The U.S. Geological Survey (USGS) has operated a water-quality monitoring network in San Francisco Bay since the late 1980s (Buchanan and others, 2015). This network includes 19 stations in the bay; currently, 8 stations are in operation (fig. 1). All eight stations are equipped with specific conductance (which can be related to salinity) and water-temperature sensors that record measurements at 15-minute intervals. Water quality in the bay constantly changes with the ocean tides and with seasonal and interannual differences in river inflows. Our network was designed to observe and characterize some of these changes in the bay across space and over time. Our data demonstrated a high degree of variability both in specific conductance and temperature at time scales from tidal to annual and also revealed longer term changes that are likely to influence overall environmental health in the bay (San Francisco Estuary Institute, 2014). Figure 1. Locations of fixed water-quality monitoring stations in San Francisco Bay, California, for the 2014 water year (October 1, 2013 to September 30, 2014).
\nIn water year (WY) 2014 (October 1, 2013, through September 30, 2014), our network measured record-high values of specific conductance and water temperature at several stations during a period of very little freshwater inflow from the Sacramento–San Joaquin Delta and other tributaries because of severe drought conditions in California. This report summarizes our observations for WY2014 and compares them to previous years that had different levels of freshwater inflow.
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The Water Availability Tool for Environmental Resources (WATER) is a decision support system (DSS) for the nontidal part of the Delaware River Basin (DRB) that provides a consistent and objective method of simulating streamflow under historical, forecasted, and managed conditions. WATER integrates geospatial sampling of landscape characteristics, including topographic and soil properties, with a regionally calibrated hillslope-hydrology model, an impervious-surface model, and hydroclimatic models that have been parameterized using three hydrologic response units—forested, agricultural, and developed land cover. It is this integration that enables the regional hydrologic-modeling approach used in WATER without requiring site-specific optimization or those stationary conditions inferred when using a statistical model. The DSS provides a “historical” database, ideal for simulating streamflow for 2001–11, in addition to land-cover forecasts that focus on 2030 and 2060. The WATER Application Utilities are provided with the DSS and apply change factors for precipitation, temperature, and potential evapotranspiration to a 1981–2011 climatic record provided with the DSS. These change factors were derived from a suite of general circulation models (GCMs) and representative concentration pathway (RCP) emission scenarios. These change factors are based on 25-year monthly averages (normals) that are centere on 2030 and 2060. The WATER Application Utilities also can be used to apply a 2010 snapshot of water use for the DRB; a factorial approach enables scenario testing of increased or decreased water use for each simulation. Finally, the WATER Application Utilities can be used to reformat streamflow time series for input to statistical or reservoir management software.
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1770 Corporate Drive, Ste. 500
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678-524-1544
http://water.usgs.gov/watercensus/
Mineral extraction and associated industries play an important role in the Afghan economy, particularly in the “transitional era” of declining foreign aid and withdrawal of foreign troops post 2014. In addition to providing a substantial source of government revenue, other potential benefits of natural resource development include boosted exports, employment opportunities, and strengthened industrialization (Joya, 2012). Continued exploration and investment in these industries has resulted in large economic improvements since 2007, when this series of studies was initiated. At that time, the “Preliminary Non-Fuel Mineral Resource Assessment of Afghanistan” was completed by members of the U.S. Geological Survey and Afghanistan Geological Survey (Peters and others, 2007). The assessment published a series of country-wide datasets, including a digital elevation model (DEM), elevation contours, hydrography, transportation routes, geophysics, and cultural datasets (Peters and others, 2007). It also delineated 20 mineralized areas for further study using a geologic-based methodology. A second data product, “Summaries of Important Areas for Mineral Investment and Production Opportunities of Nonfuel Minerals in Afghanistan,” was released by Peters and others in 2011. This work highlighted geologic, geohydrologic, and hyperspectral studies that were carried out in specific Areas of Interest (AOIs) to assess the location and characteristics of mineral resources. Also included in the 2011 publication is a collection of appendixes and inventories of Geographic Information System (GIS) datasets for each of the 24 identified AOIs. A third data product was released in 2013 (Casey and Chirico, 2013), publishing datasets for five different AOIs, two subareas, and one AOI extension. Each dataset contains vector shapefiles of the AOI boundary, streams, roads, and contours at 25-, 50-, and 100-meter (m) intervals, as well as raster files of the AOI’s DEM and hillshade.
\nThis work represents the fourth installment of the series, and publishes a dataset of eight new AOIs and one subarea within Afghanistan. These areas include Dasht-e-Nawar, Farah, North Ghazni, South Ghazni, Chakhansur, Godzareh East, Godzareh West, and Namaksar-e-Herat AOIs and the Central Bamyan subarea of the South Bamyan AOI (datasets for South Bamyan were published previously in Casey and Chirico, 2013). For each AOI and subarea, this dataset collection consists of the areal extent boundaries, elevation contours at 25-, 50-, and 100-m intervals, and an enhanced DEM. Hydrographic datasets covering the extent of four AOIs and one subarea are also included in the collection. The resulting raster and vector layers are intended for use by government agencies, developmental organizations, and private companies in Afghanistan to support mineral assessments, monitoring, management, and investment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151181","collaboration":"Prepared in cooperation with the Afghanistan Geological Survey under the auspices of the U.S. Department of Defense Task Force for Business and Stability Operations","usgsCitation":"DeWitt, J.D., Chirico, P.G., and Malpeli, K.C., 2015, Topographic and hydrographic GIS datasets for the Afghanistan Geological Survey and U.S. Geological Survey 2014 mineral areas of interest: U.S. Geological Survey Open-File Report 2015−1181, 27 p., https://dx.doi.org/10.3133/ofr20151181.","productDescription":"Report: iii, 23 p.; Metadata","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-058768","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":311065,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1181/metadata","text":"Metadata","size":"429 MB","description":"OFR 2015-1181"},{"id":311064,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1181/ofr20151181.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1181"},{"id":311063,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1181/coverthb.jpg"}],"country":"Afghanistan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[61.21082,35.65007],[62.23065,35.27066],[62.98466,35.40404],[63.19354,35.85717],[63.9829,36.00796],[64.54648,36.31207],[64.74611,37.11182],[65.58895,37.30522],[65.74563,37.66116],[66.21738,37.39379],[66.51861,37.36278],[67.07578,37.35614],[67.83,37.14499],[68.13556,37.02312],[68.85945,37.34434],[69.19627,37.15114],[69.51879,37.609],[70.11658,37.58822],[70.27057,37.73516],[70.3763,38.1384],[70.80682,38.48628],[71.34813,38.25891],[71.2394,37.95327],[71.54192,37.90577],[71.44869,37.06564],[71.84464,36.73817],[72.19304,36.94829],[72.63689,37.04756],[73.26006,37.49526],[73.9487,37.42157],[74.98,37.41999],[75.15803,37.13303],[74.57589,37.02084],[74.06755,36.83618],[72.92002,36.72001],[71.84629,36.50994],[71.26235,36.07439],[71.49877,35.65056],[71.61308,35.1532],[71.11502,34.73313],[71.15677,34.34891],[70.8818,33.98886],[69.93054,34.02012],[70.32359,33.35853],[69.68715,33.1055],[69.26252,32.50194],[69.31776,31.90141],[68.92668,31.62019],[68.55693,31.71331],[67.79269,31.58293],[67.68339,31.30315],[66.93889,31.30491],[66.38146,30.7389],[66.34647,29.88794],[65.04686,29.47218],[64.35042,29.56003],[64.148,29.34082],[63.55026,29.46833],[62.54986,29.31857],[60.87425,29.82924],[61.78122,30.73585],[61.69931,31.37951],[60.94194,31.54807],[60.86365,32.18292],[60.53608,32.98127],[60.9637,33.52883],[60.52843,33.67645],[60.80319,34.4041],[61.21082,35.65007]]]},\"properties\":{\"name\":\"Afghanistan\"}}]}","contact":"Eastern Geology and Paleoclimate Science Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://geology.er.usgs.gov/egpsc/
Or
Jessica D. DeWitt
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Birds can cause extensive crop damage in the United States. In some regions, depredating species comprise a substantial portion of the total avian population, emphasizing their importance both economically and ecologically. We used the National Audubon Society Christmas Bird Count data from the south-central United States and mixed-effects models to identify habitat factors associated with population trend and abundance for 5 species: red-winged blackbird (Agelaius phoeniceus), common grackle (Quiscalus quiscula), rusty blackbird (Euphagus carolinus), Brewer’s blackbird (Euphagus cyanocephalus), and European starling (Sturnus vulgaris). Overall, we found positive associations between bird abundance and agricultural land-cover for all species. Relationships between abundance and other land-cover types were species-specific, often with contrasting relationships among species. Likewise, we found no consistent patterns among abundance and climate. Of the 5 species, only red-winged blackbirds had a significant population trend in our study area, increasing annually by 2.4%. There was marginal evidence to suggest population increases for rusty blackbirds, whereas all other species showed no trend in population size within our study area. Our study provides managers who are interested in limiting crop damage in the south-central United States with novel information on habitat associations in the region that could be used to improve management and control actions.
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Berryman Institute","publisherLocation":"Logan, UT","doi":"10.26077/mej7-v607","usgsCitation":"Strassburg, M., Crimmins, S.M., McKann, P.C., and Thogmartin, W.E., 2015, Winter habitat associations of blackbirds and starlings wintering in the south-central United States: Human-Wildlife Interactions, v. 9, no. 2, p. 171-179, https://doi.org/10.26077/mej7-v607.","productDescription":"9 p.","startPage":"171","endPage":"179","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053796","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":311495,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Kansas, Louisiana, Mississippi, Missouri, Oklahoma, Tennessee, Texas","otherGeospatial":"Mississippi Flyway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.0849609375,\n 40.04443758460859\n ],\n [\n -95.2294921875,\n 40.111688665595956\n ],\n [\n -95.80078125,\n 40.613952441166596\n ],\n [\n -91.23046875,\n 40.613952441166596\n ],\n [\n -90.5712890625,\n 39.33429742980725\n ],\n [\n -89.4287109375,\n 37.50972584293751\n ],\n [\n -88.9013671875,\n 36.5978891330702\n ],\n [\n -81.5185546875,\n 36.77409249464195\n ],\n [\n -82.353515625,\n 35.24561909420681\n ],\n [\n -83.4521484375,\n 34.813803317113155\n ],\n [\n -85.60546875,\n 34.88593094075317\n ],\n [\n -85.0341796875,\n 32.509761735919426\n ],\n [\n -84.9462890625,\n 30.86451022625836\n ],\n [\n -87.62695312499999,\n 30.90222470517144\n ],\n [\n -87.3193359375,\n 30.29701788337205\n ],\n [\n -88.1982421875,\n 30.031055426540206\n ],\n [\n -89.07714843749999,\n 29.99300228455108\n ],\n [\n -88.72558593749999,\n 29.726222319395504\n ],\n [\n -88.59374999999999,\n 29.267232865200878\n ],\n [\n -89.82421875,\n 29.036960648558267\n ],\n [\n -91.4501953125,\n 29.036960648558267\n ],\n [\n -92.4169921875,\n 29.38217507514529\n ],\n [\n -93.8232421875,\n 29.34387539941801\n ],\n [\n -95.1416015625,\n 29.036960648558267\n ],\n [\n -96.328125,\n 28.22697003891834\n ],\n [\n -96.8994140625,\n 27.72243591897343\n ],\n [\n -97.0751953125,\n 26.980828590472107\n ],\n [\n -97.119140625,\n 26.352497858154\n ],\n [\n -97.998046875,\n 26.70635985763354\n ],\n [\n -99.49218749999999,\n 28.34306490482549\n ],\n [\n -101.0302734375,\n 31.80289258670676\n ],\n [\n -101.953125,\n 35.42486791930556\n ],\n [\n -102.9638671875,\n 36.949891786813296\n ],\n [\n -102.12890625,\n 37.125286284966776\n ],\n [\n -102.0849609375,\n 40.04443758460859\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564da137e4b0112df6c62dd7","contributors":{"authors":[{"text":"Strassburg, Matthew","contributorId":139647,"corporation":false,"usgs":false,"family":"Strassburg","given":"Matthew","email":"","affiliations":[{"id":12813,"text":"Department of Biological Sciences, North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":542195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crimmins, Shawn M. 0000-0001-6229-5543 scrimmins@usgs.gov","orcid":"https://orcid.org/0000-0001-6229-5543","contributorId":5498,"corporation":false,"usgs":true,"family":"Crimmins","given":"Shawn","email":"scrimmins@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":542193,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKann, Patrick C.","contributorId":149776,"corporation":false,"usgs":false,"family":"McKann","given":"Patrick","email":"","middleInitial":"C.","affiliations":[{"id":6733,"text":"former UMESC employee, USGS","active":true,"usgs":false}],"preferred":false,"id":542196,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":542197,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70142276,"text":"sir20105090V - 2015 - Porphyry copper assessment of the Tethys region of western and southern Asia: Chapter V in Global mineral resource assessment","interactions":[{"subject":{"id":70142276,"text":"sir20105090V - 2015 - Porphyry copper assessment of the Tethys region of western and southern Asia: Chapter V in Global mineral resource assessment","indexId":"sir20105090V","publicationYear":"2015","noYear":false,"chapter":"V","title":"Porphyry copper assessment of the Tethys region of western and southern Asia: Chapter V in Global mineral resource assessment"},"predicate":"IS_PART_OF","object":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"id":1}],"isPartOf":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"lastModifiedDate":"2020-08-18T22:18:50.767782","indexId":"sir20105090V","displayToPublicDate":"2015-11-18T08:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5090","chapter":"V","title":"Porphyry copper assessment of the Tethys region of western and southern Asia: Chapter V in Global mineral resource assessment","docAbstract":"A probabilistic mineral resource assessment of undiscovered resources in porphyry copper deposits in the Tethys region of western and southern Asia was carried out as part of a global mineral resource assessment led by the U.S. Geological Survey (USGS). The purpose of the study was to delineate geographic areas as permissive tracts for the occurrence of porphyry copper deposits at a scale of 1:1,000,000 and to provide probabilistic estimates of amounts of copper likely to be contained in undiscovered porphyry copper deposits in those tracts. The team did the assessment using the USGS three-part form of mineral resource assessment, which is based on (1) mineral deposit and grade-tonnage models constructed from known deposits as analogs for undiscovered deposits, (2) delineation of permissive tracts based on geoscientific information, and (3) estimation of numbers of undiscovered deposits.
\nThe assessment area includes the Asian part of Turkey and Georgia, Armenia, Azerbaijan, Iran, western Pakistan, and southwestern Afghanistan. Selected tracts also extend marginally into southwesternmost Russia and northeasternmost Iraq. This region is located in the central part of the larger Tethyan Eurasian Metallogenic Belt, which extends from western Europe to eastern Asia. Mining in this part of the Tethyan Eurasian Metallogenic Belt has occurred for thousands of years; in 2011 the region produced 420,000 metric tons (t) of copper (2.6 percent of global production), 8,300 t of molybdenum (3 percent), and 29,600 kilograms of gold (1 percent).
\nThe assessment team defined 26 tracts permissive for Late Triassic to Holocene porphyry copper-molybdenum and porphyry copper-gold deposits. Permissive tracts range in extent from 2,960 to 194,000 square kilometers (km2) and cover a total area of 924,000 km2. Younger tracts overlap older tracts in several areas. Three permissive tracts include sub-tracts in order to separate tract segments on the basis of geography, data quality, or likelihood of occurrence of undiscovered deposits. About 65 percent of all known porphyry sites occur in only five tracts, which also host most of the identified copper resources. In terms of tectonic setting, 58 percent of the permissive tracts are related to continental arcs; 19 percent to island arcs or back arcs; and 24 percent to postcollisional settings. Of the known porphyry copper deposits, subequal fractions are spread among these three settings.
\nThe spatial distribution of known porphyry deposits and prospects is also related to the level of erosion. Magmatic belts with numerous known porphyry sites exhibit subequal areas of coeval plutonic and volcanic units and lesser amounts of cover rocks. Belts with fewer known porphyry sites display either high or low volcanic-to-plutonic ratios and (or) greater cover, indicating crustal levels that are too shallow or too deep for exposure of porphyry deposits.
\nProbabilistic estimates of numbers of undiscovered porphyry copper deposits were made for 18 of the 26 tracts. The undiscovered porphyry copper endowment for 8 tracts is discussed qualitatively.
\nThe assessment estimates that the Tethys region contains 47 undiscovered deposits within 1 kilometer of the surface. Probabilistic estimates of numbers of undiscovered deposits were combined with grade and tonnage models in a Monte Carlo simulation to estimate probable amounts of contained metal. The 47 undiscovered deposits are estimated to contain a mean of 180 million metric tons (Mt) of copper distributed among the 18 tracts for which probabilistic estimates were made, in addition to the 62 Mt of copper already identified in the 42 known porphyry deposits in the study area. Results of Monte Carlo simulations show that 80 percent of the estimated undiscovered porphyry copper resources in the Tethys region are located in four tracts or sub-tracts.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Global mineral resource assessment (Scientific Investigations Report 2010-5090)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105090V","issn":"2328-0328","collaboration":"Prepared in cooperation with the Natural History Museum, London","usgsCitation":"Zürcher, L., Bookstrom A.A., Hammarstrom, J.M., Mars, J.C., Ludington, S., Zientek, M.L., Dunlap, P., and Wallis, J.C., with contributions from Drew, L.J., Sutphin, D.M., Berger, B.R., Herrington, R.J., Billa, M., Kuşcu, I., Moon, C.J. ,and Richards, J.P., 2015, Porphyry copper assessment of the Tethys region of western and southern Asia: U.S. Geological Survey Scientific Investigations Report 2010–5090–V, 232 p., and spatial data, https://dx.doi.org/10.3133/sir20105090V.","productDescription":"Report: xvii, 232 p.; 7 Figures: 17.0 x 11.0 inches; Appendices A-C; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053054","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":311125,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/sir20105090v_fig07.pdf","text":"Tabloid Figure 7","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090V Figure 7","linkHelpText":"Eocene to Miocene permissive tracts for porphyry copper deposits in the Tethys region of western and southern Asia."},{"id":311126,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/sir20105090v_fig08.pdf","text":"Tabloid Figure 8","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090V Figure 8","linkHelpText":"Pliocene to Holocene permissive tract for porphyry copper deposits in the Tethys region of western and southern Asia."},{"id":311127,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/sir20105090v_fig57.pdf","text":"Tabloid Figure 57","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090V Figure 57","linkHelpText":"Map showing the distribution of permissive intrusive and extrusive rocks used to define tract 142pCu9017, Plio-Quaternary— Afghanistan, Armenia, Azerbaijan, Georgia, Iran, Pakistan, Russian Federation, and Turkey. Sub-tracts: 142pCu9017a, Plio-Quaternary—Konya, Turkey; 142pCu9017b, Plio-Quaternary—Postcollisional, Armenia, Azerbaijan, Georgia, Iran, Russian Federation, and Turkey; and 142pCu9017c, Plio-Quaternary—Bazman, Afghanistan, Iran, Pakistan."},{"id":311122,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/sir20105090v_fig03.pdf","text":"Tabloid Figure 3","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090V Figure 3","linkHelpText":"Map showing tectono-stratigraphic terranes, accretionary prisms, and metamorphic belts of the Tethys region of western and southern Asia. After Abdullah and Chmyriov (1977b) and Peters and others (2011) for Afghanistan, Kazmi and Rana (1982) for Pakistan, Stöcklin (1968) for Iran, Pollastro and others (1998) for Iraq, Kaymakci and others (2010) and Yigit (2009) for Turkey, and Kekelia and others (2001) for the Caucasus."},{"id":311121,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/sir20105090v_fig02.pdf","text":"Tabloid Figure 2","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090V Figure 2","linkHelpText":"Map showing major sutures, faults, and geologic and geographic features in the Tethys region of western and southern Asia (assessment area) and vicinity on a digital elevation base."},{"id":311129,"rank":11,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/sir20105090v_gis.zip","text":"GIS Data","size":"88.1 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2010-5090V GIS Data"},{"id":311128,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/sir20105090v_appendixes_a_c.xlsx","text":"Appendices A–C","size":"516 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2010-5090V Appendixes A–C"},{"id":311124,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/sir20105090v_fig06.pdf","text":"Tabloid Figure 6","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090V Figure 6","linkHelpText":"Late Cretaceous to late Eocene permissive tracts for porphyry copper deposits in the Tethys region of western and southern Asia."},{"id":311123,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/sir20105090v_fig05.pdf","text":"Tabloid Figure 5","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090V Figure 5","linkHelpText":"Late Triassic to Early Cretaceous permissive tracts for porphyry copper deposits in the Tethys region of western and southern Asia."},{"id":311120,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/sir20105090v.pdf","text":"Report","size":"28.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090V"},{"id":311118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2010/5090/v/coverthb.jpg"}],"country":"Afghanistan, Armenia, Azerbaijan, Georgia, Iran, Iraq, Pakistan, Russia, Turkey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 24.609375,\n 39.90973623453719\n ],\n [\n 25.13671875,\n 41.83682786072714\n ],\n [\n 26.630859375,\n 42.87596410238254\n ],\n [\n 27.94921875,\n 43.32517767999296\n ],\n [\n 34.27734375,\n 42.87596410238254\n ],\n [\n 38.759765625,\n 42.22851735620852\n ],\n [\n 40.341796875,\n 42.94033923363181\n ],\n [\n 40.517578125,\n 43.96119063892024\n ],\n [\n 44.12109374999999,\n 43.644025847699496\n ],\n [\n 46.93359375,\n 43.32517767999296\n ],\n [\n 49.39453125,\n 42.68243539838623\n ],\n [\n 50.2734375,\n 41.244772343082076\n ],\n [\n 50.009765625,\n 39.232253141714885\n ],\n [\n 50.88867187499999,\n 38.47939467327645\n ],\n [\n 52.82226562499999,\n 38.272688535980976\n ],\n [\n 55.107421875,\n 38.685509760012\n ],\n [\n 58.447265625,\n 38.89103282648849\n ],\n [\n 61.17187499999999,\n 37.50972584293751\n ],\n [\n 64.6875,\n 36.1733569352216\n ],\n [\n 68.5546875,\n 35.88905007936091\n ],\n [\n 74.00390625,\n 32.91648534731439\n ],\n [\n 74.70703125,\n 32.10118973232094\n ],\n [\n 73.916015625,\n 30.751277776257812\n ],\n [\n 72.24609375,\n 29.22889003019423\n ],\n [\n 69.9609375,\n 27.839076094777816\n ],\n [\n 68.5546875,\n 27.21555620902969\n ],\n [\n 66.181640625,\n 26.43122806450644\n ],\n [\n 62.9296875,\n 25.48295117535531\n ],\n [\n 59.765625,\n 25.3241665257384\n ],\n [\n 56.42578125,\n 25.799891182088334\n ],\n [\n 53.26171875,\n 26.745610382199022\n ],\n [\n 50.009765625,\n 28.76765910569123\n ],\n [\n 47.548828125,\n 29.53522956294847\n ],\n [\n 43.2421875,\n 31.728167146023935\n ],\n [\n 38.408203125,\n 34.30714385628804\n ],\n [\n 33.83789062499999,\n 35.60371874069731\n ],\n [\n 27.773437499999996,\n 35.31736632923788\n ],\n [\n 25.224609375,\n 36.24427318493909\n ],\n [\n 24.169921875,\n 38.685509760012\n ],\n [\n 24.609375,\n 39.90973623453719\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Mineral Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
913 National Center
Reston, VA 20192
http://minerals.usgs.gov/
Population viability analyses provide a quantitative approach that seeks to predict the possible future status of a species of interest under different scenarios and, therefore, can be important components of large-scale species’ conservation programs. We created a model and simulated range-wide population and breeding habitat dynamics for an endangered woodland warbler, the golden-cheeked warbler (Setophaga chrysoparia). Habitat-transition probabilities were estimated across the warbler's breeding range by combining National Land Cover Database imagery with multistate modeling. Using these estimates, along with recently published demographic estimates, we examined if the species can remain viable into the future given the current conditions. Lastly, we evaluated if protecting a greater amount of habitat would increase the number of warblers that can be supported in the future by systematically increasing the amount of protected habitat and comparing the estimated terminal carrying capacity at the end of 50 years of simulated habitat change. The estimated habitat-transition probabilities supported the hypothesis that habitat transitions are unidirectional, whereby habitat is more likely to diminish than regenerate. The model results indicated population viability could be achieved under current conditions, depending on dispersal. However, there is considerable uncertainty associated with the population projections due to parametric uncertainty. Model results suggested that increasing the amount of protected lands would have a substantial impact on terminal carrying capacities at the end of a 50-year simulation. Notably, this study identifies the need for collecting the data required to estimate demographic parameters in relation to changes in habitat metrics and population density in multiple regions, and highlights the importance of establishing a common definition of what constitutes protected habitat, what management goals are suitable within those protected areas, and a standard operating procedure to identify areas of priority for habitat conservation efforts. Therefore, we suggest future efforts focus on these aspects of golden-cheeked warbler conservation and ecology.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.ecolmodel.2015.09.018","usgsCitation":"Adam Duarte, Hatfield, J., Swannack, T.M., Forstner, M.R., Green, M.C., and Floyd W. Weckerly, 2015, Simulating range-wide population and breeding habitat dynamics for an endangered woodland warbler in the face of uncertainty: Ecological Modelling, v. 320, no. 7691, p. 52-61, https://doi.org/10.1016/j.ecolmodel.2015.09.018.","productDescription":"10 p.","startPage":"52","endPage":"61","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068943","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471640,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2015.09.018","text":"Publisher Index Page"},{"id":311531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"320","issue":"7691","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564daf53e4b0112df6c62e2e","chorus":{"doi":"10.1016/j.ecolmodel.2015.09.018","url":"http://dx.doi.org/10.1016/j.ecolmodel.2015.09.018","publisher":"Elsevier BV","authors":"Duarte Adam, Hatfield Jeff S., Swannack Todd M., Forstner Michael R.J., Green M. Clay, Weckerly Floyd W.","journalName":"Ecological Modelling","publicationDate":"1/2016"},"contributors":{"authors":[{"text":"Adam Duarte","contributorId":149349,"corporation":false,"usgs":false,"family":"Adam Duarte","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":578041,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatfield, Jeffrey 0000-0002-6517-2925 jhatfield@usgs.gov","orcid":"https://orcid.org/0000-0002-6517-2925","contributorId":139261,"corporation":false,"usgs":true,"family":"Hatfield","given":"Jeffrey","email":"jhatfield@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":578040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swannack, Todd M.","contributorId":149350,"corporation":false,"usgs":false,"family":"Swannack","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":17715,"text":"U.S. Army Engineering Research and Development Center","active":true,"usgs":false}],"preferred":false,"id":578042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Forstner, Michael R. J.","contributorId":149351,"corporation":false,"usgs":false,"family":"Forstner","given":"Michael","email":"","middleInitial":"R. J.","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":578043,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Green, M. Clay","contributorId":149352,"corporation":false,"usgs":false,"family":"Green","given":"M.","email":"","middleInitial":"Clay","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":578044,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Floyd W. Weckerly","contributorId":149353,"corporation":false,"usgs":false,"family":"Floyd W. Weckerly","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":578045,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70159660,"text":"70159660 - 2015 - Web based visualization of large climate data sets","interactions":[],"lastModifiedDate":"2015-11-17T14:07:35","indexId":"70159660","displayToPublicDate":"2015-11-17T15:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Web based visualization of large climate data sets","docAbstract":"We have implemented the USGS National Climate Change Viewer (NCCV), which is an easy-to-use web application that displays future projections from global climate models over the United States at the state, county and watershed scales. We incorporate the NASA NEX-DCP30 statistically downscaled temperature and precipitation for 30 global climate models being used in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), and hydrologic variables we simulated using a simple water-balance model. Our application summarizes very large, complex data sets at scales relevant to resource managers and citizens and makes climate-change projection information accessible to users of varying skill levels. Tens of terabytes of high-resolution climate and water-balance data are distilled to compact binary format summary files that are used in the application. To alleviate slow response times under high loads, we developed a map caching technique that reduces the time it takes to generate maps by several orders of magnitude. The reduced access time scales to >500 concurrent users. We provide code examples that demonstrate key aspects of data processing, data exporting/importing and the caching technique used in the NCCV.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2015.02.016","usgsCitation":"Alder, J.R., and Hostetler, S.W., 2015, Web based visualization of large climate data sets: Environmental Modelling and Software, v. 68, p. 175-180, https://doi.org/10.1016/j.envsoft.2015.02.016.","productDescription":"6 p.","startPage":"175","endPage":"180","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057365","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":311437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564c4fbee4b0ebfbef0d345f","contributors":{"authors":[{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":579955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":579956,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159678,"text":"70159678 - 2015 - Karst mapping in the United States: Past, present and future","interactions":[],"lastModifiedDate":"2017-04-14T10:20:04","indexId":"70159678","displayToPublicDate":"2015-11-17T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1727,"text":"GSA Special Papers","active":true,"publicationSubtype":{"id":10}},"title":"Karst mapping in the United States: Past, present and future","docAbstract":"The earliest known comprehensive karst map of the entire USA was published by Stringfield and LeGrand (1969), based on compilations of William E. Davies of the U.S. Geological Survey (USGS). Various versions of essentially the same map have been published since. The USGS recently published new digital maps and databases depicting the extent of known karst, potential karst, and pseudokarst areas of the United States of America including Puerto Rico and the U.S. Virgin Islands (Weary and Doctor, 2014). These maps are based primarily on the extent of potentially karstic soluble rock types, and rocks with physical properties conducive to the formation of pseudokarst features. These data were compiled and refined from multiple sources at various spatial resolutions, mostly as digital data supplied by state geological surveys. The database includes polygons delineating areas with potential for karst and that are tagged with attributes intended to facilitate classification of karst regions. Approximately 18% of the surface of the fifty United States is underlain by significantly soluble bedrock. In the eastern United States the extent of outcrop of soluble rocks provides a good first-approximation of the distribution of karst and potential karst areas. In the arid western states, the extent of soluble rock outcrop tends to overestimate the extent of regions that might be considered as karst under current climatic conditions, but the new dataset encompasses those regions nonetheless. This database will be revised as needed, and the present map will be updated as new information is incorporated.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2015.2516(04)","usgsCitation":"Weary, D.J., and Doctor, D.H., 2015, Karst mapping in the United States: Past, present and future: GSA Special Papers, v. 516, p. 177-211, https://doi.org/10.1130/2015.2516(04).","productDescription":"15 p.","startPage":"177","endPage":"211","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062964","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":311430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"516","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564c4fbbe4b0ebfbef0d3459","contributors":{"authors":[{"text":"Weary, David J. 0000-0002-6115-6397 dweary@usgs.gov","orcid":"https://orcid.org/0000-0002-6115-6397","contributorId":545,"corporation":false,"usgs":true,"family":"Weary","given":"David","email":"dweary@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":580049,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":580050,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159236,"text":"ofr20151191 - 2015 - California State Waters map series — Offshore of Scott Creek, California","interactions":[],"lastModifiedDate":"2022-04-18T21:30:15.119324","indexId":"ofr20151191","displayToPublicDate":"2015-11-17T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1191","title":"California State Waters map series — Offshore of Scott Creek, California","docAbstract":"In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within the 3-nautical-mile limit of California’s State Waters. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data, acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow subsurface geology.
\nThe Offshore of Scott Creek map area is located in central California, on the Pacific Coast about 65 km south of San Francisco and 12 km northwest of Santa Cruz. The onshore part of the map area is sparsely populated; the only cultural center is Davenport, a small community with a population of less than 500. The hilly coastal area is virtually undeveloped, and a large percentage of coastal land is incorporated in open-space trusts. Agricultural land is almost entirely limited to coastal areas between the shoreline and the northwest-trending Santa Cruz Mountains, on Pleistocene alluvial fan deposits and the lowest emergent marine terrace. The Santa Cruz Mountains are part of the northwest-trending Coast Ranges that run roughly parallel to the San Andreas Fault Zone.
\nThe map area is cut by the San Gregorio Fault Zone, and it lies a few kilometers southwest of the San Andreas Fault Zone. Regional folding and uplift along the coast has been attributed to a westward bend in the San Andreas Fault Zone and also to right-lateral movement along the San Gregorio Fault Zone. The irregular coastal geomorphology of this area, which consists of low, rocky cliffs and sparse, small pocket beaches backed by low, terraced hills, is partly attributable to this ongoing deformation.
\nThe shelf in the map area is underlain by variable amounts (0 to 25 m) of upper Quaternary shelf, nearshore, and fluvial sediments deposited as sea level fluctuated in the late Pleistocene. The northernmost part of the map area is characterized by the presence of uplifted bedrock that has been linked to a local transpressional zone in the San Gregorio Fault Zone. This uplift, coupled with high wave energy, has resulted in little or no sediment cover in this area where exposures of bedrock are present at water depths of as much as 45 m. The thickest deposits of sediment lie offshore of both Davenport and the mouth of Waddell Creek.
\nCoastal sediment transport in the map area is characterized by north-to-south littoral transport of sediment that is derived mainly from streams in the Santa Cruz Mountains and also from local coastal erosion. Shoreline-change studies indicate long-term erosion; within the region between San Francisco and Davenport, the highest long- and short-term coastal-erosion rates occur north of the map area, just north of Point Año Nuevo. During the last approximately 300 years, as much as 18 million cubic yards (14 million cubic meters) of sand-sized sediment has been eroded from the area between Año Nuevo Island and Point Año Nuevo and transported south. Once widened by this pulse of eroded sediment, beaches in the map area are now narrowing as the tail end of this mass of sand progresses farther south.
\nThe Offshore of Scott Creek map area lies within the cold-temperate biogeographic zone that is called either the “Oregonian province” or the “northern California ecoregion.” This biogeographic province is maintained by the long-term stability of the southward-flowing California Current, the eastern limb of the North Pacific subtropical gyre that flows from southern British Columbia to Baja California. At its midpoint off central California, the California Current transports subarctic surface (0–500 m deep) waters southward, about 150 to 1,300 km from shore. Seasonal northwesterly winds that are, in part, responsible for the California Current, generate coastal upwelling. The south end of the Oregonian province is at Point Conception (about 320 km south of the map area), although its associated phylogeographic group of marine fauna may extend beyond to the area offshore of Los Angeles in southern California. The ocean off of central California has experienced a warming over the last 50 years that is driving an ecosystem shift away from the productive subarctic regime towards a depopulated subtropical environment.
\nSeafloor habitats in the Offshore of Scott Creek map area, which lie within the Shelf (continental shelf) megahabitat, range from significant rocky outcrops that support kelp-forest communities nearshore to rocky-reef communities in deeper water. Biological productivity resulting from coastal upwelling supports populations of Sooty Shearwater, Western Gull, Common Murre, Cassin’s Auklet, and many other less populous bird species. In addition, an observable recovery of Humpback and Blue Whales has occurred in the area; both species are dependent on coastal upwelling to provide nutrients. The large extent of exposed inner shelf bedrock supports large forests of “bull kelp,” which is well adapted for high-wave-energy environments. The kelp beds are the northernmost known habitat for the population of southern sea otters. Common fish species found in the kelp beds and rocky reefs include lingcod and various species of rockfish and greenling.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151191","usgsCitation":"Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series — Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015–1191, pamphlet 40 p., 10 sheets, scale 1:24,000, https://dx.doi.org/10.3133/ofr20151191.","productDescription":"Pamphlet: iv, 40 p.; 10 Sheets: 51 x 36 inches or less; Dataset; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-058155","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438667,"rank":20,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CJ8BJW","text":"USGS data release","linkHelpText":"California State Waters Map Series Data Catalog--Offshore of Scott Creek, California"},{"id":310185,"rank":18,"type":{"id":28,"text":"Dataset"},"url":"https://dx.doi.org/10.5066/F7CJ8BJW","text":"Data Catalog","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1191 Data Catalog","linkHelpText":"The GIS data layers for this map are accessible from “Data Catalog—Offshore of Scott Creek, California,” which is part of California State Waters Map Series Data Catalog. Each GIS data file is listed with a brief description, a small image, and links to the metadata files and the downloadable data files."},{"id":310184,"rank":17,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_metadata.html","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1191 Metadata"},{"id":310104,"rank":15,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet10.pdf","text":"Sheet 10","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 10","linkHelpText":"Offshore and Onshore Geology and Geomorphology, Offshore of Scott Creek Map Area, California By Stephen R. Hartwell, Samuel Y. Johnson, and Clifton W. Davenport"},{"id":310103,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet9.pdf","text":"Sheet 9","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 9","linkHelpText":"Local (Offshore of Scott Creek Map Area) and Regional (Offshore from Pigeon Point to Southern Monterey Bay) Shallow-Subsurface Geology and Structure, California By Samuel Y. Johnson, Stephen R. Hartwell, Janet T. Watt, Ray W. Sliter, and Katherine L. Maier"},{"id":310102,"rank":13,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet8.pdf","text":"Sheet 8","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 8","linkHelpText":"Seismic-Reflection Profiles, Offshore of Scott Creek Map Area, California by Samuel Y. Johnson, Stephen R. Hartwell, and Ray W. Sliter"},{"id":310101,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet7.pdf","text":"Sheet 7","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 7","linkHelpText":"Potential Marine Benthic Habitats, Offshore of Scott Creek Map Area, California By Charles A. Endris, H. Gary Greene, Bryan E. Dieter, and Mercedes D. Erdey"},{"id":310093,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/of/2014/1214/","text":"Open-File Report 2014–1214","description":"Open-File Report 2014–1214","linkHelpText":"California State Waters Map Series—Offshore of Half Moon Bay, California, by Guy R. Cochrane and others"},{"id":399010,"rank":19,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_103676.htm"},{"id":310168,"rank":16,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Pamphlet"},{"id":310100,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet6.pdf","text":"Sheet 6","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 6","linkHelpText":"Ground-Truth Studies, Offshore of Scott Creek Map Area, California By Nadine E. Golden, Guy R. Cochrane, and Lisa M. Krigsman"},{"id":310099,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet5.pdf","text":"Sheet 5","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 5","linkHelpText":"Seafloor Character, Offshore of Scott Creek Map Area, California By Mercedes D. Erdey and Guy R. Cochrane"},{"id":310098,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet4.pdf","text":"Sheet 4","linkFileType":{"id":1,"text":"pdf"},"description":"OFR2015-1191 Sheet 4","linkHelpText":"Data Integration and Visualization, Offshore of Scott Creek Map Area, California By Peter Dartnell"},{"id":310097,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet3.pdf","text":"Sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 3","linkHelpText":"Acoustic Backscatter, Offshore of Scott Creek Map Area, California By Peter Dartnell, Andrew C. Ritchie, David P. Finlayson, and Rikk G. Kvitek"},{"id":310096,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 2","linkHelpText":"Shaded-Relief Bathymetry, Offshore of Scott Creek Map Area, California By Peter Dartnell, Andrew C. Ritchie, David P. Finlayson, and Rikk G. Kvitek"},{"id":310095,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 1","linkHelpText":"Colored Shaded-Relief Bathymetry, Offshore of Scott Creek Map Area, California By Peter Dartnell, Andrew C. Ritchie, David P. Finlayson, and Rikk G. Kvitek"},{"id":310094,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/of/2014/1260/","text":"Open-File Report 2014–1260","description":"Open-File Report 2014–1260","linkHelpText":"California State Waters Map Series—Offshore of Pacifica, California, by Brian D. Edwards and others."},{"id":310092,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/sim/3306/","text":"Scientific Investigations Map 3306","description":"Scientific Investigations Map 3306","linkHelpText":"California State Waters Map Series—Offshore of San Gregorio, California, by Guy R. Cochrane and others."},{"id":310091,"rank":2,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/781/","text":"Data Series 781","description":"Data Series 781","linkHelpText":"California State Waters Map Series Data Catalog"},{"id":310090,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1191/coverthb.jpg"}],"scale":"24000","country":"United States","state":"California","otherGeospatial":"Scott Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.3867,\n 36.9428\n ],\n [\n -122.1881,\n 36.9428\n ],\n [\n -122.1881,\n 37.1022\n ],\n [\n -122.3867,\n 37.1022\n ],\n [\n -122.3867,\n 36.9428\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
In the Pacific Northwest, coastal wetlands support a wealth of ecosystem services including habitat provision for wildlife and fisheries and flood protection. The tidal marshes, mudflats, and shallow bays of coastal estuaries link marine, freshwater, and terrestrial habitats, and provide economic and recreational benefits to local communities. Climate change effects such as sea-level rise are altering these habitats, but we know little about how these areas will change over the next 50–100 years. Our study examined the effects of sea-level rise on nine tidal marshes in Washington and Oregon between 2012 and 2015, with the goal of providing scientific data to support future coastal planning and conservation. We compiled physical and biological data, including coastal topography, tidal inundation, vegetation structure, as well as recent and historical sediment accretion rates, to assess and model how sea-level rise may alter these ecosystems in the future. Multiple factors, including initial elevation, marsh productivity, sediment availability, and rates of sea-level rise, affected marsh persistence. Under a low sea-level rise scenario, all marshes remained vegetated with little change in the present configuration of communities of marsh plants or gradually increased proportions of middle-, high-, or transition-elevation zones of marsh vegetation. However, at most sites, mid sea-level rise projections led to loss of habitat of middle and high marshes and a gain of low marshes. Under a high sea-level rise scenario, marshes at most sites eventually converted to intertidal mudflats. Two sites (Grays Harbor and Willapa) seemed to have the most resilience to a high rate of rise in sea-level, persisting as low marsh until at least 2110. Our main model finding is that most tidal marsh study sites are resilient to sea-level rise over the next 50–70 years, but that sea-level rise will eventually outpace marsh accretion and drown most habitats of high and middle marshes by 2110.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151204","collaboration":"Prepared in cooperation with the Northwest Climate Science Center","usgsCitation":"Thorne, K.M., Dugger, B.D., Buffington, K.J., Freeman, C.M., Janousek, C.N., Powelson, K.W., Gutenspergen, G.R., and Takekawa, J.Y., 2015, Marshes to mudflats—Effects of sea-level rise on tidal marshes along a latitudinal gradient in the Pacific Northwest: U.S. Geological Survey Open-File Report 2015-1204, 54 p. plus appendixes, https://dx.doi.org/10.3133/ofr20151204.","productDescription":"Report: vi, 54 p.; Appendixes: A-I","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2012-01-01","temporalEnd":"2015-12-31","ipdsId":"IP-063198","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":311426,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1204/ofr20151204.pdf","text":"Report","size":"8. MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1204 PDF"},{"id":311427,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1204/ofr20151204_appendixesA-I.pdf","text":"Appendixes A-I","size":"6.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1204 Appendix PDF"},{"id":311425,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1204/coverthb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.4111328125,\n 32.58384932565662\n ],\n [\n -121.53076171875,\n 38.09998264736481\n ],\n [\n -123.28857421875,\n 42.00032514831621\n ],\n [\n -122.03613281249999,\n 49.009050809382046\n ],\n [\n -124.73876953125,\n 48.3416461723746\n ],\n [\n -124.69482421875,\n 42.74701217318067\n ],\n [\n -124.51904296875,\n 40.26276066437183\n ],\n [\n -122.87109375,\n 36.98500309285596\n ],\n [\n -120.80566406250001,\n 34.252676117101515\n ],\n [\n -118.2568359375,\n 33.55970664841198\n ],\n [\n -117.333984375,\n 32.509761735919426\n ],\n [\n -116.4111328125,\n 32.58384932565662\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://www.werc.usgs.gov/
An evaluation was conducted to estimate dam passage survival of juvenile Chinook salmon (Oncorhynchus tshawytscha) at Detroit Dam during a period of spill. To estimate dam passage survival, we used a paired-release recapture study design and released groups of tagged fish upstream (997 fish) and downstream (625 fish) of Detroit Dam. A total of 43 fish (6.8 percent) passed Detroit Dam from the upstream release group and passage occurred through regulating outlets (54.8 percent), spill bays (31.0 percent), and turbines (14.3 percent). We do not present dam passage survival estimates from 2014 because these estimates would have been highly uncertain due to the low number of fish that passed Detroit Dam during the study. Secondary objectives were addressed using data collected from tagged fish that were released at the downstream release site.
\nJuvenile salmonids have multiple passage options at the Bennett Dam complex, which includes a series of dams and braided channels. A pair of Passive Integrated Transponder (PIT) monitoring arrays were installed at Upper Bennett Dam and in the Stayton Canal by the U.S. Army Corps of Engineers and the Oregon Department of Fish and Wildlife during 2014. We deployed acoustic telemetry hydrophones near these arrays to detect acoustic-tagged fish from our study and used these detections to quantify proportions of tagged fish passing through the two routes. About one-fourth (0.257) of the tagged fish that were released downstream of Big Cliff Dam were detected on the new PIT tag array while passing the Bennett Dam complex. A total of 402 acoustic-tagged fish were detected at the complex and many (248 fish; 62 percent) eventually entered the Stayton Canal. Median residence time in the canal was 6.5 hours, but 12.7 percent of the fish had extended residence times (7–37 days). Passage also was monitored at the Sullivan Project at Willamette Falls and about 40 percent (0.398) of the tagged fish passing the project were detected on the PIT tag array.
\nA Cormack-Jolly-Seber mark-recapture model was developed to provide reach-specific survival estimates for juvenile Chinook salmon. A portion of the tagged population overwintered in the Willamette River Basin and outmigrated several months after release. As a result, survival estimates from the model would have been negatively biased by factors such as acoustic tag failure and tag loss. Data from laboratory studies were incorporated into the model to provide survival estimates that accounted for these factors. In the North Santiam River between Minto Dam and the Bennett Dam complex, a distance of 37.2 kilometers, survival was estimated to be 0.844 (95-percent confidence interval 0.795–0.893). The survival estimate for the 203.7 kilometer reach between the Bennett Dam complex and Portland, Oregon, was 0.279 (95-percent confidence interval 0.234–0.324), and included portions of the North Santiam, Santiam, and Willamette Rivers. The cumulative survival estimate in the 240.9 kilometer reach from the Minto Dam tailrace to Portland was 0.236 (95-percent confidence interval 0.197–0.275).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151220","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Kock, T.J., Beeman, J.W., Hansen, A.C., Hansel, H.C., Hansen, G.S., Hatton, T.W., Kofoot, E.E., Sholtis, M.D., and Sprando, J.M., 2015, Behavior, passage, and downstream migration of juvenile Chinook salmon from Detroit Reservoir to Portland, Oregon, 2014–15: U.S. Geological Survey Open-File Report 2015-1220, 30 p., https://dx.doi.org/10.3133/ofr20151220.","productDescription":"vi, 30 p.","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2014-08-01","temporalEnd":"2015-06-30","ipdsId":"IP-067565","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":311361,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1220/coverthb.jpg"},{"id":311362,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1220/ofr20151220.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1220 PDF"}],"country":"United States","state":"Oregon","city":"Portland","otherGeospatial":"Detroit Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.20068359374999,\n 44.65693173288727\n ],\n [\n -123.20068359374999,\n 45.5679096098613\n ],\n [\n -122.07733154296875,\n 45.5679096098613\n ],\n [\n -122.07733154296875,\n 44.65693173288727\n ],\n [\n -123.20068359374999,\n 44.65693173288727\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
Biostimulation is increasingly used to accelerate microbial remediation of recalcitrant groundwater contaminants. Effective application of biostimulation requires successful emplacement of amendment in the contaminant target zone. Verification of remediation performance requires postemplacement assessment and contaminant monitoring. Sampling-based approaches are expensive and provide low-density spatial and temporal information. Time-lapse electrical resistivity tomography (ERT) is an effective geophysical method for determining temporal changes in subsurface electrical conductivity. Because remedial amendments and biostimulation-related biogeochemical processes often change subsurface electrical conductivity, ERT can complement and enhance sampling-based approaches for assessing emplacement and monitoring biostimulation-based remediation.
Field studies demonstrating the ability of time-lapse ERT to monitor amendment emplacement and behavior were performed during a biostimulation remediation effort conducted at the Department of Defense Reutilization and Marketing Office (DRMO) Yard, in Brandywine, Maryland, United States. Geochemical fluid sampling was used to calibrate a petrophysical relation in order to predict groundwater indicators of amendment distribution. The petrophysical relations were field validated by comparing predictions to sequestered fluid sample results, thus demonstrating the potential of electrical geophysics for quantitative assessment of amendment-related geochemical properties. Crosshole radar zero-offset profile and borehole geophysical logging were also performed to augment the data set and validate interpretation.
In addition to delineating amendment transport in the first 10 months after emplacement, the time-lapse ERT results show later changes in bulk electrical properties interpreted as mineral precipitation. Results support the use of more cost-effective surface-based ERT in conjunction with limited field sampling to improve spatial and temporal monitoring of amendment emplacement and remediation performance.
","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.12291","usgsCitation":"Johnson, T.C., Versteeg, R.J., Day-Lewis, F.D., Major, W., and Lane, J.W., 2015, Time-lapse electrical geophysical monitoring of amendment-based biostimulation: Ground Water, v. 53, no. 6, p. 920-932, https://doi.org/10.1111/gwat.12291.","productDescription":"13 p.","startPage":"920","endPage":"932","ipdsId":"IP-059263","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":349017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","city":"Brandywine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.8456,\n 38.7\n ],\n [\n -76.8456,\n 38.6964\n ],\n [\n -76.8420,\n 38.6964\n ],\n [\n -76.8420,\n 38.7\n ],\n [\n -76.8456,\n 38.7\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"53","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2014-12-02","publicationStatus":"PW","scienceBaseUri":"5a60fe57e4b06e28e9c252e8","contributors":{"authors":[{"text":"Johnson, Timothy C.","contributorId":199842,"corporation":false,"usgs":false,"family":"Johnson","given":"Timothy","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":720185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Versteeg, Roelof J.","contributorId":199843,"corporation":false,"usgs":false,"family":"Versteeg","given":"Roelof","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":720186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":720183,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Major, William","contributorId":199844,"corporation":false,"usgs":false,"family":"Major","given":"William","email":"","affiliations":[],"preferred":false,"id":720187,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lane, John W. Jr. 0000-0002-3558-243X jwlane@usgs.gov","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":189168,"corporation":false,"usgs":true,"family":"Lane","given":"John","suffix":"Jr.","email":"jwlane@usgs.gov","middleInitial":"W.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":false,"id":720184,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70159577,"text":"70159577 - 2015 - Holocene environmental changes inferred from biological and sedimentological proxies in a high elevation Great Basin lake in the northern Ruby Mountains, Nevada, USA","interactions":[],"lastModifiedDate":"2015-11-12T10:50:35","indexId":"70159577","displayToPublicDate":"2015-11-12T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3217,"text":"Quaternary International","active":true,"publicationSubtype":{"id":10}},"title":"Holocene environmental changes inferred from biological and sedimentological proxies in a high elevation Great Basin lake in the northern Ruby Mountains, Nevada, USA","docAbstract":"Multi-proxy analyses were conducted on a sediment core from Favre Lake, a high elevation cirque lake in the northern Ruby Mountains, Nevada, and provide a ca. 7600 year record of local and regional environmental change. Data indicate that lake levels were lower from 7600-5750 cal yr BP, when local climate was warmer and/or drier than today. Effective moisture increased after 5750 cal yr BP and remained relatively wet, and possibly cooler, until ca. 3750 cal yr BP. Results indicate generally dry conditions but also enhanced climatic variability from 3750-1750 cal yr BP, after which effective moisture increased. The timing of major changes in the Favre Lake proxy data are roughly coeval and in phase with those recorded in several paleoclimate studies across the Great Basin, suggesting regional climatic controls on local conditions and similar responses at high and low altitudes.
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An isotopic mixing model based on mass-dependent (MDF) and mass-independent fractionation (MIF) (δ202Hg and Δ199Hg) identified three primary Hg sources for sediments: atmospheric, industrial, and watershed-derived. Results indicate atmospheric sources dominate in Lakes Huron, Superior, and Michigan sediments while watershed-derived and industrial sources dominate in Lakes Erie and Ontario sediments. Anomalous Δ200Hg signatures, also apparent in sediments, provided independent validation of the model. Comparison of Δ200Hg signatures in predatory fish from three lakes reveals that bioaccumulated Hg is more isotopically similar to atmospherically derived Hg than a lake’s sediment. Previous research suggests Δ200Hg is conserved during biogeochemical processing and odd mass-independent fractionation (MIF) is conserved during metabolic processing, so it is suspected even is similarly conserved. 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Reactive transport models help elucidate how diverse geochemical reactions control the spatiotemporal evolution of these impacts. Using extensive monitoring data from a crude oil spill site near Bemidji, Minnesota (USA), we implemented a comprehensive model that simulates secondary plumes of depleted dissolved O2 and elevated concentrations of Mn2+, Fe2+, CH4, and Ca2+ over a two-dimensional cross section for 30 years following the spill. The model produces observed changes by representing multiple oil constituents and coupled carbonate and hydroxide chemistry. The model includes reactions with carbonates and Fe and Mn mineral phases, outgassing of CH4 and CO2 gas phases, and sorption of Fe, Mn, and H+. Model results demonstrate that most of the carbon loss from the oil (70%) occurs through direct outgassing from the oil source zone, greatly limiting the amount of CH4 cycled down-gradient. The vast majority of reduced Fe is strongly attenuated on sediments, with most (91%) in the sorbed form in the model. Ferrous carbonates constitute a small fraction of the reduced Fe in simulations, but may be important for furthering the reduction of ferric oxides. The combined effect of concomitant redox reactions, sorption, and dissolved CO2 inputs from source-zone degradation successfully reproduced observed pH. The model demonstrates that secondary water quality impacts may depend strongly on organic carbon properties, and impacts may decrease due to sorption and direct outgassing from the source zone.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015WR016964","usgsCitation":"Ng, G.C., Bekins, B.A., Cozzarelli, I.M., Baedecker, M.J., Bennett, P.C., Amos, R.T., and Herkelrath, W.N., 2015, Reactive transport modeling of geochemical controls on secondary water quality impacts at a crude oil spill site near Bemidji, MN: Water Resources Research, v. 51, no. 6, p. 4156-4183, https://doi.org/10.1002/2015WR016964.","productDescription":"28 p.","startPage":"4156","endPage":"4183","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064817","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":471651,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015wr016964","text":"Publisher Index Page"},{"id":311418,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","county":"Bemidji","otherGeospatial":"Bemindji Oil Spill site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.13130187988281,\n 47.5363264438391\n ],\n [\n -95.0456428527832,\n 47.5363264438391\n ],\n [\n -95.0456428527832,\n 47.57316730158045\n ],\n [\n -95.13130187988281,\n 47.57316730158045\n ],\n [\n -95.13130187988281,\n 47.5363264438391\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-06-11","publicationStatus":"PW","scienceBaseUri":"564c5de4e4b0ebfbef0d348b","contributors":{"authors":[{"text":"Ng, Gene-Hua Crystal gng@usgs.gov","contributorId":5313,"corporation":false,"usgs":true,"family":"Ng","given":"Gene-Hua","email":"gng@usgs.gov","middleInitial":"Crystal","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":579996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bekins, Barbara A. 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":1348,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","middleInitial":"A.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":579997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":579998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baedecker, Mary Jo mjbaedec@usgs.gov","contributorId":3346,"corporation":false,"usgs":true,"family":"Baedecker","given":"Mary","email":"mjbaedec@usgs.gov","middleInitial":"Jo","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":579999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennett, Philip C.","contributorId":30567,"corporation":false,"usgs":true,"family":"Bennett","given":"Philip","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":580000,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Amos, Richard T.","contributorId":69081,"corporation":false,"usgs":true,"family":"Amos","given":"Richard","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":580001,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Herkelrath, William N. 0000-0002-6149-5524 wnherkel@usgs.gov","orcid":"https://orcid.org/0000-0002-6149-5524","contributorId":2612,"corporation":false,"usgs":true,"family":"Herkelrath","given":"William","email":"wnherkel@usgs.gov","middleInitial":"N.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":580002,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70159182,"text":"sim3341 - 2015 - Seabed maps showing topography, ruggedness, backscatter intensity, sediment mobility, and the distribution of geologic substrates in Quadrangle 6 of the Stellwagen Bank National Marine Sanctuary Region offshore of Boston, Massachusetts","interactions":[],"lastModifiedDate":"2026-04-02T18:55:39.361302","indexId":"sim3341","displayToPublicDate":"2015-11-10T15:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3341","title":"Seabed maps showing topography, ruggedness, backscatter intensity, sediment mobility, and the distribution of geologic substrates in Quadrangle 6 of the Stellwagen Bank National Marine Sanctuary Region offshore of Boston, Massachusetts","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration's National Marine Sanctuary Program, has conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary (SBNMS) region since 1993. The area is approximately 3,700 square kilometers (km2) and is subdivided into 18 quadrangles. Seven maps, at a scale of 1:25,000, of quadrangle 6 (211 km2) depict seabed topography, backscatter, ruggedness, geology, substrate mobility, mud content, and areas dominated by fine-grained or coarse-grained sand. Interpretations of bathymetric and seabed backscatter imagery, photographs, video, and grain-size analyses were used to create the geology-based maps. In all, data from 420 stations were analyzed, including sediment samples from 325 locations. The seabed geology map shows the distribution of 10 substrate types ranging from boulder ridges to immobile, muddy sand to mobile, rippled sand. Mapped substrate types are defined on the basis of sediment grain-size composition, surface morphology, sediment layering, the mobility or immobility of substrate surfaces, and water depth range. This map series is intended to portray the major geological elements (substrates, topographic features, processes) of environments within quadrangle 6. Additionally, these maps will be the basis for the study of the ecological requirements of invertebrate and vertebrate species that utilize these substrates and guide seabed management in the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3341","collaboration":"Prepared in cooperation with the National Oceanic and Atmospheric Administration","usgsCitation":"Valentine, P.C., and Gallea, L.B., 2015, Seabed maps showing topography, ruggedness, backscatter intensity, sediment mobility, and the distribution of geologic substrates in quadrangle 6 of the Stellwagen Bank National Marine Sanctuary region offshore of Boston, Massachusetts: U.S. Geological Survey Scientific Investigations Map 3341, 10 sheets, scale 1:25,000, and 21-p. pamphlet, https://dx.doi.org/10.3133/sim3341.","productDescription":"Report: vii, 21 p.; 10 Plates: 28.0 x 36.0 inches; Table; Spatial Data","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-026747","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":426835,"rank":18,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sim3515","text":"Scientific Investigations Map 3515","linkHelpText":"- Seabed Maps Showing Topography, Ruggedness, Backscatter Intensity, Sediment Mobility, and the Distribution of Geologic Substrates in Quadrangle 5 of the Stellwagen Bank National Marine Sanctuary Region Offshore of Boston, Massachusetts"},{"id":311079,"rank":17,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3341/data/SIM3341_stations_geology.zip","text":"Station location data and metadata","size":"0.3 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3341"},{"id":311078,"rank":16,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3341/data/bathy/SIM3341_13mbathy.zip","text":"Bathymetry data and metadata","size":"2.5 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3341"},{"id":311077,"rank":15,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3341/data/SIM3341_1m_contours.zip","text":"1-meter contour data and metadata","size":"0.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3341"},{"id":311027,"rank":14,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3341/data/SIM3341_geologic_interp.zip","text":"Geologic interpretation data and metadata","size":"0.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3341"},{"id":311026,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_table4.xlsx","text":"Table 4 - Sediment sample grain-size analyses and assignment to geologic substrates","size":"136 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIM 3341"},{"id":311025,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_mapG.pdf","text":"Map G - Distribution of substrate mud content","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341","linkHelpText":"(28” x 36”)"},{"id":311024,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_mapF.pdf","text":"Map F - Distribution of fine- and coarse-grained sand","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341","linkHelpText":"(28” x 36”)"},{"id":311023,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_mapE.pdf","text":"Map E - Sediment mobility","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341","linkHelpText":"(28” x 36”)"},{"id":311022,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_mapD_sheet4.pdf","text":"Map D, Distribution of geologic substrates, Sheet 4 - Seabed geology and sun-illuminated topography","size":"4.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341","linkHelpText":"(28” x 36”)"},{"id":311021,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_mapD_sheet3.pdf","text":"Map D, Distribution of geologic substrates, Sheet 3 - Seabed geology and station data types","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341","linkHelpText":"(28” x 36”)"},{"id":311020,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_mapD_sheet2.pdf","text":"Map D, Distribution of geologic substrates, Sheet 2 - Seabed geology and stations","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341","linkHelpText":"(28” x 38.5”)"},{"id":311019,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_mapD_sheet1.pdf","text":"Map D, Distribution of geologic substrates, Sheet 1 - Seabed geology","size":"1.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341","linkHelpText":"(28” x 36”)"},{"id":311017,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_mapC.pdf","text":"Map C - Backscatter intensity and sun-illuminated topography","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341","linkHelpText":"(28” x 36”)"},{"id":311016,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_mapB.pdf","text":"Map B - Seabed ruggedness","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341","linkHelpText":"(28” x 36”)"},{"id":311015,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3341/downloads/sim3341_mapA.pdf","text":"Map A - Sun-illuminated topography and boulder ridges","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341","linkHelpText":"(28” x 36”)"},{"id":502034,"rank":20,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sim3544","text":"Scientific Investigations Map 3544","linkHelpText":"- Seabed Maps Showing Topography, Ruggedness, Backscatter Intensity, Sediment Mobility, and the Distribution of Geologic Substrates in Quadrangle 3 of the Stellwagen Bank National Marine Sanctuary Region Offshore of Boston, Massachusetts"},{"id":465154,"rank":19,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sim3530","text":"Scientific Investigations Map 3530","linkHelpText":"- Seabed Maps Showing Topography, Ruggedness, Backscatter Intensity, Sediment Mobility, and the Distribution of Geologic Substrates in Quadrangle 2 of the Stellwagen Bank National Marine Sanctuary Region Offshore of Boston, Massachusetts"},{"id":311014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3341/sim3341.pdf","text":"Pamphlet","size":"6.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3341"},{"id":311013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3341/coverthb.jpg"}],"country":"United States","state":"Massachusetts","city":"Boston","otherGeospatial":"Stellwagen Bank National Marine Sanctuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.565185546875,\n 42.09822241118974\n ],\n [\n -70.565185546875,\n 42.64204079304428\n ],\n [\n -69.993896484375,\n 42.64204079304428\n ],\n [\n -69.993896484375,\n 42.09822241118974\n ],\n [\n -70.565185546875,\n 42.09822241118974\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Coastal and Marine Geology Program Coordinator
U.S. Geological Survey
13 National Center
Reston, VA 20192
http://marine.usgs.gov
Cyanobacteria are a genetically diverse group of photosynthetic microorganisms that occupy a broad range of habitats on land and water all over the world. They release toxins that can cause lung and skin irritation, alter the taste and odor of potable water, and cause human and animal illness. Cyanobacteria blooms occur worldwide, and climate change may increase the frequency, duration, and extent of these bloom events.
\nRapid detection of potentially harmful blooms is essential to protect humans and animals from exposure. Information about potential for exposure, such as bloom duration, frequency, and extent, is especially critical for developing environmental management decisions during periods of limited resources and funding.
\nThe National Research Council (NRC) report Exposure Science in the 21st Century suggested that effectively assessing and mitigating exposures requires techniques for rapid measurement of a stressor, such as an algal bloom, across diverse geographic, temporal, and biologic scales (e.g., various bloom concentrations) and an enhanced infrastructure to address threats [NRC, 2012]. The report specifically calls for approaches that use diverse information, such as satellite remote sensing, to identify and understand exposures that may pose a threat to ecosystems or human health.
\nA collaborative effort integrates the work of the U.S. Environmental Protection Agency (EPA), NASA, the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Geological Survey (USGS) to provide an approach for using satellite ocean color capabilities in U.S. fresh and brackish water quality management decisions. The overarching goal of this collaborative project is to detect and quantify cyanobacteria blooms using satellite data records in order to support the environmental management and public use of U.S. lakes and reservoirs.
\nSatellite remote sensing tools may enable policy makers and environmental managers to assess the sustainability of watershed ecosystems and the services they provide, now and in the future. Satellite technology allows us to develop early-warning indicators of cyanobacteria blooms at the local scale while maintaining continuous national coverage.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2015EO038809","usgsCitation":"Schaeffer, B., Loftin, K.A., Stumpf, R., and Werdell, P., 2015, Agencies collaborate, develop a cyanobacteria assessment network: Eos, Earth and Space Science News, v. 96, HTML Document, https://doi.org/10.1029/2015EO038809.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063681","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":471657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2015eo038809","text":"Publisher Index Page"},{"id":314537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56a0bdc5e4b0961cf280dc0a","contributors":{"authors":[{"text":"Schaeffer, Blake A.","contributorId":152172,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake A.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":588361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":588360,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stumpf, Richard P.","contributorId":7739,"corporation":false,"usgs":true,"family":"Stumpf","given":"Richard P.","affiliations":[],"preferred":false,"id":588362,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Werdell, P. Jeremy","contributorId":152173,"corporation":false,"usgs":false,"family":"Werdell","given":"P. Jeremy","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":588363,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159582,"text":"70159582 - 2015 - Multiple estimates of effective population size for monitoring a long-lived vertebrate: An application to Yellowstone grizzly bears","interactions":[],"lastModifiedDate":"2016-02-05T10:08:43","indexId":"70159582","displayToPublicDate":"2015-11-10T11:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Multiple estimates of effective population size for monitoring a long-lived vertebrate: An application to Yellowstone grizzly bears","docAbstract":"Effective population size (Ne) is a key parameter for monitoring the genetic health of threatened populations because it reflects a population's evolutionary potential and risk of extinction due to genetic stochasticity. However, its application to wildlife monitoring has been limited because it is difficult to measure in natural populations. The isolated and well-studied population of grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem provides a rare opportunity to examine the usefulness of different Ne estimators for monitoring. We genotyped 729 Yellowstone grizzly bears using 20 microsatellites and applied three single-sample estimators to examine contemporary trends in generation interval (GI), effective number of breeders (Nb) and Ne during 1982–2007. We also used multisample methods to estimate variance (NeV) and inbreeding Ne (NeI). Single-sample estimates revealed positive trajectories, with over a fourfold increase in Ne (≈100 to 450) and near doubling of the GI (≈8 to 14) from the 1980s to 2000s. NeV (240–319) and NeI (256) were comparable with the harmonic mean single-sample Ne (213) over the time period. Reanalysing historical data, we found NeV increased from ≈80 in the 1910s–1960s to ≈280 in the contemporary population. The estimated ratio of effective to total census size (Ne/Nc) was stable and high (0.42–0.66) compared to previous brown bear studies. These results support independent demographic evidence for Yellowstone grizzly bear population growth since the 1980s. They further demonstrate how genetic monitoring of Ne can complement demographic-based monitoring of Nc and vital rates, providing a valuable tool for wildlife managers.
","language":"English","publisher":"Wiley-Blackwell","doi":"10.1111/mec.13398","collaboration":"Prepared in collaboration with U.S. Fish and Wildlife Service","usgsCitation":"Kamath, P.L., Haroldson, M.A., Luikart, G., Paetkau, D., Whitman, C., and van Manen, F.T., 2015, Multiple estimates of effective population size for monitoring a long-lived vertebrate: An application to Yellowstone grizzly bears: Molecular Ecology, v. 24, no. 22, p. 5507-5521, https://doi.org/10.1111/mec.13398.","productDescription":"15 p.","startPage":"5507","endPage":"5521","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066690","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":311150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Grand Teton National Park, Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.181396484375,\n 42.44778143462245\n ],\n [\n -112.181396484375,\n 45.69083283645816\n ],\n [\n -108.62182617187499,\n 45.69083283645816\n ],\n [\n -108.62182617187499,\n 42.44778143462245\n ],\n [\n -112.181396484375,\n 42.44778143462245\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"22","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-28","publicationStatus":"PW","scienceBaseUri":"56431534e4b0aafbcd017fb2","contributors":{"authors":[{"text":"Kamath, Pauline L. pkamath@usgs.gov","contributorId":4517,"corporation":false,"usgs":true,"family":"Kamath","given":"Pauline","email":"pkamath@usgs.gov","middleInitial":"L.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":579569,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":579570,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luikart, Gordon","contributorId":97409,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":6580,"text":"University of Montana, Flathead Lake Biological Station, Polson, Montana 59860, USA","active":true,"usgs":false}],"preferred":false,"id":579571,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paetkau, David","contributorId":97712,"corporation":false,"usgs":false,"family":"Paetkau","given":"David","email":"","affiliations":[],"preferred":false,"id":579572,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitman, Craig L. cwhitman@usgs.gov","contributorId":4313,"corporation":false,"usgs":true,"family":"Whitman","given":"Craig L.","email":"cwhitman@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":579573,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":579574,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70169021,"text":"70169021 - 2015 - Shaping species with ephemeral boundaries: The distribution and genetic structure of desert tortoise (Gopherus morafkai) in the Sonoran Desert region","interactions":[],"lastModifiedDate":"2016-03-21T12:59:44","indexId":"70169021","displayToPublicDate":"2015-11-10T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Shaping species with ephemeral boundaries: The distribution and genetic structure of desert tortoise (Gopherus morafkai) in the Sonoran Desert region","docAbstract":"We examine the role biogeographical features played in the evolution of Morafka's desert tortoise (Gopherus morafkai) and test the hypothesis that G. morafkai maintains genetically distinct lineages associated with different Sonoran Desert biomes. Increased knowledge of the past and present distribution of the Sonoran Desert region's biota provides insight into the forces that drive and maintain its biodiversity.
\nSonoran Desert biogeographical region; Sonora and Sinaloa, Mexico and Arizona, USA.
\nWe examined wild tortoises from Mexico (n = 155) and Arizona (n = 78), spanning their known distribution. We used mtDNA sequences to reconstruct matrilineal relationships and 25 microsatellite (STR) loci for Bayesian analyses of gene flow. We performed clinal analyses on both mtDNA and STR loci to determine the position and amount of introgression where lineages co-occur. We used GIS to assess the association of genetic structuring with ecological features. We used these data in a hypothesis-driven approach to assess different models of how genetic diversity is maintained and distributed in G. morafkai.
\nGopherus morafkai was found to comprise genetically and geographically distinct ‘Sonoran’ and ‘Sinaloan’ lineages. Both lineages occurred in a relatively narrow zone of overlap in Sinaloan thornscrub, where it transitions into Sonoran desertscrub. Limited introgression occurred at the contact zone. The best-fit model suggests that these lineages diverged in parapatry where the distribution of genotypes is environment-dependent and introgression is inhibited by exogenous selection.
\nThe historically shifting ecotone between tropical deciduous forest and Sonoran desertscrub appears to be a boundary that fostered divergence between parapatric lineages of tortoises. The sharp genetic cline between the two lineages suggests that periods of isolation in temporary refugia due to Pleistocene climatic cycling influenced divergence. Despite incomplete reproductive isolation, the Sonoran and Sinaloan lineages of G. morafkai are on separate evolutionary trajectories.
","language":"English","publisher":"John Wiley & Sons Ltd.","doi":"10.1111/jbi.12664","usgsCitation":"Edwards, T., Vaughn, M., Rosen, P.C., Torres, M.C., Karl, A.E., Culver, M., and Murphy, R.W., 2015, Shaping species with ephemeral boundaries: The distribution and genetic structure of desert tortoise (Gopherus morafkai) in the Sonoran Desert region: Journal of Biogeography, v. 43, p. 484-497, https://doi.org/10.1111/jbi.12664.","productDescription":"14 p.","startPage":"484","endPage":"497","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061035","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471659,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jbi.12664","text":"Publisher Index Page"},{"id":319095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, 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Alice E., Culver Melanie, Murphy Robert W.","journalName":"Journal of Biogeography","publicationDate":"11/10/2015","auditedOn":"11/12/2015"},"contributors":{"authors":[{"text":"Edwards, Taylor","contributorId":62337,"corporation":false,"usgs":true,"family":"Edwards","given":"Taylor","affiliations":[],"preferred":false,"id":623131,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vaughn, Mercy","contributorId":21881,"corporation":false,"usgs":true,"family":"Vaughn","given":"Mercy","email":"","affiliations":[],"preferred":false,"id":623132,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosen, Philip C.","contributorId":70311,"corporation":false,"usgs":true,"family":"Rosen","given":"Philip","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":623133,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Torres, Ma. Cristina Melendez","contributorId":138858,"corporation":false,"usgs":false,"family":"Torres","given":"Ma.","email":"","middleInitial":"Cristina Melendez","affiliations":[{"id":12550,"text":"Comision de Ecologia y Desarrollo Sustentable del Estado de Sonora","active":true,"usgs":false}],"preferred":false,"id":623134,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Karl, Alice E.","contributorId":32844,"corporation":false,"usgs":true,"family":"Karl","given":"Alice","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":623135,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Culver, Melanie 0000-0001-5380-3059 mculver@usgs.gov","orcid":"https://orcid.org/0000-0001-5380-3059","contributorId":4327,"corporation":false,"usgs":true,"family":"Culver","given":"Melanie","email":"mculver@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":127,"text":"Arizona Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true},{"id":12625,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA","active":true,"usgs":false}],"preferred":false,"id":622558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Murphy, Robert W.","contributorId":147498,"corporation":false,"usgs":false,"family":"Murphy","given":"Robert","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":623136,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70160098,"text":"70160098 - 2015 - Accounting for time- and space-varying changes in the gravity field to improve the network adjustment of relative-gravity data","interactions":[],"lastModifiedDate":"2015-12-14T11:38:47","indexId":"70160098","displayToPublicDate":"2015-11-09T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for time- and space-varying changes in the gravity field to improve the network adjustment of relative-gravity data","docAbstract":"The relative gravimeter is the primary terrestrial instrument for measuring spatially and temporally varying gravitational fields. The background noise of the instrument—that is, non-linear drift and random tares—typically requires some form of least-squares network adjustment to integrate data collected during a campaign that may take several days to weeks. Here, we present an approach to remove the change in the observed relative-gravity differences caused by hydrologic or other transient processes during a single campaign, so that the adjusted gravity values can be referenced to a single epoch. The conceptual approach is an example of coupled hydrogeophysical inversion, by which a hydrologic model is used to inform and constrain the geophysical forward model. The hydrologic model simulates the spatial variation of the rate of change of gravity as either a linear function of distance from an infiltration source, or using a 3-D numerical groundwater model. The linear function can be included in and solved for as part of the network adjustment. Alternatively, the groundwater model is used to predict the change of gravity at each station through time, from which the accumulated gravity change is calculated and removed from the data prior to the network adjustment. Data from a field experiment conducted at an artificial-recharge facility are used to verify our approach. Maximum gravity change due to hydrology (observed using a superconducting gravimeter) during the relative-gravity field campaigns was up to 2.6 μGal d−1, each campaign was between 4 and 6 d and one month elapsed between campaigns. The maximum absolute difference in the estimated gravity change between two campaigns, two months apart, using the standard network adjustment method and the new approach, was 5.5 μGal. The maximum gravity change between the same two campaigns was 148 μGal, and spatial variation in gravity change revealed zones of preferential infiltration and areas of relatively high groundwater storage. The accommodation for spatially varying gravity change would be most important for long-duration campaigns, campaigns with very rapid changes in gravity and (or) campaigns where especially precise observed relative-gravity differences are used in the network adjustment.
","language":"English","publisher":"Published for the Royal Astronomical Society, the Deutsche Geophysikalische Gesellschaft, and the European Geophysical Society by Blackwell Scientific Publications","publisherLocation":"Oxford, UK","doi":"10.1093/gji/ggv493","usgsCitation":"Kennedy, J.R., and Ferre, T.P., 2015, Accounting for time- and space-varying changes in the gravity field to improve the network adjustment of relative-gravity data: Geophysical Journal International, v. 2, no. 204, p. 892-906, https://doi.org/10.1093/gji/ggv493.","productDescription":"15 p.","startPage":"892","endPage":"906","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067380","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":471661,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggv493","text":"Publisher Index Page"},{"id":312246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"204","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-10","publicationStatus":"PW","scienceBaseUri":"566ff63be4b09cfe53ca7965","contributors":{"authors":[{"text":"Kennedy, Jeffrey R. 0000-0002-3365-6589 jkennedy@usgs.gov","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":2172,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","email":"jkennedy@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":581889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferre, Ty P.A.","contributorId":102167,"corporation":false,"usgs":true,"family":"Ferre","given":"Ty","email":"","middleInitial":"P.A.","affiliations":[],"preferred":false,"id":581890,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70133111,"text":"ds896 - 2015 - Introductory text","interactions":[],"lastModifiedDate":"2019-11-08T06:28:40","indexId":"ds896","displayToPublicDate":"2015-11-07T12:40:20","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"896","title":"Introductory text","docAbstract":"The U.S. Geological Survey (USGS) provides information on the current use and flow of minerals and mineral-based materials in the U.S. and world economies. This Data Series report on “Historical Global Statistics for Mineral and Material Commodities” contains information on the production of selected commodities from 1990 to the most current year. The data may be used in the analysis of socioeconomic developments and trends and in the study of environmental issues associated with the extraction and processing of the selected commodities.\n\nThis report on global statistics includes U.S. data and is a companion to Data Series 140 on “Historical Statistics for Mineral and Material Commodities in the United States.”","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ds896","usgsCitation":"Matos, G.R., Miller, L.D., and Barry, J.J., 2015, Introductory text: U.S. Geological Survey Data Series 896, HTML, https://doi.org/10.3133/ds896.","productDescription":"HTML","ipdsId":"IP-053968","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":369046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":299384,"type":{"id":15,"text":"Index Page"},"url":"https://minerals.usgs.gov/minerals/pubs/historical-statistics/global/"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551d089ee4b0256c24f42152","contributors":{"authors":[{"text":"Matos, Grecia R. 0000-0002-3285-3070 gmatos@usgs.gov","orcid":"https://orcid.org/0000-0002-3285-3070","contributorId":2656,"corporation":false,"usgs":true,"family":"Matos","given":"Grecia","email":"gmatos@usgs.gov","middleInitial":"R.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":544080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Lisa D. millerl@usgs.gov","contributorId":1304,"corporation":false,"usgs":true,"family":"Miller","given":"Lisa","email":"millerl@usgs.gov","middleInitial":"D.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":544081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barry, James J. jbarry@usgs.gov","contributorId":501,"corporation":false,"usgs":true,"family":"Barry","given":"James","email":"jbarry@usgs.gov","middleInitial":"J.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":544082,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159443,"text":"sir20155120 - 2015 - Water Quality, Cyanobacteria, and Environmental Factors and Their Relations to Microcystin Concentrations for Use in Predictive Models at Ohio Lake Erie and Inland Lake Recreational Sites, 2013-14","interactions":[],"lastModifiedDate":"2015-11-10T13:25:43","indexId":"sir20155120","displayToPublicDate":"2015-11-06T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5120","title":"Water Quality, Cyanobacteria, and Environmental Factors and Their Relations to Microcystin Concentrations for Use in Predictive Models at Ohio Lake Erie and Inland Lake Recreational Sites, 2013-14","docAbstract":"Harmful cyanobacterial “algal” blooms (cyanoHABs) and associated toxins, such as microcystin, are a major water-quality issue for Lake Erie and inland lakes in Ohio. Predicting when and where a bloom may occur is important to protect the public that uses and consumes a water resource; however, predictions are complicated and likely site specific because of the many factors affecting toxin production. Monitoring for a variety of environmental and water-quality factors, for concentrations of cyanobacteria by molecular methods, and for algal pigments such as chlorophyll and phycocyanin by using optical sensors may provide data that can be used to predict the occurrence of cyanoHABs.
\nTo test these monitoring approaches, water-quality samples were collected at Ohio recreational sites during May–November in 2013 and 2014. In 2013, samples were collected monthly at eight sites at eight lakes to facilitate an initial assessment and select sites for more intensive sampling during 2014. In 2014, samples were collected approximately weekly at five sites at three lakes. Physical water-quality parameters were measured at the time of sampling. Composite samples were preserved and analyzed for dissolved and total nutrients, toxins, phytoplankton abundance and biovolume, and cyanobacterial genes by molecular methods. Molecular assays were done to enumerate (1) general cyanobacteria, (2) general Microcystis and Dolichospermum (Anabaena), (3) mcyE genes forMicrocystis, Dolichospermum (Anabaena), and Planktothrix targeting deoxyribonucleic acid (DNA), and (4) mcyE transcripts for Microcystis, Dolichospermum (Anabaena), and Planktothrix targeting ribonucleic acid (RNA).The DNA assays for the mcyE gene provide data on cyanobacteria that have the potential to produce microcystin, whereas the RNA assays provide data on cyanobacteria that are actively transcribing the toxin gene. Environmental data were obtained from available online sources. Quality-control (QC) samples were collected and analyzed for all constituents to characterize bias and variability; however, QC data for molecular assays were examined in more detail than for the other constituents. The QC data for molecular assays suggested that sampling variability and qPCR variability were small in comparison with the combined variability associated with sample filtering, extraction and purification, and the matrix itself.
\nA total of 46 water-quality samples were collected during 2013 at 8 beach sites—Buck Creek, Buckeye Crystal, Deer Creek, Harsha Main, Maumee Bay State Park (MBSP) Inland (negative control site), MBSP Lake Erie, Port Clinton, and Sandusky Bay. Microcystin was detected in 67–100 percent of samples at all sites except for MBSP Inland, where microcystin was detected in only 20 percent of samples. Microcystin concentrations ranged from <0.10 to 48 micrograms per liter (µ/L), with the widest range found at MBSP Lake Erie and the highest concentrations found at Buckeye Crystal. Saxitoxin was detected in five samples, and cylindrospermopsin was not detected in any samples.
\nA total of 65 water-quality samples were collected during 2014 at 5 sites on 3 lakes—Buckeye Fairfield and Onion Island, Harsha Main and Campers, and MBSP Lake Erie beach. Four of the sites were bathing beaches and one site, Onion Island, was an offshore boater swim area. Concentrations of microcystin ranged from <0.10 to 240 µ/L and, as in 2013, the widest range was found at MBSP Lake Erie. At Buckeye Lake, microcystin concentrations were consistently high (greater than 20 µ/L), ranging from 23 to 81 µ/L. At Harsha Main and Campers, microcystin concentrations ranged from <0.10 to 15 µ/L. Saxitoxin was detected in four samples collected at MBSP Lake Erie. Throughout the 2014 season, the cyanobacterial community, as determined by molecular and microscopy methods, and the dominance associated with the highest microcystin concentrations were unique to individual lakes. At Buckeye Lake, Planktothrix dominated the cyanobacterial community throughout the season and Planktothrix DNA and RNA were found in 100 percent of samples; Microcystis mcyE DNA was found in low concentrations. At Harsha Lake, Dolichospermum and Microcystis were a substantial percentage of the community from late May through August, and the highest microcystin concentrations occurred in June and July. At MBSP Lake Erie, Microcystis generally dominated from mid-July through early November, and the highest microcystin concentrations occurred in August.
\nSpearman’s correlation coefficient (rho) was computed to determine the relations between environmental and water-quality factors and microcystin concentrations at four sites—Buckeye Fairfield, Buckeye Onion Island, Harsha Main, and MBSP Lake Erie. Factors were evaluated for use as potential independent variables in two types of predictive models—daily and long-term models. Easily or continuously measured water-quality factors and available environmental data are used for daily predictions that do not require a site visit. Data from factors used in daily predictions and results from samples collected and analyzed in a laboratory are used for long-term predictions (a few days to several weeks). A few statistically significant correlations (p ≤ 0.05) between microcystin concentrations and factors for both daily and long-term predictions were found at Buckeye Onion Island, and many were found at Harsha Main and MBSP Lake Erie. There were only a few statistically significant factors for daily predictions at Buckeye Fairfield, likely because of the lack of variability in microcystin concentrations. Among factors for daily predictions, phycocyanin had the highest Spearman’s correlation to microcystin concentrations (rho = 0.79 to 0.93) at all sites except for Buckeye Fairfield. Turbidity, pH, algae category, and Secchi depth were significantly correlated to microcystin concentrations at Harsha Main and MBSP Lake Erie. Algae categories were observational categories from 0 (none) to 4 (extreme). Several discharge variables (Maumee River at Waterville, river mouth is approximately 3.5 miles from the beach) at MBSP Lake Erie were promising environmental factors for daily predictions. In addition to discrete water-quality measurements recorded at Harsha Main at the time of sampling, many manipulated measurements (factors derived from mathematical manipulation of time-series data) available from a nearby continuous monitor were strongly correlated to microcystin concentrations; the highest correlation was found for the relation between microcystin concentrations and the antecedent 7-day average phycocyanin (rho = 0.98). For long-term predictions, the most highly correlated molecular assays were Planktothrix mcyE DNA at Buckeye Onion Island and Microcystis mcyE DNA at Harsha Main and MBSP Lake Erie. Concentrations of several nutrient constituents were significantly correlated to microcystin concentrations including total nitrogen at Buckeye Onion Island, ammonia and nitrate plus nitrite (both negatively correlated) at Harsha Main and MBSP Lake Erie, and total phosphorus at MBSP Lake Erie.
\nThe results of this study showed that water-quality and environmental variables are promising for use in site-specific daily or long-term predictive models. In order to develop more accurate models to predict toxin concentrations at freshwater lake sites, data need to be collected more frequently and for consecutive days in future studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155120","collaboration":"Prepared in cooperation with the Ohio Water Development Authority","usgsCitation":"Francy, D.S., Graham, J.L., Stelzer, E.A., Ecker, C.D., Brady, A.M.G., Struffolino, Pamela, and Loftin, K.A., 2015, Water quality, cyanobacteria, and environmental factors and their relations to microcystin concentrations for use in predictive models at Ohio Lake Erie and inland lake recreational sites, 2013–14: U.S. Geological Survey Scientific Investigations Report 2015–5120, 58 p., https://dx.doi.org/10.3133/sir20155120.","productDescription":"Report: vii, 58 p.; Appendix","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2013-05-01","temporalEnd":"2014-11-01","ipdsId":"IP-064699","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":310974,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5120/sir20155120.pdf","text":"Report","size":"9.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":310973,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5120/coverthb.jpg"},{"id":310975,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5120/sir20155120_appendix2_phytoplanktondata.xlsx","text":"Appendix 2","size":"181 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 2","linkHelpText":"Phytoplankton abundance and community composition at Ohio recreational lake sites, 2013–14."}],"country":"United States","state":"Ohio","otherGeospatial":"Buck Creek State Park, Buckeye Lake State Park, Deer Creek State Park, East Fork State Park, Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.638916015625,\n 41.413895564677304\n ],\n [\n -83.638916015625,\n 41.7672146942102\n ],\n [\n -82.6556396484375,\n 41.7672146942102\n ],\n [\n -82.6556396484375,\n 41.413895564677304\n ],\n [\n -83.638916015625,\n 41.413895564677304\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.54440307617188,\n 39.89498716884207\n ],\n [\n -82.54440307617188,\n 39.94712141785606\n ],\n [\n -82.40432739257812,\n 39.94712141785606\n ],\n [\n -82.40432739257812,\n 39.89498716884207\n ],\n [\n -82.54440307617188,\n 39.89498716884207\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.17449951171875,\n 38.983965305617666\n ],\n [\n -84.17449951171875,\n 39.0533181067413\n ],\n [\n -84.05982971191406,\n 39.0533181067413\n ],\n [\n -84.05982971191406,\n 38.983965305617666\n ],\n [\n -84.17449951171875,\n 38.983965305617666\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.7546157836914,\n 39.94212035820183\n ],\n [\n -83.7546157836914,\n 39.99369266969988\n ],\n [\n -83.70586395263672,\n 39.99369266969988\n ],\n [\n -83.70586395263672,\n 39.94212035820183\n ],\n [\n -83.7546157836914,\n 39.94212035820183\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.27104568481445,\n 39.59748778978444\n ],\n [\n -83.27104568481445,\n 39.64535376010791\n ],\n [\n -83.21285247802734,\n 39.64535376010791\n ],\n [\n -83.21285247802734,\n 39.59748778978444\n ],\n [\n -83.27104568481445,\n 39.59748778978444\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio Water Science Center
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A collection of 24 datasets, including streamflow, well characteristics, groundwater elevations, and discrete water-quality concentrations, is provided to produce a consistent set of example data to demonstrate typical data manipulations or statistical analysis of hydrologic data. These example data are provided in an R package called smwrData. The data in the package have been collected by the U.S. Geological Survey or published in its reports, for example Helsel and Hirsch (2002). The R package provides a convenient mechanism for distributing the data to users of R within the U.S. Geological Survey and other users in the R community.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151103","usgsCitation":"Lorenz, D.L., 2015, smwrData—An R package of example hydrologic data, version 1.1.1: U.S. Geological\nSurvey Open-File Report 2015–1103, 5 p., https://dx.doi.org/10.3133/ofr20151103.","productDescription":"Report: iii, 3 p.; Appendix","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-040392","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":311067,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1103/coverthb.jpg"},{"id":311068,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1103/ofr20151103.pdf","text":"Report","size":"316 kB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":311069,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1103/downloads/smwrData-manual.pdf","text":"Appendix","size":"193 kB","linkFileType":{"id":1,"text":"pdf"},"description":"Appendix"}],"contact":"Director, Minnesota Water Science Center
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Crocker Reef, located on the outer reef tract of the Florida Keys (fig. 1), was the site of an integrated “reefscape characterization” effort focused on calcification and related biogeochemical processes as part of the U.S. Geological Survey (USGS) Coral Reef Ecosystem STudies (CREST) project. It is characterized as a senile or dead reef, with only scattered stony coral colonies and areas of sand and rubble. It was chosen as an end-member for later comparison to sites with a healthy, growing reef framework. The CREST reefscape characterization included two intensive seasonal sampling trips to capture summer (July 8–17, 2014) and winter (January 29–February 5, 2015) conditions. This report presents water column microbial and environmental data collected for use as metadata in future publications examining reef metabolic processes via metagenomes derived from water samples and fine-scale temporal and spatial carbonate chemistry measurements.
\nMicrobial measurements included enumeration of total bacteria, enumeration of virus-like particles, and plate counts of Vibrio spp. colony-forming units (CFU). These measurements were intended to give a sense of any seasonal changes in the total microbial load and to provide an indication of water quality. Additional environmental parameters measured included water temperature, salinity, dissolved oxygen, and pH. Four sites (table 1) were intensively sampled for periods of approximately 48 hours during summer (July 2014) and winter (January–February 2015), during which water samples were collected every 4 hours for analysis, except when prevented by weather conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151203","usgsCitation":"Kellogg, C.A., Yates, K.K., Lawler, S.N., Moore, C.S., and Smiley, N.A., 2015, Seasonal microbial and environmental parameters at Crocker Reef, Florida Keys, 2014–2015: U.S. Geological Survey Open-File Report 2015–1203, 12 p., https://dx.doi.org/10.3133/ofr20151203.","productDescription":"Report: iv, 12 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-068435","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":310880,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1203/coverthb.jpg"},{"id":310883,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://dx.doi.org/10.5066/F74Q7S25","text":"Data Release","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1203","linkHelpText":"Microbial and environmental dataset from Crocker Reef, Florida Keys, 2014-2015"},{"id":310881,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1203/ofr20151203.pdf","text":"Report","size":"776 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1203"}],"country":"United States","state":"Florida","otherGeospatial":"Crocker Reef","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.58677673339842,\n 24.94186090727989\n ],\n [\n -80.58677673339842,\n 25.027750592082853\n ],\n [\n -80.49407958984375,\n 25.027750592082853\n ],\n [\n -80.49407958984375,\n 24.94186090727989\n ],\n [\n -80.58677673339842,\n 24.94186090727989\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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