The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service collected data on water and bottom-sediment chemistry to be used to evaluate a new water rights acquisition program designed to enhance wetland habitat in Stillwater National Wildlife Refuge and in Lahontan Valley, Churchill County, Nevada. The area supports habitat critical to the feeding and resting of migratory birds travelling the Pacific Flyway. Information about how water rights acquisitions may affect the quality of water delivered to the wetlands is needed by stakeholders and Stillwater National Wildlife Refuge managers in order to evaluate the effectiveness of this approach to wetlands management. A network of six sites on waterways that deliver the majority of water to Refuge wetlands was established to monitor the quality of streamflow and bottom sediment. Each site was visited every 4 to 6 weeks and selected water-quality field parameters were measured when flowing water was present. Water samples were collected at varying frequencies and analyzed for major ions, silica, and organic carbon, and for selected species of nitrogen and phosphorus, trace elements, pharmaceuticals, and other trace organic compounds. Bottom-sediment samples were collected for analysis of selected trace elements.
Dissolved-solids concentrations exceeded the recommended criterion for protection of aquatic life (500 milligrams per liter) in 33 of 62 filtered water samples. The maximum arsenic criterion (340 micrograms per liter) was exceeded twice and the continuous criterion was exceeded seven times. Criteria protecting aquatic life from continuous exposure to aluminum, cadmium, lead, and mercury (87, 0.72, 2.5, and 0.77 micrograms per liter, respectively) were exceeded only once in filtered samples (27, 40, 32, and 36 samples, respectively). Mercury was the only trace element analyzed in bottom-sediment samples to exceed the published probable effect concentration (1,060 micrograms per kilogram).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1072","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Thodal, C.E., 2017, Chemical quality of water and bottom sediment, Stillwater National Wildlife Refuge, Lahontan Valley, Nevada: U.S. Geological Survey Data Series Report 1072, 38 p., plus supplemental data, https://doi.org/10.3133/ds1072.","productDescription":"Report: vi, 38 p.; Supplemental Data","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-083557","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":350181,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1072/ds1072.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1072"},{"id":350180,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1072/coverthb.jpg"},{"id":350182,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/1072/ds1072_suppData.xlsx","text":"Supplemental Data","size":"575 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 1072"}],"country":"United States","state":"Nevada","otherGeospatial":"Stillwater National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.55484008789061,\n 39.46164364205549\n ],\n [\n -118.49853515625,\n 39.46111351521458\n ],\n [\n -118.50265502929688,\n 39.47489550075251\n ],\n [\n -118.48205566406251,\n 39.47807557129829\n ],\n [\n -118.48068237304686,\n 39.50615988027491\n ],\n [\n -118.46214294433592,\n 39.505100301007545\n ],\n [\n -118.46420288085936,\n 39.54958871848275\n ],\n [\n -118.44291687011719,\n 39.549059262117225\n ],\n [\n -118.4497833251953,\n 39.564941195531496\n ],\n [\n -118.4051513671875,\n 39.564411856338054\n ],\n [\n -118.40789794921875,\n 39.57552713084889\n ],\n [\n -118.38867187500001,\n 39.576585635482296\n ],\n [\n -118.39073181152344,\n 39.607804249995105\n ],\n [\n -118.37013244628905,\n 39.60727523813919\n ],\n [\n -118.37150573730467,\n 39.63530729658601\n ],\n [\n -118.36395263671875,\n 39.63636488778663\n ],\n [\n -118.36051940917969,\n 39.678126804900295\n ],\n [\n -118.3447265625,\n 39.680240661158805\n ],\n [\n -118.34541320800781,\n 39.72303232864369\n ],\n [\n -118.553466796875,\n 39.72303232864369\n ],\n [\n -118.55484008789061,\n 39.46164364205549\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, NV 89701
Streamflow distribution maps for the Cannon River and St. Louis River drainage basins were developed by the U.S. Geological Survey, in cooperation with the Legislative-Citizen Commission on Minnesota Resources, to illustrate relative and cumulative streamflow distributions. The Cannon River was selected to provide baseline data to assess the effects of potential surficial sand mining, and the St. Louis River was selected to determine the effects of ongoing Mesabi Iron Range mining. Each drainage basin (Cannon, St. Louis) was subdivided into nested drainage basins: the Cannon River was subdivided into 152 nested drainage basins, and the St. Louis River was subdivided into 353 nested drainage basins. For each smaller drainage basin, the estimated volumes of groundwater discharge (as base flow) and surface runoff flowing into all surface-water features were displayed under the following conditions: (1) extreme low-flow conditions, comparable to an exceedance-probability quantile of 0.95; (2) low-flow conditions, comparable to an exceedance-probability quantile of 0.90; (3) a median condition, comparable to an exceedance-probability quantile of 0.50; and (4) a high-flow condition, comparable to an exceedance-probability quantile of 0.02.
Streamflow distribution maps were developed using flow-duration curve exceedance-probability quantiles in conjunction with Soil-Water-Balance model outputs; both the flow-duration curve and Soil-Water-Balance models were built upon previously published U.S. Geological Survey reports. The selected streamflow distribution maps provide a proactive water management tool for State cooperators by illustrating flow rates during a range of hydraulic conditions. Furthermore, after the nested drainage basins are highlighted in terms of surface-water flows, the streamflows can be evaluated in the context of meeting specific ecological flows under different flow regimes and potentially assist with decisions regarding groundwater and surface-water appropriations. Presented streamflow distribution maps are foundational work intended to support the development of additional streamflow distribution maps that include statistical constraints on the selected flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3390","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources","usgsCitation":"Smith, E.A., Sanocki, C.A., Lorenz, D.L., and Jacobsen, K.E., 2017, Streamflow distribution maps for the Cannon River drainage basin, southeast Minnesota, and the St. Louis River drainage basin, northeast Minnesota: U.S. Geological Survey Scientific Investigations Map 3390, pamphlet 16 p., 2 sheets, https://doi.org/10.3133/sim3390.","productDescription":"Pamphlet: vii, 16 p.; 2 Sheets: 22.0 inches x 11.0 inches; Data Releases","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060395","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":350215,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3390/sim3390.pdf","text":"Pamphlet","size":"976 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3390 Pamphlet"},{"id":350216,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3390/sim3390_sheet1.pdf","text":"Sheet 1","size":"480 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3390 Sheet 1"},{"id":350217,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3390/sim3390_sheet2.pdf","text":"Sheet 2","size":"590 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":350214,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3390/coverthb.jpg"},{"id":350218,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72V2DMN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-Water-Balance model data sets for the Cannon River drainage basin, southeast Minnesota, 1995-2010"},{"id":350219,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z60MJ0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-Water-Balance model data sets for the St. Louis River drainage basin, northeast Minnesota, 1995-2010"}],"country":"United States","state":"Minnesota","otherGeospatial":"Cannon River Drainage Basin, St. Louis River Drainage Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.25,\n 46.63435070293566\n ],\n [\n -91.5,\n 46.63435070293566\n ],\n [\n -91.5,\n 47.89056441663247\n ],\n [\n -93.25,\n 47.89056441663247\n ],\n [\n -93.25,\n 46.63435070293566\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.8,\n 43.75\n ],\n [\n -92.75,\n 43.75\n ],\n [\n -92.75,\n 44.6\n ],\n [\n -93.8,\n 44.6\n ],\n [\n -93.8,\n 43.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Populations of migratory birds present unique conservation challenges given the often vast distances separating critical resources throughout the annual cycle. Migration areas close to the breeding grounds represent a link between two key stages of the annual cycle, and understanding migration ecology as birds exit the breeding grounds may be particularly informative for successful conservation. We studied migration phenology and stopover ecology of an endangered subspecies of the Red Knot Calidris canutus rufa at a migration area relatively close to its breeding range. Using mark-recapture/resight data and a Jolly-Seber model for open populations, we described the arrival and departure schedules, stopover duration, and passage population size at the Mingan Archipelago, Quebec, Canada. Red Knots arrived at the study area in two distinct waves of birds separated by approximately 22 days. Nearly 30% of the passage population arrived in the first wave of arrivals during 15–18 July, and approximately 22% arrived in a second wave during 8–11 August. The sex-ratio in the stopover population at the time of the first wave was slightly skewed toward females, whereas the second wave was heavily skewed toward males. Because males remain on the breeding grounds to care for young, this may reflect successful
breeding in the year of our study. The estimated stopover duration (population mean) was 11 days (95% credible interval: 10.3–11.7 days), but stopover persistence was variable throughout the season. We estimated a passage population size of 9,450 birds (8,355–10,710), a minimum estimate for reasons related to the duration of our sampling. Mingan Archipelago is thus an important migration area for this endangered subspecies and could be a priority in conservation planning. Our results also emphasize the advantages of mark-recapture/resight approaches for estimating migration phenology and stopover persistence.
The Great Lakes Areas of Concern (AOCs) are considered to be the most severely degraded areas within the Great Lakes basin, as defined in the Great Lakes Water Quality Agreement and amendments. Among the 43 designated AOCs are four Lake Michigan AOCs in the State of Wisconsin. The smallest of these AOCs is the Sheboygan River AOC, which was designated as an AOC because of sediment contamination from polychlorinated biphenyl compounds (PCBs), polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and heavy metals. The Sheboygan River AOC has 9 of 14 possible Beneficial Use Impairments (BUIs), which must be addressed to improve overall water-quality, and to ultimately delist the AOC. One of the BUIs associated with this AOC is the “degradation of phytoplankton and zooplankton populations,” which can be removed from the list of impairments when it has been determined that zooplankton community composition and structure at the AOC do not differ significantly from communities at non-AOC comparison sites. In 2012 and 2014, the U.S. Geological Survey collected plankton (phytoplankton and zooplankton) community samples at the Sheboygan River AOC and selected non-AOC sites as part of a larger Great Lakes Restoration Initiative study evaluating both the benthos and plankton communities in all four of Wisconsin’s Lake Michigan AOCs. Although neither richness nor diversity of phytoplankton or zooplankton in the Sheboygan River AOC were found to differ significantly from the non-AOC sites in 2012, results from the 2014 data indicated that zooplankton diversity was significantly lower, and so rated as degraded, when compared to the Manitowoc and Kewaunee Rivers, two non-AOC sites of similar size, land use, and close geographic proximity.
As a follow-up to the 2014 results, zooplankton samples were collected at the same locations in the AOC and non-AOC sites during three sampling trips in spring, summer, and fall 2016. An analysis of similarity indicated no significant difference between the zooplankton community composition and structure in the AOC and non-AOC sites. Zooplankton taxa richness in the AOC was rated as “not degraded” in 2016 because of significantly higher taxa richness values in samples collected from the Sheboygan River AOC, compared with the non-AOC sites as a group (that is, data pooled from both non-AOC sites). Zooplankton diversity in 2016, however, was characterized as “degraded” in the AOC on the basis of significantly lower (p<0.05) values in samples collected from the AOC compared with those collected from the non-AOC sites as a group. Annual variation in zooplankton community composition and structure at the Sheboygan River AOC was significantly different among all 3 years sampled, as indicated by an analysis of similarity test. Zooplankton richness was significantly higher in 2014 than in both 2012 and 2016, and diversity was significantly higher in 2012 than in both 2014 and 2016. Postremediation recovery can often be complicated by non-AOC-related stressors such as nutrients, invasive species, and extremes in flow, which could affect the recovery of zooplankton communities in the Sheboygan River AOC. The effect of the stressors on postremediation recovery underscores the importance of sampling multiple years when assessing the effectiveness of remediation activities. The results from this study will be used by the Wisconsin Department of Natural Resources and the U.S. Environmental Protection Agency to determine if restoration efforts have been effective in removing the plankton BUI and to monitor future conditions in the AOC.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175131","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources and the U.S. Environmental Protection Agency","usgsCitation":"Olds, H.T., Scudder Eikenberry, B.C., Burns, D.J., and Bell, A.H., 2017, An evaluation of the zooplankton community at the Sheboygan River Area of Concern and non-Area of Concern comparison sites in western Lake Michigan rivers and harbors in 2016: U.S. Geological Survey Scientific Investigations Report 2017–5131, 15 p., https://doi.org/10.3133/sir20175131.\n","productDescription":"Report: vii, 15 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-090820","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":349967,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5131/sir20175131.pdf","text":"Report","size":"2.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5131"},{"id":349966,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5131/coverthb.jpg"},{"id":349968,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QV3KD9","text":"USGS data release","linkHelpText":"Zooplankton Community Data at the Sheboygan River Area of Concern and Non-Areas of Concern Comparison Sites in Western Lake Michigan Rivers and Harbors in 2016"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.7313232421875,\n 43.74530493763506\n ],\n [\n -87.4676513671875,\n 43.74530493763506\n ],\n [\n -87.4676513671875,\n 44.484749436619964\n ],\n [\n -87.7313232421875,\n 44.484749436619964\n ],\n [\n -87.7313232421875,\n 43.74530493763506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Located in south-central Texas, the Geronimo Creek and Plum Creek watersheds have long been characterized by elevated nitrate concentrations. From April 2015 through March 2016, an assessment was done by the U.S. Geological Survey, in cooperation with the Guadalupe-Blanco River Authority and the Texas State Soil and Water Conservation Board, to characterize nitrate concentrations and to document possible sources of elevated nitrate in these two watersheds. Water-quality samples were collected from stream, spring, and groundwater sites distributed across the two watersheds, along with precipitation samples and wastewater treatment plant (WWTP) effluent samples from the Plum Creek watershed, to characterize endmember concentrations and isotopic compositions from April 2015 through March 2016. Stream, spring, and groundwater samples from both watersheds were collected during four synoptic sampling events to characterize spatial and temporal variations in water quality and chemical loadings. Water-quality and -quantity data from the WWTPs and stream discharge data also were considered. Samples were analyzed for major ions, selected trace elements, nutrients, and stable isotopes of water and nitrate.
The dominant land use in both watersheds is agriculture (cultivated crops, rangeland, and grassland and pasture). The upper part of the Plum Creek watershed is more highly urbanized and has five major WWTPs; numerous smaller permitted wastewater outfalls are concentrated in the upper and central parts of the Plum Creek watershed. The Geronimo Creek watershed, in contrast, has no WWTPs upstream from or near the sampling sites.
Results indicate that water quality in the Geronimo Creek watershed, which was evaluated only during base-flow conditions, is dominated by groundwater, which discharges to the stream by numerous springs at various locations. Nitrate isotope values for most Geronimo Creek samples were similar, which indicates that they likely have a common source (or sources) of nitrate. Nitrate sources in the Geronimo Creek watershed include a predominance of nitrate from fertilizer applications, as well as a contribution from septic systems. Additional nitrate loading from these sources is ongoing. Chemical loadings of dissolved solids, chloride, and sulfate varied little among sampling events and were low at most sites because of low streamflow.
In contrast to the Geronimo Creek watershed, nitrate sources in the Plum Creek watershed are dominated by effluent discharge from the major WWTPs in the upper and central parts of the watershed. Results indicate that discharge from these WWTPs accounts for the majority of base flow in the watershed. Nitrate concentrations in Plum Creek were dependent on flow conditions, with the highest concentrations measured at lower flows, when flow is dominated by WWTP effluent discharge. In addition to WWTP effluent discharge, the Plum Creek watershed, similar to the Geronimo Creek watershed, also is affected by historical and current loading of nitrate from fertilizer applications and from septic systems in the watershed. Chemical loadings of dissolved solids, chloride, sulfate, and nitrate in Plum Creek at lower flow conditions are highest at the upstream sites and decrease downstream as distance from the WWTPs increases, which is consistent with WWTP effluent as an important control on water quality. Under higher flow conditions, however, nitrate loads to Plum Creek increased by about a factor of three. These higher nitrate loads cannot be accounted for by WWTP effluent discharge from the five major WWTPs in the watershed. This additional loading indicates that nitrate is exported from the northeastern part of the watershed. In the lower part of the Plum Creek watershed, higher concentrations of dissolved solids, chloride, and sulfate occur, which might be affected by produced water associated with oil and gas exploration, or mixing with saline groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175121","collaboration":"Prepared in cooperation with the Guadalupe-Blanco River Authority and the Texas State Soil and Water Conservation Board","usgsCitation":"Lambert, R.B., Opsahl, S.P., and Musgrove, MaryLynn, 2017, Water quality, sources of nitrate, and chemical loadings in the Geronimo Creek and Plum Creek watersheds, south-central Texas, April 2015–March 2016: U.S. Geological Survey Scientific Investigations Report 2017–5121, 49 p., https://doi.org/10.3133/sir20175121.","productDescription":"Report: x, 49 p.; Data Release","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087883","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":350189,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q23Z5S","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data release for water-quality and chemical loading data from the Geronimo Creek and Plum Creek watersheds, south-central Texas, April 2015–March 2016"},{"id":350187,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5121/coverthb.jpg"},{"id":350188,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5121/sir20175121.pdf","text":"Report","size":"3.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5121"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.1667,\n 29.5\n ],\n [\n -97.4167,\n 29.5\n ],\n [\n -97.4167,\n 30.1667\n ],\n [\n -98.1667,\n 30.1667\n ],\n [\n -98.1667,\n 29.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Pavlof Volcano is one of the most frequently active volcanoes in the Aleutian Island arc, having erupted more than 40 times since observations were first recorded in the early 1800s . The volcano is located on the Alaska Peninsula (lat 55.4173° N, long 161.8937° W), near Izembek National Wildlife Refuge. The towns and villages closest to the volcano are Cold Bay, Nelson Lagoon, Sand Point, and King Cove, which are all within 90 kilometers (km) of the volcano (fig. 1). Pavlof is a symmetrically shaped stratocone that is 2,518 meters (m) high, and has about 2,300 m of relief. The volcano supports a cover of glacial ice and perennial snow roughly 2 to 4 cubic kilometers (km3) in volume, which is mantled by variable amounts of tephra fall, rockfall debris, and pyroclastic-flow deposits produced during historical eruptions. Typical Pavlof eruptions are characterized by moderate amounts of ash emission, lava fountaining, spatter-fed lava flows, explosions, and the accumulation of unstable mounds of spatter on the upper flanks of the volcano. The accumulation and subsequent collapse of spatter piles on the upper flanks of the volcano creates hot granular avalanches, which erode and melt snow and ice, and thereby generate watery debris-flow and hyperconcentrated-flow lahars.
Seismic instruments were first installed on Pavlof Volcano in the early 1970s, and since then eruptive episodes have been better characterized and specific processes have been documented with greater certainty. The application of remote sensing techniques, including the use of infrasound data, has also aided the study of more recent eruptions. Although Pavlof Volcano is located in a remote part of Alaska, it is visible from Cold Bay, Sand Point, and Nelson Lagoon, making distal observations of eruptive activity possible, weather permitting. A busy air-travel corridor that is utilized by a numerous transcontinental and regional air carriers passes near Pavlof Volcano. The frequency of air travel across the region results in a relatively large number of airborne observations of eruptive activity. During the 2014 Pavlof eruptions, the Alaska Volcano Observatory received observations and photographs from pilots and local observers, which aided evaluation of the eruptive activity and the areas affected by eruptive products.
This report outlines the chronology of events associated with the 2014 eruptive activity at Pavlof Volcano, provides documentation of the style and character of the eruptive episodes, and reports briefly on the eruptive products and impacts. The principal observations are described and portrayed on maps and photographs, and the 2014 eruptive activity is compared to historical eruptions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175129","usgsCitation":"Waythomas, C.F., Haney, M.M., Wallace, K.L., Cameron, C.E., and Schneider, D.J., 2017, The 2014 eruptions of Pavlof Volcano, Alaska: U.S. Geological Survey Scientific Investigations Report 2017-5129, 27 p., https://doi.org/10.3133/sir20175129. ","productDescription":"vi, 27 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-075355","costCenters":[{"id":121,"text":"Alaska Volcano Observatory","active":false,"usgs":true}],"links":[{"id":350485,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix1.xlsx","text":"Appendix 1","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350486,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix2a.xlsx","text":"Appendix 2A","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350487,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix2b.xlsx","text":"Appendix 2B","size":"150 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350186,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5129"},{"id":350185,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5129/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Pavlof Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.2,\n 55.25\n ],\n [\n -161.7,\n 55.25\n ],\n [\n -161.7,\n 55.547280698640805\n ],\n [\n -162.2,\n 55.547280698640805\n ],\n [\n -162.2,\n 55.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
The Chukchi Borderland is both a stand-alone petroleum province and assessment unit (AU) that lies north of the Chukchi Sea. It is a bathymetrically high-standing block of continental crust that was probably rifted from the Canadian continental margin. The sum of our knowledge of this province is based upon geophysical data (seismic, gravity, and magnetic) and a limited number of seafloor core and dredge samples.
As expected from the limited data set, the basin’s petroleum potential is poorly known. A single assessment unit, the Chukchi Borderland AU, was defined and assigned an overall probability of about a 5 percent chance of at least one petroleum accumulation >50 million barrels of oil equivalent (MMBOE). No quantitative assessment of sizes and numbers of petroleum accumulations was conducted for this AU.
Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
The U.S. Geological Survey (USGS), in support of the Massachusetts Estuaries Project (MEP), delineated groundwater-contributing areas to various hydrologic receptors including ponds, streams, and coastal water bodies throughout southeastern Massachusetts, including portions of the Plymouth-Carver aquifer system and all of Cape Cod. These contributing areas were delineated over a 6-year period from 2003 through 2008 by using previously published regional USGS groundwater-flow models for the Plymouth-Carver region (Masterson and others, 2009), the Sagamore (western) and Monomoy (eastern) flow lenses of Cape Cod (Walter and Whealan, 2005), and lower Cape Cod (Masterson, 2004). The original USGS groundwater-contributing areas were subsequently revised in some locations by the MEP to remove modeling artifacts or to make the contributing areas more consistent with site-specific hydrologic conditions without further USGS review. This report describes the process used to create the USGS groundwater-contributing areas and provides these model results in their original format in a single, publicly accessible publication.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1074","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Carlson, C.S., Masterson, J.P., Walter, D.A., and Barbaro, J.R., 2017, Development of simulated groundwater-contributing areas to selected streams, ponds, coastal water bodies, and production wells in the Plymouth-Carver region and Cape Cod, Massachusetts: U.S. Geological Survey Data Series 1074, 17 p., https://doi.org/10.3133/ds1074.","productDescription":"Report: iv, 17 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087594","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":350104,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7V69H2Z","text":"USGS data release","description":"USGS data release","linkHelpText":"Simulated Groundwater-Contributing Areas to Selected Streams, Ponds, Coastal Water Bodies, and Production Wells, Plymouth-Carver Region and Cape Cod, Massachusetts"},{"id":350102,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1074/coverthb.jpg"},{"id":350103,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1074/ds1074.pdf","text":"Report","size":"5.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1074"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod, Plymouth-Carver Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.015625,\n 41.50857729743935\n ],\n [\n -69.88128662109375,\n 41.50857729743935\n ],\n [\n -69.88128662109375,\n 42.167475010395336\n ],\n [\n -71.015625,\n 42.167475010395336\n ],\n [\n -71.015625,\n 41.50857729743935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Agricultural producers in North Dakota are aware of concerns about degrading water quality, and many of the producers are interested in implementing conservation practices to reduce the export of nutrients from their farms. Producers often implement conservation practices without knowledge of the water quality of the runoff from their farm or if conservation practices they may implement have any effect on water quality. In response to this lack of information, the U.S. Geological Survey, in cooperation with North Dakota State University Extension Service and in coordination with an advisory group consisting of State agencies, agricultural producers, and commodity groups, implemented a monitoring study as part of a Discovery Farms program in North Dakota in 2007. Three data-collection sites were established at each of three farms near Underwood, Embden, and Dazey, North Dakota. The purpose of this report is to describe runoff and water-quality characteristics using data collected at the three Discovery Farms during 2008–16. Runoff and water-quality data were used to help describe the implications of agricultural conservation practices on runoff and water-quality patterns.
Runoff characteristics of monitoring sites at the three farms were determined by measuring flow volume and precipitation. Runoff at the Underwood farm monitoring sites generally was controlled by precipitation in the area, antecedent soil moisture conditions, and, after 2012, possibly by the diversion ditch constructed by the producer. Most of the annual runoff was in March and April each year during spring snowmelt. Runoff characteristics at the Embden farm are complex because of the mix of surface runoff and flow through two separate drainage tile systems. Annual flow volumes for the drainage tiles sites (sites E2 and E3) were several orders of magnitude greater than measured at the surface water site E1. Site E1 generally only had runoff briefly in March and April during spring snowmelt and during only a few large rain events throughout 2009–16. Flow was somewhat continuous at sites E2 and E3 throughout the year during years of increased precipitation, such as in 2010 and 2011. At Dazey farm, annual flow volumes at the most downstream site D3 for 2010–15 ranged from 88 acre-feet (2012) to 12,060 acre-feet (2010). The largest monthly runoff volumes at D1 (most upstream site; combination of data from site D1a [original site] and site D1b [relocated site]) and D3 were in March and April during spring snowmelt runoff and rain events.
At Underwood farm, total ammonia and total phosphorus had the highest concentrations at the most upstream site (U1) and decreased sequentially at sites U2 and U3 downstream. Total ammonia and total phosphorus concentrations at the sites for Underwood farm also generally were higher than measured at sites for the Dazey and Embden farms. At Embden farm, nitrate plus nitrite concentrations were lowest at site E1 (surface-water site) and highest at sites E2 and E3 (drainage tile sites). Nitrate plus nitrite concentrations at sites E2 and E3 also were the highest among all the sites at all three farms. Median total nitrate plus nitrite concentrations for sites E1, E2, and E3 were 0.22, 13, and 10 milligrams per liter as nitrogen, respectively. Nutrient concentrations generally were greater at site D1 (most upstream site) compared to site D3 (most downstream site) at Dazey farm. Higher concentrations at site D1, which is farther upstream and closer to potential sources of nutrients, compared to lower concentrations at site D3, which is farther downstream and receives more runoff, indicates that dilution may be the reason concentrations decrease downstream.
Annual loads for chloride at all three Underwood sites were the greatest in 2011 and the least in 2012, which coincided with years of the greatest and least annual flow volume, respectively. Total ammonia had a similar pattern at the three sites. Nitrate plus nitrite loads displayed a different pattern than chloride and total ammonia, indicating possible different sources. Chloride, total ammonia, total phosphorus, and suspended sediment were transported past site U1 mostly in March and the least from July through October. Monthly nitrate plus nitrite loads had a different pattern than the other constituents, indicating other possible sources such as fertilizer application in the surrounding cropland.
Annual loads for Embden farm were considerably greater at sites E2 and E3 compared to site E1. Annual yields for all constituents also were substantially greater at sites E2 and E3 compared to site E1, mainly because of a combination of higher flow volumes and small contributing drainage areas at sites E2 and E3 compared to site E1.
The greatest annual loads at Dazey farm site D3 for chloride, nitrate plus nitrite, and suspended sediment were in 2010 and 2011, and zero loads were estimated for 2012 because no flow was measured at the site. Mean monthly loads generally were greatest for most constituents in March and April at sites D1 and D3 except for suspended sediment that had the greatest monthly loads in May.
To mitigate runoff and water-quality effects of their operations, the producers implemented various agricultural conservation practices before and during the Discovery Farms monitoring. Even though it was difficult to quantify the effects of the agricultural conservation practices implemented at the farms, the data collected from the Discovery Farms program provided a better understanding of some of the variables that affect runoff and water quality.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175124","collaboration":"Prepared in cooperation with North Dakota State University Extension Service","usgsCitation":"Galloway, J.M., and Nustad, R.A., 2017, Runoff and water-quality characteristics of three Discovery Farms in North Dakota, 2008–16: U.S. Geological Survey Scientific Investigations Report 2017–5124, 68 p.,\nhttps://doi.org/10.3133/sir20175124.","productDescription":"ix, 68 p.","numberOfPages":"82","onlineOnly":"Y","ipdsId":"IP-088875","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":350167,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5124/sir20175124.pdf","text":"Report","size":"3.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Dakota Water Science Center, North Dakota Office
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
Earthquake-triggered ground failure, such as landsliding and liquefaction, can contribute significantly to losses, but our current ability to accurately include them in earthquake-hazard analyses is limited. The development of robust and widely applicable models requires access to numerous inventories of ground failures triggered by earthquakes that span a broad range of terrains, shaking characteristics, and climates. We present an openly accessible, centralized earthquake-triggered groundfailure inventory repository in the form of a ScienceBase Community to provide open access to these data with the goal of accelerating research progress. The ScienceBase Community hosts digital inventories created by both U.S. Geological Survey (USGS) and non-USGS authors. We present the original digital inventory files (when available) as well as an integrated database with uniform attributes. We also summarize the mapping methodology and level of completeness as reported by the original author(s) for each inventory. This document describes the steps taken to collect, process, and compile the inventories and the process for adding additional ground-failure inventories to the ScienceBase Community in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1064","usgsCitation":"Schmitt, R.G., Tanyas, Hakan, Nowicki Jessee, M.A., Zhu, Jing, Biegel, K.M., Allstadt, K.E., Jibson, R.W., Thompson, E.M., van Westen, C.J., Sato, H.P., Wald, D.J., Godt, J.W., Gorum, Tolga, Xu, Chong, Rathje, E.M., Knudsen, K.L., 2017, An open repository of earthquake-triggered ground-failure inventories: U.S. Geological Survey Data Series 1064, 17 p., https://doi.org/10.3133/ds1064.","productDescription":"Report: iii, 17 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-088662","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":438123,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DN43Z6","text":"USGS data release","linkHelpText":"landslides-metadata"},{"id":350095,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1064/coverthb.jpg"},{"id":350096,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1064/ds1064.pdf","text":"Report","size":"2.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1064"},{"id":350097,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H70DB4","text":"USGS data release","description":"USGS data release","linkHelpText":"An Open Repository of Earthquake-Triggered Ground-Failure Inventories"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS–966
Denver, CO 80225–0046
Recent developments of community abundance models (CAMs) enable us to analyze communities subject to imperfect detection. However, existing CAMs assume spatial closure, that is, that individuals are always present in the sampling plots, which is often violated in field surveys. Violation of this assumption, such as in the presence of spatial temporary emigration, can lead to the underestimates of detection probability and overestimates of population densities and diversity metrics. Here, we propose a model that simultaneously accommodates both temporary emigration and imperfect detection by integrating CAMs and a form of hierarchical distance sampling for open populations. Expected values of species richness are obtained via the summation of occupancy (or incidence) probabilities, based on species‐level densities, across all species of the community. Simulations were used to examine the effects of spatial temporary emigration on the estimation of biological communities. We also applied the proposed model to empirical data and constructed area‐based rarefaction curves accounting for temporary emigration. Simulation experiments showed that temporary emigration can decrease the local species richness (α diversity) based on densities and increase the species turnover (β diversity). Raw species counts can overestimate or underestimate α diversity in the presence of temporary emigration, but the specific biases depend on the values of detection and emigration probabilities. Our newly proposed model yielded unbiased estimates of α, β, and γ diversity in the presence of temporary emigration. The application to empirical data suggested that accounting for temporary emigration lowered area‐based rarefaction curves because availability probabilities of individual species were estimated to be <1. Temporary emigration prevails in field surveys and has broad significance for understanding the ecology and function of biological communities and separation of imperfect detection and temporary emigration resolves long‐standing issues in the use of count data. We therefore suggest that the consideration of temporary emigration would contribute to understanding the nature and role of biological communities.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2028","usgsCitation":"Yamaura, Y., and Royle, A., 2017, Community distance sampling models allowing for imperfect detection and temporary emigration: Ecosphere, v. 8, no. 12, p. 1-15, https://doi.org/10.1002/ecs2.2028.","productDescription":"e02028, 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-089901","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469230,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2028","text":"Publisher Index Page"},{"id":363415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"12","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Yamaura, Yuichi","contributorId":173122,"corporation":false,"usgs":false,"family":"Yamaura","given":"Yuichi","email":"","affiliations":[{"id":16855,"text":"Hokkaido University","active":true,"usgs":false}],"preferred":false,"id":761807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":761806,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70176622,"text":"pp1802C - 2017 - Antimony","interactions":[{"subject":{"id":70176622,"text":"pp1802C - 2017 - Antimony","indexId":"pp1802C","publicationYear":"2017","noYear":false,"chapter":"C","title":"Antimony"},"predicate":"IS_PART_OF","object":{"id":70158974,"text":"pp1802 - 2017 - Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply","indexId":"pp1802","publicationYear":"2017","noYear":false,"title":"Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply"},"id":1}],"isPartOf":{"id":70158974,"text":"pp1802 - 2017 - Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply","indexId":"pp1802","publicationYear":"2017","noYear":false,"title":"Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply"},"lastModifiedDate":"2018-03-13T16:10:08","indexId":"pp1802C","displayToPublicDate":"2017-12-19T09:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1802","chapter":"C","title":"Antimony","docAbstract":"Antimony is an important mineral commodity used widely in modern industrialized societies. The element imparts strength, hardness, and corrosion resistance to alloys that are used in many areas of industry, including in lead-acid storage batteries. Antimony’s leading use is as a fire retardant in safety equipment and in household goods, such as mattresses. The U.S. Government has considered antimony to be a critical mineral mainly because of its use in military applications. The great majority of the world’s antimony comes from China, and much of the remainder is shipped to China for smelting. Antimony resources are unevenly distributed around the world. China has the bulk of the world’s identified resources; other countries that have identified antimony resources include Bolivia, Canada, Mexico, Russia, South Africa, Tajikistan, and Turkey. Resources in the United States are located mainly in Alaska, Idaho, Montana, and Nevada. The most significant antimony mineral deposits occur in geologic environments with a thick sequence of siliciclastic sedimentary rocks in areas with significant fault and fracture systems. The most common antimony ore mineral is stibnite (Sb2 S3 ), but more than 100 other minerals also contain antimony. The presence of antimony in surface waters and groundwaters results primarily from rock weathering, soil runoff, and anthropogenic sources. Global emissions of antimony to the atmosphere average 6,100 metric tons per year. Empirical data suggest that the acid-generating potential of antimony mine waste is low.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1802C","isbn":"978-1-4113-3991-0","usgsCitation":"Seal, R.R., II, Schulz, K.J., and DeYoung, J.H., Jr., with contributions from David M. Sutphin, Lawrence J. Drew, James F. Carlin, Jr., and Byron R. Berger, 2017, Antimony, chap. C of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. C1–C17, https://doi.org/10.3133/pp1802C.","productDescription":"vii, 17 p.","numberOfPages":"30","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-078901","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":352475,"rank":3,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/pp/1802/pp1802_erratum-march132018.txt","text":"Erratum","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":339520,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1802/c/coverthb1.jpg"},{"id":339513,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1802/c/pp1802c.pdf","text":"Report","size":"7.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1802 C"}],"contact":"Mineral Resources Program Coordinator
U.S. Geological Survey
913 National Center
Reston, VA 20192
Email: minerals@usgs.gov
https://minerals.usgs.gov
Tellurium (Te) is a very rare element that averages only 3 parts per billion in Earth’s upper crust. It shows a close association with gold and may be present in orebodies of most gold deposit types at levels of tens to hundreds of parts per million. In large-tonnage mineral deposits, such as porphyry copper and seafloor volcanogenic massive sulfide deposits, sulfide minerals may contain hundreds of parts per million tellurium, although the orebodies likely have overall concentrations of 0.1 to 1.0 parts per million tellurium. Tellurium is presently recovered as a primary ore from only two districts in the world; these are the gold-tellurium epithermal vein deposits located adjacent to one another at Dashuigou and Majiagou (Sichuan Province) in southwestern China, and the epithermal-like mineralization at the Kankberg deposit in the Skellefteå VMS district of Västerbotten County, Sweden. Combined, these two groups of deposits account for about 15 percent (about 70 metric tons) of the annual global production of between 450 and 470 metric tons of tellurium. Most of the world’s tellurium, however, is produced as a byproduct of the mining of porphyry copper deposits. These deposits typically yield concentrations of 1 to 4 percent tellurium in the anode slimes recovered during copper refining. Present production of tellurium from the United States is solely from the anode slimes at ASARCO LLC’s copper refinery in Amarillo, Texas, and may total about 50 metric tons per year. The main uses of tellurium are in photovoltaic solar cells and as an additive to copper, lead, and steel alloys in various types of machinery. The environmental data available regarding the mining of tellurium are limited; most concerns to date have focused on the more-abundant metals present in the large-tonnage deposits from which tellurium is recovered as a byproduct. Global reserves of tellurium are estimated to be 24,000 metric tons, based on the amount of tellurium likely contained in global copper reserves and on a 50 percent recovery rate from refinery anode slimes during the commonly used electrolytic process, also known as solvent extraction-electrolytic refining. If the more economical solvent-leach process—a process that does not recover tellurium—is increasingly used in the future to recover lower grades of copper from porphyry and other large-tonnage deposits, then additional high-grade tellurium-rich gold deposits may become new primary sources for tellurium, particularly epithermal vein deposits associated with alkaline magmatism.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1802R","isbn":"978-1-4113-3991-0","usgsCitation":"Goldfarb, R.J., Berger, B.R., George, M.W., and Seal, R.R., II, 2017, Tellurium, chap. R of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. R1–R27, https://doi.org/10.3133/pp1802R.","productDescription":"viii, 27 p.","numberOfPages":"40","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069567","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":334839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1802/r/coverthb1.jpg"},{"id":334840,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1802/r/pp1802r.pdf","text":"Report","size":"3.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1802 R"}],"contact":"Mineral Resources Program Coordinator
U.S. Geological Survey
913 National Center
Reston, VA 20192
Email: minerals@usgs.gov
https://minerals.usgs.gov
Barite (barium sulfate, BaSO4) is vital to the oil and gas industry because it is a key constituent of the mud used to drill oil and gas wells. Elemental barium is an additive in optical glass, ceramic glazes, and other products. Within the United States, barite is produced mainly from mines in Nevada. Imports in 2011 (the latest year for which complete data were available) accounted for 78 percent of domestic consumption and came mostly from China.
Barite deposits can be divided into the following four main types: bedded-sedimentary; bedded-volcanic; vein, cavity-fill, and metasomatic; and residual. Bedded-sedimentary deposits, which are found in sedimentary rocks with characteristics of high biological productivity during sediment accumulation, are the major sources of barite production and account for the majority of reserves, both in the United States and worldwide. In 2013, China and India were the leading producers of barite, and they have large identified resources that position them to be significant producers for the foreseeable future. The potential for undiscovered barite resources in the United States and in many other countries is considerable, however. The expected tight supply and rising costs in the coming years will likely be met by increased production from such countries as Kazakhstan, Mexico, Morocco, and Vietnam.
Barium has limited mobility in the environment and exposed barium in the vicinity of barite mines poses minimal risk to human or ecosystem health. Of greater concern is the potential for acidic metal-bearing drainage at sites where the barite ores or waste rocks contain abundant sulfide minerals. This risk is lessened naturally if the host rocks at the site are acid-neutralizing, and the risk can also be lessened by engineering measures.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1802D","isbn":"978-1-4113-3991-0","usgsCitation":"Johnson, C.A., Piatak, N.M., and Miller, M.M., 2017, Barite (Barium), chap. D of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. D1–D18, https://doi.org/10.3133/pp1802D.","productDescription":"vii, 18 p.","numberOfPages":"30","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045302","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":334561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1802/d/coverthb1.jpg"},{"id":334562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1802/d/pp1802d.pdf","text":"Report","size":"3.91 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Mineral Resources Program Coordinator
U.S. Geological Survey
913 National Center
Reston, VA 20192
Email: minerals@usgs.gov
httsp://minerals.usgs.gov
Tsala Apopka Lake is a complex system of lakes and wetlands, with intervening uplands, located in Citrus County in west-central Florida. It is located within the 2,100 square mile watershed of the Withlacoochee River, which drains north and northwest towards the Gulf of Mexico. The lake system is managed by the Southwest Florida Water Management District as three distinct “pools,” which from upstream to downstream are referred to as the Floral City Pool, Inverness Pool, and Hernando Pool. Each pool contains a mixture of deep-water lakes that remain wet year round, ephemeral (seasonal) ponds and wetlands, and dry uplands. Many of the major deep-water lakes are interconnected by canals. Flow from the Withlacoochee River, when conditions allow, can be diverted into the lake system. Flow thorough the canals can be used to control the distribution of water between the three pools. Flow in the canals is controlled using structures, such as gates and weirs.
Hydrogeologic units in the study area include a surficial aquifer consisting of Quaternary-age sediments, a discontinuous intermediate confining unit consisting of Miocene- and Pliocene-age sediments, and the underlying Upper Floridan aquifer, which consists of Eocene- and Oligocene-age carbonates. The fine-grained quartz sands that constitute the surficial aquifer are generally thin, typically less than 25 feet thick, within the vicinity of Tsala Apopka Lake. A thin, discontinuous, sandy clay layer forms the intermediate confining unit. The Upper Floridan aquifer is generally unconfined in the vicinity of Tsala Apopka Lake because the intermediate confining unit is discontinuous and breached by numerous karst features. In the study area, the Upper Floridan aquifer includes the upper Avon Park Formation and Ocala Limestone. The Ocala Limestone is the primary source of drinking water and spring flow in the area.
The objectives of this study are to document the interaction of Tsala Apopka Lake, the surficial aquifer, and the Upper Floridan aquifer; and to estimate an annual water budget for each pool and for the entire lake system for 2004–12. The hydrologic interactions were evaluated using hydraulic head and geochemical data. Geochemical data, including major ion, isotope, and age-tracer data, were used to evaluate sources of water and to distinguish flow paths. Hydrologic connection of the surficial environment (lakes, ponds, wetlands, and the surficial aquifer) was quantified on the basis of a conceptualized annual water-budget model. The model included the change in surface water and groundwater storage, precipitation, evapotranspiration, surface-water inflow and outflow, and net groundwater exchange with the underlying Upper Floridan aquifer. The control volume for each pool extended to the base of the surficial aquifer and covered an area defined to exceed the maximum inundated area for each pool during 2004–12 by 0.5 foot. Net groundwater flow was computed as a lumped value and was either positive or negative, with a negative value indicating downward or lateral leakage from the control volume and a positive value indicating upward leakage to the control volume.
The annual water budget for Tsala Apopka Lake was calculated using a combination of field observations and remotely sensed data for each of three pools and for the composite three pool area. A digital elevation model at a 5-foot grid spacing and bathymetric survey data were used to define the land-surface elevation and volume of each pool and to calculate the changes in inundated area with change in lake stage. Continuous lake-stage and groundwater-level data were used to define the change in storage for each pool. The rainfall data used in the water-budget calculations were based on daily radar reflectance data and measured rainfall from weather stations. Evapotranspiration was computed as a function of reference evapotranspiration, adjusted to actual evapotranspiration using a monthly land-cover coefficient (based on evapotranspiration measurements at stations located in representative landscapes). Surface-water inflows and outflows were determined using stage data collected at a series of streamgages installed primarily at the water-control structures. Discharge was measured under varying flow regimes and ratings were developed for the water-control structures. The discharge data collected during the study period were used to calibrate a surface-water flow model for 2004–12. Flows predicted by the model were used in the water-budget analysis. Net groundwater flow was determined as the residual term in the water-budget equation.
The results of the water-budget analysis indicate that rainfall was the largest input of water to Tsala Apopka Lake, whereas evapotranspiration was the largest output. For the 2004–12 analysis period, surface-water inflow accounted for 11 percent of the inputs, net groundwater inflow accounted for 1 percent of inputs (annual periods with positive net groundwater flow were included as inputs, while annual periods with negative net groundwater flow were counted as outputs), and rainfall accounted for the remaining 88 percent. For the same period, the outputs consisted of 2 percent surface-water outflow, 12 percent net groundwater outflow, and 86 percent evapotranspiration. Net groundwater inflows and surface-water/groundwater storage were negligible during the water-budget period but could be important components of the budget in individual years.
The net groundwater flow was negative (downward) for 8 out of the 9 years modeled (2004–12), indicating that the Tsala Apopka Lake study area was primarily a recharge area for the underlying Upper Floridan aquifer during this time period. Groundwater-level elevation in paired wells (adjacent wells completed in the surficial aquifer and Upper Floridan aquifer) typically was higher in the surficial aquifer than the Upper Floridan aquifer. However, hydraulic head data indicate that the surficial aquifer often has discharge potential to the surface-water system, especially in the low lying areas near the major lakes. Surficial-aquifer water levels were often higher than lake stages, especially during wet periods, which is likely an indication of aquifer-to-lake seepage in these areas. East of the major lakes, hydraulic head data were nearly equal in the surficial aquifer and Upper Floridan aquifer, which is an indication that the Upper Floridan aquifer is unconfined. Based on deuterium and oxygen stable isotope data collected in December 2011 and December 2012, there was no evidence of recharge to the Upper Floridan aquifer from the wetlands east of the major lakes; aquifer isotopic ratios did not indicate an enriched source, which is typical of lake and wetland sources. West of the major lakes, there was evidence of enriched isotopic ratios in water samples from the Upper Floridan aquifer. Differences in hydraulic head at paired wells in the surficial aquifer and Upper Floridan aquifer indicated that the surficial aquifer has the potential to recharge the Upper Floridan aquifer in the western part of the pools and west of the major lakes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175132","collaboration":"Prepared in cooperation with the Southwest Florida Water Management District","usgsCitation":"McBride, W.S., Metz, P.A., Ryan, P.J., Fulkerson, Mark, and Downing, H.C., 2017, Groundwater levels, geochemistry, and water budget of the Tsala Apopka Lake system, west-central Florida, 2004–12: U.S. Geological Survey Scientific Investigations Report 2017–5132, 100 p., https://doi.org/10.3133/sir20175132.","productDescription":"xi, 100 p.","numberOfPages":"116","onlineOnly":"Y","ipdsId":"IP-059771","costCenters":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"links":[{"id":350056,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5132/sir20175132.pdf","text":"Report","size":"14.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5132"},{"id":350055,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5132/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Tsala Apopka Lake System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.452392578125,\n 28.66890107414433\n ],\n [\n -82.0520782470703,\n 28.66890107414433\n ],\n [\n -82.0520782470703,\n 29.00693934321682\n ],\n [\n -82.452392578125,\n 29.00693934321682\n ],\n [\n -82.452392578125,\n 28.66890107414433\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Sequoia Scientific’s LISST-ABS is an acoustic backscatter sensor designed to measure suspended-sediment concentration at a point source. Three LISST-ABS were evaluated at the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF). Serial numbers 6010, 6039, and 6058 were assessed for accuracy in solutions with varying particle-size distributions and for the effect of temperature on sensor accuracy. Certified sediment samples composed of different ranges of particle size were purchased from Powder Technology Inc. These sediment samples were 30–80-micron (µm) Arizona Test Dust; less than 22-µm ISO 12103-1, A1 Ultrafine Test Dust; and 149-µm MIL-STD 810E Silica Dust. The sensor was able to accurately measure suspended-sediment concentration when calibrated with sediment of the same particle-size distribution as the measured. Overall testing demonstrated that sensors calibrated with finer sized sediments overdetect sediment concentrations with coarser sized sediments, and sensors calibrated with coarser sized sediments do not detect increases in sediment concentrations from small and fine sediments. These test results are not unexpected for an acoustic-backscatter device and stress the need for using accurate site-specific particle-size distributions during sensor calibration. When calibrated for ultrafine dust with a less than 22-µm particle size (silt) and with the Arizona Test Dust with a 30–80-µm range, the data from sensor 6039 were biased high when fractions of the coarser (149-µm) Silica Dust were added. Data from sensor 6058 showed similar results with an elevated response to coarser material when calibrated with a finer particle-size distribution and a lack of detection when subjected to finer particle-size sediment. Sensor 6010 was also tested for the effect of dissimilar particle size during the calibration and showed little effect. Subsequent testing revealed problems with this sensor, including an inadequate temperature compensation, making this data questionable. The sensor was replaced by Sequoia Scientific with serial number 6039. Results from the extended temperature testing showed proper temperature compensation for sensor 6039, and results from the dissimilar calibration/testing particle-size distribution closely corroborated the results from sensor 6058.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171154","usgsCitation":"Snazelle, T.T., 2017, Laboratory evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor: U.S. Geological Survey Open-File Report 2017–1154, 21 p., https://doi.org/10.3133/ofr20171154.","productDescription":"Report: vii, 21 p.; Data; Metadata","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-083385","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":350020,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1154/ofr20171154.pdf","text":"Report","size":"921 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1154"},{"id":350021,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://www.sciencebase.gov/catalog/item/59ba9376e4b091459a563ba7","text":"Data and Metadata","linkHelpText":"HIF evaluation of LISST-ABS"},{"id":350019,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1154/coverthb.jpg"}],"contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
The U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility evaluated three Hydrolab HL4 multiparameter water-quality sondes by OTT Hydromet. The sondes were equipped with temperature, conductivity, pH, dissolved oxygen (DO), and turbidity sensors. The sensors were evaluated for compliance with the USGS National Field Manual for the Collection of Water-Quality Data (NFM) criteria for continuous water-quality monitors and to verify the validity of the manufacturer’s technical specifications. The conductivity sensors were evaluated for the accuracy of the specific conductance (SC) values (conductance at 25 degrees Celsius [oC]), that were calculated by using the vendor default method, Hydrolab Fresh. The HL4’s communication protocols and operating temperature range along with accuracy of the water-quality sensors were tested in a controlled laboratory setting May 1–19, 2016. To evaluate the sonde’s performance in a surface-water field application, an HL4 equipped with temperature, conductivity, pH, DO, and turbidity sensors was deployed June 20–July 22, 2016, at USGS water-monitoring site 02492620, Pearl River at National Space Technology Laboratories (NSTL) Station, Mississippi, located near Bay Saint Louis, Mississippi, and compared to the adjacent well-maintained EXO2 site sonde.
The three HL4 sondes met the USGS temperature testing criteria and the manufacturer’s technical specifications for temperature based upon the median room temperature difference between the measured and standard temperatures, but two of the three sondes exceeded the allowable difference criteria at the temperature extremes of approximately 5 and 40 ºC. Two sondes met the USGS criteria for SC. One of the sondes failed the criteria for SC when evaluated in a 100,000-microsiemens-per-centimeter (μS/cm) standard at room temperature, and also failed in a 10,000-μS/cm standard at 5, 15, and 40 ºC. All three sondes met the USGS criteria for pH and DO at room temperature, but one sonde exceeded the allowable difference criteria when tested in pH 5.00 buffer and at 40 ºC. The USGS criteria and the technical specifications for turbidity were met by one sonde in standards ranging from 10 to 3,000 nephelometric turbidity units (NTU). A second sonde met the USGS criteria and the technical specifications except in the 3,000-NTU standard, and the third sonde exceeded the USGS calibration criteria in the 10- and 20-NTU standards and the technical specifications in the 20-NTU standard.
Results of the field test showed acceptable performance and revealed that differences in data sample processing between sonde manufacturers may result in variances between the reported measurements when comparing one sonde to another. These variances in data would be more pronounced in dynamic site conditions. The lack of a wiper or other sensor-cleaning device on the DO sensor could prove problematic, and could limit the use of the HL4 to profiling applications or at sites with limited biofouling.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171153","usgsCitation":"Snazelle, T.T., 2017, Evaluation of the Hydrolab HL4 water-quality sonde and sensors: U.S. Geological Survey Open-File Report 2017–1153, 20 p., https://doi.org/10.3133/ofr20171153.","productDescription":"Report: v, 20 p.; Data; Metadata","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072173","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":350018,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://www.sciencebase.gov/catalog/item/59b94eaae4b091459a54d8f9","text":"Data and Metadata ","linkHelpText":"Evaluation of Hydrolab HL4 Water-Quality Sondes and Sensors"},{"id":350016,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1153/coverthb.jpg"},{"id":350017,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1153/ofr20171153.pdf","text":"Report","size":"602 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1153"}],"contact":"Chief, Hydrologic Instrumentation Facility
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Anticipating and mitigating the effects of climate change is a fundamental challenge for natural resource conservation. In this report, we respond to research needs identified by Catoctin Mountain Park (CATO) for native Brook Trout (Salvelinus fontinalis) conservation and management as part of the US Geological Survey (USGS) Natural Resources Preservation Program in FY15-16. We addressed three overarching research questions: (1) How will anticipated changes in air temperature affect stream habitats? (2) How will changes to stream habitat affect the distribution of Brook Trout? (3) Which stream segments are most and least vulnerable to the effects of climate change?
First, we surveyed Brook Trout abundance and fish community composition using electrofishing techniques within three watersheds: Owens Creek, upper Big Hunting Creek, and Blue Blazes Creek (a tributary to Big Hunting Creek). Second, we deployed a network of stream temperature gages to assess spatial variation in stream temperature and groundwater (GW) influence. Third, we used modeling techniques to forecast future stream temperatures that account for GW influences and air temperature scenarios.
Fish sampling detected 13 species and 15,345 individual fish, the majority of which were Blacknose Dace (60%), Blue Ridge Sculpin (26%), and Brook Trout (6%). Brook Trout were not observed in Blue Blazes Creek and exhibited higher densities in Owens Creek than upper Big Hunting Creek (average densities = 19 fish/100 m and 4 fish/100 m, respectively). In contrast, Brown Trout were present in Blue Blazes Creek and exhibited greater density in Blue Blazes Creek than either Owens Creek or upper Big Hunting Creek (average densities = 3.0 fish/100 m,
0.3 fish/100 m, and 1.7 fish/100 m, respectively). Brown Trout occurred in sympatry with Brook Trout in Owens Creek and upper Big Hunting Creek, but appeared to have replaced Brook Trout
in Blue Blazes Creek. Our fish surveys also revealed important locations for Brook Trout reproduction and young-of-year (YOY) dispersal within the Owens Creek watershed.
Our study also revealed surprising differences in the distribution of Blue Ridge Sculpin among CATO streams. This species was abundant in Owens Creek (average density = 83 fish/100 m) but was less common in Blue Blazes Creek (average density = 12 fish/100 m) and was not detected in upper Big Hunting Creek. Histological examination of several specimens from Blue Blazes Creek by V. Blazer at the USGS Leetown Science Center revealed the presence of a novel parasite (Dermosystidium sp.) which has been linked to fish population declines elsewhere (Blazer et al. 2016). The parasite was not detected in Blue Ridge Sculpin samples from Owens Creek, and all trout appeared to be uninfected. Our survey results suggest that Blue Ridge Sculpin have been extirpated from upper Big Hunting Creek and have not recolonized from downstream source populations due to the fish passage barrier of Cunningham Falls. We recommend additional research to (1) evaluate the feasibility of reintroducing Blue Ridge Sculpin into upper Big Hunting Creek and (2) continue monitoring the distribution and potential spread of Dermocystidium in downstream waters.
Stream temperatures ranged from 9.6 – 27.6 ºC during baseflow conditions in 2015 and 2016. Sites within upper Big Hunting Creek were consistently warmer than in Owens Creek or Blue Blazes Creek, suggesting an effect of headwater ponds outside CATO on upper Big Hunting Creek temperatures. For instance, in 2016 the maximum observed temperature in upper Big Hunting Creek was 27.6 ºC whereas Owens Creek reached a maximum of 23.7 ºC that year. Stream temperature data also revealed that 2016 was warmer than 2015 throughout the study area but did not exceed thermal tolerance limits for Brook Trout in either year.
We estimated the influence of GW on stream temperatures using a statistical modeling approach based on the relationship between daily mean air temperature and stream temperature over time. Results indicated that effects of GW were generally stronger in the Owens Creek watershed than in Blue Blazes or upper Big Hunting Creek. However, we detected substantial spatial variation in GW influence among Owens Creek sites, with stream temperatures at some locations showing relatively little GW influence and others showing very strong influences (and correspondingly small influence of daily mean air temperatures). Although incoming lateral seeps were detected in upper Big Hunting Creek (D. Ferrier, Hood College, personal communication), the strongest effects of GW in the study area were due to GW upwelling within portions of the Owens Creek watershed (i.e., Tributary C in Figure 4) where we also observed high numbers of Brook Trout juveniles. Our results therefore identified potential high-priority areas for Brook Trout conservation in CATO.
Finally, we modeled future stream temperatures based on scenarios characterizing GW sensitivity to air temperature and future air temperature increases. Stream temperature forecasts revealed important differences in habitat suitability for Brook Trout within and among watersheds. Big Hunting Creek sites were generally more sensitive to air temperature increases than sites in Owens Creek or Blue Blazes Creek. For instance, an increase in mean annual air temperature of 1.5 ºC (lowest level evaluated) exceeded thermal thresholds for Brook Trout in the majority of sites within that watershed, regardless of GW influence levels. In contrast, an air temperature increase of 1.5 ºC did not exceed thermal thresholds for Brook Trout in Owens Creek. However, modeled air temperature increases of 5 ºC resulted in a loss of Brook Trout thermal suitability throughout the study area. Model results revealed spatially patchy responses to air temperature increases that could provide an early-warning system for trout monitoring
designs in CATO.
The New England cottontail (Sylvilagus transitionalis) is a species of high conservation priority in the Northeastern United States, and was a candidate for federal listing under the Endangered Species Act until a recent decision determined that conservation actions were sufficient to preclude listing. The aim of this study was to develop a suite of microsatellite loci to guide future research efforts such as the analysis of population genetic structure, genetic variation, dispersal, and genetic mark-recapture population estimation.
Thirty-five microsatellite markers containing tri- and tetranucleotide sequences were developed from shotgun genomic sequencing of tissue from S. transitionalis, S. obscurus, and S. floridanus. These loci were screened in n = 33 wild S. transitionalis sampled from a population in eastern Massachusetts, USA. Thirty-two of the 35 loci were polymorphic with 2–6 alleles, and observed heterozygosities of 0.06–0.82. All loci conformed to Hardy–Weinberg Equilibrium proportions and there was no evidence of linkage disequilibrium or null alleles. Primers for 33 of the 35 loci amplified DNA extracted from n = 6 eastern cottontail (S. floridanus) samples, of which nine revealed putative species-diagnostic alleles. These loci will provide a useful tool for conservation genetics investigations of S. transitionalis and a potential diagnostic species assay for differentiating sympatric eastern and New England cottontails.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13104-017-3062-2","usgsCitation":"King, T.L., Eackles, M.S., Aunins, A.W., McGreevy, T.J., Husband, T.P., Tur, A., and Kovach, A.I., 2017, Microsatellite marker development from next-generation sequencing in the New England cottontail (Sylvilagus transitionalis) and cross-amplification in the eastern cottontail (S. floridanus): BMC Research Notes, no. 10, 741, 7 p., https://doi.org/10.1186/s13104-017-3062-2.","productDescription":"741, 7 p.","ipdsId":"IP-089217","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":469233,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13104-017-3062-2","text":"Publisher Index Page"},{"id":367381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.2520751953125,\n 42.06356771883277\n ],\n [\n -70.19989013671875,\n 42.002366213375524\n ],\n [\n -70.16143798828125,\n 42.04317376494972\n ],\n [\n -70.07904052734375,\n 41.89818843043047\n ],\n [\n -70.02960205078125,\n 41.80407814427234\n ],\n [\n -70.43060302734375,\n 41.759019938155404\n ],\n [\n -70.48004150390625,\n 41.777456667491066\n ],\n [\n -70.55419921875,\n 41.77336007442076\n ],\n [\n -70.63934326171875,\n 41.73033005046653\n ],\n [\n -70.69976806640625,\n 41.549700145132725\n ],\n [\n -70.6201171875,\n 41.50446357504803\n ],\n [\n -70.477294921875,\n 41.539421883822854\n ],\n [\n -70.28228759765625,\n 41.60928183876483\n ],\n [\n -69.99114990234375,\n 41.64213096472801\n ],\n [\n -69.85107421874999,\n 41.654445072031244\n ],\n [\n -69.99938964843749,\n 42.049292638686836\n ],\n [\n -70.13397216796875,\n 42.10026033308264\n ],\n [\n -70.25482177734375,\n 42.09822241118974\n ],\n [\n -70.2520751953125,\n 42.06356771883277\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"10","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"King, Tim L. tlking@usgs.gov","contributorId":3520,"corporation":false,"usgs":true,"family":"King","given":"Tim","email":"tlking@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":770733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":770732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":770731,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGreevy, Thomas J. 0000-0002-8542-4210","orcid":"https://orcid.org/0000-0002-8542-4210","contributorId":218938,"corporation":false,"usgs":false,"family":"McGreevy","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":770734,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Husband, Thomas P.","contributorId":174902,"corporation":false,"usgs":false,"family":"Husband","given":"Thomas","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":770735,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tur, Anthony","contributorId":218956,"corporation":false,"usgs":false,"family":"Tur","given":"Anthony","email":"","affiliations":[],"preferred":false,"id":770737,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kovach, Adrienne I. 0000-0002-6791-0610","orcid":"https://orcid.org/0000-0002-6791-0610","contributorId":218939,"corporation":false,"usgs":false,"family":"Kovach","given":"Adrienne","email":"","middleInitial":"I.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":770736,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70190920,"text":"sir20175107 - 2017 - Peak discharge, flood frequency, and peak stage of floods on Big Cottonwood Creek at U.S. Highway 50 near Coaldale, Colorado, and Fountain Creek below U.S. Highway 24 in Colorado Springs, Colorado, 2016","interactions":[],"lastModifiedDate":"2017-12-14T15:35:01","indexId":"sir20175107","displayToPublicDate":"2017-12-14T13:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5107","title":"Peak discharge, flood frequency, and peak stage of floods on Big Cottonwood Creek at U.S. Highway 50 near Coaldale, Colorado, and Fountain Creek below U.S. Highway 24 in Colorado Springs, Colorado, 2016","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the Colorado Department of Transportation, determined the peak discharge, annual exceedance probability (flood frequency), and peak stage of two floods that took place on Big Cottonwood Creek at U.S. Highway 50 near Coaldale, Colorado (hereafter referred to as “Big Cottonwood Creek site”), on August 23, 2016, and on Fountain Creek below U.S. Highway 24 in Colorado Springs, Colorado (hereafter referred to as “Fountain Creek site”), on August 29, 2016. A one-dimensional hydraulic model was used to estimate the peak discharge. To define the flood frequency of each flood, peak-streamflow regional-regression equations or statistical analyses of USGS streamgage records were used to estimate annual exceedance probability of the peak discharge. A survey of the high-water mark profile was used to determine the peak stage, and the limitations and accuracy of each component also are presented in this report. Collection and computation of flood data, such as peak discharge, annual exceedance probability, and peak stage at structures critical to Colorado’s infrastructure are an important addition to the flood data collected annually by the USGS.
The peak discharge of the August 23, 2016, flood at the Big Cottonwood Creek site was 917 cubic feet per second (ft3/s) with a measurement quality of poor (uncertainty plus or minus 25 percent or greater). The peak discharge of the August 29, 2016, flood at the Fountain Creek site was 5,970 ft3/s with a measurement quality of poor (uncertainty plus or minus 25 percent or greater).
The August 23, 2016, flood at the Big Cottonwood Creek site had an annual exceedance probability of less than 0.01 (return period greater than the 100-year flood) and had an annual exceedance probability of greater than 0.005 (return period less than the 200-year flood). The August 23, 2016, flood event was caused by a precipitation event having an annual exceedance probability of 1.0 (return period of 1 year, or the 1-year storm), which is a statistically common (high probability) storm. The Big Cottonwood Creek site is downstream from the Hayden Pass Fire burn area, which dramatically altered the hydrology of the watershed and caused this statistically rare (low probability) flood from a statistically common (high probability) storm. The peak flood stage at the cross section closest to the U.S. Highway 50 culvert was 6,438.32 feet (ft) above the North American Datum of 1988 (NAVD 88).
The August 29, 2016, flood at the Fountain Creek site had an estimated annual exceedance probability of 0.5505 (return period equal to the 1.8-year flood). The August 29, 2016, flood event was caused by a precipitation event having an annual exceedance probability of 1.0 (return period of 1 year, or the 1-year storm). The peak stage during this flood at the cross section closest to the U.S. Highway 24 bridge was 5,832.89 ft (NAVD 88).
Slope-area indirect discharge measurements were carried out at the Big Cottonwood Creek and Fountain Creek sites to estimate peak discharge of the August 23, 2016, flood and August 29, 2016, flood, respectively. The USGS computer program Slope-Area Computation Graphical User Interface was used to compute the peak discharge by adding the surveyed cross sections with Manning roughness coefficient assignments to the high-water marks. The Manning roughness coefficients for each cross section were estimated in the field using the Cowan method.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175107","collaboration":"Prepared in cooperation with the Colorado Department of Transportation","usgsCitation":"Kohn, M.S., Stevens, M.R., Mommandi, Amanullah, and Khan, A.R., 2017, Peak discharge, flood frequency, and peak stage of floods on Big Cottonwood Creek at U.S. Highway 50 near Coaldale, Colorado, and Fountain Creek below U.S. Highway 24 in Colorado Springs, Colorado, 2016: U.S. Geological Survey Scientific Investigations Report 2017–5107, 58 p., https://doi.org/10.3133/sir20175107.","productDescription":"Report: vii, 58 p.; Appendixes","numberOfPages":"70","onlineOnly":"Y","ipdsId":"IP-083372","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":349894,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5107/sir20175107_Appendix2_BigCottonwoodCr_LeftBank.zip","text":"Appendix 2, Big Cottonwood Creek, Left Bank—","size":"177 MB","linkFileType":{"id":6,"text":"zip"},"description":"Appendix 2 Left Bank","linkHelpText":"Photos of left bank high-water marks from Big Cottonwood Creek at U.S. Highway 50 near Coaldale, Colorado"},{"id":349892,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5107/coverthb.jpg"},{"id":349923,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5107/sir20175107_Appendix7_FountainCr_LeftBank.zip","text":"Appendix 7, Fountain Creek, Left Bank—","size":"303 MB","linkFileType":{"id":6,"text":"zip"},"description":"Appendix 7 Left Bank","linkHelpText":"Photos of left bank high-water marks from Fountain Creek below U.S. Highway 24 in Colorado Springs, Colorado"},{"id":349893,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5107/sir20175107.pdf","text":"Report","size":"19.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5107"},{"id":349921,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5107/sir20175107_Appendix3_BigCottonwoodCr.zip","text":"Appendix 3, Big Cottonwood Creek—","size":"154 MB","linkFileType":{"id":6,"text":"zip"},"description":"Appendix 3","linkHelpText":"Photos of cross Sections from Big Cottonwood Creek at U.S. Highway 50 near Coaldale, Colorado"},{"id":349925,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5107/sir20175107_Appendix7_FountainCr_RightBank.zip","text":"Appendix 7, Fountain Creek, Right Bank—","size":"305 MB","linkFileType":{"id":6,"text":"zip"},"description":"Appendix 7 Right Bank","linkHelpText":"Photos of right bank high-water marks from Fountain Creek below U.S. Highway 24 in Colorado Springs, Colorado"},{"id":349926,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5107/sir20175107_Appendix8_FountainCr.zip","text":"Appendix 8, Fountain Creek—","size":"220 MB","linkFileType":{"id":6,"text":"zip"},"description":"Appendix 8","linkHelpText":"Photos of cross sections from Fountain Creek below U.S. Highway 24 in Colorado Springs, Colorado"},{"id":349920,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5107/sir20175107_Appendix2_BigCottonwoodCr_RightBank.zip","text":"Appendix 2, Big Cottonwood Creek, Right Bank—","size":"142 MB","linkFileType":{"id":6,"text":"zip"},"description":"Appendix 2 Right Bank","linkHelpText":"Photos of right bank high-water marks from Big Cottonwood Creek at U.S. Highway 50 near Coaldale, Colorado"}],"country":"United States","state":"Colorado","city":"Coaldale, Colorado Springs","otherGeospatial":"Big Cottonwood Creek, Fountain Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.80493545532227,\n 38.79868097286392\n ],\n [\n -104.78673934936523,\n 38.79868097286392\n ],\n [\n -104.78673934936523,\n 38.80944982778107\n ],\n [\n -104.80493545532227,\n 38.80944982778107\n ],\n [\n -104.80493545532227,\n 38.79868097286392\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.76083183288574,\n 38.36297641178211\n ],\n [\n -105.7474637031555,\n 38.36297641178211\n ],\n [\n -105.7474637031555,\n 38.37083318856711\n ],\n [\n -105.76083183288574,\n 38.37083318856711\n ],\n [\n -105.76083183288574,\n 38.36297641178211\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
The enhancement of agricultural lands through the use of artificial drainage systems is a common practice throughout the United States, and recently the use of this practice has expanded in the Prairie Pothole Region. Many wetlands are afforded protection from the direct effects of drainage through regulation or legal agreements, and drainage setback distances typically are used to provide a buffer between wetlands and drainage systems. A field study was initiated to assess the potential for subsurface drainage to affect wetland surface-water characteristics through a reduction in precipitation runoff, and to examine the efficacy of current U.S. Department of Agriculture drainage setback distances for limiting these effects. Surface-water levels, along with primary components of the catchment water balance, were monitored over 3 y at four seasonal wetland catchments situated in a high-relief terrain (7–11% slopes). During the second year of the study, subsurface drainage systems were installed in two of the catchments using drainage setbacks, and the drainage discharge volumes were monitored. A catchment water-balance model was used to assess the potential effect of subsurface drainage on wetland hydrology and to assess the efficacy of drainage setbacks for mitigating these effects. Results suggest that overland precipitation runoff can be an important component of the seasonal water balance of Prairie Pothole Region wetlands, accounting on average for 34% (19–49%) or 45% (39–49%) of the annual (includes snowmelt runoff) or seasonal (does not include snowmelt) input volumes, respectively. Seasonal (2014–2015) discharge volumes from the localized drainage systems averaged 81 m3 (31–199 m3), and were small when compared with average combined inputs of 3,745 m3 (1,214–6,993 m3) from snowmelt runoff, direct precipitation, and precipitation runoff. Model simulations of reduced precipitation runoff volumes as a result of subsurface drainage systems showed that ponded wetland surface areas were reduced by an average of 590 m2 (141–1,787 m2), or 24% (3–46%), when no setbacks were used (drainage systems located directly adjacent to wetland). Likewise, wetland surface areas were reduced by an average of 141 m2 (23–464 m2), or 7% (1–28%), when drainage setbacks (buffer) were used. In totality, the field data and model simulations suggest that the drainage setbacks should reduce, but not eliminate, impacts to the water balance of the four wetlands monitored in this study that were located in a high-relief terrain. However, further study is required to assess the validity of these conclusions outside of the limited parameters (e.g., terrain, weather, soils) of this study and to examine potential ecological effects of altered wetland hydrology.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/022017-JFWM-012","usgsCitation":"Tangen, B., and Finocchiaro, R., 2017, A case study examining the efficacy of drainage setbacks for limiting effects to wetlands in the Prairie Pothole Region, USA: Journal of Fish and Wildlife Management, v. 8, no. 2, p. 513-529, https://doi.org/10.3996/022017-JFWM-012.","productDescription":"17 p.","startPage":"513","endPage":"529","ipdsId":"IP-084102","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":461323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/022017-jfwm-012","text":"Publisher Index Page"},{"id":350010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","county":"Stutsman County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.2669,47.3268],[-98.8466,47.327],[-98.8392,47.327],[-98.8232,47.3272],[-98.8152,47.3271],[-98.4991,47.327],[-98.467,47.3266],[-98.4677,47.2402],[-98.4685,46.9788],[-98.4412,46.9789],[-98.4396,46.6296],[-98.7894,46.6294],[-99.0379,46.6309],[-99.1616,46.6317],[-99.4122,46.6316],[-99.4498,46.6319],[-99.4477,46.8044],[-99.4476,46.9788],[-99.4821,46.9795],[-99.4824,47.0089],[-99.4822,47.0162],[-99.4821,47.0249],[-99.4826,47.0396],[-99.4827,47.1558],[-99.4801,47.3267],[-99.2669,47.3268]]]},\"properties\":{\"name\":\"Stutsman\",\"state\":\"ND\"}}]}","volume":"8","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-01","publicationStatus":"PW","scienceBaseUri":"5a60fae7e4b06e28e9c2294a","contributors":{"authors":[{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":725082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finocchiaro, Raymond 0000-0002-5514-8729 rfinocchiaro@usgs.gov","orcid":"https://orcid.org/0000-0002-5514-8729","contributorId":167278,"corporation":false,"usgs":true,"family":"Finocchiaro","given":"Raymond","email":"rfinocchiaro@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":725083,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194710,"text":"70194710 - 2017 - Phylogenetics of a fungal invasion: Origins and widespread dispersal of white-nose syndrome","interactions":[],"lastModifiedDate":"2017-12-13T15:22:19","indexId":"70194710","displayToPublicDate":"2017-12-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3819,"text":"mBio","active":true,"publicationSubtype":{"id":10}},"title":"Phylogenetics of a fungal invasion: Origins and widespread dispersal of white-nose syndrome","docAbstract":"Globalization has facilitated the worldwide movement and introduction of pathogens, but epizoological reconstructions of these invasions are often hindered by limited sampling and insufficient genetic resolution among isolates. Pseudogymnoascus destructans, a fungal pathogen causing the epizootic of white-nose syndrome in North American bats, has exhibited few genetic polymorphisms in previous studies, presenting challenges for both epizoological tracking of the spread of this fungus and for determining its evolutionary history. We used single nucleotide polymorphisms (SNPs) from whole-genome sequencing and microsatellites to construct high-resolution phylogenies of P. destructans. Shallow genetic diversity and the lack of geographic structuring among North American isolates support a recent introduction followed by expansion via clonal reproduction across the epizootic zone. Moreover, the genetic relationships of isolates within North America suggest widespread mixing and long-distance movement of the fungus. Genetic diversity among isolates of P. destructans from Europe was substantially higher than in those from North America. However, genetic distance between the North American isolates and any given European isolate was similar to the distance between the individual European isolates. In contrast, the isolates we examined from Asia were highly divergent from both European and North American isolates. Although the definitive source for introduction of the North American population has not been conclusively identified, our data support the origin of the North American invasion by P. destructans from Europe rather than Asia.
","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/mBio.01941-17","usgsCitation":"Drees, K.P., Lorch, J.M., Puechmaille, S.J., Parise, K.L., Wibbelt, G., Hoyt, J.R., Sun, K., Jargalsaikhan, A., Dalannast, M., Palmer, J.M., Linder, D.L., Kilpatrick, M., Pearson, T., Keim, P.S., Blehert, D.S., and Foster, J.T., 2017, Phylogenetics of a fungal invasion: Origins and widespread dispersal of white-nose syndrome: mBio, v. 8, no. 6, p. 1-15, https://doi.org/10.1128/mBio.01941-17.","productDescription":"e01941-17; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-091970","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":469237,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1128/mbio.01941-17","text":"Publisher Index Page"},{"id":349971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"6","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fae8e4b06e28e9c22964","contributors":{"authors":[{"text":"Drees, Kevin P.","contributorId":201308,"corporation":false,"usgs":false,"family":"Drees","given":"Kevin","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":724964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health 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R.","contributorId":201314,"corporation":false,"usgs":false,"family":"Hoyt","given":"Joseph","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":724972,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sun, Keping","contributorId":201315,"corporation":false,"usgs":false,"family":"Sun","given":"Keping","email":"","affiliations":[],"preferred":false,"id":724973,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jargalsaikhan, Ariunbold","contributorId":201317,"corporation":false,"usgs":false,"family":"Jargalsaikhan","given":"Ariunbold","email":"","affiliations":[],"preferred":false,"id":724975,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dalannast, Munkhnast","contributorId":201318,"corporation":false,"usgs":false,"family":"Dalannast","given":"Munkhnast","email":"","affiliations":[],"preferred":false,"id":724976,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Palmer, Jonathan M.","contributorId":172601,"corporation":false,"usgs":false,"family":"Palmer","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[{"id":27066,"text":"Center for Forest Mycology Research, Northern Research Station, US Forest Service, Madison, Wisconsin, USAb","active":true,"usgs":false}],"preferred":false,"id":724977,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Linder, Daniel L.","contributorId":127718,"corporation":false,"usgs":false,"family":"Linder","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":724978,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kilpatrick, Marm","contributorId":201316,"corporation":false,"usgs":false,"family":"Kilpatrick","given":"Marm","email":"","affiliations":[],"preferred":false,"id":724974,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Pearson, Talima","contributorId":201312,"corporation":false,"usgs":false,"family":"Pearson","given":"Talima","email":"","affiliations":[],"preferred":false,"id":724969,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Keim, Paul S.","contributorId":201311,"corporation":false,"usgs":false,"family":"Keim","given":"Paul","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":724968,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"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":724963,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Foster, Jeffrey T.","contributorId":177905,"corporation":false,"usgs":false,"family":"Foster","given":"Jeffrey","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":724970,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70191318,"text":"sir20175114 - 2017 - Groundwater discharge to the Mississippi River and groundwater balances for the Interstate 94 Corridor surficial aquifer, Clearwater to Elk River, Minnesota, 2012–14","interactions":[],"lastModifiedDate":"2017-12-13T15:59:22","indexId":"sir20175114","displayToPublicDate":"2017-12-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5114","title":"Groundwater discharge to the Mississippi River and groundwater balances for the Interstate 94 Corridor surficial aquifer, Clearwater to Elk River, Minnesota, 2012–14","docAbstract":"The Interstate 94 Corridor has been identified as 1 of 16 Minnesota groundwater areas of concern because of its limited available groundwater resources. The U.S. Geological Survey, in cooperation with the Minnesota Department of Natural Resources, completed six seasonal and annual groundwater balances for parts of the Interstate 94 Corridor surficial aquifer to better understand its long-term (next several decades) sustainability. A high-precision Mississippi River groundwater discharge measurement of 5.23 cubic feet per second per mile was completed at low-flow conditions to better inform these groundwater balances. The recharge calculation methods RISE program and Soil-Water-Balance model were used to inform the groundwater balances. For the RISE-derived recharge estimates, the range was from 3.30 to 11.91 inches per year; for the SWB-derived recharge estimates, the range was from 5.23 to 17.06 inches per year.
Calculated groundwater discharges ranged from 1.45 to 5.06 cubic feet per second per mile, a ratio of 27.7 to 96.4 percent of the measured groundwater discharge. Ratios of groundwater pumping to total recharge ranged from 8.6 to 97.2 percent, with the longer-term groundwater balances ranging from 12.9 to 19 percent. Overall, this study focused on the surficial aquifer system and its interactions with the Mississippi River. During the study period (October 1, 2012, through November 30, 2014), six synoptic measurements, along with continuous groundwater hydrographs, rainfall records, and a compilation of the pertinent irrigation data, establishes the framework for future groundwater modeling efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175114","collaboration":"Prepared in cooperation with the Minnesota Department of Natural Resources","usgsCitation":"Smith, E.A., Lorenz, D.L., Kessler, E.W., Berg, A.M., and Sanocki, C.A., 2017, Groundwater discharge to the Mississippi River and groundwater balances for the Interstate 94 Corridor surficial aquifer, Clearwater to Elk River, Minnesota, 2012–14: U.S. Geological Survey Scientific Investigations Report 2017–5114, 54 p., https://doi.org/10.3133/sir20175114.","productDescription":"Report: ix, 54 p.; Appendix Tables; Data Release","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-027699","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":349965,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5114/sir20175114_appendix_tables.xlsx","text":"Appendix Tables 1–4","size":"171 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5114 Appendix Tables"},{"id":349961,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5114/sir20175114.pdf","text":"Report","size":"4.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5114"},{"id":349960,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5114/coverthb.jpg"},{"id":349962,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NZ864G","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-Water-Balance model data sets for the Interstate 94 corridor surficial aquifer, Clearwater to Elk River, Minnesota, 2010-2014"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.10202026367188,\n 45.25\n ],\n [\n -93.52249145507812,\n 45.25\n ],\n [\n -93.52249145507812,\n 45.47650323381734\n ],\n [\n -94.10202026367188,\n 45.47650323381734\n ],\n [\n -94.10202026367188,\n 45.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
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2280 Woodale Drive
Mounds View, MN 55112–4900
Bighead carp (Hypophthalmichthys nobilis) have been introduced throughout Europe, mostly unintentionally, and little attention has been given to their potential for natural reproduction. We investigated the presence of young-of-the-year bighead carp in an irrigation canal network of Northern Italy and the environmental conditions associated with spawning in 2011–2015. The adult bighead carp population of the canal network was composed by large, likely mature, individuals with an average density of 45.2 kg/ha (over 10 fold more than in the main river). The 29 juvenile bighead carp found were 7.4–13.1 cm long (TL) and weighed 9.5–12.7 g. Using otolith-derived spawning dates we estimated that these juveniles were 94–100 days old, placing their fertilization and hatch dates in mid-to-end-June. Using this information in combination with thermal and hydraulic data, we examined the validity of existing models predicting the onset of spawning conditions and the viability of egg pathways to elucidate spawning location of the species. While evidence of reproduction was not found every year, we determined that potentially viable spawning conditions (annual degree-days and temperature thresholds) and pathways of egg drift suitable for hatching are present in short, slow-flowing canals.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0189517","usgsCitation":"Milardi, M., Chapman, D., Long, J.M., and Castaldelli, G., 2017, First evidence of bighead carp wild recruitment in Western Europe, and its relation to hydrology and temperature: PLoS ONE, p. 1-13, https://doi.org/10.1371/journal.pone.0189517.","productDescription":"e0189517; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-083648","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":469238,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0189517","text":"Publisher Index Page"},{"id":350048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-12","publicationStatus":"PW","scienceBaseUri":"5a60fae9e4b06e28e9c2296d","contributors":{"authors":[{"text":"Milardi, Marco","contributorId":201384,"corporation":false,"usgs":false,"family":"Milardi","given":"Marco","email":"","affiliations":[],"preferred":false,"id":725157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":725156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":725159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Castaldelli, Giuseppe","contributorId":201385,"corporation":false,"usgs":false,"family":"Castaldelli","given":"Giuseppe","email":"","affiliations":[],"preferred":false,"id":725158,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194294,"text":"ofr20171139 - 2017 - Deepwater Program: Lophelia II, continuing ecological research on deep-sea corals and deep-reef habitats in the Gulf of Mexico","interactions":[],"lastModifiedDate":"2017-12-12T10:20:33","indexId":"ofr20171139","displayToPublicDate":"2017-12-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1139","title":"Deepwater Program: Lophelia II, continuing ecological research on deep-sea corals and deep-reef habitats in the Gulf of Mexico","docAbstract":"The deep sea is a rich environment composed of diverse habitat types. While deep-sea coral habitats have been discovered within each ocean basin, knowledge about the ecology of these habitats and associated inhabitants continues to grow. This report presents information and results from the Lophelia II project that examined deep-sea coral habitats in the Gulf of Mexico. The Lophelia II project focused on Lophelia pertusa habitats along the continental slope, at depths ranging from 300 to 1,000 meters. The chapters are authored by several scientists from the U.S. Geological Survey, National Oceanic and Atmospheric Administration, University of North Carolina Wilmington, and Florida State University who examined the community ecology (from microbes to fishes), deep-sea coral age, growth, and reproduction, and population connectivity of deep-sea corals and inhabitants. Data from these studies are presented in the chapters and appendixes of the report as well as in journal publications. This study was conducted by the Ecosystems Mission Area of the U.S. Geological Survey to meet information needs identified by the Bureau of Ocean Energy Management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171139","collaboration":"Prepared in collaboration with the Bureau of Ocean Energy Management and the National Oceanic and Atmospheric Administration","usgsCitation":"Demopoulos, A.W.J., Ross, S.W., Kellogg, C.A., Morrison, C.L., Nizinski, M., Prouty, N.G., Bourque, J.R., Galkiewicz, J.P., Gray, M.A., Springmann, M.J., Coykendall, D.K., Miller, A., Rhode, M., Quattrini, A., Ames, C.L., Brooke, S., McClain-Counts, J., Roark, E.B., Buster, N.A., Phillips, R.M., and Frometa, J., 2017, Deepwater Program: Lophelia II, continuing ecological research on deep-sea corals and deep-reef habitats in the Gulf of Mexico: U.S. Geological Survey Open-File Report 2017–1139, 269 p., https://doi.org/10.3133/ofr20171139.","productDescription":"xviii, 269 p.","numberOfPages":"287","onlineOnly":"Y","ipdsId":"IP-057758","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":349831,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1139/coverthb2.jpg"},{"id":349832,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1139/ofr20171139.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1139"}],"country":"United States","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.48193359375,\n 24\n ],\n [\n -74.81689453125,\n 24\n ],\n [\n -74.81689453125,\n 35\n ],\n [\n -96.48193359375,\n 35\n ],\n [\n -96.48193359375,\n 24\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
7920 NW 71st St.
Gainesville, FL 32653