The total nitrogen load to Long Island Sound from Connecticut and contributing areas to the north was estimated for October 1998 to September 2009. Discrete measurements of total nitrogen concentrations and continuous flow data from 37 water-quality monitoring stations in the Long Island Sound watershed were used to compute total annual nitrogen yields and loads. Total annual computed yields and basin characteristics were used to develop a generalized-least squares regression model for use in estimating the total nitrogen yields from unmonitored areas in coastal and central Connecticut. Significant variables in the regression included the percentage of developed land, percentage of row crops, point-source nitrogen yields from wastewater-treatment facilities, and annual mean streamflow. Computed annual median total nitrogen yields at individual monitoring stations ranged from less than 2,000 pounds per square mile in mostly forested basins (typically less than 10 percent developed land) to more than 13,000 pounds per square mile in urban basins (greater than 40 percent developed) with wastewater-treatment facilities and in one agricultural basin. Medians of computed total annual nitrogen yields for water years 1999–2009 at most stations were similar to those previously computed for water years 1988–98. However, computed medians of annual yields at several stations, including the Naugatuck River, Quinnipiac River, and Hockanum River, were lower than during 1988–98. Nitrogen yields estimated for 26 unmonitored areas downstream from monitoring stations ranged from less than 2,000 pounds per square mile to 34,000 pounds per square mile. Computed annual total nitrogen loads at the farthest downstream monitoring stations were combined with the corresponding estimates for the downstream unmonitored areas for a combined estimate of the total nitrogen load from the entire study area. Resulting combined total nitrogen loads ranged from 38 to 68 million pounds per year during water years 1999–2009. Total annual loads from the monitored basins represent 63 to 74 percent of the total load. Computed annual nitrogen loads from four stations near the Massachusetts border with Connecticut represent 52 to 54 percent of the total nitrogen load during water years 2008–9, the only years with data for all the border sites. During the latter part of the 1999–2009 study period, total nitrogen loads to Long Island Sound from the study area appeared to increase slightly. The apparent increase in loads may be due to higher than normal streamflows, which consequently increased nonpoint nitrogen loads during the study, offsetting major reductions of nitrogen from wastewater-treatment facilities. Nitrogen loads from wastewater treatment facilities declined as much as 2.3 million pounds per year in areas of Connecticut upstream from the monitoring stations and as much as 5.8 million pounds per year in unmonitored areas downstream in coastal and central Connecticut.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135171","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Mullaney, J.R., and Schwarz, G., 2013, Estimated nitrogen loads from selected tributaries in Connecticut draining to Long Island Sound, 1999–2009: U.S. Geological Survey Scientific Investigations Report 2013-5171, vii, 65 p., https://doi.org/10.3133/sir20135171.","productDescription":"vii, 65 p.","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-050762","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":279610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135171.jpg"},{"id":279608,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5171/"},{"id":279609,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5171/pdf/sir2013-5171.pdf"}],"country":"United States","state":"Connecticut;Massachusetts;New Hampshire;Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.62487792968749,\n 40.925964939514294\n ],\n [\n -73.7896728515625,\n 41.11660732012896\n ],\n [\n -73.6138916015625,\n 41.24890252240322\n ],\n [\n -73.5809326171875,\n 41.53325414281322\n ],\n [\n -73.729248046875,\n 41.64418347939748\n ],\n [\n -73.6962890625,\n 42.00032514831621\n ],\n [\n -73.4820556640625,\n 42.15118709351198\n ],\n [\n -73.487548828125,\n 42.39912215986002\n ],\n [\n -73.070068359375,\n 42.75911283724358\n ],\n [\n -72.75146484374999,\n 43.48481212891603\n ],\n [\n -72.9327392578125,\n 43.739352079154706\n ],\n [\n -72.6361083984375,\n 44.629573191951046\n ],\n [\n -71.9219970703125,\n 44.90646871709883\n ],\n [\n -71.4166259765625,\n 45.29034662473615\n ],\n [\n -71.2353515625,\n 45.336701909968106\n ],\n [\n -71.026611328125,\n 45.24008561090264\n ],\n [\n -71.2628173828125,\n 44.09153051045218\n ],\n [\n -71.83959960937499,\n 43.76712702120528\n ],\n [\n -71.7352294921875,\n 42.69454866207692\n ],\n [\n -71.707763671875,\n 42.00848901572399\n ],\n [\n -71.510009765625,\n 41.69752591075902\n ],\n [\n -71.3177490234375,\n 41.40565583808169\n ],\n [\n -71.861572265625,\n 41.261291493919856\n ],\n [\n -72.9217529296875,\n 41.10832999732833\n ],\n [\n -73.62487792968749,\n 40.925964939514294\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52907ce1e4b0bbdcf23ed2b1","contributors":{"authors":[{"text":"Mullaney, John R. 0000-0003-4936-5046 jmullane@usgs.gov","orcid":"https://orcid.org/0000-0003-4936-5046","contributorId":1957,"corporation":false,"usgs":true,"family":"Mullaney","given":"John","email":"jmullane@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":543,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"preferred":false,"id":486334,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70160619,"text":"70160619 - 2013 - Broad-scale patterns of Brook Trout responses to introduced Brown Trout in New York","interactions":[],"lastModifiedDate":"2019-12-13T06:39:07","indexId":"70160619","displayToPublicDate":"2013-11-22T12:45:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Broad-scale patterns of Brook Trout responses to introduced Brown Trout in New York","docAbstract":"Brook Trout Salvelinus fontinalis and Brown Trout Salmo trutta are valuable sport fish that coexist in many parts of the world due to stocking introductions. Causes for the decline of Brook Trout within their native range are not clear but include competition with Brown Trout, habitat alteration, and repetitive stocking practices. New York State contains a large portion of the Brook Trout's native range, where both species are maintained by stocking and other management actions. We used artificial neural network models, regression, principal components analysis, and simulation to evaluate the effects of Brown Trout, environmental conditions, and stocking on the distribution of Brook Trout in the center of their native range. We found evidence for the decline of Brook Trout in the presence of Brown Trout across many watersheds; 22% of sampled reaches where both species were expected to occur contained only Brown Trout. However, a model of the direct relationship between Brook Trout and Brown Trout abundance explained less than 1% of data variation. Ordination showed extensive overlap of Brook Trout and Brown Trout habitat conditions, with only small components of the hypervolume (multidimensional space) being distinctive. Subsequent analysis indicated higher abundances of Brook Trout in highly forested areas, while Brown Trout were more abundant in areas with relatively high proportions of agriculture. Simulation results indicated that direct interactions and habitat conditions were relatively minor factors compared with the effects of repeated stocking of Brown Trout into Brook Trout habitat. Intensive annual stocking of Brown Trout could eliminate resident Brook Trout in less than a decade. Ecological differences, harvest behavior, and other habitat changes can exacerbate Brook Trout losses. Custom stocking scenarios with Brown Trout introductions at relatively low proportions of resident Brook Trout populations may be able to sustain healthy populations of both species within their present range.
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Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":627,"corporation":false,"usgs":true,"family":"McKenna","given":"James E.","suffix":"Jr.","email":"jemckenna@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":583357,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slattery, Michael T. mslattery@usgs.gov","contributorId":5470,"corporation":false,"usgs":true,"family":"Slattery","given":"Michael","email":"mslattery@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":583358,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clifford, Kean M.","contributorId":150867,"corporation":false,"usgs":false,"family":"Clifford","given":"Kean","email":"","middleInitial":"M.","affiliations":[{"id":18127,"text":"State University of New York, College of Environmental Science and Foresty","active":true,"usgs":false}],"preferred":false,"id":583359,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70049000,"text":"sir20135188 - 2013 - Methods for estimating water consumption for thermoelectric power plants in the United States","interactions":[],"lastModifiedDate":"2014-11-24T14:26:19","indexId":"sir20135188","displayToPublicDate":"2013-11-22T08:26:00","publicationYear":"2013","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":"2013-5188","title":"Methods for estimating water consumption for thermoelectric power plants in the United States","docAbstract":"Water consumption at thermoelectric power plants represents a small but substantial share of total water consumption in the U.S. However, currently available thermoelectric water consumption data are inconsistent and incomplete, and coefficients used to estimate consumption are contradictory. The U.S. Geological Survey (USGS) has resumed the estimation of thermoelectric water consumption, last done in 1995, based on the use of linked heat and water budgets to complement reported water consumption. This report presents the methods used to estimate freshwater consumption at a study set of 1,284 power plants based on 2010 plant characteristics and operations data.
\nPower plants were categorized for estimation of water consumption in two tiers. First, generating units were assigned to categories based on the technology used to generate electricity. These generation-type categories are combustion steam, combined-cycle, nuclear, geothermal, and solar thermal. Second, cooling systems were separately categorized as either wet cooling towers or surface-water cooling systems, and the surface-water cooling systems were subcategorized as cooling ponds, lakes, and rivers.
\nHeat budgets were constructed for the first four generation-type categories; data at solar thermal plants were insufficient for heat budgets. These heat budgets yielded estimates of the amount of heat transferred to the condenser. The ratio of evaporation to the heat discharged through the condenser was estimated using existing heat balance models that are sensitive to environmental data; this feature allows estimation of consumption under different climatic conditions. These two estimates were multiplied to yield an estimate of consumption at each power plant.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135188","collaboration":"USGS National Water Census and National Streamflow Information Program","usgsCitation":"Diehl, T.H., Harris, M., Murphy, J.C., Hutson, S.S., and Ladd, D.E., 2013, Methods for estimating water consumption for thermoelectric power plants in the United States: U.S. Geological Survey Scientific Investigations Report 2013-5188, Report: vii, 78 p.; Appendix 4, https://doi.org/10.3133/sir20135188.","productDescription":"Report: vii, 78 p.; Appendix 4","numberOfPages":"90","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-045152","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"links":[{"id":279513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135188.jpg"},{"id":279511,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5188/pdf/sir2013-5188.pdf"},{"id":279512,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5188/appendix/sir2013-5188_appendix4_fews_version_3.104_edit_20141106.xlsx","description":"Appendix 4"},{"id":279502,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5188/"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 173,16.916667 ], [ 173,71.833333 ], [ -66.95,71.833333 ], [ -66.95,16.916667 ], [ 173,16.916667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52907d0ee4b0bbdcf23ed313","contributors":{"authors":[{"text":"Diehl, Timothy H. 0000-0001-9691-2212 thdiehl@usgs.gov","orcid":"https://orcid.org/0000-0001-9691-2212","contributorId":546,"corporation":false,"usgs":true,"family":"Diehl","given":"Timothy","email":"thdiehl@usgs.gov","middleInitial":"H.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Melissa","contributorId":17519,"corporation":false,"usgs":true,"family":"Harris","given":"Melissa","affiliations":[],"preferred":false,"id":485972,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":4281,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hutson, Susan S. sshutson@usgs.gov","contributorId":2040,"corporation":false,"usgs":true,"family":"Hutson","given":"Susan","email":"sshutson@usgs.gov","middleInitial":"S.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485970,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ladd, David E. 0000-0002-9247-7839 deladd@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7839","contributorId":1646,"corporation":false,"usgs":true,"family":"Ladd","given":"David","email":"deladd@usgs.gov","middleInitial":"E.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485969,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70057098,"text":"70057098 - 2013 - Habitat heterogeneity and intraguild interactions modify distribution and injury rates in two coexisting genera of damselflies","interactions":[],"lastModifiedDate":"2013-11-22T08:22:58","indexId":"70057098","displayToPublicDate":"2013-11-22T08:17:34","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Habitat heterogeneity and intraguild interactions modify distribution and injury rates in two coexisting genera of damselflies","docAbstract":"1. Sublethal effects of predation can affect both population and community structure. Despite this, little is known about how the frequency of injury varies in relation to habitat, aquatic community characteristics or between trophically similar, coexisting taxa.\n2. In a tidal freshwater ecosystem, we first examined injuries (lamellar autotomy) of Enallagma and Ischnura damselfly larvae, which have unique behaviours and susceptibilities to predation, as a function of habitat type, body size and overall odonate density. We also examined relative abundance of these genera and potential anisopteran predators as a function of habitat type.\n3. The frequency of injury to Enallagma was high when larvae were small and overall odonate density was high. For Ischnura, however, the frequency of injury depended on habitat and was high for small larvae in less disturbed habitats low on the shore. Ischnura were most frequently found in more disturbed habitats high on the shore, whereas Enallagma were more frequently found in less disturbed habitats low on the shore.\n4. The relative importance of factors hypothesised to structure odonate communities varied between coexisting Enallagma and Ischnura. Distinctive distributions and patterns of injury for each genus provided new insights on the potential for intraguild interactions to modify habitat associations in tidal freshwater ecosystems.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Freshwater Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/fwb.12217","usgsCitation":"Witt, J., Forkner, R.E., and Kraus, R.T., 2013, Habitat heterogeneity and intraguild interactions modify distribution and injury rates in two coexisting genera of damselflies: Freshwater Biology, v. 58, no. 11, p. 2380-2388, https://doi.org/10.1111/fwb.12217.","productDescription":"9 p.","startPage":"2380","endPage":"2388","ipdsId":"IP-049265","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":279508,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279507,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/fwb.12217"}],"country":"United States","state":"Virginia","otherGeospatial":"Thompson Creek","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -77.537,38.604 ], [ -77.537,39.057 ], [ -77.04,39.057 ], [ -77.04,38.604 ], [ -77.537,38.604 ] ] ] } } ] }","volume":"58","issue":"11","noUsgsAuthors":false,"publicationDate":"2013-08-19","publicationStatus":"PW","scienceBaseUri":"52907d0be4b0bbdcf23ed304","contributors":{"authors":[{"text":"Witt, Jonathan W.","contributorId":44071,"corporation":false,"usgs":true,"family":"Witt","given":"Jonathan W.","affiliations":[],"preferred":false,"id":486639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forkner, Rebecca E.","contributorId":11934,"corporation":false,"usgs":true,"family":"Forkner","given":"Rebecca","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":486638,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":486637,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70055684,"text":"sir20135142 - 2013 - Land subsidence along the Delta-Mendota Canal in the northern part of the San Joaquin Valley, California, 2003-10","interactions":[],"lastModifiedDate":"2013-11-21T12:47:43","indexId":"sir20135142","displayToPublicDate":"2013-11-21T12:40:00","publicationYear":"2013","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":"2013-5142","title":"Land subsidence along the Delta-Mendota Canal in the northern part of the San Joaquin Valley, California, 2003-10","docAbstract":"Extensive groundwater withdrawal from the unconsolidated deposits in the San Joaquin Valley caused widespread aquifer-system compaction and resultant land subsidence from 1926 to 1970—locally exceeding 8.5 meters. The importation of surface water beginning in the early 1950s through the Delta-Mendota Canal and in the early 1970s through the California Aqueduct resulted in decreased pumping, initiation of water-level recovery, and a reduced rate of compaction in some areas of the San Joaquin Valley. However, drought conditions during 1976–77 and 1987–92, and drought conditions and regulatory reductions in surface-water deliveries during 2007–10, decreased surface-water availability, causing pumping to increase, water levels to decline, and renewed compaction. Land subsidence from this compaction has reduced freeboard and flow capacity of the Delta-Mendota Canal, the California Aqueduct, and other canals that deliver irrigation water and transport floodwater.\n\nThe U.S. Geological Survey, in cooperation with the U.S. Bureau of Reclamation and the San Luis and Delta-Mendota Water Authority, assessed land subsidence in the vicinity of the Delta-Mendota Canal as part of an effort to minimize future subsidence-related damages to the canal. The location, magnitude, and stress regime of land-surface deformation during 2003–10 were determined by using extensometer, Global Positioning System (GPS), Interferometric Synthetic Aperture Radar (InSAR), spirit leveling, and groundwater-level data. Comparison of continuous GPS, shallow extensometer, and groundwater-level data, combined with results from a one-dimensional model, indicated the vast majority of the compaction took place beneath the Corcoran Clay, the primary regional confining unit.\n\nLand-surface deformation measurements indicated that much of the northern portion of the Delta-Mendota Canal (Clifton Court Forebay to Check 14) was fairly stable or minimally subsiding on an annual basis; some areas showed seasonal periods of subsidence and of uplift that resulted in little or no longer-term elevation loss. Many groundwater levels in this northern area did not reach historical lows during 2003–10, indicating that deformation in this region was primarily elastic.\n\nAlthough the northern portion of the Delta-Mendota Canal was relatively stable, land-surface deformation measurements indicated the southern portion of the Delta-Mendota Canal (Checks 15–21) subsided as part of a large subsidence feature centered about 15 kilometers northeast of the Delta-Mendota Canal, south of the town of El Nido. Results of InSAR analysis indicated at least 540 millimeters of subsidence near the San Joaquin River and the Eastside Bypass during 2008–10, which is part of a 3,200 square-kilometer area—including the southern part of the Delta-Mendota Canal—affected by 20 millimeters or more of subsidence during the same period. Calculations indicated that the subsidence rate doubled in 2008 in some areas. The GPS surveys done in 2008 and 2010 confirmed the high subsidence rate measured by using InSAR for the same period. Water levels in many shallow and deep wells in this area declined during 2007–10; water levels in many deep wells reached historical lows, indicating that subsidence measured during this period was largely inelastic. InSAR-derived subsidence maps for various periods during 2003–10 showed that the area of maximum active subsidence (that is, the largest rates of subsidence) shifted from its historical (1926–70) location southwest of Mendota to south of El Nido.\n\nContinued groundwater-level and land-subsidence monitoring in the San Joaquin Valley is important because (1) regulatory- and drought-related reductions in surface-water deliveries since 1976 have resulted in increased groundwater pumping and associated land subsidence, and (2) land use and associated groundwater pumping continue to change throughout the valley. The availability of surface water remains uncertain; even during record-setting precipitation years, such as 2010–11, water deliveries have fallen short of requests and groundwater pumping was required to meet the irrigation demand. Due to the expected continued demand for irrigation supply water and the limitations and uncertainty of surface-water supplies, groundwater pumping and associated land subsidence is likely to continue in the future. Spatially detailed information on land subsidence is needed to facilitate minimization of future subsidence-related damages to the Delta-Mendota Canal and other infrastructure in the San Joaquin Valley. The integration of subsidence, deformation, and water-level measurements—particularly continuous measurements—enables the analysis of aquifer-system response to increased groundwater pumping, which in turn, enables identification of the preconsolidation head and calculation of aquifer-system storage properties. This information can be used to improve numerical model simulations of groundwater flow and aquifer-system compaction and allow for consideration of land subsidence in the evaluation of water-resource management alternatives.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135142","issn":"2328-0328","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation and the San Luis and Delta-Mendota Water Authority","usgsCitation":"Sneed, M., Brandt, J.T., and Solt, M., 2013, Land subsidence along the Delta-Mendota Canal in the northern part of the San Joaquin Valley, California, 2003-10: U.S. Geological Survey Scientific Investigations Report 2013-5142, Report: x, 86 p.; 2 Appendices, https://doi.org/10.3133/sir20135142.","productDescription":"Report: x, 86 p.; 2 Appendices","numberOfPages":"100","onlineOnly":"Y","temporalStart":"2003-01-01","temporalEnd":"2010-12-31","ipdsId":"IP-037140","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":279412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135142.jpg"},{"id":279407,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5142/pdf/sir2013-5142_appendixE.pdf"},{"id":279408,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5142/sir2013-5142_appendixE_tables.xlsx"},{"id":279405,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5142/"},{"id":279406,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5142/pdf/sir2013-5142.pdf"}],"country":"United States","state":"California","otherGeospatial":"Delta-mendota Canal;San Joaquin Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.5,35.5 ], [ -122.5,38.0 ], [ -119.5,38.0 ], [ -119.5,35.5 ], [ -122.5,35.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528f53c9e4b0660d392bed6f","contributors":{"authors":[{"text":"Sneed, Michelle 0000-0002-8180-382X micsneed@usgs.gov","orcid":"https://orcid.org/0000-0002-8180-382X","contributorId":155,"corporation":false,"usgs":true,"family":"Sneed","given":"Michelle","email":"micsneed@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brandt, Justin T. 0000-0002-9397-6824 jbrandt@usgs.gov","orcid":"https://orcid.org/0000-0002-9397-6824","contributorId":157,"corporation":false,"usgs":true,"family":"Brandt","given":"Justin","email":"jbrandt@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Solt, Mike","contributorId":88258,"corporation":false,"usgs":true,"family":"Solt","given":"Mike","email":"","affiliations":[],"preferred":false,"id":486202,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048999,"text":"ofr20131242 - 2013 - Mechanical properties of simulated Mars materials: gypsum-rich sandstones and lapilli tuff","interactions":[],"lastModifiedDate":"2014-01-08T10:23:53","indexId":"ofr20131242","displayToPublicDate":"2013-11-21T12:35:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1242","title":"Mechanical properties of simulated Mars materials: gypsum-rich sandstones and lapilli tuff","docAbstract":"Observations by the Mars Exploration Rover (MER) Opportunity, and other recent studies on diagenesis in the extensive equatorial layered deposits on Mars, suggest that the likely lithologies of these deposits are gypsum-rich sandstones and tuffaceous sediments (for example, Murchie and others, 2009; Squyres and others, 2012; Zimbelman and Scheidt, 2012). Of particular interest is how the diagenesis history of these sediments (degree of cementation and composition) influences the strength and brittle behavior of the material. For instance, fractures are more common in lower porosity materials under strain, whereas deformation bands, characterized by distributed strain throughout a broader discontinuity in a material, are common in higher porosity sedimentary materials. Such discontinuities can either enhance or restrict fluid flow; hence, failure mode plays an important role in determining the mechanics of fluid migration through sediments (Antonellini and Aydin, 1994; 1995; Taylor and Pollard, 2000; Ogilvie and Glover, 2001). As part of a larger study to characterize processes of fault-controlled fluid flow in volcaniclastic and gypsum-rich sediments on Mars, we have completed a series of laboratory experiments to focus on how gypsum clast content and degree of authigenic cementation affects the strength behavior of simulated Mars rocks. Both axial deformation and hydrostatic pressure tests were done at room temperature under dry conditions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131242","usgsCitation":"Morrow, C., Lockner, D., and Okubo, C., 2013, Mechanical properties of simulated Mars materials: gypsum-rich sandstones and lapilli tuff: U.S. Geological Survey Open-File Report 2013-1242, iii, 11 p., https://doi.org/10.3133/ofr20131242.","productDescription":"iii, 11 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-044873","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":279397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131242.PNG"},{"id":279396,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1242/pdf/of2013-1242.pdf"},{"id":279348,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1242/"}],"otherGeospatial":"Mars","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528f53cbe4b0660d392bed78","contributors":{"authors":[{"text":"Morrow, Carolyn","contributorId":71874,"corporation":false,"usgs":true,"family":"Morrow","given":"Carolyn","affiliations":[],"preferred":false,"id":485967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lockner, David","contributorId":21058,"corporation":false,"usgs":true,"family":"Lockner","given":"David","affiliations":[],"preferred":false,"id":485965,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Okubo, Chris","contributorId":36278,"corporation":false,"usgs":true,"family":"Okubo","given":"Chris","affiliations":[],"preferred":false,"id":485966,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048958,"text":"sir20135166 - 2013 - Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches","interactions":[],"lastModifiedDate":"2013-11-21T12:41:47","indexId":"sir20135166","displayToPublicDate":"2013-11-21T11:40:00","publicationYear":"2013","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":"2013-5166","title":"Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches","docAbstract":"Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as Escherichia coli (E. coli.). Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010-12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report.\n\nLocal agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development.
\n\nPredictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches that had at least 2 years of data (2010-11 and sometimes earlier) and for 1 beach that had 1 year of data. For most models, software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 hours, wave height, wind direction and speed, and antecedent rainfall for various time periods. Forty-two predictive models were validated against data collected during an independent year (2012) and compared to the current method for assessing recreational water quality-using the previous day’s E. coli concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 percent greater than the persistence model and overall correct responses greater than or equal to 80 percent, sensitivities (percentage of exceedances of the bathing-water standard that were correctly predicted by the model) greater than or equal to 50 percent, and specificities (percentage of nonexceedances correctly predicted by the model) greater than or equal to 85 percent. Out of 42 predictive models, 24 models yielded over-all correct responses that were at least 5 percent greater than the use of the persistence model. Predictive-model responses met the performance goals more often than the persistence-model responses in terms of overall correctness (28 versus 17 models, respectively), sensitivity (17 versus 4 models), and specificity (34 versus 25 models). Gaining knowledge of each beach and the factors that affect E. coli concentrations is important for developing good predictive models. Collection of additional years of data with a wide range of environmental conditions may also help to improve future model performance. The USGS will continue to work with local agencies in 2013 and beyond to develop and validate predictive models at beaches and improve existing nowcasts, restructuring monitoring activities to accommodate future uncertainties in funding and resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135166","collaboration":"Coastal and Marine Geology Program Prepared in cooperation with the U.S. Environmental Protection Agency, Great Lakes Restoration Initiative","usgsCitation":"Francy, D.S., Brady, A., Carvin, R.B., Corsi, S., Fuller, L.M., Harrison, J.H., Hayhurst, B.A., Lant, J., Nevers, M.B., Terrio, P.J., and Zimmerman, T.M., 2013, Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches: U.S. Geological Survey Scientific Investigations Report 2013-5166, Report: vii, 68 p.; 3 Appendices, Downloads Directory, https://doi.org/10.3133/sir20135166.","productDescription":"Report: vii, 68 p.; 3 Appendices, Downloads Directory","ipdsId":"IP-037603","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":279395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135166.jpg"},{"id":279349,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5166/"},{"id":279400,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5166/pdf/sir2013-5166_appendix2.pdf"},{"id":279401,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5166/table/sir2013-5166_appendix3.xlsx"},{"id":279398,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5166/pdf/sir2013-5166.pdf"},{"id":279399,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5166/table/sir2013-5166_appendix1.xls"},{"id":279402,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5166/Downloads"}],"country":"Canada;United States","otherGeospatial":"Great Lakes","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -92.33,41.25 ], [ -92.33,49.12 ], [ -75.73,49.12 ], [ -75.73,41.25 ], [ -92.33,41.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528f53c8e4b0660d392bed69","contributors":{"authors":[{"text":"Francy, Donna S. 0000-0001-9229-3557 dsfrancy@usgs.gov","orcid":"https://orcid.org/0000-0001-9229-3557","contributorId":1853,"corporation":false,"usgs":true,"family":"Francy","given":"Donna","email":"dsfrancy@usgs.gov","middleInitial":"S.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brady, Amie M. G.","contributorId":29774,"corporation":false,"usgs":true,"family":"Brady","given":"Amie M. G.","affiliations":[],"preferred":false,"id":485876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carvin, Rebecca B. 0000-0001-7778-4841 rbcarvin@usgs.gov","orcid":"https://orcid.org/0000-0001-7778-4841","contributorId":4456,"corporation":false,"usgs":true,"family":"Carvin","given":"Rebecca","email":"rbcarvin@usgs.gov","middleInitial":"B.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485875,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corsi, Steven R. srcorsi@usgs.gov","contributorId":511,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":485869,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuller, Lori M. lmfuller@usgs.gov","contributorId":2100,"corporation":false,"usgs":true,"family":"Fuller","given":"Lori","email":"lmfuller@usgs.gov","middleInitial":"M.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":485871,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harrison, John H.","contributorId":34011,"corporation":false,"usgs":true,"family":"Harrison","given":"John","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":485878,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hayhurst, Brett A. 0000-0002-1717-2015 bhayhurs@usgs.gov","orcid":"https://orcid.org/0000-0002-1717-2015","contributorId":3398,"corporation":false,"usgs":true,"family":"Hayhurst","given":"Brett","email":"bhayhurs@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485874,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lant, Jeremiah","contributorId":30904,"corporation":false,"usgs":true,"family":"Lant","given":"Jeremiah","affiliations":[],"preferred":false,"id":485877,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nevers, Meredith B.","contributorId":91803,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":485879,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Terrio, Paul J. 0000-0002-1515-9570 pjterrio@usgs.gov","orcid":"https://orcid.org/0000-0002-1515-9570","contributorId":3313,"corporation":false,"usgs":true,"family":"Terrio","given":"Paul","email":"pjterrio@usgs.gov","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485873,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zimmerman, Tammy M. 0000-0003-0842-6981 tmzimmer@usgs.gov","orcid":"https://orcid.org/0000-0003-0842-6981","contributorId":2359,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Tammy","email":"tmzimmer@usgs.gov","middleInitial":"M.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":485872,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70048949,"text":"ds802 - 2013 - Water quality, sediment characteristics, aquatic habitat, geomorphology, and mussel population status of the Clinch River, Virginia and Tennessee, 2009-2011","interactions":[],"lastModifiedDate":"2016-07-08T12:21:37","indexId":"ds802","displayToPublicDate":"2013-11-21T10:40:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"802","title":"Water quality, sediment characteristics, aquatic habitat, geomorphology, and mussel population status of the Clinch River, Virginia and Tennessee, 2009-2011","docAbstract":"Chemical, physical, and biological data were collected during 2009-2011 as part of a study of the Clinch River in Virginia and Tennessee. The data from this study, data-collection methods, and laboratory analytical methods used in the study are documented in this report. The study was conducted to describe the conditions of the Clinch River and to determine if there are measurable differences in chemical, physical, or biological characteristics in a segment of the river where freshwater mussel populations are in decline, have low density, richness, little to no recruitment, and lack endangered species (low-quality reach) compared to a segment of the river where mussel assemblages have relatively high density, richness, evidence of recruitment, and support endangered species (high-quality reach). Five continuous water-quality monitors were installed and operated on the mainstem of the Clinch River and two tributaries. Discrete water-quality sample sets were collected during base-flow and stormflow conditions two sites on the Clinch River and on the Guest River, a tributary to the Clinch River predominantly in the Appalachian Plateaus Physiographic Province. Base-flow water-quality samples were collected in July and August 2011 at 15 sites along the mainstem of the Clinch River. Other analyses included longitudinal sampling along the mainstem of the Clinch River at 10 sites to evaluate bed-sediment chemistry, habitat condition, and mollusk community status. In situ freshwater mussel growth and mortality experiments were conducted with hatchery propogated Villosa iris (rainbow mussels). Tissue from the V. iris as well as tissue from 16 Actinonaias pectorosa mussels were analyzed for trace metals, and V. iris mussel tissue was analyzed for organic compounds. Data collected during this investigation were analyzed by various U.S. Geological Survey or U.S. Fish and Wildlife Service laboratories.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds802","collaboration":"Prepared in cooperation with the Tennessee Wildlife Resources Agency and the Tennessee Department of Environment and Conservation","usgsCitation":"Krstolic, J.L., Johnson, G.C., and Ostby, B.J., 2013, Water quality, sediment characteristics, aquatic habitat, geomorphology, and mussel population status of the Clinch River, Virginia and Tennessee, 2009-2011: U.S. Geological Survey Data Series 802, Report: vi, 14 p.; 2 Appendices; 1 Figure: 17 inches x 11 inches, https://doi.org/10.3133/ds802.","productDescription":"Report: vi, 14 p.; 2 Appendices; 1 Figure: 17 inches x 11 inches","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-034903","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":279326,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0802/pdf/ds802.pdf","text":"Report","size":"753.47 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":279327,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0802/table/ds802_appendix_tables_1-2_4-25.xlsx","text":"Tables 1-2, 4-25","size":"542 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Tables 1-2, 4-25"},{"id":279328,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0802/table/ds802_appendix_table3.xlsx","text":"Table 3","size":"11.4 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 3"},{"id":279329,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/ds/0802/pdf/ds802_figure1_clinch_river_11x17.pdf","text":"Figure 1","size":"984.67 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Figure 1"},{"id":279330,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds802.gif"},{"id":279318,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0802/"}],"country":"United States","state":"Tennessee, Virginia","otherGeospatial":"Clinch River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.980224609375,\n 37.405073750176946\n ],\n [\n -81.23291015625,\n 37.405073750176946\n ],\n [\n -81.683349609375,\n 37.26530995561875\n ],\n [\n -82.628173828125,\n 37.01132594307015\n ],\n [\n -82.96875,\n 36.84446074079564\n ],\n [\n -83.199462890625,\n 36.721273880045004\n ],\n [\n -83.726806640625,\n 36.633162095586556\n ],\n [\n -83.990478515625,\n 36.53612263184686\n ],\n [\n -84.00146484374999,\n 36.33282808737919\n ],\n [\n -84.012451171875,\n 36.1733569352216\n ],\n [\n -83.94653320312499,\n 36.04021586880111\n ],\n [\n -83.8037109375,\n 35.99578538642032\n ],\n [\n -83.51806640624999,\n 35.98689628443791\n ],\n [\n -83.3203125,\n 35.98689628443791\n ],\n [\n -83.1005859375,\n 35.969115075774845\n ],\n [\n -82.85888671875,\n 36.02244668175846\n ],\n [\n -82.694091796875,\n 36.05798104702501\n ],\n [\n -82.254638671875,\n 36.27085020723905\n ],\n [\n -81.88110351562499,\n 36.46547188679816\n ],\n [\n -81.683349609375,\n 36.50963615733049\n ],\n [\n -81.474609375,\n 36.58906837139909\n ],\n [\n -80.947265625,\n 36.70365959719453\n ],\n [\n -80.870361328125,\n 36.84446074079564\n ],\n [\n -80.870361328125,\n 37.046408899699564\n ],\n [\n -80.980224609375,\n 37.405073750176946\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528f53cce4b0660d392bed81","contributors":{"authors":[{"text":"Krstolic, Jennifer L. 0000-0003-2253-9886 jkrstoli@usgs.gov","orcid":"https://orcid.org/0000-0003-2253-9886","contributorId":3677,"corporation":false,"usgs":true,"family":"Krstolic","given":"Jennifer","email":"jkrstoli@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Gregory C. 0000-0003-3683-5010 gcjohnso@usgs.gov","orcid":"https://orcid.org/0000-0003-3683-5010","contributorId":1420,"corporation":false,"usgs":true,"family":"Johnson","given":"Gregory","email":"gcjohnso@usgs.gov","middleInitial":"C.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ostby, Brett J.K.","contributorId":42863,"corporation":false,"usgs":true,"family":"Ostby","given":"Brett","email":"","middleInitial":"J.K.","affiliations":[],"preferred":false,"id":485842,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70055694,"text":"sir20135186 - 2013 - Organic wastewater compounds in water and sediment in and near restored wetlands, Great Marsh, Indiana Dunes National Lakeshore, 2009–11","interactions":[],"lastModifiedDate":"2013-11-21T10:52:40","indexId":"sir20135186","displayToPublicDate":"2013-11-21T10:38:39","publicationYear":"2013","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":"2013-5186","title":"Organic wastewater compounds in water and sediment in and near restored wetlands, Great Marsh, Indiana Dunes National Lakeshore, 2009–11","docAbstract":"A cooperative investigation between the U.S. Geological Survey and the National Park Service was completed from 2009 through 2011 to understand the occurrence, distribution, and environmental processes affecting concentrations of organic wastewater compounds in water and sediment in and near Great Marsh at the Indiana Dunes National Lakeshore in Beverly Shores, Indiana. Sampling sites were selected to represent hydrologic inputs to the restored wetlands from adjacent upstream residential and less developed areas and to represent discharge points of cascading cells within the restored wetland. A multiphase approach was used for the investigation. Discrete water samples and time-integrated passive samples were analyzed for 69 organic wastewater compounds. Continuous water-level information and periodic streamflow measurements characterized flow conditions at discharge points from restored wetland cells. Wetland sediments were collected and analyzed for sorptive losses of organic wastewater compounds and to evaluate of the potential for wetland sediments to biotransform organic wastewater compounds. A total of 52 organic wastewater compounds were detected in discrete water samples at 1 or more sites. Detections of organic wastewater compounds were widespread, but concentrations were generally low and 95 percent were less than 2.1 micrograms per liter. Six compounds were detected at concentrations greater than 2.1 micrograms per liter—four fecal sterols (beta-sitosterol, cholesterol, beta-stigmastanol, and 2-beta coprostanol), one plasticizer (bis-2-ethylhex ylphthalate), and a non-ionic detergent (4-nonylphenol diethoxylate). Two 1-month deployments of time-integrative passive samplers, called polar organic chemical integrative samplers, detected organic wastewater compounds at lower concentrations than were possible with discrete water samples. Isopropyl benzene (solvent), caffeine (plant alkaloid, stimulant), and hexahydrohexamethyl cyclopentabenzopyran (fragrance) were detected in more than half of the extracts from passive samplers, but they were not detected in any discrete water sample. The Yeast Estrogen Screen assay identified measurable estrogenicity in one passive sampler extract from the most downstream wetland site in both the April and November–December 2011 deployments and in passive sampler extracts from one residential and one upstream site in the November–December 2011 deployment only. Surface-water levels in the restored wetland cells were monitored continuously using submersible pressure transducers in hand-driven well points screened in the surface water. Surface-water levels in the wetland cells responded quickly to precipitation and substantially receded within 2 days following the largest rainfall events. Seasonal patterns in water levels generally showed higher and more variable surface-water levels in the wetland cells during spring and early summer. Water levels in the wetland cells fell below the elevation of the control structures and ceased to flow over the spillways during extended dry periods (primarily late summer and early fall). Daily loads of seven organic wastewater compounds, as indicators of septic system effluent, were estimated for samples collected at wetland outlet spillways when flow measurements could be made. Median daily loads of the indicator organic wastewater compounds increased in downstream order, and the largest median loads were measured at the most downstream site. Median daily loads were higher for samples collected in spring and summer than those collected in fall, as the higher seasonal water levels increased streamflow at the wetland outlet spillways. Wetland sediment samples were analyzed for 84 organic wastewater compounds, polycyclic aromatic hydrocarbons, and semivolatile organic compounds to investigate the fate of contaminants in Great Marsh. The top five detected compounds by total mass in wetland sediment samples were beta-sitosterol, beta-stigmastanol, cholesterol, bis(2-ethylhexyl) phthalate, and phenol. Polycyclic aromatic hydrocarbons also were frequently detected in wetland sediment samples. Source apportionment of polycyclic aromatic hydrocarbon detections indicated atmospheric sources of pyrogenic compounds, rather than residential sources. Comparisons of polycyclic aromatic hydrocarbon concentrations in wetland sediment samples to sediment quality target guidelines indicated the potential for harmful effects on sediment-dwelling organisms at several sites. Biodegradation of select endocrine-disrupting compounds (17α-ethinylestradiol, 4-nonylphenol, triclocarban, and bisphenol A) in shallow wetland sediments was evaluated in laboratory experiments by using carbon-14 radiolabeled model contaminants. Substantial biodegradation of certain organic wastewater compounds were demonstrated, primarily in oxic (oxygen containing) environments. One of four modeled compounds, bisphenol A, was biodegraded in anoxic (oxygen free) environments. Only sediments collected nearest residential areas exhibited degradation of the synthetic birth control pharmaceutical, 17α-ethinylestradiol, possibly owing to adaptation and acclimation of the indigenous microbial community to septic discharge and the resultant selection of a microbial capability for biodegradation of 17α-ethinylestradiol.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135186","collaboration":"National Park Service / U.S. Geological Survey–Water Quality Partnership Program","usgsCitation":"Egler, A.L., Risch, M.R., Alvarez, D., and Bradley, P.M., 2013, Organic wastewater compounds in water and sediment in and near restored wetlands, Great Marsh, Indiana Dunes National Lakeshore, 2009–11: U.S. Geological Survey Scientific Investigations Report 2013-5186, Report: viii, 52 p.; 3 Appendices, https://doi.org/10.3133/sir20135186.","productDescription":"Report: viii, 52 p.; 3 Appendices","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-039563","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":279325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135186.jpg"},{"id":279323,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5186/table/sir2013-5186_Appendix_tables_2-1_to_2-2.xls"},{"id":279321,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5186/pdf/sir2013-5186.pdf"},{"id":279322,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5186/table/sir2013-5186_Appendix_tables_1-1_to_1-6.xls"},{"id":279324,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5186/table/sir2013-5186_Appendix_table_3-1.xls"},{"id":279317,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5186/"}],"scale":"100000","projection":"Universal Transverse Mercator projection, Zone 16","datum":"North American Datum 1983","country":"United States","state":"Indiana","otherGeospatial":"Great Marsh;Indiana Dunes National Lakeshore","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -8.1175,4.018055555555556 ], [ -8.1175,0.0011111111111111111 ], [ -8.102222222222222,0.0011111111111111111 ], [ -8.102222222222222,4.018055555555556 ], [ -8.1175,4.018055555555556 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528f53cbe4b0660d392bed7b","contributors":{"authors":[{"text":"Egler, Amanda L. 0000-0001-5621-6810","orcid":"https://orcid.org/0000-0001-5621-6810","contributorId":103221,"corporation":false,"usgs":true,"family":"Egler","given":"Amanda","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":486216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Risch, Martin R. 0000-0002-7908-7887 mrrisch@usgs.gov","orcid":"https://orcid.org/0000-0002-7908-7887","contributorId":2118,"corporation":false,"usgs":true,"family":"Risch","given":"Martin","email":"mrrisch@usgs.gov","middleInitial":"R.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486214,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alvarez, David A.","contributorId":72755,"corporation":false,"usgs":true,"family":"Alvarez","given":"David A.","affiliations":[],"preferred":false,"id":486215,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradley, Paul M. 0000-0001-7522-8606 pbradley@usgs.gov","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":361,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul","email":"pbradley@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486213,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70056510,"text":"70056510 - 2013 - Climate change and fire effects on a prairie-woodland ecotone: projecting species range shifts with a dynamic global vegetation model","interactions":[],"lastModifiedDate":"2017-12-29T12:57:53","indexId":"70056510","displayToPublicDate":"2013-11-21T09:58:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Climate change and fire effects on a prairie-woodland ecotone: projecting species range shifts with a dynamic global vegetation model","docAbstract":"Large shifts in species ranges have been predicted under future climate scenarios based primarily on niche-based species distribution models. However, the mechanisms that would cause such shifts are uncertain. Natural and anthropogenic fires have shaped the distributions of many plant species, but their effects have seldom been included in future projections of species ranges. Here, we examine how the combination of climate and fire influence historical and future distributions of the ponderosa pine–prairie ecotone at the edge of the Black Hills in South Dakota, USA, as simulated by MC1, a dynamic global vegetation model that includes the effects of fire, climate, and atmospheric CO2 concentration on vegetation dynamics. For this purpose, we parameterized MC1 for ponderosa pine in the Black Hills, designating the revised model as MC1-WCNP. Results show that fire frequency, as affected by humidity and temperature, is central to the simulation of historical prairies in the warmer lowlands versus woodlands in the cooler, moister highlands. Based on three downscaled general circulation model climate projections for the 21st century, we simulate greater frequencies of natural fire throughout the area due to substantial warming and, for two of the climate projections, lower relative humidity. However, established ponderosa pine forests are relatively fire resistant, and areas that were initially wooded remained so over the 21st century for most of our future climate x fire management scenarios. This result contrasts with projections for ponderosa pine based on climatic niches, which suggest that its suitable habitat in the Black Hills will be greatly diminished by the middle of the 21st century. We hypothesize that the differences between the future predictions from these two approaches are due in part to the inclusion of fire effects in MC1, and we highlight the importance of accounting for fire as managed by humans in assessing both historical species distributions and future climate change effects.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecology and Evolution","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/ece3.877","usgsCitation":"King, D.A., Bachelet, D.M., and Symstad, A., 2013, Climate change and fire effects on a prairie-woodland ecotone: projecting species range shifts with a dynamic global vegetation model: Ecology and Evolution, v. 3, no. 15, p. 5076-5097, https://doi.org/10.1002/ece3.877.","productDescription":"22 p.","startPage":"5076","endPage":"5097","onlineOnly":"Y","ipdsId":"IP-044811","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":473437,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.877","text":"Publisher Index Page"},{"id":279312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279311,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/ece3.877"}],"country":"United States","state":"South Dakota","otherGeospatial":"Wind Cave National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -103.550635,43.497251 ], [ -103.550635,43.640543 ], [ -103.337034,43.640543 ], [ -103.337034,43.497251 ], [ -103.550635,43.497251 ] ] ] } } ] }","volume":"3","issue":"15","noUsgsAuthors":false,"publicationDate":"2013-11-18","publicationStatus":"PW","scienceBaseUri":"528f53aee4b0660d392bed66","contributors":{"authors":[{"text":"King, David A.","contributorId":7160,"corporation":false,"usgs":true,"family":"King","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":486574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bachelet, Dominique M.","contributorId":89042,"corporation":false,"usgs":true,"family":"Bachelet","given":"Dominique","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":486576,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Symstad, Amy J.","contributorId":11721,"corporation":false,"usgs":true,"family":"Symstad","given":"Amy J.","affiliations":[],"preferred":false,"id":486575,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70056139,"text":"sir20125208 - 2013 - Evaluation of intake efficiencies and associated sediment-concentration errors in US D-77 bag-type and US D-96-type depth-integrating suspended-sediment samplers","interactions":[],"lastModifiedDate":"2018-03-21T15:48:11","indexId":"sir20125208","displayToPublicDate":"2013-11-21T09:29:00","publicationYear":"2013","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":"2012-5208","title":"Evaluation of intake efficiencies and associated sediment-concentration errors in US D-77 bag-type and US D-96-type depth-integrating suspended-sediment samplers","docAbstract":"Accurate measurements of suspended-sediment concentration require suspended-sediment samplers to operate isokinetically, within an intake-efficiency range of 1.0 ± 0.10, where intake efficiency is defined as the ratio of the velocity of the water through the sampler intake to the local ambient stream velocity. Local ambient stream velocity is defined as the velocity of the water in the river at the location of the nozzle, unaffected by the presence of the sampler. Results from Federal Interagency Sedimentation Project (FISP) laboratory experiments published in the early 1940s show that when the intake efficiency is less than 1.0, suspended-sediment samplers tend to oversample sediment relative to water, leading to potentially large positive biases in suspended-sediment concentration that are positively correlated with grain size. Conversely, these experiments show that, when the intake efficiency is greater than 1.0, suspended‑sediment samplers tend to undersample sediment relative to water, leading to smaller negative biases in suspended-sediment concentration that become slightly more negative as grain size increases.\n\nThe majority of FISP sampler development and testing since the early 1990s has been conducted under highly uniform flow conditions via flume and slack-water tow tests, with relatively little work conducted under the greater levels of turbulence that exist in actual rivers. Additionally, all of this recent work has been focused on the hydraulic characteristics and intake efficiencies of these samplers, with no field investigations conducted on the accuracy of the suspended-sediment data collected with these samplers. When depth-integrating suspended-sediment samplers are deployed under the more nonuniform and turbulent conditions that exist in rivers, multiple factors may contribute to departures from isokinetic sampling, thus introducing errors into the suspended-sediment data collected by these samplers that may not be predictable on the basis of flume and tow tests alone.\n\nThis study has three interrelated goals. First, the intake efficiencies of the older US D-77 bag-type and newer, FISP-approved US D-96-type1 depth-integrating suspended‑sediment samplers are evaluated at multiple cross‑sections under a range of actual-river conditions. The intake efficiencies measured in these actual-river tests are then compared to those previously measured in flume and tow tests. Second, other physical effects, mainly water temperature and the duration of sampling at a vertical, are examined to determine whether these effects can help explain observed differences in intake efficiency both between the two types of samplers and between the laboratory and field tests. Third, the signs and magnitudes of the likely errors in suspendedsand concentration in measurements made with both types of samplers are predicted based the intake efficiencies of these two types of depth-integrating samplers. Using the relative difference in isokinetic sampling observed between the US D-77 bag-type and D-96-type samplers during river tests, measured differences in suspended-sediment concentration in a variety of size classes were evaluated between paired equal-discharge-increment (EDI) and equal-width-increment (EWI) measurements made with these two types of samplers to determine whether these differences in concentration are consistent with the differences in concentrations expected on the basis of the 1940s FISP laboratory experiments. In addition, sequential single-vertical depth-integrated samples were collected (concurrent with velocity measurements) with the US D-96-type bag sampler and two different rigidcontainer samplers to evaluate whether the predicted errors in suspended-sand concentrations measured with the US D-96- type sampler are consistent with those expected on the basis of the 1940s FISP laboratory experiments.\n\nResults from our study indicate that the intake efficiency of the US D-96-type sampler is superior to that of the US D-77 bag-type sampler under actual-river conditions, with overall performance of the US D-96-type sampler being closer to, yet still typically below, the FISP-acceptable range of isokinetic operation. These results are in contrast to the results from FISP-conducted flume tests that showed that both the US D-77 bag-type and US D-96-type samplers sampled isokinetically in the laboratory. Results from our study indicate that the single largest problem with the behavior of both the US D-77 bag-type and the US D-96-type samplers under actual‑river conditions is that both samplers are prone to large time‑dependent decreases in intake efficiency as sampling duration increases. In the case of the US D-96-type sampler, this problem may be at least partially overcome by shortening the duration of sampling (or, instead, perhaps by a simple design improvement); in the case of the US D-77 bag-type sampler, although shortening the sampling duration improves the intake efficiency, it does not bring it into agreement with the FISP‑accepted range of isokinetic operation.\n\nThe predicted errors in suspended-sand concentration in EDI or EWI measurements made with the US-96-type sampler are much smaller than those associated with EDI or EWI measurements made with the US D-77 bag-type sampler, especially when the results are corrected for the effects of water temperature and sampling duration. The bias in the concentration in each size class measured using the US D-77 bag-type relative to the concentration measured using the US D-96-type sampler behaves in a manner consistent with that expected on the basis of the observed differences in intake efficiency between the two samplers in conjunction with the results from the 1940s FISP laboratory experiments. In addition, the bias in the concentration in each size class measured using the US D-96‑type sampler relative to the concentration measured using the truly isokinetic rigid-container samplers is in excellent agreement with that predicted on the basis of the 1940s FISP laboratory experiments. Because suspended-sediment samplers can respond differently between laboratory and field conditions, actual-river tests such as those in this study should be conducted when models of suspended-sediment samplers are changed from one type to another during the course of long-term monitoring programs. Otherwise, potential large differences in the suspended-sediment data collected by different types of samplers would lead to large step changes in sediment loads that may be misinterpreted as real, when, in fact, they are associated with the change in suspended‑sediment sampling equipment.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125208","issn":"2328-0328","usgsCitation":"Sabol, T., and Topping, D.J., 2013, Evaluation of intake efficiencies and associated sediment-concentration errors in US D-77 bag-type and US D-96-type depth-integrating suspended-sediment samplers: U.S. Geological Survey Scientific Investigations Report 2012-5208, viii, 88 p., https://doi.org/10.3133/sir20125208.","productDescription":"viii, 88 p.","numberOfPages":"100","ipdsId":"IP-027819","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":279306,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20125208.jpg"},{"id":279305,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5208/pdf/sir2012-5208.pdf"},{"id":279302,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5208/"}],"projection":"North America Albers Equal Area","datum":"North American Datum of 1983","country":"United States","state":"Arizona;Nevada;Utah","otherGeospatial":"Grand Canyon;Marble Canyon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -114.5,35.5 ], [ -114.5,37.5 ], [ -111.0,37.5 ], [ -111.0,35.5 ], [ -114.5,35.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528f53c9e4b0660d392bed6c","contributors":{"authors":[{"text":"Sabol, Thomas A.","contributorId":67186,"corporation":false,"usgs":true,"family":"Sabol","given":"Thomas A.","affiliations":[],"preferred":false,"id":486324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577 dtopping@usgs.gov","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":715,"corporation":false,"usgs":true,"family":"Topping","given":"David","email":"dtopping@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":486323,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70048894,"text":"ofr20131261 - 2013 - Landscape consequences of natural gas extraction in Sullivan and Wyoming Counties, Pennsylvania, 2004–2010","interactions":[],"lastModifiedDate":"2016-08-19T18:07:33","indexId":"ofr20131261","displayToPublicDate":"2013-11-21T08:30:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1261","title":"Landscape consequences of natural gas extraction in Sullivan and Wyoming Counties, Pennsylvania, 2004–2010","docAbstract":"Increased demands for cleaner burning energy, coupled with the relatively recent technological advances in accessing unconventional hydrocarbon-rich geologic formations, have led to an intense effort to find and extract natural gas from various underground sources around the country. One of these sources, the Marcellus Shale, located in the Allegheny Plateau, is currently undergoing extensive drilling and production. The technology used to extract gas in the Marcellus Shale is known as hydraulic fracturing and has garnered much attention because of its use of large amounts of fresh water, its use of proprietary fluids for the hydraulic-fracturing process, its potential to release contaminants into the environment, and its potential effect on water resources. Nonetheless, development of natural gas extraction wells in the Marcellus Shale is only part of the overall natural gas story in this area of Pennsylvania. Conventional natural gas wells, which sometimes use the same technique, are commonly located in the same general area as the Marcellus Shale and are frequently developed in clusters across the landscape. The combined effects of these two natural gas extraction methods create potentially serious patterns of disturbance on the landscape. This document quantifies the landscape changes and consequences of natural gas extraction for Sullivan County and Wyoming County in Pennsylvania between 2004 and 2010. Patterns of landscape disturbance related to natural gas extraction activities were collected and digitized using National Agriculture Imagery Program (NAIP) imagery for 2004, 2005/2006, 2008, and 2010. The disturbance patterns were then used to measure changes in land cover and land use using the National Land Cover Database (NLCD) of 2001. A series of landscape metrics is also used to quantify these changes and is included in this publication.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131261","usgsCitation":"Slonecker, T.E., Milheim, L., Roig-Silva, C.M., and Malizia, A.R., 2013, Landscape consequences of natural gas extraction in Sullivan and Wyoming Counties, Pennsylvania, 2004–2010: U.S. Geological Survey Open-File Report 2013-1261, v, 34 p., https://doi.org/10.3133/ofr20131261.","productDescription":"v, 34 p.","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-042856","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":279303,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131261.jpg"},{"id":279298,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1261/pdf/of2013-1261.pdf","text":"Report","size":"4.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":279297,"type":{"id":15,"text":"Index 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croig@usgs.gov","contributorId":4213,"corporation":false,"usgs":true,"family":"Roig-Silva","given":"Coral","email":"croig@usgs.gov","middleInitial":"M.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":485791,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Malizia, Alexander R.","contributorId":83018,"corporation":false,"usgs":true,"family":"Malizia","given":"Alexander","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":485792,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70049027,"text":"ofr20131263 - 2013 - Landscape consequences of natural gas extraction in Armstrong and Indiana Counties, Pennsylvania, 2004–2010","interactions":[],"lastModifiedDate":"2016-08-19T18:05:36","indexId":"ofr20131263","displayToPublicDate":"2013-11-21T08:29:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1263","title":"Landscape consequences of natural gas extraction in Armstrong and Indiana Counties, Pennsylvania, 2004–2010","docAbstract":"Increased demands for cleaner burning energy, coupled with the relatively recent technological advances in accessing unconventional hydrocarbon-rich geologic formations, have led to an intense effort to find and extract natural gas from various underground sources around the country. One of these sources, the Marcellus Shale, located in the Allegheny Plateau, is currently undergoing extensive drilling and production. The technology used to extract gas in the Marcellus Shale is known as hydraulic fracturing and has garnered much attention because of its use of large amounts of fresh water, its use of proprietary fluids for the hydraulic-fracturing process, its potential to release contaminants into the environment, and its potential effect on water resources. Nonetheless, development of natural gas extraction wells in the Marcellus Shale is only part of the overall natural gas story in this area of Pennsylvania. Conventional natural gas wells are commonly located in the same general area as the Marcellus Shale and are frequently developed in clusters across the landscape. The combined effects of these two natural gas extraction methods create potentially serious patterns of disturbance on the landscape. This document quantifies the landscape changes and consequences of natural gas extraction for Armstrong County and Indiana County in Pennsylvania between 2004 and 2010. Patterns of landscape disturbance related to natural gas extraction activities were collected and digitized using National Agriculture Imagery Program (NAIP) imagery for 2004, 2005/2006, 2008, and 2010. The disturbance patterns were then used to measure changes in land cover and land use using the National Land Cover Database (NLCD) of 2001. A series of landscape metrics is also used to quantify these changes and is included in this publication.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131263","usgsCitation":"Slonecker, T.E., Milheim, L., Roig-Silva, C.M., and Malizia, A.R., 2013, Landscape consequences of natural gas extraction in Armstrong and Indiana Counties, Pennsylvania, 2004–2010: U.S. Geological Survey Open-File Report 2013-1263, v, 35 p., https://doi.org/10.3133/ofr20131263.","productDescription":"v, 35 p.","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-049110","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":279304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131263.jpg"},{"id":279299,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1263/"},{"id":279300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1263/pdf/of2013-1263.pdf","text":"Report","size":"5.30 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programs","docAbstract":"Mineralogy is the link between ore formation and ore extraction. It is the most fundamental component of geomet programs, and the most important aspect of a life-of-project approach to mineral resource projects. Understanding orebodies is achieved by understanding the mineralogy\r\nand texture of the materials, throughout the process, because minerals hold the information required to unlock the value they contain. Geomet mineralogy programs absolutely require the appropriate expertise and at least three steps of mineral characterisation prior to using semi-automated or other methods: field examination, thorough core logging, and optical microscopy. Economic geological inputs for orebody characterisation are necessary for orebody understanding, and are exemplified by current research in the Zambian Copperbelt, where revised sequence stratigraphy\r\nand understanding of alteration, metasomatism and metamorphism can be used to predict topical issues at mine sites. Environmental inputs for sustainability characterisation are demonstrated by recent work on tailings from the Leadville, Colorado, USA area, including linking mineralogy to water quality issues. Risk assessments need to take into account the technical uncertainties around geological variability and mineral extractability, and mineralogy is the only metric that can be used to make this risk contribution.","conferenceTitle":"The Second AusIMM International Geometallurgy Conference ","conferenceDate":"September 30 - October 2, 2013","conferenceLocation":"Brisbane, Australia","language":"English","publisher":"The Australasian Institute of Mining and Metallurgy","usgsCitation":"Hoal, K.O., Woodhead, J., and Smith, K.S., 2013, The importance of mineralogical input into geometallurgy programs, The Second AusIMM International Geometallurgy Conference , Brisbane, Australia, September 30 - October 2, 2013, p. 17-26.","productDescription":"10 p.","startPage":"17","endPage":"26","ipdsId":"IP-048842","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":342108,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59366dace4b0f6c2d0d7d642","contributors":{"authors":[{"text":"Hoal, K. 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We examined enrichment and energy flow for pallid sturgeon in three segments of the Missouri River (Montana/North Dakota, Nebraska/South Dakota, and Nebraska/Iowa) and made comparisons between species in the two downstream segments (Nebraska/South Dakota and Nebraska/Iowa). Patterns in isotopic composition for pallid sturgeon were consistent with gut content analyses indicating an ontogenetic diet shift from invertebrates to fish prey at sizes of >500-mm fork length (FL) in all three segments of the Missouri River. Isotopic patterns revealed shovelnose sturgeon did not experience an ontogenetic shift in diet and used similar prey resources as small (<500-mm FL) pallid sturgeon in the two downstream segments. We found stable isotope analysis to be an effective tool for evaluating the trophic position of sturgeons within a large river food web.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/022012-JFWM-013","usgsCitation":"French, W.E., Graeb, B.D., Bertrand, K.N., Chipps, S.R., and Klumb, R.A., 2013, Size-dependent trophic patterns of pallid sturgeon and shovelnose sturgeon in a large river system: Journal of Fish and Wildlife Management, v. 4, no. 1, p. 41-52, https://doi.org/10.3996/022012-JFWM-013.","productDescription":"12 p.","startPage":"41","endPage":"52","ipdsId":"IP-034023","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":473438,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/022012-jfwm-013","text":"Publisher Index Page"},{"id":340886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": 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S.","contributorId":171851,"corporation":false,"usgs":false,"family":"Graeb","given":"Brian","email":"","middleInitial":"D. S.","affiliations":[{"id":26956,"text":"Departement of Natural Resource Management, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":694282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bertrand, Katie N.","contributorId":191792,"corporation":false,"usgs":false,"family":"Bertrand","given":"Katie","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":694339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":694281,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Klumb, Robert A.","contributorId":86606,"corporation":false,"usgs":true,"family":"Klumb","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":561,"text":"South Dakota Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true},{"id":5089,"text":"South Dakota State University","active":true,"usgs":false},{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":694284,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70057382,"text":"70057382 - 2013 - Location-only and use-availability data: analysis methods converge","interactions":[],"lastModifiedDate":"2013-11-22T13:40:17","indexId":"70057382","displayToPublicDate":"2013-11-20T13:34:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Location-only and use-availability data: analysis methods converge","docAbstract":"This Special Feature arose from a session on a topic of the same name that took place during The Wildlife Society meeting in Kona, Hawaii, from 5 to 10 November, 2011. The purpose of that session and this Special Feature is to compare methods for predictive modelling of species geographical distributions and the modelling of habitat (resource) selection by animals. The predictive modelling of species geographical distributions and the modelling of habitat selection based on the environmental conditions at sites where animals are known to occur are essentially the same problem. Presence-only and used-available data\nboth consist of a sample of locations with known presence of a species or an individual. A separate sample of locations from a study area, with unknown presence (pseudo-absence), is also assumed to exist. The probability or relative probability of presence of a species or individual is modelled and estimated across a certain time implicitly defined by the sampling mechanism, for example, by the time period during which museum specimens or radiotelemetry data were collected. A number of modelling methods have appeared in the literature over the last couple of decades. Many of these methods were made feasible\nby the availability of geographical information systems (GIS), global positioning system (GPS) radiotelemetry and public online data access initiatives (e.g. global biodiversity information facility). The papers in this Special Feature are intended to present the state of the methodological art in their subject area, with particular attention paid to contrasting the advantages and disadvantages of alternative methods of analysis for data.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Animal Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"British Ecologyical Society","doi":"10.1111/1365-2656.12145","usgsCitation":"McDonald, L., Manly, B., Huettmann, F., and Thogmartin, W., 2013, Location-only and use-availability data: analysis methods converge: Journal of Animal Ecology, v. 82, no. 6, p. 1120-1124, https://doi.org/10.1111/1365-2656.12145.","productDescription":"5 p.","startPage":"1120","endPage":"1124","ipdsId":"IP-051603","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":473439,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.12145","text":"Publisher Index Page"},{"id":279605,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279604,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/1365-2656.12145"},{"id":279584,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1111/1365-2656.12145/abstract"}],"volume":"82","issue":"6","noUsgsAuthors":false,"publicationDate":"2013-10-24","publicationStatus":"PW","scienceBaseUri":"52908b09e4b0bbdcf23f092c","contributors":{"authors":[{"text":"McDonald, Lyman","contributorId":23588,"corporation":false,"usgs":true,"family":"McDonald","given":"Lyman","affiliations":[],"preferred":false,"id":486665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manly, Bryan","contributorId":64292,"corporation":false,"usgs":true,"family":"Manly","given":"Bryan","affiliations":[],"preferred":false,"id":486667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huettmann, Falk","contributorId":15663,"corporation":false,"usgs":false,"family":"Huettmann","given":"Falk","email":"","affiliations":[],"preferred":false,"id":486664,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thogmartin, Wayne","contributorId":36974,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","affiliations":[],"preferred":false,"id":486666,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70055549,"text":"fs20133075 - 2013 - Water resources of Lafourche Parish, Louisiana","interactions":[],"lastModifiedDate":"2013-11-20T12:52:03","indexId":"fs20133075","displayToPublicDate":"2013-11-20T12:47:00","publicationYear":"2013","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":"2013-3075","title":"Water resources of Lafourche Parish, Louisiana","docAbstract":"This fact sheet presents a brief overview of groundwater and surface-water resources in Lafourche Parish, Louisiana. Information on the availability, past and current use trends, and water quality from groundwater and surface-water sources in the parish is discussed. Previously published reports and data stored in the U.S. Geological Survey’s National Water Information System (http://waterdata.usgs.gov/nwis) are the primary sources of the information presented here.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133075","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Prakken, L., and Lovelace, J.K., 2013, Water resources of Lafourche Parish, Louisiana: U.S. Geological Survey Fact Sheet 2013-3075, 4 p., https://doi.org/10.3133/fs20133075.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-050772","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"links":[{"id":279254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20133075.jpg"},{"id":279252,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3075/pdf/fs2013-3075.pdf"},{"id":279253,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3075/"}],"country":"United States","state":"Louisiana","county":"Lafourche Parish","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -91.0365,28.942 ], [ -91.0365,30.222 ], [ -89.7565,30.222 ], [ -89.7565,28.942 ], [ -91.0365,28.942 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52a64090e4b0a6d6958827b7","contributors":{"authors":[{"text":"Prakken, Lawrence B.","contributorId":73978,"corporation":false,"usgs":true,"family":"Prakken","given":"Lawrence B.","affiliations":[],"preferred":false,"id":486141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lovelace, John K. 0000-0002-8532-2599 jlovelac@usgs.gov","orcid":"https://orcid.org/0000-0002-8532-2599","contributorId":999,"corporation":false,"usgs":true,"family":"Lovelace","given":"John","email":"jlovelac@usgs.gov","middleInitial":"K.","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486140,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70057791,"text":"70057791 - 2013 - Performance of quantitative vegetation sampling methods across gradients of cover in Great Basin plant communities","interactions":[],"lastModifiedDate":"2013-11-27T12:03:15","indexId":"70057791","displayToPublicDate":"2013-11-20T11:56:26","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Performance of quantitative vegetation sampling methods across gradients of cover in Great Basin plant communities","docAbstract":"Resource managers and scientists need efficient, reliable methods for quantifying vegetation to conduct basic research, evaluate land management actions, and monitor trends in habitat conditions. We examined three methods for quantifying vegetation in 1-ha plots among different plant communities in the northern Great Basin: photography-based grid-point intercept (GPI), line-point intercept (LPI), and point-quarter (PQ). We also evaluated each method for within-plot subsampling adequacy and effort requirements relative to information gain. We found that, for most functional groups, percent cover measurements collected with the use of LPI, GPI, and PQ methods were strongly correlated. These correlations were even stronger when we used data from the upper canopy only (i.e., top “hit” of pin flags) in LPI to estimate cover. PQ was best at quantifying cover of sparse plants such as shrubs in early successional habitats. As cover of a given functional group decreased within plots, the variance of the cover estimate increased substantially, which required more subsamples per plot (i.e., transect lines, quadrats) to achieve reliable precision. For GPI, we found that that six–nine quadrats per hectare were sufficient to characterize the vegetation in most of the plant communities sampled. All three methods reasonably characterized the vegetation in our plots, and each has advantages depending on characteristics of the vegetation, such as cover or heterogeneity, study goals, precision of measurements required, and efficiency needed.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Rangeland Ecology and Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The Society for Range Management","doi":"10.2111/REM-D-13-00063.1","usgsCitation":"Pilliod, D., and Arkle, R., 2013, Performance of quantitative vegetation sampling methods across gradients of cover in Great Basin plant communities: Rangeland Ecology and Management, v. 66, no. 6, p. 634-647, https://doi.org/10.2111/REM-D-13-00063.1.","productDescription":"14 p.","startPage":"634","endPage":"647","ipdsId":"IP-045275","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":473440,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/642750","text":"External Repository"},{"id":279868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279866,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2111/REM-D-13-00063.1"},{"id":279867,"type":{"id":15,"text":"Index Page"},"url":"https://www.bioone.org/doi/full/10.2111/REM-D-13-00063.1"}],"country":"United States","state":"Idaho;Oregon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.61,41.99 ], [ -124.61,49.0 ], [ -111.04,49.0 ], [ -111.04,41.99 ], [ -124.61,41.99 ] ] ] } } ] }","volume":"66","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52972290e4b08e44bf670c47","contributors":{"authors":[{"text":"Pilliod, David S.","contributorId":101760,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[],"preferred":false,"id":486875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arkle, Robert S.","contributorId":55679,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert S.","affiliations":[],"preferred":false,"id":486874,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045458,"text":"fs20133037 - 2013 - USGS science at work in the San Francisco Bay and Sacramento-San Joaquin Delta estuary","interactions":[],"lastModifiedDate":"2017-10-30T11:44:56","indexId":"fs20133037","displayToPublicDate":"2013-11-20T10:57:00","publicationYear":"2013","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":"2013-3037","title":"USGS science at work in the San Francisco Bay and Sacramento-San Joaquin Delta estuary","docAbstract":"The San Francisco Bay and Sacramento-San Joaquin Delta form one of the largest estuaries in the United States. The “Bay-Delta” system provides water to more than 25 million California residents and vast farmlands, as well as key habitat for birds, fish, and other wildlife. To help ensure the health of this crucial estuary, the U.S. Geological Survey, in close cooperation with partner agencies and organizations, is providing science essential to addressing societal issues associated with water quantity and quality, sediment transportation, environmental contamination, animal health and status, habitat restoration, hazards, ground subsidence, and climate change.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133037","usgsCitation":"Shouse, M.K., and Cox, D.A., 2013, USGS science at work in the San Francisco Bay and Sacramento-San Joaquin Delta estuary: U.S. Geological Survey Fact Sheet 2013-3037, 6 p., https://doi.org/10.3133/fs20133037.","productDescription":"6 p.","numberOfPages":"6","ipdsId":"IP-042810","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":279247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20133037.jpg"},{"id":279245,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3037/"},{"id":279246,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3037/pdf/fs2013-3037.pdf"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta;San Francisco Bay","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.6321,37.297 ], [ -122.6321,38.6877 ], [ -121.1847,38.6877 ], [ -121.1847,37.297 ], [ -122.6321,37.297 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52a6408be4b0a6d69588277c","contributors":{"authors":[{"text":"Shouse, Michelle K. mkshouse@usgs.gov","contributorId":5407,"corporation":false,"usgs":true,"family":"Shouse","given":"Michelle","email":"mkshouse@usgs.gov","middleInitial":"K.","affiliations":[],"preferred":true,"id":477525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cox, Dale A. dacox@usgs.gov","contributorId":165,"corporation":false,"usgs":true,"family":"Cox","given":"Dale","email":"dacox@usgs.gov","middleInitial":"A.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":477524,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70055658,"text":"ofr20131172 - 2013 - Borehole-explosion and air-gun data acquired in the 2011 Salton Seismic Imaging Project (SSIP), southern California: description of the survey","interactions":[],"lastModifiedDate":"2013-11-20T11:16:29","indexId":"ofr20131172","displayToPublicDate":"2013-11-20T10:53:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1172","title":"Borehole-explosion and air-gun data acquired in the 2011 Salton Seismic Imaging Project (SSIP), southern California: description of the survey","docAbstract":"The Imperial and Coachella Valleys are being formed by active plate-tectonic processes. From the Imperial Valley southward into the Gulf of California, plate motions are rifting the continent apart. In the Coachella Valley, the plates are sliding past one another along the San Andreas and related faults (fig. 1). These processes build the stunning landscapes of the region, but also produce damaging earthquakes.
\nRupture of the southern section of the San Andreas Fault (SAF), from the Coachella Valley to the Mojave Desert, is believed to be the greatest natural hazard that California will face in the near future. With an estimated magnitude between 7.2 and 8.1, such an event would result in violent shaking, loss of life, and disruption of infrastructure (freeways, aqueducts, power, petroleum, and communication lines) that might bring much of southern California to a standstill. As part of the nation’s efforts to avert a catastrophe of this magnitude, a number of projects have been undertaken to more fully understand and mitigate the effects of such an event. The Salton Seismic Imaging Project (SSIP), funded jointly by the National Science Foundation (NSF) and the U.S. Geological Survey (USGS), seeks to understand, through seismic imaging, the structure of the Earth surrounding the SAF, including the sedimentary basins on which cities are built.
\nThe principal investigators (PIs) of this collaborative project represent the USGS, Virginia Polytechnic Institute and State University (Virginia Tech), California Institute of Technology (Caltech), Scripps Institution of Oceanography (Scripps), University of Nevada, Reno (UNR), and Stanford University.
\nSSIP will create images of underground structure and sediments in the Imperial and Coachella Valleys and adjacent mountain ranges to investigate the earthquake hazards posed to cities in this area. Importantly, the images will help determine the underground geometry of the SAF, how deep the sediments are, and how fast earthquake energy can travel through the sediments. All of these factors determine how hard the earth will shake during a major earthquake. If we can improve on our understanding of how and where earthquakes will occur, and how strong their resultant shaking will be, then buildings can be designed or retrofitted accordingly in order to resist damage and collapse, and emergency plans can be adequately prepared. In addition, SSIP will investigate the processes of rifting and magmatism in the Salton Trough in order to better understand this important plate-boundary region. The Salton Trough is a unique rift in that subsidence is accompanied by huge influxes of infilling sediment from the Colorado River. Volcanism that accompanies the subsidence here is muted by these influxes of sediment. The Salton Trough, in the central part of the Imperial Valley, is apparently made up of entirely new crust: young sediment in the upper crust and basaltic intrusive rocks in the mid-to-lower crust (Fuis and others, 1984).
\nSimilar to the ultrasound and computed tomography (CT) scans performed by the medical industry, seismic imaging is a collection of techniques that enable scientists to obtain a picture of what is underground. The petroleum industry routinely uses these techniques to search for oil and gas at relatively shallow depths; however, the scope of this project demanded that we image as much as 30 km into the Earth’s crust. This project generated and recorded seismic waves, similar to sound waves, which move downward into the Earth and are bent (refracted) or echoed (reflected) back to the surface.
\nSSIP acquired data in a series of intersecting lines that cover key areas of the Salton Trough. The sources of sound waves were detonations (shots) in deep boreholes, designed to create energy equivalent to magnitude 1–2 earthquakes. The study region routinely experiences earthquakes of these magnitudes, but earthquakes are not located in such a way as to permit us to create the detailed images we need for earthquake hazard assessment. Air gun bursts, generated in the Salton Sea along extensions of our onshore seismic lines, also were utilized as sound-wave sources. Temporary deployments of portable land seismometers, as well as ocean-bottom seismometers (OBSs) on the floor of the Salton Sea, recorded the energy from the land shots and air gun bursts.
\nSSIP is similar to the Los Angeles Regional Seismic Experiments of 1994 and 1999 (LARSE I and II, respectively; Murphy and others, 1996; Fuis and others, 2001). The LARSE surveys demonstrated that the USGS and collaborators can safely and effectively conduct seismic imaging surveys in urban and nonurban areas, on lands owned and/or managed by many different types of agencies and entities. Information was produced that could not have been obtained any other way, and this information was key to changing the leading ideas about earthquake hazards at that time in the Los Angeles region. These surveys produced no significant environmental impact or damage to structures, and they did not trigger earthquakes.
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For the State of Alabama, elevation data are critical for flood risk management; infrastructure and construction management; wildfire management, planning, and response; natural resources conservation; geologic resource assessment and hazards mitigation; and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data.
\nThe National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The new 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A-16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.
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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52a6407be4b0a6d6958826e8","contributors":{"authors":[{"text":"Carswell, William J. Jr. carswell@usgs.gov","contributorId":1787,"corporation":false,"usgs":true,"family":"Carswell","given":"William J.","suffix":"Jr.","email":"carswell@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":false,"id":486101,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70055859,"text":"ofr20131149 - 2013 - Geochemistry of soils from the San Rafael Valley, Santa Cruz County, Arizona","interactions":[],"lastModifiedDate":"2026-03-13T19:34:25.330134","indexId":"ofr20131149","displayToPublicDate":"2013-11-20T10:20:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1149","title":"Geochemistry of soils from the San Rafael Valley, Santa Cruz County, Arizona","docAbstract":"This study was conducted to determine whether surficial geochemical methods can be used to identify subsurface mineraldeposits covered by alluvium derived from surrounding areas. The geochemical investigation focused on an anomalous geo-physical magnetic high located in the San Rafael Valley in Santa Cruz County, Arizona. The magnetic high, inferred to be asso-ciated with a buried granite intrusion, occurs beneath Quaternary alluvial and terrace deposits. Soil samples were collected at a depth of 10 to 30 centimeters below land surface along transects that traverse the inferred granite. The samples were analyzed by inductively coupled plasma-mass spectrometry and by the partial-leach Mobile Metal Ion™ method. Principal component and factor analyses showed a strong correlation between the soils and source rocks hosting base-metal replacement deposits in the Harshaw and Patagonia Mining Districts. Factor analysis also indicated areas of high metal concentrations associated with the Meadow Valley Flat. Although no definitive geochemical signature was identified for the inferred granite, concentrations otungsten and iron in the surrounding area were slightly elevated.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131149","usgsCitation":"Folger, H.W., and Gray, F., 2013, Geochemistry of soils from the San Rafael Valley, Santa Cruz County, Arizona: U.S. Geological Survey Open-File Report 2013-1149, Report: vi, 30 p.; 5 Tables, https://doi.org/10.3133/ofr20131149.","productDescription":"Report: vi, 30 p.; 5 Tables","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-033576","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":279226,"rank":8,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131149.jpg"},{"id":279062,"rank":7,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1149"},{"id":279223,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1149/tables/of2013-1149_Table3.xls","text":"Table 3"},{"id":279224,"rank":2,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1149/tables/of2013-1149_Table2.xls","text":"Table 2"},{"id":279221,"rank":1,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1149/tables/of2013-1149_Table1.xls","text":"Table 1"},{"id":279220,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1149/pdf/of2013-1149.pdf"},{"id":279222,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1149/tables/of2013-1149_Table4.xls","text":"Table 4"},{"id":279225,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1149/tables/of2013-1149_Table5.xls","text":"Table 5"}],"datum":"North American Datum of 1983","country":"United States","state":"Arizona","county":"Santa Cruz County","otherGeospatial":"San Rafael Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.450142,31.299327 ], [ -111.450142,31.930394 ], [ -110.400193,31.930394 ], [ -110.400193,31.299327 ], [ -111.450142,31.299327 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52a6402fe4b0a6d6958823d2","contributors":{"authors":[{"text":"Folger, Helen W. 0000-0003-1376-5996 hfolger@usgs.gov","orcid":"https://orcid.org/0000-0003-1376-5996","contributorId":3219,"corporation":false,"usgs":true,"family":"Folger","given":"Helen","email":"hfolger@usgs.gov","middleInitial":"W.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, Floyd 0000-0002-0223-8966 fgray@usgs.gov","orcid":"https://orcid.org/0000-0002-0223-8966","contributorId":603,"corporation":false,"usgs":true,"family":"Gray","given":"Floyd","email":"fgray@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486265,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70057129,"text":"70057129 - 2013 - Ups and Downs of Burbot and their predator Lake Trout in Lake Superior, 1953-2011","interactions":[],"lastModifiedDate":"2013-11-22T08:30:24","indexId":"70057129","displayToPublicDate":"2013-11-20T08:24:32","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Ups and Downs of Burbot and their predator Lake Trout in Lake Superior, 1953-2011","docAbstract":"The fish community of Lake Superior has undergone a spectacular cycle of decline and recovery over the past 60 years. A combination of Sea Lamprey Petromyzon marinus depredation and commercial overfishing resulted in severe declines in Lake Trout Salvelinus namaycush, which served as the primary top predator of the community. Burbot Lota lota populations also declined as a result of Sea Lamprey depredation, largely owing to the loss of adult fish. After Sea Lamprey control measures were instituted in the early 1960s, Burbot populations rebounded rapidly but Lake Trout populations recovered more slowly and recovery was not fully evident until the mid-1980s. As Lake Trout populations recovered, Burbot populations began to decline, and predation on small Burbot was identified as the most likely cause. By 2000, Burbot densities had dropped below their nadir in the early 1960s and have continued to decline, with the densities of juveniles and small adults falling below that of large adults. Although Burbot populations are at record lows in Lake Superior, the density of large reproductive adults remains stable and a large reserve of adult Burbot is present in deep offshore waters. The combination of the Burbot's early maturation, long life span, and high fecundity provides the species with the resiliency to remain a viable member of the Lake Superior fish community into the foreseeable future.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Transactions of the American Fisheries Society","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Fisheries Society","doi":"10.1080/00028487.2013.824918","usgsCitation":"Gorman, O.T., and Sitar, S., 2013, Ups and Downs of Burbot and their predator Lake Trout in Lake Superior, 1953-2011: Transactions of the American Fisheries Society, v. 142, no. 6, p. 1757-1772, https://doi.org/10.1080/00028487.2013.824918.","productDescription":"16 p.","startPage":"1757","endPage":"1772","ipdsId":"IP-049451","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":279510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279509,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/00028487.2013.824918"}],"country":"United States","otherGeospatial":"Lake Superior","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -92.1122,46.41 ], [ -92.1122,49.0195 ], [ -84.3544,49.0195 ], [ -84.3544,46.41 ], [ -92.1122,46.41 ] ] ] } } ] }","volume":"142","issue":"6","noUsgsAuthors":false,"publicationDate":"2013-11-07","publicationStatus":"PW","scienceBaseUri":"52908b14e4b0bbdcf23f0983","contributors":{"authors":[{"text":"Gorman, Owen T. 0000-0003-0451-110X otgorman@usgs.gov","orcid":"https://orcid.org/0000-0003-0451-110X","contributorId":2888,"corporation":false,"usgs":true,"family":"Gorman","given":"Owen","email":"otgorman@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":486640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sitar, Shawn P.","contributorId":34421,"corporation":false,"usgs":true,"family":"Sitar","given":"Shawn P.","affiliations":[],"preferred":false,"id":486641,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70056317,"text":"70056317 - 2013 - Hyporheic zone denitrification: controls on effective reaction depth and contribution to whole-stream mass balance","interactions":[],"lastModifiedDate":"2013-12-02T10:42:25","indexId":"70056317","displayToPublicDate":"2013-11-19T14:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Hyporheic zone denitrification: controls on effective reaction depth and contribution to whole-stream mass balance","docAbstract":"Stream denitrification is thought to be enhanced by hyporheic transport but there is little direct evidence from the field. To demonstrate at a field site, we injected 15NO3−, Br (conservative tracer), and SF6 (gas exchange tracer) and compared measured whole-stream denitrification with in situ hyporheic denitrification in shallow and deeper flow paths of contrasting geomorphic units. Hyporheic denitrification accounted for between 1 and 200% of whole-stream denitrification. The reaction rate constant was positively related to hyporheic exchange rate (greater substrate delivery), concentrations of substrates DOC and nitrate, microbial denitrifier abundance (nirS), and measures of granular surface area and presence of anoxic microzones. The dimensionless product of the reaction rate constant and hyporheic residence time, λhzτhz define a Damköhler number, Daden-hz that was optimal in the subset of hyporheic flow paths where Daden-hz ≈ 1. Optimal conditions exclude inefficient deep pathways transport where substrates are used up and also exclude inefficient shallow pathways that require repeated hyporheic entries and exits to complete the reaction. The whole-stream reaction significance, Rs (dimensionless), was quantified by multiplying Daden-hz by the proportion of stream discharge passing through the hyporheic zone. Together these two dimensionless metrics, one flow-path scale and the other reach-scale, quantify the whole-stream significance of hyporheic denitrification. One consequence is that the effective zone of significant denitrification often differs from the full depth of the hyporheic zone, which is one reason why whole-stream denitrification rates have not previously been explained based on total hyporheic-zone metrics such as hyporheic-zone size or residence time.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Water Resources Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/wrcr.20492","usgsCitation":"Harvey, J.W., Böhlke, J., Voytek, M.A., Scott, D., and Tobias, C., 2013, Hyporheic zone denitrification: controls on effective reaction depth and contribution to whole-stream mass balance: Water Resources Research, v. 49, no. 10, p. 6298-6316, https://doi.org/10.1002/wrcr.20492.","productDescription":"19 p.","startPage":"6298","endPage":"6316","numberOfPages":"19","ipdsId":"IP-050807","costCenters":[{"id":628,"text":"Water Resources Discipline","active":false,"usgs":true}],"links":[{"id":473442,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wrcr.20492","text":"Publisher Index Page"},{"id":279189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279166,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/wrcr.20492"}],"country":"United States","state":"Indiana","otherGeospatial":"Sugar Creek","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -88.5718,39.8423 ], [ -88.5718,41.2654 ], [ -86.2482,41.2654 ], [ -86.2482,39.8423 ], [ -88.5718,39.8423 ] ] ] } } ] }","volume":"49","issue":"10","noUsgsAuthors":false,"publicationDate":"2013-10-07","publicationStatus":"PW","scienceBaseUri":"528c888ce4b0c629af44a96e","contributors":{"authors":[{"text":"Harvey, Judson W. 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":1796,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","middleInitial":"W.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":486523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Böhlke, John Karl 0000-0001-5693-6455","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":22843,"corporation":false,"usgs":true,"family":"Böhlke","given":"John Karl","affiliations":[],"preferred":false,"id":486524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Voytek, Mary A.","contributorId":91943,"corporation":false,"usgs":true,"family":"Voytek","given":"Mary","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":486527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scott, Durelle","contributorId":62513,"corporation":false,"usgs":true,"family":"Scott","given":"Durelle","affiliations":[],"preferred":false,"id":486526,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tobias, Craig R.","contributorId":23410,"corporation":false,"usgs":false,"family":"Tobias","given":"Craig R.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":486525,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70056319,"text":"70056319 - 2013 - Obligate brood parasites show more functionally effective innate immune responses: an eco-immunological hypothesis","interactions":[],"lastModifiedDate":"2013-11-19T13:18:17","indexId":"70056319","displayToPublicDate":"2013-11-19T13:15:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1602,"text":"Evolutionary Biology","active":true,"publicationSubtype":{"id":10}},"title":"Obligate brood parasites show more functionally effective innate immune responses: an eco-immunological hypothesis","docAbstract":"Immune adaptations of obligate brood parasites attracted interest when three New World cowbird species (Passeriformes, Icteridae, genus Molothrus) proved unusually resistant to West Nile virus. We have used cowbirds as models to investigate the eco-immunological hypothesis that species in parasite-rich environments characteristically have enhanced immunity as a life history adaptation. As part of an ongoing program to understand the cowbird immune system, in this study we measured degranulation and oxidative burst, two fundamental responses of the innate immune system. Innate immunity provides non-specific, fast-acting defenses against a variety of invading pathogens, and we hypothesized that innate immunity experiences particularly strong selection in cowbirds, because their life history strategy exposes them to diverse novel and unpredictable parasites. We compared the relative effectiveness of degranulation and oxidative burst responses in two cowbird species and one related, non-parasitic species. Both innate immune defenses were significantly more functionally efficient in the two parasitic cowbird species than in the non-parasitic red-winged blackbird (Icteridae, Agelaius phoeniceus). Additionally, both immune defenses were more functionally efficient in the brown-headed cowbird (M. ater), an extreme host-generalist brood parasite, than in the bronzed cowbird (M. aeneus), a moderate host-specialist with lower exposure to other species and their parasites. Thus the relative effectiveness of these two innate immune responses corresponds to the diversity of parasites in the niche of each species and to their relative resistance to WNV. This study is the first use of these two specialized assays in a comparative immunology study of wild avian species.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Evolutionary Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s11692-013-9231-x","usgsCitation":"Hahn, D., Summers, S.G., Genovese, K.J., He, H., and Kogut, M.H., 2013, Obligate brood parasites show more functionally effective innate immune responses: an eco-immunological hypothesis: Evolutionary Biology, v. 40, no. 4, p. 554-561, https://doi.org/10.1007/s11692-013-9231-x.","productDescription":"8 p.","startPage":"554","endPage":"561","numberOfPages":"8","ipdsId":"IP-043917","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":279182,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279171,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s11692-013-9231-x"}],"volume":"40","issue":"4","noUsgsAuthors":false,"publicationDate":"2013-04-16","publicationStatus":"PW","scienceBaseUri":"528c888de4b0c629af44a974","contributors":{"authors":[{"text":"Hahn, D. Caldwell 0000-0002-5242-2059","orcid":"https://orcid.org/0000-0002-5242-2059","contributorId":26055,"corporation":false,"usgs":true,"family":"Hahn","given":"D. Caldwell","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":486528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Summers, Scott G.","contributorId":45612,"corporation":false,"usgs":true,"family":"Summers","given":"Scott","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":486530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Genovese, Kenneth J.","contributorId":45613,"corporation":false,"usgs":true,"family":"Genovese","given":"Kenneth","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":486531,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"He, Haiqi","contributorId":31289,"corporation":false,"usgs":true,"family":"He","given":"Haiqi","email":"","affiliations":[],"preferred":false,"id":486529,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kogut, Michael H.","contributorId":98203,"corporation":false,"usgs":true,"family":"Kogut","given":"Michael","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":486532,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}