Sediment accumulation threatens the viability and hydrologic functioning of many naturally formed depressional wetlands across the interior regions of North America. These wetlands provide many ecosystem services and vital habitats for diverse plant and animal communities. Climate change may further impact sediment accumulation rates in the context of current land use patterns. We estimated sediment accretion in wetlands within a region renowned for its large populations of breeding waterfowl and migrant shorebirds and examined the relative roles of precipitation and land use context in the sedimentation process. We modeled rates of sediment accumulation from 1971 through 2100 using the Revised Universal Soil Loss Equation (RUSLE) with a sediment delivery ratio and the Unit Stream Power Erosion Deposition model (USPED). These models predicted that by 2100, 21–33 % of wetlands filled completely with sediment and 27–46 % filled by half with sediments; estimates are consistent with measured sediment accumulation rates in the region reported by empirical studies. Sediment accumulation rates were strongly influenced by size of the catchment, greater coverage of tilled landscape within the catchment, and steeper slopes. Conservation efforts that incorporate the relative risk of infilling of wetlands with sediments, thus emphasizing areas of high topographic relief and large watersheds, may benefit wetland-dependent biota.
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rainfall occurred across South Carolina during October 1–5, 2015, as a result of an upper atmospheric low-pressure system that funneled tropical moisture from Hurricane Joaquin into the State. The storm caused major flooding in the central and coastal parts of South Carolina. Almost 27 inches of rain fell near Mount Pleasant in Charleston County during this period. U.S. Geological Survey (USGS) streamgages recorded peaks of record at 17 locations, and 15 other locations had peaks that ranked in the top 5 for the period of record. During the October 2015 flood event, USGS personnel made about 140 streamflow measurements at 86 locations to verify, update, or extend existing rating curves (which are used to compute streamflow from monitored river stage). Immediately after the storm event, USGS personnel documented 602 high-water marks, noting the location and height of the water above land surface. Later in October, 50 additional high-water marks were documented near bridges for South Carolina Department of Transportation. Using a subset of these high-water marks, 20 flood-inundation maps of 12 communities were created. Digital datasets of the inundation area, modeling boundary, and water depth rasters are all available for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161019","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Musser, J.W., Watson, K.M., Painter, J.A., and Gotvald, A.J., 2016, Flood-inundation maps of selected areas affected by the flood of October 2015 in central and coastal South Carolina: U.S. Geological Survey Open-File Report 2016–1019, 81 p., https://dx.doi.org/10.3133/ofr20161019.","productDescription":"Report: v, 81 p.; Raw Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072657","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":318176,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1019/ofr20161019.pdf","text":"Report","size":"47.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1019"},{"id":318175,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1019/coverthb.jpg"},{"id":318184,"rank":3,"type":{"id":19,"text":"Raw Data"},"url":"https://water.usgs.gov/floods/events/2015/Joaquin/data_ofr20161019","text":"USGS Flood Information (Data Download)"}],"country":"United States","state":"South Carolina","otherGeospatial":"Salkehatchie River Basin, Savannah River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.541259765625,\n 33.86129311351553\n ],\n [\n -78.64013671875,\n 33.76088200086917\n ],\n [\n -78.94775390625,\n 33.61461929233378\n ],\n [\n -79.1015625,\n 33.348884792201694\n ],\n [\n -79.1455078125,\n 33.22030778968541\n ],\n [\n -79.34326171875,\n 32.99945000822837\n ],\n [\n -79.530029296875,\n 32.96258644191747\n ],\n [\n -79.60693359375,\n 32.85190345738802\n ],\n [\n -79.815673828125,\n 32.74108223150125\n ],\n [\n -80.057373046875,\n 32.55607364492029\n ],\n [\n -80.2880859375,\n 32.491230287947594\n ],\n [\n -80.386962890625,\n 32.41706632846282\n ],\n [\n -80.474853515625,\n 32.287132632616384\n ],\n [\n -80.628662109375,\n 32.175612478499346\n ],\n [\n -80.7275390625,\n 32.06395559466043\n ],\n [\n -80.85937499999999,\n 31.942839972853083\n ],\n [\n -80.936279296875,\n 31.99875937194732\n ],\n [\n -81.123046875,\n 32.12910537866886\n ],\n [\n -81.14501953125,\n 32.25926542645936\n ],\n [\n -81.14501953125,\n 32.36140331527543\n ],\n [\n -81.2548828125,\n 32.44488496716713\n ],\n [\n -81.298828125,\n 32.537551746769\n ],\n [\n -81.375732421875,\n 32.58384932565662\n ],\n [\n -81.4306640625,\n 32.685619853722\n ],\n [\n -81.474609375,\n 32.879587173066305\n ],\n [\n -81.507568359375,\n 33.00866349457558\n ],\n [\n -81.58447265624999,\n 33.073130945006625\n ],\n [\n -81.71630859375,\n 33.146750228776455\n ],\n [\n -81.815185546875,\n 33.23868752757414\n ],\n [\n -81.88110351562499,\n 33.32134852669881\n ],\n [\n -81.990966796875,\n 33.37641235124676\n ],\n [\n -81.947021484375,\n 33.458942753687644\n ],\n [\n -82.078857421875,\n 33.568861182555544\n ],\n [\n -82.166748046875,\n 33.642062504753675\n ],\n [\n -82.19970703125,\n 33.715201644740844\n ],\n [\n -82.28759765625,\n 33.80653802509606\n ],\n [\n -82.430419921875,\n 33.897777013859475\n ],\n [\n -82.55126953124999,\n 34.00713506435885\n ],\n [\n -82.496337890625,\n 34.17999758688084\n ],\n [\n -82.254638671875,\n 34.75966612466248\n ],\n [\n -81.947021484375,\n 35.11990857099681\n ],\n [\n -81.837158203125,\n 35.191766965947394\n ],\n [\n -81.39770507812499,\n 35.16482750605027\n ],\n [\n -81.046142578125,\n 35.146862906756304\n ],\n [\n -81.024169921875,\n 35.074964853989556\n ],\n [\n -80.91430664062499,\n 35.110921809704756\n ],\n [\n -80.771484375,\n 34.95799531086792\n ],\n [\n -80.782470703125,\n 34.84987503195418\n ],\n [\n -80.5517578125,\n 34.831841149828676\n ],\n [\n -80.255126953125,\n 34.804782919572425\n ],\n [\n -79.716796875,\n 34.82282272723702\n ],\n [\n -78.541259765625,\n 33.86129311351553\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
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720 Gracern Road
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
Net fluxes (change between upstream and downstream margins) for water, methylmercury (MeHg), total mercury (THg), dissolved organic carbon (DOC), and chloride (Cl) were assessed twice in an Adirondack stream reach (Sixmile Brook, USA), to test the hypothesized importance of wetland-stream hydraulic and chemical gradients as fundamental controls on fluvial mercury (Hg) supply. The 500 m study reach represented less than 4% of total upstream basin area. During a snowmelt high-flow event in May 2009 surface water, DOC, and chloride fluxes increased by 7.1±1.3%, 8.0±1.3%, and 9.0±1.3%, respectively, within the reach, demonstrating that the adjacent wetlands are important sources of water and solutes to the stream. However, shallow groundwater Hg concentrations lower than in the surface water limited groundwater-surface water Hg exchange and no significant changes in Hg (filtered MeHg and THg) fluxes were observed within the reach despite the favorable hydraulic gradient. In August 2009, the lack of significant wetland-stream hydraulic gradient resulted in no net flux of water or solutes (MeHg, THg, DOC, or Cl) within the reach. The results are consistent with the wetland-Hg-source hypothesis and indicate that hydraulic and chemical gradient (direction and magnitude) interactions are fundamental controls on the supply of wetland Hg to the stream.
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A preliminary analysis","interactions":[],"lastModifiedDate":"2017-04-21T16:05:35","indexId":"70169298","displayToPublicDate":"2016-02-22T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Are the Columbia River Basalts, Columbia Plateau, Idaho, Oregon, and Washington, USA, a viable geothermal target? A preliminary analysis","docAbstract":"The successful development of a geothermal electric power generation facility relies on (1) the identification of sufficiently high temperatures at an economically viable depth and (2) the existence of or potential to create and maintain a permeable zone (permeability >10-14 m2) of sufficient size to allow efficient long-term extraction of heat from the reservoir host rock. If both occur at depth under the Columbia Plateau, development of geothermal resources there has the potential to expand both the magnitude and spatial extent of geothermal energy production. However, a number of scientific and technical issues must be resolved in order to evaluate the likelihood that the Columbia River Basalts, or deeper geologic units under the Columbia Plateau, are viable geothermal targets.
Recent research has demonstrated that heat flow beneath the Columbia Plateau Regional Aquifer System may be higher than previously measured in relatively shallow (<600 m depth) wells, indicating that sufficient temperatures for electricity generation occur at depths 5 km. The remaining consideration is evaluating the likelihood that naturally high permeability exists, or that it is possible to replicate the high average permeability (approximately 10-14 to 10-12 m2) characteristic of natural hydrothermal reservoirs. From a hydraulic perspective, Columbia River Basalts are typically divided into dense, impermeable flow interiors and interflow zones comprising the top of one flow, the bottom of the overlying flow, and any sedimentary interbed. Interflow zones are highly variable in texture but, at depths <600 m, some of them form highly permeable regional aquifers with connectivity over many tens of kilometers. Below depths of ~600 m, permeability reduction occurs in many interflow zones, caused by the formation of low-temperature hydrothermal alteration minerals (corresponding to temperatures above ~35 °C). However, some high permeability (>10-14 m2) interflows are documented at depths up to ~1,400 m. If the elevated permeability in these zones persists to greater depths, they may provide natural permeability of sufficient magnitude to allow their exploitation as conventional geothermal reservoirs. Alternatively, if the permeability in these interflow zones is less than 10-14 m2 at depth, it may be possible to use hydraulic and thermal stimulation to enhance the permeability of both the interflow zones and the natural jointing within the low-permeability interior portions of individual basalt flows in order to develop Enhanced/Engineered Geothermal System (EGS) reservoirs. The key challenge for an improved Columbia Plateau geothermal assessment is acquiring and interpreting comprehensive field data that can provide quantitative constraints on the recovery of heat from the Columbia River Basalts at depths greater than those currently tested by deep boreholes.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings, 41st Workshop on Geothermal Reservoir Engineering","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"conferenceTitle":"41st Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 22-24, 2016","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","publisherLocation":"Stanford, CA","usgsCitation":"Burns, E.R., Williams, C.F., Tolan, T., and Kaven, J.O., 2016, Are the Columbia River Basalts, Columbia Plateau, Idaho, Oregon, and Washington, USA, a viable geothermal target? A preliminary analysis, in Proceedings, 41st Workshop on Geothermal Reservoir Engineering, Stanford, CA, February 22-24, 2016, 11 p.","productDescription":"11 p.","ipdsId":"IP-071284","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":340099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Columbia Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.25,\n 44.25\n ],\n [\n -115.25,\n 44.25\n ],\n [\n -115.25,\n 48.5\n ],\n [\n -122.25,\n 48.5\n ],\n [\n -122.25,\n 44.25\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4ee4b0c3010a8087c5","contributors":{"authors":[{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":3094,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":623483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Colin F. 0000-0003-2196-5496 colin@usgs.gov","orcid":"https://orcid.org/0000-0003-2196-5496","contributorId":274,"corporation":false,"usgs":true,"family":"Williams","given":"Colin","email":"colin@usgs.gov","middleInitial":"F.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":623484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tolan, Terry","contributorId":55489,"corporation":false,"usgs":true,"family":"Tolan","given":"Terry","affiliations":[],"preferred":false,"id":623485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kaven, Joern Ole","contributorId":148002,"corporation":false,"usgs":false,"family":"Kaven","given":"Joern","email":"","middleInitial":"Ole","affiliations":[],"preferred":false,"id":623486,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170108,"text":"70170108 - 2016 - Efficiency of portable antennas for detecting passive integrated transponder tags in stream-dwelling salmonids","interactions":[],"lastModifiedDate":"2016-04-06T17:03:16","indexId":"70170108","displayToPublicDate":"2016-02-22T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Efficiency of portable antennas for detecting passive integrated transponder tags in stream-dwelling salmonids","docAbstract":"Portable antennas have become an increasingly common technique for tracking fish marked with passive integrated transponder (PIT) tags. We used logistic regression to evaluate how species, fish length, and physical habitat characteristics influence portable antenna detection efficiency in stream-dwelling brown trout (Salmo trutta), bull trout (Salvelinus confluentus), and redband trout (Oncorhynchus mykiss newberrii) marked with 12-mm PIT tags. We redetected 56% (20/36) of brown trout, 34% (68/202) of bull trout, and 33% (20/61) of redband trout after a recovery period of 21 to 46 hours. Models indicate support for length and species and minor support for percent boulder, large woody debris, and percent cobble as parameters important for describing variation in detection efficiency, although 95% confidence intervals for estimates were large. The odds of detecting brown trout (1.5 ± 2.2 [mean ± SE]) are approximately four times as high as bull trout (0.4 ± 1.6) or redband trout (0.3 ± 1.8) and species-specific differences may be related to length. Our reported detection efficiency for brown trout falls within the range of other studies, but is the first reported for bull trout and redband trout. Portable antennas may be a relatively unbiased way of redetecting varying sizes of all three salmonid species.
","language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0149898","usgsCitation":"Banish, N.P., Burdick, S.M., and Moyer, K.R., 2016, Efficiency of portable antennas for detecting passive integrated transponder tags in stream-dwelling salmonids: PLoS ONE, v. 11, no. 2, e0149898, 10 p., https://doi.org/10.1371/journal.pone.0149898.","productDescription":"e0149898, 10 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052642","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":471214,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0149898","text":"Publisher Index Page"},{"id":319877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","county":"Klamath County, Lake County","otherGeospatial":"Boulder Creek, Brownsworth Creek, Klamath River basin, Leonard Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.9,\n 42.45\n ],\n [\n -120.9,\n 42.55\n ],\n [\n -120.8,\n 42.55\n ],\n [\n -120.8,\n 42.45\n ],\n [\n -120.9,\n 42.45\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-22","publicationStatus":"PW","scienceBaseUri":"572485fde4b0b13d39159452","contributors":{"authors":[{"text":"Banish, Nolan P.","contributorId":168511,"corporation":false,"usgs":false,"family":"Banish","given":"Nolan","email":"","middleInitial":"P.","affiliations":[{"id":25313,"text":"U.S. Fish and Wildlife Service, Klamath Falls Fish and Wildlife Office, 1936 California Avenue, Klamath Falls, Oregon, 97601, USA","active":true,"usgs":false}],"preferred":false,"id":626212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":626211,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moyer, Katherine R.","contributorId":168512,"corporation":false,"usgs":false,"family":"Moyer","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":25314,"text":"Conservation and Land Management Internship Program, Chicago Botanic Garden, 1000 Lake Cook Road, Glencoe, Illinois, 60022, USA","active":true,"usgs":false}],"preferred":false,"id":626213,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70177889,"text":"70177889 - 2016 - Toward a quantitative and empirical dissolved organic carbon budget for the Gulf of Maine, a semienclosed shelf sea","interactions":[],"lastModifiedDate":"2016-10-26T14:12:02","indexId":"70177889","displayToPublicDate":"2016-02-20T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"Toward a quantitative and empirical dissolved organic carbon budget for the Gulf of Maine, a semienclosed shelf sea","docAbstract":"A time series of organic carbon export from Gulf of Maine (GoM) watersheds was compared to a time series of biological, chemical, bio-optical, and hydrographic properties, measured across the GoM between Yarmouth, NS, Canada, and Portland, ME, U.S. Optical proxies were used to quantify the dissolved organic carbon (DOC) and particulate organic carbon in the GoM. The Load Estimator regression model applied to river discharge data demonstrated that riverine DOC export (and its decadal variance) has increased over the last 80 years. Several extraordinarily wet years (2006–2010) resulted in a massive pulse of chromophoric dissolved organic matter (CDOM; proxy for DOC) into the western GoM along with unidentified optically scattering material (<0.2 μm diameter). A survey of DOC in the GoM and Scotian Shelf showed the strong influence of the Gulf of Saint Lawrence on the DOC that enters the GoM. A deep plume of CDOM-rich water was observed near the coast of Maine which decreased in concentration eastward. The Forel-Ule color scale was derived and compared to the same measurements made in 1912–1913 by Henry Bigelow. Results show that the GoM has yellowed in the last century, particularly in the region of the extension of the Eastern Maine Coastal Current. Time lags between DOC discharge and its appearance in the GoM increased with distance from the river mouths. Algae were also a significant source of DOC but not CDOM. Gulf-wide algal primary production has decreased. Increases in precipitation and DOC discharge to the GoM are predicted over the next century.","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015GB005332","usgsCitation":"Balch, W., Huntington, T.G., Aiken, G.R., Drapeau, D., Bowler, B., Lubelczyk, L., and Butler, K.D., 2016, Toward a quantitative and empirical dissolved organic carbon budget for the Gulf of Maine, a semienclosed shelf sea: Global Biogeochemical Cycles, v. 30, no. 2, p. 268-292, https://doi.org/10.1002/2015GB005332.","productDescription":"25 p.","startPage":"268","endPage":"292","ipdsId":"IP-072221","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471216,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gb005332","text":"Publisher Index Page"},{"id":330416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Gulf of Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72,\n 42\n ],\n [\n -72,\n 47\n ],\n [\n -65,\n 47\n ],\n [\n -65,\n 42\n ],\n [\n -72,\n 42\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-20","publicationStatus":"PW","scienceBaseUri":"5811c0f3e4b0f497e79a5a7f","contributors":{"authors":[{"text":"Balch, William","contributorId":176267,"corporation":false,"usgs":false,"family":"Balch","given":"William","affiliations":[],"preferred":false,"id":652037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huntington, Thomas G. 0000-0002-9427-3530 thunting@usgs.gov","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":1884,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas","email":"thunting@usgs.gov","middleInitial":"G.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drapeau, David","contributorId":176268,"corporation":false,"usgs":false,"family":"Drapeau","given":"David","email":"","affiliations":[],"preferred":false,"id":652039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowler, Bruce","contributorId":176269,"corporation":false,"usgs":false,"family":"Bowler","given":"Bruce","email":"","affiliations":[],"preferred":false,"id":652040,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lubelczyk, Laura","contributorId":176270,"corporation":false,"usgs":false,"family":"Lubelczyk","given":"Laura","email":"","affiliations":[],"preferred":false,"id":652041,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Butler, Kenna D. kebutler@usgs.gov","contributorId":3283,"corporation":false,"usgs":true,"family":"Butler","given":"Kenna","email":"kebutler@usgs.gov","middleInitial":"D.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":652042,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70173794,"text":"70173794 - 2016 - Potential foraging decisions by a desert ungulate to balance water and nutrient intake in a water-stressed environment","interactions":[],"lastModifiedDate":"2016-06-10T13:21:40","indexId":"70173794","displayToPublicDate":"2016-02-19T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Potential foraging decisions by a desert ungulate to balance water and nutrient intake in a water-stressed environment","docAbstract":"Arid climates have unpredictable precipitation patterns, and wildlife managers often provide supplemental water to help desert ungulates endure the hottest, driest periods. When surface water is unavailable, the only source of water for ungulates comes from the forage they consume, and they must make resourceful foraging decisions to meet their requirements. We compared two desert bighorn sheep (Ovis canadensis nelsoni) populations in Arizona, USA: a treatment population with supplemental water removed during treatment, and a control population. We examined whether sheep altered their seasonal diets without supplemental water. We calculated water and nutrient intake and metabolic water production from dry matter intake and forage moisture and nitrogen content, to determine whether sheep could meet their seasonal daily water and nutrient requirements solely from forage. Diets of sheep were higher in protein (all seasons) and moisture (autumn and winter) during treatment compared to pretreatment. During treatment, sheep diet composition was similar between the treatment and control populations, which suggests, under the climatic conditions of this study, water removal did not influence sheep diets. We estimated that under drought conditions, without any surface water available (although small ephemeral potholes would contain water after rains), female and male sheep would be unable to meet their daily water requirements in all seasons, except winter, when reproductive females had a nitrogen deficit. We determined that sheep could achieve water and nutrient balances in all seasons by shifting their total diet proportions by 8–55% from lower to higher moisture and nitrogen forage species. We elucidate how seasonal forage quality and foraging decisions by desert ungulates allow them to cope with their xeric and uncertain environment, and suggest that, with the forage conditions observed in our study area during this study period, providing supplemental water during water-stressed periods may not be necessary for desert bighorn sheep.
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\"}}]}","volume":"11","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-19","publicationStatus":"PW","scienceBaseUri":"575be4ace4b04f417c27f531","contributors":{"authors":[{"text":"Gedir, Jay V.","contributorId":171735,"corporation":false,"usgs":false,"family":"Gedir","given":"Jay","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":638454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":638371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krausman, Paul R.","contributorId":31467,"corporation":false,"usgs":true,"family":"Krausman","given":"Paul","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":638455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allen, Jamison D.","contributorId":171736,"corporation":false,"usgs":false,"family":"Allen","given":"Jamison","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":638456,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duff, Glenn C.","contributorId":171737,"corporation":false,"usgs":false,"family":"Duff","given":"Glenn","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":638457,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morgart, John R.","contributorId":10891,"corporation":false,"usgs":true,"family":"Morgart","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":638458,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70164420,"text":"ds980 - 2016 - Terrestrial-based lidar beach topography of Fire Island, New York, June 2014","interactions":[],"lastModifiedDate":"2016-08-03T08:45:50","indexId":"ds980","displayToPublicDate":"2016-02-19T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"980","title":"Terrestrial-based lidar beach topography of Fire Island, New York, June 2014","docAbstract":"The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Florida and the USGS Lower Mississippi-Gulf Water Science Center (LMG WSC) in Montgomery, Alabama, collaborated to gather alongshore terrestrial-based lidar beach elevation data at Fire Island, New York. This high-resolution elevation dataset was collected on June 11, 2014, to characterize beach topography and document ongoing beach evolution and recovery, and is part of the ongoing beach monitoring within the Hurricane Sandy Supplemental Project GS2-2B. This USGS data series includes the resulting processed elevation point data (xyz) and an interpolated digital elevation model (DEM).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds980","usgsCitation":"Brenner, O.T., Hapke, C.J., Lee, K.G., and Kimbrow, D.R., 2016, Terrestrial-based lidar beach topography of Fire Island, New York, June 2014: U.S. Geological Survey Data Series 980, https://dx.doi.org/10.3133/ds980.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070657","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":318152,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":318151,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0980/index.html","text":"Report (HTML)","description":"DS 980"},{"id":325917,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://dx.doi.org/10.3133/ds921","text":"Data Series 921- Ground-Based Lidar Beach Topography of Fire Island, New York, April 2013","description":"DS 980"},{"id":325918,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F77H1GNN","text":"USGS data release - Ground-Based Lidar Beach Topography of Fire Island, New York, April 2014"},{"id":325919,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7862DKH","text":"USGS data release - Terrestrial-Based Lidar Beach Topography of Fire Island, New York, May 2015"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.83197402954102,\n 40.743225290205785\n ],\n [\n -72.83111572265625,\n 40.74101426921151\n ],\n [\n -72.84811019897461,\n 40.73633186448861\n ],\n [\n -72.86785125732422,\n 40.7302182289573\n ],\n [\n -72.88639068603516,\n 40.72384384060296\n ],\n [\n -72.894287109375,\n 40.721892375167045\n ],\n [\n -72.89445877075195,\n 40.72345355209305\n ],\n [\n -72.88055419921875,\n 40.728397037445035\n ],\n [\n -72.86476135253906,\n 40.73360030952804\n ],\n [\n -72.84965515136719,\n 40.738152838822934\n ],\n [\n -72.83197402954102,\n 40.743225290205785\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.92415618896484,\n 40.71577741296778\n ],\n [\n -72.9550552368164,\n 40.704586878965245\n ],\n [\n -72.99522399902344,\n 40.689229364982054\n ],\n [\n -73.04328918457031,\n 40.67360792349548\n ],\n [\n -73.07968139648438,\n 40.66188943992171\n ],\n [\n -73.10474395751953,\n 40.65720146993478\n ],\n [\n -73.14319610595703,\n 40.64912697157757\n ],\n [\n -73.16654205322266,\n 40.64339608963903\n ],\n [\n -73.19503784179688,\n 40.63740418690266\n ],\n [\n -73.22628021240234,\n 40.63115119323159\n ],\n [\n -73.22525024414062,\n 40.62541876792774\n ],\n [\n -73.18920135498047,\n 40.634017221357695\n ],\n [\n -73.15074920654297,\n 40.64261456761013\n ],\n [\n -73.10405731201172,\n 40.65251317049883\n ],\n [\n -73.09341430664062,\n 40.65459689980922\n ],\n [\n -73.08860778808594,\n 40.653294576616204\n ],\n [\n -73.08002471923828,\n 40.65772237175813\n ],\n [\n -73.05461883544922,\n 40.6639728763869\n ],\n [\n -73.037109375,\n 40.669441587473884\n ],\n [\n -72.98355102539062,\n 40.68844837985795\n ],\n [\n -72.92381286621094,\n 40.71135347314246\n ],\n [\n -72.91145324707031,\n 40.71577741296778\n ],\n [\n -72.91351318359375,\n 40.718379593199494\n ],\n [\n -72.92415618896484,\n 40.71577741296778\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
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(727) 502-8000
http://coastal.er.usgs.gov/
Seasonal mean daily flow data from 119 U.S. Geological Survey streamflow-gaging stations in North Dakota; the surrounding states of Montana, Minnesota, and South Dakota; and the Canadian provinces of Manitoba and Saskatchewan with 10 or more years of unregulated flow record were used to develop regression equations for flow duration, n-day high flow and n-day low flow using ordinary least-squares and Tobit regression techniques. Regression equations were developed for seasonal flow durations at the 10th, 25th, 50th, 75th, and 90th percent exceedances; the 1-, 7-, and 30-day seasonal mean high flows for the 10-, 25-, and 50-year recurrence intervals; and the 1-, 7-, and 30-day seasonal mean low flows for the 2-, 5-, and 10-year recurrence intervals. Basin and climatic characteristics determined to be significant explanatory variables in one or more regression equations included drainage area, percentage of basin drainage area that drains to isolated lakes and ponds, ruggedness number, stream length, basin compactness ratio, minimum basin elevation, precipitation, slope ratio, stream slope, and soil permeability. The adjusted coefficient of determination for the n-day high-flow regression equations ranged from 55.87 to 94.53 percent. The Chi2 values for the duration regression equations ranged from 13.49 to 117.94, whereas the Chi2 values for the n-day low-flow regression equations ranged from 4.20 to 49.68.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155184","collaboration":"Prepared in cooperation with the North Dakota State Water Commission, North Dakota Department of Transportation, North Dakota Department of Health, Red River Joint Water Resources Board, and Devils Lake Basin Joint Water Resource Board","usgsCitation":"Williams-Sether, Tara, and Gross, T.A., 2016, Regression equations to estimate seasonal flow duration, n-day high-flow frequency, and n-day low-flow frequency at sites in North Dakota using data through water year 2009: U.S. Geological Survey Scientific Investigations Report 2015–5184, 12 p., https://dx.doi.org/10.3133/sir20155184.","productDescription":"Report: iv, 12 p.; 1 Table","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069878","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":316691,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2015/5184/sir20155184_table1.xlsx","text":"Table 1","size":"72.0 kb","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5184 Table 1"},{"id":316670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5184/sir20155184.pdf","text":"Report","size":"3.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5184"},{"id":316669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5184/coverthb.jpg"}],"country":"Canada, United States","state":"Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.31591796875,\n 49.63917719651036\n ],\n [\n -101.854248046875,\n 49.94415035164548\n ],\n [\n -103.128662109375,\n 50.21206446065373\n ],\n [\n -104.359130859375,\n 50.240178884797025\n ],\n [\n -105.13916015625,\n 49.90171121726089\n ],\n [\n -105.3369140625,\n 48.64016871811908\n ],\n [\n -105.567626953125,\n 47.39834920035926\n ],\n [\n -105.27099609375,\n 46.59661864884465\n ],\n [\n -105.128173828125,\n 45.71385093029221\n ],\n [\n -105.46875,\n 44.574817404670306\n ],\n [\n -103.9306640625,\n 44.62175409623324\n ],\n [\n -103.524169921875,\n 45.54483149242463\n ],\n [\n -102.513427734375,\n 45.43700828867389\n ],\n [\n -101.019287109375,\n 45.205263456162385\n ],\n [\n -99.635009765625,\n 44.98811302615805\n ],\n [\n -98.668212890625,\n 45.0502402697946\n ],\n [\n -97.85522460937499,\n 44.89479576469787\n ],\n [\n -96.339111328125,\n 44.56699093657141\n ],\n [\n -95.185546875,\n 44.91813929958515\n ],\n [\n -95.987548828125,\n 45.95878764035642\n ],\n [\n -95.701904296875,\n 47.03269459852135\n ],\n [\n -95.767822265625,\n 47.82053186746053\n ],\n [\n -96.0205078125,\n 48.62564740882851\n ],\n [\n -96.427001953125,\n 49.24629332459796\n ],\n [\n -96.99829101562499,\n 49.986552130506176\n ],\n [\n -97.5146484375,\n 49.66762782262192\n ],\n [\n -97.97607421875,\n 49.50380954152213\n ],\n [\n -98.6572265625,\n 49.75997752330658\n ],\n [\n -99.29443359375,\n 49.61070993807422\n ],\n [\n -100.008544921875,\n 49.489538473066474\n ],\n [\n -100.34912109375,\n 49.36806633482156\n ],\n [\n -101.31591796875,\n 49.63917719651036\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS North Dakota Water Science Center
821 East Interstate Avenue
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The noble gases, which are chemically inert under normal terrestrial conditions but vary systematically across a wide range of atomic mass and diffusivity, offer a multicomponent approach to investigating gas dynamics in unsaturated soil horizons, including transfer of gas between saturated zones, unsaturated zones, and the atmosphere. To evaluate the degree to which fractionation of noble gases in the presence of an advective–diffusive flux agrees with existing theory, a simple laboratory sand column experiment was conducted. Pure CO2 was injected at the base of the column, providing a series of constant CO2 fluxes through the column. At five fixed sampling depths within the system, samples were collected for CO2 and noble gas analyses, and ambient pressures were measured. Both the advection–diffusion and dusty gas models were used to simulate the behavior of CO2 and noble gases under the experimental conditions, and the simulations were compared with the measured depth-dependent concentration profiles of the gases. Given the relatively high permeability of the sand column (5 ´ 10−11 m2), Knudsen diffusion terms were small, and both the dusty gas model and the advection–diffusion model accurately predicted the concentration profiles of the CO2 and atmospheric noble gases across a range of CO2 flux from ?700 to 10,000 g m−2 d−1. The agreement between predicted and measured gas concentrations demonstrated that, when applied to natural systems, the multi-component capability provided by the noble gases can be exploited to constrain component and total gas fluxes of non-conserved (CO2) and conserved (noble gas) species or attributes of the soil column relevant to gas transport, such as porosity, tortuosity, and gas saturation.
","language":"English","publisher":"Soil Science Society of America","doi":"10.2136/vzj2015.06.0095","usgsCitation":"Ding, X., Kennedy, B.M., Evans, W.C., and Stonestrom, D.A., 2016, Experimental studies and model analysis of noble gas fractionation in porous media: Vadose Zone Journal, v. 15, no. 2, p. 1-12, https://doi.org/10.2136/vzj2015.06.0095.","productDescription":"13 p.","startPage":"1","endPage":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066461","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":471219,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2136/vzj2015.06.0095","text":"Publisher Index Page"},{"id":318477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-19","publicationStatus":"PW","scienceBaseUri":"56d6cb5de4b015c306f32cef","contributors":{"authors":[{"text":"Ding, Xin","contributorId":167275,"corporation":false,"usgs":false,"family":"Ding","given":"Xin","email":"","affiliations":[{"id":6670,"text":"Lawrence Berkeley National Laboratory, Berkeley, CA","active":true,"usgs":false}],"preferred":false,"id":621640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kennedy, B. Mack.","contributorId":167276,"corporation":false,"usgs":false,"family":"Kennedy","given":"B.","email":"","middleInitial":"Mack.","affiliations":[{"id":6670,"text":"Lawrence Berkeley National Laboratory, Berkeley, CA","active":true,"usgs":false}],"preferred":false,"id":621641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, William C. 0000-0001-5942-3102 wcevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5942-3102","contributorId":2353,"corporation":false,"usgs":true,"family":"Evans","given":"William","email":"wcevans@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":621642,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stonestrom, David A. 0000-0001-7883-3385 dastones@usgs.gov","orcid":"https://orcid.org/0000-0001-7883-3385","contributorId":2280,"corporation":false,"usgs":true,"family":"Stonestrom","given":"David","email":"dastones@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":621639,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168641,"text":"70168641 - 2016 - Structured heterogeneity in a marine terrace chronosequence: Upland mottling","interactions":[],"lastModifiedDate":"2016-02-22T13:48:55","indexId":"70168641","displayToPublicDate":"2016-02-19T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3674,"text":"Vadose Zone Journal","active":true,"publicationSubtype":{"id":10}},"title":"Structured heterogeneity in a marine terrace chronosequence: Upland mottling","docAbstract":"Soil mottles generally are interpreted as a product of reducing conditions during periods of water saturation. The upland soils of the Santa Cruz, CA, marine terrace chronosequence display an evolving sequence of reticulate mottling from the youngest soil (65 ka) without mottles to the oldest soil (225 ka) with well-developed mottles. The mottles consist of an interconnected network of clay and C-enriched regions (gray, 2.5Y 6/1) bordered by leached parent material (white, 2.5Y 8/1) within a diminishing matrix of oxidized parent material (orange, 7.5YR 5/8). The mottles develop in soils that formed from relatively uniform nearshore sediments and occur below the depth of soil bioturbation. To explore how a presumably wetland feature occurs in an unsaturated upland soil, physical and chemical characteristics of mottle separates (orange, gray, and white) were compared through the deep time represented by the soil chronosequence. Mineralogical, isotopic, and surface-area differences among mottle separates indicate that rhizogenic centimeter-scale mass transfer acting across millennia is an integral part of weathering, pedogenesis, and C and nutrient transfer. Elemental analysis, electron microscopy, and Fe-isotope systematics indicate that mottle development is driven by deep roots together with their fungal and microbial symbionts. Taken together, these data suggest that deep soil horizons on old stable landforms can develop reticulate mottling as the long-term imprint of rhizospheric processes. The processes of rhizogenic mottle formation appear to regulate pedogenesis, nutrients, and C sequestration at depth in unsaturated zones.
","language":"English","publisher":"Soil Science Society of America","publisherLocation":"Madison, WI","doi":"10.2136/vzj2015.07.0102","usgsCitation":"Schulz, M., Stonestrom, D.A., Lawrence, C.R., Bullen, T.D., Fitzpatrick, J., Kyker-Snowman, E., Manning, J., and Mnich, M., 2016, Structured heterogeneity in a marine terrace chronosequence: Upland mottling: Vadose Zone Journal, v. 15, no. 2, 14 p., https://doi.org/10.2136/vzj2015.07.0102.","productDescription":"14 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066928","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":471218,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2136/vzj2015.07.0102","text":"Publisher Index Page"},{"id":318284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.15,\n 36.95\n ],\n [\n -122.15,\n 37\n ],\n [\n -122.1,\n 37\n ],\n [\n -122.1,\n 36.95\n ],\n [\n -122.15,\n 36.95\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-19","publicationStatus":"PW","scienceBaseUri":"56cc4003e4b059daa47e46b5","contributors":{"authors":[{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - 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Western Branch","active":true,"usgs":true}],"preferred":true,"id":621147,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70168541,"text":"70168541 - 2016 - Bivalve grazing can shape phytoplankton communities","interactions":[],"lastModifiedDate":"2017-10-30T09:49:38","indexId":"70168541","displayToPublicDate":"2016-02-18T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Bivalve grazing can shape phytoplankton communities","docAbstract":"The ability of bivalve filter feeders to limit phytoplankton biomass in shallow waters is well-documented, but the role of bivalves in shaping phytoplankton communities is not. The coupled effect of bivalve grazing at the sediment-water interface and sinking of phytoplankton cells to that bottom filtration zone could influence the relative biomass of sinking (diatoms) and non-sinking phytoplankton. Simulations with a pseudo-2D numerical model showed that benthic filter feeding can interact with sinking to alter diatom:non-diatom ratios. Cases with the smallest proportion of diatom biomass were those with the fastest sinking speeds and strongest bivalve grazing rates. Hydrodynamics modulated the coupled sinking-grazing influence on phytoplankton communities. For example, in simulations with persistent stratification, the non-sinking forms accumulated in the surface layer away from bottom grazers while the sinking forms dropped out of the surface layer toward bottom grazers. Tidal-scale stratification also influenced vertical gradients of the two groups in opposite ways. The model was applied to Suisun Bay, a low-salinity habitat of the San Francisco Bay system that was transformed by the introduction of the exotic clam Potamocorbula amurensis. Simulation results for this Bay were similar to (but more muted than) those for generic habitats, indicating that P. amurensis grazing could have caused a disproportionate loss of diatoms after its introduction. Our model simulations suggest bivalve grazing affects both phytoplankton biomass and community composition in shallow waters. We view these results as hypotheses to be tested with experiments and more complex modeling approaches.
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SCR has been applied to individual encounter data collected noninvasively using methods such as camera traps, hair snares, and scat surveys. Despite the widespread use of capture-based surveys to monitor amphibians and reptiles, there are few applications of SCR in the herpetological literature. We demonstrate the utility of the application of SCR for studies of reptiles and amphibians by analyzing capture–recapture data from Red-Backed Salamanders, Plethodon cinereus, collected using artificial cover boards. Using SCR to analyze spatial encounter histories of marked individuals, we found evidence that density differed little among four sites within the same forest (on average, 1.59 salamanders/m2) and that salamander detection probability peaked in early October (Julian day 278) reflecting expected surface activity patterns of the species. The spatial scale of detectability, a measure of space use, indicates that the home range size for this population of Red-Backed Salamanders in autumn was 16.89 m2. Surveying reptiles and amphibians using artificial cover boards regularly generates spatial encounter history data of known individuals, which can readily be analyzed using SCR methods, providing estimates of absolute density and inference about the spatial scale of habitat use.
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Current and hypothetical operations and structures at Detroit Dam were imposed on boundary conditions derived from downscaled General Circulation Models in base (1990–1999) and future (2059–2068) periods. Compared with the base period, future air temperatures were about 2 °C warmer year-round. Higher air temperature and lower precipitation under the future period resulted in a 23% reduction in mean annual PRMS-simulated discharge and a 1 °C increase in mean annual estimated stream temperatures flowing into the lake compared to the base period. Simulations incorporating current operational rules and minimum release rates at Detroit Dam to support downstream habitat, irrigation, and water supply during key times of year resulted in lower future lake levels. That scenario results in a lake level that is above the dam’s spillway crest only about half as many days in the future compared to historical frequencies. Managing temperature downstream of Detroit Dam depends on the ability to blend warmer water from the lake’s surface with cooler water from deep in the lake, and the spillway is an important release point near the lake’s surface. Annual average in-lake and release temperatures from Detroit Lake warmed 1.1 °C and 1.5 °C from base to future periods under present-day dam operational rules and fill schedules. Simulated dam operations such as beginning refill of the lake 30 days earlier or reducing minimum release rates (to keep more water in the lake to retain the use of the spillway) mitigated future warming to 0.4 and 0.9 °C below existing operational scenarios during the critical autumn spawning period for endangered salmonids. A hypothetical floating surface withdrawal at Detroit Dam improved temperature control in summer and autumn (0.6 °C warmer in summer, 0.6 °C cooler in autumn compared to existing structures) without altering release rates or lake level management rules.
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We applied this concept to a red abalone fishery so impacted by an infectious disease (withering syndrome) that stock densities plummeted and managers closed the fishery. In addition to the non-selective fishing strategy considered by past disease-fishing models, we modelled targeting (culling) infected individuals, which is plausible in red abalone because modern diagnostic tools can determine infection without harming landed abalone and the diagnostic cost is minor relative to the catch value. The non-selective abalone fishing required to eradicate parasites exceeded thresholds for abalone sustainability, but targeting infected abalone allowed the fishery to generate yield and reduce parasite prevalence while maintaining stock densities at or above the densities attainable if the population was closed to fishing. The effect was strong enough that stock and yield increased even when the catch was one-third uninfected abalone. These results could apply to other fisheries as the diagnostic costs decline relative to catch value.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2015.0211","usgsCitation":"Ben-Horin, T., Lafferty, K.D., Bidegain, G., and Lenihan, H.S., 2016, Fishing diseased abalone to promote yield and conservation: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 371, no. 1689, art20150211, https://doi.org/10.1098/rstb.2015.0211.","productDescription":"art20150211","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071571","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471224,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2015.0211","text":"Publisher Index Page"},{"id":318125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"371","issue":"1689","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-05","publicationStatus":"PW","scienceBaseUri":"56c6eb28e4b0946c6523b0c5","contributors":{"authors":[{"text":"Ben-Horin, Tal","contributorId":58137,"corporation":false,"usgs":false,"family":"Ben-Horin","given":"Tal","email":"","affiliations":[],"preferred":false,"id":620737,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":620736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bidegain, Gorka","contributorId":167008,"corporation":false,"usgs":false,"family":"Bidegain","given":"Gorka","email":"","affiliations":[{"id":13403,"text":"University of Southern Mississippi, Department of Biological Sciences, Hattiesburg, Mississippi, USA","active":true,"usgs":false}],"preferred":false,"id":620738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lenihan, Hunter S.","contributorId":94227,"corporation":false,"usgs":true,"family":"Lenihan","given":"Hunter","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":620739,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168659,"text":"70168659 - 2016 - Testing the suitability of geologic frameworks for extrapolating hydraulic properties across regional scales","interactions":[],"lastModifiedDate":"2016-12-16T10:51:16","indexId":"70168659","displayToPublicDate":"2016-02-18T10:30:00","publicationYear":"2016","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":"Testing the suitability of geologic frameworks for extrapolating hydraulic properties across regional scales","docAbstract":"The suitability of geologic frameworks for extrapolating hydraulic conductivity (K) to length scales commensurate with hydraulic data is difficult to assess. A novel method is presented for evaluating assumed relations between K and geologic interpretations for regional-scale groundwater modeling. The approach relies on simultaneous interpretation of multiple aquifer tests using alternative geologic frameworks of variable complexity, where each framework is incorporated as prior information that assumes homogeneous K within each model unit. This approach is tested at Pahute Mesa within the Nevada National Security Site (USA), where observed drawdowns from eight aquifer tests in complex, highly faulted volcanic rocks provide the necessary hydraulic constraints. The investigated volume encompasses 40 mi3 (167 km3) where drawdowns traversed major fault structures and were detected more than 2 mi (3.2 km) from pumping wells. Complexity of the five frameworks assessed ranges from an undifferentiated mass of rock with a single unit to 14 distinct geologic units. Results show that only four geologic units can be justified as hydraulically unique for this location. The approach qualitatively evaluates the consistency of hydraulic property estimates within extents of investigation and effects of geologic frameworks on extrapolation. Distributions of transmissivity are similar within the investigated extents irrespective of the geologic framework. In contrast, the extrapolation of hydraulic properties beyond the volume investigated with interfering aquifer tests is strongly affected by the complexity of a given framework. Testing at Pahute Mesa illustrates how this method can be employed to determine the appropriate level of geologic complexity for large-scale groundwater modeling.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-016-1375-1","usgsCitation":"Mirus, B.B., Halford, K.J., Sweetkind, D.S., and Fenelon, J.M., 2016, Testing the suitability of geologic frameworks for extrapolating hydraulic properties across regional scales: Hydrogeology Journal, v. 24, no. 5, p. 1133-1146, https://doi.org/10.1007/s10040-016-1375-1.","productDescription":"14 p.","startPage":"1133","endPage":"1146","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-033309","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":490008,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-016-1375-1","text":"Publisher Index Page"},{"id":318311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.7,\n 37.3\n ],\n [\n -116.7,\n 37\n ],\n [\n -116.3,\n 37\n ],\n [\n -116.3,\n 37.3\n ],\n [\n -116.7,\n 37.3\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"5","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-18","publicationStatus":"PW","scienceBaseUri":"56cc4007e4b059daa47e46e5","contributors":{"authors":[{"text":"Mirus, Benjamin B.","contributorId":12348,"corporation":false,"usgs":false,"family":"Mirus","given":"Benjamin","email":"","middleInitial":"B.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":621173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halford, Keith J. 0000-0002-7322-1846 khalford@usgs.gov","orcid":"https://orcid.org/0000-0002-7322-1846","contributorId":1374,"corporation":false,"usgs":true,"family":"Halford","given":"Keith","email":"khalford@usgs.gov","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":621176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetkind, Donald S. 0000-0003-0892-4796 dsweetkind@usgs.gov","orcid":"https://orcid.org/0000-0003-0892-4796","contributorId":139913,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald","email":"dsweetkind@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":621174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fenelon, Joseph M. 0000-0003-4449-245X jfenelon@usgs.gov","orcid":"https://orcid.org/0000-0003-4449-245X","contributorId":2355,"corporation":false,"usgs":true,"family":"Fenelon","given":"Joseph","email":"jfenelon@usgs.gov","middleInitial":"M.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":621175,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173993,"text":"70173993 - 2016 - Highly pathogenic avian influenza viruses and generation of novel reassortants,United States, 2014–2015","interactions":[],"lastModifiedDate":"2016-06-23T15:15:07","indexId":"70173993","displayToPublicDate":"2016-02-18T05:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1493,"text":"Emerging Infectious Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Highly pathogenic avian influenza viruses and generation of novel reassortants,United States, 2014–2015","docAbstract":"Asian highly pathogenic avian influenza A(H5N8) viruses spread into North America in 2014 during autumn bird migration. Complete genome sequencing and phylogenetic analysis of 32 H5 viruses identified novel H5N1, H5N2, and H5N8 viruses that emerged in late 2014 through reassortment with North American low-pathogenicity avian influenza viruses.
","language":"English","publisher":"Center for Disease Control","publisherLocation":"Atlanta, GA","doi":"10.3201/eid2207.160048","usgsCitation":"Dong-Hun Lee, Bahl, J., Mia Kim Torchetti, Killian, M.L., Ip, S., and Swayne, D.E., 2016, Highly pathogenic avian influenza viruses and generation of novel reassortants,United States, 2014–2015: Emerging Infectious Diseases, v. 22, no. 7, p. 1283-1285, https://doi.org/10.3201/eid2207.160048.","productDescription":"3 p.","startPage":"1283","endPage":"1285","numberOfPages":"3","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069995","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":471225,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3201/eid2207.160048","text":"Publisher Index Page"},{"id":324146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"7","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576a653ce4b07657d1a11db3","contributors":{"authors":[{"text":"Dong-Hun Lee","contributorId":172259,"corporation":false,"usgs":false,"family":"Dong-Hun Lee","affiliations":[{"id":27016,"text":"USDA, ARS, Southeast Poultry Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":640091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bahl, Justin","contributorId":172260,"corporation":false,"usgs":false,"family":"Bahl","given":"Justin","email":"","affiliations":[{"id":27017,"text":"University of Texas School of Public Health, Center for Infectious Diseases","active":true,"usgs":false}],"preferred":false,"id":640092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mia Kim Torchetti","contributorId":172261,"corporation":false,"usgs":false,"family":"Mia Kim Torchetti","affiliations":[{"id":27018,"text":"USDA, APHIS, NVSL","active":true,"usgs":false}],"preferred":false,"id":640093,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Killian, Mary Lea","contributorId":172262,"corporation":false,"usgs":false,"family":"Killian","given":"Mary","email":"","middleInitial":"Lea","affiliations":[{"id":27018,"text":"USDA, APHIS, NVSL","active":true,"usgs":false}],"preferred":false,"id":640094,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ip, S. 0000-0003-4844-7533 hip@usgs.gov","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":727,"corporation":false,"usgs":true,"family":"Ip","given":"S.","email":"hip@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":640090,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Swayne, David E","contributorId":172263,"corporation":false,"usgs":false,"family":"Swayne","given":"David","email":"","middleInitial":"E","affiliations":[{"id":27016,"text":"USDA, ARS, Southeast Poultry Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":640095,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70168509,"text":"70168509 - 2016 - Cumulative drought and land-use impacts on perennial vegetation across a North American dryland region","interactions":[],"lastModifiedDate":"2016-06-15T16:18:08","indexId":"70168509","displayToPublicDate":"2016-02-17T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":849,"text":"Applied Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"Cumulative drought and land-use impacts on perennial vegetation across a North American dryland region","docAbstract":"The decline and loss of perennial vegetation in dryland ecosystems due to global change pressures can alter ecosystem properties and initiate land degradation processes. We tracked changes of perennial vegetation using remote sensing to address the question of how prolonged drought and land-use intensification have affected perennial vegetation cover across a desert region in the early 21st century?
\nMojave Desert, southeastern California, southern Nevada, southwestern Utah and northwestern Arizona, USA.
\nWe coupled the Moderate-Resolution Imaging Spectroradiometer Enhanced Vegetation Index (MODIS-EVI) with ground-based measurements of perennial vegetation cover taken in about 2000 and about 2010. Using the difference between these years, we determined perennial vegetation changes in the early 21st century and related these shifts to climate, soil and landscape properties, and patterns of land use.
\nWe found a good fit between MODIS-EVI and perennial vegetation cover (2000: R2 = 0.83 and 2010: R2 = 0.74). The southwestern, far southeastern and central Mojave Desert had large declines in perennial vegetation cover in the early 21st century, while the northeastern and southeastern portions of the desert had increases. These changes were explained by 10-yr precipitation anomalies, particularly in the cool season and during extreme dry or wet years. Areas heavily impacted by visitor use or wildfire lost perennial vegetation cover, and vegetation in protected areas increased to a greater degree than in unprotected areas.
\nWe find that we can extrapolate previously documented declines of perennial plant cover to an entire desert, and demonstrate that prolonged water shortages coupled with land-use intensification create identifiable patterns of vegetation change in dryland regions.
\nA newly identified fungal pathogen, Batrachochytrium salamandrivorans (Bsal), is responsible for mass mortality events and severe population declines in European salamanders. The eastern USA has the highest diversity of salamanders in the world and the introduction of this pathogen is likely to be devastating. Although data are inevitably limited for new pathogens, disease-risk assessments use best available data to inform management decisions. Using characteristics of Bsal ecology, spatial data on imports and pet trade establishments, and salamander species diversity, we identify high-risk areas with both a high likelihood of introduction and severe consequences for local salamanders. We predict that the Pacific coast, southern Appalachian Mountains and mid-Atlantic regions will have the highest relative risk from Bsal. Management of invasive pathogens becomes difficult once they are established in wildlife populations; therefore, import restrictions to limit pathogen introduction and early detection through surveillance of high-risk areas are priorities for preventing the next crisis for North American salamanders.
","language":"English","publisher":"Royal Society Publishing","doi":"10.1098/rsos.150616","usgsCitation":"Richgels, K.L., Russell, R.E., Adams, M.J., White, C.L., and Campbell Grant, E., 2016, Spatial variation in risk and consequence of Batrachochytrium salamandrivorans introduction in the USA: Royal Society Open Science, v. 3, Article 150616; 9 p., https://doi.org/10.1098/rsos.150616.","productDescription":"Article 150616; 9 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066112","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":471227,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsos.150616","text":"Publisher Index Page"},{"id":318118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56c599ace4b0946c6521edf8","contributors":{"authors":[{"text":"Richgels, Katherine L. D. 0000-0003-2834-9477 krichgels@usgs.gov","orcid":"https://orcid.org/0000-0003-2834-9477","contributorId":151205,"corporation":false,"usgs":true,"family":"Richgels","given":"Katherine","email":"krichgels@usgs.gov","middleInitial":"L. D.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":620760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Robin E. 0000-0001-8726-7303 rerussell@usgs.gov","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":3998,"corporation":false,"usgs":true,"family":"Russell","given":"Robin","email":"rerussell@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":620761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, M. J. 0000-0001-8844-042X mjadams@usgs.gov","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":3133,"corporation":false,"usgs":false,"family":"Adams","given":"M.","email":"mjadams@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":620762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, C. LeAnn 0000-0002-5004-5165 clwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-5004-5165","contributorId":4315,"corporation":false,"usgs":true,"family":"White","given":"C.","email":"clwhite@usgs.gov","middleInitial":"LeAnn","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":620764,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":23233,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan H.","affiliations":[],"preferred":false,"id":620763,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70164338,"text":"ds979 - 2016 - Post-Hurricane Irene coastal oblique aerial photographs collected from Ocracoke Inlet, North Carolina, to Virginia Beach, Virginia, August 30-31, 2011","interactions":[],"lastModifiedDate":"2016-12-02T12:29:46","indexId":"ds979","displayToPublicDate":"2016-02-17T15:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"979","title":"Post-Hurricane Irene coastal oblique aerial photographs collected from Ocracoke Inlet, North Carolina, to Virginia Beach, Virginia, August 30-31, 2011","docAbstract":"The U.S. Geological Survey (USGS), as part of the National Assessment of Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On August 30-31, 2011, the USGS conducted an oblique aerial photographic survey from Ocracoke Inlet, North Carolina, to Virginia Beach, Virginia, aboard a Piper Navajo Chieftain (aircraft) at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was flown to collect post-Hurricane Irene data for assessing incremental changes in the beach and nearshore area since the last survey, flown in May 2008, and the data can be used in the assessment of future coastal change.
\nThe photographs provided in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft at the time the photograph was taken and do not indicate the location of any feature in the images (see the Navigation Data page). These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in 5-minute segments of flight time. These segments can be found on the Photos and Maps page. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
\nTable 1 provides detailed information about the GPS location, image name, date, and time for each of the 2,688 photographs that were taken along with links to each photograph.
In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML also shows the track of Hurricane Irene. The KML files were created using the photographic navigation files. These KML file(s) can be found in the kml folder.
St. Petersburg Coastal and Marine Science Center
600 4th Street South
St. Petersburg, FL 33701
(727) 502-8000
http://coastal.er.usgs.gov/
This circular provides an overview of selected activities that were conducted within the U.S. Geological Survey (USGS) Integrated Methods Development Project, an interdisciplinary project designed to develop new tools and conduct innovative research requiring integration of geologic, geophysical, geochemical, and remote-sensing expertise. The project was supported by the USGS Mineral Resources Program, and its products and acquired capabilities have broad applications to missions throughout the USGS and beyond.
In addressing challenges associated with understanding the location, quantity, and quality of mineral resources, and in investigating the potential environmental consequences of resource development, a number of field and laboratory capabilities and interpretative methodologies evolved from the project that have applications to traditional resource studies as well as to studies related to ecosystem health, human health, disaster and hazard assessment, and planetary science. New or improved tools and research findings developed within the project have been applied to other projects and activities. Specifically, geophysical equipment and techniques have been applied to a variety of traditional and nontraditional mineral- and energy-resource studies, military applications, environmental investigations, and applied research activities that involve climate change, mapping techniques, and monitoring capabilities. Diverse applied geochemistry activities provide a process-level understanding of the mobility, chemical speciation, and bioavailability of elements, particularly metals and metalloids, in a variety of environmental settings. Imaging spectroscopy capabilities maintained and developed within the project have been applied to traditional resource studies as well as to studies related to ecosystem health, human health, disaster assessment, and planetary science. Brief descriptions of capabilities and laboratory facilities and summaries of some applications of project products and research findings are included in this circular. The work helped support the USGS mission to “provide reliable scientific information to describe and understand the Earth; minimize loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; and enhance and protect our quality of life.” Activities within the project include the following:
This circular consists of eight sections that contain summaries of various activities under the project. The eight sections are listed below:
Center Director, USGS Crustal Geophysics and Geochemistry Science Center
Box 25046, Mail Stop 964
Denver, CO 80225
Or visit the Crustal Geophysics and Geochemistry Science Center Web site at:
http://crustal.usgs.gov/
Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one-dimensional summary of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function to describe the response of a tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts reasonable climate scenarios given tree ring width data.
","language":"English","publisher":"International Environmetrics Society","doi":"10.1002/env.2368","usgsCitation":"Tipton, J., Hooten, M., Pederson, N., Tingley, M., and Bishop, D., 2016, Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models: Environmetrics, v. 27, no. 1, p. 42-54, https://doi.org/10.1002/env.2368.","productDescription":"13 p.","startPage":"42","endPage":"54","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065402","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":498967,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/env.2368","text":"Publisher Index Page"},{"id":318106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-29","publicationStatus":"PW","scienceBaseUri":"56c599abe4b0946c6521edf1","contributors":{"authors":[{"text":"Tipton, John","contributorId":166999,"corporation":false,"usgs":false,"family":"Tipton","given":"John","affiliations":[],"preferred":false,"id":620683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":619800,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pederson, Neil","contributorId":149422,"corporation":false,"usgs":false,"family":"Pederson","given":"Neil","email":"","affiliations":[{"id":17731,"text":"Research Scientist, Tree Ring Laboratory, Lamont-Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":620684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tingley, Martin","contributorId":167000,"corporation":false,"usgs":false,"family":"Tingley","given":"Martin","email":"","affiliations":[],"preferred":false,"id":620685,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bishop, Daniel","contributorId":141104,"corporation":false,"usgs":false,"family":"Bishop","given":"Daniel","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":620686,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70173983,"text":"70173983 - 2016 - High-resolution seismic reflection imaging of growth folding and shallow faults beneath the Southern Puget Lowland, Washington State","interactions":[],"lastModifiedDate":"2016-06-21T15:49:13","indexId":"70173983","displayToPublicDate":"2016-02-17T06:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"High-resolution seismic reflection imaging of growth folding and shallow faults beneath the Southern Puget Lowland, Washington State","docAbstract":"Marine seismic reflection data from southern Puget Sound, Washington, were collected to investigate the nature of shallow structures associated with the Tacoma fault zone and the Olympia structure. Growth folding and probable Holocene surface deformation were imaged within the Tacoma fault zone beneath Case and Carr Inlets. Shallow faults near potential field anomalies associated with the Olympia structure were imaged beneath Budd and Eld Inlets. Beneath Case Inlet, the Tacoma fault zone includes an ∼350-m wide section of south-dipping strata forming the upper part of a fold (kink band) coincident with the southern edge of an uplifted shoreline terrace. An ∼2 m change in the depth of the water bottom, onlapping postglacial sediments, and increasing stratal dips with increasing depth are consistent with late Pleistocene to Holocene postglacial growth folding above a blind fault. Geologic data across a topographic lineament on nearby land indicate recent uplift of late Holocene age. Profiles acquired in Carr Inlet 10 km to the east of Case Inlet showed late Pleistocene or Holocene faulting at one location with ∼3 to 4 m of vertical displacement, south side up. North of this fault the data show several other disruptions and reflector terminations that could mark faults within the broad Tacoma fault zone. Seismic reflection profiles across part of the Olympia structure beneath southern Puget Sound show two apparent faults about 160 m apart having 1 to 2 m of displacement of subhorizontal bedding. Directly beneath one of these faults, a dipping reflector that may mark the base of a glacial channel shows the opposite sense of throw, suggesting strike-slip motion. Deeper seismic reflection profiles show disrupted strata beneath these faults but little apparent vertical offset, consistent with strike-slip faulting. These faults and folds indicate that the Tacoma fault and Olympia structure include active structures with probable postglacial motion.
","language":"English","publisher":"Seismological Society of America","publisherLocation":"Albany, CA","doi":"10.1785/0120080306","usgsCitation":"Odum, J., Stephenson, W.J., Pratt, T.L., and Blakely, R.J., 2016, High-resolution seismic reflection imaging of growth folding and shallow faults beneath the Southern Puget Lowland, Washington State: Bulletin of the Seismological Society of America, v. 100, no. 4, p. 1710-1723, https://doi.org/10.1785/0120080306.","productDescription":"14 p.","startPage":"1710","endPage":"1723","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-076890","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":488465,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1785/0120080306","text":"External Repository"},{"id":324161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","county":"Island, Jefferson, King Kitsap, Mason, Pierce, San Juan, Skagit, Snohomish, Thurston, Whatcom","city":"Seattle","otherGeospatial":"Northwest coast of Washington State; part of the Saliah Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.86010742187499,\n 48.88639177703194\n ],\n [\n -123.18969726562499,\n 48.672826384100354\n ],\n [\n -123.18969726562499,\n 48.574789910928864\n ],\n [\n -123.06884765625,\n 48.425555463221045\n ],\n [\n -123.20068359374999,\n 48.23565029755306\n ],\n [\n -123.4149169921875,\n 46.81885778879603\n ],\n [\n -121.2176513671875,\n 46.90149244734082\n ],\n [\n -121.3275146484375,\n 48.86832824998009\n ],\n [\n -122.86010742187499,\n 48.88639177703194\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"100","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2010-07-27","publicationStatus":"PW","scienceBaseUri":"576a653be4b07657d1a11db0","contributors":{"authors":[{"text":"Odum, Jackson K. 0000-0003-4697-2430 odum@usgs.gov","orcid":"https://orcid.org/0000-0003-4697-2430","contributorId":1365,"corporation":false,"usgs":true,"family":"Odum","given":"Jackson K.","email":"odum@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":640150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":640151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":640152,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blakely, Richard J. 0000-0003-1701-5236 blakely@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-5236","contributorId":1540,"corporation":false,"usgs":true,"family":"Blakely","given":"Richard","email":"blakely@usgs.gov","middleInitial":"J.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":640153,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70164455,"text":"70164455 - 2016 - Wetting and drying of soil in response to precipitation: Data analysis, modeling, and forecasting","interactions":[],"lastModifiedDate":"2016-12-20T11:32:48","indexId":"70164455","displayToPublicDate":"2016-02-17T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Wetting and drying of soil in response to precipitation: Data analysis, modeling, and forecasting","docAbstract":"This paper investigates methods to analyze and forecast soil moisture time series. We extend an existing Antecedent Water Index (AWI) model, which expresses soil moisture as a function of time and rainfall. Unfortunately, the existing AWI model does not forecast effectively for time periods beyond a few hours. To overcome this limitation, we develop a novel AWI-based model. Our model accumulates rainfall over a time interval and can fit a diverse range of wetting and drying curves. In addition, parameters in our model reflect hydrologic redistribution processes of gravity and suction.We validate our models using experimental soil moisture and rainfall time series data collected from steep gradient post-wildfire sites in Southern California, where rapid landscape change was observed in response to small to moderate rain storms. We found that our novel model fits the data for three distinct soil textures, occurring at different depths below the ground surface (5, 15, and 30 cm). Our model also successfully forecasts soil moisture trends, such as drying and wetting rate.","conferenceTitle":"13th Conference of the Association for the Advancement of Artificial Intelligence","conferenceDate":"February 12–17, 2016","conferenceLocation":" Phoenix, Arizona ","language":"English","publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","collaboration":"Carnegie Mellon University","usgsCitation":"Basak, A., Kulkarni, C., Schmidt, K.M., and Mengshoel, O., 2016, Wetting and drying of soil in response to precipitation: Data analysis, modeling, and forecasting, 13th Conference of the Association for the Advancement of Artificial Intelligence, Phoenix, Arizona , February 12–17, 2016.","ipdsId":"IP-068964","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":332337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":316604,"type":{"id":15,"text":"Index Page"},"url":"https://www.aaai.org/Conferences/AAAI/aaai16.php"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"585a51bfe4b01224f329b5ed","contributors":{"authors":[{"text":"Basak, Aniruddha","contributorId":156329,"corporation":false,"usgs":false,"family":"Basak","given":"Aniruddha","email":"","affiliations":[{"id":20319,"text":"Carnegie Mellon University, Silicon Valley","active":true,"usgs":false}],"preferred":false,"id":597456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kulkarni, Chinmay","contributorId":156330,"corporation":false,"usgs":false,"family":"Kulkarni","given":"Chinmay","email":"","affiliations":[{"id":20319,"text":"Carnegie Mellon University, Silicon Valley","active":true,"usgs":false}],"preferred":false,"id":597457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmidt, Kevin M. 0000-0003-2365-8035 kschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-2365-8035","contributorId":1985,"corporation":false,"usgs":true,"family":"Schmidt","given":"Kevin","email":"kschmidt@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":597455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mengshoel, Ole","contributorId":156331,"corporation":false,"usgs":false,"family":"Mengshoel","given":"Ole","email":"","affiliations":[{"id":20319,"text":"Carnegie Mellon University, Silicon Valley","active":true,"usgs":false}],"preferred":false,"id":597458,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168362,"text":"70168362 - 2016 - Ecology and conservation of Lesser Prairie-Chickens in sand shinnery oak prairies","interactions":[],"lastModifiedDate":"2017-11-27T12:51:11","indexId":"70168362","displayToPublicDate":"2016-02-17T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Ecology and conservation of Lesser Prairie-Chickens in sand shinnery oak prairies","docAbstract":"Sand shinnery oak (Quercus havardii) prairies are unique ecosystems endemic to sandy soils of eastern New Mexico, northwestern Texas, and western Oklahoma; the historic and current distribution of the Lesser Prairie-Chicken (Tympanuchus pallidicinctus) overlaps these prairie systems. Lesser Prairie-Chicken populations in sand shinnery oak prairies of the Southern Great Plains have declined substantially since the late 1980s, most likely due to conversion of nesting and brood-rearing habitat to row-crop agriculture and extended periods of drought. In addition to threats universal throughout the species distribution, this population is susceptible to a changing climate in an area that is already representative of an extreme environment for ground-nesting birds. Recent studies of Lesser Prairie-Chicken ecology in sand shinnery oak prairies have expanded our knowledge on the ecology and management of the species, but a thorough review of the historic and current literature is lacking. In addition, current management guidelines exist for Lesser Prairie-Chickens in mixed grass and sand sagebrush prairies, but there are no comprehensive management guidelines for the species in sand shinnery oak prairies. This information is paramount given unique aspects of the vegetation community, relative ecosystem drivers, and environmental variation in sand shinnery oak prairie and the species’ current status as a proposed threatened species under the United States Endangered Species Act. Herein, we provide a thorough synthesis of literature pertaining to the life history, habitat requirements, habitat management, and population management for Lesser Prairie-Chickens in sand shinnery oak prairie, provide management guidelines and recommendations for the species in this ecoregion, and highlight current and future research needs. Within our objectives, we place emphasis on two recently completed long-term investigations into Lesser Prairie-Chicken ecology in sand shinnery oak prairie - a 10-year vegetation data set collected in Roosevelt County, New Mexico, 2001–2011 and a 6-year Lesser Prairie-Chicken data set
collected in Roosevelt County, New Mexico and Cochran, Hockley, Terry, and Yoakum counties, Texas, 2006–2012.