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The increased integration of ecosystem service concepts into natural resource management places renewed emphasis on prediction and mapping of fish biomass as a major provisioning service of rivers. The goals of this study were to predict and map patterns of fish biomass as a proxy for the availability of catchable fish for anglers in rivers and to identify the strongest landscape constraints on fish productivity. We examined hypotheses about fish responses to total phosphorus (TP), as TP is a growth-limiting nutrient known to cause increases (subsidy response) and/or decreases (stress response) in fish biomass depending on its concentration and the species being considered. Boosted regression trees were used to define nonlinear functions that predicted the standing crops of Brook Trout Salvelinus fontinalis, Brown Trout Salmo trutta, Smallmouth Bass Micropterus dolomieu, panfishes (seven centrarchid species), and Walleye Sander vitreus by using landscape and modeled local-scale predictors. Fitted models were highly significant and explained 22–56% of the variation in validation data sets. Nonlinear and threshold responses were apparent for numerous predictors, including TP concentration, which had significant effects on all except the Walleye fishery. Brook Trout and Smallmouth Bass exhibited both subsidy and stress responses, panfish biomass exhibited a subsidy response only, and Brown Trout exhibited a stress response. Maps of reach-specific standing crop predictions showed patterns of predicted fish biomass that corresponded to spatial patterns in catchment area, water temperature, land cover, and nutrient availability. Maps illustrated predictions of higher trout biomass in coldwater streams draining glacial till in northern Michigan, higher Smallmouth Bass and panfish biomasses in warmwater systems of southern Michigan, and high Walleye biomass in large main-stem rivers throughout the state. Our results allow fisheries managers to examine the biomass potential of streams, describe geographic patterns of fisheries, explore possible nutrient management targets, and identify habitats that are candidates for species management.

","language":"English","publisher":"American Fisheries Society","publisherLocation":"Lawrence, KS","doi":"10.1080/02755947.2014.987887","usgsCitation":"Esselman, P.C., Stevenson, R.J., Lupi, F., Riseng, C.M., and Wiley, M., 2015, Landscape prediction and mapping of game fish biomass, an ecosystem service of Michigan rivers: North American Journal of Fisheries Management, v. 35, no. 2, p. 302-320, https://doi.org/10.1080/02755947.2014.987887.","productDescription":"19 p.","startPage":"302","endPage":"320","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057022","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":472157,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/journal_contribution/Landscape_Prediction_and_Mapping_of_Game_Fish_Biomass_an_Ecosystem_Service_of_Michigan_Rivers/1378927","text":"External Repository"},{"id":306583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.3515625,\n 46.604167162931844\n ],\n [\n -90.087890625,\n 46.45299704748289\n ],\n [\n -89.9560546875,\n 46.30140615437332\n ],\n [\n -88.79150390625,\n 46.042735653846506\n ],\n [\n -88.30810546875,\n 45.98169518512228\n ],\n [\n -88.00048828124999,\n 45.73685954736049\n ],\n [\n -87.64892578125,\n 45.166547157856016\n ],\n [\n -87.099609375,\n 45.67548217560647\n ],\n [\n -86.68212890625,\n 45.62940492064501\n ],\n [\n -85.7373046875,\n 45.935870621190546\n ],\n [\n -85.14404296875,\n 45.98169518512228\n ],\n [\n -84.9462890625,\n 45.84410779560204\n ],\n [\n -85.1220703125,\n 45.61403741135093\n ],\n [\n -85.517578125,\n 45.22848059584359\n ],\n [\n -86.28662109375,\n 44.68427737181225\n ],\n [\n -86.572265625,\n 43.77109381775651\n ],\n [\n -86.24267578125,\n 42.924251753870685\n ],\n [\n -86.37451171875,\n 42.342305278572816\n ],\n [\n -86.77001953125,\n 41.934976500546604\n ],\n [\n -86.7919921875,\n 41.80407814427237\n ],\n [\n -83.34228515625,\n 41.705728515237524\n ],\n [\n -83.056640625,\n 42.147114459220994\n ],\n [\n -82.81494140625,\n 42.48830197960227\n ],\n [\n -82.4853515625,\n 42.827638636242284\n ],\n [\n -82.44140625,\n 43.11702412135048\n ],\n [\n -82.59521484375,\n 43.78695837311561\n ],\n [\n -82.79296874999999,\n 44.05601169578525\n ],\n [\n -83.25439453125,\n 44.43377984606825\n ],\n [\n -83.27636718749999,\n 44.85586880735725\n ],\n [\n -83.38623046875,\n 45.321254361171476\n ],\n [\n -84.26513671875,\n 45.73685954736049\n ],\n [\n -84.0234375,\n 45.90529985724796\n ],\n [\n -83.6279296875,\n 46.08847179577592\n ],\n [\n -84.13330078125,\n 46.46813299215554\n ],\n [\n -84.79248046875,\n 46.81509864599243\n ],\n [\n -86.1328125,\n 46.73986059969267\n ],\n [\n -87.12158203125,\n 46.619261036171515\n ],\n [\n -87.6708984375,\n 46.90524554642923\n ],\n [\n -88.0224609375,\n 47.11499982620772\n ],\n [\n -87.5390625,\n 47.428087261714275\n ],\n [\n -88.2861328125,\n 47.56170075451973\n ],\n [\n -89.18701171875,\n 47.15984001304432\n ],\n [\n -90.3515625,\n 46.604167162931844\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-07","publicationStatus":"PW","scienceBaseUri":"55cb1caee4b08400b1fdd477","contributors":{"authors":[{"text":"Esselman, Peter C. 0000-0002-0085-903X pesselman@usgs.gov","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":5965,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter","email":"pesselman@usgs.gov","middleInitial":"C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":540045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevenson, R. 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During 2005-8, the U.S. Geological Survey, in cooperation with the Cambridge, Massachusetts, Water Department, measured concentrations of sodium and chloride, plant nutrients, commonly used pesticides, and caffeine in base-flow and stormwater samples collected from 11 tributaries in the Cambridge drinking-water source area. These data were used to characterize current water-quality conditions, to establish a baseline for future comparisons, and to describe trends in surface-water quality. The data also were used to assess the effects of watershed characteristics on surface-water quality and to inform future watershed management.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153030","collaboration":"Prepared in cooperation with the Cambridge, Massachusetts, Water Department","usgsCitation":"Smith, K.P., and Waldron, M.C., 2015, Water quality in the Cambridge, Massachusetts, drinking-water source area, 2005-8: U.S. Geological Survey Fact Sheet 2015-3030, 6 p., https://doi.org/10.3133/fs20153030.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2005-01-01","temporalEnd":"2008-12-31","ipdsId":"IP-046036","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"links":[{"id":299465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20153030.jpg"},{"id":299464,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3030/pdf/fs2015-3030.pdf","text":"Report","size":"1.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3030 Report"},{"id":299463,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2015/3030/"}],"country":"United States","state":"Massachusetts","city":"Cambridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.06420516967773,\n 42.38504955243599\n ],\n [\n -71.15741729736328,\n 42.39531906359705\n ],\n [\n -71.23191833496094,\n 42.42700448967684\n ],\n [\n -71.24839782714844,\n 42.45411449876218\n ],\n [\n -71.2957763671875,\n 42.456647545121605\n ],\n [\n -71.33663177490234,\n 42.44296787761998\n ],\n [\n -71.33251190185545,\n 42.36133451106724\n ],\n [\n -71.26556396484375,\n 42.34154398944032\n ],\n [\n -71.06403350830078,\n 42.348648996207956\n ],\n [\n -71.06420516967773,\n 42.38504955243599\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5524f19fe4b027f0aee3d461","contributors":{"authors":[{"text":"Smith, Kirk P. 0000-0003-0269-474X kpsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-474X","contributorId":1516,"corporation":false,"usgs":true,"family":"Smith","given":"Kirk","email":"kpsmith@usgs.gov","middleInitial":"P.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":543281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waldron, Marcus C. mwaldron@usgs.gov","contributorId":1867,"corporation":false,"usgs":true,"family":"Waldron","given":"Marcus","email":"mwaldron@usgs.gov","middleInitial":"C.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":543282,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70140114,"text":"sir20155017 - 2015 - Physical subdivision and description of the water-bearing sediments of the Santa Clara Valley, California","interactions":[],"lastModifiedDate":"2015-04-07T08:39:02","indexId":"sir20155017","displayToPublicDate":"2015-04-07T09:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5017","title":"Physical subdivision and description of the water-bearing sediments of the Santa Clara Valley, California","docAbstract":"

A thick Quaternary alluvial section fills a sedimentary basin beneath the Santa Clara Valley, California, located within the San Andreas Fault system at the south end of San Francisco Bay. This section consists of an upper sequence about 1,000 feet thick containing eight sedimentary cycles and a lower fine-grained unit as thick as several hundred feet. Together these constitute the Quaternary Santa Clara Basin. The section overlies an irregular unconformity with more than 1,200 feet of relief cut into the underlying bedrock. This stratigraphy is determined through study of new wells and seismic reflection profiles, together with a sample of the many thousands of water wells in the valley. It represents a major change and improvement in understanding of the basin, particularly with regard to the upper cyclic sequence, which forms a large groundwater system that is an important resource in the San Francisco Bay region.

\n

Each of the eight sedimentary cycles consists of a coarse-grained bottom interval overlain by a fine-grained top, with the coarse bottom forming a permeable sheet that is more or less continuous around the basin and the fine top forming a similarly extensive, relatively impermeable confining layer. This stratigraphic organization contrasts with most previous views, which have considered the coarse sediment in the basin to occur as scattered, discrete lenses and (or) sinuous channel sands, all embedded in a predominantly fine-grained section. Temperature logs in several wells demonstrate that the fine cycle tops do limit vertical movement of groundwater, although this may not be the case where those tops are thin to perhaps locally absent around parts of the basin margin.

\n

Age control has been obtained from previous work, in which the sedimentary cycles were correlated with the marine oxygen isotope record and the ages of two deeper Quaternary unconformities were estimated, and from detailed paleomagnetic study of cores from the new wells by E.A. Mankinen. Despite careful search of the cores, very few fossils were found, and none that are helpful in subdividing the section. No tephra (volcanic ash) was recovered, and the few carbon samples found and dated radiometrically are limited to the upper 120 feet of the section. The upper cyclic section ranges in age from 0 to somewhat older than 718 thousand years (ka), and the lower fine-grained section lies between unconformities with estimated ages of 950 and 1500 ka.

\n

Reflections in the seismic profiles indicate that layering in the basin is subparallel to the ground surface, and this fact, together with the continuous stratigraphic detail provided by geophysical logs of the new wells, allows the confident interwell correlation required to delineate the sedimentary cycles. The sequence of layers within any one cycle tends to persist laterally between the wells in the dataset, which are spaced 1 to 3 km apart, with most changes occurring gradually. The eight cycles, in contrast, tend to differ from each other in the details of their internal organization.

\n

Maps and cross sections show the elevations of cycle boundaries and the underlying bedrock surface, the varying thicknesses of the cycles and of their fine tops and coarse bottoms, and the aggregate thickness of coarse layers in those bottom intervals. Coarse sediment is more abundant toward some parts of the basin margin and in the southern part of the basin. Cycle boundary surfaces are relatively smooth, and their shapes are consistent with having been intercycle topographic surfaces. The underlying bedrock surface has a relief of more than 1,200 feet and deepens toward the center of the basin and the west edge of the fault-bounded Evergreen Basin, which is concealed beneath the east side of the Quaternary basin. The absence of consistent abrupt changes in thicknesses or boundary elevations across the basin or in cross section indicates that the interior of the basin is largely unfaulted, with the Silver Creek strand of the San Andreas system at the west edge of the Evergreen Basin being the sole exception. The east and west margins of the Santa Clara Basin, in contrast, are marked by reverse and thrust fault systems.

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III jtinsley@usgs.gov","contributorId":3266,"corporation":false,"usgs":true,"family":"Tinsley","given":"John C.","suffix":"III","email":"jtinsley@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":544151,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hanson, Randall T. 0000-0002-9819-7141 rthanson@usgs.gov","orcid":"https://orcid.org/0000-0002-9819-7141","contributorId":801,"corporation":false,"usgs":true,"family":"Hanson","given":"Randall","email":"rthanson@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":544152,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70138875,"text":"ds913 - 2015 - EAARL-B coastal topography: Chandeleur Islands, Louisiana, 2012: seamless (bare earth and submerged)","interactions":[],"lastModifiedDate":"2015-04-07T08:13:02","indexId":"ds913","displayToPublicDate":"2015-04-07T09:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"913","title":"EAARL-B coastal topography: Chandeleur Islands, Louisiana, 2012: seamless (bare earth and submerged)","docAbstract":"

These remotely sensed, geographically referenced elevation measurements of light detection and ranging (lidar)-derived seamless (bare-earth and submerged) topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

\n

This project provides datasets acquired on August 7 and 8, 2012, for a portion of the Chandeleur Islands, Louisiana. An innovative airborne lidar system, known as the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), was used during data acquisition. The EAARL-B system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometers) lidar designed to map nearshore bathymetry, topography, and vegetation structure simultaneously.

\n

For more information about similar projects, please visit the Lidar for Science and Resource Management Web site.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds913","usgsCitation":"Wright, C.W., Klipp, E.S., Kranenburg, C., Troche, R.J., Fredericks, X., Masessa, M.L., and Nagle, D.B., 2015, EAARL-B coastal topography: Chandeleur Islands, Louisiana, 2012: seamless (bare earth and submerged): U.S. Geological Survey Data Series 913, HTML Document, https://doi.org/10.3133/ds913.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2012-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-059846","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":299417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds913.jpg"},{"id":299416,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0913/html/ds_home.html","linkFileType":{"id":5,"text":"html"}},{"id":299415,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0913/"}],"country":"United States","state":"Louisiana","otherGeospatial":"Chandeleur Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.88076782226562,\n 30.05720849081894\n ],\n [\n -88.86154174804688,\n 30.045915969180676\n ],\n [\n -88.84368896484375,\n 30.0286775329042\n ],\n [\n -88.82789611816405,\n 30.00073301042315\n ],\n [\n -88.8189697265625,\n 29.9793233715619\n ],\n [\n -88.81484985351562,\n 29.952554831950962\n ],\n [\n -88.81072998046875,\n 29.931134809868713\n ],\n [\n -88.81004333496094,\n 29.903758071027738\n ],\n [\n -88.81278991699219,\n 29.881136828132842\n ],\n [\n -88.81553649902344,\n 29.859701442126756\n ],\n [\n -88.82308959960938,\n 29.832900734318393\n ],\n [\n -88.83819580078125,\n 29.798346993042582\n ],\n [\n -88.84780883789062,\n 29.774509870772462\n ],\n [\n -88.8629150390625,\n 29.7470901645232\n ],\n [\n -88.88076782226562,\n 29.719066630304063\n ],\n [\n -88.9068603515625,\n 29.6880527498568\n ],\n [\n -88.92951965332031,\n 29.66180275761379\n ],\n [\n -88.95904541015625,\n 29.639723597620257\n ],\n [\n -88.98513793945312,\n 29.622414924968727\n ],\n [\n -89.00505065917969,\n 29.621221113784504\n ],\n [\n -89.00436401367188,\n 29.628980633662827\n ],\n [\n -88.98788452148438,\n 29.634949088440198\n ],\n [\n -88.9679718017578,\n 29.651658880141365\n ],\n [\n -88.95355224609375,\n 29.66597935109997\n ],\n [\n -88.93844604492188,\n 29.68328053373362\n ],\n [\n -88.92196655273438,\n 29.701771608904792\n ],\n [\n -88.90274047851562,\n 29.727414884638474\n ],\n [\n -88.890380859375,\n 29.75364773335698\n ],\n [\n -88.87664794921875,\n 29.78523727825047\n ],\n [\n -88.86909484863281,\n 29.806688644587354\n ],\n [\n -88.85398864746094,\n 29.827539729697882\n ],\n [\n -88.84506225585938,\n 29.858510452312036\n ],\n [\n -88.84162902832031,\n 29.886494954894626\n ],\n [\n -88.83750915527344,\n 29.920423069383865\n ],\n [\n -88.84300231933592,\n 29.95552947018257\n ],\n [\n -88.84506225585938,\n 29.97753902660092\n ],\n [\n -88.85467529296875,\n 30.00965233044293\n ],\n [\n -88.87390136718749,\n 30.035811042667792\n ],\n [\n -88.88282775878906,\n 30.050670872077248\n ],\n [\n -88.88076782226562,\n 30.05720849081894\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5524f198e4b027f0aee3d459","contributors":{"authors":[{"text":"Wright, C. 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Groundwater recharge is one of the most difficult components of a water budget to ascertain, yet is an important boundary condition necessary for the quantification of water resources. In Minnesota, improved estimates of recharge are necessary because approximately 75 percent of drinking water and 90 percent of agricultural irrigation water in Minnesota are supplied from groundwater. The water that is withdrawn must be supplied by some combination of (1) increased recharge, (2) decreased discharge to streams, lakes, and other surface-water bodies, and (3) removal of water that was stored in the system. Recent pressure on groundwater resources has highlighted the need to provide more accurate recharge estimates for various tools that can assess the sustainability of long-term water use. As part of this effort, the U.S. Geological Survey, in cooperation with the Minnesota Pollution Control Agency, used the Soil-Water-Balance model to calculate gridded estimates of potential groundwater recharge across Minnesota for 1996‒2010 at a 1-kilometer (0.621-mile) resolution. The potential groundwater recharge estimates calculated for Minnesota from the Soil-Water Balance model included gridded values (1-kilometer resolution) of annual mean estimates (that is, the means for individual years from 1996 through 2010) and mean annual estimates (that is, the mean for the 15-year period 1996−2010).

\n

The Soil-Water-Balance model uses a modified Thornthwaite-Mather soil-water-balance approach, with components of the soil-water balance calculated on a daily basis. A key advantage of this approach includes the use of commonly available geographic information system data layers that incorporate land cover, soil properties, and daily meteorological data to produce temporally and spatially variable gridded estimates of potential recharge. The Soil-Water-Balance model was calibrated by using a combination of parameter estimation techniques, making manual adjustments of model parameters, and using parameter values from previously published Soil-Water-Balance models. Each calibration simulation compared the potential recharge estimate from the model against base-flow estimates derived from three separate hydrograph separation techniques. A total of 35 Minnesota watersheds were selected for the model calibration.

\n

Meteorological data necessary for the model included daily precipitation, minimum daily temperature, and maximum daily temperature. All of the meteorological data were provided by the Daymet dataset, which included daily continuous surfaces of key climatological data. Land-cover data were provided by the 2001 and 2006 National Land Cover Database: the 2001 classification was used from 1994 through 2003, and the 2006 classification was used from 2004 through 2010. Soil data used in the model included hydrologic soils group and the available soil-water capacity. These soil data were obtained from the Natural Resources Conservation Service Soil Survey Geographic (SSURGO) database and the State Soil Geographic (STATSGO) database.

\n

The statewide mean annual potential recharge rate from 1996–2010 was 4.9 inches per year. Potential recharge estimates increased from west to east across Minnesota. The mean annual potential recharge estimates across Minnesota at a 1-km resolution for the overall simulation period (1996–2010) ranged from less than 0.1 to 17.8 inches per year. Some of the lowest potential recharge rates for the simulation period were in the Red River of the North Basin of northwestern Minnesota, and generally were between 1.0 and 1.5 inches per year. The highest potential recharge rates were in northeastern Minnesota and the Anoka Sand Plain in central Minnesota. Eighty-eight percent of the potential recharge rates (by grid cell) were between 2 and 8 inches per year from 1996–2010. Only about 3 percent of all the potential recharge estimates (by grid cell) were less than 2 inches per year, and 9 percent of estimates were greater than 8 inches per year.

\n

On an annual basis, however, potential recharge rates were as high as 27.2 inches per year. The highest annual mean recharge estimate across the State was for 2010, and the lowest mean recharge estimate was for 2003. Although precipitation variability partially explained the annual differences in potential recharge estimates, precipitation alone did not account for these differences, and other factors such as antecedent moisture conditions likely were important. Also, because precipitation gradients across the State can vary from year to year, the dominant land-cover class and hydrologic soil group combinations for a particular region had a large effect on the resulting potential recharge value. During 1996–2010, April had the greatest monthly mean potential recharge compared to all other months, accounting for a mean of 30 percent of annual potential recharge in this single month.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155038","collaboration":"Prepared in cooperation with the Minnesota Pollution Control Agency","usgsCitation":"Smith, E.A., and Westenbroek, S.M., 2015, Potential groundwater recharge for the State of Minnesota using the Soil-Water-Balance model, 1996-2010: U.S. Geological Survey Scientific Investigations Report 2015-5038, vii, 85 p., https://doi.org/10.3133/sir20155038.","productDescription":"vii, 85 p.","startPage":"85","numberOfPages":"98","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"1996-01-01","temporalEnd":"2010-12-31","ipdsId":"IP-034584","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":299402,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155038.jpg"},{"id":299399,"type":{"id":15,"text":"Index 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,{"id":70142541,"text":"sir20155042 - 2015 - Multilevel groundwater monitoring of hydraulic head and temperature in the eastern Snake River Plain aquifer, Idaho National Laboratory, Idaho, 2011-13","interactions":[],"lastModifiedDate":"2015-04-02T16:56:44","indexId":"sir20155042","displayToPublicDate":"2015-04-02T17:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5042","title":"Multilevel groundwater monitoring of hydraulic head and temperature in the eastern Snake River Plain aquifer, Idaho National Laboratory, Idaho, 2011-13","docAbstract":"

From 2011 to 2013, the U.S. Geological Survey’s Idaho National Laboratory (INL) Project Office, in cooperation with the U.S. Department of Energy, collected depth-discrete measurements of fluid pressure and temperature in 11 boreholes located in the eastern Snake River Plain aquifer. Each borehole was instrumented with a multilevel monitoring system (MLMS) consisting of a series of valved measurement ports, packer bladders, casing segments, and couplers.

\n

Multilevel monitoring at the INL has been ongoing since 2006 and this report summarizes data collected from 2011 to 2013 in 11 multilevel monitoring wells. Hydraulic head (head) and groundwater temperature data were collected from 11 multilevel monitoring wells, including 177 hydraulically isolated depth intervals from 448.0 to 1,377.6 feet below land surface. One port (port 3) within borehole USGS 134 was not monitored because of a valve failure.

\n

Head and temperature profiles reveal unique patterns for vertical examination of the aquifer’s complex basalt and sediment stratigraphy, proximity to aquifer recharge and discharge, and groundwater flow. These features contribute to some of the localized variability even though the general profile shape remained consistent over the period of record. Twenty-two major head inflections were described for 9 of 11 MLMS boreholes and almost always coincided with low‑permeability sediment layers and occasionally thick layers of dense basalt. However, the presence of a sediment layer or dense basalt layer was insufficient for identifying the location of a major head change within a borehole without knowing the true areal extent and relative transmissivity of the lithologic unit. Temperature profiles for boreholes completed within the Big Lost Trough indicate linear conductive trends; whereas, temperature profiles for boreholes completed within volcanic rift zones and near the southern boundary of the Idaho National Laboratory, indicate mostly convective heat transfer. Select boreholes along the southern boundary show a temperature reversal and cooler water deeper in the aquifer resulting from the vertical movement of groundwater.

\n

Vertical head and temperature change were quantified for each of the 11 multilevel monitoring systems. Vertical head gradients defined for the major inflections in the head profiles were as high as 2.9 feet per foot. In general, fractured basalt zones displayed relatively small vertical head differences and show a high occurrence within volcanic rift zones. Poor connectivity between fractures and higher vertical gradients were generally attributed to sediment layers and layers of dense basalt, or both. Groundwater temperatures in all boreholes ranged from 10.8 to 16.3 °C.

\n

Normalized mean head values were analyzed for all 11 multilevel monitoring wells for the period of record (2007–13). The mean head values suggest a moderately positive correlation among all boreholes and generally reflect regional fluctuations in water levels in response to seasonal climatic changes. Boreholes within volcanic rift zones and near the southern boundary (USGS 103, USGS 105, USGS 108, USGS 132, USGS 135, USGS 137A) display a temporal correlation that is strongly positive. Boreholes in the Big Lost Trough display some variations in temporal correlations that may result from proximity to the mountain front to the northwest and episodic flow in the Big Lost River drainage system. For example, during June 2012, boreholes MIDDLE 2050A and MIDDLE 2051 showed head buildup within the upper zones when compared to the June 2010 profile event, which correlates to years when surface water was reported for the Big Lost River several months preceding the measurement period. With the exception of borehole USGS 134, temporal correlation between MLMS wells completed within the Big Lost Trough is generally positive. Temporal correlation for borehole USGS 134 shows the least agreement with other MLMS boreholes located within the Big Lost Trough; however, borehole USGS 134 is close to the mountain front where tributary valley subsurface inflow is suspected.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155042","collaboration":"Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Twining, B.V., and Fisher, J.C., 2015, Multilevel groundwater monitoring of hydraulic head and temperature in the eastern Snake River Plain aquifer, Idaho National Laboratory, Idaho, 2011-13: U.S. Geological Survey Scientific Investigations Report 2015-5042, Report: vii, 49 p.; 8 Appendices, https://doi.org/10.3133/sir20155042.","productDescription":"Report: vii, 49 p.; 8 Appendices","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2011-01-01","temporalEnd":"2013-12-31","ipdsId":"IP-056607","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":299324,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155042.jpg"},{"id":299323,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5042/pdf/sir20155042_AppH.pdf","text":"Appendix H","size":"161 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":299314,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5042/"},{"id":299315,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5042/pdf/sir2015-5042.pdf","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":299316,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5042/pdf/sir20155042_AppA.pdf","text":"Appendix A","size":"98 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":299317,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5042/pdf/sir20155042_AppB.pdf","text":"Appendix B","size":"202 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":299318,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5042/pdf/sir20155042_AppC.pdf","text":"Appendix C","size":"125 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":299319,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5042/pdf/sir20155042_AppD.pdf","text":"Appendix D","size":"109 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":299320,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5042/pdf/sir20155042_AppE.pdf","text":"Appendix E","size":"592 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":299321,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5042/pdf/sir20155042_AppF.pdf","text":"Appendix F","size":"103 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":299322,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5042/pdf/sir20155042_AppG.pdf","text":"Appendix G","size":"148 KB","linkFileType":{"id":1,"text":"pdf"}}],"scale":"24000","projection":"Universal Transverse Mercator projection","datum":"North American Datum of 1927","country":"United States","state":"Idaho","otherGeospatial":"Eastern Snake River Plain aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.104248046875,\n 43.52664646047308\n ],\n [\n -113.104248046875,\n 43.880077621969065\n ],\n [\n -112.61123657226562,\n 43.880077621969065\n ],\n [\n -112.61123657226562,\n 43.52664646047308\n ],\n [\n -113.104248046875,\n 43.52664646047308\n ]\n ]\n ]\n }\n }\n ]\n}","publicComments":"DOE/ID-22235","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551e5a1fe4b027f0aee3b87d","contributors":{"authors":[{"text":"Twining, Brian V. 0000-0003-1321-4721 btwining@usgs.gov","orcid":"https://orcid.org/0000-0003-1321-4721","contributorId":2387,"corporation":false,"usgs":true,"family":"Twining","given":"Brian","email":"btwining@usgs.gov","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":543938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisher, Jason C. 0000-0001-9032-8912 jfisher@usgs.gov","orcid":"https://orcid.org/0000-0001-9032-8912","contributorId":2523,"corporation":false,"usgs":true,"family":"Fisher","given":"Jason","email":"jfisher@usgs.gov","middleInitial":"C.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":543939,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70144294,"text":"ofr20151058 - 2015 - An evaluation of the accuracy of modeled and computed streamflow time-series data for the Ohio River at Hannibal Lock and Dam and at a location upstream from Sardis, Ohio","interactions":[],"lastModifiedDate":"2015-04-09T08:31:36","indexId":"ofr20151058","displayToPublicDate":"2015-04-02T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1058","title":"An evaluation of the accuracy of modeled and computed streamflow time-series data for the Ohio River at Hannibal Lock and Dam and at a location upstream from Sardis, Ohio","docAbstract":"

Between July 2013 and June 2014, the U.S. Geological Survey (USGS) made 10 streamflow measurements on the Ohio River about 1.5 miles (mi) downstream from the Hannibal Lock and Dam (near Hannibal, Ohio) and 11 streamflow measurements near the USGS Sardis gage (station number 03114306) located approximately 2.4 mi upstream from Sardis, Ohio. The measurement results were used to assess the accuracy of modeled or computed instantaneous streamflow time series created and supplied by the USGS, U.S. Army Corps of Engineers (USACE), and National Weather Service (NWS) for the Ohio River at Hannibal Lock and Dam and (or) at the USGS streamgage. Hydraulic or hydrologic models were used to create the modeled time series; index-velocity methods or gate-opening ratings coupled with hydropower operation data were used to create the computed time series. The time step of the various instantaneous streamflow time series ranged from 15 minutes to 24 hours (once-daily values at 12:00 Coordinated Universal Time [UTC]). The 15-minute time-series data, computed by the USGS for the Sardis gage, also were downsampled to 1-hour and 24-hour time steps to permit more direct comparisons with other streamflow time series.

\n

To facilitate comparisons between measurement results and time-series data, streamflows corresponding to the times of the streamflow measurements were computed from the time-series data by time-based linear interpolation. Prior to doing interpolations, measurement times for the Hannibal Lock and Dam location were adjusted for traveltime to account for the fact that the streamflow measurements were made about 1.5 mi downstream from the location corresponding to the modeled/computed time-series data. Measured and interpolated streamflows were tabulated along with residuals (the difference between measured and interpolated streamflows) and selected summary statistics.

\n

Overall, streamflows interpolated from the USGS computed 15-minute time-series data (hereafter referred to as the USGS 15-minute time-series data) had the smallest root-mean-square error (RMSE) (3,939 cubic feet per second [ft3/s]) and the second smallest mean absolute residual (2,636 ft3/s), whereas streamflows interpolated from the USACE 12 UTC time series had the largest RMSE (14,590 ft3/s) and the largest mean absolute residual (10,800 ft3/s). The larger RMSEs for streamflows interpolated from the USACE 12 UTC time series likely resulted in part from the coarser time step of that time series. Streamflows interpolated from the USGS downsampled 1-hour time series had the second smallest RMSE (4,025 ft3/s) and the smallest mean absolute residual (2,600 ft3/s). Somewhat surprisingly, streamflows interpolated from the NWS 6-hour model time series had the third smallest RMSE (4,483 ft3/s) and mean absolute residual (4,050 ft3/s) in spite of being determined from a time series with a coarser time step than the USACE 1-hour modeled and computed time series.

\n

Measured streamflows at the Sardis gage and at the Hannibal Lock and Dam measurement location were plotted versus residuals (expressed as a percentage of the measured streamflows) of corresponding interpolated time-series streamflow values. Results for each of the time series exhibited some anomaly, possibly indicating the need and (or) potential for improvement in the streamflow computational/modeling processes.

\n

Streamflow hydrographs were plotted for modeled/computed time series for the Ohio River near the USGS Sardis gage and the Ohio River at the Hannibal Lock and Dam. In general, the time series at these two locations compared well. Some notable differences include the exclusive presence of short periods of negative streamflows in the USGS 15-minute time-series data for the gage on the Ohio River above Sardis, Ohio, and the occurrence of several peak streamflows in the USACE gate/hydropower time series for the Hannibal Lock and Dam that were appreciably larger than corresponding peaks in the other time series, including those modeled/computed for the downstream Sardis gage

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151058","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Koltun, G., 2015, An evaluation of the accuracy of modeled and computed streamflow time-series data for the Ohio River at Hannibal Lock and Dam and at a location upstream from Sardis, Ohio: U.S. Geological Survey Open-File Report 2015-1058, viii, 23 p., https://doi.org/10.3133/ofr20151058.","productDescription":"viii, 23 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063449","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":299300,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151058.jpg"},{"id":299296,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1058/"},{"id":299297,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1058/pdf/ofr2015-1058.pdf","text":"Report","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Ohio","otherGeospatial":"Ohio River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.96099853515624,\n 39.57817336212527\n ],\n [\n -80.96099853515624,\n 39.68182601089365\n ],\n [\n -80.82092285156249,\n 39.68182601089365\n ],\n [\n -80.82092285156249,\n 39.57817336212527\n ],\n [\n -80.96099853515624,\n 39.57817336212527\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551e5a1be4b027f0aee3b86b","contributors":{"authors":[{"text":"Koltun, G. F. 0000-0003-0255-2960 gfkoltun@usgs.gov","orcid":"https://orcid.org/0000-0003-0255-2960","contributorId":1852,"corporation":false,"usgs":true,"family":"Koltun","given":"G. F.","email":"gfkoltun@usgs.gov","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":false,"id":543454,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70146790,"text":"70146790 - 2015 - Managing habitat to slow or reverse population declines of the Columbia spotted frog in the Northern Great Basin","interactions":[],"lastModifiedDate":"2017-11-22T18:01:11","indexId":"70146790","displayToPublicDate":"2015-04-01T16:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Managing habitat to slow or reverse population declines of the Columbia spotted frog in the Northern Great Basin","docAbstract":"

Evaluating the effectiveness of habitat management actions is critical to adaptive management strategies for conservation of imperiled species. We quantified the response of a Great Basin population of the Columbia spotted frog (Rana luteiventris) to multiple habitat improvement actions aimed to reduce threats and reverse population declines. We used mark-recapture data for 1,394 adult frogs that had been marked by state, federal, and university biologists in 9 ponds representing a single population over a 16-year period from 1997 to 2012. With the use of demographic models, we assessed population-level effects of 1) a grazing exclosure constructed around 6 stock ponds that had been used to water livestock for decades before being fully fenced in 2003, and 2) the construction of 3 new stock ponds in 2003 to provide alternative water sources for livestock and, secondarily, to provide additional frog habitat. These management actions were implemented in response to a decline of more than 80% in population size from 1997 to 2002. We found evidence that excluding cattle from ponds and surrounding riparian habitats resulted in higher levels of frog production (more egg masses), higher adult frog recruitment and survival, and higher population growth rate. We also found that frogs colonized the newly constructed stock ponds within 3 years and frogs began breeding in 2 of them after 5 years. The positive effects of the cattle exclosure and additional production from the new ponds, although notable, did not result in full recovery of the population even 9 years later. This slow recovery may be partly explained by the effects of weather on recruitment rates, particularly the negative effects of harsher winters with late springs and higher fall temperatures. Although our findings point to potential successes of habitat management aimed at slowing or reversing rapidly declining frog populations, our study also suggests that recovering from severe population declines can take many years because of demographic and environmental processes. 

","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.868","usgsCitation":"Pilliod, D., and Scherer, R.D., 2015, Managing habitat to slow or reverse population declines of the Columbia spotted frog in the Northern Great Basin: Journal of Wildlife Management, v. 79, no. 4, p. 579-590, https://doi.org/10.1002/jwmg.868.","productDescription":"12 p.","startPage":"579","endPage":"590","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059456","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":299813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","county":"Owyhee County","otherGeospatial":"Sam Noble Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.02911376953124,\n 43.67979094030124\n ],\n [\n -116.97967529296874,\n 43.667871610117494\n ],\n [\n -116.95220947265624,\n 43.63806292753483\n ],\n [\n -116.91925048828124,\n 43.6102281417864\n ],\n [\n -116.86981201171875,\n 43.59431676355149\n ],\n [\n -116.817626953125,\n 43.5843700152048\n ],\n [\n -116.79290771484374,\n 43.538593801442374\n ],\n [\n -116.77093505859375,\n 43.48082639482503\n ],\n [\n -116.67205810546874,\n 43.389081939117496\n ],\n [\n -116.63635253906249,\n 43.37311218382002\n ],\n [\n -116.59240722656249,\n 43.31718491566708\n ],\n [\n -116.54296874999999,\n 43.29120116988416\n ],\n [\n -116.49627685546874,\n 43.28920196020127\n ],\n [\n -116.43035888671875,\n 43.28920196020127\n ],\n [\n -116.38641357421875,\n 43.23119629494612\n ],\n [\n -116.37542724609374,\n 43.193162620926074\n ],\n [\n -116.30950927734374,\n 43.153101551466385\n ],\n [\n -116.2847900390625,\n 43.111009147075116\n ],\n [\n -116.21887207031249,\n 43.09095496313368\n ],\n [\n -116.18316650390625,\n 43.06086137134326\n ],\n [\n -116.13922119140624,\n 43.04079076668198\n ],\n [\n -116.12823486328126,\n 43.01268088642034\n ],\n [\n -116.07330322265625,\n 42.98656732912335\n ],\n [\n -116.03759765625,\n 42.97049193148623\n ],\n [\n -115.97442626953125,\n 42.94637095514085\n ],\n [\n -115.97442626953125,\n 42.99259451971113\n ],\n [\n -115.94146728515625,\n 43.00866413845207\n ],\n [\n -115.9112548828125,\n 42.99058552185121\n ],\n [\n -115.8563232421875,\n 42.98656732912335\n ],\n [\n -115.81237792968749,\n 42.98053954751642\n ],\n [\n -115.77667236328125,\n 42.9524020856897\n ],\n [\n -115.69976806640625,\n 42.95039177450287\n ],\n [\n -115.65032958984375,\n 42.962452656668795\n ],\n [\n -115.6036376953125,\n 42.954412331213284\n ],\n [\n -115.55419921875,\n 42.9524020856897\n ],\n [\n -115.477294921875,\n 42.93229601903058\n ],\n [\n -115.43884277343749,\n 42.93430692117159\n ],\n [\n -115.4168701171875,\n 42.93430692117159\n ],\n [\n -115.3399658203125,\n 42.91620643817353\n ],\n [\n -115.3179931640625,\n 42.80144655484334\n ],\n [\n -115.01312255859375,\n 42.80144655484334\n ],\n [\n -114.95819091796876,\n 41.99828401778616\n ],\n [\n -115.037841796875,\n 41.99828401778616\n ],\n [\n -117.02911376953124,\n 41.99624282178583\n ],\n [\n -117.02911376953124,\n 43.67979094030124\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"79","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-24","publicationStatus":"PW","scienceBaseUri":"553774ade4b0b22a15808510","contributors":{"authors":[{"text":"Pilliod, David S. dpilliod@usgs.gov","contributorId":140097,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","email":"dpilliod@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":545354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scherer, Richard D.","contributorId":140359,"corporation":false,"usgs":false,"family":"Scherer","given":"Richard","email":"","middleInitial":"D.","affiliations":[{"id":13470,"text":"Conservation Science Partners","active":true,"usgs":false}],"preferred":false,"id":545355,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188785,"text":"70188785 - 2015 - A sea-level database for the Pacific coast of central North America","interactions":[],"lastModifiedDate":"2018-08-07T14:38:07","indexId":"70188785","displayToPublicDate":"2015-04-01T14:37:58","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"A sea-level database for the Pacific coast of central North America","docAbstract":"

A database of published and new relative sea-level (RSL) data for the past 16 ka constrains the sea-level histories of the Pacific coast of central North America (southern British Columbia to central California). Our reevaluation of the stratigraphic context and radiocarbon age of sea-level indicators from geological and archaeological investigations yields 600 sea-level index points and 241 sea-level limiting points. We subdivided the database into 12 regions based on the availability of data, tectonic setting, and distance from the former Cordilleran ice sheet. Most index (95%) and limiting points (54%) are <7 ka; older data come mainly from British Columbia and San Francisco Bay. The stratigraphic position of points was used as a first-order assessment of compaction. Formerly glaciated areas show variable RSL change; where data are present, highstands of RSL occur immediately post-deglaciation and in the mid to late Holocene. Sites at the periphery and distant to formerly glaciated areas demonstrate a continuous rise in RSL with a decreasing rate through time due to the collapse of the peripheral forebulge and the reduction in meltwater input during deglaciation. Late Holocene RSL change varies spatially from falling at 0.7 ± 0.8 mm a−1 in southern British Columbia to rising at 1.5 ± 0.3 mm a−1 in California. The different sea-level histories are an ongoing isostatic response to deglaciation of the Cordilleran and Laurentide Ice Sheets.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2014.12.001","usgsCitation":"Engelhart, S.E., Vacchi, M., Horton, B.P., Nelson, A.R., and Kopp, R.E., 2015, A sea-level database for the Pacific coast of central North America: Quaternary Science Reviews, v. 113, p. 78-92, https://doi.org/10.1016/j.quascirev.2014.12.001.","productDescription":"15 p.","startPage":"78","endPage":"92","ipdsId":"IP-061597","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":488783,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://durham-repository.worktribe.com/output/1290872","text":"External Repository"},{"id":356298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fcc2de4b0f5d57878eccf","contributors":{"authors":[{"text":"Engelhart, Simon E.","contributorId":60104,"corporation":false,"usgs":false,"family":"Engelhart","given":"Simon","email":"","middleInitial":"E.","affiliations":[{"id":6923,"text":"University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":700351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vacchi, Matteo","contributorId":193395,"corporation":false,"usgs":false,"family":"Vacchi","given":"Matteo","email":"","affiliations":[],"preferred":false,"id":700352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horton, Benjamin P.","contributorId":192807,"corporation":false,"usgs":false,"family":"Horton","given":"Benjamin","email":"","middleInitial":"P.","affiliations":[{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false},{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":700353,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, Alan R. 0000-0001-7117-7098 anelson@usgs.gov","orcid":"https://orcid.org/0000-0001-7117-7098","contributorId":812,"corporation":false,"usgs":true,"family":"Nelson","given":"Alan","email":"anelson@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":700354,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kopp, Robert E.","contributorId":193396,"corporation":false,"usgs":false,"family":"Kopp","given":"Robert","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":700355,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70147071,"text":"70147071 - 2015 - Targeting climate diversity in conservation planning to build resilience to climate change","interactions":[],"lastModifiedDate":"2018-09-18T10:34:24","indexId":"70147071","displayToPublicDate":"2015-04-01T13:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Targeting climate diversity in conservation planning to build resilience to climate change","docAbstract":"

Climate change is raising challenging concerns for systematic conservation planning. Are methods based on the current spatial patterns of biodiversity effective given long-term climate change? Some conservation scientists argue that planning should focus on protecting the abiotic diversity in the landscape, which drives patterns of biological diversity, rather than focusing on the distribution of focal species, which shift in response to climate change. Climate is one important abiotic driver of biodiversity patterns, as different climates host different biological communities and genetic pools. We propose conservation networks that capture the full range of climatic diversity in a region will improve the resilience of biotic communities to climate change compared to networks that do not. In this study we used historical and future hydro-climate projections from the high resolution Basin Characterization Model to explore the utility of directly targeting climatic diversity in planning. Using the spatial planning tool, Marxan, we designed conservation networks to capture the diversity of climate types, at the regional and sub-regional scale, and compared them to networks we designed to capture the diversity of vegetation types. By focusing on the Conservation Lands Network (CLN) of the San Francisco Bay Area as a real-world case study, we compared the potential resilience of networks by examining two factors: the range of climate space captured, and climatic stability to 18 future climates, reflecting different emission scenarios and global climate models. We found that the climate-based network planned at the sub-regional scale captured a greater range of climate space and showed higher climatic stability than the vegetation and regional based-networks. At the same time, differences among network scenarios are small relative to the variance in climate stability across global climate models. Across different projected futures, topographically heterogeneous areas consistently show greater climate stability than homogenous areas. The analysis suggests that utilizing high-resolution climate and hydrological data in conservation planning improves the likely resilience of biodiversity to climate change. We used these analyses to suggest new conservation priorities for the San Francisco Bay Area.

","language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1890/ES14-00313.1","usgsCitation":"Heller, N.E., Kreitler, J.R., Ackerly, D., Weiss, S., Recinos, A., Branciforte, R., Flint, L.E., Flint, A.L., and Micheli, E., 2015, Targeting climate diversity in conservation planning to build resilience to climate change: Ecosphere, v. 6, no. 4, p. 1-20, https://doi.org/10.1890/ES14-00313.1.","productDescription":"20 p.","startPage":"1","endPage":"20","numberOfPages":"20","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058616","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":472162,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1890/es14-00313.1","text":"External Repository"},{"id":299894,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-24","publicationStatus":"PW","scienceBaseUri":"553f5dbbe4b0a658d7938cfc","contributors":{"authors":[{"text":"Heller, Nicole E.","contributorId":140429,"corporation":false,"usgs":false,"family":"Heller","given":"Nicole","email":"","middleInitial":"E.","affiliations":[{"id":13495,"text":"Dwight Center for Conservation Science at Pepperwood Preserve","active":true,"usgs":false}],"preferred":false,"id":545619,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreitler, Jason R. 0000-0002-0243-5281 jkreitler@usgs.gov","orcid":"https://orcid.org/0000-0002-0243-5281","contributorId":4050,"corporation":false,"usgs":true,"family":"Kreitler","given":"Jason","email":"jkreitler@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":545618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ackerly, David","contributorId":139541,"corporation":false,"usgs":false,"family":"Ackerly","given":"David","affiliations":[{"id":7102,"text":"University of California, Berkeley, Dept. of Civil & Envir. Engineering","active":true,"usgs":false}],"preferred":false,"id":545620,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weiss, Stuart","contributorId":7590,"corporation":false,"usgs":true,"family":"Weiss","given":"Stuart","email":"","affiliations":[],"preferred":false,"id":545621,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Recinos, Amanda","contributorId":140430,"corporation":false,"usgs":false,"family":"Recinos","given":"Amanda","email":"","affiliations":[{"id":13496,"text":"GreenInfo Network","active":true,"usgs":false}],"preferred":false,"id":545622,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Branciforte, Ryan","contributorId":140431,"corporation":false,"usgs":false,"family":"Branciforte","given":"Ryan","email":"","affiliations":[{"id":13497,"text":"Bay Area Open Space Council","active":true,"usgs":false}],"preferred":false,"id":545623,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":545624,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":545625,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Micheli, Elisabeth","contributorId":105615,"corporation":false,"usgs":true,"family":"Micheli","given":"Elisabeth","email":"","affiliations":[],"preferred":false,"id":545626,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70157067,"text":"70157067 - 2015 - Terrestrial ecology of semi-aquatic giant gartersnakes (Thamnophis gigas)","interactions":[],"lastModifiedDate":"2015-09-09T11:30:14","indexId":"70157067","displayToPublicDate":"2015-04-01T12:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"title":"Terrestrial ecology of semi-aquatic giant gartersnakes (Thamnophis gigas)","docAbstract":"

Wetlands are a vital component of habitat for semiaquatic herpetofauna, but for most species adjacent terrestrial habitats are also essential. We examined the use of terrestrial environments by Giant Gartersnakes (Thamnophis gigas) to provide behavioral information relevant to conservation of this state and federally listed threatened species. We used radio telemetry data collected 1995–2011 from adults at several sites throughout the Sacramento Valley, California, USA, to examine Giant Gartersnake use of the terrestrial environment. We found Giant Gartersnakes in terrestrial environments more than half the time during the summer, with the use of terrestrial habitats increasing to nearly 100% during brumation. While in terrestrial habitats, we found Giant Gartersnakes underground more than half the time in the early afternoon during summer, and the probability of being underground increased to nearly 100% of the time at all hours during brumation. Extreme temperatures also increased the probability that we would find Giant Gartersnakes underground. Under most conditions, we found Giant Gartersnakes to be within 10 m of water at 95% of observations. For females during brumation and individuals that we found underground, however, the average individual had a 10% probability of being located > 20 m from water. Individual variation in each of the response variables was extensive; therefore, predicting the behavior of an individual was fraught with uncertainty. Nonetheless, our estimates provide resource managers with valuable information about the importance of protecting and carefully managing terrestrial habitats for conserving a rare semiaquatic snake.

","language":"English","publisher":"Partners in Amphibian and Reptile Conservation","publisherLocation":"Texarkana, TX","usgsCitation":"Halstead, B., Skalos, S.M., Wylie, G.D., and Casazza, M.L., 2015, Terrestrial ecology of semi-aquatic giant gartersnakes (Thamnophis gigas): Herpetological Conservation and Biology, v. 10, no. 2, p. 633-644.","productDescription":"12 p.","startPage":"633","endPage":"644","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065175","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":308010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":307932,"type":{"id":11,"text":"Document"},"url":"https://www.herpconbio.org/Volume_10/Issue_2/Halstead_etal_2015.pdf"}],"volume":"10","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55f15834e4b0dacf699eb985","contributors":{"authors":[{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":571467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Skalos, Shannon M. sskalos@usgs.gov","contributorId":147372,"corporation":false,"usgs":true,"family":"Skalos","given":"Shannon","email":"sskalos@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":571468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Glenn D. 0000-0002-7061-6658 glenn_wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7061-6658","contributorId":3052,"corporation":false,"usgs":true,"family":"Wylie","given":"Glenn","email":"glenn_wylie@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":571469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":571470,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70143983,"text":"ofr20151056 - 2015 - Hydrologic conditions in Massachusetts during water year 2014","interactions":[],"lastModifiedDate":"2015-04-01T10:01:52","indexId":"ofr20151056","displayToPublicDate":"2015-04-01T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1056","title":"Hydrologic conditions in Massachusetts during water year 2014","docAbstract":"

Hydrologic data and conditions throughout Massachusetts during water year 2014 (October 1, 2013, to September 30, 2014) are presented in this report. Stream discharge and groundwater levels during water year 2014 varied geographically across the State. The data are described as being above, below, or near normal in relation to long-term averages for the period of record.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151056","usgsCitation":"Verdi, R.J., 2015, Hydrologic conditions in Massachusetts during water year 2014: U.S. Geological Survey Open-File Report 2015-1056, iii, 9 p., https://doi.org/10.3133/ofr20151056.","productDescription":"iii, 9 p.","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2013-10-01","temporalEnd":"2014-09-30","ipdsId":"IP-063076","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"links":[{"id":299138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151056.jpg"},{"id":299135,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1056/"},{"id":299136,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1056/pdf/ofr2015-1056.pdf","text":"Report","size":"736 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.2733154296875,\n 42.74701217318067\n ],\n [\n -73.50677490234375,\n 42.087012670088214\n ],\n [\n -73.49853515625,\n 42.049292638686836\n ],\n [\n -72.81463623046875,\n 42.03603433407788\n ],\n [\n -72.8173828125,\n 41.99828401778616\n ],\n [\n -72.76657104492188,\n 42.0064481470799\n ],\n [\n -72.75009155273438,\n 42.037054301883806\n ],\n [\n -72.61001586914062,\n 42.03195429915656\n ],\n [\n -72.60726928710938,\n 42.02481360781777\n ],\n [\n -72.57568359375,\n 42.02685388718981\n ],\n [\n -72.53036499023438,\n 42.03603433407788\n ],\n [\n -71.79977416992188,\n 42.02379344357796\n ],\n [\n -71.79977416992188,\n 42.00848901572399\n ],\n [\n -71.38092041015625,\n 42.01767211439039\n ],\n [\n -71.3836669921875,\n 41.89307729913167\n ],\n [\n -71.33697509765625,\n 41.898188430430444\n ],\n [\n -71.34658813476562,\n 41.81943165932007\n ],\n [\n -71.3287353515625,\n 41.781552998900345\n ],\n [\n -71.26007080078125,\n 41.75287318430239\n ],\n [\n -71.19827270507812,\n 41.67496335351134\n ],\n [\n -71.13372802734375,\n 41.66060124302088\n ],\n [\n -71.12548828125,\n 41.498292501398545\n ],\n [\n -70.784912109375,\n 41.290189955885644\n ],\n [\n -69.95956420898438,\n 41.20138969929042\n ],\n [\n -69.89227294921875,\n 41.775408403663285\n ],\n [\n -70.00213623046875,\n 42.0615286181226\n ],\n [\n -70.16693115234375,\n 42.108411365705855\n ],\n [\n -70.6256103515625,\n 42.10229818948117\n ],\n [\n -70.70594787597656,\n 42.21071942977117\n ],\n [\n -70.76019287109375,\n 42.26054098165532\n ],\n [\n -70.86456298828124,\n 42.34585783931204\n ],\n [\n -70.86868286132812,\n 42.46247316292525\n ],\n [\n -70.6475830078125,\n 42.57533232110996\n ],\n [\n -70.56381225585938,\n 42.62991729384455\n ],\n [\n -70.56243896484375,\n 42.66527089792358\n ],\n [\n -70.61737060546875,\n 42.70464124398721\n ],\n [\n -70.6585693359375,\n 42.69959515809203\n ],\n [\n -70.697021484375,\n 42.67031977251906\n ],\n [\n -70.76156616210938,\n 42.696567309696974\n ],\n [\n -70.7958984375,\n 42.789354160502775\n ],\n [\n -70.806884765625,\n 42.87495770751784\n ],\n [\n -70.84808349609375,\n 42.86338298693097\n ],\n [\n -70.88241577148438,\n 42.88149898076182\n ],\n [\n -70.90507507324219,\n 42.88803956056295\n ],\n [\n -70.93391418457031,\n 42.88602714832883\n ],\n [\n -70.96549987792969,\n 42.870931963933735\n ],\n [\n -71.03416442871094,\n 42.85985981506279\n ],\n [\n -71.04995727539062,\n 42.848785680348875\n ],\n [\n -71.06575012207031,\n 42.80799561516835\n ],\n [\n -71.13235473632812,\n 42.82209892875648\n ],\n [\n -71.16737365722656,\n 42.81051429991593\n ],\n [\n -71.18728637695312,\n 42.79187360418141\n ],\n [\n -71.18247985839844,\n 42.73843944000847\n ],\n [\n -71.22505187988281,\n 42.74751641468717\n ],\n [\n -71.2470245361328,\n 42.744995166137286\n ],\n [\n -71.29440307617188,\n 42.69858589169842\n ],\n [\n -73.2733154296875,\n 42.74701217318067\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551a65aee4b032384278342a","contributors":{"authors":[{"text":"Verdi, Richard J. 0000-0002-7093-9203 rverdi@usgs.gov","orcid":"https://orcid.org/0000-0002-7093-9203","contributorId":1098,"corporation":false,"usgs":true,"family":"Verdi","given":"Richard","email":"rverdi@usgs.gov","middleInitial":"J.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":543218,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70144126,"text":"ofr20151052 - 2015 - Evaluation of the Ott Hydromet Qliner for measuring discharge in laboratory and field conditions","interactions":[],"lastModifiedDate":"2015-04-01T11:55:09","indexId":"ofr20151052","displayToPublicDate":"2015-04-01T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1052","title":"Evaluation of the Ott Hydromet Qliner for measuring discharge in laboratory and field conditions","docAbstract":"

The U.S. Geological Survey, in collaboration with the University of Iowa IIHR – Hydroscience and Engineering, evaluated the use of the Ott Hydromet Qliner using laboratory flume tests along with field validation tests. Analysis of the flume testing indicates the velocities measured by the Qliner at a 40-second exposure time results in higher dispersion of velocities from the mean velocity of data collected with a 5-minute exposure time. The percent data spread from the mean of a 100-minute mean of Qliner velocities for a 40-second exposure time averaged 16.6 percent for the entire vertical, and a 5-minute mean produced a 6.2 percent data spread from the 100-minute mean. This 16.6 percent variation in measured velocity would result in a 3.32 percent variation in computed discharge assuming 25 verticals while averaging 4 bins in each vertical. The flume testing also provided results that indicate the blanking distance of 0.20 meters is acceptable when using beams 1 and 2, however beam 3 is negatively biased near the transducer and the 0.20-meter blanking distance is not sufficient. Field testing included comparing the measured discharge by the Qliner to the discharge measured by a Price AA mechanical current meter and a Teledyne RDI Rio Grande 1200 kilohertz acoustic Doppler current profiler. The field tests indicated a difference between the discharges measured with the Qliner and the field reference discharge between -14.0 and 8.0 percent; however the average percent difference for all 22 field comparisons was 0.22, which was not statistically significant.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151052","collaboration":"Prepared in cooperation with the University of Iowa IIHR – Hydroscience and Engineering","usgsCitation":"McVay, J.C., 2015, Evaluation of the Ott Hydromet Qliner for measuring discharge in laboratory and field conditions: U.S. Geological Survey Open-File Report 2015-1052, v, 13 p., https://doi.org/10.3133/ofr20151052.","productDescription":"v, 13 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061080","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":299250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151052.jpg"},{"id":299248,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1052/"},{"id":299249,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1052/pdf/ofr2015-1052.pdf","text":"Report","size":"2.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"projection":"Universal Transverse Mercator, Zone 15","datum":"North American Datum of 1983","country":"United States","state":"Iowa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.141357421875,\n 41.97582726102573\n ],\n [\n -94.6142578125,\n 42.87596410238254\n ],\n [\n -93.22998046875,\n 42.89206418807337\n ],\n [\n -90.90087890624999,\n 42.187829010590825\n ],\n [\n -91.417236328125,\n 40.9218144123785\n ],\n [\n -92.39501953125,\n 40.94671366508002\n ],\n [\n -96.075439453125,\n 41.795888098191426\n ],\n [\n -96.141357421875,\n 41.97582726102573\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551d089be4b0256c24f4214e","contributors":{"authors":[{"text":"McVay, Jason C. jcmcvay@usgs.gov","contributorId":139902,"corporation":false,"usgs":true,"family":"McVay","given":"Jason","email":"jcmcvay@usgs.gov","middleInitial":"C.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":false,"id":543397,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70148418,"text":"70148418 - 2015 - Radar attenuation and temperature within the Greenland Ice Sheet","interactions":[],"lastModifiedDate":"2015-06-03T10:47:45","indexId":"70148418","displayToPublicDate":"2015-04-01T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Radar attenuation and temperature within the Greenland Ice Sheet","docAbstract":"

The flow of ice is temperature-dependent, but direct measurements of englacial temperature are sparse. The dielectric attenuation of radio waves through ice is also temperature-dependent, and radar sounding of ice sheets is sensitive to this attenuation. Here we estimate depth-averaged radar-attenuation rates within the Greenland Ice Sheet from airborne radar-sounding data and its associated radiostratigraphy. Using existing empirical relationships between temperature, chemistry, and radar attenuation, we then infer the depth-averaged englacial temperature. The dated radiostratigraphy permits a correction for the confounding effect of spatially varying ice chemistry. Where radar transects intersect boreholes, radar-inferred temperature is consistently higher than that measured directly. We attribute this discrepancy to the poorly recognized frequency dependence of the radar-attenuation rate and correct for this effect empirically, resulting in a robust relationship between radar-inferred and borehole-measured depth-averaged temperature. Radar-inferred englacial temperature is often lower than modern surface temperature and that of a steady state ice-sheet model, particularly in southern Greenland. This pattern suggests that past changes in surface boundary conditions (temperature and accumulation rate) affect the ice sheet's present temperature structure over a much larger area than previously recognized. This radar-inferred temperature structure provides a new constraint for thermomechanical models of the Greenland Ice Sheet.

","language":"English","publisher":"American Geophysical Union","publisherLocation":"Richmond, VA","doi":"10.1002/2014JF003418","usgsCitation":"MacGregor, J.A., Li, J., Paden, J.D., Catania, G.A., Clow, G.D., Fahnestock, M.A., Gogineni, P.S., Grimm, R.E., Morlighem, M., Nandi, S., Seroussi, H., and Stillman, D.E., 2015, Radar attenuation and temperature within the Greenland Ice Sheet: Journal of Geophysical Research F: Earth Surface, v. 120, no. 4, p. 1-26, https://doi.org/10.1002/2014JF003418.","productDescription":"26 p.","startPage":"1","endPage":"26","numberOfPages":"26","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063363","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":472164,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/17r372tq","text":"External 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E.","contributorId":141054,"corporation":false,"usgs":false,"family":"Grimm","given":"Robert","email":"","middleInitial":"E.","affiliations":[{"id":13664,"text":"Southwest Research Institute, Boulder CO","active":true,"usgs":false}],"preferred":false,"id":548103,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morlighem, Mathieu","contributorId":141050,"corporation":false,"usgs":false,"family":"Morlighem","given":"Mathieu","email":"","affiliations":[{"id":6976,"text":"University of California, Irvine","active":true,"usgs":false}],"preferred":false,"id":548099,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nandi, Soumyaroop","contributorId":141051,"corporation":false,"usgs":false,"family":"Nandi","given":"Soumyaroop","email":"","affiliations":[{"id":13663,"text":"Center for Remote Sensing or Ice Sheets, University of Kansas","active":true,"usgs":false}],"preferred":false,"id":548100,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Seroussi, Helene","contributorId":141052,"corporation":false,"usgs":false,"family":"Seroussi","given":"Helene","email":"","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":548101,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Stillman, David E","contributorId":141053,"corporation":false,"usgs":false,"family":"Stillman","given":"David","email":"","middleInitial":"E","affiliations":[{"id":13664,"text":"Southwest Research Institute, Boulder CO","active":true,"usgs":false}],"preferred":false,"id":548102,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70146516,"text":"70146516 - 2015 - Geologic control on the evolution of the inner shelf morphology offshore of the Mississippi barrier islands, northern Gulf of Mexico, USA","interactions":[],"lastModifiedDate":"2015-04-22T15:27:54","indexId":"70146516","displayToPublicDate":"2015-04-01T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1333,"text":"Continental Shelf Research","active":true,"publicationSubtype":{"id":10}},"title":"Geologic control on the evolution of the inner shelf morphology offshore of the Mississippi barrier islands, northern Gulf of Mexico, USA","docAbstract":"

Between 2008 and 2013, high-resolution geophysical surveys were conducted around the Mississippi barrier islands and offshore. The sonar surveys included swath and single-beam bathymetry, sidescan, and chirp subbottom data collection. The geophysical data were groundtruthed using vibracore sediment collection. The results provide insight into the evolution of the inner shelf and the relationship between the near surface geologic framework and the morphology of the coastal zone. This study focuses on the buried Pleistocene fluvial deposits and late Holocene shore-oblique sand ridges offshore of Petit Bois Island and Petit Bois Pass. Prior to this study, the physical characteristics, evolution, and interrelationship of the ridges between both the shelf geology and the adjacent barrier island platform had not been evaluated. Numerous studies elsewhere along the coastal margin attribute shoal origin and sand-ridge evolution to hydrodynamic processes in shallow water (<20 m). Here we characterize the correlation between the geologic framework and surface morphology and demonstrate that the underlying stratigraphy must also be considered when developing an evolutionary conceptual model. It is important to understand this near surface, nearshore dynamic in order to understand how the stratigraphy influences the long-term response of the coastal zone to sea-level rise. The study also contributes to a growing body of work characterizing shore-oblique sand ridges which, along with the related geology, are recognized as increasingly important components to a nearshore framework whose origins and evolution must be understood and inventoried to effectively manage the coastal zone.

","language":"English","publisher":"North Pacific Marine Science Organization","publisherLocation":"New York, NY","doi":"10.1016/j.csr.2015.04.008","collaboration":"U.S. Geological Survey Northern GOM Hazards and Susceptibility Project, and the U.S. Army Corps of Engineers (USACE) Mississippi Coastal Improvement Project","usgsCitation":"Flocks, J.G., Kindinger, J.L., and Kelso, K.W., 2015, Geologic control on the evolution of the inner shelf morphology offshore of the Mississippi barrier islands, northern Gulf of Mexico, USA: Continental Shelf Research, v. 101, p. 59-70, https://doi.org/10.1016/j.csr.2015.04.008.","productDescription":"12 p.","startPage":"59","endPage":"70","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061522","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":299777,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":299700,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0278434315000898"}],"volume":"101","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5536233be4b0b22a15807a98","contributors":{"authors":[{"text":"Flocks, James G. 0000-0002-6177-7433 jflocks@usgs.gov","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":816,"corporation":false,"usgs":true,"family":"Flocks","given":"James","email":"jflocks@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":544990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kindinger, Jack L. jkindinger@usgs.gov","contributorId":815,"corporation":false,"usgs":true,"family":"Kindinger","given":"Jack","email":"jkindinger@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":544991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelso, Kyle W. 0000-0003-0615-242X kkelso@usgs.gov","orcid":"https://orcid.org/0000-0003-0615-242X","contributorId":4307,"corporation":false,"usgs":true,"family":"Kelso","given":"Kyle","email":"kkelso@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":544992,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70144482,"text":"70144482 - 2015 - Foraging range movements of the endangered Hawaiian hoary bat, Lasiurus cinereus semotus (Chiroptera: Vespertilionidae)","interactions":[],"lastModifiedDate":"2018-01-04T12:56:28","indexId":"70144482","displayToPublicDate":"2015-04-01T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Foraging range movements of the endangered Hawaiian hoary bat, Lasiurus cinereus semotus (Chiroptera: Vespertilionidae)","docAbstract":"

We documented nightly movements of Hawaiian hoary bats (Lasiurus cinereus semotus) on the island of Hawai’i. Based on data from 28 radiotagged individuals mean foraging range (FR) was 230.7±72.3 ha, core-use area (CUA) was 25.5±6.9 ha (or 11.1% of mean FR), and the mean long axis (LAX) across the FR was 3,390.8±754.3 m. There was almost no overlap in CUAs among 4 adult males having overlapping foraging areas and tracked simultaneously or within a 90-day window of each other. CUAs of subadults partially overlapped with multiple adult males or with one other subadult. High variance in FRs, cores use areas, and LAX across the FR perhaps reflect localized stochastic variables such as weather, habitat, and food resources. Hawaiian hoary bats use moderately large FRs among insectivorous bats studied with comparable methodologies; however, foraging activity indicated by documentation of acoustic feeding buzzes is concentrated within one or a few disjunct areas cumulatively forming the 50% fixed kernel of CUA. The concentration of feeding activity, low values of individual overlap, and agonistic chasing behavior within CUAs all demonstrate a structured use of individual space by Hawaiian hoary bats.

","language":"English","publisher":"Oxford University Press","doi":"10.1093/jmammal/gyu003","usgsCitation":"Bonaccorso, F.J., Todd, C.M., Miles, A.C., and Gorresen, P.M., 2015, Foraging range movements of the endangered Hawaiian hoary bat, Lasiurus cinereus semotus (Chiroptera: Vespertilionidae): Journal of Mammalogy, v. 96, no. 1, p. 64-71, https://doi.org/10.1093/jmammal/gyu003.","productDescription":"8 p.","startPage":"64","endPage":"71","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055423","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":472168,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyu003","text":"Publisher Index Page"},{"id":438709,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TI6MK3","text":"USGS data release","linkHelpText":"Hawaii Island Hawaiian hoary bat foraging location data 2004-2010"},{"id":299245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.60279846191406,\n 18.966039089744722\n ],\n [\n -155.67901611328125,\n 18.90888487914695\n ],\n [\n -155.93444824218747,\n 19.06990562064469\n ],\n [\n -155.89324951171875,\n 19.334470141510725\n ],\n [\n -156.0765838623047,\n 19.756364230752375\n ],\n [\n -155.89942932128906,\n 19.913965885756145\n ],\n [\n -155.8293914794922,\n 20.02174221755155\n ],\n [\n -155.87745666503906,\n 20.0926923075457\n ],\n [\n -155.9124755859375,\n 20.17198936032983\n ],\n [\n -155.89874267578125,\n 20.254467080673578\n ],\n [\n -155.863037109375,\n 20.269282659332276\n ],\n [\n -155.82733154296875,\n 20.26992678284155\n ],\n [\n -155.555419921875,\n 20.13073412578307\n ],\n [\n -155.40847778320312,\n 20.079794677837523\n ],\n [\n -155.26771545410156,\n 20.015290619684468\n ],\n [\n -155.1654052734375,\n 19.941723716612646\n ],\n [\n -155.0836944580078,\n 19.856498158184017\n ],\n [\n -155.08163452148438,\n 19.724049534671522\n ],\n [\n -155.03631591796875,\n 19.738915107131543\n ],\n [\n -155.00198364257812,\n 19.73439094891939\n ],\n [\n -154.94224548339844,\n 19.632886918987804\n ],\n [\n -154.80560302734375,\n 19.52355289169168\n ],\n [\n -154.82070922851562,\n 19.47500813674323\n ],\n [\n -154.97314453125,\n 19.342244996771804\n ],\n [\n -155.12489318847656,\n 19.2748506284423\n ],\n [\n -155.1983642578125,\n 19.256701248948485\n ],\n [\n -155.27252197265625,\n 19.269665296502332\n ],\n [\n -155.35079956054688,\n 19.210670607390455\n ],\n [\n -155.5499267578125,\n 19.08028870094175\n ],\n [\n -155.55130004882812,\n 19.04719036505186\n ],\n [\n -155.60279846191406,\n 18.966039089744722\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"96","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-27","publicationStatus":"PW","scienceBaseUri":"551d089de4b0256c24f42150","contributors":{"authors":[{"text":"Bonaccorso, Frank J. fbonaccorso@usgs.gov","contributorId":3088,"corporation":false,"usgs":true,"family":"Bonaccorso","given":"Frank","email":"fbonaccorso@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":543650,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Todd, Christopher M.","contributorId":64548,"corporation":false,"usgs":true,"family":"Todd","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":543651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miles, Adam C.","contributorId":139982,"corporation":false,"usgs":false,"family":"Miles","given":"Adam","email":"","middleInitial":"C.","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":543653,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorresen, P. 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Clutch size commonly decreases with increasing elevation among temperate-zone and subtropical songbird species. Tropical songbirds typically lay small clutches, thus the ability to evolve even smaller clutch sizes at higher elevations is unclear and untested. We conducted a comparative phylogenetic analysis using data gathered from the literature to test whether clutch size varied with elevation among forest passerines from three tropical biogeographic regions—the Venezuelan Andes and adjacent lowlands, Malaysian Borneo, and New Guinea. We found a significant negative effect of elevation on variation in clutch size among species. We found the same pattern using field data sampled across elevational gradients in Venezuela and Malaysian Borneo. Field data were not available for New Guinea. Both sets of results demonstrate that tropical montane species across disparate biogeographic realms lay smaller clutches than closely related low-elevation species. The environmental sources of selection underlying this pattern remain uncertain and merit further investigation.

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The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models that generate daily water-level and water-depth data, and applications that compute derived hydrologic data across the freshwater part of the greater Everglades landscape. The U.S. Geological Survey Greater Everglades Priority Ecosystems Science provides support for EDEN in order for EDEN to provide quality-assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan.

\n

The EDEN surface-water model, version 2 (V2), interpolates water-level data from a network of 240 gages to generate gridded daily water-level surfaces for the freshwater domain of the Everglades. When these spatiotemporal continuous surfaces are combined with EDEN’s digital elevation model of ground surface, derived hydrologic data provide scientists and water managers working in the Everglades with data necessary to analyze ecological and biotic responses to hydrologic changes in the Everglades. Derived datasets include water depth, recession rates, days since last dry, water-surface slopes, and hydroperiod. The V2 model includes enhancements from the previous model (version 1; V1) to accommodate changes in the water-level gage network, adjustments to water-level data, improved understanding of the flow dynamics (particularly near canals), and installation of an elevation benchmark network. Enhancements to the V2 model included

\n\n\n

Model performance statistics show a general improvement in the V2 model as compared to the V1 model. Overall, the root mean squared error (RMSE) was reduced by 2.42 centimeters (cm) to 4.68 cm. In Water Conservation Area 3A North and Water Conservation Area 3B, the RMSE was reduced by 10.88 and 9.15 cm, respectively. In addition to evaluating model performance statistics, 2-cm water-level maps were generated and evaluated for irregular contours that would indicate a potential problem either with data input or water-level estimates.

\n

Three applications of the EDEN-modeled water surfaces and other EDEN datasets are presented in the report to show how scientists and resource managers are using EDEN datasets to analyze biological and ecological responses to hydrologic changes in the Everglades. The biological responses of two important Everglades species, alligators and wading birds, to changes in hydrology are described. The effects of hydrology on fire dynamics in the Everglades are also discussed.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145209","collaboration":"Prepared as part of the U.S. Geological Survey Greater Everglades Priority Ecosystem Science and in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Telis, P., Xie, Z., Liu, Z., Li, Y., and Conrads, P., 2015, The Everglades Depth Estimation Network (EDEN) surface-water model, version 2: U.S. Geological Survey Scientific Investigations Report 2014-5209, Report: viii, 42 p. ; 3 Appendices, https://doi.org/10.3133/sir20145209.","productDescription":"Report: viii, 42 p. ; 3 Appendices","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-050914","costCenters":[{"id":269,"text":"FLWSC-Ft. 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Water-level gages used to develop the EDEN surface-water model, version 2."},{"id":299242,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5209/appendix/sir2014-5209_appendix2.xlsx","text":"Appendix 2","size":"14.3 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Appendix 2","linkHelpText":"This is an electronic copy of Appendix 2. Network of benchmarks in greater Everglades used to evaluate EDEN surface-water model."},{"id":299243,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5209/appendix/sir2014-5209_appendix3.xlsx","text":"Appendix 3","size":"39.6 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Appendix 3","linkHelpText":"This is an electronic copy of Appendix 3. Water-level measurements at elevation benchmarks and differences between the modeled surfaces for the EDEN surface-water model, versions 1 and 2."}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.93603515625,\n 25.12539261151203\n ],\n [\n -81.93603515625,\n 26.41155054662258\n ],\n [\n -80.00244140625,\n 26.41155054662258\n ],\n [\n -80.00244140625,\n 25.12539261151203\n ],\n [\n -81.93603515625,\n 25.12539261151203\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551d08a0e4b0256c24f42159","contributors":{"authors":[{"text":"Telis, Pamela A. patelis@usgs.gov","contributorId":140030,"corporation":false,"usgs":true,"family":"Telis","given":"Pamela A.","email":"patelis@usgs.gov","affiliations":[{"id":269,"text":"FLWSC-Ft. 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Larval fishes provide a direct indication of spawning activity and may therefore be useful for long-term monitoring efforts in relation to spawning habitat restoration. However, larval fish sampling can be time intensive and costly. We sought to understand the spatial and temporal structure of larval fish communities in the St. Clair–Detroit River system, Michigan–Ontario, to determine whether targeted larval fish sampling can be made more efficient for long-term monitoring. We found that larval fish communities were highly nested, with lower river segments and late-spring samples containing the highest genus richness of larval fish. We created four sampling scenarios for each river system: (1) using all available data, (2) limiting temporal sampling to late spring, (3) limiting spatial sampling to lower river segments only, and (4) limiting both spatial and temporal sampling. By limiting the spatial extent of sampling to lower river sites and/or limiting the temporal extent to the late-spring period, we found that effort could be reduced by more than 50% while maintaining over 75% of the observed and estimated total genus richness. Similarly, limiting the sampling effort to lower river sites and/or the late-spring period maintained between 65% and 93% of the observed richness of lithophilic-spawning genera and invasive genera. In general, community composition remained consistent among sampling scenarios. Targeted sampling offers a lower-cost alternative to exhaustive spatial and temporal sampling and may be more readily incorporated into long-term monitoring.

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PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-01","publicationStatus":"PW","scienceBaseUri":"55c333b0e4b033ef52106aa8","contributors":{"authors":[{"text":"Pritt, Jeremy J. jpritt@usgs.gov","contributorId":139770,"corporation":false,"usgs":true,"family":"Pritt","given":"Jeremy J.","email":"jpritt@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":564648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roseman, Edward F. eroseman@usgs.gov","contributorId":139766,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","email":"eroseman@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":564647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Jason E. jeross@usgs.gov","contributorId":5748,"corporation":false,"usgs":true,"family":"Ross","given":"Jason","email":"jeross@usgs.gov","middleInitial":"E.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":564649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeBruyne, Robin L. 0000-0002-9232-7937 rdebruyne@usgs.gov","orcid":"https://orcid.org/0000-0002-9232-7937","contributorId":4936,"corporation":false,"usgs":true,"family":"DeBruyne","given":"Robin","email":"rdebruyne@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":564650,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169236,"text":"70169236 - 2015 - Simulated high-latitude soil thermal dynamics during the past four decades","interactions":[],"lastModifiedDate":"2016-03-24T12:01:23","indexId":"70169236","displayToPublicDate":"2015-04-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1350,"text":"Cryosphere Discussions","active":true,"publicationSubtype":{"id":10}},"title":"Simulated high-latitude soil thermal dynamics during the past four decades","docAbstract":"

Soil temperature (Ts ) change is a key indicator of the dynamics of permafrost. On seasonal and inter-annual time scales, the variability of Ts determines the active layer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing T 5 s not only drives permafrost thaw/retreat, but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960–2000, to characterize the warming rate of Ts 10 in permafrost regions. There is a large spread of Ts trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 ◦C yr−1 . Most models show smaller increase in Ts with increasing depth. Air temperature (Ta ) and longwave downward radiation (LWDR) are the main drivers of Ts trends, but their relative contributions differ 15 amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in Ts trends, while trends of Ta only explain 5 % of the differences in Ts trends. Uncertain climate forcing contributes a larger uncertainty in Ts trends (0.021 ± 0.008 ◦C yr−1 , mean ± SD) than the uncertainty of model structure (0.012 ± 0.001 ◦C yr−1 ), diagnosed from the range of response between different mod- 20 els, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active layer thickness (ALT) is less than 3 m loss rate is found to be significantly correlated with the magnitude of the trends of Ts at 1 m depth across the models (R = −0.85, P = 0.003), but not with the initial total near-surface permafrost area (R = −0.30, P = 0.438). The sensitivity of the total boreal near-surface permafrost area to T 25 s at 1 m, is estimated to be of −2.80 ± 0.67 million km2 ◦C −1 . Finally, by using two long-term LWDR datasets and relationships between trends of LWDR and Ts across models, we infer an observationconstrained total boreal near-surface permafrost area decrease comprised between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr−1 from 1960 to 2000. This corresponds to 9– 18 % degradation of the current permafrost area.

","language":"English","publisher":"European Geosciences Union","doi":"10.5194/tc-10-179-2016","usgsCitation":"Peng, S., Ciais, P., Wang, T., Gouttevin, I., McGuire, A., Lawrence, D., Burke, E., Chen, X., Delire, C., Koven, C., MacDougall, A., Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T.J., Decharme, B., Hajima, T., Ji, D., Lettenmaier, D., Miller, P., Moore, J., Smith, B., and Sueyoshi, T., 2015, Simulated high-latitude soil thermal dynamics during the past four decades: Cryosphere Discussions, v. 9, p. 2301-2337, https://doi.org/10.5194/tc-10-179-2016.","productDescription":"37 p.","startPage":"2301","endPage":"2337","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063588","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":472178,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-10-179-2016","text":"Publisher Index Page"},{"id":319364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-20","publicationStatus":"PW","scienceBaseUri":"56f50fd2e4b0f59b85e1ebbb","contributors":{"authors":[{"text":"Peng, S.","contributorId":68688,"corporation":false,"usgs":true,"family":"Peng","given":"S.","email":"","affiliations":[],"preferred":false,"id":623658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ciais, P.","contributorId":39604,"corporation":false,"usgs":true,"family":"Ciais","given":"P.","affiliations":[],"preferred":false,"id":623659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, T.","contributorId":53707,"corporation":false,"usgs":true,"family":"Wang","given":"T.","affiliations":[],"preferred":false,"id":623660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gouttevin, I.","contributorId":167818,"corporation":false,"usgs":false,"family":"Gouttevin","given":"I.","affiliations":[],"preferred":false,"id":623661,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGuire, A. D.","contributorId":16552,"corporation":false,"usgs":true,"family":"McGuire","given":"A. D.","affiliations":[],"preferred":false,"id":623662,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lawrence, D.","contributorId":167819,"corporation":false,"usgs":false,"family":"Lawrence","given":"D.","affiliations":[],"preferred":false,"id":623663,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burke, E.","contributorId":167820,"corporation":false,"usgs":false,"family":"Burke","given":"E.","affiliations":[],"preferred":false,"id":623664,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chen, X.","contributorId":76527,"corporation":false,"usgs":true,"family":"Chen","given":"X.","affiliations":[],"preferred":false,"id":623665,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Delire, C.","contributorId":167821,"corporation":false,"usgs":false,"family":"Delire","given":"C.","affiliations":[],"preferred":false,"id":623666,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Koven, C.","contributorId":39655,"corporation":false,"usgs":true,"family":"Koven","given":"C.","email":"","affiliations":[],"preferred":false,"id":623667,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"MacDougall, A.","contributorId":167822,"corporation":false,"usgs":false,"family":"MacDougall","given":"A.","affiliations":[],"preferred":false,"id":623668,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rinke, A.","contributorId":13118,"corporation":false,"usgs":true,"family":"Rinke","given":"A.","email":"","affiliations":[],"preferred":false,"id":623669,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Saito, K.","contributorId":167823,"corporation":false,"usgs":false,"family":"Saito","given":"K.","email":"","affiliations":[],"preferred":false,"id":623670,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Zhang, W.","contributorId":92399,"corporation":false,"usgs":true,"family":"Zhang","given":"W.","email":"","affiliations":[],"preferred":false,"id":623671,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Alkama, R.","contributorId":167824,"corporation":false,"usgs":false,"family":"Alkama","given":"R.","affiliations":[],"preferred":false,"id":623672,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Bohn, T. J.","contributorId":167813,"corporation":false,"usgs":false,"family":"Bohn","given":"T.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":623673,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Decharme, B.","contributorId":167825,"corporation":false,"usgs":false,"family":"Decharme","given":"B.","affiliations":[],"preferred":false,"id":623674,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Hajima, T.","contributorId":167826,"corporation":false,"usgs":false,"family":"Hajima","given":"T.","affiliations":[],"preferred":false,"id":623675,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Ji, D.","contributorId":167827,"corporation":false,"usgs":false,"family":"Ji","given":"D.","email":"","affiliations":[],"preferred":false,"id":623676,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Lettenmaier, D.P.","contributorId":61175,"corporation":false,"usgs":true,"family":"Lettenmaier","given":"D.P.","email":"","affiliations":[],"preferred":false,"id":623677,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Miller, P.A.","contributorId":89414,"corporation":false,"usgs":true,"family":"Miller","given":"P.A.","email":"","affiliations":[],"preferred":false,"id":623678,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Moore, J.C.","contributorId":95141,"corporation":false,"usgs":true,"family":"Moore","given":"J.C.","email":"","affiliations":[],"preferred":false,"id":623679,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Smith, B.","contributorId":53740,"corporation":false,"usgs":true,"family":"Smith","given":"B.","affiliations":[],"preferred":false,"id":623680,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Sueyoshi, T.","contributorId":167828,"corporation":false,"usgs":false,"family":"Sueyoshi","given":"T.","affiliations":[],"preferred":false,"id":623681,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70159878,"text":"70159878 - 2015 - Slab melting beneath the Cascades Arc driven by dehydration of altered oceanic peridotite","interactions":[],"lastModifiedDate":"2015-12-03T10:07:20","indexId":"70159878","displayToPublicDate":"2015-04-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Slab melting beneath the Cascades Arc driven by dehydration of altered oceanic peridotite","docAbstract":"

Water is returned to Earth’s interior at subduction zones. However, the processes and pathways by which water leaves the subducting plate and causes melting beneath volcanic arcs are complex; the source of the water—subducting sediment, altered oceanic crust, or hydrated mantle in the downgoing plate—is debated; and the role of slab temperature is unclear. Here we analyse the hydrogen-isotope and trace-element signature of melt inclusions in ash samples from the Cascade Arc, where young, hot lithosphere subducts. Comparing these data with published analyses, we find that fluids in the Cascade magmas are sourced from deeper parts of the subducting slab—hydrated mantle peridotite in the slab interior—compared with fluids in magmas from the Marianas Arc, where older, colder lithosphere subducts. We use geodynamic modelling to show that, in the hotter subduction zone, the upper crust of the subducting slab rapidly dehydrates at shallow depths. With continued subduction, fluids released from the deeper plate interior migrate into the dehydrated parts, causing those to melt. These melts in turn migrate into the overlying mantle wedge, where they trigger further melting. Our results provide a physical model to explain melting of the subducted plate and mass transfer from the slab to the mantle beneath arcs where relatively young oceanic lithosphere is subducted.

","language":"English","publisher":"MacMillan Publishers Limited","doi":"10.1038/NGEO2417","usgsCitation":"Walowski, K., Wallace, P., Hauri, E., Wada, I., and Clynne, M.A., 2015, Slab melting beneath the Cascades Arc driven by dehydration of altered oceanic peridotite: Nature Geoscience, v. 8, p. 404-408, https://doi.org/10.1038/NGEO2417.","productDescription":"5 p.","startPage":"404","endPage":"408","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057922","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":311851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Cascade Arc","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.4091796875,\n 40.12849105685408\n ],\n [\n -124.4091796875,\n 47.29413372501023\n ],\n [\n -118.89404296875,\n 47.29413372501023\n ],\n [\n -118.89404296875,\n 40.12849105685408\n ],\n [\n -124.4091796875,\n 40.12849105685408\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-20","publicationStatus":"PW","scienceBaseUri":"566175e0e4b06a3ea36c56e8","contributors":{"authors":[{"text":"Walowski, Kristina J","contributorId":150156,"corporation":false,"usgs":false,"family":"Walowski","given":"Kristina J","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":580862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wallace, Paul J.","contributorId":29308,"corporation":false,"usgs":true,"family":"Wallace","given":"Paul J.","affiliations":[],"preferred":false,"id":580863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hauri, E.H.","contributorId":66009,"corporation":false,"usgs":true,"family":"Hauri","given":"E.H.","email":"","affiliations":[],"preferred":false,"id":580864,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wada, I.","contributorId":150157,"corporation":false,"usgs":false,"family":"Wada","given":"I.","email":"","affiliations":[{"id":17923,"text":"Tohoku University, Sendai, Japan","active":true,"usgs":false}],"preferred":false,"id":580865,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clynne, Michael A. 0000-0002-4220-2968 mclynne@usgs.gov","orcid":"https://orcid.org/0000-0002-4220-2968","contributorId":2032,"corporation":false,"usgs":true,"family":"Clynne","given":"Michael","email":"mclynne@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":580861,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70187280,"text":"70187280 - 2015 - Post-White-nose syndrome trends in Virginia’s cave bats, 2008-2013","interactions":[],"lastModifiedDate":"2026-02-05T16:51:03.287246","indexId":"70187280","displayToPublicDate":"2015-04-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5085,"text":"Journal of Ecology and the Natural Environment","active":true,"publicationSubtype":{"id":10}},"title":"Post-White-nose syndrome trends in Virginia’s cave bats, 2008-2013","docAbstract":"

Since its 2009 detection in Virginia hibernacula, the fungal pathogen Pseudogymnoascus destructans causing White-nose Syndrome (WNS) has had a marked impact on cave bats locally. From 2008-2013, we documented numeric and physiologic changes in cave bats through fall swarm (FS), early hibernation (EH), and late hibernation (LH) capture and banding surveys at 18 hibernacula in western Virginia. We coupled active surveys with passive biennial winter counts in 2009, 2011, and 2013. We compared individual body mass index (BMI) across years for FS, EH, and LH hibernation to determine if WNS impacts on extant bats would be manifested by changes in body condition (as anecdotally observed elsewhere for WNS-impacted bats) as well as a population reduction. To estimate percent declines in bat presence or relative activity, we used FS capture per-unit-effort data, and the winter hibernacula absolute counts. We captured 4,524 bats of eight species, with species-specific capture success declining by 75-100% post-WNS. Little brown bats (Myotis lucifugus) exhibited the greatest declines in winter hibernacula counts (AVG. = 99.0% decline), followed by tri-colored bats (Perimyotis subflavus; 89.5% decline) and Indiana bats (M. sodalis; 33.5% decline). Graphical analyses of captures-per-trap-hour in FS showed declines for little brown bats, tri-colored bats, and northern long-eared bats (M. septentrionalis), but suggest a modest rebound of Indiana bat numbers. Fall swarm trends in BMI suggested some drops post-WNS exposure, but these trends were not consistent across sexes or seasonal time blocks. Our inconclusive BMI metrics and little brown bat band recapture data suggest little competitive advantage or selection for surviving bats. Lesser (but apparent) declines in Indiana bat numbers mirrors trends seen elsewhere regionally, and band recoveries do show that some individuals are persisting. Additional surveys will determine if bats in Virginia will persist or face extirpation due to presumed low recruitment and survivorship.

","language":"English","publisher":"Academic Journals","doi":"10.5897/JENE2015.0507","usgsCitation":"Powers, K.E., Reynolds, R., Orndorff, W., Ford, W.M., and Hobson, C.S., 2015, Post-White-nose syndrome trends in Virginia’s cave bats, 2008-2013: Journal of Ecology and the Natural Environment, v. 7, no. 4, p. 113-123, https://doi.org/10.5897/JENE2015.0507.","productDescription":"11 p.","startPage":"113","endPage":"123","ipdsId":"IP-059994","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":472182,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5897/jene2015.0507","text":"Publisher Index Page"},{"id":340598,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","volume":"7","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-15","publicationStatus":"PW","scienceBaseUri":"590454a7e4b022cee40dc254","contributors":{"authors":[{"text":"Powers, Karen E.","contributorId":171456,"corporation":false,"usgs":false,"family":"Powers","given":"Karen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":693443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reynolds, Richard J.","contributorId":343175,"corporation":false,"usgs":false,"family":"Reynolds","given":"Richard J.","affiliations":[{"id":56188,"text":"Virginia Department of Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":693444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orndorff, Wil","contributorId":127487,"corporation":false,"usgs":false,"family":"Orndorff","given":"Wil","affiliations":[{"id":6970,"text":"Virginia Department of Conservation and Recreation, Natural Heritage Program","active":true,"usgs":false}],"preferred":false,"id":693445,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark wford@usgs.gov","contributorId":3858,"corporation":false,"usgs":true,"family":"Ford","given":"W.","email":"wford@usgs.gov","middleInitial":"Mark","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":693211,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hobson, Christopher S.","contributorId":171458,"corporation":false,"usgs":false,"family":"Hobson","given":"Christopher","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":693446,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192718,"text":"70192718 - 2015 - Polygonal tundra geomorphological change in response to warming alters future CO2 and CH4 flux on the Barrow Peninsula","interactions":[],"lastModifiedDate":"2017-11-08T14:14:17","indexId":"70192718","displayToPublicDate":"2015-04-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Polygonal tundra geomorphological change in response to warming alters future CO2 and CH4 flux on the Barrow Peninsula","docAbstract":"

The landscape of the Barrow Peninsula in northern Alaska is thought to have formed over centuries to millennia, and is now dominated by ice-wedge polygonal tundra that spans drained thaw-lake basins and interstitial tundra. In nearby tundra regions, studies have identified a rapid increase in thermokarst formation (i.e., pits) over recent decades in response to climate warming, facilitating changes in polygonal tundra geomorphology. We assessed the future impact of 100 years of tundra geomorphic change on peak growing season carbon exchange in response to: (i) landscape succession associated with the thaw-lake cycle; and (ii) low, moderate, and extreme scenarios of thermokarst pit formation (10%, 30%, and 50%) reported for Alaskan arctic tundra sites. We developed a 30 × 30 m resolution tundra geomorphology map (overall accuracy:75%; Kappa:0.69) for our ~1800 km² study area composed of ten classes; drained slope, high center polygon, flat-center polygon, low center polygon, coalescent low center polygon, polygon trough, meadow, ponds, rivers, and lakes, to determine their spatial distribution across the Barrow Peninsula. Land-atmosphere CO2 and CH4 flux data were collected for the summers of 2006–2010 at eighty-two sites near Barrow, across the mapped classes. The developed geomorphic map was used for the regional assessment of carbon flux. Results indicate (i) at present during peak growing season on the Barrow Peninsula, CO2 uptake occurs at -902.3 106gC-CO2 day−1(uncertainty using 95% CI is between −438.3 and −1366 106gC-CO2 day−1) and CH4 flux at 28.9 106gC-CH4 day−1(uncertainty using 95% CI is between 12.9 and 44.9 106gC-CH4 day−1), (ii) one century of future landscape change associated with the thaw-lake cycle only slightly alter CO2 and CH4 exchange, while (iii) moderate increases in thermokarst pits would strengthen both CO2uptake (−166.9 106gC-CO2 day−1) and CH4 flux (2.8 106gC-CH4 day−1) with geomorphic change from low to high center polygons, cumulatively resulting in an estimated negative feedback to warming during peak growing season.

","language":"English","publisher":"Wiley","doi":"10.1111/gcb.12757","usgsCitation":"Lara, M.J., McGuire, A.D., Euskirchen, E., Tweedie, C.E., Hinkel, K.M., Skurikhin, A.N., Romanovsky, V.E., Grosse, G., Bolton, W.R., and Genet, H., 2015, Polygonal tundra geomorphological change in response to warming alters future CO2 and CH4 flux on the Barrow Peninsula: Global Change Biology, v. 21, no. 4, p. 1634-1651, https://doi.org/10.1111/gcb.12757.","productDescription":"18 p.","startPage":"1634","endPage":"1651","ipdsId":"IP-057369","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487818,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1400670","text":"External Repository"},{"id":348469,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Barrow Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.65380859375,\n 70.8356582274894\n ],\n [\n -155.50048828125,\n 70.8356582274894\n ],\n [\n -155.50048828125,\n 71.39390732213532\n ],\n [\n -157.65380859375,\n 71.39390732213532\n ],\n [\n -157.65380859375,\n 70.8356582274894\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2014-11-17","publicationStatus":"PW","scienceBaseUri":"5a0425c3e4b0dc0b45b4540b","contributors":{"authors":[{"text":"Lara, Mark J.","contributorId":194640,"corporation":false,"usgs":false,"family":"Lara","given":"Mark","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":721293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, A. David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":716769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Euskirchen, Eugénie S.","contributorId":83378,"corporation":false,"usgs":false,"family":"Euskirchen","given":"Eugénie S.","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":721294,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tweedie, Craig E.","contributorId":200176,"corporation":false,"usgs":false,"family":"Tweedie","given":"Craig","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":721295,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hinkel, Kenneth M.","contributorId":15405,"corporation":false,"usgs":true,"family":"Hinkel","given":"Kenneth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721296,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Skurikhin, Alexei N.","contributorId":200177,"corporation":false,"usgs":false,"family":"Skurikhin","given":"Alexei","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":721297,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Romanovsky, Vladimir E.","contributorId":40113,"corporation":false,"usgs":true,"family":"Romanovsky","given":"Vladimir","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":721298,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":721299,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bolton, W. Robert","contributorId":187610,"corporation":false,"usgs":false,"family":"Bolton","given":"W.","email":"","middleInitial":"Robert","affiliations":[],"preferred":false,"id":721300,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Genet, Helene","contributorId":95370,"corporation":false,"usgs":true,"family":"Genet","given":"Helene","affiliations":[],"preferred":false,"id":721301,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70194175,"text":"70194175 - 2015 - Evaluating physical habitat and water chemistry data from statewide stream monitoring programs to establish least-impacted conditions in Washington State","interactions":[],"lastModifiedDate":"2017-12-18T10:48:30","indexId":"70194175","displayToPublicDate":"2015-04-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Evaluating physical habitat and water chemistry data from statewide stream monitoring programs to establish least-impacted conditions in Washington State","docAbstract":"

Various GIS-generated land-use predictor variables, physical habitat metrics, and water chemistry variables from 75 reference streams and 351 randomly sampled sites throughout Washington State were evaluated for effectiveness at discriminating reference from random sites within level III ecoregions. A combination of multivariate clustering and ordination techniques were used. 

We describe average observed conditions for a subset of predictor variables as well as proposing statistical criteria for establishing reference conditions for stream habitat in Washington. Using these criteria, we determined whether any of the random sites met expectations for reference condition and whether any of the established reference sites failed to meet expectations for reference condition. Establishing these criteria will set a benchmark from which future data will be compared.

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Remote sensing technology is advancing at an unprecedented rate. At the forefront of the new technological developments are unmanned aircraft systems (UAS). The advent of small, lightweight, low-cost, and user-friendly UAS is greatly expanding the potential applications of remote sensing technology and improving the set of tools available to researchers seeking to map and monitor terrain from above. In this article, we explore the applications of a small UAS for mapping informal diamond mining sites in Africa. We found that this technology provides aerial imagery of unparalleled resolution in a data-sparse, difficult to access, and remote terrain.

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