Analytical solutions that use diurnal temperature signals to estimate vertical fluxes between groundwater and surface water based on either amplitude ratios (Ar) or phase shifts (Δϕ) produce results that rarely agree. Analytical solutions that simultaneously utilize Ar and Δϕ within a single solution have more recently been derived, decreasing uncertainty in flux estimates in some applications. Benefits of combined (ArΔϕ) methods also include that thermal diffusivity and sensor spacing can be calculated. However, poor identification of either Ar or Δϕ from raw temperature signals can lead to erratic parameter estimates from ArΔϕ methods. An add-on program for VFLUX 2 is presented to address this issue. Using thermal diffusivity selected from an ArΔϕ method during a reliable time period, fluxes are recalculated using an Ar method. This approach maximizes the benefits of the Ar and ArΔϕ methods. Additionally, sensor spacing calculations can be used to identify periods with unreliable flux estimates, or to assess streambed scour. Using synthetic and field examples, the use of these solutions in series was particularly useful for gaining conditions where fluxes exceeded 1 m/d.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12436","usgsCitation":"Irvine, D.J., Briggs, M.A., Cartwright, I., Scruggs, C.R., and Lautz, L.K., 2017, Improved vertical streambed flux estimation using multiple diurnal temperature methods in series: Groundwater, v. 55, no. 1, p. 73-80, https://doi.org/10.1111/gwat.12436.","productDescription":"8 p.","startPage":"73","endPage":"80","ipdsId":"IP-074583","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":339119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-22","publicationStatus":"PW","scienceBaseUri":"58e4b0b2e4b09da67999777f","contributors":{"authors":[{"text":"Irvine, Dylan J.","contributorId":190404,"corporation":false,"usgs":false,"family":"Irvine","given":"Dylan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":688352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":688351,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cartwright, Ian","contributorId":190405,"corporation":false,"usgs":false,"family":"Cartwright","given":"Ian","affiliations":[],"preferred":false,"id":688353,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scruggs, Courtney R. 0000-0002-1744-3233 cscruggs@usgs.gov","orcid":"https://orcid.org/0000-0002-1744-3233","contributorId":190406,"corporation":false,"usgs":true,"family":"Scruggs","given":"Courtney","email":"cscruggs@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":688354,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lautz, Laura K.","contributorId":124523,"corporation":false,"usgs":false,"family":"Lautz","given":"Laura","email":"","middleInitial":"K.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":688355,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70186332,"text":"70186332 - 2017 - Fisheries research and monitoring activities of the Lake Erie Biological Station, 2016","interactions":[],"lastModifiedDate":"2023-04-07T16:33:01.002928","indexId":"70186332","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Fisheries research and monitoring activities of the Lake Erie Biological Station, 2016","docAbstract":"We conducted a biomass-based assessment of the Lake Erie Western Basin fish community using data collected from 2013-2016 Western Basin (spring and autumn) bottom trawl surveys. Biomass of total catch per hectare has decreased 75 percent since 2013. Declines were observed across all functional groups, but most notable was the decline of Emerald Shiner, which decreased from 25.3 kg/ha in spring 2013 to <0.01 kg/ha by autumn 2013. The four primary predator species – Walleye, Yellow Perch, White Perch, and White Bass – all decreased from 2013 to 2015. In 2016, White Bass and Yellow Perch (all lifestages combined) continued to decline, while Walleye and White Perch (all ages combined) increased slightly from 5.6 kg/ha and 3.4 kg/ha to 9.0 kg/ha and 5.0 kg/ha, respectively (autumn catches). Despite decreasing trends in biomass, there was little change in biodiversity. Declines in forage biomass, i.e. Emerald Shiner and age-0 White Perch, resulted in an increased mean trophic level of catches. Forage fish to piscivore ratios reflected marked shifts in species composition toward greater forage in 2014 and 2016.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Bodamer Scarbro, B.L., Kraus, R.T., Kocovsky, P., and Vandergoot, C., 2017, Fisheries research and monitoring activities of the Lake Erie Biological Station, 2016.","ipdsId":"IP-084961","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":352813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.83042561866203,\n 42.83947998725651\n ],\n [\n -79.05419559710549,\n 42.84445044266204\n ],\n [\n -79.34723947607836,\n 42.867505439860906\n ],\n [\n -79.63042902700818,\n 42.82979611449315\n ],\n [\n -80.19300640647401,\n 42.78683833970689\n ],\n [\n -80.48946185253618,\n 42.56830684920794\n ],\n [\n -80.75505536977545,\n 42.63058927982553\n ],\n [\n -81.2649125281849,\n 42.65947723113632\n ],\n [\n -81.68254937098362,\n 42.473095041315446\n ],\n [\n -81.94779341039089,\n 42.28566242550343\n ],\n [\n -82.45261137533379,\n 42.096617816981166\n ],\n [\n -82.50875433470178,\n 41.97702258755987\n ],\n [\n -82.6129085481856,\n 42.05303192857255\n ],\n [\n -82.96796357699394,\n 41.990378987810516\n ],\n [\n -83.11885623553695,\n 42.06549924415452\n ],\n [\n -83.11015681923705,\n 42.20201225480912\n ],\n [\n -83.20183479728604,\n 42.17539076035257\n ],\n [\n -83.24447886978825,\n 42.02138217144653\n ],\n [\n -83.29964209129852,\n 41.9183418231263\n ],\n [\n -83.46689589644896,\n 41.71125094657418\n ],\n [\n -83.17885165327081,\n 41.629872982739954\n ],\n [\n -82.94864727645425,\n 41.530145155769816\n ],\n [\n -82.94754108395134,\n 41.46190281418288\n ],\n [\n -82.68519761720665,\n 41.430232650244335\n ],\n [\n -82.45731725777486,\n 41.38919538555325\n ],\n [\n -82.18488329829222,\n 41.44165749902834\n ],\n [\n -82.00315923989179,\n 41.50180730835157\n ],\n [\n -81.76429181925941,\n 41.46784487565424\n ],\n [\n -81.43346949978508,\n 41.65468891025833\n ],\n [\n -81.07814633503381,\n 41.806259431479276\n ],\n [\n -80.6642163266489,\n 41.91355784022605\n ],\n [\n -80.08467398415922,\n 42.10240274001927\n ],\n [\n -79.65015538905237,\n 42.30010672051813\n ],\n [\n -79.23498337452526,\n 42.488602008238786\n ],\n [\n -78.83042561866203,\n 42.83947998725651\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee8f8e4b0da30c1bfc500","contributors":{"authors":[{"text":"Bodamer Scarbro, Betsy L. 0000-0002-9022-7027 bbodamerscarbro@usgs.gov","orcid":"https://orcid.org/0000-0002-9022-7027","contributorId":5857,"corporation":false,"usgs":true,"family":"Bodamer Scarbro","given":"Betsy","email":"bbodamerscarbro@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":688356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":688357,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kocovsky, Patrick 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":688358,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergoot, Christopher 0000-0003-4128-3329 cvandergoot@usgs.gov","orcid":"https://orcid.org/0000-0003-4128-3329","contributorId":178356,"corporation":false,"usgs":true,"family":"Vandergoot","given":"Christopher","email":"cvandergoot@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":688359,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195843,"text":"70195843 - 2017 - Dissolution of fluorapatite by Pseudomonas fluorescens P35 resulting in fluorine release","interactions":[],"lastModifiedDate":"2018-03-06T10:53:54","indexId":"70195843","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1800,"text":"Geomicrobiology Journal","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Dissolution of fluorapatite by Pseudomonas fluorescens P35 resulting in fluorine release","title":"Dissolution of fluorapatite by Pseudomonas fluorescens P35 resulting in fluorine release","docAbstract":"Chemical weathering of fluorine-bearing minerals is widely accepted as the main mechanism for the release of fluorine (F) to groundwater. Here, we propose a potential mechanism of F release via microbial dissolution of fluorapatite (Ca5(PO4)3F), which has been neglected previously. Batch culture experiments were conducted at 30°C with a phosphate-solubilizing bacteria strain, Pseudomonas fluorescens P35, and rock phosphates as the sole source of phosphate for microbial growth in parallel with abiotic controls. Rock phosphates consisted of 55–91% of fluorapatite and 5–10% of dolomite before microbial dissolution as indicated by X-ray diffraction (XRD). Mineral composition and morphology changed after microbial dissolution characterized by the disappearance of dolomite and the development of etched cavities on rock phosphate surfaces. The pH of media used was approximately 7.4 at the beginning and increased gradually to 7.7 in abiotic controls; with the inoculum, the pH decreased to acidic values of 3.7–3.8 after 27 h. Phosphate, calcium, and fluoride were released from the rock phosphate to the acidified medium. At 42 h, the concentration of F reached 8.1–10.3 mg L−1. The elevated F concentration was two times higher than the F levels in groundwater in regions diagnosed with fluorosis, and was toxic to the bacteria, as demonstrated by a precipitous decrease in live cells. Geochemical modeling demonstrated that the oxidation of glucose (the carbon source for microbial growth in the medium) to gluconic acid could decrease the pH to 3.7–3.8 and result in the dissolution of fluorapatite and dolomite. Dolomite and fluorapatite remained unsaturated, while concentrations of dissolved phosphorus (P), calcium (Ca), and F increased throughout the time course Fluorite reached saturation [saturation index (SI) 0.22–0.42] after 42 h in rock phosphate–amended biotic systems. However, fluorite was not detected in XRD patterns of the final residue from microcosms. Given that phosphate-solubilizing bacteria are ubiquitous in soil and groundwater ecosystems, they could play an important role in fluorapatite dissolution and the release of F to groundwater.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01490451.2016.1204376","usgsCitation":"Zhou, J., Wang, H., Cravotta, C., Dong, Q., and Xiang, X., 2017, Dissolution of fluorapatite by Pseudomonas fluorescens P35 resulting in fluorine release: Geomicrobiology Journal, v. 34, no. 5, p. 421-433, https://doi.org/10.1080/01490451.2016.1204376.","productDescription":"13 p.","startPage":"421","endPage":"433","ipdsId":"IP-059740","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":352249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-20","publicationStatus":"PW","scienceBaseUri":"5afee8ebe4b0da30c1bfc4d2","contributors":{"authors":[{"text":"Zhou, Jianping","contributorId":202968,"corporation":false,"usgs":false,"family":"Zhou","given":"Jianping","email":"","affiliations":[{"id":36564,"text":"State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, P R China","active":true,"usgs":false}],"preferred":false,"id":730277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Hongmei","contributorId":202967,"corporation":false,"usgs":false,"family":"Wang","given":"Hongmei","email":"","affiliations":[{"id":36565,"text":"Laboratory of Basin Hydrology and Wetland Eco-restoration, China University of Geosciences, Wuhan, 430074, P R China","active":true,"usgs":false}],"preferred":false,"id":730276,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684 cravotta@usgs.gov","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":138829,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","email":"cravotta@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":730274,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dong, Qiang","contributorId":202966,"corporation":false,"usgs":false,"family":"Dong","given":"Qiang","email":"","affiliations":[{"id":36564,"text":"State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, P R China","active":true,"usgs":false}],"preferred":false,"id":730275,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xiang, Xing","contributorId":202964,"corporation":false,"usgs":false,"family":"Xiang","given":"Xing","email":"","affiliations":[{"id":36564,"text":"State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, P R China","active":true,"usgs":false}],"preferred":false,"id":730273,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195842,"text":"70195842 - 2017 - Differences in flood hazard projections in Europe – their causes and consequences for decision making","interactions":[],"lastModifiedDate":"2018-03-06T11:01:34","indexId":"70195842","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1927,"text":"Hydrological Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"Differences in flood hazard projections in Europe – their causes and consequences for decision making","docAbstract":"This paper interprets differences in flood hazard projections over Europe and identifies likely sources of discrepancy. Further, it discusses potential implications of these differences for flood risk reduction and adaptation to climate change. The discrepancy in flood hazard projections raises caution, especially among decision makers in charge of water resources management, flood risk reduction, and climate change adaptation at regional to local scales. Because it is naïve to expect availability of trustworthy quantitative projections of future flood hazard, in order to reduce flood risk one should focus attention on mapping of current and future risks and vulnerability hotspots and improve the situation there. Although an intercomparison of flood hazard projections is done in this paper and differences are identified and interpreted, it does not seems possible to recommend which large-scale studies may be considered most credible in particular areas of Europe.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02626667.2016.1241398","usgsCitation":"Kundzewicz, Z., Krysanova, V., Dankers, R., Hirabayashi, Y., Kanae, S., Hattermann, F.F., Huang, S., Milly, P., Stoffel, M., Driessen, P., Matczak, P., Quevauviller, P., and Schellnhuber, H., 2017, Differences in flood hazard projections in Europe – their causes and consequences for decision making: Hydrological Sciences Journal, v. 62, no. 1, p. 1-14, https://doi.org/10.1080/02626667.2016.1241398.","productDescription":"14 p.","startPage":"1","endPage":"14","ipdsId":"IP-079346","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":470232,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02626667.2016.1241398","text":"Publisher Index Page"},{"id":352251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-29","publicationStatus":"PW","scienceBaseUri":"5afee8ebe4b0da30c1bfc4d4","contributors":{"authors":[{"text":"Kundzewicz, Z. W.","contributorId":202952,"corporation":false,"usgs":false,"family":"Kundzewicz","given":"Z. W.","affiliations":[{"id":36556,"text":"Institute for Agricultural and Forest Environment, Polish Academy of Sciences, Poznań, Poland","active":true,"usgs":false}],"preferred":false,"id":730261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krysanova, V.","contributorId":202953,"corporation":false,"usgs":false,"family":"Krysanova","given":"V.","affiliations":[{"id":32972,"text":"Potsdam Institute for Climate Impact Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":730262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dankers, R.","contributorId":202954,"corporation":false,"usgs":false,"family":"Dankers","given":"R.","email":"","affiliations":[{"id":36557,"text":"Met Office, Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":730263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hirabayashi, Y.","contributorId":202955,"corporation":false,"usgs":false,"family":"Hirabayashi","given":"Y.","email":"","affiliations":[{"id":36558,"text":"Institute of Engineering Innovation, University of Tokyo, Tokyo, Japan","active":true,"usgs":false}],"preferred":false,"id":730264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kanae, S.","contributorId":202956,"corporation":false,"usgs":false,"family":"Kanae","given":"S.","email":"","affiliations":[{"id":36559,"text":"Department of Mechanical and Environmental Informatics, Tokyo Institute of Technology, Tokyo, Japan","active":true,"usgs":false}],"preferred":false,"id":730265,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hattermann, F. F.","contributorId":202957,"corporation":false,"usgs":false,"family":"Hattermann","given":"F.","email":"","middleInitial":"F.","affiliations":[{"id":32972,"text":"Potsdam Institute for Climate Impact Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":730266,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, S.","contributorId":202958,"corporation":false,"usgs":false,"family":"Huang","given":"S.","email":"","affiliations":[{"id":36560,"text":"The Norwegian Water Resources and Energy Directorate, Oslo, Norway","active":true,"usgs":false}],"preferred":false,"id":730267,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Milly, Paul C.D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":2119,"corporation":false,"usgs":true,"family":"Milly","given":"Paul C.D.","email":"cmilly@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":730260,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stoffel, M.","contributorId":202959,"corporation":false,"usgs":false,"family":"Stoffel","given":"M.","email":"","affiliations":[{"id":36561,"text":"Climatic Change and Climate Impacts, Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":730268,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Driessen, P.P.J.","contributorId":202960,"corporation":false,"usgs":false,"family":"Driessen","given":"P.P.J.","email":"","affiliations":[{"id":36562,"text":"Utrecht University, Copernicus Institute of Sustainable Development, Utrecht, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":730269,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Matczak, P.","contributorId":202961,"corporation":false,"usgs":false,"family":"Matczak","given":"P.","email":"","affiliations":[{"id":36556,"text":"Institute for Agricultural and Forest Environment, Polish Academy of Sciences, Poznań, Poland","active":true,"usgs":false}],"preferred":false,"id":730270,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Quevauviller, P.","contributorId":202962,"corporation":false,"usgs":false,"family":"Quevauviller","given":"P.","affiliations":[{"id":36563,"text":"Vrije Universiteit Brussel, Belgium","active":true,"usgs":false}],"preferred":false,"id":730271,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Schellnhuber, H.-J.","contributorId":202963,"corporation":false,"usgs":false,"family":"Schellnhuber","given":"H.-J.","email":"","affiliations":[{"id":32972,"text":"Potsdam Institute for Climate Impact Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":730272,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70196074,"text":"70196074 - 2017 - Geologic map of the Bateman Spring Quadrangle, Lander County, Nevada","interactions":[],"lastModifiedDate":"2018-03-16T10:47:54","indexId":"70196074","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5655,"text":"Nevada Bureau of Mines and Geology Map","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"185","title":"Geologic map of the Bateman Spring Quadrangle, Lander County, Nevada","docAbstract":"This 1:24,000-scale geologic map of the Bateman Spring 7.5-minute quadrangle in Lander County, Nevada contains descriptions of 24 geologic units and one cross section. Accompanying text includes full unit descriptions and references. This quadrangle includes lower Paleozoic siliciclastic sedimentary rocks of the Roberts Mountain allochthon, Miocene intrusive dikes, alluvial deposits of the northern Shoshone Range piedmont, and riverine deposits of the Reese and Humboldt rivers.
Significant findings include: refined age estimates for the Ordovician-Cambrian Valmy Formation and Devonian Slaven Chert, based on new fossil information; and detailed mapping of late Quaternary fault traces along the Shoshone Range fault system.
Waterfalls create barriers to fish migration, yet hundreds of isolated salmonid populations exist above barriers and have persisted for thousands of years in steep mountainous terrain. Ecological theory indicates that small isolated populations in disturbance-prone landscapes are at greatest risk of extirpation because immigration and recolonization are not possible. On the contrary, many above-barrier populations are currently thriving while their downstream counterparts are dwindling. This quandary led us to explore geomorphic knickpoints as a mechanism for disconnecting hillslope and channel processes by limiting channel incision and decreasing the pace of base-level lowering. Using LiDAR from the Oregon Coast Range, we found gentler channel gradients, wider valleys, lower gradient hillslopes, and less shallow landslide potential in an above-barrier catchment compared to a neighboring catchment devoid of persistent knickpoints. Based on this unique geomorphic template, above-barrier channel networks are less prone to debris flows and other episodic sediment fluxes. These above-barrier catchments also have greater resiliency to flooding, owing to wider valleys with greater floodplain connectivity. Habitat preference models further indicate that salmonid habitat is present in greater quantity and quality in these above-barrier networks. Therefore the paradox of the persistence of small isolated fish populations may be facilitated by a geomorphic mechanism that both limits their connectivity to larger fish populations yet dampens the effect of disturbance by decreasing connections between hillslope and channel processes above geomorphic knickpoints.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2016.03.029","usgsCitation":"May, C., Roering, J., Snow, K., Griswold, K., and Gresswell, R.E., 2017, The waterfall paradox: How knickpoints disconnect hillslope and channel processes, isolating salmonid populations in ideal habitats: Geomorphology, v. 277, p. 228-236, https://doi.org/10.1016/j.geomorph.2016.03.029.","productDescription":"9 p.","startPage":"228","endPage":"236","ipdsId":"IP-066828","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":343992,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"277","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596f1e26e4b0d1f9f0640763","contributors":{"authors":[{"text":"May, Christine","contributorId":99619,"corporation":false,"usgs":true,"family":"May","given":"Christine","affiliations":[],"preferred":false,"id":705363,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roering, Joshua J.","contributorId":194297,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua J.","affiliations":[],"preferred":false,"id":705364,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snow, Kyle","contributorId":194786,"corporation":false,"usgs":false,"family":"Snow","given":"Kyle","email":"","affiliations":[],"preferred":false,"id":705365,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Griswold, Kitty","contributorId":194787,"corporation":false,"usgs":false,"family":"Griswold","given":"Kitty","email":"","affiliations":[],"preferred":false,"id":705366,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gresswell, Robert E. 0000-0003-0063-855X bgresswell@usgs.gov","orcid":"https://orcid.org/0000-0003-0063-855X","contributorId":147914,"corporation":false,"usgs":true,"family":"Gresswell","given":"Robert","email":"bgresswell@usgs.gov","middleInitial":"E.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":false,"id":705367,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189500,"text":"70189500 - 2017 - Relative contributions of copper oxide nanoparticles and dissolved copper to Cu uptake kinetics of Gulf killifish (Fundulus grandis) embryos","interactions":[],"lastModifiedDate":"2017-07-13T16:27:55","indexId":"70189500","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Relative contributions of copper oxide nanoparticles and dissolved copper to Cu uptake kinetics of Gulf killifish (Fundulus grandis) embryos","title":"Relative contributions of copper oxide nanoparticles and dissolved copper to Cu uptake kinetics of Gulf killifish (Fundulus grandis) embryos","docAbstract":"The toxicity of soluble metal-based nanomaterials may be due to the uptake of metals in both dissolved and nanoparticulate forms, but the relative contributions of these different forms to overall metal uptake rates under environmental conditions are not quantitatively defined. Here, we investigated the linkage between the dissolution rates of copper(II) oxide (CuO) nanoparticles (NPs) and their bioavailability to Gulf killifish (Fundulus grandis) embryos, with the aim of quantitatively delineating the relative contributions of nanoparticulate and dissolved species for Cu uptake. Gulf killifish embryos were exposed to dissolved Cu and CuO NP mixtures comprising a range of pH values (6.3–7.5) and three types of natural organic matter (NOM) isolates at various concentrations (0.1–10 mg-C L–1), resulting in a wide range of CuO NP dissolution rates that subsequently influenced Cu uptake. First-order dissolution rate constants of CuO NPs increased with increasing NOM concentration and for NOM isolates with higher aromaticity, as indicated by specific ultraviolet absorbance (SUVA), while Cu uptake rate constants of both dissolved Cu and CuO NP decreased with NOM concentration and aromaticity. As a result, the relative contribution of dissolved Cu and nanoparticulate CuO species for the overall Cu uptake rate was insensitive to NOM type or concentration but largely determined by the percentage of CuO that dissolved. These findings highlight SUVA and aromaticity as key NOM properties affecting the dissolution kinetics and bioavailability of soluble metal-based nanomaterials in organic-rich waters. These properties could be used in the incorporation of dissolution kinetics into predictive models for environmental risks of nanomaterials.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.6b04672","usgsCitation":"Jiang, C., Castellon, B.T., Matson, C., Aiken, G.R., and Hsu-Kim, H., 2017, Relative contributions of copper oxide nanoparticles and dissolved copper to Cu uptake kinetics of Gulf killifish (Fundulus grandis) embryos: Environmental Science & Technology, v. 51, no. 3, p. 1395-1404, https://doi.org/10.1021/acs.est.6b04672.","productDescription":"10 p.","startPage":"1395","endPage":"1404","ipdsId":"IP-080135","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":343831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"3","noUsgsAuthors":false,"publicationDate":"2017-01-27","publicationStatus":"PW","scienceBaseUri":"596886a0e4b0d1f9f05f5992","contributors":{"authors":[{"text":"Jiang, Chuanjia","contributorId":194659,"corporation":false,"usgs":false,"family":"Jiang","given":"Chuanjia","email":"","affiliations":[],"preferred":false,"id":704919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Castellon, Benjamin T.","contributorId":194660,"corporation":false,"usgs":false,"family":"Castellon","given":"Benjamin","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":704920,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matson, Cole W.","contributorId":141222,"corporation":false,"usgs":false,"family":"Matson","given":"Cole W.","affiliations":[{"id":13716,"text":"Baylor University","active":true,"usgs":false}],"preferred":false,"id":704921,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":704922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hsu-Kim, Heileen","contributorId":49041,"corporation":false,"usgs":false,"family":"Hsu-Kim","given":"Heileen","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":704923,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193154,"text":"70193154 - 2017 - A network model framework for prioritizing wetland conservation in the Great Plains","interactions":[],"lastModifiedDate":"2017-11-20T16:32:30","indexId":"70193154","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A network model framework for prioritizing wetland conservation in the Great Plains","docAbstract":"Context
Playa wetlands are the primary habitat for numerous wetland-dependent species in the Southern Great Plains of North America. Plant and wildlife populations that inhabit these wetlands are reciprocally linked through the dispersal of individuals, propagules and ultimately genes among local populations.
Objective
To develop and implement a framework using network models for conceptualizing, representing and analyzing potential biological flows among 48,981 spatially discrete playa wetlands in the Southern Great Plains.
Methods
We examined changes in connectivity patterns and assessed the relative importance of wetlands to maintaining these patterns by targeting wetlands for removal based on network centrality metrics weighted by estimates of habitat quality and probability of inundation.
Results
We identified several distinct, broad-scale sub networks and phase transitions among playa wetlands in the Southern Plains. In particular, for organisms that can disperse >2 km a dense and expansive wetland sub network emerges in the Southern High Plains. This network was characterized by localized, densely connected wetland clusters at link distances (h) >2 km but <5 km and was most sensitive to changes in wetland availability (p) and configuration when h = 4 km, and p = 0.2–0.4. It transitioned to a single, large connected wetland system at broader spatial scales even when the proportion of inundated wetland was relatively low (p = 0.2).
Conclusions
Our findings suggest that redundancy in the potential for broad and fine-scale movements insulates this system from damage and facilitates system-wide connectivity among populations with different dispersal capacities.
Loss of native grasslands by anthropogenic disturbances has reduced availability and connectivity of habitat for many grassland species. A primary threat to contiguous grasslands is the encroachment of woody vegetation, which is spurred by disturbances that take on many forms from energy development, fire suppression, and grazing. These disturbances are exacerbated by natural- and human-driven cycles of changes in climate punctuated by drought and desertification conditions. Encroachment of honey mesquite (Prosopis glandulosa) into the prairies of southeastern New Mexico has potentially limited habitat for numerous grassland species, including lesser prairie-chickens (Tympanuchus pallidicinctus). To determine the magnitude of impacts of distribution of mesquite and how lesser prairie-chickens respond to mesquite presence on the landscape in southeastern New Mexico, we evaluated seasonal space use of lesser prairie-chickens in the breeding and nonbreeding seasons. We derived several remotely sensed spatial metrics to characterize the distribution of mesquite. We then used these data to create population-level resource utilization functions and predict intensity of use of lesser prairie-chickens across our study area. Home ranges were smaller in the breeding season compared with the nonbreeding season; however, habitat use was similar across seasons. During both seasons, lesser prairie-chickens used areas closer to leks and largely avoided areas with mesquite. Relative to the breeding season, during the nonbreeding season habitat use suggested a marginal increase in mesquite within areas of low intensity of use, yet aversion to mesquite was strong in areas of medium to high intensity of use. To our knowledge, our study is the first to demonstrate a negative behavioral response by lesser prairie-chickens to woody encroachment in native grasslands. To mitigate one of the possible limiting factors for lesser prairie-chickens, we suggest future conservation strategies be employed by land managersto reduce mesquite abundance in the southern portion of their current range.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2016.09.006","usgsCitation":"Boggie, M.A., Strong, C.R., Lusk, D., Carleton, S.A., Gould, W., Howard, R.L., Nichols, C.T., Falkowski, M.J., and Hagen, C.A., 2017, Impacts of mesquite distribution on seasonal space use of lesser prairie-chickens: Rangeland Ecology and Management, v. 70, no. 1, p. 68-77, https://doi.org/10.1016/j.rama.2016.09.006.","productDescription":"10 p.","startPage":"68","endPage":"77","ipdsId":"IP-073814","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470250,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2016.09.006","text":"Publisher Index Page"},{"id":347478,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","county":"Chaves County, Lea 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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07e954e4b09af898c8cc13","contributors":{"authors":[{"text":"Boggie, Matthew A.","contributorId":198068,"corporation":false,"usgs":false,"family":"Boggie","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":716389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strong, Cody R.","contributorId":198550,"corporation":false,"usgs":false,"family":"Strong","given":"Cody","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":716390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lusk, Daniel","contributorId":198551,"corporation":false,"usgs":false,"family":"Lusk","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":716391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carleton, Scott A. 0000-0001-9609-650X 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T.","contributorId":193024,"corporation":false,"usgs":false,"family":"Nichols","given":"Clay","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":716415,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Falkowski, Michael J.","contributorId":198547,"corporation":false,"usgs":false,"family":"Falkowski","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":716416,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hagen, Christian A.","contributorId":177795,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":716417,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70189668,"text":"70189668 - 2017 - Mercury bioaccumulation in estuarine fishes: Novel insights from sulfur stable isotopes","interactions":[],"lastModifiedDate":"2017-11-22T17:03:21","indexId":"70189668","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Mercury bioaccumulation in estuarine fishes: Novel insights from sulfur stable isotopes","docAbstract":"Estuaries are transitional habitats characterized by complex biogeochemical and ecological gradients that result in substantial variation in fish total mercury concentrations (THg). We leveraged these gradients and used carbon (δ13C), nitrogen (δ15N), and sulfur (δ34S) stable isotopes to examine the ecological and biogeochemical processes underlying THg bioaccumulation in fishes from the San Francisco Bay Estuary. We employed a tiered approach that first examined processes influencing variation in fish THg among wetlands, and subsequently examined the roles of habitat and within-wetland processes in generating larger-scale patterns in fish THg. We found that δ34S, an indicator of sulfate reduction and habitat specific-foraging, was correlated with fish THg at all three spatial scales. Over the observed ranges of δ34S, THg concentrations in fish increased by up to 860% within wetlands, 560% among wetlands, and 291% within specific impounded wetland habitats. In contrast, δ13C and δ15N were not correlated with THg among wetlands and were only important in low salinity impounded wetlands, possibly reflecting more diverse food webs in this habitat. Together, our results highlight the key roles of sulfur biogeochemistry and ecology in influencing estuarine fish THg, as well as the importance of fish ecology and habitat in modulating the relationships between biogeochemical processes and Hg bioaccumulation.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.6b05325","usgsCitation":"Willacker, J.J., Eagles-Smith, C.A., and Ackerman, J., 2017, Mercury bioaccumulation in estuarine fishes: Novel insights from sulfur stable isotopes: Environmental Science & Technology, v. 51, no. 4, p. 2131-2139, https://doi.org/10.1021/acs.est.6b05325.","productDescription":"9 p.","startPage":"2131","endPage":"2139","ipdsId":"IP-080770","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":344071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay Estuary","volume":"51","issue":"4","noUsgsAuthors":false,"publicationDate":"2017-02-01","publicationStatus":"PW","scienceBaseUri":"59706fb6e4b0d1f9f065a88a","contributors":{"authors":[{"text":"Willacker, James J. jwillacker@usgs.gov","contributorId":5614,"corporation":false,"usgs":true,"family":"Willacker","given":"James","email":"jwillacker@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":705690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":705691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":705692,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192567,"text":"70192567 - 2017 - Fish assemblages","interactions":[],"lastModifiedDate":"2018-01-26T14:20:55","indexId":"70192567","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Fish assemblages","docAbstract":"Methods to sample fishes in stream ecosystems and to analyze the raw data, focusing primarily on assemblage-level (all fish species combined) analyses, are presented in this chapter. We begin with guidance on sample site selection, permitting for fish collection, and information-gathering steps to be completed prior to conducting fieldwork. Basic sampling methods (visual surveying, electrofishing, and seining) are presented with specific instructions for estimating population sizes via visual, capture-recapture, and depletion surveys, in addition to new guidance on environmental DNA (eDNA) methods. Steps to process fish specimens in the field including the use of anesthesia and preservation of whole specimens or tissue samples (for genetic or stable isotope analysis) are also presented. Data analysis methods include characterization of size-structure within populations, estimation of species richness and diversity, and application of fish functional traits. We conclude with three advanced topics in assemblage-level analysis: multidimensional scaling (MDS), ecological networks, and loop analysis.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Methods in stream ecology, 3rd Edition","language":"English","publisher":"Academic Press","doi":"10.1016/B978-0-12-416558-8.00016-0","isbn":"9780124165588","usgsCitation":"McGarvey, D.J., Falke, J.A., Li, H.W., and Li, J., 2017, Fish assemblages, chap. of Methods in stream ecology, 3rd Edition, p. 321-353, https://doi.org/10.1016/B978-0-12-416558-8.00016-0.","productDescription":"33 p.","startPage":"321","endPage":"353","ipdsId":"IP-070364","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":350703,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6c4c95e4b06e28e9cabb04","contributors":{"editors":[{"text":"Hauer, F. Richard","contributorId":189116,"corporation":false,"usgs":false,"family":"Hauer","given":"F.","email":"","middleInitial":"Richard","affiliations":[],"preferred":false,"id":725976,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Lamberti, G. A.","contributorId":44229,"corporation":false,"usgs":false,"family":"Lamberti","given":"G.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":725977,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"McGarvey, Daniel J.","contributorId":201505,"corporation":false,"usgs":false,"family":"McGarvey","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":725973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716229,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Hiram W.","contributorId":18724,"corporation":false,"usgs":true,"family":"Li","given":"Hiram","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":725974,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Judith","contributorId":74622,"corporation":false,"usgs":true,"family":"Li","given":"Judith","email":"","affiliations":[],"preferred":false,"id":725975,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192670,"text":"70192670 - 2017 - Post-rift magmatic evolution of the eastern North American “passive-aggressive” margin","interactions":[],"lastModifiedDate":"2017-11-29T13:55:13","indexId":"70192670","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Post-rift magmatic evolution of the eastern North American “passive-aggressive” margin","docAbstract":"Understanding the evolution of passive margins requires knowledge of temporal and chemical constraints on magmatism following the transition from supercontinent to rifting, to post-rifting evolution. The Eastern North American Margin (ENAM) is an ideal study location as several magmatic pulses occurred in the 200 My following rifting. In particular, the Virginia-West Virginia region of the ENAM has experienced two postrift magmatic pulses at ∼152 Ma and 47 Ma, and thus provides a unique opportunity to study the long-term magmatic evolution of passive margins. Here we present a comprehensive set of geochemical data that includes new 40Ar/39Ar ages, major and trace-element compositions, and analysis of radiogenic isotopes to further constrain their magmatic history. The Late Jurassic volcanics are bimodal, from basanites to phonolites, while the Eocene volcanics range from picrobasalt to rhyolite. Modeling suggests that the felsic volcanics from both the Late Jurassic and Eocene events are consistent with fractional crystallization. Sr-Nd-Pb systematics for the Late Jurassic event suggests HIMU and EMII components in the magma source that we interpret as upper mantle components rather than crustal interaction. Lithospheric delamination is the best hypothesis for magmatism in Virginia/West Virginia, due to tectonic instabilities that are remnant from the long-term evolution of this margin, resulting in a “passive-aggressive” margin that records multiple magmatic events long after rifting ended.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GC006646","usgsCitation":"Mazza, S.E., Gazel, E., Johnson, E.A., Bizmis, M., McAleer, R., and Biryol, C.B., 2017, Post-rift magmatic evolution of the eastern North American “passive-aggressive” margin: Geochemistry, Geophysics, Geosystems, v. 18, no. 1, p. 3-22, https://doi.org/10.1002/2016GC006646.","productDescription":"20 p.","startPage":"3","endPage":"22","ipdsId":"IP-079810","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":349550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.6667,\n 38.1667\n ],\n [\n -78.6667,\n 38.1667\n ],\n [\n -78.6667,\n 38.6667\n ],\n [\n -79.6667,\n 38.6667\n ],\n [\n -79.6667,\n 38.1667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-09","publicationStatus":"PW","scienceBaseUri":"5a60fc3de4b06e28e9c23bff","contributors":{"authors":[{"text":"Mazza, Sarah E. 0000-0001-8091-1186","orcid":"https://orcid.org/0000-0001-8091-1186","contributorId":198664,"corporation":false,"usgs":false,"family":"Mazza","given":"Sarah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":716690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gazel, Esteban","contributorId":192876,"corporation":false,"usgs":false,"family":"Gazel","given":"Esteban","email":"","affiliations":[],"preferred":false,"id":716691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Elizabeth A. 0000-0001-7244-6122","orcid":"https://orcid.org/0000-0001-7244-6122","contributorId":198665,"corporation":false,"usgs":false,"family":"Johnson","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":716692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bizmis, Michael 0000-0002-4611-6928","orcid":"https://orcid.org/0000-0002-4611-6928","contributorId":198666,"corporation":false,"usgs":false,"family":"Bizmis","given":"Michael","email":"","affiliations":[],"preferred":false,"id":716693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":5301,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan J.","email":"rmcaleer@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":716689,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Biryol, C. Berk","contributorId":198667,"corporation":false,"usgs":false,"family":"Biryol","given":"C.","email":"","middleInitial":"Berk","affiliations":[],"preferred":false,"id":716694,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195830,"text":"70195830 - 2017 - Understanding the past to interpret the future: Comparison of simulated groundwater recharge in the upper Colorado River basin (USA) using observed and general-circulation-model historical climate data","interactions":[],"lastModifiedDate":"2020-12-10T13:20:04.696686","indexId":"70195830","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Understanding the past to interpret the future: Comparison of simulated groundwater recharge in the upper Colorado River basin (USA) using observed and general-circulation-model historical climate data","docAbstract":"In evaluating potential impacts of climate change on water resources, water managers seek to understand how future conditions may differ from the recent past. Studies of climate impacts on groundwater recharge often compare simulated recharge from future and historical time periods on an average monthly or overall average annual basis, or compare average recharge from future decades to that from a single recent decade. Baseline historical recharge estimates, which are compared with future conditions, are often from simulations using observed historical climate data. Comparison of average monthly results, average annual results, or even averaging over selected historical decades, may mask the true variability in historical results and lead to misinterpretation of future conditions. Comparison of future recharge results simulated using general circulation model (GCM) climate data to recharge results simulated using actual historical climate data may also result in an incomplete understanding of the likelihood of future changes. In this study, groundwater recharge is estimated in the upper Colorado River basin, USA, using a distributed-parameter soil-water balance groundwater recharge model for the period 1951–2010. Recharge simulations are performed using precipitation, maximum temperature, and minimum temperature data from observed climate data and from 97 CMIP5 (Coupled Model Intercomparison Project, phase 5) projections. Results indicate that average monthly and average annual simulated recharge are similar using observed and GCM climate data. However, 10-year moving-average recharge results show substantial differences between observed and simulated climate data, particularly during period 1970–2000, with much greater variability seen for results using observed climate data.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-016-1481-0","usgsCitation":"Tillman, F., Gangopadhyay, S., and Pruitt, T., 2017, Understanding the past to interpret the future: Comparison of simulated groundwater recharge in the upper Colorado River basin (USA) using observed and general-circulation-model historical climate data: Hydrogeology Journal, v. 25, no. 2, p. 347-358, https://doi.org/10.1007/s10040-016-1481-0.","productDescription":"12 p.","startPage":"347","endPage":"358","ipdsId":"IP-076138","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":352218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Colorado River basin","volume":"25","issue":"2","noUsgsAuthors":false,"publicationDate":"2016-10-19","publicationStatus":"PW","scienceBaseUri":"5afee8ebe4b0da30c1bfc4d8","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":1629,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred D.","email":"ftillman@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":false,"id":730201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":730202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pruitt, Tom 0000-0002-3543-1324","orcid":"https://orcid.org/0000-0002-3543-1324","contributorId":173440,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","email":"","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":730203,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192594,"text":"70192594 - 2017 - Conservation Reserve Program mitigates grassland loss in the lesser prairie-chicken range of Kansas","interactions":[],"lastModifiedDate":"2017-11-17T11:39:08","indexId":"70192594","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Conservation Reserve Program mitigates grassland loss in the lesser prairie-chicken range of Kansas","docAbstract":"Since the beginning of the 20th century, the overall occupied range of the lesser prairie-chicken (Tympanuchus pallidicinctus) has declined by 84% commensurate with population trends. Much of this decline has been attributed to the loss and fragmentation of native grasslands throughout the lesser prairie-chicken range. However, quantification of changes in land cover in the distribution of the lesser prairie-chicken is lacking. Our objectives were to (1) document changes in the areal extent and connectivity of grasslands in the identified lesser prairie-chicken range in Kansas, USA, (>60% of extant lesser prairie-chicken population) from the 1950s to 2013 using remotely sensed data and (2) assess the potential of the Conservation Reserve Program (U.S. Department of Agriculture Program converting cropland to permanent cover; CRP) to mitigate grassland loss. Digital land cover maps were generated on a decadal time step through spectral classification of LANDSAT images and visual analysis of aerial photographs (1950s and 1960s). Landscape composition and configuration were assessed using FRAGSTATS to compute a variety of landscape metrics measuring changes in the amount of grassland present as well as changes in the size and configuration of grassland patches. With the exception of a single regional portion of the range, nearly all of the grassland converted to cropland in the lesser prairie-chicken range of Kansas occurred prior to the 1950s. Prior to the implementation of CRP, the amount of grassland decreased 3.6% between the 1950s and 1985 from 18,455 km2 to 17,788 km2. Since 1985, the overall amount of grassland in the lesser prairie-chicken range has increased 11.9% to 19,898 km2 due to implementation of CRP, although the area of grassland decreased between 1994 and 2013 as CRP contracts were not renewed by landowners. Since 1986 grassland in Kansas became more connected and less fragmented in response to the CRP. While the CRP has been successful in increasing grassland quantity and connectivity throughout the lesser prairie-chicken range in Kansas, offsetting loss of grassland since the 1950s, abundance and occupied range of lesser prairie-chickens has declined since the 1980s, suggesting that habitat quality is the principal factor influencing population demography of the species. Although the CRP is contributing to conservation actions for lesser prairie-chickens, efforts to improve habitat quality throughout the range of the lesser prairie-chicken are likely necessary to meet management goals. Continuation of the CRP faces an uncertain future in the face of rising commodity prices, energy development, and reduction in program scope, leaving open the possibility that these areas that have created habitat for lesser prairie-chickens could be lost.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2016.11.004","usgsCitation":"Haukos, D.A., Spencer, D., Hagen, C.A., Daniels, M.D., and Goodin, D., 2017, Conservation Reserve Program mitigates grassland loss in the lesser prairie-chicken range of Kansas: Global Ecology and Conservation, v. 9, p. 21-38, https://doi.org/10.1016/j.gecco.2016.11.004.","productDescription":"18 p.","startPage":"21","endPage":"38","ipdsId":"IP-078839","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":470169,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2016.11.004","text":"Publisher Index Page"},{"id":349063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.01904296874999,\n 36.96744946416934\n ],\n [\n -97.8662109375,\n 36.96744946416934\n ],\n [\n -97.8662109375,\n 40.027614437486655\n ],\n [\n -102.01904296874999,\n 40.027614437486655\n ],\n [\n -102.01904296874999,\n 36.96744946416934\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc3de4b06e28e9c23c02","contributors":{"authors":[{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":716485,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spencer, David","contributorId":200553,"corporation":false,"usgs":false,"family":"Spencer","given":"David","affiliations":[],"preferred":false,"id":722646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hagen, Christian A.","contributorId":177795,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":722647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniels, Melinda D.","contributorId":166701,"corporation":false,"usgs":false,"family":"Daniels","given":"Melinda","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":722648,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goodin, Doug","contributorId":200554,"corporation":false,"usgs":false,"family":"Goodin","given":"Doug","email":"","affiliations":[],"preferred":false,"id":722649,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193100,"text":"70193100 - 2017 - Geology and biostratigraphy of the Potomac River cliffs at Stratford Hall, Westmoreland County, Virginia","interactions":[],"lastModifiedDate":"2018-02-02T13:33:31","indexId":"70193100","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geology and biostratigraphy of the Potomac River cliffs at Stratford Hall, Westmoreland County, Virginia","docAbstract":"The cliffs along the Potomac River at Stratford Hall display extensive exposures of Miocene marine strata that belong successively to the Calvert, Choptank, St. Marys, and Eastover Formations. Within the lower part of this sequence, in the Calvert and Choptank Formations, there is well-developed cyclic stratigraphy. Above the Miocene units lies the marginal marine to deltaic Pleistocene Bacons Castle Formation, which is the highest and youngest formation exposed in the cliffs. The goals of this field trip guide are to (1) show the Miocene formations exposed in the cliffs and discuss the paleoenvironments within which they formed, (2) demonstrate the cyclicity in the Miocene marine formations and discuss its origin, (3) compare and contrast the section exposed at the Stratford and Nomini Cliffs with the classic Miocene Calvert Cliffs sequence exposed to the northeast in Calvert County, Maryland, and the Miocene sequence recovered in the Haynesville cores to the southeast in Richmond County, Virginia, (4) discuss and explain why a detailed correlation among these three places has been so difficult to attain, and (5) show typical lithologies of the Bacons Castle Formation and discuss the paleoenvironments in which they formed.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Blue Ridge to the Beach: Geological Field Excursions across Virginia","language":"English","publisher":"Geological Society of America","doi":"10.1130/2017.0047(05)","usgsCitation":"Weems, R.E., Edwards, L.E., and Landacre, B.D., 2017, Geology and biostratigraphy of the Potomac River cliffs at Stratford Hall, Westmoreland County, Virginia, chap. of Blue Ridge to the Beach: Geological Field Excursions across Virginia, v. 47, p. 125-152, https://doi.org/10.1130/2017.0047(05).","productDescription":"28 p.","startPage":"125","endPage":"152","ipdsId":"IP-082636","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":350973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","county":"Westmoreland 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Nature-based approaches are being applied globally, as signified by the breadth of geographic coverage in this book. The author’s institutions and locations of study span the East, Gulf, and West Coasts of the United States, including the states of Massachusetts, New York, New Jersey, Maryland, Virginia, North Carolina, Florida, Alabama, Mississippi, Louisiana, Texas, California, Washington, and several national perspectives, including Hawaii; British Columbia in Canada; the Netherlands, as well as perspectives across Europe also including Belgium, Denmark, France, Germany, Spain, and the United Kingdom; Sydney Harbor in Australia; and Belize. Living shoreline techniques are very diverse and practices can vary by region, salinity and tidal regime, and degrees of natural and artificial components. Techniques covered in this book include restoring oyster reefs, eelgrass, and mangroves, planting marshes with and without supportive sills (e.g., stone, oyster shell bags, coir logs), incorporating structures such as logs and reef balls, nourishing beaches and dunes with sediment, engineering habitat features into seawalls, and managed realignment. All of these can have a variety of components, such as permitting, land acquisition, design, and monitoring. However, given the diverse representation, there are some shared commonalities that can help inform and direct shoreline management moving forward.
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","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Water in Kentucky: Natural history, communities, and conservation","language":"English","publisher":"University Press of Kentucky","usgsCitation":"Lee, B.D., Williamson, T.N., and Crain, A.S., 2017, Water quality and natural resources in the Green River Basin, chap. of Water in Kentucky: Natural history, communities, and conservation, p. 133-150.","productDescription":"18 p.","startPage":"133","endPage":"150","ipdsId":"IP-046141","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":350972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350971,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.kentuckypress.com/live/title_detail.php?titleid=2917#.WnS7r7enFhE"}],"country":"United States","state":"Kentucky","otherGeospatial":"Green River Basin","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a7586d9e4b00f54eb1d81f8","contributors":{"authors":[{"text":"Lee, Brad D.","contributorId":138937,"corporation":false,"usgs":false,"family":"Lee","given":"Brad","email":"","middleInitial":"D.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":715703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":715702,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crain, Angela S. 0000-0003-0969-6238 ascrain@usgs.gov","orcid":"https://orcid.org/0000-0003-0969-6238","contributorId":3090,"corporation":false,"usgs":true,"family":"Crain","given":"Angela","email":"ascrain@usgs.gov","middleInitial":"S.","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":715701,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191191,"text":"70191191 - 2017 - Investigating impacts of oil and gas development on greater sage-grouse","interactions":[],"lastModifiedDate":"2017-10-02T13:54:33","indexId":"70191191","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Investigating impacts of oil and gas development on greater sage-grouse","docAbstract":"The sagebrush (Artemisia spp.) ecosystem is one of the largest ecosystems in western North America providing habitat for species found nowhere else. Sagebrush habitats have experienced dramatic declines since the 1950s, mostly due to anthropogenic disturbances. The greater sage-grouse (Centrocercus urophasianus) is a sagebrush-obligate species that has experienced population declines over the last several decades, which are attributed to a variety of disturbances including the more recent threat of oil and gas development. We developed a hierarchical, Bayesian state-space model to investigate the impacts of 2 measures of oil and gas development, and environmental and habitat conditions, on sage-grouse populations in Wyoming, USA using male lek counts from 1984 to 2008. Lek attendance of male sage-grouse declined by approximately 2.5%/year and was negatively related to oil and gas well density. We found little support for the influence of sagebrush cover and precipitation on changes in lek counts. Our results support those of other studies reporting negative impacts of oil and gas development on sage-grouse populations and our modeling approach allowed us to make inference to a longer time scale and larger spatial extent than in previous studies. In addition to sage-grouse, development may also negatively affect other sagebrush-obligate species, and active management of sagebrush habitats may be necessary to maintain some species.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21179","usgsCitation":"Green, A., Aldridge, C.L., and O’Donnell, M.S., 2017, Investigating impacts of oil and gas development on greater sage-grouse: Journal of Wildlife Management, v. 81, no. 1, p. 46-57, https://doi.org/10.1002/jwmg.21179.","productDescription":"12 p.","startPage":"46","endPage":"57","ipdsId":"IP-071286","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":346317,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"81","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-18","publicationStatus":"PW","scienceBaseUri":"59d35027e4b05fe04cc34d56","contributors":{"authors":[{"text":"Green, Adam agreen@usgs.gov","contributorId":196774,"corporation":false,"usgs":true,"family":"Green","given":"Adam","email":"agreen@usgs.gov","affiliations":[],"preferred":true,"id":711532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":711533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":140876,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711534,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189468,"text":"70189468 - 2017 - Replication and shedding kinetics of infectious hematopoietic necrosis virus in juvenile rainbow trout","interactions":[],"lastModifiedDate":"2018-03-26T12:20:32","indexId":"70189468","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3699,"text":"Virus Research","active":true,"publicationSubtype":{"id":10}},"title":"Replication and shedding kinetics of infectious hematopoietic necrosis virus in juvenile rainbow trout","docAbstract":"Viral replication and shedding are key components of transmission and fitness, the kinetics of which are heavily dependent on virus, host, and environmental factors. To date, no studies have quantified the shedding kinetics of infectious hematopoietic necrosis virus (IHNV) in rainbow trout (Oncorhynchus mykiss), or how they are associated with replication, making it difficult to ascertain the transmission dynamics of this pathogen of high agricultural and conservation importance. Here, the replication and shedding kinetics of two M genogroup IHNV genotypes were examined in their naturally co-evolved rainbow trout host. Within host virus replication began rapidly, approaching maximum values by day 3 post-infection, after which viral load was maintained or gradually dropped through day 7. Host innate immune response measured as stimulation of Mx-1 gene expression generally followed within host viral loads. Shedding also began very quickly and peaked within 2 days, defining a generally uniform early peak period of shedding from 1 to 4 days after exposure to virus. This was followed by a post-peak period where shedding declined, such that the majority of fish were no longer shedding by day 12 post-infection. Despite similar kinetics, the average shedding rate over the course of infection was significantly lower in mixed compared to single genotype infections, suggesting a competition effect, however, this did not significantly impact the total amount of virus shed. The data also indicated that the duration of shedding, rather than peak amount of virus shed, was correlated with fish mortality. Generally, the majority of virus produced during infection appeared to be shed into the environment rather than maintained in the host, although there was more retention of within host virus during the post-peak period. Viral virulence was correlated with shedding, such that the more virulent of the two genotypes shed more total virus. This fundamental understanding of IHNV shedding kinetics and variation at the individual fish level could assist with management decisions about how to respond to disease outbreaks when they occur.
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Many efforts to model spatially distributed slope stability, for example to assess susceptibility to shallow landslides, have relied on estimates of constant thickness, constant depth, or simple models of thickness (or depth) based on slope and other topographic variables. Assumptions of constant depth or thickness rarely give satisfactory results. Geomorphologists have devised a number of different models to represent the spatial variability of regolith depth and applied them to various settings. I have applied some of these models that can be implemented numerically to different study areas with different types of terrain and tested the results against available depth measurements and landslide inventories. The areas include crystalline rocks of the Colorado Front Range, and gently dipping sedimentary rocks of the Oregon Coast Range. Model performance varies with model, terrain type, and with quality of the input topographic data. Steps in contour-derived 10-m digital elevation models (DEMs) introduce significant errors into the predicted distribution of regolith and landslides. Scan lines, facets, and other artifacts further degrade DEMs and model predictions. Resampling to a lower grid-cell resolution can mitigate effects of facets in lidar DEMs of areas where dense forest severely limits ground returns. Due to its higher accuracy and ability to penetrate vegetation, lidar-derived topography produces more realistic distributions of cover and potential landslides than conventional photogrammetrically derived topographic data.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Landslides: Putting Experience, Knowledge and Emerging Technologies into Practice--Proceedings of the 3rd North American Symposium on Landslides: Association of Environmental and Engineering Geologists Special Publication 27","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Association of Environmental and Engineering Geologists","usgsCitation":"Baum, R.L., 2017, Effects of topographic data quality on estimates of shallow slope stability using different regolith depth models, in Landslides: Putting Experience, Knowledge and Emerging Technologies into Practice--Proceedings of the 3rd North American Symposium on Landslides: Association of Environmental and Engineering Geologists Special Publication 27, p. 807-818.","productDescription":"12 p.","startPage":"807","endPage":"818","ipdsId":"IP-085830","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":352031,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee8ebe4b0da30c1bfc4e6","contributors":{"authors":[{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":717052,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70179853,"text":"70179853 - 2017 - Human footprint affects US carbon balance more than climate change","interactions":[],"lastModifiedDate":"2017-01-19T16:06:14","indexId":"70179853","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Human footprint affects US carbon balance more than climate change","docAbstract":"The MC2 model projects an overall increase in carbon capture in conterminous United States during the 21st century while also simulating a rise in fire causing much carbon loss. Carbon sequestration in soils is critical to prevent carbon losses from future disturbances, and we show that natural ecosystems store more carbon belowground than managed systems do. Natural and human-caused disturbances affect soil processes that shape ecosystem recovery and competitive interactions between native, exotics, and climate refugees. Tomorrow's carbon budgets will depend on how land use, natural disturbances, and climate variability will interact and affect the balance between carbon capture and release.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference Module in Earth Systems and Environmental Sciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-409548-9.09770-0","usgsCitation":"Bachelet, D., Ferschweiler, K., Sheehan, T., Baker, B., Sleeter, B.M., and Zhu, Z., 2017, Human footprint affects US carbon balance more than climate change, chap. of Reference Module in Earth Systems and Environmental Sciences, https://doi.org/10.1016/B978-0-12-409548-9.09770-0.","ipdsId":"IP-083251","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":333485,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5881ded3e4b01192927d9f79","contributors":{"authors":[{"text":"Bachelet, Dominique","contributorId":178454,"corporation":false,"usgs":false,"family":"Bachelet","given":"Dominique","affiliations":[],"preferred":false,"id":658956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferschweiler, Ken","contributorId":127604,"corporation":false,"usgs":false,"family":"Ferschweiler","given":"Ken","affiliations":[{"id":7074,"text":"Conservation Biology Institute, Covallis OR","active":true,"usgs":false}],"preferred":false,"id":658957,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sheehan, Tim","contributorId":178455,"corporation":false,"usgs":false,"family":"Sheehan","given":"Tim","email":"","affiliations":[],"preferred":false,"id":658958,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baker, Barry","contributorId":178456,"corporation":false,"usgs":false,"family":"Baker","given":"Barry","email":"","affiliations":[],"preferred":false,"id":658959,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":658955,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":658960,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191830,"text":"70191830 - 2017 - Extreme geomagnetic storms: Probabilistic forecasts and their uncertainties","interactions":[],"lastModifiedDate":"2017-10-19T13:24:21","indexId":"70191830","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3456,"text":"Space Weather","active":true,"publicationSubtype":{"id":10}},"title":"Extreme geomagnetic storms: Probabilistic forecasts and their uncertainties","docAbstract":"Extreme space weather events are low-frequency, high-risk phenomena. Estimating their rates of occurrence, as well as their associated uncertainties, is difficult. In this study, we derive statistical estimates and uncertainties for the occurrence rate of an extreme geomagnetic storm on the scale of the Carrington event (or worse) occurring within the next decade. We model the distribution of events as either a power law or lognormal distribution and use (1) Kolmogorov-Smirnov statistic to estimate goodness of fit, (2) bootstrapping to quantify the uncertainty in the estimates, and (3) likelihood ratio tests to assess whether one distribution is preferred over another. Our best estimate for the probability of another extreme geomagnetic event comparable to the Carrington event occurring within the next 10 years is 10.3% 95% confidence interval (CI) [0.9,18.7] for a power law distribution but only 3.0% 95% CI [0.6,9.0] for a lognormal distribution. However, our results depend crucially on (1) how we define an extreme event, (2) the statistical model used to describe how the events are distributed in intensity, (3) the techniques used to infer the model parameters, and (4) the data and duration used for the analysis. We test a major assumption that the data represent time stationary processes and discuss the implications. If the current trends persist, suggesting that we are entering a period of lower activity, our forecasts may represent upper limits rather than best estimates.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016SW001470","usgsCitation":"Riley, P., and Love, J.J., 2017, Extreme geomagnetic storms: Probabilistic forecasts and their uncertainties: Space Weather, v. 15, no. 1, p. 53-64, https://doi.org/10.1002/2016SW001470.","productDescription":"12 p.","startPage":"53","endPage":"64","ipdsId":"IP-081721","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":346971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-10","publicationStatus":"PW","scienceBaseUri":"59e9b996e4b05fe04cd65cba","contributors":{"authors":[{"text":"Riley, Pete","contributorId":145704,"corporation":false,"usgs":false,"family":"Riley","given":"Pete","email":"","affiliations":[{"id":16202,"text":"Predictive Science Inc.","active":true,"usgs":false}],"preferred":false,"id":713249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":713250,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70181775,"text":"70181775 - 2017 - Detecting spatial regimes in ecosystems","interactions":[],"lastModifiedDate":"2017-02-14T10:28:26","indexId":"70181775","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Detecting spatial regimes in ecosystems","docAbstract":"Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory-based method, on both terrestrial and aquatic animal data (U.S. Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps and multivariate analyses such as nMDS and cluster analysis. We successfully detected spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.12709","usgsCitation":"Sundstrom, S.M., Eason, T., Nelson, R.J., Angeler, D., Barichievy, C., Garmestani, A.S., Graham, N.A., Granholm, D., Gunderson, L., Knutson, M., Nash, K.L., Spanbauer, T., Stow, C., and Allen, C.R., 2017, Detecting spatial regimes in ecosystems: Ecology Letters, v. 20, no. 1, p. 19-32, https://doi.org/10.1111/ele.12709.","productDescription":"14 p.","startPage":"19","endPage":"32","ipdsId":"IP-079617","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":461805,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/ele.12709","text":"External Repository"},{"id":335306,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-20","publicationStatus":"PW","scienceBaseUri":"58a2d3b6e4b0c82512869a05","chorus":{"doi":"10.1111/ele.12709","url":"http://dx.doi.org/10.1111/ele.12709","publisher":"Wiley-Blackwell","authors":"Sundstrom Shana M., Eason Tarsha, Nelson R. 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