A previously developed regional groundwater flow model was used to simulate the effects of changes in pumping rates on groundwater-flow paths and extent of recharge discharging to wells for a contaminated fractured bedrock aquifer in southeastern Pennsylvania. Groundwater in the vicinity of the North Penn Area 7 Superfund site, Montgomery County, Pennsylvania, was found to be contaminated with organic compounds, such as trichloroethylene (TCE), in 1979. At the time contamination was discovered, groundwater from the underlying fractured bedrock (shale) aquifer was the main source of supply for public drinking water and industrial use. As part of technical support to the U.S. Environmental Protection Agency (EPA) during the Remedial Investigation of the North Penn Area 7 Superfund site from 2000 to 2005, the U.S. Geological Survey (USGS) developed a model of regional groundwater flow to describe changes in groundwater flow and contaminant directions as a result of changes in pumping. Subsequently, large decreases in TCE concentrations (as much as 400 micrograms per liter) were measured in groundwater samples collected by the EPA from selected wells in 2010 compared to 2005‒06 concentrations.
To provide insight on the fate of potentially contaminated groundwater during the period of generally decreasing pumping rates from 1990 to 2010, steady-state simulations were run using the previously developed groundwater-flow model for two conditions prior to extensive remediation, 1990 and 2000, two conditions subsequent to some remediation 2005 and 2010, and a No Pumping case, representing pre-development or cessation of pumping conditions. The model was used to (1) quantify the amount of recharge, including potentially contaminated recharge from sources near the land surface, that discharged to wells or streams and (2) delineate the areas contributing recharge that discharged to wells or streams for the five conditions.
In all simulations, groundwater divides differed from surface-water divides, partly because of differences in stream elevations and because of geologic structure and pumping. In the 1990 and 2000 simulations, all recharge in and near the vicinity of North Penn Area 7 discharged to wells, but in the 2005 and 2010 simulations some recharge in this area discharged to streams, indicating possible discharge of contaminated groundwater from North Penn Area 7 sources to streams. As the amount of groundwater withdrawals by wells has declined since 1990, the area contributing recharge to wells in the vicinity of North Penn Area 7 has decreased.
To determine the effect of changes in pumping on flow paths and possible flow-path-related contributions to the observed changes in spatial distribution of contaminants in groundwater from 2005 to 2010, the USGS conducted simulations using the previously developed regional groundwater-flow model using reported pumping and estimated recharge rates for 2005 and 2010. Flow paths from recharge at known contaminant source areas to discharge locations at wells or streams were simulated under steady-state conditions for the two periods. Simulated groundwater-flow paths shifted only slightly from 2005 to 2010 as a result of changes in pumping rates. These slight changes in groundwater-flow paths from known sources of contamination are not coincident with the spatial distribution of observed changes in TCE concentrations from 2005 to 2010, indicating that the decreases of TCE concentrations may be a result of other processes, such as source removal or degradation. Results of the simulations and the absence of increases in TCE-degradation-product concentrations indicate that the decreases of TCE concentrations observed in 2010 may be at least partly related to contaminant-source removal by soil excavation completed in 2005, although additional data would be needed to confirm this preliminary explanation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175014","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Senior, L.A., and Goode, D.J., 2017, Effects of changes in pumping on regional groundwater-flow paths, 2005 and 2010, and areas contributing recharge to discharging wells, 1990–2010, in the vicinity of North Penn Area 7 Superfund site, Montgomery County, Pennsylvania: U.S. Geological Survey Scientific Investigations Report 2017–5014, 36 p., https://doi.org/10.3133/sir20175014.","productDescription":"Report: vi, 36 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077142","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":341375,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7FN14BQ","text":"USGS data release","description":"USGS data release"},{"id":341337,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5014/sir20175014.pdf","text":"Report","size":"4.61 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":341336,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5014/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Montgomery County","otherGeospatial":"North Penn Area 7 Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.304167,\n 40.244444\n ],\n [\n -75.263333,\n 40.244444\n ],\n [\n -75.263333,\n 40.205556\n ],\n [\n -75.304167,\n 40.205556\n ],\n [\n -75.304167,\n 40.244444\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
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There is little information on the oceanography in the National Park of American Samoa (NPSA). The transport pathways for potentially harmful constituents of land-derived runoff, as well as larvae and other planktonic organisms, are driven by nearshore circulation patterns. To evaluate the processes affecting coral reef ecosystem health, it is first necessary to understand the oceanographic processes driving nearshore circulation, residence times, exposure rates, and transport pathways. Information on how the NPSA’s natural resources may be affected by anthropogenic sources of pollution, sediment runoff, larval transport, or modifications to the marine protected areas is critical to NPSA resource managers for understanding and ultimately managing coastal and marine resources. To address this need, U.S. Geological Survey and U.S. National Park Service researchers conducted a collaborative study in 2015 to determine coastal circulation patterns and water-column properties along north-central Tutuila, American Samoa, in an area focused on NPSA’s Tutuila Unit and its coral reef ecosystem. The continuous measurements of waves, currents, tides, and water-column properties from these instrument deployments over 150 days, coupled with available meteorological measurements of wind and rainfall, provide information on nearshore circulation and the variability in these hydrodynamic properties for NPSA’s Tutuila Unit. In general, circulation was strongly driven by regional winds at longer (greater than day) timescales and by tides at shorter (less than day) timescales. Flows were primarily directed along shore, with current speeds faster offshore to the north and slower closer to shore, especially in embayments. Water-column properties exhibit strong seasonality coupled to the shift from non-trade wind season to trade wind season. During the non-trade wind season that was characterized by variable winds and larger waves in the NPSA, waters were warmer, slightly more saline, relatively less optically clear, and more stratified. When winds shifted to a more consistent trade wind pattern in the austral fall, the waters cooled and became less stratified because of decreased insolation. There are consistent spatial patterns in water column characteristics—Waters were warmer and less saline near the surface and closer to shore, especially in embayments, which tended to be more turbid, less clear, and characterized by higher chlorophyll than waters offshore. Water residence times were shorter farther offshore and longer closer to shore and in embayments, but varied spatially because of different forcing. Warmer, lower salinity, higher chlorophyll, and more turbid waters in embayments tend to reside in those locations for much greater durations, resulting in greater exposure of embayment ecosystems to those waters. This is in contrast with waters farther offshore, where the combination of shorter residence times and cooler, higher salinity water results in less exposure to land runoff. Understanding coastal circulation patterns and water-column properties in NPSA’s waters along north-central Tutuila may help to better understand how meteorological and oceanographic processes, at the regional and local scale, affect coral reef health and sustainability in this region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171060","usgsCitation":"Storlazzi, C.D., Cheriton, O.M., Rosenberger, K.J., Logan, J.B., and Clark, T.B., 2017, Coastal circulation and water-column properties in the National Park of American Samoa, February–July 2015: U.S. Geological Survey Open-File Report 2017–1060, 104 p., https://doi.org/10.3133/ofr20171060.","productDescription":"Report: ix, 104 p.; Data Release","numberOfPages":"113","onlineOnly":"Y","ipdsId":"IP-083344","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":341992,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1060/coverthb.jpg"},{"id":342023,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RN362H","text":"USGS data release","description":"USGS data release","linkHelpText":"Data from coastal circulation and water-column properties in the National Park of American Samoa, February–July 2015"},{"id":341994,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1060/ofr20171060.pdf","text":"Report","size":"6.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1060"}],"otherGeospatial":"National Park of American Samoa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -170.73646545410156,\n -14.295658268883681\n ],\n [\n -170.62454223632812,\n -14.295658268883681\n ],\n [\n -170.62454223632812,\n -14.219791816003891\n ],\n [\n -170.73646545410156,\n -14.219791816003891\n ],\n [\n -170.73646545410156,\n -14.295658268883681\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
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Nonnative fishes have been increasingly implicated in the decline of native fishes in the Pacific Northwest. Smallmouth Bass Micropterus dolomieu were introduced into the Umpqua River in southwest Oregon in the early 1960s. The spread of Smallmouth Bass throughout the basin coincided with a decline in counts of upstream-migrating Pacific Lampreys Entosphenus tridentatus. This suggested the potential for ecological interactions between Smallmouth Bass and Pacific Lampreys, as well as freshwater-resident Western Brook Lampreys Lampetra richardsoni. To evaluate the potential effects of Smallmouth Bass on lampreys, we sampled diets of Smallmouth Bass and used bioenergetics models to estimate consumption of larval lampreys in a segment of Elk Creek, a tributary to the lower Umpqua River. We captured 303 unique Smallmouth Bass (mean: 197 mm and 136 g) via angling in July and September. We combined information on Smallmouth Bass diet and energy density with other variables (temperature, body size, growth, prey energy density) in a bioenergetics model to estimate consumption of larval lampreys. Larval lampreys were found in 6.2% of diet samples, and model estimates indicated that the Smallmouth Bass we captured consumed 925 larval lampreys in this 2-month study period. When extrapolated to a population estimate of Smallmouth Bass in this segment, we estimated 1,911 larval lampreys were consumed between July and September. Although the precision of these estimates was low, this magnitude of consumption suggests that Smallmouth Bass may negatively affect larval lamprey populations.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/02755947.2017.1317677","usgsCitation":"Schultz, L., Heck, M., Kowalski, B., Eagles-Smith, C.A., Coates, K.C., and Dunham, J.B., 2017, Bioenergetics models to estimate numbers of larval lampreys consumed by smallmouth bass in Elk Creek, Oregon: North American Journal of Fisheries Management, v. 37, no. 4, p. 714-723, https://doi.org/10.1080/02755947.2017.1317677.","productDescription":"11 p. ","startPage":"714","endPage":"723","ipdsId":"IP-071356","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":342883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Elk Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.50469589233397,\n 43.65818541118326\n ],\n [\n -123.50297927856445,\n 43.66215942544592\n ],\n [\n -123.5024642944336,\n 43.66501558572499\n ],\n [\n -123.49353790283202,\n 43.66588482492509\n ],\n [\n -123.48701477050783,\n 43.66898914788972\n ],\n [\n -123.4859848022461,\n 43.670975830331784\n ],\n [\n -123.48701477050783,\n 43.67432820783561\n ],\n [\n -123.49010467529295,\n 43.67594224873435\n ],\n [\n -123.49336624145506,\n 43.67705963623325\n ],\n [\n -123.50263595581053,\n 43.67656302213585\n ],\n [\n -123.50881576538086,\n 43.674700682665446\n ],\n [\n -123.51379394531249,\n 43.673334930320166\n ],\n [\n -123.5170555114746,\n 43.67072749862279\n ],\n [\n -123.51568222045898,\n 43.66576064866714\n ],\n [\n -123.51465225219728,\n 43.66389797397276\n ],\n [\n -123.52203369140625,\n 43.66377379360479\n ],\n [\n -123.52546691894531,\n 43.6626561587339\n ],\n [\n -123.52718353271484,\n 43.659054749279555\n ],\n [\n -123.52323532104492,\n 43.65309332100122\n ],\n [\n -123.52083206176758,\n 43.65234810085346\n ],\n [\n -123.51722717285155,\n 43.65135445960513\n ],\n [\n -123.51551055908203,\n 43.65123025329318\n ],\n [\n -123.51207733154297,\n 43.650112384926544\n ],\n [\n -123.50950241088867,\n 43.65060921788041\n ],\n [\n -123.50727081298827,\n 43.65359012929569\n ],\n [\n -123.50435256958008,\n 43.65483213205091\n ],\n [\n -123.50469589233397,\n 43.65818541118326\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-06","publicationStatus":"PW","scienceBaseUri":"59521d1fe4b062508e3c365d","contributors":{"authors":[{"text":"Schultz, Luke 0000-0002-6751-4626 lschultz@usgs.gov","orcid":"https://orcid.org/0000-0002-6751-4626","contributorId":193171,"corporation":false,"usgs":true,"family":"Schultz","given":"Luke","email":"lschultz@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":700649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heck, Michael 0000-0001-8858-7325 mheck@usgs.gov","orcid":"https://orcid.org/0000-0001-8858-7325","contributorId":4796,"corporation":false,"usgs":true,"family":"Heck","given":"Michael","email":"mheck@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":700651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kowalski, Brandon M","contributorId":193503,"corporation":false,"usgs":false,"family":"Kowalski","given":"Brandon M","affiliations":[],"preferred":false,"id":700650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":700652,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coates, Kelly C.","contributorId":193504,"corporation":false,"usgs":false,"family":"Coates","given":"Kelly","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":700653,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":700654,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70188347,"text":"70188347 - 2017 - A note on adding viscoelasticity to earthquake simulators","interactions":[],"lastModifiedDate":"2017-06-06T16:04:59","indexId":"70188347","displayToPublicDate":"2017-06-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"A note on adding viscoelasticity to earthquake simulators","docAbstract":"Here, I describe how time‐dependent quasi‐static stress transfer can be implemented in an earthquake simulator code that is used to generate long synthetic seismicity catalogs. Most existing seismicity simulators use precomputed static stress interaction coefficients to rapidly implement static stress transfer in fault networks with typically tens of thousands of fault patches. The extension to quasi‐static deformation, which accounts for viscoelasticity of Earth’s ductile lower crust and mantle, involves the precomputation of additional interaction coefficients that represent time‐dependent stress transfer among the model fault patches, combined with defining and evolving additional state variables that track this stress transfer. The new approach is illustrated with application to a California‐wide synthetic fault network.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160192","usgsCitation":"Pollitz, F., 2017, A note on adding viscoelasticity to earthquake simulators: Bulletin of the Seismological Society of America, v. 107, no. 1, p. 468-474, https://doi.org/10.1785/0120160192.","productDescription":"7 p.","startPage":"468","endPage":"474","ipdsId":"IP-076554","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":342185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-27","publicationStatus":"PW","scienceBaseUri":"5937bf2ce4b0f6c2d0d9c73a","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697346,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70188342,"text":"70188342 - 2017 - System identification based on deconvolution and cross correlation: An application to a 20‐story instrumented building in Anchorage, Alaska","interactions":[],"lastModifiedDate":"2017-06-06T16:30:14","indexId":"70188342","displayToPublicDate":"2017-06-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"System identification based on deconvolution and cross correlation: An application to a 20‐story instrumented building in Anchorage, Alaska","docAbstract":"Deconvolution and cross‐correlation techniques are used for system identification of a 20‐story steel, moment‐resisting frame building in downtown Anchorage, Alaska. This regular‐plan midrise structure is instrumented with a 32‐channel accelerometer array at 10 levels. The impulse response functions (IRFs) and correlation functions (CFs) are computed based on waveforms recorded from ambient vibrations and five local and regional earthquakes. The earthquakes occurred from 2005 to 2014 with moment magnitudes between 4.7 and 6.2 over a range of azimuths at epicenter distances of 13.3–183 km. The building’s fundamental frequencies and mode shapes are determined using a complex mode indicator function based on singular value decomposition of multiple reference frequency‐response functions. The traveling waves, identified in IRFs with a virtual source at the roof, and CFs are used to estimate the intrinsic attenuation associated with the fundamental modes and shear‐wave velocity in the building. Although the cross correlation of the waveforms at various levels with the corresponding waveform at the first floor provides more complicated wave propagation than that from the deconvolution with virtual source at the roof, the shear‐wave velocities identified by both techniques are consistent—the largest difference in average values is within 8%. The median shear‐wave velocity from the IRFs of five earthquakes is 191 m/s for the east–west (E‐W), 205 m/s for the north–south (N‐S), and 176 m/s for the torsional responses. The building’s average intrinsic‐damping ratio is estimated to be 3.7% and 3.4% in the 0.2–1 Hz frequency band for the E‐W and N‐S directions, respectively. These results are intended to serve as reference for the undamaged condition of the building, which may be used for tracking changes in structural integrity during and after future earthquakes.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160069","usgsCitation":"Wen, W., and Kalkan, E., 2017, System identification based on deconvolution and cross correlation: An application to a 20‐story instrumented building in Anchorage, Alaska: Bulletin of the Seismological Society of America, v. 107, no. 2, p. 718-740, https://doi.org/10.1785/0120160069.","productDescription":"23 p.","startPage":"718","endPage":"740","ipdsId":"IP-068688","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":342192,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Anchorage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.90917682647705,\n 61.20793488105243\n ],\n [\n -149.88252639770508,\n 61.20793488105243\n ],\n [\n -149.88252639770508,\n 61.21661483933352\n ],\n [\n -149.90917682647705,\n 61.21661483933352\n ],\n [\n -149.90917682647705,\n 61.20793488105243\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-14","publicationStatus":"PW","scienceBaseUri":"5937bf2ce4b0f6c2d0d9c73c","contributors":{"authors":[{"text":"Wen, Weiping","contributorId":192669,"corporation":false,"usgs":false,"family":"Wen","given":"Weiping","email":"","affiliations":[],"preferred":false,"id":697331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697330,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188609,"text":"70188609 - 2017 - Application of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta","interactions":[],"lastModifiedDate":"2017-06-16T15:28:02","indexId":"70188609","displayToPublicDate":"2017-06-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1587,"text":"Estuarine, Coastal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"Application of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta","docAbstract":"A linked modeling approach has been undertaken to understand the impacts of climate and infrastructure on aquatic ecology and water quality in the San Francisco Bay-Delta region. The Delft3D Flexible Mesh modeling suite is used in this effort for its 3D hydrodynamics, salinity, temperature and sediment dynamics, phytoplankton and water-quality coupling infrastructure, and linkage to a habitat suitability model. The hydrodynamic model component of the suite is D-Flow FM, a new 3D unstructured finite-volume model based on the Delft3D model. In this paper, D-Flow FM is applied to the San Francisco Bay-Delta to investigate tidal, seasonal and annual dynamics of water levels, river flows and salinity under historical environmental and infrastructural conditions. The model is driven by historical winds, tides, ocean salinity, and river flows, and includes federal, state, and local freshwater withdrawals, and regional gate and barrier operations. The model is calibrated over a 9-month period, and subsequently validated for water levels, flows, and 3D salinity dynamics over a 2 year period.
Model performance was quantified using several model assessment metrics and visualized through target diagrams. These metrics indicate that the model accurately estimated water levels, flows, and salinity over wide-ranging tidal and fluvial conditions, and the model can be used to investigate detailed circulation and salinity patterns throughout the Bay-Delta. The hydrodynamics produced through this effort will be used to drive affiliated sediment, phytoplankton, and contaminant hindcast efforts and habitat suitability assessments for fish and bivalves. The modeling framework applied here will serve as a baseline to ultimately shed light on potential ecosystem change over the current century.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2017.04.024","usgsCitation":"Martyr-Koller, R., Kernkamp, H., Van Dam, A., Mick van der Wegen, Lucas, L., Knowles, N., Jaffe, B., and Fregoso, T., 2017, Application of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta: Estuarine, Coastal and Shelf Science, v. 192, p. 86-107, https://doi.org/10.1016/j.ecss.2017.04.024.","productDescription":"22 p. 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Genetic tools, such as DNA fingerprinting by terminal restriction fragment length polymorphism (T-RFLP) analysis, high-throughput DNA sequencing (HTS), and real-time, quantitative polymerase chain reaction (qPCR), provide more sensitive and rapid assessments of bloom ecology than traditional techniques. The objectives of this study were (1) to characterize the microbial community at one site in Upper Klamath Lake and determine changes in the cyanobacterial community through time using T-RFLP and HTS in comparison with traditional light microscopy; (2) to determine relative abundances and changes in abundance over time of toxigenic Microcystis using qPCR; and (3) to determine relative abundances and changes in abundance over time of Aphanizomenon, Microcystis, and total cyanobacteria using qPCR. T-RFLP analysis of total cyanobacteria showed a dominance of only one or two distinct genotypes in samples from 2013, but results of HTS in 2013 and 2014 showed more variations in the bloom cycle that fit with the previous understanding of bloom dynamics in Upper Klamath Lake and indicated that potentially toxigenic Microcystis was more prevalent in 2014 than in years prior. The qPCR-estimated copy numbers of all target genes were higher in 2014 than in 2013, when microcystin concentrations also were higher. Total Microcystis density was shown with qPCR to be a better predictor of late-season increases in microcystin concentrations than the relative proportions of potentially toxigenic cells. In addition, qPCR targeting Aphanizomenon at one site in Upper Klamath Lake indicated a moderate bloom of this species (corresponding to chlorophyll a concentrations between approximately 75 and 200 micrograms per liter) from mid-June to mid-August, 2014. After August 18, the Aphanizomenon bloom was overtaken by Microcystis late in the season as microcystin concentrations peaked. Overall, results of this study showed how DNA-based, genetic methods may provide rapid and sensitive diagnoses for the presence of toxigenic cyanobacteria and that they are useful for general monitoring or ecological studies and identification of cyanobacterial community members in complex aquatic habitats. These same methods can also be used to simultaneously address spatial (horizontal and vertical) and temporal variation and under different conditions. Additionally, with some modifications, the same techniques can be applied to different sample types, including water, sediment, and tissue.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175026","usgsCitation":"Eldridge, S.L.C., Driscoll, Connor, and Dreher, T.W., 2017, Using high-throughput DNA sequencing, genetic fingerprinting, and quantitative PCR as tools for monitoring bloom-forming and toxigenic cyanobacteria in Upper Klamath Lake, Oregon, 2013 and 2014: U.S. Geological Survey Scientific Investigations Report 2017–5026, 50 p., https://doi.org/10.3133/sir20175026.","productDescription":"Report: vi, 50 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-075955","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":342012,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7C53J3V","text":"USGS data release","description":"USGS data release","linkHelpText":"Datasets of High-throughput DNA Sequencing, Genetic Fingerprinting, and Quantitative PCR from Upper Klamath Lake, Oregon, 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U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Nest box programs are frequently implemented for the conservation of cavity-nesting birds, but their effectiveness is rarely evaluated in comparison to birds not using nest boxes. In the European Palearctic, Red-footed Falcon Falco vespertinus populations are both of high conservation concern and are strongly associated with nest box programs in heavily managed landscapes. We used a 21-year monitoring dataset collected on 753 nesting attempts by Red-footed Falcons in unmanaged natural or semi-natural habitats to provide basic information on this poorly known species; to evaluate long-term demographic trends; and to evaluate response of demographic parameters of Red-footed Falcons to environmental factors including use of nest boxes. We observed significant differences among years in laying date, offspring loss, and numbers of fledglings produced, but not in egg production. Of these four parameters, offspring loss and, to a lesser extent, number of fledglings exhibited directional trends over time. Variation in laying date and in numbers of eggs were not well explained by any one model, but instead by combinations of models, each with informative terms for nest type. Nevertheless, laying in nest boxes occurred 2.10 ± 0.70 days earlier than in natural nests. In contrast, variation in both offspring loss and numbers of fledglings produced were fairly well explained by a single model including terms for nest type, nest location, and an interaction between the two parameters (65% and 81% model weights respectively), with highest offspring loss in nest boxes on forest edges. Because, for other species, earlier laying dates are associated with more fit individuals, this interaction highlighted a possible ecological trap, whereby birds using nest boxes on forest edges lay eggs earlier but suffer greater offspring loss and produce lower numbers of fledglings than do those in other nesting settings. If nest boxes increase offspring loss for Red-footed Falcons in heavily managed landscapes where populations are at greater risk, or for the many other species of rare or endangered birds supported by nest box programs, these processes could have important demographic and conservation consequences.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.12503","usgsCitation":"Bragin, E.A., Bragin, A.E., and Katzner, T., 2017, Demographic consequences of nest box use for Red-footed Falcons Falco vespertinus in Central Asia: Ibis, v. 159, no. 4, p. 841-853, https://doi.org/10.1111/ibi.12503.","productDescription":"13 p.","startPage":"841","endPage":"853","ipdsId":"IP-081874","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":342604,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kazakhstan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[70.96231,42.26615],[70.38896,42.08131],[69.07003,41.38424],[68.63248,40.66868],[68.2599,40.66232],[67.98586,41.13599],[66.71405,41.16844],[66.51065,41.98764],[66.02339,41.99465],[66.09801,42.99766],[64.90082,43.72808],[63.18579,43.65007],[62.0133,43.50448],[61.05832,44.40582],[60.23997,44.78404],[58.68999,45.50001],[58.50313,45.5868],[55.92892,44.99586],[55.96819,41.30864],[55.45525,41.25986],[54.75535,42.04397],[54.07942,42.32411],[52.94429,42.11603],[52.50246,41.78332],[52.44634,42.02715],[52.69211,42.4439],[52.50143,42.7923],[51.34243,43.13297],[50.89129,44.03103],[50.33913,44.28402],[50.30564,44.60984],[51.2785,44.51485],[51.3169,45.246],[52.16739,45.40839],[53.04088,45.25905],[53.22087,46.23465],[53.04274,46.85301],[52.04202,46.80464],[51.19195,47.0487],[50.03408,46.60899],[49.10116,46.39933],[48.59324,46.56103],[48.69473,47.07563],[48.05725,47.74375],[47.31523,47.71585],[46.46645,48.39415],[47.04367,49.15204],[46.7516,49.35601],[47.54948,50.4547],[48.57784,49.87476],[48.70238,50.60513],[50.76665,51.69276],[52.32872,51.71865],[54.53288,51.02624],[55.71694,50.62172],[56.77796,51.04355],[58.36329,51.06365],[59.64228,50.54544],[59.93281,50.84219],[61.33742,50.79907],[61.588,51.27266],[59.96753,51.96042],[60.92727,52.44755],[60.73999,52.71999],[61.69999,52.98],[60.97807,53.66499],[61.43659,54.00626],[65.17853,54.35423],[65.66688,54.60127],[68.1691,54.97039],[69.06817,55.38525],[70.86527,55.16973],[71.18013,54.13329],[72.22415,54.37666],[73.50852,54.03562],[73.42568,53.48981],[74.38485,53.54686],[76.8911,54.49052],[76.52518,54.177],[77.80092,53.40441],[80.03556,50.86475],[80.56845,51.38834],[81.94599,50.8122],[83.383,51.06918],[83.93511,50.88925],[84.41638,50.3114],[85.11556,50.1173],[85.54127,49.69286],[86.82936,49.82667],[87.35997,49.21498],[86.59878,48.54918],[85.76823,48.45575],[85.72048,47.45297],[85.16429,47.00096],[83.18048,47.33003],[82.45893,45.53965],[81.94707,45.31703],[79.96611,44.91752],[80.86621,43.18036],[80.18015,42.92007],[80.25999,42.35],[79.64365,42.49668],[79.14218,42.85609],[77.65839,42.96069],[76.00035,42.98802],[75.63696,42.8779],[74.21287,43.29834],[73.6453,43.09127],[73.48976,42.50089],[71.84464,42.8454],[71.18628,42.70429],[70.96231,42.26615]]]},\"properties\":{\"name\":\"Kazakhstan\"}}]}","volume":"159","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-23","publicationStatus":"PW","scienceBaseUri":"5944ee13e4b062508e3335e9","contributors":{"authors":[{"text":"Bragin, Evgeny A.","contributorId":194894,"corporation":false,"usgs":false,"family":"Bragin","given":"Evgeny","email":"","middleInitial":"A.","affiliations":[{"id":35656,"text":"Science Department, Naurzum National Nature Reserve, Kostanay Oblast, Naurzumski Raijon, Karamendy, Kazakhstan","active":true,"usgs":false}],"preferred":false,"id":698515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bragin, Alexander E.","contributorId":193027,"corporation":false,"usgs":false,"family":"Bragin","given":"Alexander","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":698516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":698514,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188184,"text":"70188184 - 2017 - Snow and ice","interactions":[],"lastModifiedDate":"2020-08-20T19:26:43.019871","indexId":"70188184","displayToPublicDate":"2017-06-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":32,"text":"General Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"PNW-GTR-950","chapter":"3","title":"Snow and ice","docAbstract":"Multiple nutrient cycles regulate biological nitrogen (N) fixation in forests, yet long-term feedbacks between N-fixation and coupled element cycles remain largely unexplored. We examined soil nutrients and heterotrophic N-fixation across a gradient of 24 temperate conifer forests shaped by legacies of symbiotic N-fixing trees. We observed positive relationships among mineral soil pools of N, carbon (C), organic molybdenum (Mo), and organic phosphorus (P) across sites, evidence that legacies of symbiotic N-fixing trees can increase the abundance of multiple elements important to heterotrophic N-fixation. Soil N accumulation lowered rates of heterotrophic N-fixation in organic horizons due to both N inhibition of nitrogenase enzymes and declines in soil organic matter quality. Experimental fertilization of organic horizon soil revealed widespread Mo limitation of heterotrophic N-fixation, especially at sites where soil Mo was scarce relative to C. Fertilization also revealed widespread absence of P limitation, consistent with high soil P:Mo ratios. Responses of heterotrophic N-fixation to added Mo (positive) and N (negative) were correlated across sites, evidence that multiple nutrient controls of heterotrophic N-fixation were more common than single-nutrient effects. We propose a conceptual model where symbiotic N-fixation promotes coupled N, C, P, and Mo accumulation in soil, leading to positive feedback that relaxes nutrient limitation of overall N-fixation, though heterotrophic N-fixation is primarily suppressed by strong negative feedback from long-term soil N accumulation.
","language":"English","publisher":"Springer","doi":"10.1007/s10533-017-0341-x","usgsCitation":"Perakis, S.S., Pett-Ridge, J.C., and Catricala, C.E., 2017, Nutrient feedbacks to soil heterotrophic nitrogen fixation in forests: Biogeochemistry, v. 134, no. 1-2, p. 41-55, https://doi.org/10.1007/s10533-017-0341-x.","productDescription":"15 p.","startPage":"41","endPage":"55","ipdsId":"IP-066856","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":342065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Oregon Coast Range","volume":"134","issue":"1-2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-24","publicationStatus":"PW","scienceBaseUri":"59366da7e4b0f6c2d0d7d609","contributors":{"authors":[{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":696978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pett-Ridge, Julie C.","contributorId":192603,"corporation":false,"usgs":false,"family":"Pett-Ridge","given":"Julie","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":696979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catricala, Christina E.","contributorId":192604,"corporation":false,"usgs":false,"family":"Catricala","given":"Christina","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":696980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187570,"text":"fs20173035 - 2017 - Magnetic monitoring in Saguaro National Park","interactions":[],"lastModifiedDate":"2020-07-13T14:38:50.422625","indexId":"fs20173035","displayToPublicDate":"2017-06-02T18:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3035","title":"Magnetic monitoring in Saguaro National Park","docAbstract":"On a sandy, arid plain, near the Rincon Mountain Visitor Center of Saguaro National Park, tucked in among brittlebush, creosote, and other hardy desert plants, is an unusual type of observatory—a small unmanned station that is used for monitoring the Earth’s variable magnetic field. Named for the nearby city of Tucson, Arizona, the observatory is 1 of 14 that the Geomagnetism Program of the U.S. Geological Survey operates at various locations across the United States and Territories.
Data from USGS magnetic observatories, including the Tucson observatory, as well as observatories operated by institutions in other countries, record a variety of signals related to a wide diversity of physical phenomena in the Earth’s interior and its surrounding outer-space environment. The data are used for geomagnetic mapping and surveying, for fundamental scientific research, and for assessment of magnetic storms, which can be hazardous for the activities and infrastructure of our modern, technologically based society. The U.S. Geological Survey observatory service is an integral part of a U.S. national project for monitoring and assessing space weather hazards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173035","usgsCitation":"Love, J.J., Finn, C.A., Gamez Valdez, Y.C., Swann, Don, 2017, Magnetic monitoring in Saguaro National Park: U.S. Geological Survey Fact Sheet 2017–3035, 2 p., https://doi.org/10.3133/fs20173035.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-084937","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3035/fs20173035.pdf","text":"Report","size":"14.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3035"},{"id":342003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3035/coverthb3.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Saguaro National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.16567611694336,\n 32.24779674710343\n ],\n [\n -111.16567611694336,\n 32.24924855874583\n ],\n [\n -111.10559463500975,\n 32.24924855874583\n ],\n [\n -111.10559463500975,\n 32.265071800242445\n ],\n [\n -111.11383438110352,\n 32.265071800242445\n ],\n [\n -111.11417770385742,\n 32.27218410189552\n ],\n [\n -111.12258911132812,\n 32.272474388080994\n ],\n [\n -111.12224578857422,\n 32.29380787959086\n ],\n [\n -111.10490798950194,\n 32.29453342030047\n ],\n [\n -111.10559463500975,\n 32.30164341194408\n ],\n [\n -111.11434936523438,\n 32.30178850800702\n ],\n [\n -111.11417770385742,\n 32.30483547166609\n ],\n [\n -111.10198974609374,\n 32.30570601389429\n ],\n [\n -111.10370635986328,\n 32.30889793050953\n ],\n [\n -111.10010147094727,\n 32.30904301495666\n ],\n [\n -111.10027313232422,\n 32.31092909162695\n ],\n [\n -111.09563827514648,\n 32.31179957530851\n ],\n [\n -111.09186172485352,\n 32.313540517578666\n ],\n [\n -111.08945846557616,\n 32.31629694107953\n ],\n [\n -111.08963012695312,\n 32.32326016365093\n ],\n [\n -111.10559463500975,\n 32.32311510197487\n ],\n [\n -111.10559463500975,\n 32.319923686309146\n ],\n [\n -111.10937118530273,\n 32.32006875309753\n ],\n [\n -111.10971450805664,\n 32.323405225094625\n ],\n [\n -111.11434936523438,\n 32.32326016365093\n ],\n [\n -111.11400604248047,\n 32.31615186824962\n ],\n [\n -111.12190246582031,\n 32.31658708604226\n ],\n [\n -111.12258911132812,\n 32.33471930167625\n ],\n [\n -111.126708984375,\n 32.33471930167625\n ],\n [\n -111.12653732299805,\n 32.3377651576181\n ],\n [\n -111.13151550292969,\n 32.33791019582042\n ],\n [\n -111.13134384155273,\n 32.35212281198644\n ],\n [\n -111.1402702331543,\n 32.352557856852634\n ],\n [\n -111.14009857177733,\n 32.35560311232581\n ],\n [\n -111.14919662475586,\n 32.355458102485485\n ],\n [\n -111.14953994750977,\n 32.352267827174344\n ],\n [\n -111.18267059326172,\n 32.352267827174344\n ],\n [\n -111.18267059326172,\n 32.33529947261555\n ],\n [\n -111.16722106933594,\n 32.335444514769286\n ],\n [\n -111.16704940795897,\n 32.306431459362386\n ],\n [\n -111.18387222290038,\n 32.306431459362386\n ],\n [\n -111.18370056152344,\n 32.31368559459092\n ],\n [\n -111.23588562011719,\n 32.31368559459092\n ],\n [\n -111.23588562011719,\n 32.306431459362386\n ],\n [\n -111.21820449829102,\n 32.30599619277848\n ],\n [\n -111.21820449829102,\n 32.29148611029876\n ],\n [\n -111.23554229736328,\n 32.29163122262151\n ],\n [\n -111.23537063598633,\n 32.27755424598\n ],\n [\n -111.24395370483398,\n 32.277699380597454\n ],\n [\n -111.24429702758789,\n 32.27029721905168\n ],\n [\n -111.23537063598633,\n 32.26986177897686\n ],\n [\n -111.23537063598633,\n 32.26260413677006\n ],\n [\n -111.21837615966795,\n 32.26274929530214\n ],\n [\n -111.21820449829102,\n 32.24837747454558\n ],\n [\n -111.16567611694336,\n 32.24779674710343\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Geologic Hazards Science Center
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An evaluation of trends in hydrologic and water quality conditions and estimation of water budgets through 2013 was done by the U.S. Geological Survey in cooperation with the Chester County Water Resources Authority. Long-term hydrologic, meteorologic, and biologic data collected in Chester County, Pennsylvania, which included streamflow, groundwater levels, surface-water quality, biotic integrity, precipitation, and air temperature were analyzed to determine possible trends or changes in hydrologic conditions. Statistically significant trends were determined by applying the Kendall rank correlation test; the magnitudes of the trends were determined using the Sen slope estimator. Water budgets for eight selected watersheds were updated and a new water budget was developed for the Marsh Creek watershed. An average water budget for Chester County was developed using the eight selected watersheds and the new Marsh Creek water budget.
Annual and monthly mean streamflow, base flow, and runoff were analyzed for trends at 10 streamgages. The periods of record at the 10 streamgages ranged from 1961‒2013 to 1988‒2013. The only statistically significant trend for annual mean streamflow was for West Branch Brandywine Creek near Honey Brook, Pa. (01480300) where annual mean streamflow increased 1.6 cubic feet per second (ft3/s) per decade. The greatest increase in monthly mean streamflow was for Brandywine Creek at Chadds Ford, Pa. (01481000) for December; the increase was 47 ft3/s per decade. No statistically significant trends in annual mean base flow or runoff were determined for the 10 streamgages. The greatest increase in monthly mean base flow was for Brandywine Creek at Chadds Ford, Pa. (01481000) for December; the increase was 26 ft3/s per decade.
The magnitude of peaks greater than a base streamflow was analyzed for trends for 12 streamgages. The period of record at the 12 stream gages ranged from 1912‒2012 to 2004–11. Fifty percent of the streamgages showed a small statistically significant increase in peaks greater than the base streamflow. The greatest increase was for Brandywine Creek at Chadds Ford, Pa. (01481000) during 1962‒2012; the increase was 1.8 ft3/s per decade. There were no statistically significant trends in the number of floods equal to or greater than the 2-year recurrence interval flood flow.
Twenty‒one monitoring wells were evaluated for statistically significant trends in annual mean water level, minimum annual water level, maximum annual water level, and annual range in water-level fluctuations. For four wells, a small statistically significant increase in annual mean water level was determined that ranged from 0.16 to 0.7 feet per decade. There was poor or no correlation between annual mean groundwater levels and annual mean streamflow and base flow. No correlation was determined between annual mean groundwater level and annual precipitation. Despite rapid population growth and land-use change since 1950, there appears to have been little or no detrimental effects on groundwater levels in 21 monitoring wells.
Long-term precipitation and temperature data were available from the West Chester (1893‒2013) and Phoenixville, Pa. (1915‒2013) National Oceanic and Atmospheric Administration (NOAA) weather stations. No statistically significant trends in annual mean precipitation or annual mean temperature were determined for either station. Both weather stations had a significant decrease in the number of days per year with precipitation greater than or equal to 0.1 inch. Annual mean minimum and maximum temperatures from the NOAA Southeastern Piedmont Climate Division increased 0.2 degrees Fahrenheit (F) per decade between 1896 and 2014. The number of days with a maximum temperature equal to or greater than 90 degrees F increased at West Chester and decreased at Phoenixville. No statistically significant trend was determined for annual snowfall amounts.
Data from 1974 to 2013 for three stream water-quality monitors in the Brandywine Creek watershed were evaluated. The monitors are on the West Branch Brandywine Creek at Modena, Pa. (01480617), East Branch Brandywine Creek below Downingtown, Pa. (01480870), and Brandywine Creek at Chadds Ford, Pa. (01481000). Statistically significant upward trends were determined for annual mean specific conductance at all three stations, indicating the total dissolved solids load has been increasing. If the current trend continues, the annual mean specific conductance could almost double from 1974 to 2050. The increase in specific conductance likely is due to increases in chloride concentrations, which have been increasing steadily over time at all three stations. No correlation was found between monthly mean specific conductance and monthly mean streamflow or base flow. Statistically significant upward trends in pH were determined for all three stations. Statistically significant upward trends in stream temperature were determined for East Branch Brandywine Creek below Downingtown, Pa. (01480870) and Brandywine Creek at Chadds Ford, Pa. (01481000). The stream water-quality data indicate substantial increases in the minimum daily dissolved oxygen concentrations in the Brandywine Creek over time.
The Chester County Index of Biotic Integrity (CC-IBI) determined for 1998‒2013 was evaluated for the five biological sampling sites collocated with streamgages. CC-IBI scores are based on a 0‒100 scale with higher scores indicating better stream quality. Statistically significant upward trends in the CC-IBI were determined for West Branch Brandywine Creek at Modena, Pa. (01480617) and East Branch Brandywine Creek below Downingtown, Pa. (01480870). No correlation was found between the CC-IBI and streamflow, precipitation, or stream specific conductance, pH, temperature, or dissolved oxygen concentration.
A Chester County average water budget was developed using the nine estimated watershed water budgets. Average precipitation was 48.4 inches, and average streamflow was 21.4 inches. Average runoff and base flow were 8.3 and 13.1 inches, respectively, and average evapotranspiration and estimation of errors was 27.2 inches.\"
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175025","collaboration":"Prepared in cooperation with the Chester County Water Resources Authority","usgsCitation":"Sloto, R.A., and Reif, A.G., 2017, Evaluation of long-term trends in hydrologic and water-quality conditions, and estimation of water budgets through 2013, Chester County, Pennsylvania (ver.1.1, July 2017): U.S. Geological Survey Scientific Investigations Report 2017–5025, 59 p., https://doi.org/10.3133/sir20175025.","productDescription":"vii, 59 p.","numberOfPages":"71","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064731","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":341981,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5025/sir20175025.pdf","text":"Report","size":"8.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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1.0: Originally posted June 2,2017; Version 1.1: July 10, 2017","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
We present a detailed example of how a subbasin develops adjacent to a transfer zone in the Rio Grande rift. The Embudo transfer zone in the Rio Grande rift is considered one of the classic examples and has been used as the inspiration for several theoretical models. Despite this attention, the history of its development into a major rift structure is poorly known along its northern extent near Taos, New Mexico. Geologic evidence for all but its young rift history is concealed under Quaternary cover. We focus on understanding the pre-Quaternary evidence that is in the subsurface by integrating diverse pieces of geologic and geophysical information. As a result, we present a substantively new understanding of the tectonic configuration and evolution of the northern extent of the Embudo fault and its adjacent subbasin.
We integrate geophysical, borehole, and geologic information to interpret the subsurface configuration of the rift margins formed by the Embudo and Sangre de Cristo faults and the geometry of the subbasin within the Taos embayment. Key features interpreted include (1) an imperfect D-shaped subbasin that slopes to the east and southeast, with the deepest point ∼2 km below the valley floor located northwest of Taos at ∼36° 26′N latitude and 105° 37′W longitude; (2) a concealed Embudo fault system that extends as much as 7 km wider than is mapped at the surface, wherein fault strands disrupt or truncate flows of Pliocene Servilleta Basalt and step down into the subbasin with a minimum of 1.8 km of vertical displacement; and (3) a similar, wider than expected (5–7 km) zone of stepped, west-down normal faults associated with the Sangre de Cristo range front fault.
From the geophysical interpretations and subsurface models, we infer relations between faulting and flows of Pliocene Servilleta Basalt and older, buried basaltic rocks that, combined with geologic mapping, suggest a revised rift history involving shifts in the locus of fault activity as the Taos subbasin developed. We speculate that faults related to north-striking grabens at the end of Laramide time formed the first west-down master faults. The Embudo fault may have initiated in early Miocene southwest of the Taos region. Normal-oblique slip on these early fault strands likely transitioned in space and time to dominantly left-lateral slip as the Embudo fault propagated to the northeast. During and shortly after eruption of Servilleta Basalt, proto-Embudo fault strands were active along and parallel to the modern, NE-aligned Rio Pueblo de Taos, ∼4–7 km basinward of the modern, mapped Embudo fault zone. Faults along the northeastern subbasin margin had northwest strikes for most of the period of subbasin formation and were located ∼5–7 km basinward of the modern Sangre de Cristo fault. The locus of fault activity shifted to more northerly striking faults within 2 km of the modern range front sometime after Servilleta volcanism had ceased. The northerly faults may have linked with the northeasterly proto-Embudo faults at this time, concurrent with the development of N-striking Los Cordovas normal faults within the interior of the subbasin. By middle Pleistocene(?) time, the Los Cordovas faults had become inactive, and the linked Embudo–Sangre de Cristo fault system migrated to the south, to the modern range front.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01425.1","usgsCitation":"Grauch, V.J., Bauer, P.W., Drenth, B.J., and Kelson, K.I., 2017, A shifting rift—Geophysical insights into the evolution of Rio Grande rift margins and the Embudo transfer zone near Taos, New Mexico: Geosphere, v. 13, no. 3, p. 870-910, https://doi.org/10.1130/GES01425.1.","productDescription":"41 p.","startPage":"870","endPage":"910","ipdsId":"IP-076788","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":469777,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01425.1","text":"Publisher Index Page"},{"id":342032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Taos","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.71456909179686,\n 36.32950909247666\n ],\n [\n -105.53466796874999,\n 36.32950909247666\n ],\n [\n -105.53466796874999,\n 36.474306755095235\n ],\n [\n -105.71456909179686,\n 36.474306755095235\n ],\n [\n -105.71456909179686,\n 36.32950909247666\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-07","publicationStatus":"PW","scienceBaseUri":"59327922e4b0e9bd0eab54ed","contributors":{"authors":[{"text":"Grauch, V. J. S. 0000-0002-0761-3489 tien@usgs.gov","orcid":"https://orcid.org/0000-0002-0761-3489","contributorId":886,"corporation":false,"usgs":true,"family":"Grauch","given":"V.","email":"tien@usgs.gov","middleInitial":"J. S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":696930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bauer, Paul W.","contributorId":145562,"corporation":false,"usgs":false,"family":"Bauer","given":"Paul","email":"","middleInitial":"W.","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":696931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":696932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelson, Keith I.","contributorId":192585,"corporation":false,"usgs":false,"family":"Kelson","given":"Keith","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":696933,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188152,"text":"70188152 - 2017 - Monitoring the cooling of the 1959 Kīlauea Iki lava lake using surface magnetic measurements","interactions":[],"lastModifiedDate":"2017-06-02T11:00:03","indexId":"70188152","displayToPublicDate":"2017-06-02T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring the cooling of the 1959 Kīlauea Iki lava lake using surface magnetic measurements","docAbstract":"Lava lakes can be considered as proxies for small magma chambers, offering a unique opportunity to investigate magma evolution and solidification. Repeated magnetic ground surveys over more than 50 years each show a large vertical magnetic intensity anomaly associated with Kīlauea Iki Crater, partly filled with a lava lake during the 1959 eruption of Kīlauea Volcano (Island of Hawai’i). The magnetic field values recorded across the Kīlauea Iki crater floor and the cooling lava lake below result from three simple effects: the static remnant magnetization of the rocks forming the steep crater walls, the solidifying lava lake crust, and the hot, but shrinking, paramagnetic non-magnetic lens (>540 °C). We calculate 2D magnetic models to reconstruct the temporal evolution of the geometry of this non-magnetic body, its depth below the surface, and its thickness. Our results are in good agreement with the theoretical increase in thickness of the solidifying crust with time. Using the 2D magnetic models and the theoretical curve for crustal growth over a lava lake, we estimate that the former lava lake will be totally cooled below the Curie temperature in about 20 years. This study shows the potential of magnetic methods for detecting and monitoring magmatic intrusions at various scales.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-017-1119-7","usgsCitation":"Gailler, L., and Kauahikaua, J.P., 2017, Monitoring the cooling of the 1959 Kīlauea Iki lava lake using surface magnetic measurements: Bulletin of Volcanology, v. 79, p. 1-7, https://doi.org/10.1007/s00445-017-1119-7.","productDescription":"Article 40; 7 p.","startPage":"1","endPage":"7","ipdsId":"IP-080299","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":342034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.2613639831543,\n 19.40443049681278\n ],\n [\n -155.23492813110352,\n 19.40443049681278\n ],\n [\n -155.23492813110352,\n 19.421430209692744\n ],\n [\n -155.2613639831543,\n 19.421430209692744\n ],\n [\n -155.2613639831543,\n 19.40443049681278\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"79","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-06","publicationStatus":"PW","scienceBaseUri":"59327922e4b0e9bd0eab54f4","contributors":{"authors":[{"text":"Gailler, Lydie 0000-0002-8132-2428","orcid":"https://orcid.org/0000-0002-8132-2428","contributorId":192584,"corporation":false,"usgs":false,"family":"Gailler","given":"Lydie","email":"","affiliations":[],"preferred":false,"id":696928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":696927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198379,"text":"70198379 - 2017 - Seasonal and spatial variabilities in northern Gulf of Alaska surface water iron concentrations driven by shelf sediment resuspension, glacial meltwater, a Yakutat eddy, and dust","interactions":[],"lastModifiedDate":"2018-08-02T11:57:27","indexId":"70198379","displayToPublicDate":"2017-06-01T11:57:20","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal and spatial variabilities in northern Gulf of Alaska surface water iron concentrations driven by shelf sediment resuspension, glacial meltwater, a Yakutat eddy, and dust","docAbstract":"Phytoplankton growth in the Gulf of Alaska (GoA) is limited by iron (Fe), yet Fe sources are poorly constrained. We examine the temporal and spatial distributions of Fe, and its sources in the GoA, based on data from three cruises carried out in 2010 from the Copper River (AK) mouth to beyond the shelf break. April data are the first to describe late winter Fe behavior before surface water nitrate depletion began. Sediment resuspension during winter and spring storms generated high “total dissolvable Fe” (TDFe) concentrations of ~1000 nmol kg−1 along the entire continental shelf, which decreased beyond the shelf break. In July, high TDFe concentrations were similar on the shelf, but more spatially variable, and driven by low‐salinity glacial meltwater. Conversely, dissolved Fe (DFe) concentrations in surface waters were far lower and more seasonally consistent, ranging from ~4 nmol kg−1 in nearshore waters to ~0.6–1.5 nmol kg−1seaward of the shelf break during April and July, despite dramatic depletion of nitrate over that period. The reasonably constant DFe concentrations are likely maintained during the year across the shelf by complexation by strong organic ligands, coupled with ample supply of labile particulate Fe. The April DFe data can be simulated using a simple numerical model that assumes a DFe flux from shelf sediments, horizontal transport by eddy diffusion, and removal by scavenging. Given how global change is altering many processes impacting the Fe cycle, additional studies are needed to examine controls on DFe in the Gulf of Alaska.
","language":"English","publisher":"Ameican Geophysical Union","doi":"10.1002/2016GB005493","usgsCitation":"Crusius, J., Schroth, A.W., Resing, J.A., Cullen, J., and Campbell, R.W., 2017, Seasonal and spatial variabilities in northern Gulf of Alaska surface water iron concentrations driven by shelf sediment resuspension, glacial meltwater, a Yakutat eddy, and dust: Global Biogeochemical Cycles, v. 31, no. 6, p. 942-960, https://doi.org/10.1002/2016GB005493.","productDescription":"19 p.","startPage":"942","endPage":"960","ipdsId":"IP-078971","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":469779,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gb005493","text":"Publisher Index Page"},{"id":438308,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7222S06","text":"USGS data release","linkHelpText":"Gulf of Alaska Shelf and Slope Iron and Nitrate data, Copper River Region, 2010"},{"id":356112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -147.5,\n 58\n ],\n [\n -143,\n 58\n ],\n [\n -143,\n 60.359564131824236\n ],\n [\n -147.5,\n 60.359564131824236\n ],\n [\n -147.5,\n 58\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-03","publicationStatus":"PW","scienceBaseUri":"5b6fc67ce4b0f5d57878eb84","contributors":{"authors":[{"text":"Crusius, John 0000-0003-2554-0831 jcrusius@usgs.gov","orcid":"https://orcid.org/0000-0003-2554-0831","contributorId":2155,"corporation":false,"usgs":true,"family":"Crusius","given":"John","email":"jcrusius@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":741299,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schroth, Andrew W.","contributorId":192042,"corporation":false,"usgs":false,"family":"Schroth","given":"Andrew","email":"","middleInitial":"W.","affiliations":[{"id":17809,"text":"University of Vermont, Burlington","active":true,"usgs":false}],"preferred":false,"id":741300,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Resing, Joseph A.","contributorId":206619,"corporation":false,"usgs":false,"family":"Resing","given":"Joseph","email":"","middleInitial":"A.","affiliations":[{"id":37351,"text":"University of Washington; Joint Institute for the Study of the Atmosphere and the Ocean","active":true,"usgs":false}],"preferred":false,"id":741301,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cullen, Jay","contributorId":206620,"corporation":false,"usgs":false,"family":"Cullen","given":"Jay","email":"","affiliations":[{"id":37352,"text":"University of Victoria; School of Earth and Ocean Sciences Victoria, B.C.","active":true,"usgs":false}],"preferred":false,"id":741302,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell, Robert W.","contributorId":206621,"corporation":false,"usgs":false,"family":"Campbell","given":"Robert","email":"","middleInitial":"W.","affiliations":[{"id":37353,"text":"Prince William Sound Science Center, Cordova, AK","active":true,"usgs":false}],"preferred":false,"id":741303,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202279,"text":"70202279 - 2017 - Thermal and petrologic constraints on lower crustal melt accumulation under the Salton Sea Geothermal Field","interactions":[],"lastModifiedDate":"2019-02-20T10:52:59","indexId":"70202279","displayToPublicDate":"2017-06-01T10:52:53","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Thermal and petrologic constraints on lower crustal melt accumulation under the Salton Sea Geothermal Field","docAbstract":"In the Salton Sea region of southern California (USA), concurrent magmatism, extension, subsidence, and sedimentation over the past 0.5 to 1.0 Ma have led to the creation of the Salton Sea Geothermal Field (SSGF)—the second largest and hottest geothermal system in the continental United States—and the small-volume rhyolite eruptions that created the Salton Buttes. In this study, we determine the flux of mantle-derived basaltic magma that would be required to produce the elevated average heat flow and sustain the magmatic roots of rhyolite volcanism observed at the surface of the Salton Sea region. We use a 2D thermal model to show that a lower-crustal, partially molten mush containing
We assessed habitat characteristics and measured daily survival rate of 72 nests of Charadrius wilsonia (Wilson's Plover) during 2012 and 2013 on South Island and Sand Island on the central coast of South Carolina. At both study areas, nest sites were located at slightly higher elevations (i.e., small platforms of sand) relative to randomly selected nearby unused sites, and nests at each study area also appeared to be situated to enhance crypsis and/or vigilance. Daily survival rate (DSR) of nests ranged from 0.969 to 0.988 among study sites and years, and the probability of nest survival ranged from 0.405 to 0.764. Flooding and predation were the most common causes of nest failure at both sites. At South Island, DSR was most strongly related to maximum tide height, which suggests that flooding and overwash may be common causes of nest loss for Wilson's Plovers at these study sites. The difference in model results between the 2 nearby study sites may be partially due to more-frequent flooding at Sand Island because of some underlying yet unmeasured physiographic feature. Remaining data gaps for the species include regional assessments of nest and chick survival and habitat requirements during chick rearing.
","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/058.016.0203","usgsCitation":"Zinsser, E., Sanders, F.J., Gerard, P., and Jodice, P.G., 2017, Daily survival rate and habitat characteristics of nests of Wilson's Plover: Southeastern Naturalist, v. 16, no. 2, p. 149-156, https://doi.org/10.1656/058.016.0203.","productDescription":"8 p.","startPage":"149","endPage":"156","ipdsId":"IP-073336","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","otherGeospatial":"Sand Island, South Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.2172622680664,\n 33.16658236914082\n ],\n [\n -79.15340423583984,\n 33.16658236914082\n ],\n [\n -79.15340423583984,\n 33.224903086263964\n ],\n [\n -79.2172622680664,\n 33.224903086263964\n ],\n [\n -79.2172622680664,\n 33.16658236914082\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-08","publicationStatus":"PW","scienceBaseUri":"5a07e8d2e4b09af898c8cbb5","contributors":{"authors":[{"text":"Zinsser, Elizabeth","contributorId":14315,"corporation":false,"usgs":false,"family":"Zinsser","given":"Elizabeth","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":720504,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanders, Felicia J.","contributorId":56574,"corporation":false,"usgs":false,"family":"Sanders","given":"Felicia","email":"","middleInitial":"J.","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":720550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gerard, Patrick D.","contributorId":140181,"corporation":false,"usgs":false,"family":"Gerard","given":"Patrick D.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":720551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X pjodice@usgs.gov","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":1119,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","email":"pjodice@usgs.gov","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":720552,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198079,"text":"70198079 - 2017 - The morphology of transverse aeolian ridges on Mars","interactions":[],"lastModifiedDate":"2018-07-13T10:08:52","indexId":"70198079","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":666,"text":"Aeolian Research","active":true,"publicationSubtype":{"id":10}},"title":"The morphology of transverse aeolian ridges on Mars","docAbstract":"A preliminary survey of publicly released high resolution digital terrain models (DTMs) produced by the High Resolution Imaging Science Experiment (HiRISE) camera on Mars Reconnaissance Orbiter identified transverse aeolian ridges (TARs) in 154 DTMs in latitudes from 50°S to 40°N. Consistent with previous surveys, the TARs identified in HiRISE DTMs are found at all elevations, irrespective of the regional thermal inertia of the surface. Ten DTMs were selected for measuring the characteristics of the TARs, including maximum height, mean height, mean spacing (wavelength), and the slope of the surface where they are located. We confined our measurements to features that were taller than 1 m and spaced more than 10 m apart.\n\nWe found a surprisingly wide variability of TAR sizes within each local region (typically 5 km by 25 km), with up to a factor of 7 difference in TAR wavelengths in a single DTM. The TAR wavelengths do not appear to be correlated to latitude or elevation, but the largest TARs in our small survey were found at lower elevations. The tallest TARs we measured were on the flat floor of Moni crater, within Kaiser crater in the southern highlands. These TARs are up to 14 m tall, with a typical wavelength of 120 m. TAR heights are weakly correlated with their wavelengths. The height-to-wavelength ratios for most TARs are far less than 1/2π (the maximum predicted for antidunes), however in two cases the ratio is close to 1/2π, and in one case (in the bend of a channel) the ratio exceeds 1/2π. TAR wavelengths are uncorrelated with surface slope, both on local and regional scales. TAR heights are weakly anti-correlated with local slope.\n\nThese results help constrain models of TAR formation, particularly a new hypothesis (Geissler, 2014) that suggests that TARs were formed from micron-sized dust that was transported in suspension. The lack of correlation between TAR wavelength and surface slope seems to rule out formation by gravity-driven dust flows such as avalanches or density currents, and suggests that the TARs were instead produced by the Martian winds.","language":"English","publisher":"Elsevier","doi":"10.1016/j.aeolia.2016.08.008","usgsCitation":"Geissler, P., and Wilgus, J., 2017, The morphology of transverse aeolian ridges on Mars: Aeolian Research, v. 26, p. 63-71, https://doi.org/10.1016/j.aeolia.2016.08.008.","productDescription":"9 p.","startPage":"63","endPage":"71","ipdsId":"IP-073238","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":355665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc67ee4b0f5d57878eb86","contributors":{"authors":[{"text":"Geissler, Paul","contributorId":206262,"corporation":false,"usgs":true,"family":"Geissler","given":"Paul","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":739923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilgus, Justin T.","contributorId":206263,"corporation":false,"usgs":false,"family":"Wilgus","given":"Justin T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":739924,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193291,"text":"70193291 - 2017 - Uncertainties in forecasting the response of polar bears to global climate change","interactions":[],"lastModifiedDate":"2021-04-26T15:04:42.409319","indexId":"70193291","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Uncertainties in forecasting the response of polar bears to global climate change","docAbstract":"Several sources of uncertainty affect how precisely the future status of polar bears (Ursus maritimus) can be forecasted. Foremost are unknowns about the future levels of global greenhouse gas emissions, which could range from an unabated increase to an aggressively mitigated reduction. Uncertainties also arise because different climate models project different amounts and rates of future warming (and sea ice loss)—even for the same emission scenario. There are also uncertainties about how global warming could affect the Arctic Ocean’s food web, so even if climate models project the presence of sea ice in the future, the availability of polar bear prey is not guaranteed. Under a worst-case emission scenario in which rates of greenhouse gas emissions continue to rise unabated to century’s end, the uncertainties about polar bear status center on a potential for extinction. If the species were to persist, it would likely be restricted to a high-latitude refugium in northern Canada and Greenland—assuming a food web also existed with enough accessible prey to fuel weight gains for surviving onshore during the most extreme years of summer ice melt. On the other hand, if emissions were to be aggressively mitigated at the levels proposed in the Paris Climate Agreement, healthy polar bear populations would probably continue to occupy all but the most southern areas of their contemporary summer range. While polar bears have survived previous warming phases—which indicate some resiliency to the loss of sea ice habitat—what is certain is that the present pace of warming is unprecedented and will increasingly expose polar bears to historically novel stressors.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Marine animal welfare","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-319-46994-2_25","usgsCitation":"Douglas, D.C., and Atwood, T.C., 2017, Uncertainties in forecasting the response of polar bears to global climate change, chap. of Marine animal welfare, p. 463-473, https://doi.org/10.1007/978-3-319-46994-2_25.","productDescription":"11 p.","startPage":"463","endPage":"473","ipdsId":"IP-076001","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":349594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-20","publicationStatus":"PW","scienceBaseUri":"5a60fbbde4b06e28e9c23530","contributors":{"editors":[{"text":"Butterworth, Andy","contributorId":45100,"corporation":false,"usgs":false,"family":"Butterworth","given":"Andy","email":"","affiliations":[],"preferred":false,"id":724155,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":718566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":718567,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194492,"text":"70194492 - 2017 - Constraining the thermal history of the North American Midcontinent Rift System using carbonate clumped isotopes and organic thermal maturity indices","interactions":[],"lastModifiedDate":"2017-11-30T11:34:28","indexId":"70194492","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"Constraining the thermal history of the North American Midcontinent Rift System using carbonate clumped isotopes and organic thermal maturity indices","docAbstract":"The Midcontinent Rift System (MRS) is a Late Mesoproterozoic (∼1.1 Ga) sequence of volcanic and sedimentary rocks exposed in the Lake Superior Region of North America. The MRS continues to be the focus of much research due to its economic mineral deposits as well as its archive of Precambrian life and tectonic processes. In order to constrain the post-depositional thermal history of the MRS, samples were analyzed for carbonate clumped isotope composition and organic thermal maturity. Clumped isotope values from sedimentary/early-diagenetic samples were partially reset during burial to temperatures between 68 and 75 °C. Solid-state reordering models indicate that maximum burial temperatures of 125–155 °C would reset the clumped isotope values to the observed temperature range prior to the onset of regional cooling and uplift. Clumped isotope results from late-stage veins in the White Pine Mine encompass a greater temperature range (49–116 °C), indicative of spatially variable hydrothermal activity and vein emplacement after burial temperatures fell below 100 °C during regional cooling and uplift. Clumped isotope and organic thermal maturity data do not indicate significant spatial differences in thermal history along the MRS. Observed variability in bulk organic matter composition and biomarker indices are therefore more likely a result of shifts in primary productivity or early-degradation processes. These results demonstrate that the MRS experienced a spatially consistent, relatively mild thermal history (125–155 °C) and is therefore a valuable archive for understanding the Late Mesoproterozoic environment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2017.03.022","usgsCitation":"Gallagher, T.M., Sheldon, N.D., Mauk, J.L., Petersen, S.V., Gueneli, N., and Brocks, J.J., 2017, Constraining the thermal history of the North American Midcontinent Rift System using carbonate clumped isotopes and organic thermal maturity indices: Precambrian Research, v. 294, p. 53-66, https://doi.org/10.1016/j.precamres.2017.03.022.","productDescription":"14 p.","startPage":"53","endPage":"66","ipdsId":"IP-080782","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":469803,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.precamres.2017.03.022","text":"Publisher Index Page"},{"id":349586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.4224853515625,\n 46.33175800051563\n ],\n [\n -87.462158203125,\n 46.33175800051563\n ],\n [\n -87.462158203125,\n 48.23565029755308\n ],\n [\n -92.4224853515625,\n 48.23565029755308\n ],\n [\n -92.4224853515625,\n 46.33175800051563\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"294","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fbbce4b06e28e9c23520","contributors":{"authors":[{"text":"Gallagher, Timothy M.","contributorId":201012,"corporation":false,"usgs":false,"family":"Gallagher","given":"Timothy","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":724101,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheldon, Nathan D.","contributorId":201013,"corporation":false,"usgs":false,"family":"Sheldon","given":"Nathan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":724102,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mauk, Jeffrey L. 0000-0002-6244-2774 jmauk@usgs.gov","orcid":"https://orcid.org/0000-0002-6244-2774","contributorId":4101,"corporation":false,"usgs":true,"family":"Mauk","given":"Jeffrey","email":"jmauk@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":724100,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Petersen, Sierra V.","contributorId":201014,"corporation":false,"usgs":false,"family":"Petersen","given":"Sierra","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":724103,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gueneli, Nur","contributorId":201015,"corporation":false,"usgs":false,"family":"Gueneli","given":"Nur","email":"","affiliations":[],"preferred":false,"id":724104,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brocks, Jochen J.","contributorId":201016,"corporation":false,"usgs":false,"family":"Brocks","given":"Jochen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":724105,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196784,"text":"70196784 - 2017 - Forecasted range shifts of arid-land fishes in response to climate change","interactions":[],"lastModifiedDate":"2021-06-04T15:37:31.242636","indexId":"70196784","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Forecasted range shifts of arid-land fishes in response to climate change","docAbstract":"Climate change is poised to alter the distributional limits, center, and size of many species. Traits may influence different aspects of range shifts, with trophic generality facilitating shifts at the leading edge, and greater thermal tolerance limiting contractions at the trailing edge. The generality of relationships between traits and range shifts remains ambiguous however, especially for imperiled fishes residing in xeric riverscapes. Our objectives were to quantify contemporary fish distributions in the Lower Colorado River Basin, forecast climate change by 2085 using two general circulation models, and quantify shifts in the limits, center, and size of fish elevational ranges according to fish traits. We examined relationships among traits and range shift metrics either singly using univariate linear modeling or combined with multivariate redundancy analysis. We found that trophic and dispersal traits were associated with shifts at the leading and trailing edges, respectively, although projected range shifts were largely unexplained by traits. As expected, piscivores and omnivores with broader diets shifted upslope most at the leading edge while more specialized invertivores exhibited minimal changes. Fishes that were more mobile shifted upslope most at the trailing edge, defying predictions. No traits explained changes in range center or size. Finally, current preference explained multivariate range shifts, as fishes with faster current preferences exhibited smaller multivariate changes. Although range shifts were largely unexplained by traits, more specialized invertivorous fishes with lower dispersal propensity or greater current preference may require the greatest conservation efforts because of their limited capacity to shift ranges under climate change.
","language":"English","publisher":"Springer","doi":"10.1007/s11160-017-9479-9","usgsCitation":"Whitney, J.E., Whittier, J.B., Paukert, C.P., Olden, J., and Strecker, A.L., 2017, Forecasted range shifts of arid-land fishes in response to climate change: Reviews in Fish Biology and Fisheries, v. 27, no. 2, p. 463-479, https://doi.org/10.1007/s11160-017-9479-9.","productDescription":"17 p.","startPage":"463","endPage":"479","ipdsId":"IP-076776","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":353871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-09","publicationStatus":"PW","scienceBaseUri":"5afee86ce4b0da30c1bfc447","contributors":{"authors":[{"text":"Whitney, James E.","contributorId":176500,"corporation":false,"usgs":false,"family":"Whitney","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":734386,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whittier, Joanna B.","contributorId":53151,"corporation":false,"usgs":false,"family":"Whittier","given":"Joanna","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":734387,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paukert, Craig P. 0000-0002-9369-8545 cpaukert@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":147821,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","email":"cpaukert@usgs.gov","middleInitial":"P.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734381,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olden, Julian D.","contributorId":66951,"corporation":false,"usgs":true,"family":"Olden","given":"Julian D.","affiliations":[],"preferred":false,"id":734388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strecker, Angela L.","contributorId":43256,"corporation":false,"usgs":true,"family":"Strecker","given":"Angela","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":734389,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197052,"text":"70197052 - 2017 - A new mechanistic approach for the further development of a population with established size bimodality","interactions":[],"lastModifiedDate":"2018-05-15T15:46:10","indexId":"70197052","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","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":"A new mechanistic approach for the further development of a population with established size bimodality","docAbstract":"Usually, the origin of a within-cohort bimodal size distribution is assumed to be caused by initial size differences or by one discrete period of accelerated growth for one part of the population. The aim of this study was to determine if more continuous pathways exist allowing shifts from the small to the large fraction within a bimodal age-cohort. Therefore, a Eurasian perch population, which had already developed a bimodal size-distribution and had differential resource use of the two size-cohorts, was examined. Results revealed that formation of a bimodal size-distribution can be a continuous process. Perch from the small size-cohort were able to grow into the large size-cohort by feeding on macroinvertebrates not used by their conspecifics. The diet shifts were accompanied by morphological shape changes. Intra-specific competition seemed to trigger the development towards an increasing number of large individuals. A stage-structured matrix model confirmed these assumptions. The fact that bimodality can be a continuous process is important to consider for the understanding of ecological processes and links within ecosystems.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0179339","usgsCitation":"Heerman, L., DeAngelis, D.L., and Borcherding, J., 2017, A new mechanistic approach for the further development of a population with established size bimodality: PLoS ONE, v. 12, no. 6, p. 1-18, https://doi.org/10.1371/journal.pone.0179339.","productDescription":"e0179339; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-075347","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469787,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0179339","text":"Publisher Index Page"},{"id":354185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"6","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-26","publicationStatus":"PW","scienceBaseUri":"5afee86ce4b0da30c1bfc443","contributors":{"authors":[{"text":"Heerman, Lisa","contributorId":204891,"corporation":false,"usgs":false,"family":"Heerman","given":"Lisa","email":"","affiliations":[{"id":37006,"text":"Institute for Zoology of the University of Cologne, Germany","active":true,"usgs":false}],"preferred":false,"id":735377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":735376,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borcherding, Jost","contributorId":204892,"corporation":false,"usgs":false,"family":"Borcherding","given":"Jost","email":"","affiliations":[{"id":37006,"text":"Institute for Zoology of the University of Cologne, Germany","active":true,"usgs":false}],"preferred":false,"id":735378,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192964,"text":"70192964 - 2017 - Temporal genetic population structure and interannual variation in migration behavior of Pacific Lamprey Entosphenus tridentatus","interactions":[],"lastModifiedDate":"2017-11-07T12:32:37","indexId":"70192964","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Temporal genetic population structure and interannual variation in migration behavior of Pacific Lamprey Entosphenus tridentatus","title":"Temporal genetic population structure and interannual variation in migration behavior of Pacific Lamprey Entosphenus tridentatus","docAbstract":"Studies using neutral loci suggest that Pacific lamprey, Entosphenus tridentatus, lack strong spatial genetic population structure. However, it is unknown whether temporal genetic population structure exists. We tested whether adult Pacific lamprey: (1) show temporal genetic population structure; and (2) migrate different distances between years. We non-lethally sampled lamprey for DNA in 2009 and 2010 and used eight microsatellite loci to test for genetic population structure. We used telemetry to record the migration behaviors of these fish. Lamprey were assignable to three moderately differentiated genetic clusters (FST = 0.16–0.24 for all pairwise comparisons): one cluster was composed of individuals from 2009, and the other two contained individuals from 2010. The FST value between years was 0.13 and between genetic clusters within 2010 was 0.20. A total of 372 (72.5%) fish were detected multiple times during their migrations. Most fish (69.9%) remained in the mainstem Willamette River; the remaining 30.1% migrated into tributaries. Eighty-two lamprey exhibited multiple back-and-forth movements among tributaries and the mainstem, which may indicate searching behaviors. All migration distances were significantly greater in 2010, when the amplitude of river discharge was greater. Our data suggest genetic structuring between and within years that may reflect different cohorts.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-017-3096-4","usgsCitation":"Clemens, B.J., Wyss, L.A., McCoun, R., Courter, I., Schwabe, L., Peery, C., Schreck, C.B., Spice, E.K., and Docker, M.F., 2017, Temporal genetic population structure and interannual variation in migration behavior of Pacific Lamprey Entosphenus tridentatus: Hydrobiologia, v. 794, no. 1, p. 223-240, https://doi.org/10.1007/s10750-017-3096-4.","productDescription":"18 p.","startPage":"223","endPage":"240","ipdsId":"IP-085011","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":348375,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.4918212890625,\n 43.64800079902171\n ],\n [\n -121.78344726562499,\n 43.64800079902171\n ],\n [\n -121.78344726562499,\n 45.706179285330855\n ],\n [\n -123.4918212890625,\n 45.706179285330855\n ],\n [\n -123.4918212890625,\n 43.64800079902171\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"794","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-27","publicationStatus":"PW","scienceBaseUri":"5a07e8dee4b09af898c8cbc5","contributors":{"authors":[{"text":"Clemens, Benjamin J.","contributorId":195098,"corporation":false,"usgs":false,"family":"Clemens","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":720919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wyss, Lance A.","contributorId":195114,"corporation":false,"usgs":false,"family":"Wyss","given":"Lance","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":720920,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCoun, Rebecca","contributorId":200082,"corporation":false,"usgs":false,"family":"McCoun","given":"Rebecca","email":"","affiliations":[],"preferred":false,"id":720921,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Courter, Ian","contributorId":173188,"corporation":false,"usgs":false,"family":"Courter","given":"Ian","affiliations":[{"id":27180,"text":"Mount Hood Environmental","active":true,"usgs":false}],"preferred":false,"id":720922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schwabe, Lawrence","contributorId":200083,"corporation":false,"usgs":false,"family":"Schwabe","given":"Lawrence","email":"","affiliations":[],"preferred":false,"id":720923,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peery, Christopher","contributorId":200084,"corporation":false,"usgs":false,"family":"Peery","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":720924,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schreck, Carl B. 0000-0001-8347-1139 carl.schreck@usgs.gov","orcid":"https://orcid.org/0000-0001-8347-1139","contributorId":878,"corporation":false,"usgs":true,"family":"Schreck","given":"Carl","email":"carl.schreck@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":717453,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Spice, Erin K.","contributorId":200085,"corporation":false,"usgs":false,"family":"Spice","given":"Erin","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":720925,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Docker, Margaret F.","contributorId":195099,"corporation":false,"usgs":false,"family":"Docker","given":"Margaret","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":720926,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70191864,"text":"70191864 - 2017 - Re-Os systematics and geochemistry of cobaltite (CoAsS) in the Idaho cobalt belt, Belt-Purcell Basin, USA: Evidence for middle Mesoproterozoic sediment-hosted Co-Cu sulfide mineralization with Grenvillian and Cretaceous remobilization","interactions":[],"lastModifiedDate":"2017-10-18T14:54:12","indexId":"70191864","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Re-Os systematics and geochemistry of cobaltite (CoAsS) in the Idaho cobalt belt, Belt-Purcell Basin, USA: Evidence for middle Mesoproterozoic sediment-hosted Co-Cu sulfide mineralization with Grenvillian and Cretaceous remobilization","docAbstract":"We report the first study of the Re-Os systematics of cobaltite (CoAsS) using disseminated grains and massive sulfides from samples of two breccia-type and two stratabound deposits in the Co-Cu-Au Idaho cobalt belt (ICB), Lemhi subbasin to the Belt-Purcell Basin, Idaho, USA. Using a 185Re + 190Os spike solution, magnetic and non-magnetic fractions of cobaltite mineral separates give reproducible Re-Os analytical data for aliquot sizes of 150 to 200 mg. Cobaltite from the ICB has highly radiogenic 187Os/188Os ratios (17–45) and high 187Re/188Os ratios (600–1800) but low Re and total Os contents (ca. 0.4–4 ppb and 14–64 ppt, respectively). Containing 30 to 74% radiogenic 187Os, cobaltite from the ICB is amenable to Re-Os age determination using the isochron regression approach.
Re-Os data for disseminated cobaltite mineralization in a quartz-tourmaline breccia from the Haynes-Stellite deposit yield a Model 1 isochron age of 1349 ± 76 Ma (2σ, n = 4, mean squared weighted deviation MSWD = 2.1, initial 187Os/188Os ratio = 4.7 ± 2.2). This middle Mesoproterozoic age is preserved despite a possible metamorphic overprint or a pulse of metamorphic-hydrothermal remobilization of pre-existing cobaltite that formed along fold cleavages during the ca. 1190–1006 Ma Grenvillian orogeny. This phase of remobilization is tentatively identified by a Model 3 isochron age of 1132 ± 240 Ma (2σ, n = 7, MSWD = 9.3, initial 187Os/188Os ratio of 9.0 ± 2.9) for cobaltite in the quartz-tourmaline breccia from the Idaho zone in the Blackbird mine.
All Mesoproterozoic cobaltite mineralization in the district was affected by greenschist- to lower amphibolite-facies (garnet zone) metamorphism during the Late Jurassic to Late Cretaceous Cordilleran orogeny. However, the fine- to coarse-grained massive cobaltite mineralization from the shear zone-hosted Chicago zone, Blackbird mine, is the only studied deposit that has severely disturbed Re-Os systematics with evidence for a linear trend of mixing with (metamorphic?) fluids.
The new Re-Os ages and extremely high initial 187Os/188Os ratios of cobaltite reported here favor a magmatic-hydrothermal genetic model for a multi-stage REE-Y-Co-Cu-Au mineralization occurring at ca. 1370 to 1349 Ma, and related to the emplacement of the Big Deer Creek granite pluton at ca. 1377 Ma. In our model, deposition of paragenetically early xenotime and gadolinite was followed by an influx of Mesoproterozoic evaporitic brines and magmatic-hydrothermal fluids containing metals and reduced sulfur derived from mafic and oceanic island-arc Archean to Paleoproterozoic rocks in the Laurentian basement. Cobaltite mineralization occurred upon cooling of these fluids at an inferred temperature of 300 °C or below.
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