Great earthquakes anticipated on the Cascadia subduction fault can potentially rupture beyond the geodetically and thermally inferred locked zone to the depths of episodic tremor and slip (ETS) or to the even deeper fore-arc mantle corner (FMC). To evaluate these extreme rupture limits, we map the FMC from southern Vancouver Island to central Oregon by combining published seismic velocity structures with a model of the Juan de Fuca plate. These data indicate that the FMC is somewhat shallower beneath Vancouver Island (36–38 km) and Oregon (35–40 km) and deeper beneath Washington (41–43 km). The updip edge of tremor follows the same general pattern, overlying a slightly shallower Juan de Fuca plate beneath Vancouver Island and Oregon (∼30 km) and a deeper plate beneath Washington (∼35 km). Similar to the Nankai subduction zone, the best constrained FMC depths correlate with the center of the tremor band suggesting that ETS is controlled by conditions near the FMC rather than directly by temperature or pressure. Unlike Nankai, a gap as wide as 70 km exists between the downdip limit of the inferred locked zone and the FMC. This gap also encompasses a ∼50 km wide gap between the inferred locked zones and the updip limit of tremor. The separation of these features offers a natural laboratory for determining the key controls on downdip rupture limits.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2013GC005144","usgsCitation":"McCrory, P.A., Hyndman, R.D., and Blair, J.L., 2014, Relationship between the Cascadia fore-arc mantle wedge, nonvolcanic tremor, and the downdip limit of seismogenic rupture: Geochemistry, Geophysics, Geosystems, v. 15, no. 4, p. 1071-1095, https://doi.org/10.1002/2013GC005144.","productDescription":"15 p.","startPage":"1071","endPage":"1095","ipdsId":"IP-051064","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":472937,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2013gc005144","text":"Publisher Index Page"},{"id":344242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Cascadia fore-arc mantle wedge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -132,\n 39\n ],\n [\n -120,\n 39\n ],\n [\n -120,\n 52\n ],\n [\n -132,\n 52\n ],\n [\n -132,\n 39\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-04-22","publicationStatus":"PW","scienceBaseUri":"59770753e4b0ec1a48889faa","contributors":{"authors":[{"text":"McCrory, Patricia A. 0000-0003-2471-0018 pmccrory@usgs.gov","orcid":"https://orcid.org/0000-0003-2471-0018","contributorId":2728,"corporation":false,"usgs":true,"family":"McCrory","given":"Patricia","email":"pmccrory@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":706215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hyndman, Roy D.","contributorId":26031,"corporation":false,"usgs":true,"family":"Hyndman","given":"Roy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":706216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blair, J. Luke 0000-0002-6980-6446 lblair@usgs.gov","orcid":"https://orcid.org/0000-0002-6980-6446","contributorId":4146,"corporation":false,"usgs":true,"family":"Blair","given":"J.","email":"lblair@usgs.gov","middleInitial":"Luke","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":706217,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70112510,"text":"70112510 - 2014 - Modeling the influence of organic acids on soil weathering","interactions":[],"lastModifiedDate":"2014-06-16T14:14:35","indexId":"70112510","displayToPublicDate":"2014-06-16T14:10:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the influence of organic acids on soil weathering","docAbstract":"Biological inputs and organic matter cycling have long been regarded as important factors in the physical and chemical development of soils. In particular, the extent to which low molecular weight organic acids, such as oxalate, influence geochemical reactions has been widely studied. Although the effects of organic acids are diverse, there is strong evidence that organic acids accelerate the dissolution of some minerals. However, the influence of organic acids at the field-scale and over the timescales of soil development has not been evaluated in detail. In this study, a reactive-transport model of soil chemical weathering and pedogenic development was used to quantify the extent to which organic acid cycling controls mineral dissolution rates and long-term patterns of chemical weathering. Specifically, oxalic acid was added to simulations of soil development to investigate a well-studied chronosequence of soils near Santa Cruz, CA. The model formulation includes organic acid input, transport, decomposition, organic-metal aqueous complexation and mineral surface complexation in various combinations. Results suggest that although organic acid reactions accelerate mineral dissolution rates near the soil surface, the net response is an overall decrease in chemical weathering. Model results demonstrate the importance of organic acid input concentrations, fluid flow, decomposition and secondary mineral precipitation rates on the evolution of mineral weathering fronts. In particular, model soil profile evolution is sensitive to kaolinite precipitation and oxalate decomposition rates. The soil profile-scale modeling presented here provides insights into the influence of organic carbon cycling on soil weathering and pedogenesis and supports the need for further field-scale measurements of the flux and speciation of reactive organic compounds.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geochimica et Cosmochimica Acta","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2014.05.003","usgsCitation":"Lawrence, C., Harden, J.W., and Maher, K., 2014, Modeling the influence of organic acids on soil weathering: Geochimica et Cosmochimica Acta, v. 139, p. 487-507, https://doi.org/10.1016/j.gca.2014.05.003.","productDescription":"21 p.","startPage":"487","endPage":"507","numberOfPages":"21","ipdsId":"IP-052158","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":288644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288642,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.gca.2014.05.003"}],"country":"United States","state":"California","city":"Santa Cruz","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.156811,36.945639 ], [ -122.156811,37.005569 ], [ -122.00901,37.005569 ], [ -122.00901,36.945639 ], [ -122.156811,36.945639 ] ] ] } } ] }","volume":"139","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae777de4b0abf75cf2c14f","contributors":{"authors":[{"text":"Lawrence, Corey R. clawrence@usgs.gov","contributorId":4478,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey R.","email":"clawrence@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":false,"id":494795,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harden, Jennifer W. 0000-0002-6570-8259 jharden@usgs.gov","orcid":"https://orcid.org/0000-0002-6570-8259","contributorId":1971,"corporation":false,"usgs":true,"family":"Harden","given":"Jennifer","email":"jharden@usgs.gov","middleInitial":"W.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":494794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maher, Kate","contributorId":97824,"corporation":false,"usgs":false,"family":"Maher","given":"Kate","affiliations":[{"id":7039,"text":"Stanford University, Department of Geoloigcal and Environmental Sciences, Stanford, CA","active":true,"usgs":false}],"preferred":false,"id":494796,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70112470,"text":"70112470 - 2014 - The use of solvent extractions and solubility theory to discern hydrocarbon associations in coal, with application to the coal-supercritical CO2 system","interactions":[],"lastModifiedDate":"2014-06-16T12:19:02","indexId":"70112470","displayToPublicDate":"2014-06-16T12:13:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2958,"text":"Organic Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"The use of solvent extractions and solubility theory to discern hydrocarbon associations in coal, with application to the coal-supercritical CO2 system","docAbstract":"Samples of three high volatile bituminous coals were subjected to parallel sets of extractions involving solvents dichloromethane (DCM), carbon disulfide (CS2), and supercritical carbon dioxide (CO2) (40 °C, 100 bar) to study processes affecting coal–solvent interactions. Recoveries of perdeuterated surrogate compounds, n-hexadecane-d34 and four polycyclic aromatic hydrocarbons (PAHs), added as a spike prior to extraction, provided further insight into these processes. Soxhlet-DCM and Soxhlet-CS2 extractions yielded similar amounts of extractable organic matter (EOM) and distributions of individual hydrocarbons. Supercritical CO2 extractions (40 °C, 100 bar) yielded approximately an order of magnitude less EOM. Hydrocarbon distributions in supercritical CO2 extracts generally mimicked distributions from the other solvent extracts, albeit at lower concentrations. This disparity increased with increasing molecular weight of target hydrocarbons. Five- and six-ring ring PAHs generally were not detected and no asphaltenes were recovered in supercritical CO2 extractions conducted at 40 °C and 100 bar. Supercritical CO2 extraction at elevated temperature (115 °C) enhanced recovery of four-ring and five-ring PAHs, dibenzothiophene (DBT), and perdeuterated PAH surrogate compounds. These results are only partially explained through comparison with previous measurements of hydrocarbon solubility in supercritical CO2. Similarly, an evaluation of extraction results in conjunction with solubility theory (Hildebrand and Hansen solubility parameters) does not fully account for the hydrocarbon distributions observed among the solvent extracts. Coal composition (maceral content) did not appear to affect surrogate recovery during CS2 and DCM extractions but might affect supercritical CO2 extractions, which revealed substantive uptake (partitioning) of PAH surrogates into the coal samples. This uptake was greatest in the sample (IN-1) with the highest vitrinite content. These findings indicate that hydrocarbon solubility does not exert a strong influence on hydrocarbon behavior in the systems studied. Other factors such as coal composition and maceral content, surface processes (physisorption), or other molecular interactions appear to affect the partitioning of hydrocarbons within the coal–supercritical CO2 system. Resolving the extent to which these factors might affect hydrocarbon behavior under different geological settings is important to efforts seeking to model petroleum generation, fractionation and expulsion from coal beds and to delineate potential hydrocarbon fate and transport in geologic CO2 sequestration settings.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Organic Geochemistry","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.orggeochem.2014.05.002","usgsCitation":"Kolak, J.J., and Burruss, R.A., 2014, The use of solvent extractions and solubility theory to discern hydrocarbon associations in coal, with application to the coal-supercritical CO2 system: Organic Geochemistry, v. 73, p. 56-69, https://doi.org/10.1016/j.orggeochem.2014.05.002.","productDescription":"14 p.","startPage":"56","endPage":"69","numberOfPages":"14","ipdsId":"IP-052577","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":288626,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288615,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.orggeochem.2014.05.002"}],"volume":"73","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7871e4b0abf75cf2d567","contributors":{"authors":[{"text":"Kolak, Jonathan J.","contributorId":59100,"corporation":false,"usgs":true,"family":"Kolak","given":"Jonathan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":494754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burruss, Robert A. 0000-0001-6827-804X burruss@usgs.gov","orcid":"https://orcid.org/0000-0001-6827-804X","contributorId":558,"corporation":false,"usgs":true,"family":"Burruss","given":"Robert","email":"burruss@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":494753,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70112450,"text":"70112450 - 2014 - Modeling regeneration responses of big sagebrush (Artemisia tridentata) to abiotic conditions","interactions":[],"lastModifiedDate":"2014-06-16T14:03:08","indexId":"70112450","displayToPublicDate":"2014-06-16T12:06:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Modeling regeneration responses of big sagebrush (Artemisia tridentata) to abiotic conditions","docAbstract":"Ecosystems dominated by big sagebrush, Artemisia tridentata Nuttall (Asteraceae), which are the most widespread ecosystems in semiarid western North America, have been affected by land use practices and invasive species. Loss of big sagebrush and the decline of associated species, such as greater sage-grouse, are a concern to land managers and conservationists. However, big sagebrush regeneration remains difficult to achieve by restoration and reclamation efforts and there is no regeneration simulation model available. We present here the first process-based, daily time-step, simulation model to predict yearly big sagebrush regeneration including relevant germination and seedling responses to abiotic factors. We estimated values, uncertainty, and importance of 27 model parameters using a total of 1435 site-years of observation. Our model explained 74% of variability of number of years with successful regeneration at 46 sites. It also achieved 60% overall accuracy predicting yearly regeneration success/failure. Our results identify specific future research needed to improve our understanding of big sagebrush regeneration, including data at the subspecies level and improved parameter estimates for start of seed dispersal, modified wet thermal-time model of germination, and soil water potential influences. We found that relationships between big sagebrush regeneration and climate conditions were site specific, varying across the distribution of big sagebrush. This indicates that statistical models based on climate are unsuitable for understanding range-wide regeneration patterns or for assessing the potential consequences of changing climate on sagebrush regeneration and underscores the value of this process-based model. We used our model to predict potential regeneration across the range of sagebrush ecosystems in the western United States, which confirmed that seedling survival is a limiting factor, whereas germination is not. Our results also suggested that modeled regeneration suitability is necessary but not sufficient to explain sagebrush presence. We conclude that future assessment of big sagebrush responses to climate change will need to account for responses of regenerative stages using a process-based understanding, such as provided by our model.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecological Modelling","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2014.04.021","usgsCitation":"Schlaepfer, D., Lauenroth, W.K., and Bradford, J.B., 2014, Modeling regeneration responses of big sagebrush (Artemisia tridentata) to abiotic conditions: Ecological Modelling, v. 286, p. 66-77, https://doi.org/10.1016/j.ecolmodel.2014.04.021.","productDescription":"12 p.","startPage":"66","endPage":"77","numberOfPages":"12","ipdsId":"IP-049397","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":288625,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288601,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecolmodel.2014.04.021"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.79,29.95 ], [ -124.79,49.0 ], [ -99.93,49.0 ], [ -99.93,29.95 ], [ -124.79,29.95 ] ] ] } } ] }","volume":"286","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae777de4b0abf75cf2c14d","contributors":{"authors":[{"text":"Schlaepfer, Daniel R.","contributorId":105189,"corporation":false,"usgs":false,"family":"Schlaepfer","given":"Daniel R.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":494742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":494741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":494740,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70110811,"text":"sir20145012 - 2014 - Dissolved-solids sources, loads, yields, and concentrations in streams of the conterminous United States","interactions":[],"lastModifiedDate":"2016-06-29T13:40:28","indexId":"sir20145012","displayToPublicDate":"2014-06-16T09:00:00","publicationYear":"2014","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":"2014-5012","title":"Dissolved-solids sources, loads, yields, and concentrations in streams of the conterminous United States","docAbstract":"Recent studies have shown that excessive dissolved-solids concentrations in water can have adverse effects on the environment and on agricultural, domestic, municipal, and industrial water users. Such effects motivated the U.S. Geological Survey’s National Water Quality Assessment Program to develop a SPAtially-Referenced Regression on Watershed Attributes (SPARROW) model that has improved the understanding of sources, loads, yields, and concentrations of dissolved solids in streams of the conterminous United States.
\n\n
Using the SPARROW model, long-term mean annual dissolved-solids loads from 2,560 water-quality monitoring stations were statistically related to several spatial datasets that are surrogates for dissolved-solids sources and land-to-water delivery processes. Specifically, sources in the model included variables representing geologic materials, road deicers, urban lands, cultivated lands, and pasture lands. Transport of dissolved solids from these sources was modulated by land-to-water delivery variables that represent precipitation, streamflow, soil, vegetation, terrain, population, irrigation, and artificial drainage characteristics. Where appropriate, the load estimates, source variables, and transport variables were statistically adjusted to represent conditions for the base year 2000. The nonlinear least-squares estimated SPARROW model was used to predict long-term mean annual conditions for dissolved-solids sources, loads, yields, and concentrations in a digital hydrologic network representing nearly 66,000 stream reaches and their corresponding incremental catchments that drain the Nation.
\n\n
Nationwide, the predominant source of dissolved solids yielded from incremental catchments and delivered to local streams is geologic materials in 89 percent of the catchments, road deicers in 5 percent of the catchments, pasture lands in 3 percent of the catchments, urban lands in 2 percent of the catchments, and cultivated lands in 1 percent of the catchments. Whereas incremental catchments with dissolved solids that originated predominantly from geologic sources or from urban lands are found across much of the Nation, incremental catchments with dissolved solids yields that originated predominantly from road deicers are largely found in the Northeast, and incremental catchments with dissolved solids that originated predominantly from cultivated or pasture lands are largely found in the West. The total amount of dissolved solids delivered to the Nation’s streams is 271.9 million metric tons (Mt) annually, of which 194.2 million Mt (71.4%) come from geologic sources, 37.7 million Mt (13.9%) come from road deicers, 18.2 million Mt (6.7%) come from pasture lands, 13.9 million Mt (5.1%) come from urban lands, and 7.9 million Mt (2.9%) come from cultivated lands.
\n\n
Nationwide, the median incremental-catchment yield delivered to local streams is 26 metric tons per year per square kilometer [(Mt/yr)/km2]. Ten percent of the incremental catchments yield less than 4 (Mt/yr)/km2, and 10 percent yield more than 90 (Mt/yr)/km2. Incremental-catchment yields greater than 50 (Mt/yr)/km2 mostly occur along the northern part of the West Coast and in a crescent shaped band south of the Great Lakes. For example, the median incremental-catchment yield is 81 (Mt/yr)/km2 for the Great Lakes, 78 (Mt/yr)/km2 for the Ohio, and 74 (Mt/yr)/km2 for the Upper Mississippi water-resources regions. Incremental-catchment yields less than 10 (Mt/yr)/km2 mostly occur in a wide band across the arid lowland of the interior West that excludes areas along the coast and the extensive, higher mountain ranges. For example, the median incremental-catchment yield is 3 (Mt/yr)/km2 for the Lower Colorado, 5 (Mt/yr)/km2 for the Rio Grande, and 8 (Mt/yr)/km2 for the Great Basin water-resources regions.
\n\n
Predicted incremental loads were cascaded down through the reach network, with loads accumulating from reach to reach. For most stream reaches, the entire incremental load of dissolved solids delivered to the reach was transported to either the ocean or to one of the large streams flowing along the U.S. international boundary without losses occurring along the way. The exceptions to this include streams in the southwestern part of the country, such as the Colorado River, Rio Grande, and streams of internally drained drainages in the Great Basin, where dissolved-solids loads decreased through streamflow diversion for off-stream use, or by infiltration through the streambed.
\n\n
Long-term mean annual flow-weighted concentrations were derived from the predicted accumulated-load and stream-discharge data. Widespread low concentrations, generally less than 100 milligrams per liter (mg/L), occur in many reaches of the New England, South Atlantic-Gulf, and Pacific Northwest water-resources regions as a result of moderate dissolved-solids yields and high runoff rates. Widespread moderate concentrations, generally between 100 and 500 mg/L, occur in many reaches of the Great Lakes, Ohio, and Upper Mississippi River water-resources regions. Whereas dissolved-solids yields are generally high in these regions, runoff rates are also high, which helps moderate concentrations in these regions. Widespread higher concentrations, generally greater than 500 mg/L, occur across a belt of reaches that extends almost continuously from Canada to Mexico in the Midwest, cutting through the Souris-Red-Rainy, Missouri, Arkansas-White-Red, Texas-Gulf, and Rio Grande water-resources regions. Although dissolved-solids yields are moderate to low in these areas, low runoff rates result in the high concentrations for these areas.
\n\n
In 12.6 percent of the Nation’s stream reaches, predicted concentrations of dissolved solids exceed 500 mg/L, the U.S. Environmental Protection Agency’s secondary, nonenforceable drinking water standard. While this standard provides a metric for evaluating predicted concentrations in the context of drinking-water supplies, it should be noted that it only applies to drinking water actually served to customers by water utilities, and it does not apply to all stream reaches in the Nation nor does it apply during times when water is not being withdrawn for use. Exceedance of 500 mg/L is more pronounced in certain water-resources regions than others. For example, about half of the reaches in the Souris-Red-Rainy region have concentrations predicted to exceed 500 mg/L, and between 25 and 37 percent of the reaches in the Missouri, Arkansas-White-Red, Texas-Gulf, Rio Grande, and Lower Colorado regions are predicted to exceed 500 mg/L.
\n\n
Development of stream-load data for use in the SPARROW model also provided long-term temporal trend information in dissolved-solids concentrations at the monitoring stations for their period of record, which was constrained between 1980 and 2009. For the 2,560 monitoring stations used in this study, long-term trends in flow-adjusted dissolved-solids concentrations increased over time at 23 percent of the stations, decreased at 18 percent of the stations, and did not change over time at 59 percent of the stations. Long-term trends show a strong regional spatial pattern where from the western parts of the Great Plains to the West Coast, concentrations mostly either did not change or decreased over time, and from the eastern parts of the Great Plains to the East Coast, concentrations mostly either did not change or increased over time.
\n\n
Results from the trend analysis and from the SPARROW model indicate that, compared to monitoring stations with no trends or decreasing trends, stations with increasing trends are associated with a smaller percentage of the predicted dissolved-solids load originating from geologic sources, and a larger percentage originating from urban lands and road deicers. Conversely, compared to stations with increasing trends or no trends, stations with decreasing trends have a larger percentage of the predicted dissolved-solids load originating from geologic sources and a smaller percentage originating from urban lands and road deicers. Stations with decreasing trends also have larger percentages of predicted dissolved-solids load originating from cultivated lands and pasture lands, compared to stations with increasing trends or no trends.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145012","collaboration":"National Water Quality Assessment Program","usgsCitation":"Anning, D.W., and Flynn, M., 2014, Dissolved-solids sources, loads, yields, and concentrations in streams of the conterminous United States: U.S. Geological Survey Scientific Investigations Report 2014-5012, Report: viii, 101 p.; Appendixes 1-4, https://doi.org/10.3133/sir20145012.","productDescription":"Report: viii, 101 p.; Appendixes 1-4","numberOfPages":"113","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-037458","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":287816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145012.jpg"},{"id":287811,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5012/pdf/sir2014-5012.pdf"},{"id":287813,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5012/downloads/sir20145012_app02.xlsx"},{"id":287812,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5012/downloads/sir20145012_app01.xlsx"},{"id":287814,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5012/downloads/sir20145012_app03.xlsx"},{"id":287815,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5012/downloads/sir20145012_app04.docx"},{"id":287810,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5012/"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -97.18505859374999, 25.97779895546436 ], [ -97.42675781249999, 25.878994400196202 ], [ -98.2177734375, 26.115985925333536 ], [ -99.0966796875, 26.47057302237511 ], [ -99.580078125, 27.625140335093324 ], [ -100.283203125, 28.265682390146477 ], [ -100.83251953125, 29.305561325527698 ], [ -101.27197265625, 29.592565403314087 ], [ -101.49169921875, 29.80251790576445 ], [ -102.67822265625, 29.76437737516313 ], [ -103.11767578124999, 29.036960648558267 ], [ -104.47998046875, 29.611670115197377 ], [ -104.7216796875, 29.973970240516614 ], [ -104.69970703125, 30.20211367909724 ], [ -104.9853515625, 30.675715404167743 ], [ -106.12792968749999, 31.44741029142872 ], [ -106.45751953125, 31.80289258670676 ], [ -108.21533203125, 31.784216884487385 ], [ -108.204345703125, 31.344254455668054 ], [ -110.98388671874999, 31.3348710339506 ], [ -114.82910156249999, 32.52828936482526 ], [ -114.7412109375, 32.731840896865684 ], [ -117.13623046874999, 32.54681317351514 ], [ -117.3779296875, 33.211116472416855 ], [ -118.23486328125, 33.779147331286474 ], [ -118.564453125, 34.043556504127444 ], [ -118.94897460937499, 34.052659421375964 ], [ -119.2236328125, 34.161818161230386 ], [ -119.66308593749999, 34.470335121217495 ], [ -120.44311523437499, 34.45221847282654 ], [ -120.640869140625, 34.56990638085636 ], [ -120.673828125, 35.137879119634185 ], [ -121.9482421875, 36.4566360115962 ], [ -121.83837890625, 36.721273880045004 ], [ -121.88232421875, 36.949891786813296 ], [ -122.05810546875, 36.96744946416931 ], [ -122.40966796874999, 37.23032838760387 ], [ -122.56347656249999, 37.71859032558816 ], [ -122.67333984374999, 37.90953361677018 ], [ -123.00292968749999, 37.996162679728116 ], [ -123.02490234375, 38.238180119798635 ], [ -123.74999999999999, 38.92522904714054 ], [ -123.85986328124999, 39.757879992021756 ], [ -124.365234375, 40.27952566881291 ], [ -124.4091796875, 40.463666324587685 ], [ -124.18945312500001, 41.07935114946899 ], [ -124.0576171875, 41.60722821271717 ], [ -124.29931640625, 42.00032514831621 ], [ -124.49707031249999, 42.827638636242284 ], [ -124.16748046874999, 43.83452678223684 ], [ -124.03564453125, 45.058001435398296 ], [ -124.03564453125, 46.30140615437332 ], [ -124.25537109375, 47.41322033016902 ], [ -124.62890625, 47.91634204016118 ], [ -124.69482421875, 48.4146186174932 ], [ -123.96972656249999, 48.188063481211415 ], [ -123.134765625, 48.17341248658084 ], [ -122.78320312499999, 48.246625590713826 ], [ -122.81616210937499, 48.42920055556841 ], [ -123.18969726562499, 48.50204750525715 ], [ -123.24462890625, 48.69096039092549 ], [ -122.728271484375, 48.77067246880509 ], [ -122.82714843749999, 49.001843917978526 ], [ -95.174560546875, 49.01625665778159 ], [ -95.152587890625, 49.38237278700955 ], [ -94.81201171875, 49.31796095602274 ], [ -94.669189453125, 48.777912755501845 ], [ -93.834228515625, 48.63290858589532 ], [ -93.8232421875, 48.516604348867475 ], [ -93.44970703125, 48.56752037390827 ], [ -93.33984375, 48.66194284607008 ], [ -92.59277343749999, 48.531157010976706 ], [ -92.08740234375, 48.37084770238363 ], [ -91.4501953125, 48.06339653776211 ], [ -91.07666015625, 48.19538740833338 ], [ -90.8349609375, 48.23930899024905 ], [ -90.791015625, 48.10743118848039 ], [ -89.62646484375, 48.019324184801185 ], [ -89.3463134765625, 47.98256841921402 ], [ -88.3795166015625, 48.31973404047173 ], [ -84.847412109375, 46.897739085507 ], [ -84.55078125, 46.464349400461124 ], [ -84.4134521484375, 46.49839225859763 ], [ -84.19921875, 46.53619267489863 ], [ -84.1058349609375, 46.51351558059737 ], [ -84.122314453125, 46.320378031062354 ], [ -84.00146484374999, 46.15700496290803 ], [ -83.95751953125, 46.06560846138691 ], [ -83.81469726562499, 46.11132565729796 ], [ -83.6334228515625, 46.11894150610708 ], [ -83.42468261718749, 46.0007775685566 ], [ -83.583984375, 45.82497145796607 ], [ -82.518310546875, 45.32897866218559 ], [ -82.12280273437499, 43.57243174740972 ], [ -82.40295410156249, 43.000629854450025 ], [ -82.474365234375, 42.79136972365016 ], [ -82.5732421875, 42.5611728553181 ], [ -82.8533935546875, 42.370720143531955 ], [ -83.07861328125, 42.32200108060303 ], [ -83.12255859375, 42.14304156290942 ], [ -83.14453125, 42.04521345501039 ], [ -83.07861328125, 41.86956082699455 ], [ -82.6776123046875, 41.68111756290652 ], [ -82.3974609375, 41.68111756290652 ], [ -81.243896484375, 42.21224516288584 ], [ -80.09033203125, 42.39506551565123 ], [ -78.9532470703125, 42.827638636242284 ], [ -78.91754150390625, 42.95039177450287 ], [ -79.06585693359375, 43.092960677116295 ], [ -79.06036376953125, 43.26120612479979 ], [ -79.19769287109375, 43.45291889355465 ], [ -78.6895751953125, 43.632099415557754 ], [ -76.7999267578125, 43.64005063334694 ], [ -76.4483642578125, 44.11125397357153 ], [ -75.7781982421875, 44.51609322284931 ], [ -75.30029296875, 44.84029065139799 ], [ -74.827880859375, 45.02695045318546 ], [ -71.510009765625, 45.02695045318546 ], [ -71.3232421875, 45.29034662473615 ], [ -70.653076171875, 45.42158812329091 ], [ -70.68603515625, 45.537136680398596 ], [ -70.301513671875, 45.920587344733654 ], [ -70.2685546875, 46.240651955001695 ], [ -70.059814453125, 46.40756396630067 ], [ -69.993896484375, 46.694667307773116 ], [ -69.246826171875, 47.46523622438362 ], [ -69.01611328125, 47.4355191531953 ], [ -69.0380859375, 47.26432008025478 ], [ -68.93920898437499, 47.19717795172789 ], [ -68.35693359375, 47.36115300722623 ], [ -67.763671875, 47.06263847995432 ], [ -67.78564453125, 45.706179285330855 ], [ -67.423095703125, 45.5679096098613 ], [ -67.423095703125, 45.22074260255366 ], [ -67.17041015625, 45.174292524076726 ], [ -66.961669921875, 44.85586880735725 ], [ -67.24731445312499, 44.63739123445585 ], [ -68.236083984375, 44.308126684886126 ], [ -68.258056640625, 44.20583500104184 ], [ -69.06005859375, 44.04811573082351 ], [ -69.80712890625, 43.73935207915473 ], [ -70.048828125, 43.78695837311561 ], [ -70.2081298828125, 43.73538317799622 ], [ -70.191650390625, 43.58039085560786 ], [ -70.587158203125, 43.25320494908846 ], [ -70.81787109374999, 42.89206418807337 ], [ -70.76568603515625, 42.70060440808085 ], [ -70.68603515625, 42.66426107379467 ], [ -70.63934326171875, 42.69051116998241 ], [ -70.59814453125, 42.65416193033991 ], [ -70.6585693359375, 42.589488572714245 ], [ -70.8782958984375, 42.54498667313236 ], [ -70.83984375, 42.508552415528634 ], [ -70.9771728515625, 42.44372793752476 ], [ -70.97442626953125, 42.391008609205045 ], [ -70.8343505859375, 42.26917949243506 ], [ -70.75469970703125, 42.24681856113825 ], [ -70.64208984375, 42.07783959017503 ], [ -70.5487060546875, 41.92680320648791 ], [ -70.53497314453125, 41.820455096140314 ], [ -70.42785644531249, 41.74672584176937 ], [ -70.21636962890625, 41.73852846935917 ], [ -70.0323486328125, 41.781552998900345 ], [ -70.0103759765625, 41.85319643776675 ], [ -70.0762939453125, 41.90432124806034 ], [ -70.09277343749999, 42.02889410108475 ], [ -70.16143798828125, 42.05948945192712 ], [ -70.1751708984375, 42.01869237684385 ], [ -70.24932861328125, 42.06356771883277 ], [ -70.22186279296875, 42.07987816698549 ], [ -70.13946533203124, 42.07580094787543 ], [ -70.02960205078125, 42.02889410108475 ], [ -69.96917724609375, 41.916585116228354 ], [ -69.93072509765625, 41.7856490686444 ], [ -69.93072509765625, 41.6770148220322 ], [ -69.971923828125, 41.61338889474735 ], [ -69.949951171875, 41.253032440653186 ], [ -70.11199951171875, 41.23238023874142 ], [ -70.279541015625, 41.304634388885916 ], [ -70.44158935546875, 41.347948493443546 ], [ -70.675048828125, 41.3500103516271 ], [ -70.7684326171875, 41.31494988250963 ], [ -70.7958984375, 41.29844430929419 ], [ -71.03759765625, 41.49623534616764 ], [ -71.10076904296875, 41.50034959128928 ], [ -71.180419921875, 41.46125371076149 ], [ -71.3616943359375, 41.45507852101139 ], [ -71.44683837890625, 41.43654942411456 ], [ -71.4935302734375, 41.36031866306708 ], [ -71.531982421875, 41.16004614168688 ], [ -71.97967529296874, 41.01928287604565 ], [ -72.86956787109375, 40.724364221722716 ], [ -73.33099365234375, 40.61812224225511 ], [ -73.75946044921875, 40.58267063809529 ], [ -73.92425537109375, 40.543026009954986 ], [ -73.9874267578125, 40.46993497635153 ], [ -73.96820068359375, 40.319325896602095 ], [ -74.07806396484375, 39.928694653732364 ], [ -74.1357421875, 39.631076770083666 ], [ -74.3994140625, 39.364032338047984 ], [ -74.6356201171875, 39.21948715423953 ], [ -74.8004150390625, 38.96795115401593 ], [ -74.96520996093749, 38.929502416386605 ], [ -75.07507324218749, 38.775499003812946 ], [ -75.0421142578125, 38.47939467327645 ], [ -75.16845703124999, 38.004819966413194 ], [ -75.574951171875, 37.65773212628274 ], [ -75.882568359375, 37.16907157713011 ], [ -75.9814453125, 36.88840804313823 ], [ -75.706787109375, 36.16448788632064 ], [ -75.443115234375, 35.7019167328534 ], [ -75.52001953125, 35.22767235493586 ], [ -76.00341796875, 35.10193405724606 ], [ -76.519775390625, 34.6241677899049 ], [ -76.783447265625, 34.66935854524543 ], [ -77.2119140625, 34.58799745550482 ], [ -77.596435546875, 34.37064492478658 ], [ -77.87109375, 34.043556504127444 ], [ -77.93701171875, 33.797408767572485 ], [ -78.167724609375, 33.87041555094183 ], [ -78.662109375, 33.8430453147447 ], [ -79.013671875, 33.568861182555565 ], [ -79.156494140625, 33.32134852669881 ], [ -79.1455078125, 33.201924189778936 ], [ -79.69482421875, 32.7872745269555 ], [ -80.43090820312499, 32.47269502206151 ], [ -80.44189453125, 32.32427558887655 ], [ -80.83740234375, 32.0732655510424 ], [ -81.265869140625, 31.42866311735861 ], [ -81.419677734375, 30.760718908944472 ], [ -81.34277343749999, 29.99300228455108 ], [ -80.936279296875, 29.1233732108192 ], [ -80.52978515625, 28.488005204159457 ], [ -80.606689453125, 28.372068829631633 ], [ -80.474853515625, 27.848790459862073 ], [ -80.068359375, 26.95145308349826 ], [ -80.079345703125, 26.2145910237943 ], [ -80.167236328125, 25.37380917154398 ], [ -80.79345703125, 24.87646991083154 ], [ -80.9033203125, 24.856534339310674 ], [ -81.177978515625, 25.21488107113259 ], [ -81.2548828125, 25.54244147012483 ], [ -81.551513671875, 25.878994400196202 ], [ -81.67236328125, 25.86910939099931 ], [ -81.89208984375, 26.441065564038418 ], [ -82.11181640625, 26.43122806450644 ], [ -82.298583984375, 26.833874515058554 ], [ -82.63916015625, 27.371767300523047 ], [ -82.869873046875, 27.89734922968426 ], [ -82.72705078125, 28.391400375817753 ], [ -82.7490234375, 29.046565622728846 ], [ -83.023681640625, 29.180941290001776 ], [ -83.8916015625, 30.0405664305846 ], [ -84.3310546875, 30.097613277217132 ], [ -84.320068359375, 29.907329376851553 ], [ -85.05615234375, 29.592565403314087 ], [ -85.36376953125, 29.66896252599253 ], [ -86.02294921875, 30.29701788337205 ], [ -87.03369140625, 30.372875188118016 ], [ -88.17626953125, 30.221101852485987 ], [ -89.27490234375, 30.240086360983426 ], [ -89.31884765624999, 29.76437737516313 ], [ -89.47265625, 29.554345125748267 ], [ -88.9013671875, 29.267232865200878 ], [ -89.27490234375, 28.8831596093235 ], [ -89.69238281249999, 29.209713225868185 ], [ -90.2197265625, 29.075375179558346 ], [ -91.01074218749999, 29.0945770775118 ], [ -91.4501953125, 29.34387539941801 ], [ -92.46093749999999, 29.53522956294847 ], [ -93.44970703125, 29.726222319395504 ], [ -94.52636718749999, 29.477861195816843 ], [ -95.2294921875, 29.05616970274342 ], [ -96.064453125, 28.5941685062326 ], [ -96.767578125, 28.16887518006332 ], [ -97.27294921875, 27.68352808378776 ], [ -97.3388671875, 26.980828590472107 ], [ -97.18505859374999, 25.97779895546436 ] ] ] } }, { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -141.15234374999997, 69.7181066990676 ], [ -142.03125, 70.0205873017406 ], [ -142.734375, 70.25945200030641 ], [ -143.96484375, 70.25945200030641 ], [ -144.84375, 70.19999407534661 ], [ -147.041015625, 70.40734767606811 ], [ -148.359375, 70.46620742226558 ], [ -149.4140625, 70.69995129442536 ], [ -150.99609375, 70.67088107015755 ], [ -151.69921875, 70.81581215931348 ], [ -152.75390624999997, 71.04552881933586 ], [ -154.16015625, 71.04552881933586 ], [ -154.86328125, 71.24435551310671 ], [ -156.26953125, 71.46912418989677 ], [ -157.32421875, 71.44117085172385 ], [ -158.02734375, 71.13098770917023 ], [ -159.43359375, 70.9883492241249 ], [ -161.279296875, 70.61261423801925 ], [ -162.421875, 70.4367988185464 ], [ -163.30078125, 70.0205873017406 ], [ -163.828125, 69.59589006237648 ], [ -164.70703125, 69.2249968541159 ], [ -166.55273437499997, 69.19379976461907 ], [ -166.81640625, 68.5924865825295 ], [ -166.55273437499997, 67.97463396204759 ], [ -165.58593749999997, 67.57571741708057 ], [ -164.53125, 67.2720426739952 ], [ -163.65234374999997, 67.03316279015066 ], [ -162.861328125, 66.51326044311188 ], [ -163.564453125, 66.30220547599842 ], [ -163.740234375, 66.65297740055279 ], [ -165.41015625, 66.687783861162 ], [ -167.255859375, 66.16051056018838 ], [ -168.837890625, 65.69447579373418 ], [ -168.662109375, 65.25670649344259 ], [ -166.640625, 64.69910544204765 ], [ -166.904296875, 64.05297838071347 ], [ -169.27734375, 63.58767529470318 ], [ -170.068359375, 63.860035895395306 ], [ -171.03515625, 63.93737246791484 ], [ -171.826171875, 63.97596090918338 ], [ -172.001953125, 63.194018438087845 ], [ -169.716796875, 62.71446210149774 ], [ -168.134765625, 62.95522304515911 ], [ -166.81640625, 63.23362741232569 ], [ -166.201171875, 63.27318217465046 ], [ -165.76171875, 62.67414334669093 ], [ -166.46484375, 61.897577621605016 ], [ -166.640625, 61.18562468142283 ], [ -167.255859375, 60.71619779357716 ], [ -168.75, 60.84491057364915 ], [ -170.771484375, 60.84491057364915 ], [ -173.056640625, 61.01572481397616 ], [ -174.19921875, 60.673178565817715 ], [ -172.177734375, 58.53959476664049 ], [ -172.177734375, 56.31653672211301 ], [ -172.880859375, 54.7246201949245 ], [ -176.1328125, 54.00776876193478 ], [ -181.05468749999997, 53.80065082633023 ], [ -182.900390625, 53.33087298301704 ], [ -186.15234374999997, 53.9560855309879 ], [ -188.08593749999997, 53.54030739150022 ], [ -188.08593749999997, 52.214338608258224 ], [ -185.625, 51.23440735163459 ], [ -181.31835937499997, 50.792047064406844 ], [ -178.2421875, 50.51342652633956 ], [ -176.572265625, 50.792047064406844 ], [ -172.08984375, 50.90303283111257 ], [ -169.27734375, 51.83577752045248 ], [ -165.58593749999997, 52.53627304145948 ], [ -158.55468749999997, 54.316523240258284 ], [ -155.390625, 55.07836723201515 ], [ -151.69921875, 57.136239319177434 ], [ -150.29296875, 58.859223547066584 ], [ -147.65625, 59.712097173322924 ], [ -145.634765625, 59.84481485969105 ], [ -142.55859375, 59.265880628258095 ], [ -140.9765625, 59.265880628258095 ], [ -139.482421875, 58.90464570302001 ], [ -138.25195312499997, 58.44773280389084 ], [ -136.93359375, 57.657157596582984 ], [ -135.966796875, 56.70450561416937 ], [ -135, 55.52863052257191 ], [ -133.505859375, 54.7246201949245 ], [ -132.626953125, 54.316523240258284 ], [ -131.396484375, 54.316523240258284 ], [ -130.341796875, 54.826007999094955 ], [ -130.078125, 55.677584411089505 ], [ -130.341796875, 56.17002298293205 ], [ -131.396484375, 56.41390137600676 ], [ -131.8359375, 56.897003921272606 ], [ -132.1875, 57.231502991478905 ], [ -133.2421875, 58.17070248348609 ], [ -133.76953125, 58.63121664342478 ], [ -134.38476562499997, 58.99531118795094 ], [ -134.736328125, 59.40036514079251 ], [ -135.615234375, 59.88893689676585 ], [ -136.0546875, 59.57885104663186 ], [ -136.31835937499997, 59.355596110016315 ], [ -137.373046875, 59.0405546167585 ], [ -137.8125, 59.22093407615045 ], [ -138.427734375, 59.712097173322924 ], [ -139.04296875, 60.10867046303597 ], [ -139.39453125, 60.45721779774397 ], [ -139.833984375, 60.37042901631508 ], [ -140.44921875, 60.50052541051131 ], [ -141.15234374999997, 60.50052541051131 ], [ -141.15234374999997, 69.7181066990676 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"538848cfe4b0318b93124a24","contributors":{"authors":[{"text":"Anning, David W. dwanning@usgs.gov","contributorId":432,"corporation":false,"usgs":true,"family":"Anning","given":"David","email":"dwanning@usgs.gov","middleInitial":"W.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flynn, Marilyn E. meflynn@usgs.gov","contributorId":1039,"corporation":false,"usgs":true,"family":"Flynn","given":"Marilyn E.","email":"meflynn@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494156,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70103153,"text":"fs20143042 - 2014 - Arsenic, iron, lead, manganese, and uranium concentrations in private bedrock wells in southeastern New Hampshire, 2012-2013","interactions":[],"lastModifiedDate":"2014-06-16T08:12:28","indexId":"fs20143042","displayToPublicDate":"2014-06-16T08:00:00","publicationYear":"2014","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":"2014-3042","title":"Arsenic, iron, lead, manganese, and uranium concentrations in private bedrock wells in southeastern New Hampshire, 2012-2013","docAbstract":"Trace metals, such as arsenic, iron, lead, manganese, and uranium, in groundwater used for drinking have long been a concern because of the potential adverse effects on human health and the aesthetic or nuisance problems that some present. Moderate to high concentrations of the trace metal arsenic have been identified in drinking water from groundwater sources in southeastern New Hampshire, a rapidly growing region of the State (Montgomery and others, 2003). During the past decade (2000–10), southeastern New Hampshire, which is composed of Hillsborough, Rockingham, and Strafford Counties, has grown in population by nearly 48,700 (or 6.4 percent) to 819,100. These three counties contain 62 percent of the State’s population but encompass only about 22 percent of the land area (New Hampshire Office of Energy and Planning, 2011). According to a 2005 water-use study (Hayes and Horn, 2009), about 39 percent of the population in these three counties in southeastern New Hampshire uses private wells as sources of drinking water, and these wells are not required by the State to be routinely tested for trace metals or other contaminants.
\nSome trace metals have associated human-health benchmarks or nonhealth guidelines that have been established by the U.S. Environmental Protection Agency (EPA) to regulate public water supplies. The EPA has established a maximum contaminant level (MCL) of 10 micrograms per liter (μg/L) for arsenic (As) and a MCL of 30 μg/L for uranium (U) because of associated health risks (U.S. Environmental Protection Agency, 2012). Iron (Fe) and manganese (Mn) are essential for human health, but Mn at high doses may have adverse cognitive effects in children (Bouchard and others, 2011; Agency for Toxic Substances and Disease Registry, 2012); therefore, the EPA has issued a lifetime health advisory (LHA) of 300 μg/L for Mn. Recommended secondary maximum contaminant levels (SMCLs) for Fe (300 μg/L) and Mn (50 μg/L) were established primarily as nonhealth guidelines—based on aesthetic considerations, such as taste or the staining of laundry and plumbing fixtures—because these contaminants, at the SMCLs, are not considered to present risks to human health. Because lead (Pb) contamination of drinking water typically results from corrosion of plumbing materials belonging to water-system customers but still poses a risk to human health, the EPA established an action level (AL) of 15 μg/L for Pb instead of an MCL or SMCL (U.S. Environmental Protection Agency, 2012). The 15-μg/L AL for Pb has been adopted by the New Hampshire Department of Environmental Services for public water systems, and if exceeded, the public water system must inform their customers and undertake additional actions to control corrosion in the pipes of the distribution system (New Hampshire Department of Environmental Services, 2013).
\nUnlike the quality of drinking water provided by public water suppliers, the quality of drinking water obtained from private wells in New Hampshire is not regulated; consequently, private wells are sampled only when individual well owners voluntarily choose to sample them. The U.S. Geological Survey (USGS), in cooperation with the EPA New England, conducted an assessment in 2012–13 to provide private well owners and State and Federal health officials with information on the distribution of trace-metal (As, Fe, Pb, Mn, and U) concentrations in groundwater from bedrock aquifers in the three counties of southeastern New Hampshire. This fact sheet analyzes data from water samples collected by a randomly selected group of private well owners from the three-county study area and describes the major findings for trace-metal concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20143042","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Flanagan, S., Belaval, M., and Ayotte, J., 2014, Arsenic, iron, lead, manganese, and uranium concentrations in private bedrock wells in southeastern New Hampshire, 2012-2013: U.S. Geological Survey Fact Sheet 2014-3042, Report: 6 p.; Appendix 1-5, https://doi.org/10.3133/fs20143042.","productDescription":"Report: 6 p.; Appendix 1-5","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"Y","temporalStart":"2012-01-01","temporalEnd":"2013-12-31","ipdsId":"IP-052568","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":288268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20143042.jpg"},{"id":288613,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2014/3042/pdf/fs2014-3042.pdf"},{"id":288614,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/fs/2014/3042/appendix/fs2014-3042_appendixes_1-5.xlsx"},{"id":288267,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2014/3042/"}],"projection":"Albers Equal-Area Conic projection","country":"United States","state":"New Hampshire","county":"Hillsborough County;Rockingham County;Strafford County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -72.062374,42.696985 ], [ -72.062374,43.573012 ], [ -70.60266,43.573012 ], [ -70.60266,42.696985 ], [ -72.062374,42.696985 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7631e4b0abf75cf2bec3","contributors":{"authors":[{"text":"Flanagan, Sarah M.","contributorId":8492,"corporation":false,"usgs":true,"family":"Flanagan","given":"Sarah M.","affiliations":[],"preferred":false,"id":493168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belaval, Marcel","contributorId":21636,"corporation":false,"usgs":true,"family":"Belaval","given":"Marcel","affiliations":[],"preferred":false,"id":493169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ayotte, Joseph D. jayotte@usgs.gov","contributorId":1802,"corporation":false,"usgs":true,"family":"Ayotte","given":"Joseph D.","email":"jayotte@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":493167,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70110814,"text":"70110814 - 2014 - Focused campaign increases activity among participants in Nature's Notebook, a citizen science project","interactions":[],"lastModifiedDate":"2017-03-27T10:28:41","indexId":"70110814","displayToPublicDate":"2014-06-15T13:56:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2836,"text":"Natural Sciences Education","active":true,"publicationSubtype":{"id":10}},"title":"Focused campaign increases activity among participants in Nature's Notebook, a citizen science project","docAbstract":"Citizen science projects, which engage non-professional scientists in one or more stages of scientific research, have been gaining popularity; yet maintaining participants’ activity level over time remains a challenge. The objective of this study was to evaluate the potential for a short-term, focused campaign to increase participant activity in a national-scale citizen science program. The campaign that we implemented was designed to answer a compelling scientific question. We invited participants in the phenology-observing program, Nature’s Notebook, to track trees throughout the spring of 2012, to ascertain whether the season arrived as early as the anomalous spring of 2010. Consisting of a series of six electronic newsletters and costing our office slightly more than 1 week of staff resources, our effort was successful; compared with previous years, the number of observations collected in the region where the campaign was run increased by 184%, the number of participants submitting observations increased by 116%, and the number of trees registered increased by 110%. In comparison, these respective metrics grew by 25, 55, and 44%, over previous years, in the southeastern quadrant of the United States, where no such campaign was carried out. The campaign approach we describe here is a model that could be adapted by a wide variety of programs to increase engagement and thereby positively influence participant retention.
","language":"English","publisher":"American Society of Agronomy","publisherLocation":"Madison, WI","doi":"10.4195/nse2013.06.0019","usgsCitation":"Crimmins, T., Weltzin, J., Rosemartin, A.H., Surina, E.M., Marsh, L., and Denny, E.G., 2014, Focused campaign increases activity among participants in Nature's Notebook, a citizen science project: Natural Sciences Education, v. 43, no. 1, p. 64-72, https://doi.org/10.4195/nse2013.06.0019.","productDescription":"9 p.","startPage":"64","endPage":"72","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-043446","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":287830,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-05-13","publicationStatus":"PW","scienceBaseUri":"538848d0e4b0318b93124a28","contributors":{"authors":[{"text":"Crimmins, Theresa","contributorId":103579,"corporation":false,"usgs":false,"family":"Crimmins","given":"Theresa","affiliations":[],"preferred":false,"id":494162,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weltzin, Jake F.","contributorId":51005,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake F.","affiliations":[],"preferred":false,"id":494160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosemartin, Alyssa H.","contributorId":30910,"corporation":false,"usgs":true,"family":"Rosemartin","given":"Alyssa","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":494159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Surina, Echo M.","contributorId":28898,"corporation":false,"usgs":true,"family":"Surina","given":"Echo","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":494158,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marsh, Lee","contributorId":16755,"corporation":false,"usgs":true,"family":"Marsh","given":"Lee","affiliations":[],"preferred":false,"id":494157,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Denny, Ellen G.","contributorId":79803,"corporation":false,"usgs":true,"family":"Denny","given":"Ellen","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":494161,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70150446,"text":"70150446 - 2014 - Health status of Largescale Sucker (Catostomus macrocheilus) collected along an organic contaminant gradient in the lower Columbia River, Oregon and Washington, USA","interactions":[],"lastModifiedDate":"2015-06-26T10:40:36","indexId":"70150446","displayToPublicDate":"2014-06-15T11:45:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Health status of Largescale Sucker (Catostomus macrocheilus) collected along an organic contaminant gradient in the lower Columbia River, Oregon and Washington, USA","docAbstract":"The health of Largescale Sucker (Catostomus macrocheilus) in the lower Columbia River (USA) was evaluated using morphometric and histopathological approaches, and its association with organic contaminants accumulated in liver was evaluated in males. Fish were sampled from three sites along a contaminant gradient In 2009, body length and mass, condition factor, gonadosomatic index, and hematocrit were measured in males and females; liver and gonad tissue were collected from males for histological analyses; and organ composites were analyzed for contaminant content in males. In 2010, additional data were collected for males and females, including external fish condition assessment, histopathologies of spleen, kidney and gill and, for males, liver contaminant content. Multivariate analysis of variance indicated that biological traits in males, but not females, differed among sites in 2009 and 2010. Discriminant function analysis indicated that site-related differences among male populations were relatively small in 2009, but in 2010, when more variables were analyzed, males differed among sites in regards to kidney, spleen, and liver histopathologies and gill parasites. Kidney tubular hyperplasia, liver and spleen macrophage aggregations, and gill parasites were generally more severe in the downstream sites compared to the reference location. The contaminant content of male livers was also generally higher downstream, and the legacy pesticide hexachlorobenzene and flame retardants BDE-47 and BDE-154 were the primary drivers for site discrimination. However, bivariate correlations between biological variables and liver contaminants retained in the discriminant models failed to reveal associations between the two variable sets. In conclusion, whereas certain non-reproductive biological traits and liver contaminant contents of male Largescale Sucker differed according to an upstream-downstream gradient in the lower Columbia River, results from this study did not reveal the specific environmental factors responsible for the differences in health status among fish populations.
","language":"English","publisher":"Elsevier Pub. Co.","publisherLocation":"Amsterdam","doi":"10.1016/j.scitotenv.2013.07.112","usgsCitation":"Torres, L., Nilsen, E.B., Grove, R.A., and Patino, R., 2014, Health status of Largescale Sucker (Catostomus macrocheilus) collected along an organic contaminant gradient in the lower Columbia River, Oregon and Washington, USA: Science of the Total Environment, v. 484, p. 353-364, https://doi.org/10.1016/j.scitotenv.2013.07.112.","productDescription":"12 p.","startPage":"353","endPage":"364","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-042213","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":472940,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/6b01n5jk","text":"External Repository"},{"id":302373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"484","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"558e77b7e4b0b6d21dd65958","chorus":{"doi":"10.1016/j.scitotenv.2013.07.112","url":"http://dx.doi.org/10.1016/j.scitotenv.2013.07.112","publisher":"Elsevier BV","authors":"Torres Leticia, Nilsen Elena, Grove Robert, Patiño Reynaldo","journalName":"Science of The Total Environment","publicationDate":"6/2014"},"contributors":{"authors":[{"text":"Torres, Leticia","contributorId":143738,"corporation":false,"usgs":false,"family":"Torres","given":"Leticia","email":"","affiliations":[],"preferred":false,"id":556955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nilsen, Elena B. 0000-0002-0104-6321 enilsen@usgs.gov","orcid":"https://orcid.org/0000-0002-0104-6321","contributorId":923,"corporation":false,"usgs":true,"family":"Nilsen","given":"Elena","email":"enilsen@usgs.gov","middleInitial":"B.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":556956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grove, Robert A.","contributorId":52134,"corporation":false,"usgs":true,"family":"Grove","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":556957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":556893,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70138504,"text":"70138504 - 2014 - Differentiating transpiration from evaporation in seasonal agricultural wetlands and the link to advective fluxes in the root zone","interactions":[],"lastModifiedDate":"2015-01-19T11:04:45","indexId":"70138504","displayToPublicDate":"2014-06-15T11:15:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Differentiating transpiration from evaporation in seasonal agricultural wetlands and the link to advective fluxes in the root zone","docAbstract":"The current state of science and engineering related to analyzing wetlands overlooks the importance of transpiration and risks data misinterpretation. In response, we developed hydrologic and mass budgets for agricultural wetlands using electrical conductivity (EC) as a natural conservative tracer. We developed simple differential equations that quantify evaporation and transpiration rates using flowrates and tracer concentrations atwetland inflows and outflows. We used two ideal reactormodel solutions, a continuous flowstirred tank reactor (CFSTR) and a plug flow reactor (PFR), to bracket real non-ideal systems. From those models, estimated transpiration ranged from 55% (CFSTR) to 74% (PFR) of total evapotranspiration (ET) rates, consistent with published values using standard methods and direct measurements. The PFR model more appropriately represents these nonideal agricultural wetlands in which check ponds are in series. Using a fluxmodel, we also developed an equation delineating the root zone depth at which diffusive dominated fluxes transition to advective dominated fluxes. This relationship is similar to the Peclet number that identifies the dominance of advective or diffusive fluxes in surface and groundwater transport. Using diffusion coefficients for inorganic mercury (Hg) and methylmercury (MeHg) we calculated that during high ET periods typical of summer, advective fluxes dominate root zone transport except in the top millimeters below the sediment–water interface. The transition depth has diel and seasonal trends, tracking those of ET. Neglecting this pathway has profound implications: misallocating loads along different hydrologic pathways; misinterpreting seasonal and diel water quality trends; confounding Fick's First Law calculations when determining diffusion fluxes using pore water concentration data; and misinterpreting biogeochemicalmechanisms affecting dissolved constituent cycling in the root zone. In addition,our understanding of internal root zone cycling of Hg and other dissolved constituents, benthic fluxes, and biological irrigation may be greatly affected.
","language":"English","publisher":"Elsevier Pub. Co.","publisherLocation":"Amsterdam","doi":"10.1016/j.scitotenv.2013.11.026","collaboration":"RWQCB","usgsCitation":"Bachand, P., Bachand, S., Fleck, J., Anderson, F.E., and Windham-Myers, L., 2014, Differentiating transpiration from evaporation in seasonal agricultural wetlands and the link to advective fluxes in the root zone: Science of the Total Environment, v. 484, p. 232-248, https://doi.org/10.1016/j.scitotenv.2013.11.026.","productDescription":"17 p.","startPage":"232","endPage":"248","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-030347","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":297376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":297375,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pubmed/24296049"}],"volume":"484","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54dd2b78e4b08de9379b33a8","contributors":{"authors":[{"text":"Bachand, P.A.M.","contributorId":9857,"corporation":false,"usgs":true,"family":"Bachand","given":"P.A.M.","email":"","affiliations":[],"preferred":false,"id":538756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bachand, S.","contributorId":138794,"corporation":false,"usgs":false,"family":"Bachand","given":"S.","email":"","affiliations":[{"id":12526,"text":"Bachand & Associates","active":true,"usgs":false}],"preferred":false,"id":538757,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleck, Jacob A. 0000-0002-3217-3972 jafleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3217-3972","contributorId":1498,"corporation":false,"usgs":true,"family":"Fleck","given":"Jacob A.","email":"jafleck@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":538755,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Frank E. 0000-0002-1418-4678 fanders@usgs.gov","orcid":"https://orcid.org/0000-0002-1418-4678","contributorId":2605,"corporation":false,"usgs":true,"family":"Anderson","given":"Frank","email":"fanders@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":538754,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":538753,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70100906,"text":"tm5B10 - 2014 - Determination of human-use pharmaceuticals in filtered water by direct aqueous injection: high-performance liquid chromatography/tandem mass spectrometry","interactions":[],"lastModifiedDate":"2014-06-13T13:58:08","indexId":"tm5B10","displayToPublicDate":"2014-06-13T13:46:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"5-B10","title":"Determination of human-use pharmaceuticals in filtered water by direct aqueous injection: high-performance liquid chromatography/tandem mass spectrometry","docAbstract":"This report describes a method for the determination of 110 human-use pharmaceuticals using a 100-microliter aliquot of a filtered water sample directly injected into a high-performance liquid chromatograph coupled to a triple-quadrupole tandem mass spectrometer using an electrospray ionization source operated in the positive ion mode. The pharmaceuticals were separated by using a reversed-phase gradient of formic acid/ammonium formate-modified water and methanol. Multiple reaction monitoring of two fragmentations of the protonated molecular ion of each pharmaceutical to two unique product ions was used to identify each pharmaceutical qualitatively. The primary multiple reaction monitoring precursor-product ion transition was quantified for each pharmaceutical relative to the primary multiple reaction monitoring precursor-product transition of one of 19 isotope-dilution standard pharmaceuticals or the pesticide atrazine, using an exact stable isotope analogue where possible. Each isotope-dilution standard was selected, when possible, for its chemical similarity to the unlabeled pharmaceutical of interest, and added to the sample after filtration but prior to analysis.
\nMethod performance for each pharmaceutical was determined for reagent water, groundwater, treated drinking water, surface water, treated wastewater effluent, and wastewater influent sample matrixes that this method will likely be applied to. Each matrix was evaluated in order of increasing complexity to demonstrate (1) the sensitivity of the method in different water matrixes and (2) the effect of sample matrix, particularly matrix enhancement or suppression of the precursor ion signal, on the quantitative determination of pharmaceutical concentrations. Recovery of water samples spiked (fortified) with the suite of pharmaceuticals determined by this method typically was greater than 90 percent in reagent water, groundwater, drinking water, and surface water. Correction for ambient environmental concentrations of pharmaceuticals hampered the determination of absolute recoveries and method sensitivity of some compounds in some water types, particularly for wastewater effluent and influent samples.
\nThe method detection limit of each pharmaceutical was determined from analysis of pharmaceuticals fortified at multiple concentrations in reagent water. The calibration range for each compound typically spanned three orders of magnitude of concentration. Absolute sensitivity for some compounds, using isotope-dilution quantitation, ranged from 0.45 to 94.1 nanograms per liter, primarily as a result of the inherent ionization efficiency of each pharmaceutical in the electrospray ionization process.
\nHolding-time studies indicate that acceptable recoveries of pharmaceuticals can be obtained from filtered water samples held at 4 °C for as long as 9 days after sample collection. Freezing samples to provide for storage for longer periods currently (2014) is under evaluation by the National Water Quality Laboratory.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Methods of the National Water Quality Laboratory in Book 5 Laboratory Analysis","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm5B10","collaboration":"This report is Chapter 10 of Section B: Methods of the National Water Quality Laboratory in Book 5 Laboratory Analysis.","usgsCitation":"Furlong, E.T., Noriega, M.C., Kanagy, C.J., Kanagy, L.K., Coffey, L.J., and Burkhardt, M.R., 2014, Determination of human-use pharmaceuticals in filtered water by direct aqueous injection: high-performance liquid chromatography/tandem mass spectrometry: U.S. Geological Survey Techniques and Methods 5-B10, Report: viii, 49 p.; Tables; Appendix Tables, https://doi.org/10.3133/tm5B10.","productDescription":"Report: viii, 49 p.; Tables; Appendix Tables","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-038894","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"links":[{"id":288592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm5B10.jpg"},{"id":288588,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/5b10/"},{"id":288589,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/5b10/pdf/tm10-b5.pdf"},{"id":288590,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/5b10/downloads/Tables1-16.xlsx"},{"id":288591,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/5b10/downloads/LS%202440%20Appendixes.xlsx"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7681e4b0abf75cf2bf75","contributors":{"authors":[{"text":"Furlong, Edward T. 0000-0002-7305-4603 efurlong@usgs.gov","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":740,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","email":"efurlong@usgs.gov","middleInitial":"T.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noriega, Mary C. mnoriega@usgs.gov","contributorId":2553,"corporation":false,"usgs":true,"family":"Noriega","given":"Mary","email":"mnoriega@usgs.gov","middleInitial":"C.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":492472,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kanagy, Christopher J. ckanagy@usgs.gov","contributorId":1201,"corporation":false,"usgs":true,"family":"Kanagy","given":"Christopher","email":"ckanagy@usgs.gov","middleInitial":"J.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":492471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanagy, Leslie K. 0000-0001-5073-8538 lkkanagy@usgs.gov","orcid":"https://orcid.org/0000-0001-5073-8538","contributorId":4543,"corporation":false,"usgs":true,"family":"Kanagy","given":"Leslie","email":"lkkanagy@usgs.gov","middleInitial":"K.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":492474,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coffey, Laura J. ljcoffey@usgs.gov","contributorId":4132,"corporation":false,"usgs":true,"family":"Coffey","given":"Laura","email":"ljcoffey@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":492473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burkhardt, Mark R.","contributorId":27872,"corporation":false,"usgs":true,"family":"Burkhardt","given":"Mark","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":492475,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70112283,"text":"70112283 - 2014 - Evidence against a Pleistocene desert refugium in the Lower Colorado River Basin","interactions":[],"lastModifiedDate":"2016-04-12T16:15:06","indexId":"70112283","displayToPublicDate":"2014-06-12T13:36:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Evidence against a Pleistocene desert refugium in the Lower Colorado River Basin","docAbstract":"Aim
The absence of Sonoran Desert plants in late Pleistocene-aged packrat middens has led to speculation that they survived glacial episodes either in refugia as intact associations (Clementsian community concept) or in dry microsites within chaparral or woodland according to individualistic species responses (Gleasonian community concept). To test these hypotheses, we developed a midden record from one likely refugium in north-eastern Baja California, Mexico. We also measured stomatal guard cell size in fossil leaves to further evaluate site-level individualistic responses of Larrea tridentata (creosote bush) ploidy races to climatic changes, including monsoonal history, over the late Quaternary.
Location
Sierra Juárez, Lower Colorado River Basin, north-eastern Baja California, Mexico.
Methods
Packrat (Neotoma) middens were collected from <300 m elevation on the eastern piedmont of the Sierra Juárez. Plant macrofossils and pollen were analysed from 50 dated middens, including determination of Larrea tridentata ploidy races.
Results
Pleistocene middens dating back to >55,000 cal. yr BP contained a mix of extralocal species characteristic of chaparral and pinyon–juniper–oak woodland, along with some modern desert elements. Many other desert taxa were absent during the Pleistocene, although most had arrived by the beginning of the Holocene 11,700 years ago.
Main conclusions
The assemblage of chaparral, woodland and select desert elements refutes the hypothesis that the Lower Colorado River Basin served as a late Pleistocene refugium for Sonoran Desert flora. The rapid arrival of most missing desert species by the early Holocene suggests they did not have far to migrate. They probably survived the last glacial period as smaller, disparate populations in dry microsites within chaparral and pinyon–juniper–oak woodlands. Diploid and tetraploid races of Larrea tridentata were present during the Pleistocene, but hexaploids did not appear until the mid-Holocene. This demonstrates that individualistic responses to climate involved genetic variants, in this case cytotypes, and not just species.
Digital flood-inundation maps for an 8.3-mile reach of the Wabash River and a 7.6-mile reach of the Eel River at Logansport, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage Wabash River at Logansport, Ind. (sta. no. 03329000) and USGS streamgage Eel River near Logansport, Ind. (sta. no. 03328500). Current conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system http:/water.weather.gov/ahps/). The NWS forecasts flood hydrographs at many places that are often colocated with USGS streamgages. NWS-forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation.
\nFor this study, flood profiles were computed for the stream reaches by means of a one-dimensional step-backwater model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the most current stage-discharge relations at USGS streamgages 03329000, Wabash River at Logansport, Ind., and 03328500, Eel River near Logansport, Ind. The calibrated hydraulic model was then used to determine five water-surface profiles for flood stage at 1-foot intervals referenced to the Wabash River streamgage datum, and four water-surface profiles for flood stages at 1-foot intervals referenced to the Eel River streamgage datum. The stages range from bankfull to approximately the highest stages that have occurred since 1967 when three flood control dams were built upstream of Logansport, Ind. The simulated water-surface profiles were then combined with a geographic information system (GIS) digital elevation model (DEM, derived from Light Detection and Ranging [lidar] data having a 0.37-foot vertical accuracy and 3.9-foot horizontal resolution) in order to delineate the area flooded at each stage.
\nThe availability of these maps, along with information available on the Internet regarding current stages from the USGS streamgages at Logansport, Ind., and forecasted stream stages from the NWS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3293","issn":"2329-132X","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Fowler, K.K., 2014, Flood inundation maps for the Wabash and Eel Rivers at Logansport, Indiana: U.S. Geological Survey Scientific Investigations Map 3293, Pamphlet: v, 12 p.; Map Sheet Low Resolution: 9 JPGs; Map Sheet High Resolution: 9 PDFs, 22.00 x 17.00 inches; Downloads Directory, https://doi.org/10.3133/sim3293.","productDescription":"Pamphlet: v, 12 p.; Map Sheet Low Resolution: 9 JPGs; Map Sheet High Resolution: 9 PDFs, 22.00 x 17.00 inches; Downloads Directory","numberOfPages":"22","ipdsId":"IP-041227","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":288319,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim3293.jpg"},{"id":288303,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3293/"},{"id":288310,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293_mapsheets/sheet02_eel_631_sim3293.pdf"},{"id":288311,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293_mapsheets/sheet03_wab_584_sim3293.pdf"},{"id":288307,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3293/images/sim3293_mapsheets/"},{"id":288308,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293_mapsheets/"},{"id":288309,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293_mapsheets/sheet01_wab_583_sim3293.pdf"},{"id":288312,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293_mapsheets/sheet04_eel_632_sim3293.pdf"},{"id":288313,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293_mapsheets/sheet05_wab_585_sim3293.pdf"},{"id":288314,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293_mapsheets/sheet06_wab_586_sim3292.pdf"},{"id":288315,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293_mapsheets/sheet07_eel_633_sim3293.pdf"},{"id":288316,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293_mapsheets/sheet08_wab_587_sim3292.pdf"},{"id":288317,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293_mapsheets/sheet09_eel_634_sim3293.pdf"},{"id":288318,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3293/downloads"},{"id":288305,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3293/pdf/sim3293.pdf"}],"projection":"Transverse Mercator projection","datum":"North American Datum of 1983","country":"United States","state":"Indiana","city":"Logansport","otherGeospatial":"Eel River;Wabash River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -86.416667,40.733333 ], [ -86.416667,40.8 ], [ -86.266667,40.8 ], [ -86.266667,40.733333 ], [ -86.416667,40.733333 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53996c4ee4b0a59b26496933","contributors":{"authors":[{"text":"Fowler, Kathleen K. 0000-0002-0107-3848 kkfowler@usgs.gov","orcid":"https://orcid.org/0000-0002-0107-3848","contributorId":2439,"corporation":false,"usgs":true,"family":"Fowler","given":"Kathleen","email":"kkfowler@usgs.gov","middleInitial":"K.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493082,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70158681,"text":"70158681 - 2014 - Multiseason occupancy models for correlated replicate surveys","interactions":[],"lastModifiedDate":"2015-10-05T13:19:20","indexId":"70158681","displayToPublicDate":"2014-06-11T10:30:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Multiseason occupancy models for correlated replicate surveys","docAbstract":"Occupancy surveys collecting data from adjacent (sometimes correlated) spatial replicates have become relatively popular for logistical reasons. Hines et al. (2010) presented one approach to modelling such data for single-season occupancy surveys. Here, we present a multiseason analogue of this model (with corresponding software) for inferences about occupancy dynamics. We include a new parameter to deal with the uncertainty associated with the first spatial replicate for both single-season and multiseason models. We use a case study, based on the brown-headed nuthatch, to assess the need for these models when analysing data from the North American Breeding Bird Survey (BBS), and we test various hypotheses about occupancy dynamics for this species in the south-eastern United States.
\nThe new model permits inference about local probabilities of extinction, colonization and occupancy for sampling conducted over multiple seasons. The model performs adequately, based on a small simulation study and on results of the case study analysis.
\nThe new model incorporating correlated replicates was strongly favoured by model selection for the BBS data for brown-headed nuthatch (Sitta pusilla). Latitude was found to be an important source of variation in local colonization and occupancy probabilities for brown-headed nuthatch, with both probabilities being higher near the centre of the species range, as opposed to more northern and southern areas.
\nWe recommend this new occupancy model for detection–nondetection studies that use potentially correlated replicates.
\nThis report presents information used to characterize the groundwater-flow system on the Kitsap Peninsula, and includes descriptions of the geology and hydrogeologic framework, groundwater recharge and discharge, groundwater levels and flow directions, seasonal groundwater-level fluctuations, interactions between aquifers and the surface‑water system, and a water budget. The Kitsap Peninsula is in the Puget Sound lowland of west-central Washington, is bounded by Puget Sound on the east and by Hood Canal on the west, and covers an area of about 575 square miles. The peninsula encompasses all of Kitsap County, the part of Mason County north of Hood Canal, and part of Pierce County west of Puget Sound. The peninsula is surrounded by saltwater and the hydrologic setting is similar to that of an island. The study area is underlain by a thick sequence of unconsolidated glacial and interglacial deposits that overlie sedimentary and volcanic bedrock units that crop out in the central part of the study area. Geologic units were grouped into 12 hydrogeologic units consisting of aquifers, confining units, and an underlying bedrock unit. A surficial hydrogeologic unit map was developed and used with well information from 2,116 drillers’ logs to construct 6 hydrogeologic sections and unit extent and thickness maps.
\nUnconsolidated aquifers typically consist of moderately to well-sorted alluvial and glacial outwash deposits of sand, gravel, and cobbles, with minor lenses of silt and clay. These units often are discontinuous or isolated bodies and are of highly variable thickness. Unconfined conditions occur in areas where aquifer units are at land surface; however, much of the study area is mantled by glacial till, and confined aquifer conditions are common. Groundwater in the unconsolidated aquifers generally flows radially off the peninsula in the direction of Puget Sound and Hood Canal. These generalized flow patterns likely are complicated by the presence of low-permeability confining units that separate discontinuous bodies of aquifer material and act as local groundwater-flow barriers.
\nGroundwater-level fluctuations observed during the monitoring period (2011–12) in wells completed in unconsolidated hydrogeologic units indicated seasonal variations ranging from 1 to about 20 feet. The largest fluctuation of 33 feet occurred in a well that was completed in the bedrock unit. Streamgage discharge measurements made during 2012 indicate that groundwater discharge to creeks in the area ranged from about 0.41 to 33.3 cubic feet per second.
\nDuring 2012, which was an above-average year of precipitation, the groundwater system received an average of about 664,610 acre-feet of recharge from precipitation and 22,122 acre-feet of recharge from return flows. Most of this annual recharge (66 percent) discharged to streams, and only about 4 percent was withdrawn from wells. The remaining groundwater recharge (30 percent) left the groundwater system as discharge to Hood Canal and Puget Sound.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145106","collaboration":"Prepared in cooperation with the Kitsap Public Utility District","usgsCitation":"Welch, W.B., Frans, L.M., and Olsen, T.D., 2014, Hydrogeologic framework, groundwater movement, and water budget of the Kitsap Peninsula, west-central Washington: U.S. Geological Survey Scientific Investigations Report 2014-5106, Report: vii, 44 p.; 2 Plates: 34.0 x 44.0 inches and 47.0 x 32.68 inches, https://doi.org/10.3133/sir20145106.","productDescription":"Report: vii, 44 p.; 2 Plates: 34.0 x 44.0 inches and 47.0 x 32.68 inches","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-055785","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":288260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145106.jpg"},{"id":288223,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5106/"},{"id":288257,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5106/pdf/sir20145106.pdf"},{"id":288258,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2014/5106/pdf/sir20145106_plate01.pdf"},{"id":288259,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2014/5106/pdf/sir20145106_plate02.pdf"}],"projection":"State Plane Washington North FIPS 4601 Feet","datum":"North American Datum of 1983","country":"United States","state":"Washington","otherGeospatial":"Kitsap Peninsula","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -123.17018,47.233146 ], [ -123.17018,47.99093 ], [ -122.347281,47.99093 ], [ -122.347281,47.233146 ], [ -123.17018,47.233146 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53996c4fe4b0a59b26496937","contributors":{"authors":[{"text":"Welch, Wendy B. wwelch@usgs.gov","contributorId":1645,"corporation":false,"usgs":true,"family":"Welch","given":"Wendy","email":"wwelch@usgs.gov","middleInitial":"B.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":false,"id":494302,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frans, Lonna M. 0000-0002-3217-1862 lmfrans@usgs.gov","orcid":"https://orcid.org/0000-0002-3217-1862","contributorId":1493,"corporation":false,"usgs":true,"family":"Frans","given":"Lonna","email":"lmfrans@usgs.gov","middleInitial":"M.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494300,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olsen, Theresa D. 0000-0003-4099-4057 tdolsen@usgs.gov","orcid":"https://orcid.org/0000-0003-4099-4057","contributorId":1644,"corporation":false,"usgs":true,"family":"Olsen","given":"Theresa","email":"tdolsen@usgs.gov","middleInitial":"D.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494301,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70112026,"text":"70112026 - 2014 - Compositional and stable carbon isotopic fractionation during non-autocatalytic thermochemical sulfate reduction by gaseous hydrocarbons","interactions":[],"lastModifiedDate":"2014-06-10T16:22:29","indexId":"70112026","displayToPublicDate":"2014-06-10T16:18:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Compositional and stable carbon isotopic fractionation during non-autocatalytic thermochemical sulfate reduction by gaseous hydrocarbons","docAbstract":"The possibility of autocatalysis during thermochemical sulfate reduction (TSR) by gaseous hydrocarbons was investigated by examination of previously reported laboratory and field data. This reaction was found to be a kinetically controlled non-autocatalytic process, and the apparent lack of autocatalysis is thought to be due to the absence of the required intermediate species. Kinetic parameters for chemical and carbon isotopic fractionations of gaseous hydrocarbons affected by TSR were calculated and found to be consistent with experimentally derived values for TSR involving long-chain hydrocarbons. Model predictions based on these kinetic values indicate that TSR by gaseous hydrocarbon requires high-temperature conditions. The oxidation of C2–5 hydrocarbons by sulfate reduction is accompanied by carbon isotopic fractionation with the residual C2–5 hydrocarbons becoming more enriched in 13C. Kinetic parameters were calculated for the stable carbon isotopic fractionation of gaseous hydrocarbons that have experienced TSR. Model predictions based on these kinetics indicate that it may be difficult to distinguish the effects of TSR from those of thermal maturation at lower levels of hydrocarbon oxidation; however, unusually heavy δ13C2+ values (>−10‰) can be diagnostic of high levels of conversion (>50%). Stoichiometric and stable carbon isotopic data show that methane is stable under the investigated reaction conditions and is likely a product of TSR by other gaseous hydrocarbons rather than a significant reactant. These results indicate that the overall TSR reaction mechanism for oxidation of organic substrates containing long-chain hydrocarbons involves three distinct phases as follows: (1) an initial slow and non-autocatalytic stage characterized by the reduction of reactive sulfate by long-chain saturated hydrocarbons; (2) a second autocatalytic reaction phase dominated by reactions involving reduced sulfur species and partially oxidized hydrocarbons; (3) and a final, or late-stage, TSR reaction in which hydrocarbon oxidation continues at a slower rate via the non-autocatalytic reduction of sulfate by gaseous hydrocarbons.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geochimica et Cosmochimica Acta","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2014.05.004","usgsCitation":"Xia, X., Ellis, G.S., Ma, Q., and Tang, Y., 2014, Compositional and stable carbon isotopic fractionation during non-autocatalytic thermochemical sulfate reduction by gaseous hydrocarbons: Geochimica et Cosmochimica Acta, v. 139, p. 472-486, https://doi.org/10.1016/j.gca.2014.05.004.","productDescription":"15 p.","startPage":"472","endPage":"486","numberOfPages":"15","ipdsId":"IP-051961","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":288222,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288221,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.gca.2014.05.004"}],"volume":"139","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53981ad1e4b09e5ae91f9d9a","contributors":{"authors":[{"text":"Xia, Xinyu","contributorId":54494,"corporation":false,"usgs":true,"family":"Xia","given":"Xinyu","email":"","affiliations":[],"preferred":false,"id":494558,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellis, Geoffrey S. 0000-0003-4519-3320 gsellis@usgs.gov","orcid":"https://orcid.org/0000-0003-4519-3320","contributorId":1058,"corporation":false,"usgs":true,"family":"Ellis","given":"Geoffrey","email":"gsellis@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":494556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ma, Qisheng","contributorId":35219,"corporation":false,"usgs":true,"family":"Ma","given":"Qisheng","email":"","affiliations":[],"preferred":false,"id":494557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tang, Yongchun","contributorId":103166,"corporation":false,"usgs":true,"family":"Tang","given":"Yongchun","affiliations":[],"preferred":false,"id":494559,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70110751,"text":"ofr20141107 - 2014 - National Land Imaging Requirements (NLIR) Pilot Project summary report: Summary of moderate resolution imaging user requirements","interactions":[],"lastModifiedDate":"2020-06-05T12:08:01.026438","indexId":"ofr20141107","displayToPublicDate":"2014-06-10T14:58:00","publicationYear":"2014","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":"2014-1107","title":"National Land Imaging Requirements (NLIR) Pilot Project summary report: Summary of moderate resolution imaging user requirements","docAbstract":"Under the National Land Imaging Requirements (NLIR) Project, the U.S. Geological Survey (USGS) is developing a functional capability to obtain, characterize, manage, maintain and prioritize all Earth observing (EO) land remote sensing user requirements. The goal is a better understanding of community needs that can be supported with land remote sensing resources, and a means to match needs with appropriate solutions in an effective and efficient way.
The NLIR Project is composed of two components. The first component is focused on the development of the Earth Observation Requirements Evaluation System (EORES) to capture, store and analyze user requirements, whereas, the second component is the mechanism and processes to elicit and document the user requirements that will populate the EORES.
To develop the second component, the requirements elicitation methodology was exercised and refined through a pilot project conducted from June to September 2013. The pilot project focused specifically on applications and user requirements for moderate resolution imagery (5–120 meter resolution) as the test case for requirements development.
The purpose of this summary report is to provide a high-level overview of the requirements elicitation process that was exercised through the pilot project and an early analysis of the moderate resolution imaging user requirements acquired to date to support ongoing USGS sustainable land imaging study needs.
The pilot project engaged a limited set of Federal Government users from the operational and research communities and therefore the information captured represents only a subset of all land imaging user requirements. However, based on a comparison of results, trends, and analysis, the pilot captured a strong baseline of typical applications areas and user needs for moderate resolution imagery.
Because these results are preliminary and represent only a sample of users and application areas, the information from this report should only be used to indicate general user needs for the applications covered. Users of the information are cautioned that use of specific numeric results may be inappropriate without additional research. Any information used or cited from this report should specifically be cited as preliminary findings.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141107","usgsCitation":"Vadnais, C., and Stensaas, G.L., 2014, National Land Imaging Requirements (NLIR) Pilot Project summary report: Summary of moderate resolution imaging user requirements: U.S. Geological Survey Open-File Report 2014-1107, vi, 46 p., https://doi.org/10.3133/ofr20141107.","productDescription":"vi, 46 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-054641","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":288212,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1107/","linkFileType":{"id":5,"text":"html"}},{"id":288213,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1107/pdf/ofr2014-1107.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":375352,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2014/1107/images/coverthb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53981ad5e4b09e5ae91f9dae","contributors":{"authors":[{"text":"Vadnais, Carolyn","contributorId":21069,"corporation":false,"usgs":true,"family":"Vadnais","given":"Carolyn","affiliations":[],"preferred":false,"id":494140,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":494141,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70111960,"text":"70111960 - 2014 - Reducing fatigue damage for ships in transit through structured decision making","interactions":[],"lastModifiedDate":"2014-06-10T10:43:53","indexId":"70111960","displayToPublicDate":"2014-06-10T10:32:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2677,"text":"Marine Structures","active":true,"publicationSubtype":{"id":10}},"title":"Reducing fatigue damage for ships in transit through structured decision making","docAbstract":"Research in structural monitoring has focused primarily on drawing inference about the health of a structure from the structure’s response to ambient or applied excitation. Knowledge of the current state can then be used to predict structural integrity at a future time and, in principle, allows one to take action to improve safety, minimize ownership costs, and/or increase the operating envelope. While much time and effort has been devoted toward data collection and system identification, research to-date has largely avoided the question of how to choose an optimal maintenance plan. This work describes a structured decision making (SDM) process for taking available information (loading data, model output, etc.) and producing a plan of action for maintaining the structure. SDM allows the practitioner to specify his/her objectives and then solves for the decision that is optimal in the sense that it maximizes those objectives. To demonstrate, we consider the problem of a Naval vessel transiting a fixed distance in varying sea-state conditions. The physics of this problem are such that minimizing transit time increases the probability of fatigue failure in the structural supports. It is shown how SDM produces the optimal trip plan in the sense that it minimizes both transit time and probability of failure in the manner of our choosing (i.e., through a user-defined cost function). The example illustrates the benefit of SDM over heuristic approaches to maintaining the vessel.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Marine Structures","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.marstruc.2014.04.002","usgsCitation":"Nichols, J., Fackler, P., Pacifici, K., Murphy, K., and Nichols, J., 2014, Reducing fatigue damage for ships in transit through structured decision making: Marine Structures, v. 38, p. 18-43, https://doi.org/10.1016/j.marstruc.2014.04.002.","productDescription":"26 p.","startPage":"18","endPage":"43","numberOfPages":"26","ipdsId":"IP-054791","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":288208,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288207,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.marstruc.2014.04.002"}],"volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53981ad5e4b09e5ae91f9db6","contributors":{"authors":[{"text":"Nichols, J.M.","contributorId":18080,"corporation":false,"usgs":true,"family":"Nichols","given":"J.M.","email":"","affiliations":[],"preferred":false,"id":494550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fackler, P.L.","contributorId":30859,"corporation":false,"usgs":true,"family":"Fackler","given":"P.L.","email":"","affiliations":[],"preferred":false,"id":494551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pacifici, K.","contributorId":71667,"corporation":false,"usgs":true,"family":"Pacifici","given":"K.","email":"","affiliations":[],"preferred":false,"id":494553,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murphy, K.D.","contributorId":50004,"corporation":false,"usgs":true,"family":"Murphy","given":"K.D.","email":"","affiliations":[],"preferred":false,"id":494552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nichols, J.D. 0000-0002-7631-2890","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":14332,"corporation":false,"usgs":true,"family":"Nichols","given":"J.D.","affiliations":[],"preferred":false,"id":494549,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70111934,"text":"70111934 - 2014 - Modeling Hawaiian ecosystem degradation due to invasive plants under current and future climates","interactions":[],"lastModifiedDate":"2014-06-10T09:37:24","indexId":"70111934","displayToPublicDate":"2014-06-10T09:14:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Modeling Hawaiian ecosystem degradation due to invasive plants under current and future climates","docAbstract":"Occupation of native ecosystems by invasive plant species alters their structure and/or function. In Hawaii, a subset of introduced plants is regarded as extremely harmful due to competitive ability, ecosystem modification, and biogeochemical habitat degradation. By controlling this subset of highly invasive ecosystem modifiers, conservation managers could significantly reduce native ecosystem degradation. To assess the invasibility of vulnerable native ecosystems, we selected a proxy subset of these invasive plants and developed robust ensemble species distribution models to define their respective potential distributions. The combinations of all species models using both binary and continuous habitat suitability projections resulted in estimates of species richness and diversity that were subsequently used to define an invasibility metric. The invasibility metric was defined from species distribution models with <0.7 niche overlap (Warrens I) and relatively discriminative distributions (Area Under the Curve >0.8; True Skill Statistic >0.75) as evaluated per species. Invasibility was further projected onto a 2100 Hawaii regional climate change scenario to assess the change in potential habitat degradation. The distribution defined by the invasibility metric delineates areas of known and potential invasibility under current climate conditions and, when projected into the future, estimates potential reductions in native ecosystem extent due to climate-driven invasive incursion. We have provided the code used to develop these metrics to facilitate their wider use (Code S1). This work will help determine the vulnerability of native-dominated ecosystems to the combined threats of climate change and invasive species, and thus help prioritize ecosystem and species management actions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS ONE","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0095427","usgsCitation":"Vorsino, A.E., Fortini, L., Amidon, F.A., Miller, S.E., Jacobi, J.D., Price, J.P., `Ohukani`ohi`a Gon, S., and Koob, G.A., 2014, Modeling Hawaiian ecosystem degradation due to invasive plants under current and future climates: PLoS ONE, v. 9, no. 5, 18 p., https://doi.org/10.1371/journal.pone.0095427.","productDescription":"18 p.","numberOfPages":"18","ipdsId":"IP-054741","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":472944,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0095427","text":"Publisher Index Page"},{"id":288204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288198,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1371/journal.pone.0095427"}],"country":"United States","state":"Hawai'i","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -159.9917,18.7288 ], [ -159.9917,22.4876 ], [ -154.4937,22.4876 ], [ -154.4937,18.7288 ], [ -159.9917,18.7288 ] ] ] } } ] }","volume":"9","issue":"5","noUsgsAuthors":false,"publicationDate":"2014-05-07","publicationStatus":"PW","scienceBaseUri":"53981ad4e4b09e5ae91f9daa","contributors":{"authors":[{"text":"Vorsino, Adam E.","contributorId":71102,"corporation":false,"usgs":true,"family":"Vorsino","given":"Adam","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":494547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fortini, Lucas B.","contributorId":10693,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas B.","affiliations":[],"preferred":false,"id":494543,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Amidon, Fred A.","contributorId":107200,"corporation":false,"usgs":true,"family":"Amidon","given":"Fred","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":494548,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Stephen E.","contributorId":31683,"corporation":false,"usgs":true,"family":"Miller","given":"Stephen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":494544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jacobi, James D. 0000-0003-2313-7862 jjacobi@usgs.gov","orcid":"https://orcid.org/0000-0003-2313-7862","contributorId":3705,"corporation":false,"usgs":true,"family":"Jacobi","given":"James","email":"jjacobi@usgs.gov","middleInitial":"D.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":494541,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Price, Jonathan P.","contributorId":8736,"corporation":false,"usgs":true,"family":"Price","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":494542,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"`Ohukani`ohi`a Gon, Sam III","contributorId":60961,"corporation":false,"usgs":true,"family":"`Ohukani`ohi`a Gon","given":"Sam","suffix":"III","email":"","affiliations":[],"preferred":false,"id":494545,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Koob, Gregory A.","contributorId":61752,"corporation":false,"usgs":true,"family":"Koob","given":"Gregory","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":494546,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70189960,"text":"70189960 - 2014 - H-binding of size- and polarity-fractionated soil and lignite humic acids after removal of metal and ash components","interactions":[],"lastModifiedDate":"2017-07-31T07:57:10","indexId":"70189960","displayToPublicDate":"2014-06-10T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1564,"text":"Environmental Science and Pollution Research","active":true,"publicationSubtype":{"id":10}},"title":"H-binding of size- and polarity-fractionated soil and lignite humic acids after removal of metal and ash components","docAbstract":"A fractionation technique, combining dialysis removal of metal and ash components with hydrofluoric acid and pH 10 citrate buffer followed by chromatography of dialysis permeate on XAD-8 resin at decreasing pH values, has been applied to lignite humic acid (lignite-HA) and soil humic acid (soil-HA). H-binding data and non ideal competitive adsorption-Donnan model parameters were obtained for the HA fractions by theoretical analysis of H-binding data which reveal a significant increase of the carboxyl and the phenolic charge for the lignite-HA fractions vs. the parental lignite humic acid (LParentalHA). The fractionated lignite-HA material consisted mainly of permeate fractions, some of which were fulvic acid-like. The fractionated soil-HA material consisted mainly of large macromolecular structures that did not permeate the dialysis membrane during deashing. Chargeable groups had comparable concentrations in soil-HA fractions and parental soil humic acid (SParentalHA), indicating minimal interference of ash components with carboxyl and phenolic (and/or enolic) groups. Fractionation of HA, combined with theoretical analysis of H-binding, can distinguish the supramolecular vs. macromolecular nature of fractions within the same parental HA.
","language":"English","publisher":"Springer","doi":"10.1007/s11356-013-2302-9","usgsCitation":"Drosos, M., Leenheer, J.A., Avgeropoulos, A., and Deligiannakis, Y., 2014, H-binding of size- and polarity-fractionated soil and lignite humic acids after removal of metal and ash components: Environmental Science and Pollution Research, v. 21, no. 5, p. 3963-3971, https://doi.org/10.1007/s11356-013-2302-9.","productDescription":"9 p.","startPage":"3963","endPage":"3971","ipdsId":"IP-025378","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344450,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2013-12-03","publicationStatus":"PW","scienceBaseUri":"5980419ce4b0a38ca278935f","contributors":{"authors":[{"text":"Drosos, Marios","contributorId":195372,"corporation":false,"usgs":false,"family":"Drosos","given":"Marios","email":"","affiliations":[],"preferred":false,"id":706896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leenheer, Jerry A.","contributorId":72420,"corporation":false,"usgs":true,"family":"Leenheer","given":"Jerry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":706895,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Avgeropoulos, Apostolos","contributorId":195398,"corporation":false,"usgs":false,"family":"Avgeropoulos","given":"Apostolos","email":"","affiliations":[],"preferred":false,"id":706948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deligiannakis, Yiannis","contributorId":195373,"corporation":false,"usgs":false,"family":"Deligiannakis","given":"Yiannis","email":"","affiliations":[],"preferred":false,"id":706897,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70102224,"text":"70102224 - 2014 - Use of genetic data to infer population-specific ecological and phenotypic traits from mixed aggregations","interactions":[],"lastModifiedDate":"2018-04-21T13:19:15","indexId":"70102224","displayToPublicDate":"2014-06-09T13:58:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Use of genetic data to infer population-specific ecological and phenotypic traits from mixed aggregations","docAbstract":"Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS ONE","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0098470","usgsCitation":"Moran, P., Bromaghin, J.F., and Masuda, M., 2014, Use of genetic data to infer population-specific ecological and phenotypic traits from mixed aggregations: PLoS ONE, v. 9, no. 6, 13 p., https://doi.org/10.1371/journal.pone.0098470.","productDescription":"13 p.","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-050953","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":472945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0098470","text":"Publisher Index Page"},{"id":288179,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288178,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1371/journal.pone.0098470"}],"volume":"9","issue":"6","noUsgsAuthors":false,"publicationDate":"2014-06-06","publicationStatus":"PW","scienceBaseUri":"5396c953e4b0f7580bc0a8c7","contributors":{"authors":[{"text":"Moran, Paul","contributorId":42140,"corporation":false,"usgs":true,"family":"Moran","given":"Paul","email":"","affiliations":[],"preferred":false,"id":492862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":492860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Masuda, Michele","contributorId":24280,"corporation":false,"usgs":true,"family":"Masuda","given":"Michele","email":"","affiliations":[],"preferred":false,"id":492861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70111747,"text":"70111747 - 2014 - Hawaiian forest bird trends: using log-linear models to assess long-term trends is supported by model diagnostics and assumptions (reply to Freed and Cann 2013)","interactions":[],"lastModifiedDate":"2014-06-09T10:37:25","indexId":"70111747","displayToPublicDate":"2014-06-09T10:27:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1318,"text":"Condor","active":true,"publicationSubtype":{"id":10}},"title":"Hawaiian forest bird trends: using log-linear models to assess long-term trends is supported by model diagnostics and assumptions (reply to Freed and Cann 2013)","docAbstract":"Freed and Cann (2013) criticized our use of linear models to assess trends in the status of Hawaiian forest birds through time (Camp et al. 2009a, 2009b, 2010) by questioning our sampling scheme, whether we met model assumptions, and whether we ignored short-term changes in the population time series. In the present paper, we address these concerns and reiterate that our results do not support the position of Freed and Cann (2013) that the forest birds in the Hakalau Forest National Wildlife Refuge (NWR) are declining, or that the federally listed endangered birds are showing signs of imminent collapse. On the contrary, our data indicate that the 21-year long-term trends for native birds in Hakalau Forest NWR are stable to increasing, especially in areas that have received active management.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Condor","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Cooper Ornithological Society","doi":"10.1650/CONDOR-13-089.1","usgsCitation":"Camp, R., Pratt, T.K., Gorresen, P.M., Woodworth, B., and Jeffrey, J.J., 2014, Hawaiian forest bird trends: using log-linear models to assess long-term trends is supported by model diagnostics and assumptions (reply to Freed and Cann 2013): Condor, v. 116, no. 1, p. 97-101, https://doi.org/10.1650/CONDOR-13-089.1.","productDescription":"5 p.","startPage":"97","endPage":"101","numberOfPages":"5","ipdsId":"IP-052204","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":472946,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-13-089.1","text":"Publisher Index Page"},{"id":288171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288161,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1650/CONDOR-13-089.1"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Hakalau Forest National Wildlife Refuge","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -155.85876,19.363457 ], [ -155.85876,19.922001 ], [ -155.223499,19.922001 ], [ -155.223499,19.363457 ], [ -155.85876,19.363457 ] ] ] } } ] }","volume":"116","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5396c952e4b0f7580bc0a8bf","contributors":{"authors":[{"text":"Camp, Richard J.","contributorId":27392,"corporation":false,"usgs":true,"family":"Camp","given":"Richard J.","affiliations":[],"preferred":false,"id":494460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pratt, Thane K. tkpratt@usgs.gov","contributorId":5495,"corporation":false,"usgs":true,"family":"Pratt","given":"Thane","email":"tkpratt@usgs.gov","middleInitial":"K.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":494459,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorresen, P. Marcos mgorresen@usgs.gov","contributorId":37020,"corporation":false,"usgs":true,"family":"Gorresen","given":"P.","email":"mgorresen@usgs.gov","middleInitial":"Marcos","affiliations":[],"preferred":false,"id":494461,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woodworth, Bethany L.","contributorId":66797,"corporation":false,"usgs":true,"family":"Woodworth","given":"Bethany L.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":494463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jeffrey, John J.","contributorId":55256,"corporation":false,"usgs":true,"family":"Jeffrey","given":"John","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":494462,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}