness and diversity in the AOC were lower and rated as more degraded in spring, these metrics were rated as less degraded in fall compared to the non-AOC, effectively balancing out so that there was no difference across seasons. Multivariate comparisons also indicated that zooplankton communities in the AOC were significantly different from those in the non-AOC for spring only but not across seasons, possibly because of lower water temperatures in spring at Waukegan Harbor than at the non-AOC site. The spring zooplankton community in Waukegan Harbor was dominated in density and biomass by the rotifer Synchaeta. Across seasons, diatom richness was significantly higher and rated as less degraded in the AOC than the non-AOC because of spring values, whereas soft algae richness was significantly lower and rated as more degraded in the AOC because of fall values. Spring richness of combined phytoplankton (soft algae and diatoms) was significantly higher in the AOC than in the non-AOC. Neither diatom diversity nor soft algae diversity differed significantly between the harbors, but combined phytoplankton diversity across seasons, if replicates were included, was significantly lower and rated as more degraded in the AOC than in the non-AOC. Multivariate tests indicated that the combined phytoplankton communities in the harbors were not significantly different across seasons. Significant differences were not found between harbors for chlorophyll-a, suspended solids, algal densities, or biomass.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175039","collaboration":"Prepared in cooperation with the Illinois Department of Natural Resources and the U.S. Environmental Protection Agency-Great Lakes National Program Office","usgsCitation":"Scudder Eikenberry, B.C., Templar, H.A., Burns, D.J., Dobrowolski, E.G., and Schmude, K.L., 2017, Comparison of benthos and plankton for Waukegan Harbor Area of Concern, Illinois, and Burns Harbor-Port of Indiana non-Area of Concern, Indiana, in 2015: U.S. Geological Survey Scientific Investigations Report 2017–5039, 29 p., https://doi.org/10.3133/sir20175039.","productDescription":"Report: viii, 29 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077261","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":342062,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CN7259","text":"USGS data release","description":"USGS data release","linkHelpText":"Benthos and Plankton data for Waukegan Harbor Area of Concern, Illinois, and Burns Harbor-Port of Indiana Non-Area of Concern, Indiana, in 2015"},{"id":342020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5039/coverthb.jpg"},{"id":342021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5039/sir20175039.pdf","text":"Report","size":"4.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5039"}],"country":"United States","state":"Illinois, Indiana","otherGeospatial":"Burns Harbor-Port of Indiana non-Area of Concern, Waukegon Harbor Area of Concern","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.827083,\n 42.374\n ],\n [\n -87.827083,\n 42.355\n ],\n [\n -87.808333,\n 42.355\n ],\n [\n -87.808333,\n 42.374\n ],\n [\n -87.827083,\n 42.374\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.163889,\n 41.649\n ],\n [\n -87.144444,\n 41.649\n ],\n [\n -87.144444,\n 41.627778\n ],\n [\n -87.163889,\n 41.627778\n ],\n [\n -87.163889,\n 41.649\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Nonnative fishes have been increasingly implicated in the decline of native fishes in the Pacific Northwest. Smallmouth Bass Micropterus dolomieu were introduced into the Umpqua River in southwest Oregon in the early 1960s. The spread of Smallmouth Bass throughout the basin coincided with a decline in counts of upstream-migrating Pacific Lampreys Entosphenus tridentatus. This suggested the potential for ecological interactions between Smallmouth Bass and Pacific Lampreys, as well as freshwater-resident Western Brook Lampreys Lampetra richardsoni. To evaluate the potential effects of Smallmouth Bass on lampreys, we sampled diets of Smallmouth Bass and used bioenergetics models to estimate consumption of larval lampreys in a segment of Elk Creek, a tributary to the lower Umpqua River. We captured 303 unique Smallmouth Bass (mean: 197 mm and 136 g) via angling in July and September. We combined information on Smallmouth Bass diet and energy density with other variables (temperature, body size, growth, prey energy density) in a bioenergetics model to estimate consumption of larval lampreys. Larval lampreys were found in 6.2% of diet samples, and model estimates indicated that the Smallmouth Bass we captured consumed 925 larval lampreys in this 2-month study period. When extrapolated to a population estimate of Smallmouth Bass in this segment, we estimated 1,911 larval lampreys were consumed between July and September. Although the precision of these estimates was low, this magnitude of consumption suggests that Smallmouth Bass may negatively affect larval lamprey populations.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/02755947.2017.1317677","usgsCitation":"Schultz, L., Heck, M., Kowalski, B., Eagles-Smith, C.A., Coates, K.C., and Dunham, J.B., 2017, Bioenergetics models to estimate numbers of larval lampreys consumed by smallmouth bass in Elk Creek, Oregon: North American Journal of Fisheries Management, v. 37, no. 4, p. 714-723, https://doi.org/10.1080/02755947.2017.1317677.","productDescription":"11 p. 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2017 - Unusual population attributes of invasive red-eared slider turtles (Trachemys scripta elegans) in Japan: do they have a performance advantage?","interactions":[],"lastModifiedDate":"2017-06-07T10:03:22","indexId":"70188350","displayToPublicDate":"2017-06-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":868,"text":"Aquatic Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Unusual population attributes of invasive red-eared slider turtles (Trachemys scripta elegans) in Japan: do they have a performance advantage?","docAbstract":"The slider turtle (Trachemys scripta Thunberg in Schoepff, 1792) is native to the USA and Mexico. Due to the popularity of their colorful hatchlings as pets, they have been exported worldwide and are now present on all continents, except Antarctica. Slider turtles are well-established in Japan and occupy aquatic habitats in urban and agricultural areas, to the detriment of native turtles with which they compete. We asked the overall question, do slider turtles in Japan have a performance advantage because they are liberated from the numerous competing turtle species in their native range and released from many of their natural predators? Traits compared included various measures of adult body size (mean, maximum), female size at maturity as measured by size of gravid females, clutch size, population density and biomass, sex ratio, and sexual size dimorphism, the latter two a partial reflection of growth and maturity differences between the sexes. We sampled slider turtle populations in three habitats in Japan and compared population attributes with published data for the species from throughout its native range in the USA. Mean male body sizes were at the lower end of values from the USA suggesting that males in Japan may mature at smaller body sizes. The smallest gravid females in Japan mature at smaller body sizes but have mean clutch sizes larger than some populations in the USA. Compared to most populations in the USA, slider turtles achieve higher densities and biomasses in Japanese wetlands, especially the lotic system we sampled. Sex ratios were female-biased, the opposite of what is reported for many populations in protected areas of the USA. Sexual size dimorphism was enhanced relative to native populations with females as the larger sex. The enhanced dimorphism is likely a result of earlier size of maturity in Japanese males and the large size of mature (gravid) Japanese females. Slider turtles appear to have a performance advantage over native turtles in Japan, possibly as a result of being released from competition with numerous sympatric turtle species in their native range, and the absence of many co-evolved predators and parasites in Japan. This slight competitive edge, coupled with the catholic diet and broad tolerance of varying aquatic habitats of slider turtles, is reflected in their dominance over native and naturalized Japanese turtles in altered aquatic habitats.
","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre","doi":"10.3391/ai.2017.12.1.10","usgsCitation":"Taniguchi, M., Lovich, J.E., Mine, K., Ueno, S., and Kamezaki, N., 2017, Unusual population attributes of invasive red-eared slider turtles (Trachemys scripta elegans) in Japan: do they have a performance advantage?: Aquatic Invasions, v. 12, no. 1, p. 97-108, https://doi.org/10.3391/ai.2017.12.1.10.","productDescription":"12 p.","startPage":"97","endPage":"108","ipdsId":"IP-064335","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469768,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/ai.2017.12.1.10","text":"Publisher Index Page"},{"id":438306,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71C1V2B","text":"USGS data release","linkHelpText":"Body size estimates for slider turtles in the United States, 1944-2010Data"},{"id":342190,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5937bf2ae4b0f6c2d0d9c738","contributors":{"authors":[{"text":"Taniguchi, Mari","contributorId":192680,"corporation":false,"usgs":false,"family":"Taniguchi","given":"Mari","email":"","affiliations":[],"preferred":false,"id":697354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":697353,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mine, Kanako","contributorId":192681,"corporation":false,"usgs":false,"family":"Mine","given":"Kanako","email":"","affiliations":[],"preferred":false,"id":697355,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ueno, Shintaro","contributorId":192682,"corporation":false,"usgs":false,"family":"Ueno","given":"Shintaro","email":"","affiliations":[],"preferred":false,"id":697356,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kamezaki, Naoki","contributorId":192683,"corporation":false,"usgs":false,"family":"Kamezaki","given":"Naoki","email":"","affiliations":[],"preferred":false,"id":697357,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188347,"text":"70188347 - 2017 - A note on adding viscoelasticity to earthquake simulators","interactions":[],"lastModifiedDate":"2017-06-06T16:04:59","indexId":"70188347","displayToPublicDate":"2017-06-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"A note on adding viscoelasticity to earthquake simulators","docAbstract":"Here, I describe how time‐dependent quasi‐static stress transfer can be implemented in an earthquake simulator code that is used to generate long synthetic seismicity catalogs. Most existing seismicity simulators use precomputed static stress interaction coefficients to rapidly implement static stress transfer in fault networks with typically tens of thousands of fault patches. The extension to quasi‐static deformation, which accounts for viscoelasticity of Earth’s ductile lower crust and mantle, involves the precomputation of additional interaction coefficients that represent time‐dependent stress transfer among the model fault patches, combined with defining and evolving additional state variables that track this stress transfer. The new approach is illustrated with application to a California‐wide synthetic fault network.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160192","usgsCitation":"Pollitz, F., 2017, A note on adding viscoelasticity to earthquake simulators: Bulletin of the Seismological Society of America, v. 107, no. 1, p. 468-474, https://doi.org/10.1785/0120160192.","productDescription":"7 p.","startPage":"468","endPage":"474","ipdsId":"IP-076554","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":342185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-27","publicationStatus":"PW","scienceBaseUri":"5937bf2ce4b0f6c2d0d9c73a","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697346,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70188342,"text":"70188342 - 2017 - System identification based on deconvolution and cross correlation: An application to a 20‐story instrumented building in Anchorage, Alaska","interactions":[],"lastModifiedDate":"2017-06-06T16:30:14","indexId":"70188342","displayToPublicDate":"2017-06-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"System identification based on deconvolution and cross correlation: An application to a 20‐story instrumented building in Anchorage, Alaska","docAbstract":"Deconvolution and cross‐correlation techniques are used for system identification of a 20‐story steel, moment‐resisting frame building in downtown Anchorage, Alaska. This regular‐plan midrise structure is instrumented with a 32‐channel accelerometer array at 10 levels. The impulse response functions (IRFs) and correlation functions (CFs) are computed based on waveforms recorded from ambient vibrations and five local and regional earthquakes. The earthquakes occurred from 2005 to 2014 with moment magnitudes between 4.7 and 6.2 over a range of azimuths at epicenter distances of 13.3–183 km. The building’s fundamental frequencies and mode shapes are determined using a complex mode indicator function based on singular value decomposition of multiple reference frequency‐response functions. The traveling waves, identified in IRFs with a virtual source at the roof, and CFs are used to estimate the intrinsic attenuation associated with the fundamental modes and shear‐wave velocity in the building. Although the cross correlation of the waveforms at various levels with the corresponding waveform at the first floor provides more complicated wave propagation than that from the deconvolution with virtual source at the roof, the shear‐wave velocities identified by both techniques are consistent—the largest difference in average values is within 8%. The median shear‐wave velocity from the IRFs of five earthquakes is 191 m/s for the east–west (E‐W), 205 m/s for the north–south (N‐S), and 176 m/s for the torsional responses. The building’s average intrinsic‐damping ratio is estimated to be 3.7% and 3.4% in the 0.2–1 Hz frequency band for the E‐W and N‐S directions, respectively. These results are intended to serve as reference for the undamaged condition of the building, which may be used for tracking changes in structural integrity during and after future earthquakes.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160069","usgsCitation":"Wen, W., and Kalkan, E., 2017, System identification based on deconvolution and cross correlation: An application to a 20‐story instrumented building in Anchorage, Alaska: Bulletin of the Seismological Society of America, v. 107, no. 2, p. 718-740, https://doi.org/10.1785/0120160069.","productDescription":"23 p.","startPage":"718","endPage":"740","ipdsId":"IP-068688","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":342192,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Anchorage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.90917682647705,\n 61.20793488105243\n ],\n [\n -149.88252639770508,\n 61.20793488105243\n ],\n [\n -149.88252639770508,\n 61.21661483933352\n ],\n [\n -149.90917682647705,\n 61.21661483933352\n ],\n [\n -149.90917682647705,\n 61.20793488105243\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-14","publicationStatus":"PW","scienceBaseUri":"5937bf2ce4b0f6c2d0d9c73c","contributors":{"authors":[{"text":"Wen, Weiping","contributorId":192669,"corporation":false,"usgs":false,"family":"Wen","given":"Weiping","email":"","affiliations":[],"preferred":false,"id":697331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697330,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188321,"text":"70188321 - 2017 - Using a gradient in food quality to infer drivers of fatty acid content in two filter-feeding aquatic consumers","interactions":[],"lastModifiedDate":"2017-09-18T15:40:04","indexId":"70188321","displayToPublicDate":"2017-06-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Using a gradient in food quality to infer drivers of fatty acid content in two filter-feeding aquatic consumers","docAbstract":"Inferences about ecological structure and function are often made using elemental or macromolecular tracers of food web structure. For example, inferences about food chain length are often made using stable isotope ratios of top predators and consumer food sources are often inferred from both stable isotopes and fatty acid (FA) content in consumer tissues. The use of FAs as tracers implies some degree of macromolecular conservation across trophic interactions, but many FAs are subject to physiological alteration and animals may produce those FAs from precursors in response to food deficiencies. We measured 41 individual FAs and several aggregate FA metrics in two filter-feeding taxa to (1) assess ecological variation in food availability and (2) identify potential drivers of among-site variation in FA content. These taxa were filter feeding caddisflies (Family Hydropyschidae) and dreissenid mussels (Genus Dreissena), which both consume seston. Stable isotopic composition (C and N) in these taxa co-varied across 13 sites in the Great Lakes region of North America, indicating they fed on very similar food resources. However, co-variation in FA content was very limited, with only one common FA co-varying across this gradient (α-linolenic acid; ALA), suggesting these taxa accumulate FAs very differently even when exposed to the same foods. Based on these results, among-site variation in ALA content in both consumers does appear to be driven by food resources, along with several other FAs in dreissenid mussels. We conclude that single-taxa measurements of FA content cannot be used to infer FA availability in food resources.
","language":"English","publisher":"Springer","doi":"10.1007/s00027-017-0537-0","usgsCitation":"Larson, J.H., Richardson, W.B., Vallazza, J.M., Bartsch, L., and Bartsch, M.R., 2017, Using a gradient in food quality to infer drivers of fatty acid content in two filter-feeding aquatic consumers: Aquatic Sciences, v. 79, no. 4, p. 855-865, https://doi.org/10.1007/s00027-017-0537-0.","productDescription":"11 p.","startPage":"855","endPage":"865","ipdsId":"IP-077546","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":342138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie, Lake Huron, Lake Michigan, Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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