The U.S. Geological Survey, in cooperation with the Puerto Rico Department of Natural and Environmental Resources, completed a study to determine whether a relation exists between the extent of forest cover and the magnitude of base flow at two sets of paired drainage basins in the highlands of the municipalities of Adjuntas and Utuado within the mountainous interior of Puerto Rico. One set of paired basins includes the Río Guaónica and Río Tanamá, both tributaries of the Río Grande de Arecibo. The other set includes two smaller basins in the drainage basin of the Río Coabey, which is a tributary of the Río Tanamá. The paired basins in each set have similar rainfall patterns, geologic substrate, and aspect; the principal difference identified in the study is the extent of forest cover and related land uses such as the cultivation of shade and sun coffee. Data describing the hydrology, hydrogeology, and streamflow were used in the analysis. The principal objective of the study was to compare base flow per unit area among basins having different areal extents of forest cover and land uses such as shade coffee and sun coffee cultivation.
Within the mountainous interior of Puerto Rico, a substantial amount of the annual rainfall (45 to 39 percent in the Rio Guaónica and Rio Tanamá, respectively) can migrate to the subsurface and later emerge as base flow in streams. The magnitude of base flow within the two sets of paired basins varies seasonally. Minimum base flows occur during the annual dry season (generally from January to March), and maximum base flows occur during the wet season (generally from August to October). During the dry season or periods of below-normal rainfall, base flow is either the primary or the sole component of streamflow. Daily mean base flow ranged from 3.2 to 20.5 cubic feet per second (ft3 /s) at the Rio Guaónica Basin, and from 4.2 to 23.0 ft3 /s at the Rio Tanamá Basin. The daily mean base flows during 2010 ranged from 0.28 to 0.98 ft3 /s at Tributary 1 and from 0.22 to 0.58 ft3 /s at Tributary 2 of the Rio Coabey. The normalized daily base flow at the Río Guaónica and Río Tanamá Basin during 2010 ranged from 1.3 to 8.1 cubic feet per second per square mile (ft3 /s)/mi2 and from 1.1 to 6.1 (ft3 /s)/mi2 , respectively. The normalized daily base flow for the basins of Tributary 1 and Tributary 2 of Río Coabey during 2010 ranged from 1.0 to 3.6 (ft3 /s)/mi2 and from 1.5 to 3.9 (ft3 /s)/mi2 , respectively.
The normalized mean annual base flow is similar within the larger paired basins of Río Tanamá (2.74 [ft3 /s]/mi2 ) and Río Guaónica (3.15 [ft3 /s]/mi2 ). The mean annual base flow per unit area for both of these basins is about 79 percent of the mean annual streamflow. In the large paired basins, the proportion of Type I land use (forest patches, shade and mixed shade/sun coffee with associated cash crops) is substantially higher in Rio Guaónica Basin (81 percent) than in the Rio Tanamá Basin (59 percent), and the base flow per unit area is also higher. In the small paired basins of Rio Coabey, the proportion of Type I land use is much higher at Tributary 1 (52 percent) than at Tributary 2 (15 percent), but, in contrast to the large basins, the mean annual base flow per unit area is lower (2.22 and 2.62 [ft3 /s]/mi2 , respectively). There is no consistent relation between land use and normalized base flow between the two sets of paired basins in the study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165142","collaboration":"Prepared in cooperation with the Puerto Rico Department of Natural and Environmental Resources","usgsCitation":"Rodríguez-Martínez, Jesús, and Santiago, Marilyn, 2017, The effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico, 2010: U.S. Geological Survey Scientific Investigations Report 2016–5142, 19 p., https://doi.org/10.3133/sir20165142.","productDescription":"Report: vii, 19 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061550","costCenters":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true}],"links":[{"id":438424,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7N58JG5","text":"USGS data release","linkHelpText":"Hydrologic data for the effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico"},{"id":336264,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7N58JG5","text":"USGS data release ","description":"USGS data release","linkHelpText":"Hydrologic data for the effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico"},{"id":336240,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5142/coverthb.jpg"},{"id":336241,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5142/sir20165142.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5142"}],"otherGeospatial":"Puerto Rico","contact":"Director, Caribbean-Florida Water Science Center
4446 Pet Lane
Suite 108
Lutz, FL 33559
https://pr.water.usgs.gov
Datum conversions from the National Geodetic Vertical Datum of 1929 to the North American Vertical Datum of 1988 among inland and coastal gages throughout the hydrologic regions of New England, the Mid-Atlantic, and the South Atlantic-Gulf have implications among river and storm surge forecasting, general commerce, and water-control operations. The process of data conversions may involve the application of a recovered National Geodetic Vertical Datum of 1929–North American Vertical Datum of 1988 offset, a simplistic datum transformation using VDatum or VERTCON software, or a survey, depending on a gaging network datum evaluation, anticipated uncertainties for data use among the cooperative water community, and methods used to derive the conversion. Datum transformations from National Geodetic Vertical Datum of 1929 to North American Vertical Datum of 1988 using VERTCON purport errors of ± 0.13 foot at the 95 percent confidence level among modeled points, claiming more consistency along the east coast. Survey methods involving differential and trigonometric leveling, along with observations using Global Navigation Satellite System technology, afford a variety of approaches to establish or perpetuate a datum during a survey. Uncertainties among leveling approaches are generally < 0.1 foot, and and Global Navigation Satellite System approaches may be categorized with uncertainties of ≤0.1 foot for a Level I quality category and ≥0.1 foot for Level II or III quality categories (defined by the U.S. Geological Survey) by observation and review of experienced practice. The conversion process is initiated with an evaluation of the inland and coastal gage network datum, beginning with altitude datum components and the history of those components queried through the U.S. Geological Survey Groundwater Site Inventory database. Subsequent edits to the Groundwater Site Inventory database may be required and a consensus reached among the U.S. Geological Survey Water Science Centers to identify the outstanding workload categorized as in-office datum transformations or offset applications versus out-of-office survey efforts. Datum conversions or datum establishment for the inland or coastal gaging network should meet datum uncertainty requirements among other Federal agencies. Datum uncertainty requirements are ±0.25 foot for U.S. Army Corps of Engineers water-control or construction projects and ±0.16 foot for Federal Emergency Management Agency field surveys and checkpoint surveys used for mapping. River level forecasts generally are defined as ± 0.10 foot among the National Oceanic and Atmospheric Administration–National Weather Service. Collaboration and communication among the cooperative water community is necessary during a datum conversion or datum change. Datum notification time-change requirements set by the National Oceanic and Atmospheric Administration–National Weather Service vary from 30 to 120 days, depending on datum conversion or datum-change case scenarios. Notification times associated with these case scenarios may be useful to the National Oceanic and Atmospheric Administration–National Weather Service and U.S. Army Corps of Engineers, because their daily operations are time sensitive, unlike the notification time change requirements of other entities that make up the cooperative water community. At the time of this writing, a future geopotential datum resulting from Gravity for the Redefinition of the American Vertical Datum is anticipated in 2022. A future version of VDatum and VERTCON is anticipated to provide a transformation among North American Vertical Datum of 1988 elevations to the new geopotential datum.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B8","usgsCitation":"Rydlund, P.H., Jr., and Noll, M.L., 2017, Vertical datum conversion process for the inland and coastal gage network located in the New England, Mid-Atlantic, and South Atlantic-Gulf hydrologic regions (ver. 1.1, July 2017) U.S. Geological Survey Techniques and Methods, book 11, chap. B8, 29 p., https://doi.org/10.3133/tm11B8.","productDescription":"ix, 29 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-078726","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":399692,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_105491.htm"},{"id":344212,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/11/b08/versionHist.txt"},{"id":336266,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b08/coverthb2.jpg"},{"id":336267,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b08/tm11B8.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 11-8B"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.6484375,\n 30.183121842195515\n ],\n [\n -89.23095703125,\n 30.278044377800153\n ],\n [\n -88.39599609375,\n 30.221101852485987\n ],\n [\n -87.07763671875,\n 30.35391637229704\n ],\n [\n -86.06689453125,\n 30.164126343161097\n ],\n [\n -85.23193359375,\n 29.49698759653577\n ],\n [\n -84.08935546875,\n 30.012030680358613\n ],\n [\n -83.60595703125,\n 29.630771207229\n ],\n [\n -82.77099609375,\n 28.671310915880834\n ],\n [\n -82.99072265625,\n 27.858503954841247\n ],\n [\n -81.93603515625,\n 26.017297563851745\n ],\n [\n -81.32080078125,\n 25.105497373014686\n ],\n [\n -80.15625,\n 25.045792240303445\n ],\n [\n -79.8486328125,\n 26.70635985763354\n ],\n [\n -80.39794921875,\n 28.013801376380712\n ],\n [\n -81.1669921875,\n 30.939924331023445\n ],\n [\n -80.22216796875,\n 32.194208672875384\n ],\n [\n -77.93701171875,\n 33.687781758439364\n ],\n [\n -75.322265625,\n 35.08395557927643\n ],\n [\n -75.1025390625,\n 35.51434313431818\n ],\n [\n -75.7177734375,\n 36.87962060502676\n ],\n [\n -73.6962890625,\n 40.36328834091583\n ],\n [\n -69.85107421874999,\n 41.178653972331674\n ],\n [\n -69.49951171875,\n 41.83682786072714\n ],\n [\n -70.51025390625,\n 42.24478535602799\n ],\n [\n -70.42236328125,\n 43.197167282501276\n ],\n [\n -69.6533203125,\n 43.644025847699496\n ],\n [\n -66.81884765625,\n 44.74673324024678\n ],\n [\n -67.7197265625,\n 45.72152152227954\n ],\n [\n -67.7197265625,\n 47.100044694025215\n ],\n [\n -68.26904296875,\n 47.35371061951363\n ],\n [\n -69.10400390625,\n 47.517200697839414\n ],\n [\n -69.9609375,\n 46.89023157359399\n ],\n [\n -70.51025390625,\n 45.767522962149876\n ],\n [\n -71.25732421875,\n 45.336701909968134\n ],\n [\n -71.71875,\n 45.02695045318546\n ],\n [\n -73.41064453125,\n 45.01141864227728\n ],\n [\n -74.5751953125,\n 44.63739123445585\n ],\n [\n -76.97021484375,\n 43.13306116240612\n ],\n [\n -78.3544921875,\n 42.114523952464246\n ],\n [\n -79.189453125,\n 39.791654835253425\n ],\n [\n -80.26611328125,\n 37.82280243352756\n ],\n [\n -80.88134765625,\n 36.65079252503471\n ],\n [\n -82.41943359375,\n 35.28150065789119\n ],\n [\n -89.49462890625,\n 35.02999636902566\n ],\n [\n -90.41748046874999,\n 32.47269502206151\n ],\n [\n -90.10986328125,\n 31.034108344903512\n ],\n [\n -89.6484375,\n 30.183121842195515\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted March 2017; Version 1.1: July 2017","publicComments":"This report is Chapter 8 in Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data.","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road, MS 100
Rolla, MO 65401
https://mo.water.usgs.gov/
The quartz monzodiorite of Mount Edith and the concentrically zoned intrusive suite of Boulder Baldy constitute the principal Late Cretaceous igneous intrusions hosted by Mesoproterozoic sedimentary rocks of the Newland Formation in the Big Belt Mountains, Montana. These calc-alkaline plutonic masses are manifestations of subduction-related magmatism that prevailed along the western edge of North America during the Cretaceous. Radiogenic isotope data for neodymium, strontium, and lead indicate that the petrogenesis of the associated magmas involved a combination of (1) sources that were compositionally heterogeneous at the scale of the geographically restricted intrusive rocks in the Big Belt Mountains and (2) variable contamination by crustal assimilants also having diverse isotopic compositions. Altered and mineralized rocks temporally, spatially, and genetically related to these intrusions manifest at least two isotopically distinct mineralizing events, both of which involve major inputs from spatially associated Late Cretaceous igneous rocks. Alteration and mineralization of rock associated with the intrusive suite of Boulder Baldy requires a component characterized by significantly more radiogenic strontium than that characteristic of the associated igneous rocks. However, the source of such a component was not identified in the Big Belt Mountains. Similarly, altered and mineralized rocks associated with the quartz monzodiorite of Mount Edith include a component characterized by significantly more radiogenic strontium and lead, particularly as defined by 207Pb/204Pb values. The source of this component appears to be fluids that equilibrated with proximal Newland Formation rocks. Oxygen isotope data for rocks of the intrusive suite of Boulder Baldy are similar to those of subduction-related magmatism that include mantle-derived components; oxygen isotope data for altered and mineralized equivalents are slightly lighter.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1040","usgsCitation":"du Bray, E.A., Unruh, D.M., and Hofstra, A.H., 2017, Isotopic data for Late Cretaceous intrusions and associated altered and mineralized rocks in the Big Belt Mountains, Montana: U.S. Geological Survey Data Series 1040, 12 p., https://doi.org/10.3133/ds1040.","productDescription":"iii, 12 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-079476","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":336901,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1040/ds1040.pdf","text":"Report","size":"5.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1040"},{"id":336899,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1040/coverthb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Big Belt Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5,\n 46.25\n ],\n [\n -111,\n 46.25\n ],\n [\n -111,\n 46.74\n ],\n [\n -111.5,\n 46.74\n ],\n [\n -111.5,\n 46.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Central Mineral and Environmental Resources Science Center
Box 25046, Mail Stop 973
Denver, CO 80225
Approximately 6.4 million tons of dissolved solids are discharged from the Upper Colorado River Basin (UCRB) to the Lower Colorado River Basin each year. This results in substantial economic damages, and tens of millions of dollars are spent annually on salinity control projects designed to reduce salinity loads in surface waters of the UCRB. Dissolved solids in surface water and groundwater have been studied extensively over the past century, and these studies have contributed to a conceptual understanding of sources and transport of dissolved solids. This conceptual understanding was incorporated into a Spatially Referenced Regressions on Watershed Attributes (SPARROW) model to examine sources and transport of dissolved solids in the UCRB. The results of this model were published in 2009. The present report documents the methods and data used to develop an updated dissolved-solids SPARROW model for the UCRB, and incorporates data defining current basin attributes not available in the previous model, including delineation of irrigated lands by irrigation type (sprinkler or flood irrigation), and calibration data from additional monitoring sites.
Dissolved-solids loads estimated for 312 monitoring sites were used to calibrate the SPARROW model, which predicted loads for each of 10,789 stream reaches in the UCRB. The calibrated model provided a good fit to the calibration data as evidenced by R2 and yield R2 values of 0.96 and 0.73, respectively, and a root-mean-square error of 0.47. The model included seven geologic sources that have estimated dissolved-solids yields ranging from approximately 1 to 45 tons per square mile (tons/mi2). Yields generated from irrigated agricultural lands are substantially greater than those from geologic sources, with sprinkler irrigated lands generating an average of approximately 150 tons/mi2 and flood irrigated lands generating between 770 and 2,300 tons/mi2 depending on underlying lithology. The coefficients estimated for six landscape transport characteristics that influence the delivery of dissolved solids from sources to streams, are consistent with the process understanding of dissolved-solids loading to streams in the UCRB.
Dissolved-solids loads and the proportion of those loads among sources in the entire UCRB as well as in major tributaries in the basin are reported, as are loads generated from irrigated lands, rangelands, Bureau of Land Management (BLM) lands, and grazing allotments on BLM lands. Model-predicted loads also are compared with load estimates from 1957 and 1991 at selected locations in three divisions of the UCRB. At the basin scale, the model estimates that 32 percent of the dissolved-solids loads are from irrigated agricultural land sources that compose less than 2 percent of the land area in the UCRB. This estimate is less than previously reported estimates of 40 to 45 percent of basin-scale dissolved-solids loads from irrigated agricultural land sources. This discrepancy could be a result of the implementation of salinity control projects in the basin. Notably, results indicate that the conversion of flood irrigated agricultural lands to sprinkler irrigated agricultural lands is a likely process contributing to the temporal decrease in dissolved-solids loads from irrigated lands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175009","collaboration":"Prepared in cooperation with the Colorado River Basin Salinity Control Forum","usgsCitation":"Miller, M.P., Buto, S.G., Lambert, P.M., and Rumsey, C.A., 2017, Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin: U.S. Geological Survey Scientific Investigations Report 2017–5009, 23 p., https://doi.org/10.3133/sir20175009.","productDescription":"vi, 23 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-076357","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":438425,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LO3JV2","text":"USGS data release","linkHelpText":"SPARROW model input datasets and predictions of total dissolved loads in streams of the Upper Colorado River Basin watershed"},{"id":336947,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5009/coverthb.jpg"},{"id":336948,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5009/sir20175009.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5009"},{"id":336949,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76T0JT4","text":"USGS data release","description":"USGS data release","linkHelpText":"Catchment-flowline network and selected model inputs for an enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin"}],"country":"United States","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.500244140625,\n 35.53222622770337\n ],\n [\n -106.14990234375,\n 35.53222622770337\n ],\n [\n -106.14990234375,\n 43.27720532212024\n ],\n [\n -111.500244140625,\n 43.27720532212024\n ],\n [\n -111.500244140625,\n 35.53222622770337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Intensification of the global hydrological cycle, ranging from larger individual precipitation events to more extreme multiyear droughts, has the potential to cause widespread alterations in ecosystem structure and function. With evidence that the incidence of extreme precipitation years (defined statistically from historical precipitation records) is increasing, there is a clear need to identify ecosystems that are most vulnerable to these changes and understand why some ecosystems are more sensitive to extremes than others. To date, opportunistic studies of naturally occurring extreme precipitation years, combined with results from a relatively small number of experiments, have provided limited mechanistic understanding of differences in ecosystem sensitivity, suggesting that new approaches are needed. Coordinated distributed experiments (CDEs) arrayed across multiple ecosystem types and focused on water can enhance our understanding of differential ecosystem sensitivity to precipitation extremes, but there are many design challenges to overcome (e.g., cost, comparability, standardization). Here, we evaluate contemporary experimental approaches for manipulating precipitation under field conditions to inform the design of ‘Drought-Net’, a relatively low-cost CDE that simulates extreme precipitation years. A common method for imposing both dry and wet years is to alter each ambient precipitation event. We endorse this approach for imposing extreme precipitation years because it simultaneously alters other precipitation characteristics (i.e., event size) consistent with natural precipitation patterns. However, we do not advocate applying identical treatment levels at all sites – a common approach to standardization in CDEs. This is because precipitation variability varies >fivefold globally resulting in a wide range of ecosystem-specific thresholds for defining extreme precipitation years. For CDEs focused on precipitation extremes, treatments should be based on each site's past climatic characteristics. This approach, though not often used by ecologists, allows ecological responses to be directly compared across disparate ecosystems and climates, facilitating process-level understanding of ecosystem sensitivity to precipitation extremes.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13504","usgsCitation":"Knapp, A., Avolio, M.L., Beier, C., Carroll, C.J., Collins, S., Dukes, J.S., Fraser, L.H., Griffin-Nolan, R.J., Hoover, D.L., Jentsch, A., Loik, M.E., Phillips, R.P., Post, A.K., Sala, O.E., Slette, I.J., Yahdjian, L., and Smith, M.D., 2017, Pushing precipitation to the extremes in distributed experiments: Recommendations for simulating wet and dry years: Global Change Biology, v. 23, no. 5, p. 1774-1782, https://doi.org/10.1111/gcb.13504.","productDescription":"9 p.","startPage":"1774","endPage":"1782","ipdsId":"IP-079614","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470023,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gcb.13504","text":"External Repository"},{"id":336943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4efe4b014cc3a3ba47a","contributors":{"authors":[{"text":"Knapp, Alan K.","contributorId":139807,"corporation":false,"usgs":false,"family":"Knapp","given":"Alan K.","affiliations":[{"id":13277,"text":"Graduate Degree Program in Ecology and Department of Biology, Colorado State University, Ft. Collins, CO","active":true,"usgs":false}],"preferred":false,"id":680953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Avolio, Meghan L.","contributorId":187573,"corporation":false,"usgs":false,"family":"Avolio","given":"Meghan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":680954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beier, Claus","contributorId":187574,"corporation":false,"usgs":false,"family":"Beier","given":"Claus","email":"","affiliations":[],"preferred":false,"id":680955,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carroll, Charles J. W.","contributorId":187575,"corporation":false,"usgs":false,"family":"Carroll","given":"Charles","email":"","middleInitial":"J. W.","affiliations":[],"preferred":false,"id":680956,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Scott L.","contributorId":71307,"corporation":false,"usgs":false,"family":"Collins","given":"Scott L.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":680957,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dukes, Jeffrey S.","contributorId":187576,"corporation":false,"usgs":false,"family":"Dukes","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":680958,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fraser, Lauchlan H.","contributorId":187577,"corporation":false,"usgs":false,"family":"Fraser","given":"Lauchlan","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":680959,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Griffin-Nolan, Robert J.","contributorId":187578,"corporation":false,"usgs":false,"family":"Griffin-Nolan","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680960,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hoover, David L. dlhoover@usgs.gov","contributorId":5843,"corporation":false,"usgs":true,"family":"Hoover","given":"David","email":"dlhoover@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":680952,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jentsch, Anke","contributorId":187579,"corporation":false,"usgs":false,"family":"Jentsch","given":"Anke","email":"","affiliations":[],"preferred":false,"id":680961,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Loik, Michael E.","contributorId":187580,"corporation":false,"usgs":false,"family":"Loik","given":"Michael","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":680962,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Phillips, Richard P.","contributorId":187581,"corporation":false,"usgs":false,"family":"Phillips","given":"Richard","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":680963,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Post, Alison K.","contributorId":187582,"corporation":false,"usgs":false,"family":"Post","given":"Alison","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":680964,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sala, Osvaldo E.","contributorId":139047,"corporation":false,"usgs":false,"family":"Sala","given":"Osvaldo","email":"","middleInitial":"E.","affiliations":[{"id":12629,"text":"Arizona State University, Tempe, AZ (DETAIL TO BE ADDED)","active":true,"usgs":false}],"preferred":false,"id":680965,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Slette, Ingrid J.","contributorId":187583,"corporation":false,"usgs":false,"family":"Slette","given":"Ingrid","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680966,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Yahdjian, Laura","contributorId":187584,"corporation":false,"usgs":false,"family":"Yahdjian","given":"Laura","email":"","affiliations":[],"preferred":false,"id":680967,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Smith, Melinda D.","contributorId":187585,"corporation":false,"usgs":false,"family":"Smith","given":"Melinda","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":680968,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70184286,"text":"70184286 - 2017 - Prediction and visualization of redox conditions in the groundwater of Central Valley, California","interactions":[],"lastModifiedDate":"2018-09-25T11:31:39","indexId":"70184286","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Prediction and visualization of redox conditions in the groundwater of Central Valley, California","docAbstract":"Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions.
Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.01.014","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., and Gronberg, J.M., 2017, Prediction and visualization of redox conditions in the groundwater of Central Valley, California: Journal of Hydrology, v. 546, p. 341-356, https://doi.org/10.1016/j.jhydrol.2017.01.014.","productDescription":"16 p.","startPage":"341","endPage":"356","ipdsId":"IP-075668","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","volume":"546","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4f0e4b014cc3a3ba483","contributors":{"authors":[{"text":"Rosecrans, Celia Z. 0000-0003-1456-4360 crosecrans@usgs.gov","orcid":"https://orcid.org/0000-0003-1456-4360","contributorId":187542,"corporation":false,"usgs":true,"family":"Rosecrans","given":"Celia","email":"crosecrans@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":680860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":680862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gronberg, JoAnn M. 0000-0003-4822-7434 jmgronbe@usgs.gov","orcid":"https://orcid.org/0000-0003-4822-7434","contributorId":3548,"corporation":false,"usgs":true,"family":"Gronberg","given":"JoAnn","email":"jmgronbe@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":680861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175427,"text":"70175427 - 2017 - Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from southwest Alaska","interactions":[],"lastModifiedDate":"2021-04-19T17:06:54.450804","indexId":"70175427","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from southwest Alaska","docAbstract":"The distribution of volcanogenic massive sulfide (VMS), porphyry-epithermal, Alaska-type ultramafic-mafic complexes, intrusion-related Au, and granitoid Sn-W ore deposits in southwest Alaska supports current metallogenic models linking the formation of these deposit types to the emplacement of different suites of igneous rocks during the evolution of this convergent plate margin. Regional-scale aeromagnetic data provide a continuous set of observations over the deposits and show contrasting patterns over the igneous rock suites hosting the various deposit types. Combined with surface geologic data and regional metallogenic constraints, aeromagnetic data—filtered to enhance the anomalous magnetic field and map magnetic domains—were used to produce a mineral potential map across this accreted island-arc setting. The reduced-to-pole, upward continuation, and total horizontal gradient transform maps show anomalies that could represent porphyry-epithermal deposits within the intraoceanic- and continental-arc terranes. The tilt derivative transform highlights lineaments within the back arc that may represent zones with potential for VMS deposits. The truncations of tilt derivative lineaments outline a major magnetic domain boundary between the back-arc and craton margin, which is prospective for granitoid Sn-W deposits. Annular tilt derivative highs outline granitoids that could be associated with intrusion-related Au deposits within the craton margin. Shallow, magnetite-rich Alaska-type ultramafic-mafic complexes are mapped by their short-wavelength, high-amplitude anomalies. Successful mineral potential mapping across southwestern Alaska as performed in the present study suggests that filtered aeromagnetic data can be effectively used in mineral exploration in convergent continental margin settings.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.112.2.375","usgsCitation":"Anderson, E., Monecke, T., Hitzman, M.W., Zhou, W., and Bedrosian, P.A., 2017, Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from southwest Alaska: Economic Geology, v. 112, no. 2, p. 375-396, https://doi.org/10.2113/econgeo.112.2.375.","productDescription":"22 p.","startPage":"375","endPage":"396","ipdsId":"IP-069250","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":327805,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.23632812499997,\n 56.992882804633986\n ],\n [\n -132.275390625,\n 56.992882804633986\n ],\n [\n -132.275390625,\n 62.55285695857292\n ],\n [\n -157.23632812499997,\n 62.55285695857292\n ],\n [\n -157.23632812499997,\n 56.992882804633986\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"112","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-07","publicationStatus":"PW","scienceBaseUri":"57c6b175e4b0f2f0cebe6f32","contributors":{"authors":[{"text":"Anderson, Eric D. 0000-0002-0138-6166 ericanderson@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":172766,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric","email":"ericanderson@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":645153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monecke, Thomas","contributorId":50423,"corporation":false,"usgs":true,"family":"Monecke","given":"Thomas","affiliations":[],"preferred":false,"id":645155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hitzman, Murray W. 0000-0002-3876-0537 mhitzman@usgs.gov","orcid":"https://orcid.org/0000-0002-3876-0537","contributorId":200913,"corporation":false,"usgs":true,"family":"Hitzman","given":"Murray","email":"mhitzman@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":false,"id":645156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhou, Wendy","contributorId":205989,"corporation":false,"usgs":false,"family":"Zhou","given":"Wendy","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":814483,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":814484,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184303,"text":"70184303 - 2017 - An evaluation of inorganic toxicity reference values for use in assessing hazards to American robins (Turdus migratorius)","interactions":[],"lastModifiedDate":"2018-08-09T12:25:08","indexId":"70184303","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2006,"text":"Integrated Environmental Assessment and Management","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of inorganic toxicity reference values for use in assessing hazards to American robins (Turdus migratorius)","docAbstract":"When performing screening-level and baseline risk assessments, assessors usually compare estimated exposures of wildlife receptor species with toxicity reference values (TRVs). We modeled the exposure of American robins (Turdus migratorius) to 10 elements (As, Cd, Cr, Cu, Hg, Mn, Pb, Se, Zn, and V) in spring and early summer, a time when earthworms are the preferred prey. We calculated soil benchmarks associated with possible toxic effects to these robins from 6 sets of published TRVs. Several of the resulting soil screening-level benchmarks were inconsistent with each other and less than soil background concentrations. Accordingly, we examined the derivations of the TRVs as a possible source of error. In the case of V, a particularly toxic chemical compound (ammonium vanadate) containing V, not normally present in soil, had been used to estimate a TRV. In the cases of Zn and Cu, use of uncertainty values of 10 in estimating TRVs led to implausibly low soil screening values. In the case of Pb, a TRV was calculated from studies demonstrating reductions in egg production in Japanese quail (Coturnix coturnix japonica) exposed to Pb concentrations well below than those causing toxic effects in other species of birds. The results on quail, which were replicated in additional trials, are probably not applicable to other, unrelated species, although we acknowledge that only a small fraction of all species of birds has been tested. These examples underscore the importance of understanding the derivation and relevance of TRVs before selecting them for use in screening or in ecological risk assessment.
","language":"English","publisher":"Wiley","doi":"10.1002/ieam.1792","usgsCitation":"Beyer, W.N., and Sample, B.E., 2017, An evaluation of inorganic toxicity reference values for use in assessing hazards to American robins (Turdus migratorius): Integrated Environmental Assessment and Management, v. 13, no. 2, p. 352-359, https://doi.org/10.1002/ieam.1792.","productDescription":"8 p.","startPage":"352","endPage":"359","ipdsId":"IP-068783","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":336938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-07","publicationStatus":"PW","scienceBaseUri":"58bfd4efe4b014cc3a3ba47f","contributors":{"authors":[{"text":"Beyer, W. Nelson 0000-0002-8911-9141 nbeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8911-9141","contributorId":3301,"corporation":false,"usgs":true,"family":"Beyer","given":"W.","email":"nbeyer@usgs.gov","middleInitial":"Nelson","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":680906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sample, Bradley E.","contributorId":61135,"corporation":false,"usgs":true,"family":"Sample","given":"Bradley","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":680942,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70184252,"text":"sim3375 - 2017 - Bathymetry of Clear Creek Reservoir, Chaffee County, Colorado, 2016","interactions":[],"lastModifiedDate":"2017-03-07T11:02:19","indexId":"sim3375","displayToPublicDate":"2017-03-06T16:15:00","publicationYear":"2017","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":"3375","title":"Bathymetry of Clear Creek Reservoir, Chaffee County, Colorado, 2016","docAbstract":"To better characterize the water supply capacity of Clear Creek Reservoir, Chaffee County, Colorado, the U.S. Geological Survey, in cooperation with the Pueblo Board of Water Works and Colorado Mountain College, carried out a bathymetry survey of Clear Creek Reservoir. A bathymetry map of the reservoir is presented here with the elevation-surface area and the elevation-volume relations. The bathymetry survey was carried out June 6–9, 2016, using a man-operated boat-mounted, multibeam echo sounder integrated with a Global Positioning System and a terrestrial survey using real-time kinematic Global Navigation Satellite Systems. The two collected datasets were merged and imported into geographic information system software. The equipment and methods used in this study allowed water-resource managers to maintain typical reservoir operations, eliminating the need to empty the reservoir to carry out the survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3375","collaboration":"Prepared in cooperation with the Pueblo Board of Water Works and Colorado Mountain College","usgsCitation":"Kohn, M.S., Kinzel, P.J., and Mohrmann, J.S., 2017, Bathymetry of Clear Creek Reservoir, Chaffee County, Colorado, 2016: U.S. Geological Survey Scientific Investigations Map 3375, 1 sheet, https://doi.org/10.3133/sim3375","productDescription":"Map: 36.01 x 28.00 inches; Data Release, Read Me","onlineOnly":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":336842,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3375/sim3375.pdf","text":"Map","size":"9.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3375"},{"id":336843,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3375/sim3375_readme.txt","size":"8.00 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Box 25046, Mail Stop 415
Denver, CO 80225
The U.S. Geological Survey completed a geology-based assessment of undiscovered, technically recoverable continuous petroleum resources in the Wolfcamp shale in the Midland Basin part of the Permian Basin Province of west Texas. This is the first U.S. Geological Survey evaluation of continuous resources in the Wolfcamp shale in the Midland Basin. Since the 1980s, the Wolfcamp shale in the Midland Basin has been part of the “Wolfberry” play. This play has traditionally been developed using vertical wells that are completed and stimulated in multiple productive stratigraphic intervals that include the Wolfcamp shale and overlying Spraberry Formation. Since the shift to horizontal wells targeting the organic-rich shale of the Wolfcamp, more than 3,000 horizontal wells have been drilled and completed in the Midland Basin Wolfcamp section. The U.S. Geological Survey assessed technically recoverable mean resources of 20 billion barrels of oil and 16 trillion cubic feet of associated gas in the Wolfcamp shale in the Midland Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171013","usgsCitation":"Gaswirth, S.B., 2017, Assessment of continuous oil resources in the Wolfcamp shale of the Midland Basin, Permian Basin Province, Texas, 2016: U.S. Geological Survey Open File-Report 2017–1013, 14 p., https://doi.org/10.3133/ofr20171013.\n","productDescription":"14 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-081644","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":336741,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1013/ofr20171013.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1013"},{"id":336740,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1013/coverthb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Midland Basin, Permian Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103,\n 30.5\n ],\n [\n -103,\n 33.8\n ],\n [\n -100,\n 33.8\n ],\n [\n -100,\n 30.5\n ],\n [\n -103,\n 30.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Central Energy Resources Science Center
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Denver, CO 80225
Groundwater is a major source of drinking water in Lycoming County and adjacent counties in north-central and northeastern Pennsylvania, which are largely forested and rural and are currently undergoing development for hydrocarbon gases. Water-quality data are needed for assessing the natural characteristics of the groundwater resource and the potential effects from energy and mineral extraction, timber harvesting, agriculture, sewage and septic systems, and other human influences.
This report, prepared in cooperation with Lycoming County, presents analytical data for groundwater samples from 75 domestic wells sampled throughout Lycoming County in June, July, and August 2014. The samples were collected using existing pumps and plumbing prior to any treatment and analyzed for physical and chemical characteristics, including nutrients, major ions, metals and trace elements, volatile organic compounds, gross-alpha particle and gross beta-particle activity, uranium, and dissolved gases, including methane and radon-222.
Results indicate groundwater quality generally met most drinking-water standards, but that some samples exceeded primary or secondary maximum contaminant levels (MCLs) for arsenic, iron, manganese, total dissolved solids (TDS), chloride, pH, bacteria, or radon-222. Arsenic concentrations were higher than the MCL of 10 micrograms per liter (µg/L) in 9 of the 75 (12 percent) well-water samples, with concentrations as high as 23.6 μg/L; arsenic concentrations were higher than the health advisory level (HAL) of 2 μg/L in 23 samples (31 percent). Total iron concentrations exceeded the secondary maximum contaminant level (SMCL) of 300 μg/L in 20 of the 75 samples. Total manganese concentrations exceeded the SMCL of 50 μg/L in 20 samples and the HAL of 300 μg/L in 2 of those samples. Three samples had chloride concentrations that exceeded the SMCL of 250 milligrams per liter (mg/L); two of those samples exceeded the SMCL of 500 mg/L for TDS. The pH ranged from 5.3 to 9.15 and did not meet the SMCL range of 6.5 to 8.5 in 22 samples, with 17 samples having a pH less than 6.5 and 8 samples having pH greater than 8.5. Generally, the samples that had elevated TDS, chloride, or arsenic concentrations had high pH.
Total coliform bacteria were detected in 39 of 75 samples (52 percent), with Escherichia coli detected in 10 of those 39 samples. Radon-222 activities ranged from non-detect to 7,420 picocuries per liter (pCi/L), with a median of 863 pCi/L, and exceeded the proposed drinking-water standard of 300 pCi/L in 50 (67 percent) of the 75 samples; radon-222 activities were higher than the alternative proposed standard of 4,000 pCi/L in 3 samples.
Water from 15 of 75 (20 percent) wells had concentrations of methane greater than the reporting level of 0.01 mg/L; detectable methane concentrations ranged from 0.04 to 16.8 mg/L. Two samples had methane concentrations (13.1 and 16.8 mg/L) exceeding the action level of 7 mg/L. Low levels of ethane (up to 0.12 mg/L) were present in the five samples with the highest methane concentrations (near or above 1 mg/L) that were analyzed for hydrocarbon compounds and isotopic composition. The isotopic composition of methane in four of these groundwater samples, from the Catskill and Lock Haven Formations and the Hamilton Group, have sample carbon isotopic ratio delta values (carbon-13/carbon-12) ranging from –42.36 to –36.08 parts per thousand (‰) and hydrogen isotopic ratio delta values (deuterium/protium) ranging from –212.0 to –188.4 ‰, which are consistent with the isotopic compositions reported for mud-gas logging samples from these geologic units and a thermogenic source of the methane. However, the isotopic composition and ratios of methane to ethane in a fifth sample indicate the methane in that sample may be of microbial origin that subsequently underwent oxidation. The fifth sample had the highest concentration of methane, 16.8 mg/L, with an carbon isotopic ratio delta values of -50.59 ‰ and a hydrogen isotopic ratio delta values of -209.7 ‰.
The six well-water samples with the highest methane concentrations also had among the highest pH values (8.25 to 9.15) and elevated concentrations of sodium, lithium, boron, fluoride, arsenic, and bromide. Relatively elevated concentrations of some other constituents, such as barium, strontium, and chloride, commonly were present in, but not limited to, those well-water samples with elevated methane.
Three of the six groundwater samples with the highest methane concentrations had chloride/bromide ratios that indicate mixing with a small amount of brine (0.02 percent or less) similar in composition to those reported at undetermined depth below the freshwater aquifer and for gas and oil well brines in Pennsylvania. The sample with the highest methane concentration and most other samples with low methane concentrations (less than about 1 mg/L) have chloride/bromide ratios that indicate predominantly anthropogenic sources of chloride, such as road-deicing salt, septic systems, and (or) animal waste. Brines that are naturally present may originate from deeper parts of the aquifer system, while anthropogenic sources are more likely to affect shallow groundwater because they occur on or near the land-surface.
The spatial distribution of groundwater compositions generally indicate that (1) uplands along the western border of Lycoming County usually have dilute, slightly acidic oxygenated, calcium-bicarbonate type waters; (2) intermediate altitudes or areas of carbonate bedrock usually have water of near neutral pH, with highest amounts of hardness (calcium and magnesium); (3) stream valleys, low elevations where groundwater may be discharging, and deep wells in uplands usually have water with pH values greater than 8 and highest arsenic, sodium, lithium, bromide concentrations. Geochemical modeling indicated that for samples with elevated pH, sodium, lithium, bromide, and alkalinity, the water chemistry could have resulted by dissolution of calcite (calcium carbonate) combined with cation-exchange and mixing with a small amount of brine. Through cation-exchange reactions between water and bedrock, which are equivalent to processes in a water softener, calcium ions released by calcite dissolution are exchanged for sodium ions on clay minerals. Thus, the assessment of groundwater quality in Lycoming County indicates groundwater is generally of good quality, but various parts of Lycoming County can have groundwater with low to moderate concentrations of methane and other constituents that appear in naturally present brine and produced waters from gas and oil wells at high concentrations.\"
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165143","collaboration":"Prepared in cooperation with the County of Lycoming, Pennsylvania","usgsCitation":"Gross, E.L., and Cravotta, C.A., III, 2017, Groundwater quality for 75 domestic wells in Lycoming County, Pennsylvania, 2014: U.S. Geological Survey Scientific Investigations Report 2016–5143, 74 p., https://doi.org/10.3133/sir20165143.","productDescription":"Report: xi, 74 p.; Appendixes 1-2","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076071","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":336804,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5143/sir20165143_appendix2.xlsx","text":"Appendix 2 - Table 2-1 - ","size":"27.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5143","linkHelpText":"Spearman rank correlation coefficient (r) matrix 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215 Limekiln Road
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http://pa.water.usgs.gov/
A Cooper’s hawk (Accipiter cooperii) was found dead in a ditch leading from a heap leach pad at a gold mine in Nevada. Observations at autopsy included an absence of external lesions, traces of subcutaneous and coronary fat, no food in the upper gastrointestinal tract, and no lesions in the viscera. Cyanide concentrations (µg/g ww) were 5.04 in blood, 3.88 in liver, and 1.79 in brain. No bacteria or viruses were isolated from tissues, and brain cholinesterase activity was within the normal range for a Cooper’s hawk.
","language":"English","publisher":"American Association of Veterinary Laboratory Diagnosticians","publisherLocation":"Lawrence, KS","doi":"10.1177/1040638716687604","usgsCitation":"Franson, J.C., 2017, Cyanide poisoning of a Cooper’s hawk (Accipiter cooperii): Journal of Veterinary Diagnostic Investigation, v. 29, no. 2, p. 258-260, https://doi.org/10.1177/1040638716687604.","productDescription":"3 p.","startPage":"258","endPage":"260","ipdsId":"IP-077866","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":461707,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/1040638716687604","text":"Publisher Index Page"},{"id":336890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-24","publicationStatus":"PW","scienceBaseUri":"58be8336e4b014cc3a3a99cf","contributors":{"authors":[{"text":"Franson, J. Christian 0000-0002-0251-4238 jfranson@usgs.gov","orcid":"https://orcid.org/0000-0002-0251-4238","contributorId":177499,"corporation":false,"usgs":true,"family":"Franson","given":"J.","email":"jfranson@usgs.gov","middleInitial":"Christian","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":680824,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70184273,"text":"70184273 - 2017 - Taxonomic revision of the South American catfish genus Ageneiosus (Siluriformes: Auchenipteridae) with the description of four new species","interactions":[],"lastModifiedDate":"2017-04-24T16:46:51","indexId":"70184273","displayToPublicDate":"2017-03-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2285,"text":"Journal of Fish Biology","active":true,"publicationSubtype":{"id":10}},"title":"Taxonomic revision of the South American catfish genus Ageneiosus (Siluriformes: Auchenipteridae) with the description of four new species","docAbstract":"The catfish genus Ageneiosus in the exclusively Neotropical family Auchenipteridae is revised. Species of Ageneiosus are widely distributed in all major South American continental drainages except the São Francisco River basin and small rivers along the Brazilian east coast. The taxonomic revision was based on examination of available type specimens, additional museum material and comparisons of original descriptions. A suite of morphometric, meristic and qualitative characters of internal and external anatomy were used to diagnose valid species and determine synonyms. Thirteen valid species are recognized in the genus Ageneiosus, some of which are widely distributed across South America. Ageneiosus pardalis is the only trans-Andean species in the genus. Ageneiosus polystictus and Ageneiosus uranophthalmus are more widely distributed than previously reported. Ageneiosus marmoratus is a junior synonym of Ageneiosus inermis. Ageneiosus dentatus is a valid species and its name is removed from the synonymy of Ageneiosus ucayalensis. Four new species are described: Ageneiosus akamai, Ageneiosus apiaka, Ageneiosus intrusus and Ageneiosus lineatus, all from the Amazon River basin. A dichotomous key for all 13 valid species of Ageneiosus species is provided.
","language":"English","publisher":"Wiley-Blackwell","doi":"10.1111/jfb.13246","usgsCitation":"Ribeiro, F., Rapp Py-Daniel, L.H., and Walsh, S.J., 2017, Taxonomic revision of the South American catfish genus Ageneiosus (Siluriformes: Auchenipteridae) with the description of four new species: Journal of Fish Biology, v. 90, no. 4, p. 1388-1478, https://doi.org/10.1111/jfb.13246.","productDescription":"91 p.","startPage":"1388","endPage":"1478","costCenters":[],"links":[{"id":336888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"South America","volume":"90","issue":"4","noUsgsAuthors":false,"publicationDate":"2017-02-09","publicationStatus":"PW","scienceBaseUri":"58be8336e4b014cc3a3a99cd","contributors":{"authors":[{"text":"Ribeiro, Frank","contributorId":140535,"corporation":false,"usgs":false,"family":"Ribeiro","given":"Frank","email":"","affiliations":[{"id":13526,"text":"Universidade Federal do Oeste do Pará, Santarem, Para, Brazil","active":true,"usgs":false}],"preferred":false,"id":680828,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rapp Py-Daniel, Lucia H.","contributorId":140536,"corporation":false,"usgs":false,"family":"Rapp Py-Daniel","given":"Lucia","email":"","middleInitial":"H.","affiliations":[{"id":13527,"text":"Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil","active":true,"usgs":false}],"preferred":false,"id":680829,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Stephen J. 0000-0002-1009-8537 swalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-1009-8537","contributorId":1456,"corporation":false,"usgs":true,"family":"Walsh","given":"Stephen","email":"swalsh@usgs.gov","middleInitial":"J.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":680830,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70184253,"text":"70184253 - 2017 - Detecting spatial ontogenetic niche shifts in complex dendritic ecological networks","interactions":[],"lastModifiedDate":"2017-03-06T09:56:27","indexId":"70184253","displayToPublicDate":"2017-03-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Detecting spatial ontogenetic niche shifts in complex dendritic ecological networks","docAbstract":"Ontogenetic niche shifts (ONS) are important drivers of population and community dynamics, but they can be difficult to identify for species with prolonged larval or juvenile stages, or for species that inhabit continuous habitats. Most studies of ONS focus on single transitions among discrete habitat patches at local scales. However, for species with long larval or juvenile periods, affinity for particular locations within connected habitat networks may differ among cohorts. The resulting spatial patterns of distribution can result from a combination of landscape-scale habitat structure, position of a habitat patch within a network, and local habitat characteristics—all of which may interact and change as individuals grow. We estimated such spatial ONS for spring salamanders (Gyrinophilus porphyriticus), which have a larval period that can last 4 years or more. Using mixture models to identify larval cohorts from size frequency data, we fit occupancy models for each age class using two measures of the branching structure of stream networks and three measures of stream network position. Larval salamander cohorts showed different preferences for the position of a site within the stream network, and the strength of these responses depended on the basin-wide spatial structure of the stream network. The isolation of a site had a stronger effect on occupancy in watersheds with more isolated headwater streams, while the catchment area, which is associated with gradients in stream habitat, had a stronger effect on occupancy in watersheds with more paired headwater streams. Our results show that considering the spatial structure of habitat networks can provide new insights on ONS in long-lived species.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1002/ecs2.1662","usgsCitation":"Fields, W.R., Grant, E., and Lowe, W.H., 2017, Detecting spatial ontogenetic niche shifts in complex dendritic ecological networks: Ecosphere, v. 8, no. 2, e01662: 10 p., https://doi.org/10.1002/ecs2.1662.","productDescription":"e01662: 10 p.","ipdsId":"IP-069867","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470024,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1662","text":"Publisher Index Page"},{"id":336846,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n 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Campbell, Lowe Winsor H.","journalName":"Ecosphere","publicationDate":"2/2017","publiclyAccessibleDate":"2/21/2017"},"contributors":{"authors":[{"text":"Fields, William R.","contributorId":152076,"corporation":false,"usgs":false,"family":"Fields","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":680743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Evan H. Campbell 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":167017,"corporation":false,"usgs":true,"family":"Grant","given":"Evan H. Campbell","email":"ehgrant@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":680744,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowe, Winsor H.","contributorId":126722,"corporation":false,"usgs":false,"family":"Lowe","given":"Winsor","email":"","middleInitial":"H.","affiliations":[{"id":6577,"text":"University of Montana, Division of Biological Sciences, Missoula, MT, 59812, USA.","active":true,"usgs":false}],"preferred":false,"id":680745,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70184205,"text":"fs20173014 - 2017 - The California stream quality assessment","interactions":[],"lastModifiedDate":"2017-03-07T08:07:21","indexId":"fs20173014","displayToPublicDate":"2017-03-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3014","title":"The California stream quality assessment","docAbstract":"In 2017, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project is assessing stream quality in coastal California, United States. The USGS California Stream Quality Assessment (CSQA) will sample streams over most of the Central California Foothills and Coastal Mountains ecoregion (modified from Griffith and others, 2016), where rapid urban growth and intensive agriculture in the larger river valleys are raising concerns that stream health is being degraded. Findings will provide the public and policy-makers with information regarding which human and natural factors are the most critical in affecting stream quality and, thus, provide insights about possible approaches to protect the health of streams in the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173014","issn":"2327-6916 (print)","usgsCitation":"Van Metre, P.C., Egler, A.L., and May, Jason, 2017, The California Stream Quality Assessment: U.S. Geological Survey Fact Sheet 2017–3014, 2 p., https://doi.org/10.3133/fs20173014.","productDescription":"2 p.","additionalOnlineFiles":"N","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":336797,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3014/cover/coverthb.jpg"},{"id":336798,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3014/fs20173014.pdf","text":"Report","size":"733 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2017-3014"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.45312499999999,\n 42.00032514831621\n ],\n [\n -124.365234375,\n 41.86956082699455\n ],\n [\n -124.23339843749999,\n 41.623655390686395\n ],\n [\n -124.18945312500001,\n 41.27780646738183\n ],\n [\n -124.27734374999999,\n 40.896905775860006\n ],\n [\n -124.365234375,\n 40.697299008636755\n ],\n [\n -124.56298828125001,\n 40.329795743702064\n ],\n [\n -123.90380859374999,\n 39.740986355883564\n ],\n [\n -123.92578125,\n 39.30029918615029\n ],\n [\n -123.662109375,\n 38.788345355085625\n ],\n [\n -123.1787109375,\n 38.151837403006766\n ],\n [\n -123.02490234375,\n 37.85750715625203\n ],\n [\n -122.67333984374999,\n 37.666429212090605\n ],\n [\n -122.4755859375,\n 37.28279464911045\n ],\n [\n -122.32177734375,\n 37.020098201368114\n ],\n [\n -121.904296875,\n 36.82687474287728\n ],\n [\n -122.2119140625,\n 36.59788913307022\n ],\n [\n -121.86035156249999,\n 36.03133177633187\n ],\n [\n -121.33300781249999,\n 35.53222622770337\n ],\n [\n -121.00341796874999,\n 35.29943548054545\n ],\n [\n -120.80566406250001,\n 35.08395557927643\n ],\n [\n -120.7177734375,\n 34.52466147177172\n ],\n [\n -119.44335937499999,\n 34.161818161230386\n ],\n [\n -118.828125,\n 33.90689555128866\n ],\n [\n -118.47656249999999,\n 33.706062655101206\n ],\n [\n -118.0810546875,\n 33.65120829920497\n ],\n [\n -117.42187500000001,\n 33.15594830078649\n ],\n [\n -117.20214843749999,\n 32.45415593941475\n ],\n [\n -116.96044921875,\n 32.39851580247402\n ],\n [\n -116.08154296875001,\n 32.43561304116276\n ],\n [\n -115.4443359375,\n 32.54681317351514\n ],\n [\n -114.7412109375,\n 32.47269502206151\n ],\n [\n -114.54345703125,\n 33.04550781490999\n ],\n [\n -114.6533203125,\n 33.33970700424026\n ],\n [\n -114.49951171875,\n 33.54139466898275\n ],\n [\n -114.47753906249999,\n 33.797408767572485\n ],\n [\n -114.14794921875,\n 34.07086232376631\n ],\n [\n -113.92822265625,\n 34.34343606848294\n ],\n [\n -114.345703125,\n 34.470335121217474\n ],\n [\n -114.43359375,\n 34.77771580360469\n ],\n [\n -120.0146484375,\n 38.89103282648846\n ],\n [\n -120.1904296875,\n 42.09822241118974\n ],\n [\n -124.45312499999999,\n 42.00032514831621\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Water-Quality Assessment (NAWQA) Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
http://water.usgs.gov/nawqa/
Cambarus (Cambarus) appalachiensis is a stream-dwelling crayfish endemic to the greater New River basins of Virginia and West Virginia. The new species is morphologically most similar to Cambarus sciotensis. Cambarus appalachiensis can be differentiated from C. sciotensis by its more elongated chelae which possess a single mesial row of tubercles, reduced to no tuberculation on the dorsal-longitudinal ridge of the dactyl, and reduced lateral impression. Cambarus sciotensis has a more subrectangular chelae with two rows of mesial margin tubercles on the chelae, as well as both a pronounced dorsal-longitudinal ridge and pronounced lateral impression. Several chelae meristic ratios also differentiate C. appalachiensis from C. sciotensis. Within the New, Gauley, and lower portions of the Greenbrier basins C. appalachiensis is the dominant tertiary burrowing Cambarus species, and as such, is considered stable across its range.
","language":"English","publisher":"Magnolia Press","doi":"10.11646/zootaxa.4243.3.2","usgsCitation":"Loughman, Z.J., Welsh, S.A., and Thoma, R.F., 2017, Cambarus (C.) appalachiensis, a new species of crayfish (Decapoda: Cambaridae) from the New River Basin of Virginia and West Virginia, USA: Zootaxa, v. 4243, no. 3, p. 432-454, https://doi.org/10.11646/zootaxa.4243.3.2.","productDescription":"23 p.","startPage":"432","endPage":"454","ipdsId":"IP-083475","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia, West Virginia","otherGeospatial":"New River Basin","volume":"4243","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-16","publicationStatus":"PW","scienceBaseUri":"5a05771de4b09af898c7086b","contributors":{"authors":[{"text":"Loughman, Zachary J.","contributorId":76157,"corporation":false,"usgs":false,"family":"Loughman","given":"Zachary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":721398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":1483,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart","email":"swelsh@usgs.gov","middleInitial":"A.","affiliations":[{"id":205,"text":"Cooperative Research Units","active":false,"usgs":true}],"preferred":false,"id":721399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thoma, Roger F.","contributorId":172206,"corporation":false,"usgs":false,"family":"Thoma","given":"Roger","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":721400,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70184270,"text":"70184270 - 2017 - Geomyces and Pseudogymnoascus: Emergence of a primary pathogen, the causative agent of bat white-nose syndrome","interactions":[],"lastModifiedDate":"2020-08-20T19:38:51.46699","indexId":"70184270","displayToPublicDate":"2017-03-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"28","displayTitle":"Geomyces and Pseudogymnoascus: Emergence of a primary pathogen, the causative agent of bat white-nose syndrome","title":"Geomyces and Pseudogymnoascus: Emergence of a primary pathogen, the causative agent of bat white-nose syndrome","docAbstract":"Geomyces and Pseudogymnoascus (Fungi, Ascomycota, Leotiomycetes, aff. Thelebolales) are closely related groups of globally occurring soil-associated fungi. Recently, these genera of fungi have received attention because a newly identified species, Pseudogymnoascus (initially classified as Geomyces) destructans, was discovered in association with significant and unusual mortality of hibernating bats in North America (Blehert et al. 2009; Gargas et al. 2009; Minnis and Linder 2013). This emergent disease called bat white-nose syndrome (WNS), has since caused drastic declines in populations of hibernating bats in the United States and Canada (Turner, Reeder, and Coleman 2011; Thogmartin et al. 2012) and threatens some species with regional extinction (Frick et al. 2010; Langwig et al. 2012; Thogmartin et al. 2013). As primary predators of insects and keystone species for cave ecosystems, the loss of bats due to WNS has important economic and ecological implications.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The fungal community: Its organization and role in the ecosystem","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","publisherLocation":"Boca Raton, FL","isbn":"978-1-4987-0665-0","usgsCitation":"Verant, M.L., Minnis, A.M., Lindner, D.L., and Blehert, D.S., 2017, Geomyces and Pseudogymnoascus: Emergence of a primary pathogen, the causative agent of bat white-nose syndrome, chap. 28 of The fungal community: Its organization and role in the ecosystem, p. 405-415.","productDescription":"11 p.","startPage":"405","endPage":"415","ipdsId":"IP-069740","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":336886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":336885,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.crcpress.com/The-Fungal-Community-Its-Organization-and-Role-in-the-Ecosystem-Fourth/Dighton-White/p/book/9781498706650"}],"edition":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58be8336e4b014cc3a3a99d1","contributors":{"authors":[{"text":"Verant, Michelle L. mverant@usgs.gov","contributorId":5566,"corporation":false,"usgs":true,"family":"Verant","given":"Michelle","email":"mverant@usgs.gov","middleInitial":"L.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":680821,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minnis, Andrew M.","contributorId":10273,"corporation":false,"usgs":false,"family":"Minnis","given":"Andrew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":680822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindner, Daniel L.","contributorId":7411,"corporation":false,"usgs":true,"family":"Lindner","given":"Daniel","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":680823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blehert, David S. 0000-0002-1065-9760 dblehert@usgs.gov","orcid":"https://orcid.org/0000-0002-1065-9760","contributorId":140397,"corporation":false,"usgs":true,"family":"Blehert","given":"David","email":"dblehert@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":680820,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193851,"text":"70193851 - 2017 - Connecting the dots: Preprocessing Apollo 15 panoramic camera images for photogrammetric control","interactions":[],"lastModifiedDate":"2017-11-06T12:33:15","indexId":"70193851","displayToPublicDate":"2017-03-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Connecting the dots: Preprocessing Apollo 15 panoramic camera images for photogrammetric control","docAbstract":"No abstract available.
","conferenceTitle":"48th Lunar and Planetary Science Conference ","conferenceDate":"March 20-24, 2017","conferenceLocation":"The Woodlands, Texas","language":"English","publisher":"Lunar and Planetary Institute","usgsCitation":"Edmundson, K., Archinal, B.A., Becker, T.L., Mapel, J., Robinson, M.S., and Shepherd, M., 2017, Connecting the dots: Preprocessing Apollo 15 panoramic camera images for photogrammetric control, 48th Lunar and Planetary Science Conference , The Woodlands, Texas, March 20-24, 2017, 2 p.","productDescription":"2 p.","numberOfPages":"2","ipdsId":"IP-085588","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":348267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":348257,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.hou.usra.edu/meetings/lpsc2017/"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07e928e4b09af898c8cbfb","contributors":{"authors":[{"text":"Edmundson, Kenneth L. kedmundson@usgs.gov","contributorId":4725,"corporation":false,"usgs":true,"family":"Edmundson","given":"Kenneth L.","email":"kedmundson@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":720674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Archinal, Brent A. 0000-0002-6654-0742 barchinal@usgs.gov","orcid":"https://orcid.org/0000-0002-6654-0742","contributorId":2816,"corporation":false,"usgs":true,"family":"Archinal","given":"Brent","email":"barchinal@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":720675,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Becker, Tammy L. tbecker@usgs.gov","contributorId":4388,"corporation":false,"usgs":true,"family":"Becker","given":"Tammy","email":"tbecker@usgs.gov","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":720676,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mapel, J.A.","contributorId":200020,"corporation":false,"usgs":false,"family":"Mapel","given":"J.A.","email":"","affiliations":[],"preferred":false,"id":720677,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robinson, Mark S.","contributorId":167665,"corporation":false,"usgs":false,"family":"Robinson","given":"Mark","email":"","middleInitial":"S.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":720678,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shepherd, M.R.","contributorId":200021,"corporation":false,"usgs":false,"family":"Shepherd","given":"M.R.","email":"","affiliations":[],"preferred":false,"id":720679,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70184250,"text":"70184250 - 2017 - Extreme oceanographic forcing and coastal response due to the 2015–2016 El Niño","interactions":[],"lastModifiedDate":"2017-03-06T10:23:24","indexId":"70184250","displayToPublicDate":"2017-03-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Extreme oceanographic forcing and coastal response due to the 2015–2016 El Niño","docAbstract":"The El Niño-Southern Oscillation is the dominant mode of interannual climate variability across the Pacific Ocean basin, with influence on the global climate. The two end members of the cycle, El Niño and La Niña, force anomalous oceanographic conditions and coastal response along the Pacific margin, exposing many heavily populated regions to increased coastal flooding and erosion hazards. However, a quantitative record of coastal impacts is spatially limited and temporally restricted to only the most recent events. Here we report on the oceanographic forcing and coastal response of the 2015–2016 El Niño, one of the strongest of the last 145 years. We show that winter wave energy equalled or exceeded measured historical maxima across the US West Coast, corresponding to anomalously large beach erosion across the region. Shorelines in many areas retreated beyond previously measured landward extremes, particularly along the sediment-starved California coast.
","language":"English","publisher":"Nature Publishing Group","publisherLocation":"London","doi":"10.1038/ncomms14365","usgsCitation":"Barnard, P., Hoover, D.J., Hubbard, D.M., Snyder, A.G., Ludka, B., Allan, J., Kaminsky, G.M., Ruggiero, Gallien, T.W., Gabel, L., McCandless, D., Weiner, H.M., Cohn, N., Anderson, D.L., and Serafin, K.A., 2017, Extreme oceanographic forcing and coastal response due to the 2015–2016 El Niño: Nature Communications, v. 8, 14365: 8 p., https://doi.org/10.1038/ncomms14365.","productDescription":"14365: 8 p.","ipdsId":"IP-075724","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470026,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/ncomms14365","text":"Publisher Index Page"},{"id":336849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Pacific 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Article"},"seriesTitle":{"id":874,"text":"Aquatic Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"The effects of fipronil and the photodegradation product fipronil desulfinyl on growth and gene expression in juvenile blue crabs, Callinectes sapidus, at different salinities","docAbstract":"Endocrine disrupting compounds (EDCs) are now widely established to be present in the environment at concentrations capable of affecting wild organisms. Although many studies have been conducted in fish, less is known about effects in invertebrates such as decapod crustaceans. Decapods are exposed to low concentrations of EDCs that may cause infertility, decreased growth, and developmental abnormalities. The objective herein was to evaluate effects of fipronil and its photodegradation product fipronil desulfinyl. Fipronil desulfinyl was detected in the eggs of the decapod Callinectes sapidus sampled off the coast of South Carolina. As such, to examine specific effects on C. sapidus exposed in early life, we exposed laboratory-reared juveniles to fipronil and fipronil desulfinyl for 96 hours at three nominal concentrations (0.01, 0.1, 0.5 μg/L) and two different salinities (10, 30 ppt). The size of individual crabs (weight, carapace width) and the expression of several genes critical to growth and reproduction were evaluated. Exposure to fipronil and fipronil desulfinyl resulted in significant size increases in all treatments compared to controls. Levels of expression for vitellogenin (Vtg), an egg yolk precursor, and the ecdysone receptor (EcR), which binds to ecdysteroids that control molting, were inversely correlated with increasing fipronil and fipronil desulfinyl concentrations. Effects on overall growth and on the expression of EcR and Vtg differ depending on the exposure salinity. The solubility of fipronil is demonstrated to decrease considerably at higher salinities. This suggests that fipronil and its photodegradation products may be more bioavailable to benthic organisms as salinity increases, as more chemical would partition to tissues. Our findings suggest that endocrine disruption is occurring through alterations to gene expression in C. sapidus populations exposed to environmental levels of fipronil, and that effects may be dependent upon the salinity at which exposure occurs.
","language":"English","publisher":"American Society for Testing and Materials","publisherLocation":"Philadelphia, PA","doi":"10.1016/j.aquatox.2017.02.027","usgsCitation":"Goff, A.D., Saranjampour, P., Ryan, L.M., Hladik, M., Covi, J.A., Armbrust, K.L., and Brander, S.M., 2017, The effects of fipronil and the photodegradation product fipronil desulfinyl on growth and gene expression in juvenile blue crabs, Callinectes sapidus, at different salinities: Aquatic Toxicology, v. 186, p. 96-104, https://doi.org/10.1016/j.aquatox.2017.02.027.","productDescription":"9 p.","startPage":"96","endPage":"104","ipdsId":"IP-082911","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":470025,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.aquatox.2017.02.027","text":"Publisher Index Page"},{"id":336891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"186","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58be8336e4b014cc3a3a99d3","contributors":{"authors":[{"text":"Goff, Andrew D.","contributorId":187543,"corporation":false,"usgs":false,"family":"Goff","given":"Andrew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":680808,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saranjampour, Parichehr","contributorId":187544,"corporation":false,"usgs":false,"family":"Saranjampour","given":"Parichehr","email":"","affiliations":[],"preferred":false,"id":680809,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryan, Lauren M.","contributorId":187547,"corporation":false,"usgs":false,"family":"Ryan","given":"Lauren","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":680834,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hladik, Michelle 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":784,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":680807,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Covi, Joseph A.","contributorId":187548,"corporation":false,"usgs":false,"family":"Covi","given":"Joseph","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":680835,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Armbrust, Kevin L.","contributorId":187545,"corporation":false,"usgs":false,"family":"Armbrust","given":"Kevin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":680810,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brander, Susanne M.","contributorId":187546,"corporation":false,"usgs":false,"family":"Brander","given":"Susanne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":680811,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70184217,"text":"fs20173018 - 2017 - Landsat eyes help guard the world's forests","interactions":[],"lastModifiedDate":"2017-03-06T12:58:39","indexId":"fs20173018","displayToPublicDate":"2017-03-03T16:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3018","title":"Landsat eyes help guard the world's forests","docAbstract":"The Landsat program is a joint effort between the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), but the partner agencies have distinct roles. NASA develops remote-sensing instruments and spacecraft, launches satellites, and validates their performance in orbit. The USGS owns and operates Landsat satellites in space and manages their data transmissions, including ground reception, archiving, product generation, and public distribution. In 2008, with support from the U.S. Department of the Interior, the USGS made its Landsat data free to anyone in the world.
The current satellites in the Landsat program, Landsat 7 (launched in 1999) and Landsat 8 (launched in 2013), provide complete coverage of the Earth every eight days. A Landsat 9 satellite is scheduled for launch in late 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173018","usgsCitation":"Campbell, Jon, 2017, Landsat eyes help guard the world's forests: U.S. Geological Survey Fact Sheet 2017–3018, 2 p., https://doi.org/10.3133/fs20173018.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084384","costCenters":[{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"links":[{"id":336823,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3018/coverthb.jpg"},{"id":336824,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3018/fs20173018.pdf","text":"Report","size":"2.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3018"}],"otherGeospatial":"Earth","contact":"Associate Director
U.S. Geological Survey
Climate and Land Use Change Mission Area
12201 Sunrise Valley Drive
Reston, VA 20192
Web site: Climate and Land Use Change
Alaska consists of more than 663,000 square miles (1,717,000 square kilometers) of land—more than a sixth of the total area of the United States—and large tracts of it have not been systematically studied or sampled for mineral-resource potential. Many regions of the State are known to have significant mineral-resource potential, and there are currently six operating mines in the State along with numerous active mineral exploration projects. The U.S. Geological Survey and the Alaska Division of Geological & Geophysical Surveys have developed a new geospatial tool that integrates and analyzes publicly available databases of geologic information and estimates the mineral-resource potential for critical minerals, which was recently used to evaluate Alaska. The results of the analyses highlight areas that have known mineral deposits and also reveal areas that were not previously considered to be prospective for these deposit types. These results will inform land management decisions by Federal, State, and private landholders, and will also help guide future exploration activities and scientific investigations in Alaska.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173012","usgsCitation":"Karl, S.M., and Labay, K.A., Geospatial analysis identifies critical mineral-resource potential in Alaska: U.S. Geological Survey Fact Sheet 2017–3012, 4 p., https://doi.org/10.3133/fs20173012.","productDescription":"4 p.","ipdsId":"IP-082372","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":336820,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3012/fs20173012.pdf","text":"Report","size":"6.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3012"},{"id":336819,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3012/coverthb.jpg"}],"country":"United 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\":{\"name\":\"Alaska\",\"nation\":\"USA \"}}]}","contact":"Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
Distinctive rock packages assigned to three provinces are overlain by younger sedimentary rocks and intruded by widely dispersed latest Cretaceous and (or) early Tertiary granitic rocks. Much of the east half of the map area lies in the Alaska-Aleutian Range province; the Jurassic to Tertiary Alaska-Aleutian Range batholith and derivative Jurassic sedimentary rocks form the core of this province, which is intruded and overlain by the Aleutian magmatic arc. The Lime Hills province, the carbonate platform, occurs in the north-central part of the map area. The Paleozoic and Mesozoic Ahklun Mountains province in the western part of the map area includes abundant chert, argillite, and graywacke and lesser limestone, basalt, and tectonic mélange. The Kuskokwim Group, an Upper Cretaceous turbidite sequence, is extensively exposed and bounds all three provinces in the west-central part of the map area.
Staff, Alaska Science Center
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Science Center
The risks to wildlife and humans from uranium (U) mining in the Grand Canyon watershed are largely unknown. In addition to U, other co-occurring ore constituents contribute to risks to biological receptors depending on their toxicological profiles. This study characterizes the pre-mining concentrations of total arsenic (As), cadmium (Cd), copper (Cu), lead (Pb), mercury (Hg), nickel (Ni), selenium (Se), thallium (Tl), U, and zinc (Zn); radiation levels; and histopathology in biota (vegetation, invertebrates, amphibians, birds, and mammals) at the Canyon Mine. Gross alpha levels were below the reporting limit (4 pCi/g) in all samples, and gross beta levels were indicative of background in vegetation (<10–17 pCi/g) and rodents (<10–43.5 pCi/g). Concentrations of U, Tl, Pb, Ni, Cu, and As in vegetation downwind from the mine were likely the result of aeolian transport. Chemical concentrations in rodents and terrestrial invertebrates indicate that surface disturbance during mine construction has not resulted in statistically significant spatial differences in fauna concentrations adjacent to the mine. Chemical concentrations in egg contents and nestlings of non-aquatic birds were less than method quantification limits or did not exceed toxicity thresholds. Bioaccumulation of As, Pb, Se, Tl, and U was evident in Western spadefoot (Spea multiplicata) tadpoles from the mine containment pond; concentrations of As (28.9–31.4 μg/g) and Se (5.81–7.20 μg/g) exceeded toxicity values and were significantly greater than in tadpoles from a nearby water source. Continued evaluation of As and Se in biota inhabiting and forging in the mine containment pond is warranted as mining progresses.
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