The Catahoula aquifer is an important source of fresh groundwater in central Louisiana. In 2010, about 3.96 million gallons per day (Mgal/d) were withdrawn from the Catahoula aquifer in Louisiana.
\nIn 2012, the U.S. Geological Survey in cooperation with the Louisiana Department of Natural Resources began a study to document current water levels in selected aquifers in Louisiana. This report presents water-level data and a map that illustrates the potentiometric surface of the Catahoula aquifer in 2013.
\nThe Catahoula aquifer crops out in a narrow band across north-central Louisiana. This band is broken by alluvial deposits in the valleys of the Red, Little, and Ouachita Rivers that have incised into the aquifer. Saltwater ridges under the Red, Little, and Tensas River Valleys divide the freshwater extents of the Catahoula aquifer and limit the flow of freshwater between these areas. The Catahoula aquifer generally ranges in thickness from about 50 feet (ft) in the outcrop area to about 450 ft in southern Vernon Parish. Sand beds in the aquifer are generally discontinuous, lenticular, and interbedded with silts and clays.
\nThe potentiometric surface of the Catahoula aquifer was constructed by using the altitude of water levels measured at 29 wells during the period May through September 2013. The altitude of water levels ranged from 0.02 ft above the National Geodetic Vertical Datum of 1929 (NGVD 29) in well Co-51 to 238 ft above NGVD 29 in well Na-317. Groundwater movement in the Catahoula aquifer is generally to the southeast and towards discharge areas beneath the Sabine, Red, Little, and Tensas River Valleys.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3339","collaboration":"Prepared in cooperation with the Louisiana Department of Natural Resources","usgsCitation":"Fendick, R.B., Jr., and Carter, Kayla, 2015, Potentiometric surface of the Catahoula aquifer in central Louisiana, 2013: U.S. Geological Survey Scientific Investigations Map 3339, 1 sheet, https://dx.doi.org/10.3133/sim3339.","productDescription":"Sheet: 32.0 x 28.0 inches","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064411","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"links":[{"id":310005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3339/coverthb.jpg"},{"id":310006,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3339/sim3339.pdf","text":"Sheet","size":"788 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3339"}],"country":"United 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U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
http://la.water.usgs.gov/
This report describes an R package called smwrBase, which consists of a collection of functions to import, transform, manipulate, and manage hydrologic data within the R statistical environment. Functions in the package allow users to import surface-water and groundwater data from the U.S. Geological Survey’s National Water Information System database and other sources. Additional functions are provided to transform, manipulate, and manage hydrologic data in ways necessary for analyzing the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151202","usgsCitation":"Lorenz, D.L., 2015, smwrBase—An R package for managing hydrologic data, version 1.1.1: U.S. Geological\nSurvey Open-File Report 2015–1202, 7 p., https://dx.doi.org/10.3133/ofr20151202.","productDescription":"Report: iii, 5 p.; Appendixes: 1-3","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-052705","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":311723,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1202/ofr20151202.pdf","text":"Report","size":"241 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1202"},{"id":311724,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ofr/2015/1202/downloads/","text":"Appendix","description":"OFR 2015-1202 Appendix","linkHelpText":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
http://mn.water.usgs.gov/
Methylmercury is a global pollutant of aquatic ecosystems, and monitoring programs need tools to predict mercury exposure of wildlife. We developed equations to estimate methylmercury exposure of piscivorous birds and sport fish using mercury concentrations in prey fish. We collected original data on western grebes (Aechmophorus occidentalis) and Clark’s grebes (Aechmophorus clarkii) and summarized the published literature to generate predictive equations specific to grebes and a general equation for piscivorous birds. We measured mercury concentrations in 354 grebes (blood averaged 1.06 ± 0.08 μg/g ww), 101 grebe eggs, 230 sport fish (predominantly largemouth bass and rainbow trout), and 505 prey fish (14 species) at 25 lakes throughout California. Mercury concentrations in grebe blood, grebe eggs, and sport fish were strongly related to mercury concentrations in prey fish among lakes. Each 1.0 μg/g dw (∼0.24 μg/g ww) increase in prey fish resulted in an increase in mercury concentrations of 103% in grebe blood, 92% in grebe eggs, and 116% in sport fish. We also found strong correlations between mercury concentrations in grebes and sport fish among lakes. Our results indicate that prey fish monitoring can be used to estimate mercury exposure of piscivorous birds and sport fish when wildlife cannot be directly sampled.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.5b02691","usgsCitation":"Ackerman, J., Hartman, C.A., Eagles-Smith, C.A., Herzog, M.P., Davis, J., Ichikawa, G., and Bonnema, A., 2015, Estimating mercury exposure of piscivorous birds and sport fish using prey fish monitoring: Environmental Science & Technology, v. 49, no. 22, p. 13596-13604, https://doi.org/10.1021/acs.est.5b02691.","productDescription":"9 p.","startPage":"13596","endPage":"13604","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067013","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":312042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"49","issue":"22","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-28","publicationStatus":"PW","scienceBaseUri":"5667ff38e4b06a3ea36c8e08","chorus":{"doi":"10.1021/acs.est.5b02691","url":"http://dx.doi.org/10.1021/acs.est.5b02691","publisher":"American Chemical Society (ACS)","authors":"Ackerman Joshua T., Hartman C. Alex, Eagles-Smith Collin A., Herzog Mark P., Davis Jay, Ichikawa Gary, Bonnema Autumn","journalName":"Environmental Science & Technology","publicationDate":"11/17/2015"},"contributors":{"authors":[{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":581541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131109,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":581542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":581543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131110,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":581544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Jay","contributorId":150405,"corporation":false,"usgs":false,"family":"Davis","given":"Jay","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":581545,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ichikawa, Gary","contributorId":140920,"corporation":false,"usgs":false,"family":"Ichikawa","given":"Gary","email":"","affiliations":[],"preferred":false,"id":581546,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bonnema, Autumn","contributorId":140921,"corporation":false,"usgs":false,"family":"Bonnema","given":"Autumn","email":"","affiliations":[],"preferred":false,"id":581547,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70157562,"text":"70157562 - 2015 - A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA","interactions":[],"lastModifiedDate":"2015-12-08T13:39:29","indexId":"70157562","displayToPublicDate":"2015-12-08T14:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA","docAbstract":"We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R2 and a version with R2 within one standard error of the maximum (the 1SE model). The former yielded CV training R2 values of 0.94–1.0. Cross-validation testing R2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2015.10.025","usgsCitation":"Nolan, B.T., Fienen, M., and Lorenz, D.L., 2015, A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA: Journal of Hydrology, v. 531, no. 3, p. 902-911, https://doi.org/10.1016/j.jhydrol.2015.10.025.","productDescription":"10 p.","startPage":"902","endPage":"911","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065964","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":471571,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2015.10.025","text":"Publisher Index Page"},{"id":312041,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.025146484375,\n 40.73893324113603\n ],\n [\n -122.93701171874999,\n 40.38002840251183\n ],\n [\n -122.16796875,\n 38.048091067457236\n ],\n [\n -119.39941406249999,\n 34.95799531086792\n ],\n [\n -118.67431640625,\n 34.97600151317591\n ],\n [\n -118.795166015625,\n 36.19995805932895\n ],\n [\n -120.92651367187499,\n 38.38472766885085\n ],\n [\n -122.025146484375,\n 40.73893324113603\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"531","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5667ff32e4b06a3ea36c8e04","contributors":{"authors":[{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":573640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":893,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","email":"mnfienen@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":573641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorenz, David L. 0000-0003-3392-4034 lorenz@usgs.gov","orcid":"https://orcid.org/0000-0003-3392-4034","contributorId":1384,"corporation":false,"usgs":true,"family":"Lorenz","given":"David","email":"lorenz@usgs.gov","middleInitial":"L.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":573642,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159406,"text":"sir20155153 - 2015 - Simulation of the effects of different inflows on hydrologic conditions in Lake Houston with a three-dimensional hydrodynamic model, Houston, Texas, 2009–10","interactions":[],"lastModifiedDate":"2020-05-19T18:00:59.387807","indexId":"sir20155153","displayToPublicDate":"2015-12-08T12:00:00","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":"2015-5153","title":"Simulation of the effects of different inflows on hydrologic conditions in Lake Houston with a three-dimensional hydrodynamic model, Houston, Texas, 2009–10","docAbstract":"Lake Houston, an important water resource for the Houston, Texas, area, receives inflows from seven major tributaries that compose the San Jacinto River Basin upstream from the reservoir. The effects of different inflows from the watersheds drained by these tributaries on the residence time of water in Lake Houston and closely associated physical and chemical properties including lake elevation, salinity, and water temperature are not well known. Accordingly, the U.S. Geological Survey (USGS), in cooperation with the City of Houston, developed a three-dimensional hydrodynamic model of Lake Houston as a tool for evaluating the effects of different inflows on residence time of water in the lake and associated physical and chemical properties. The Environmental Fluid Dynamics Code (EFDC), a grid-based, surface-water modeling package for simulating three-dimensional circulation, mass transport, sediments, and biogeochemical processes, was used to develop the model of Lake Houston. The Lake Houston EFDC model was developed and calibrated by using 2009 data and verified by using 2010 data. Three statistics (mean error, root mean square error, and the Nash-Sutcliffe model efficiency coefficient) were used to evaluate how well the Lake Houston EFDC model simulated lake elevation, salinity, and water temperature. The residence time of water in reservoirs is associated with various physical and chemical properties (including lake elevation, salinity, and water temperature). Simulated and measured lake-elevation values were compared at USGS reservoir station 08072000 Lake Houston near Sheldon, Tex. The accuracy of simulated salinity and water temperature values was assessed by using the salinity (computed from measured specific conductance) and water temperature at two USGS monitoring stations: 295826095082200 Lake Houston south Union Pacific Railroad Bridge near Houston, Tex., and 295554095093401 Lake Houston at mouth of Jack’s Ditch near Houston, Tex. Specific conductance and water temperature were measured at as many as four different depths at each of the two monitoring stations during 2009 and then used for assessing the accuracy of simulated values of salinity and water temperature during 2010. The performance evaluation statistics indicate that the model performed satisfactorily. The calibrated model was used to simulate two possible inflow scenarios to evaluate the changes in the residence time of water in Lake Houston. The two scenarios tested were an increased inflow of approximately 300 cubic feet per second for 1 month (May 2010) from two watersheds: the West Fork San Jacinto River and Luce Bayou. These scenarios were chosen to mimic the effects of possible small releases or diversions of water from outside the San Jacinto River Basin into the basin (or directly into the lake) on the residence time of water in Lake Houston. During the time of increased inflow for the two scenarios tested, maximum residence time decreased slightly from approximately 106 to 97 days.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155153","collaboration":"Prepared in cooperation with the City of Houston","usgsCitation":"Rendon, S.H., and Lee, M.T., 2015, Simulation of the effects of different inflows on hydrologic conditions in Lake Houston with a three-dimensional hydrodynamic model, Houston, Texas, 2009–10: U.S. Geological Survey Scientific Investigations Report 2015–5153, 42 p., https://dx.doi.org/10.3133/sir20155153.","productDescription":"vi, 42 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-060635","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":311980,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5153/sir20155153.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5153"},{"id":311979,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5153/coverthb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lake Houston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.99578857421875,\n 29.90256760730233\n ],\n [\n -95.99578857421875,\n 30.62845887475364\n ],\n [\n -95.10040283203125,\n 30.62845887475364\n ],\n [\n -95.10040283203125,\n 29.90256760730233\n ],\n [\n -95.99578857421875,\n 29.90256760730233\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
http://tx.usgs.gov/
The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/sdata.2015.66","usgsCitation":"Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J., 2015, The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes: Scientific Data, v. 2, Article 150066: 21 p., https://doi.org/10.1038/sdata.2015.66.","productDescription":"Article 150066: 21 p.","ipdsId":"IP-066224","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2015.66","text":"Publisher Index Page"},{"id":341859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-08","publicationStatus":"PW","scienceBaseUri":"592e84bbe4b092b266f10d3d","contributors":{"authors":[{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, Pete","contributorId":192379,"corporation":false,"usgs":false,"family":"Peterson","given":"Pete","affiliations":[],"preferred":false,"id":696432,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Landsfeld, Martin","contributorId":192380,"corporation":false,"usgs":false,"family":"Landsfeld","given":"Martin","affiliations":[],"preferred":false,"id":696433,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pedreros, Diego 0000-0001-9943-7373 pedreros@usgs.gov","orcid":"https://orcid.org/0000-0001-9943-7373","contributorId":4195,"corporation":false,"usgs":true,"family":"Pedreros","given":"Diego","email":"pedreros@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696371,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Verdin, James 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":145830,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696376,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shukla, Shraddhanand","contributorId":145802,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":696372,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":696373,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696375,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Harrison, Laura","contributorId":78859,"corporation":false,"usgs":true,"family":"Harrison","given":"Laura","affiliations":[],"preferred":false,"id":696374,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hoell, Andrew","contributorId":145803,"corporation":false,"usgs":false,"family":"Hoell","given":"Andrew","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":696434,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Michaelsen, Joel","contributorId":149202,"corporation":false,"usgs":false,"family":"Michaelsen","given":"Joel","affiliations":[],"preferred":false,"id":696435,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70159636,"text":"sir20155166 - 2015 - Occurrence and transport of selected constituents in streams near the Stibnite mining area, Central Idaho, 2012–14","interactions":[],"lastModifiedDate":"2016-01-05T08:28:48","indexId":"sir20155166","displayToPublicDate":"2015-12-07T17:45:00","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":"2015-5166","title":"Occurrence and transport of selected constituents in streams near the Stibnite mining area, Central Idaho, 2012–14","docAbstract":"Mining of stibnite (antimony sulfide), tungsten, gold, silver, and mercury near the town of Stibnite in central Idaho has left a legacy of trace element contamination in local streams. Water-quality and streamflow monitoring data from a network of five streamflow-gaging stations were used to estimate trace-element and suspended-sediment loads and flow-weighted concentrations in the Stibnite mining area between 2012 and 2014. Measured concentrations of arsenic exceeded human health-based water-quality criteria at each streamflow-gaging station, except for Meadow Creek (site 2), which was selected to represent background conditions in the study area. Measured concentrations of antimony exceeded human health-based water-quality criteria at sites 3, 4, and 5.
\nRegression models developed using the U.S. Geological Survey LOAD ESTimation (LOADEST) program showed that concentrated sources of arsenic and antimony are present in specific reaches along Meadow Creek and the East Fork of South Fork of the Salmon River (EFSFSR) between the EFSFSR at Stibnite (site 3) and the EFSFSR above Sugar Creek (site 4). Eighty percent of the arsenic and antimony loads were attributable to discrete reaches that accounted for 25 percent of the total streamflow in the study area. Streamflow was negatively correlated with arsenic and antimony concentrations, indicating groundwater sources. Continuously monitored specific conductance, alone or combined with continuously computed streamflow, was more significant than streamflow alone as a surrogate measure of dissolved arsenic and antimony concentrations. Surrogate regression models (with coefficients of determination ranging from 0.96 to 0.65) can be used to estimate arsenic and antimony concentrations in real time at all five streamflow-gaging stations.
\nLOADEST model simulation results indicated hysteresis in transport of suspended sediment and sediment-associated constituents. Predictor variables that account for streamflow variability reduced model bias and root mean square error when included in regression models used to estimate concentrations and loads of suspended sediment, total aluminum, total lead, and total mercury.
\nNinety-eight percent of the estimated total mercury load transported downstream of the study area is attributable to Sugar Creek. A maximum concentration of 26 micrograms per liter was measured in Sugar Creek during May 2013 when snowmelt runoff occurred during a single peak in the hydrograph. Monitoring and modeling results indicate sediment and sediment-associated constituent concentrations and loads increase along Meadow Creek, likely because of the inflow of the East Fork of Meadow Creek, and decrease between sites 3 and 4 because the Glory Hole is trapping sediments. Sugar Creek (site 5) accounted for most of the sediment and sediment-associated constituent loading leaving the study area because loads from the East Fork of Meadow Creek remained trapped in the Glory Hole. Additionally, total mercury was detected at all five streamflow-gaging stations, and sampled mercury concentrations exceeded Idaho ambient water-quality criteria at all five streamflow-gaging stations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155166","collaboration":"Prepared in cooperation with the Idaho Department of Lands and the Midas Gold Corporation","usgsCitation":"Etheridge, A.B., 2015, Occurrence and transport of selected constituents in streams near the Stibnite mining area, central Idaho, 2012–14: U.S. Geological Survey Scientific Investigations Report 2015–5166, 47 p., https://dx.doi.org/10.3133/sir20155166.","productDescription":"Report: vii, 47 p.; Appendix B","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2011-10-01","temporalEnd":"2014-09-30","ipdsId":"IP-060615","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":311962,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5166/coverthb.jpg"},{"id":311964,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5166/sir20155166_appendixb.xlsx","text":"Appendix B","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5166 Appendix B"},{"id":311963,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5166/sir20155166.pdf","text":"Report","size":"4.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5166 PDF"}],"country":"United States","state":"Idaho","otherGeospatial":"Stibnite Mining Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {\n \"stroke\": \"#555555\",\n \"stroke-width\": 2,\n \"stroke-opacity\": 1,\n \"fill\": \"#555555\",\n \"fill-opacity\": 0.5\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.36502838134766,\n 44.82397775537488\n ],\n [\n -115.36502838134766,\n 44.98034238084973\n ],\n [\n -115.20195007324217,\n 44.98034238084973\n ],\n [\n -115.20195007324217,\n 44.82397775537488\n ],\n [\n -115.36502838134766,\n 44.82397775537488\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov
Geomorphic and habitat data were collected along Underwood Creek as part of a larger study of stream water quality conditions in the greater Milwaukee, Wisconsin, area. The data were collected to characterize baseline physical conditions in Underwood Creek prior to a potential discharge of wastewater return flow to the stream from the city of Waukesha, Wis. Geomorphic and habitat assessments were conducted in the summer and fall of 2012 by the U.S. Geological Survey (USGS) in cooperation with the Milwaukee Metropolitan Sewerage District. The assessments used a transect based, reach scale assessment at a total of eight reaches—six reaches along Underwood Creek and two reaches along the Menomonee River upstream and downstream of its confluence with Underwood Creek. The reach scale assessment was an updated version of the USGS National Water Quality Assessment Program habitat assessment integrated with an intensive geomorphic assessment. Channel cross sections and longitudinal profiles were surveyed along each of the eight reaches, and discharge and water temperature were measured. Additionally, a geomorphic river walk-through was completed along a 10 kilometer reach that spanned the individual assessment reaches and the sections of channel between them. The assessments and river walk-through described channel and bank stability, channel shape and size, sediment and riparian conditions along these areas of Underwood Creek and the Menomonee River. Since the time of the data collection, focus has turned to other Lake Michigan tributary watersheds for possible Waukesha return-flow discharges; however, the data collected for this effort remains a valuable asset for the baseline characterization, design, and prioritization of planned stream rehabilitation activities in Underwood Creek. The data files presented in this report include a variety of formats including geographic information system files, spreadsheets, photos, and scans of field forms.
\nA subset of habitat-specific data collected during the baseline study can be retrieved through USGS BioData https://aquatic.biodata.usgs.gov/landing.action.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds947","collaboration":"Prepared in cooperation with Milwaukee Metropolitan Sewerage District","usgsCitation":"Young, B.M., Fitzpatrick, F.A., and Blount, J.D., 2015, Stream geomorphic and habitat data from a baseline study of Underwood Creek, Wisconsin, 2012: U.S. Geological Survey Data Series 947, 14 p., plus data files, https://dx.doi.org/10.3133/ds947.","productDescription":"Report: v, 14 p.; 3 Tables; Figures; ReadMe; Spatial Data; Photos; Downloads Directory","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053458","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":311608,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/0947/downloads/ds947_USGS-Underwood-Creek-Site-Surveys-Cross-Sections.xlsx","text":"Underwood Creek Site Survey - Cross Section","size":"239 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 947"},{"id":311606,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/0947/downloads/photos","text":"Photos","description":"DS 947","linkHelpText":"Geomorphic and Habit Assessment Site Photos (108 files, 270 MB), River Walk-Through PhotosDirector, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, Wisconsin 53562-3586
http://wi.water.usgs.gov/
Previous studies by the U.S. Geological Survey identified Alkali Flat as an area of groundwater upwelling, with increases in concentrations of total dissolved solids, and streamflow loss, but additional study was needed to better characterize these observations. The U.S. Geological Survey, in cooperation with the Bureau of Land Management, White River Field Office, conducted a study to characterize the hydrology and water quality of Piceance Creek in the Alkali Flat area of Rio Blanco County, Colorado.
\nWater-quality samples were collected at five springs on March 27, 2012, to determine field properties, major ions, trace elements, and stable isotopes of water. Major-ion and trace-element chemistry indicated that the springs sampled as part of this study were likely recharged by the bedrock aquifer. Isotopic values for the springs plotted close to that of groundwater from the Parachute Creek Member of the Green River Formation, and the isotopic values from both of these sources are similar to the values for Grand Mesa snow. Based on fluoride, lithium, and strontium concentrations, one spring appeared to have different source water than the other four springs. The spring also had higher concentrations of calcium, magnesium, and sulfate relative to the other four springs. Trace-element and major-ion data indicate that this spring was sourced from the Uinta Formation. It was likely the other four springs were primarily sourced from the lower part of the Parachute Creek Member of the Green River Formation as indicated by low sulfate concentrations and high fluoride, lithium, and boron concentrations.
\nWater-quality samples were collected at 16 surface-water-quality sites on March 14, 2012, to determine field properties, major ions, and trace elements. Sodium was the dominant cation and concentrations increased steadily from upstream to downstream along the study reach. Calcium, magnesium, and potassium concentrations remained relatively stable along the study reach. Strontium concentrations were relatively stable along the study reach, whereas boron and lithium concentrations increased appreciably at site PC22031 and remained elevated to the end of the study reach.
\nLoading profiles were used to further refine areas of spring and groundwater input and streamflow gains and losses. Although there was a minor gain in streamflow from sites PC21543 to PC21816 (58 to 59 cubic feet per second (ft3/s) during March 2014), the observed increase in dissolved solids load indicated groundwater contribution to Piceance Creek between these two sites. From sites PC22737 to PC22980, dissolved solids load decreased, which was not observed in concentration profiles and indicated that streamflow loss occurred between these two sites. Barium, boron, lithium, and strontium loads showed similar patterns to that of the major ions along the study reach and indicated similar areas of groundwater gain and loss. Boron and lithium load were not observed to decrease in a similar pattern to that of barium and strontium load which would suggest the contribution to the stream from sources with similar chemistry to that of spring sites PCSP2 through PCSP5. Sodium, chloride, and bicarbonate loads increased and decreased along the study reach in a pattern similar to that of dissolved solids load. A chemical mass balance was used to estimate the amount of groundwater and (or) spring water that contributed to the observed changes in water quality along Piceance Creek. This analysis indicated only 5 percent spring water would need to reach Piceance Creek to result in the observed changes in water quality.
\nInstantaneous streamflow was measured from sites PC20133 to PC23721 during field reconnaissance (February 2012) and during synoptic sampling (March 2012). During both February and March, the study reach from sites PC20133 to PC23721 was a losing reach with net losses that ranged from 0.5 ft3/s (February) to 3 ft3/s (March). Observed changes in streamflow along the study reach helped to depict interactions between groundwater and surface water in the Alkali Flat area.
\nWater-quality samples were collected at five surface-water sites in December 2010 that were sampled as part of a previous USGS study in 2000. Water-quality data collected during December 2010 showed no appreciable difference from water-quality data collected during December 2000 at the five sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20155147","collaboration":"Prepared in cooperation with the Bureau of Land Management, White River Field Office","usgsCitation":"Thomas, J.C., 2015, Characterization of hydrology and water quality of Piceance Creek in the Alkali Flat area, Rio Blanco County, Colorado, March 2012: U.S. Geological Survey Scientific Investigations Report 2015–5147, 23 p., https://dx.doi.org/10.3133/sir20155147.","productDescription":"iv, 23 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065008","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":311970,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5147/sir20155147.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5147"},{"id":311969,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5147/coverthb.jpg"}],"country":"United States","state":"Colorado","county":"Rio Blanco County","otherGeospatial":"Alkali Flat Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109,\n 39\n ],\n [\n -109,\n 40.1\n ],\n [\n -107.8,\n 40.1\n ],\n [\n -107.8,\n 39\n ],\n [\n -109,\n 39\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
http://co.water.usgs.gov
Hydrodynamic-assessment data for the Kalamazoo River were collected by the U.S. Geological Survey (USGS) during 2012–14 to augment other hydrodynamic data-collection efforts by Enbridge Energy L.P. and the U.S. Environmental Protection Agency associated with the 2010 Enbridge Line 6B oil spill. Specifically, the USGS data-collection efforts were focused on additional background data needed for 2013–14 updates to Enbridge’s 2012 hydrodynamic and sediment-transport models for simulating resuspension and deposition of submerged oil. The main data-collection activities consisted of the following along the Kalamazoo River: (1) a survey done by use of a Real-Time Network Global Navigation Satellite System, (2) water-level measurements in impounded sections, (3) velocity, discharge, and bathymetry measurements at transects and stationary points along the oil-affected reach of the river and in Morrow Delta and Lake, (4) estimates of tributary inflows, and (5) suspended-sediment concentrations and particle-size data at USGS streamgages along the Kalamazoo River. The method used to estimate bed shear stress from stationary velocity data is described. Averaged transect-based velocity data that were processed to match model grids also are included. In addition to model inputs and checks, these hydrodynamic-related data were used in submerged oil containment and recovery operations focused in impoundments and designated sediment traps. This report contains a description of the scope and methods associated with the hydrodynamic data collection and supplementary files of the USGS data that were used in modeling activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151205","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Reneau, P.C., Soong, D.T., Hoard, C.J., and Fitzpatrick, F.A., 2015, Hydrodynamic-assessment data associated with the July 2010 Line 6B spill into the Kalamazoo River, Michigan, 2012–14: U.S. Geological Survey Open-File Report 2015–1205, 26 p., https://dx.doi.org/10.3133/ofr20151205.","productDescription":"Report: v, 26 p.; 4 Appendixes","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059841","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":311838,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1205/ofr20151205.pdf","text":"Report","size":"8.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1205"},{"id":311837,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1205/coverthb.jpg"},{"id":311868,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1205/downloads","text":"Report Appendixes - Downloads","size":"772 MB","description":"OFR 2015-1205"},{"id":311843,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1205/downloads/ofr20151205_appendixc.xlsx","text":"Appendix C - Tributary Inflows Estimates","size":"1.51 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1205"},{"id":311842,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1205/downloads/ofr20151205_appendixb/ofr20151205_appendixb.zip","text":"Appendix B - Velocity, Discharge and Bathymetry Data","size":"225 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2015-1205","linkHelpText":"Downloads include raw and processedDirector, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, Wisconsin 53562
http://wi.water.usgs.gov/
Sagebrush steppe ecosystems in the United States currently (2015) occur on only about one-half of their historical land area because of changes in land use, urban growth, and degradation of land, including invasions of non-native plants. The existence of many animal species depends on the existence of sagebrush steppe habitat. The greater sage-grouse (Centrocercus urophasianus) is a landscape-dependent bird that requires intact habitat and combinations of sagebrush and perennial grasses to exist. In addition, other sagebrush-obligate animals also have similar requirements and restoration of landscapes for greater sage-grouse also will benefit these animals. Once sagebrush lands are degraded, they may require restoration actions to make those lands viable habitat for supporting sagebrush-obligate animals.
\nLand managers do not have resources to restore all locations because of the extent of the restoration need and because some land uses are not likely to change, therefore, restoration decisions made at the landscape to regional scale may improve the effectiveness of restoration to achieve landscape and local restoration objectives. We present a landscape restoration decision tool intended to assist decision makers in determining landscape objectives, to identify and prioritize landscape areas where sites for priority restoration projects might be located, and to aid in ultimately selecting restoration sites guided by criteria used to define the landscape objectives. The landscape restoration decision tool is structured in five sections that should be addressed sequentially. Each section has a primary question or statement followed by related questions and statements to assist the user in addressing the primary question or statement. This handbook will guide decision makers through the important process steps of identifying appropriate questions, gathering appropriate data, developing landscape objectives, and prioritizing landscape patches where potential sites for restoration projects may be located. Once potential sites are selected, land managers can move to the site-specific decision tool to guide restoration decisions at the site level.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1418","isbn":"9781411339972","collaboration":"Prepared in cooperation with U.S. Joint Fire Science Program and National Interagency Fire Center, Bureau of Land Management, Great Northern Landscape Conservation, and Western Association of Fish and Wildlife Agencies","usgsCitation":"Pyke, D.A., Knick, S.T., Chambers, J.C., Pellant, M., Miller, R.F., Beck, J.L., Doescher, P.S., Schupp, E.W., Roundy,\nB.A., Brunson, M., and McIver, J.D., 2015, Restoration handbook for sagebrush steppe ecosystems with emphasis\non greater sage-grouse habitat—Part 2. Landscape level restoration decisions: U.S. Geological Survey Circular 1418,\n21 p., https://dx.doi.org/10.3133/cir1418.","productDescription":"vi, 21 p.","numberOfPages":"31","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061720","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":337389,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/cir1416","text":"Circular 1416 –","linkHelpText":"Restoration handbook for sagebrush steppe ecosystems with emphasis on greater sage-grouse habitat—Part 1. Concepts for understanding and applying restoration"},{"id":311669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1418/coverthb.jpg"},{"id":337390,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/cir1426","text":"Circular 1426 –","linkHelpText":"Restoration handbook for sagebrush steppe ecosystems with emphasis on greater sage-grouse habitat—Part 3. Site level restoration decisions"},{"id":311670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1418/circ1418.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1418 PDF"}],"country":"United States","state":"California, Idaho, Montana, Nevada, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.025390625,\n 37.75334401310656\n ],\n [\n -121.025390625,\n 48.980216985374994\n ],\n [\n -104.1064453125,\n 48.980216985374994\n ],\n [\n -104.1064453125,\n 37.75334401310656\n ],\n [\n -121.025390625,\n 37.75334401310656\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov
1. Spatially clustered populations create unique challenges for conservation monitoring programmes. Advances in methodology typically are focused on either the design or the modelling stage of the study but do not involve integration of both.
\n2. We integrate adaptive cluster sampling and spatial occupancy modelling by developing two models to handle the dependence induced by cluster sampling. We compare these models to scenarios using simple random sampling and traditional occupancy models via simulation and data collected on a rare plant species, Tamarix ramosissima, found in China.
\n3. Our simulations show a marked improvement in confidence interval coverage for the new models combined with cluster sampling compared to simple random sampling and traditional occupancy models, with greatest improvement in the presence of low detection probability and spatial correlation in occupancy.
\n4. Accounting for the design using the simple cluster random-effects model reduces bias considerably, and full spatial modelling reduces bias further, especially for large n when the spatial covariance parameters can be estimated reliably. Both new models build on the strength of occupancy modelling and adaptive sampling and perform at least as well, and often better, than occupancy modelling alone.
\n5. We believe our approach is unique and potentially useful for a variety of studies directed at patchily distributed, clustered or rare species exhibiting spatial variation.
","language":"English","publisher":"Wiley","publisherLocation":"Hoboken, NJ","doi":"10.1111/2041-210X.12499","collaboration":"Krishna Pacifici; Brian Reich; Michael Conroy","usgsCitation":"Pacifici, K., Reich, B.J., Dorazio, R., and Conroy, M.J., 2015, Occupancy estimation for rare species using a spatially-adaptive sampling design: Methods in Ecology and Evolution, v. 7, no. 3, p. 285-293, https://doi.org/10.1111/2041-210X.12499.","productDescription":"9 p.","startPage":"285","endPage":"293","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066383","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":471577,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.12499","text":"Publisher Index Page"},{"id":312878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-06","publicationStatus":"PW","scienceBaseUri":"56826b46e4b0a04ef4925b8b","contributors":{"authors":[{"text":"Pacifici, Krishna","contributorId":26564,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":583365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reich, Brian J.","contributorId":150871,"corporation":false,"usgs":false,"family":"Reich","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":583366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dorazio, Robert 0000-0003-2663-0468 bob_dorazio@usgs.gov","orcid":"https://orcid.org/0000-0003-2663-0468","contributorId":149286,"corporation":false,"usgs":true,"family":"Dorazio","given":"Robert","email":"bob_dorazio@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":583364,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conroy, Michael J.","contributorId":20871,"corporation":false,"usgs":false,"family":"Conroy","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":13266,"text":"Warnell School of Forestry and Natural Resources, The University of Georgia","active":true,"usgs":false}],"preferred":false,"id":583367,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168643,"text":"70168643 - 2015 - A hybrid model for mapping relative differences in belowground biomass and root: Shoot ratios using spectral reflectance, foliar N and plant biophysical data within coastal marsh","interactions":[],"lastModifiedDate":"2016-02-22T14:05:39","indexId":"70168643","displayToPublicDate":"2015-12-05T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A hybrid model for mapping relative differences in belowground biomass and root: Shoot ratios using spectral reflectance, foliar N and plant biophysical data within coastal marsh","docAbstract":"Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground biomass and % foliar N to explore biomass allocation in low diversity freshwater impounded peatlands (Sacramento-San Joaquin River Delta, CA, USA). We developed a hybrid modeling approach to relate remotely-sensed estimates of % foliar N (a surrogate for environmental N and plant available nutrients) and aboveground biomass to field-measured belowground biomass for species specific and mixed species models. We estimated up to 90% of variation in foliar N concentration using partial least squares (PLS) regression of full-spectrum field spectrometer reflectance data. Landsat 7 reflectance data explained up to 70% of % foliar N and 67% of aboveground biomass. Spectrally estimated foliar N or aboveground biomass had negative relationships with belowground biomass and root:shoot ratio in both Schoenoplectus acutus and Typha, consistent with a balanced growth model, which suggests plants only allocate growth belowground when additional nutrients are necessary to support shoot development. Hybrid models explained up to 76% of variation in belowground biomass and 86% of variation in root:shoot ratio. Our modeling approach provides a method for developing maps of spatial variation in wetland belowground biomass.
\n.
","language":"English","publisher":"Molecular Diversity Preservation International","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs71215837","usgsCitation":"Jessica L. O'Connell, Byrd, K.B., and Kelly, M., 2015, A hybrid model for mapping relative differences in belowground biomass and root: Shoot ratios using spectral reflectance, foliar N and plant biophysical data within coastal marsh: Remote Sensing, v. 12, no. 7, p. 16480-16503, https://doi.org/10.3390/rs71215837.","productDescription":"24 p.","startPage":"16480","endPage":"16503","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059688","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471578,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs71215837","text":"Publisher Index Page"},{"id":318288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mayberry Slough, Sacramento-San Joaquin River Delta, Sherman Island, Twitchell Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6,\n 38.2\n ],\n [\n -121.6,\n 38\n ],\n [\n -121.8,\n 38\n ],\n [\n -121.8,\n 38.2\n ],\n [\n -121.6,\n 38.2\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-05","publicationStatus":"PW","scienceBaseUri":"56cc3f3ce4b059daa47e4388","contributors":{"authors":[{"text":"Jessica L. O'Connell","contributorId":167125,"corporation":false,"usgs":false,"family":"Jessica L. O'Connell","affiliations":[{"id":7102,"text":"University of California, Berkeley, Dept. of Civil & Envir. Engineering","active":true,"usgs":false}],"preferred":false,"id":621138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":621137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelly, Maggi","contributorId":150360,"corporation":false,"usgs":false,"family":"Kelly","given":"Maggi","email":"","affiliations":[{"id":7102,"text":"University of California, Berkeley, Dept. of Civil & Envir. Engineering","active":true,"usgs":false}],"preferred":false,"id":621139,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159476,"text":"ofr20151206 - 2015 - Coastwide Reference Monitoring System (CRMS) Vegetation Volume Index: An assessment tool for marsh habitat focused on the three-dimensional structure at CRMS vegetation monitoring stations","interactions":[],"lastModifiedDate":"2015-12-07T08:57:56","indexId":"ofr20151206","displayToPublicDate":"2015-12-04T11:30:00","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":"2015-1206","title":"Coastwide Reference Monitoring System (CRMS) Vegetation Volume Index: An assessment tool for marsh habitat focused on the three-dimensional structure at CRMS vegetation monitoring stations","docAbstract":"A Vegetation Volume (VV) variable and Vegetation Volume Index (VVI) have been developed for the Coastwide Reference Monitoring System (CRMS). The VV is a measure of the amount of three-dimensional vegetative structure present at each CRMS site and is based on vegetation data collected annually. The VV uses 10 stations per CRMS site to quantify four vegetation layers: carpet, herbaceous, shrub, and tree. For each layer an overall live vegetation percent cover and height are collected to create a layer volume; the individual layer volumes are then summed to generate a site vegetation volume profile. The VV uses the two-dimensional area of live vegetative cover (in square meters) multiplied by the height (in meters) of each layer to produce a volume (in cubic meters) for each layer present in a 2-meter by 2-meter station. These layers are additive, yielding a total volume for each of the 10 herbaceous vegetation stations and an overall CRMS marsh site average.
\nThe VV is an assessment of the quantity of vegetation present and is directly related to plant community structure. The VV differs from the previously developed Floristic Quality Index (FQI) in that the VV makes no assumptions about vegetation quality, giving each species equal weight; the FQI scores species with consistent site fidelity more favorably. We adapted the VV data into the VVI, which creates a representative score for all coastal marsh types. A VV and VVI will be generated annually for CRMS site, project, and basin-level analysis. The index is designed to assess areas undergoing habitat conversion, creation, and disturbance and to document project effectiveness when goals are to create, increase, or maintain emergent vegetation.
\nThe VV and VVI will be used to establish trends, to make comparisons, and to evaluate restoration projects. Assessments that rely on the VVI will be included in appropriate Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) project reports and analyses. Implementation of the VVI will give coastal managers a new tool to design, implement, and monitor coastal restoration projects. A yearly trajectory of site, project, basin, and coastwide VVI will be posted on the CRMS Web site as data are collected. The primary purpose of the tool is to assess CWPPRA restoration project effectiveness, but it will also be useful in identifying areas in need of restoration and in coastwide vegetation assessments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151206","collaboration":"Prepared in cooperation with the Coastal Wetlands Planning, Protection and Restoration Act Task Force","usgsCitation":"Wood, W.B., Visser, J.M., Piazza, S.C., Sharp, L.A., Hundy, L.C., and McGinnis, T.E., 2015, Coastwide Reference Monitoring System (CRMS) Vegetation Volume Index—An assessment tool for marsh habitat focused on the three-dimensional structure at CRMS vegetation monitoring stations: U.S. Geological Survey Open-File Report 2015–1206, 14 p., https://dx.doi.org/10.3133/ofr20151206.","productDescription":"iv, 14 p.","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065105","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":311813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1206/coverthb.jpg"},{"id":311814,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1206/ofr20151206.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1206"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.944091796875,\n 28.92163128242129\n ],\n [\n -93.944091796875,\n 30.609549797190844\n ],\n [\n -88.96728515624999,\n 30.609549797190844\n ],\n [\n -88.96728515624999,\n 28.92163128242129\n ],\n [\n -93.944091796875,\n 28.92163128242129\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd
Lafayette, LA 70506
http://www.nwrc.usgs.gov/
Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we use a Bayesian network (BN) to test the importance of multiple interactions between barrier island geomorphic variables. This approach models complex interactions and handles uncertainty, which is intrinsic to future sea level rise, storminess, or anthropogenic processes (e.g., beach nourishment and other forms of coastal management). The BN was developed and tested at Assateague Island, Maryland/Virginia, USA, a barrier island with sufficient geomorphic and temporal variability to evaluate our approach. We tested the ability to predict dune height, beach width, and beach height variables using inputs that included longer-term, larger-scale, or external variables (historical shoreline change rates, distances to inlets, barrier width, mean barrier elevation, and anthropogenic modification). Data sets from three different years spanning nearly a decade sampled substantial temporal variability and serve as a proxy for analysis of future conditions. We show that distinct geomorphic conditions are associated with different long-term shoreline change rates and that the most skillful predictions of dune height, beach width, and beach height depend on including multiple input variables simultaneously. The predictive relationships are robust to variations in the amount of input data and to variations in model complexity. The resulting model can be used to evaluate scenarios related to coastal management plans and/or future scenarios where shoreline change rates may differ from those observed historically.
","language":"English","publisher":"Wiley","doi":"10.1002/2015JF003671","usgsCitation":"Gutierrez, B.T., Plant, N.G., Thieler, E.R., and Turecek, A., 2015, Using a Bayesian network to predict barrier island geomorphologic characteristics: Journal of Geophysical Research, v. 120, no. 12, p. 2452-2475, https://doi.org/10.1002/2015JF003671.","productDescription":"24 p.","startPage":"2452","endPage":"2475","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049088","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471579,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jf003671","text":"Publisher Index Page"},{"id":311958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland and Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.1025390625,\n 38.32334305552793\n ],\n [\n -75.157470703125,\n 38.23710146774334\n ],\n [\n -75.19317626953125,\n 38.18098951438852\n ],\n [\n -75.1959228515625,\n 38.13239618602296\n ],\n [\n -75.27008056640625,\n 38.05566088242076\n ],\n [\n -75.29754638671875,\n 38.00698412839117\n ],\n [\n -75.31539916992188,\n 37.97234987199528\n ],\n [\n -75.33187866210936,\n 37.93553306183642\n ],\n [\n -75.3826904296875,\n 37.90411590881245\n ],\n [\n -75.39779663085938,\n 37.86943313301452\n ],\n [\n -75.39230346679688,\n 37.84883250647402\n ],\n [\n -75.3497314453125,\n 37.86509663749013\n ],\n [\n -75.1739501953125,\n 38.11943249695316\n ],\n [\n -75.08880615234375,\n 38.3211882645322\n ],\n [\n -75.09292602539062,\n 38.32765244536364\n ],\n [\n -75.1025390625,\n 38.32334305552793\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","issue":"12","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-04","publicationStatus":"PW","scienceBaseUri":"5662b935e4b06a3ea36c679e","chorus":{"doi":"10.1002/2015jf003671","url":"http://dx.doi.org/10.1002/2015jf003671","publisher":"Wiley-Blackwell","authors":"Gutierrez Benjamin T., Plant Nathaniel G., Thieler E. Robert, Turecek Aaron","journalName":"Journal of Geophysical Research: Earth Surface","publicationDate":"12/2015"},"contributors":{"authors":[{"text":"Gutierrez, Benjamin T. 0000-0002-1879-7893 bgutierrez@usgs.gov","orcid":"https://orcid.org/0000-0002-1879-7893","contributorId":2924,"corporation":false,"usgs":true,"family":"Gutierrez","given":"Benjamin","email":"bgutierrez@usgs.gov","middleInitial":"T.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":581202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":581203,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thieler, E. Robert 0000-0003-4311-9717 rthieler@usgs.gov","orcid":"https://orcid.org/0000-0003-4311-9717","contributorId":2488,"corporation":false,"usgs":true,"family":"Thieler","given":"E.","email":"rthieler@usgs.gov","middleInitial":"Robert","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":581204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turecek, Aaron aturecek@usgs.gov","contributorId":4940,"corporation":false,"usgs":true,"family":"Turecek","given":"Aaron","email":"aturecek@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":581205,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191891,"text":"70191891 - 2015 - Underwater videography outperforms above-water videography and in-person surveys for monitoring the spawning of Devils Hole Pupfish","interactions":[],"lastModifiedDate":"2017-10-18T16:30:19","indexId":"70191891","displayToPublicDate":"2015-12-04T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Underwater videography outperforms above-water videography and in-person surveys for monitoring the spawning of Devils Hole Pupfish","docAbstract":"The monitoring of threatened and endangered fishes in remote environments continues to challenge fisheries biologists. The endangered Devils Hole Pupfish Cyprinodon diabolis, which is confined to a single warm spring in Death Valley National Park, California–Nevada, has recently experienced record declines, spurring renewed conservation and recovery efforts. In February–December 2010, we investigated the timing and frequency of spawning in the species' native habitat by using three survey methods: underwater videography, above-water videography, and in-person surveys. Videography methods incorporated fixed-position, solar-powered cameras to record continuous footage of a shallow rock shelf that Devils Hole Pupfish use for spawning. In-person surveys were conducted from a platform placed above the water's surface. The underwater camera recorded more overall spawning throughout the year (mean ± SE = 0.35 ± 0.06 events/sample) than the above-water camera (0.11 ± 0.03 events/sample). Underwater videography also recorded more peak-season spawning (March: 0.83 ± 0.18 events/sample; April: 2.39 ± 0.47 events/sample) than above-water videography (March: 0.21 ± 0.10 events/sample; April: 0.9 ± 0.32 events/sample). Although the overall number of spawning events per sample did not differ significantly between underwater videography and in-person surveys, underwater videography provided a larger data set with much less variability than data from in-person surveys. Fixed videography was more cost efficient than in-person surveys (\\$1.31 versus \\$605 per collected data-hour), and underwater videography provided more usable data than above-water videography. Furthermore, video data collection was possible even under adverse conditions, such as the extreme temperatures of the region, and could be maintained successfully with few study site visits. Our results suggest that self-contained underwater cameras can be efficient tools for monitoring remote and sensitive aquatic ecosystems.
","language":"English","publisher":"Informa UK Limited","doi":"10.1080/02755947.2015.1094155","usgsCitation":"Chaudoin, A.L., Feuerbacher, O., Bonar, S.A., and Barrett, P.J., 2015, Underwater videography outperforms above-water videography and in-person surveys for monitoring the spawning of Devils Hole Pupfish: North American Journal of Fisheries Management, v. 35, no. 6, p. 1252-1262, https://doi.org/10.1080/02755947.2015.1094155.","productDescription":"11 p.","startPage":"1252","endPage":"1262","ipdsId":"IP-069027","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":346915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Ash Meadows National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.29220902919769,\n 36.42484356033192\n ],\n [\n -116.29077136516571,\n 36.42484356033192\n ],\n [\n -116.29077136516571,\n 36.42601761391104\n ],\n [\n -116.29220902919769,\n 36.42601761391104\n ],\n [\n -116.29220902919769,\n 36.42484356033192\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-04","publicationStatus":"PW","scienceBaseUri":"59e8683ae4b05fe04cd4d222","contributors":{"authors":[{"text":"Chaudoin, Ambre L.","contributorId":197535,"corporation":false,"usgs":false,"family":"Chaudoin","given":"Ambre","email":"","middleInitial":"L.","affiliations":[{"id":127,"text":"Arizona Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":713691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feuerbacher, Olin","contributorId":187760,"corporation":false,"usgs":false,"family":"Feuerbacher","given":"Olin","affiliations":[],"preferred":false,"id":713692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":713549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barrett, Paul J.","contributorId":187761,"corporation":false,"usgs":false,"family":"Barrett","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":713693,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159928,"text":"70159928 - 2015 - Evaluation of a formula that categorizes female gray wolf breeding status by nipple size","interactions":[],"lastModifiedDate":"2017-09-08T10:20:22","indexId":"70159928","displayToPublicDate":"2015-12-03T15:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of a formula that categorizes female gray wolf breeding status by nipple size","docAbstract":"The proportion by age class of wild Canis lupus (Gray Wolf) females that reproduce in any given year remains unclear; thus, we evaluated the applicability to our long-term (1972–2013) data set of the Mech et al. (1993) formula that categorizes female Gray Wolf breeding status by nipple size and time of year. We used the formula to classify Gray Wolves from 68 capture events into 4 categories (yearling, adult non-breeder, former breeder, current breeder). To address issues with small sample size and variance, we created an ambiguity index to allow some Gray Wolves to be classed into 2 categories. We classified 20 nipple measurements ambiguously: 16 current or former breeder, 3 former or adult non-breeder, and 1 yearling or adult non-breeder. The formula unambiguously classified 48 (71%) of the nipple measurements; based on supplemental field evidence, at least 5 (10%) of these were incorrect. When used in conjunction with an ambiguity index we developed and with corrections made for classifications involving very large nipples, and supplemented with available field evidence, the Mech et al. (1993) formula provided reasonably reliable classification of breeding status in wild female Gray Wolves.
","language":"English","publisher":"Northeastern Naturalist","doi":"10.1656/045.022.0402","usgsCitation":"Barber-Meyer, S., and Mech, L.D., 2015, Evaluation of a formula that categorizes female gray wolf breeding status by nipple size: Northeastern Naturalist, v. 22, no. 4, p. 652-657, https://doi.org/10.1656/045.022.0402.","productDescription":"6 p.","startPage":"652","endPage":"657","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060793","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":311887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Superior National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.812744140625,\n 47.58764167941513\n ],\n [\n -90.0274658203125,\n 47.58764167941513\n ],\n [\n -90.0274658203125,\n 48.17707562779612\n ],\n [\n -91.812744140625,\n 48.17707562779612\n ],\n [\n -91.812744140625,\n 47.58764167941513\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-18","publicationStatus":"PW","scienceBaseUri":"566167b9e4b06a3ea36c565f","contributors":{"authors":[{"text":"Barber-Meyer, Shannon M. 0000-0002-3048-2616 sbarber-meyer@usgs.gov","orcid":"https://orcid.org/0000-0002-3048-2616","contributorId":147904,"corporation":false,"usgs":true,"family":"Barber-Meyer","given":"Shannon M.","email":"sbarber-meyer@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":581093,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mech, L. David 0000-0003-3944-7769 david_mech@usgs.gov","orcid":"https://orcid.org/0000-0003-3944-7769","contributorId":2518,"corporation":false,"usgs":true,"family":"Mech","given":"L.","email":"david_mech@usgs.gov","middleInitial":"David","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":581094,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159914,"text":"70159914 - 2015 - Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations","interactions":[],"lastModifiedDate":"2017-01-18T09:55:38","indexId":"70159914","displayToPublicDate":"2015-12-03T15:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations","docAbstract":"Accurately estimating aboveground vegetation biomass productivity is essential for local ecosystem assessment and best land management practice. Satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. A 250-m grassland biomass productivity map for the Greater Platte River Basin had been developed based on the relationship between Moderate Resolution Imaging Spectroradiometer (MODIS) GSN and Soil Survey Geographic (SSURGO) annual grassland productivity. However, the 250-m MODIS grassland biomass productivity map does not capture detailed ecological features (or patterns) and may result in only generalized estimation of the regional total productivity. Developing a high or moderate spatial resolution (e.g., 30-m) productivity map to better understand the regional detailed vegetation condition and ecosystem services is preferred. The 30-m Landsat data provide spatial detail for characterizing human-scale processes and have been successfully used for land cover and land change studies. The main goal of this study is to develop a 30-m grassland biomass productivity estimation map for central Nebraska, leveraging 250-m MODIS GSN and 30-m Landsat data. A rule-based piecewise regression GSN model based on MODIS and Landsat (r = 0.91) was developed, and a 30-m MODIS equivalent GSN map was generated. Finally, a 30-m grassland biomass productivity estimation map, which provides spatially detailed ecological features and conditions for central Nebraska, was produced. The resulting 30-m grassland productivity map was generally supported by the SSURGO biomass production map and will be useful for regional ecosystem study and local land management practices.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2015.10.018","usgsCitation":"Gu, Y., and Wylie, B.K., 2015, Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations: Remote Sensing of Environment, v. 171, p. 291-298, https://doi.org/10.1016/j.rse.2015.10.018.","productDescription":"8 p.","startPage":"291","endPage":"298","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068578","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":311883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.162353515625,\n 41.00477542222949\n ],\n [\n -100.162353515625,\n 42.52879629320373\n ],\n [\n -96.624755859375,\n 42.52879629320373\n ],\n [\n -96.624755859375,\n 41.00477542222949\n ],\n [\n -100.162353515625,\n 41.00477542222949\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"171","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"566167b8e4b06a3ea36c565b","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":581015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":581016,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159003,"text":"sir20155151 - 2015 - Regression Equations for Monthly and Annual Mean and Selected Percentile Streamflows for Ungaged Rivers in Maine","interactions":[],"lastModifiedDate":"2015-12-31T10:46:01","indexId":"sir20155151","displayToPublicDate":"2015-12-03T12:00:00","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":"2015-5151","title":"Regression Equations for Monthly and Annual Mean and Selected Percentile Streamflows for Ungaged Rivers in Maine","docAbstract":"In an effort to delineate hydrologic conditions in Maine, the U.S. Geological Survey, in cooperation with the Maine Department of Transportation, used streamflow data to develop dependent variables for 130 regression equations for estimating monthly and annual mean and 1, 5, 10, 25, 50, 75, 90, 95, and 99 percentile streamflows for ungaged, unregulated rivers in Maine. Daily streamflow data from 24 rural unregulated basins with drainage areas between 14.9 and 1,419 square miles in Maine and northern New Hampshire were used in the derivation of the equations. Streamflow data collected from October 1, 1982, through September 30, 2012, were used to derive the dependent variables for this study to represent current [2015] hydrologic conditions in Maine and northern New Hampshire. Weighted least squares regression techniques were used to derive the final coefficients and measures of uncertainty for the regression equations. Eight basin characteristics serve as the explanatory variables: drainage area, distance from the coast, mean and maximum basin elevation, mean basin slope, mean basin percentage of hydrologic soil group A, fraction of sand and gravel aquifers, and percentage of open water.
\nThe largest average errors of prediction are associated with regression equations for the lowest streamflows derived for months during which the lowest streamflows of the year occur (such as the 5 and 1 monthly percentiles for August and September). The regression equations have been derived on the basis of streamflow and basin characteristics data for unregulated, rural drainage basins without substantial streamflow or drainage modifications (for example, diversions and (or) regulation by dams or reservoirs, tile drainage, irrigation, channelization, and impervious paved surfaces), therefore using the equations for regulated or urbanized basins with substantial streamflow or drainage modifications will yield results of unknown error. Input basin characteristics derived using techniques or datasets other than those documented in this report or using values outside the ranges used to develop these regression equations also will yield results of unknown error.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155151","collaboration":"Prepared in cooperation with the Maine Department of Transportation","usgsCitation":"Dudley, R.W., 2015, Regression equations for monthly and annual mean and selected percentile streamflows for ungaged rivers in Maine (ver. 1.1, December 21, 2015): U.S. Geological Survey Scientific Investigations Report 2015–5151, 35 p., https://dx.doi.org/10.3133/sir20155151.","productDescription":"viii, 35 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066284","costCenters":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"links":[{"id":311685,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5151/sir20155151.pdf","text":"Report","size":"8.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5151"},{"id":312572,"rank":3,"type":{"id":25,"text":"Version 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\"}}]}","edition":"Version 1: Originally posted December 3, 2015; Version 1.1: December 21, 2015","contact":"Director, New England Water Science Center
U.S. Geological Survey
196 Whitten Road
Augusta, ME 04330
Or visit our Web site at:
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Estimating the size of bird populations is central to effective conservation planning and prudent management. I updated estimated regional bird populations for the East Gulf Coastal Plain of Mississippi using data from 275 North American Breeding Bird Surveys from 2009 to 2013. However, regional bird populations estimated from count surveys of breeding birds may be biased due to lack of empirical knowledge of the distance at which a species is effectively detected and the probability of detecting a species if it is present. I used data recorded within two distance classes (0–50 m and >50–400 m) and three 1-min time intervals on 130 Breeding Bird Surveys to estimate detection probability and effective detection distance for 77 species. Incorporating these empirical estimates of detection probability and detection distance resulted in estimated regional populations for these species that were markedly greater than regional populations estimated without species-specific estimates of detection parameters. Using the same Breeding Bird Survey data, I also estimated probability of site occupancy for 66 species and extrapolated this to the proportion of area occupied in the East Gulf Coastal Plain of Mississippi. I combined the area occupied with the reported range of breeding territory size for 54 species to obtain independent estimates of regional bird populations. Although the true population of these species is unknown, estimated populations that incorporated empirical estimates of detection probability and detection distance were more likely to be within the range of independently estimated, occupancy-based, regional population estimates than were population estimates that lacked empirical detection and distance information.
","language":"English","doi":"10.1111/jofo.12118","usgsCitation":"Twedt, D.J., 2015, Estimating regional landbird populations from enhanced North American Breeding Bird Surveys: Journal of Field Ornithology, v. 86, no. 4, p. 352-368, https://doi.org/10.1111/jofo.12118.","productDescription":"17 p.","startPage":"352","endPage":"368","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058291","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":311839,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.39599609375,\n 30.363396239603716\n ],\n [\n -88.472900390625,\n 31.868227816180674\n ],\n [\n -88.099365234375,\n 34.89944783005726\n ],\n [\n -88.209228515625,\n 35.007502842952896\n ],\n [\n -90.15380859375,\n 34.99850370014629\n ],\n [\n -90.054931640625,\n 34.89494244739732\n ],\n [\n -90.0933837890625,\n 34.773203753940734\n ],\n [\n -90.0714111328125,\n 34.619647359797185\n ],\n [\n -90.0274658203125,\n 34.492975402501536\n ],\n [\n -89.9176025390625,\n 34.17999758688084\n ],\n [\n -89.879150390625,\n 33.89321737944089\n ],\n [\n -89.8187255859375,\n 33.5459730276919\n ],\n [\n -89.9560546875,\n 33.23409295522519\n ],\n [\n -90.24169921875,\n 32.90726224488304\n ],\n [\n -90.47241210937499,\n 32.62549671451373\n ],\n [\n -90.9613037109375,\n 32.310348764525806\n ],\n [\n -91.175537109375,\n 32.194208672875355\n ],\n [\n -91.109619140625,\n 32.091882620021785\n ],\n [\n -91.19750976562499,\n 31.970803930433096\n ],\n [\n -91.318359375,\n 31.840232667909365\n ],\n [\n -91.47216796875,\n 31.700129553985924\n ],\n [\n -91.58203125,\n 31.606609719226917\n ],\n [\n -91.505126953125,\n 31.522361470421437\n ],\n [\n -91.58203125,\n 31.38177878211098\n ],\n [\n -91.571044921875,\n 31.28793989264176\n ],\n [\n -91.68090820312499,\n 31.25037814985571\n ],\n [\n -91.60400390625,\n 31.11879439598953\n ],\n [\n -91.636962890625,\n 30.977609093348686\n ],\n [\n -89.71435546875,\n 31.005862904624205\n ],\n [\n -89.84619140625,\n 30.770159115784214\n ],\n [\n -89.835205078125,\n 30.609549797190844\n ],\n [\n -89.703369140625,\n 30.439202087235607\n ],\n [\n -89.56054687499999,\n 30.135626231134612\n ],\n [\n -89.3408203125,\n 30.278044377800153\n ],\n [\n -89.000244140625,\n 30.38235321766959\n ],\n [\n -88.83544921874999,\n 30.38235321766959\n ],\n [\n -88.681640625,\n 30.315987718557867\n ],\n [\n -88.61572265625,\n 30.363396239603716\n ],\n [\n -88.48388671874999,\n 30.29701788337205\n ],\n [\n -88.39599609375,\n 30.363396239603716\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"86","issue":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-19","publicationStatus":"PW","scienceBaseUri":"566167b8e4b06a3ea36c565d","contributors":{"authors":[{"text":"Twedt, Daniel J. 0000-0003-1223-5045 dtwedt@usgs.gov","orcid":"https://orcid.org/0000-0003-1223-5045","contributorId":398,"corporation":false,"usgs":true,"family":"Twedt","given":"Daniel","email":"dtwedt@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":580929,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70159537,"text":"ofr20151211 - 2015 - California State Waters Map Series — Offshore of Fort Ross, California","interactions":[],"lastModifiedDate":"2022-04-18T21:34:02.125304","indexId":"ofr20151211","displayToPublicDate":"2015-12-03T08:00:00","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":"2015-1211","title":"California State Waters Map Series — Offshore of Fort Ross, California","docAbstract":"In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within the 3-nautical-mile limit of California’s State Waters. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data, acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow subsurface geology.
\nThe Offshore of Fort Ross map area is located in northern California, on the Pacific coast of Sonoma County, about 90 km north of San Francisco and 60 km south of Point Arena. The onshore part of the map area is largely undeveloped, used primarily for grazing and recreation; the small town of Jenner (population, 136), located at the mouth of the Russian River, is the largest cultural center. The coast and shoreline are rugged and scenic, characterized by rocky promontories, kelp-rich coves, and nearshore rocks and sea stacks. U.S. Highway 1 extends along the coast through the map area, crossing the Russian River and passing through Sonoma Coast State Park and Fort Ross State Historic Park.
\nThe Offshore of Fort Ross map area is cut by the northwest-striking San Andreas Fault, the right-lateral transform boundary between the North American and Pacific plates. The fault intersects the shoreline a few kilometers south of Fort Ross at Timber Gulch, and it juxtaposes Jurassic, Cretaceous, Paleocene, and Eocene rocks of the Franciscan Complex to the northeast and Tertiary sedimentary rocks to the southwest. In this area, the San Andreas Fault has an estimated slip rate of 17 to 24 mm/yr. The devastating great 1906 California earthquake (M7.8) is thought to have nucleated on the San Andreas Fault offshore of San Francisco, about 90 km to the south, with the rupture extending northward through the Offshore of Fort Ross map area to the south flank of Cape Mendocino. Approximately 3.6 m of lateral offset occurred at Timber Gulch during this event.
\nThe San Andreas Fault has an important influence on coastal geomorphology. The coastline in the northern part of the map area, southwest of the onshore San Andreas Fault, is characterized by steep shoreline bluffs and as many as four uplifted, relatively flat marine terraces that range in elevation from about 15 to 100 m. Northeast of the San Andreas Fault, about 12 km of coastline is marked by steep, landslide-prone cliffs that commonly are 200 to 300 m high.
\nThe mouth of the Russian River and its estuary cut through the steep coastal topography in the southern part of the Offshore of Fort Ross map area. The Russian River drains a large watershed (3,470 km2), and it has an annual discharge of about 2 km3 (1,600,000 acre-feet) and an annual sediment load of about 900,000 metric tons. The map area is part of the Russian River littoral cell, in which the predominant longshore drift is to the south. Small pocket beaches are most common along the shoreline, but longer linear beaches are present near the mouth of the Russian River.
\nThe seafloor in the north half of the map area is characterized by rocky outcrops of Tertiary sedimentary rocks. The rugged nearshore zone and the inner shelf area (to water depths of about 50 m) typically slopes gently seaward, whereas the smooth midshelf area within California’s State Waters (about 50 to 85 m deep) is relatively flat. In contrast, the nearshore to midshelf area in the south half of the map area, which lies directly offshore of the mouth of the Russian River, has a more uniform, relatively flat slope. Shallow-marine and shelf sediments were deposited in the last about 21,000 years during the sea-level rise that followed the Last Glacial Maximum (LGM). Sea level was about 125 m lower than present during the LGM, at which time the entire Offshore of Fort Ross map area was emergent and the shoreline was about 20 km west of its present location.
\nCirculation over the continental shelf in the map area (and in the broader northern California region) is dominated by the southward-flowing California Current, the eastern limb of the North Pacific Gyre. Associated upwelling brings cool, nutrient-rich waters to the surface, resulting in high biological productivity. The current flow generally is southeastward during the spring and summer; however, during the fall and winter, the otherwise persistent northwest winds are sometimes weak or absent, causing the California Current to move farther offshore and the Davidson Current, a weaker, northward-flowing countercurrent, to become active.
\nThroughout the year, this part of the northern California coast is exposed to four wave climate regimes: the north Pacific swell, the southern swell, northwest wind waves, and local wind waves. The north Pacific swell dominates in winter months (typically November through March). During summer months, the largest waves come from the southern swell, generated by storms in the south Pacific and offshore of Central America. Northwest wind waves affect the coast throughout the year, whereas local wind waves are most common from October to April.
\nPotential marine benthic habitat types in the Offshore of Fort Ross map area include unconsolidated continental-shelf sediments, mixed continental-shelf substrate, and hard continental-shelf substrate. Rocky shelf outcrops and rubble are considered the primary habitat type for rockfish and lingcod, both of which are recreationally and commercially important species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151211","usgsCitation":"Johnson, S.Y., Dartnell, P., Golden, N.E., Hartwell, S.R., Erdey, M.D., Greene, H.G., Cochrane, G.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Watt, J.T., Krigsman, L.M., Sliter, R.W., Lowe, E.N., and Chin, J.L. (S.Y. Johnson and S.A. Cochran, eds.), 2015, California State Waters Map Series — Offshore of Fort Ross, California: U.S. Geological Survey Open-File Report 2015–1211, pamphlet 37 p., 10 sheets, scale 1:24,000, https://dx.doi.org/10.3133/ofr20151211.","productDescription":"Pamphlet: iv, 37 p.; 10 Sheets: 47.0 x 36.0 inches or smaller; Data Catalog; Metadata","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-056321","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399011,"rank":21,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_103728.htm"},{"id":311811,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_sheet9.pdf","text":"Sheet 9","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Sheet 9 PDF","linkHelpText":"Local (Offshore of Fort Ross Map Area) and Regional (Offshore from Salt Point to Drakes Bay) Shallow-Subsurface Geology and Structure, California By Samuel Y. Johnson, Stephen R. Hartwell, Janet T. Watt, and Ray W. Sliter"},{"id":311810,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_sheet8.pdf","text":"Sheet 8","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Sheet 8 PDF","linkHelpText":"Seismic-Reflection Profiles, Offshore of Fort Ross Map Area, California by Samuel Y. Johnson, Ray W. Sliter, Stephen R. Hartwell, and John L. Chin"},{"id":311809,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_sheet7.pdf","text":"Sheet 7","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Sheet 7 PDF","linkHelpText":"Potential Marine Benthic Habitats, Offshore of Fort Ross Map Area, California By Bryan E. Dieter, H. Gary Greene, Charles A. Endris, Mercedes D. Erdey, and Erik N. Lowe"},{"id":311819,"rank":17,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/of/2015/1088/","text":"Open-File Report 2015–1088","linkHelpText":"California State Waters Map Series—Offshore of Tomales Point, California, by Samuel Y. Johnson and others."},{"id":311818,"rank":16,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/of/2015/1041/","text":"Open-File Report 2015–1041","linkHelpText":"California State Waters Map Series—Drakes Bay and Vicinity, California, by Janet T. Watt and others."},{"id":311817,"rank":15,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/781/","text":"Data Series 781","linkHelpText":"California State Waters Map Series Data Catalog"},{"id":311816,"rank":14,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_metadata.html","linkFileType":{"id":5,"text":"html"}},{"id":311815,"rank":13,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/ds/781/OffshoreFortRoss/data_catalog_OffshoreFortRoss.html","text":"Data Catalog","linkFileType":{"id":5,"text":"html"},"linkHelpText":"The GIS data layers for this map are accessible from “Data Catalog—Offshore Fort Ross, California,” which is part of California State Waters Map Series Data Catalog. Each GIS data file is listed with a brief description, a small image, and links to the metadata files and the downloadable data files."},{"id":311812,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_sheet10.pdf","text":"Sheet 10","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Sheet 10 PDF","linkHelpText":"Offshore and Onshore Geology and Geomorphology, Offshore of Fort Ross Map Area, California By Samuel Y. Johnson, Stephen R. Hartwell, and Michael W. Manson"},{"id":311808,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_sheet6.pdf","text":"Sheet 6","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Sheet 6 PDF","linkHelpText":"Ground-Truth Studies, Offshore of Fort Ross Map Area, California By Nadine E. Golden, Guy R. Cochrane, and Lisa M. Krigsman"},{"id":311807,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_sheet5.pdf","text":"Sheet 5","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Sheet 5 PDF","linkHelpText":"Seafloor Character, Offshore of Fort Ross Map Area, California By Mercedes D. Erdey and Guy R. Cochrane"},{"id":311803,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Sheet 1 PDF","linkHelpText":"Colored Shaded-Relief Bathymetry, Offshore of Fort Ross Map Area, California By Peter Dartnell and Rikk G. Kvitek"},{"id":311801,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1211/coverthb.jpg"},{"id":311802,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Pamphlet PDF"},{"id":311806,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_sheet4.pdf","text":"Sheet 4","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Sheet 4 PDF","linkHelpText":"Data Integration and Visualization, Offshore of Fort Ross Map Area, California By Peter Dartnell"},{"id":311805,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_sheet3.pdf","text":"Sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Sheet 3 PDF","linkHelpText":"Acoustic Backscatter, Offshore of Fort Ross Map Area, California By Peter Dartnell, Mercedes D. Erdey, and Rikk G. Kvitek"},{"id":311804,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1211/ofr20151211_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1211 Sheet 2 PDF","linkHelpText":"Shaded-Relief Bathymetry, Offshore of Fort Ross Map Area, California By Peter Dartnell and Rikk G. Kvitek"},{"id":311820,"rank":18,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20151140","text":"Open-File Report 2015–1140","linkHelpText":"California State Waters Map Series—Offshore of Bodega Head, California, by Samuel Y. Johnson and others."},{"id":311821,"rank":19,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/of/2015/1098/","text":"Open-File Report 2015–1098","linkHelpText":"California State Waters Map Series—Offshore of Salt Point, California, by Samuel Y. Johnson and others."},{"id":311822,"rank":20,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/of/2015/1114/","text":"Open-File Report 2015–1114","linkHelpText":"California State Waters Map Series—Offshore of Point Reyes and Vicinity, California, by Janet T. Watt and others."}],"scale":"24000","country":"United States","state":"California","otherGeospatial":"Fort Ross","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.3056,\n 38.3967\n ],\n [\n -123.3056,\n 38.5558\n ],\n [\n -123.1028,\n 38.5558\n ],\n [\n -123.1028,\n 38.3967\n ],\n [\n -123.3056,\n 38.3967\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
As Arctic regions warm and frozen soils thaw, the large organic carbon pool stored in permafrost becomes increasingly vulnerable to decomposition or transport. The transfer of newly mobilized carbon to the atmosphere and its potential influence upon climate change will largely depend on the degradability of carbon delivered to aquatic ecosystems. Dissolved organic carbon (DOC) is a key regulator of aquatic metabolism, yet knowledge of the mechanistic controls on DOC biodegradability is currently poor due to a scarcity of long-term data sets, limited spatial coverage of available data, and methodological diversity. Here, we performed parallel biodegradable DOC (BDOC) experiments at six Arctic sites (16 experiments) using a standardized incubation protocol to examine the effect of methodological differences commonly used in the literature. We also synthesized results from 14 aquatic and soil leachate BDOC studies from across the circum-arctic permafrost region to examine pan-arctic trends in BDOC.
An increasing extent of permafrost across the landscape resulted in higher DOC losses in both soil and aquatic systems. We hypothesize that the unique composition of (yedoma) permafrost-derived DOC combined with limited prior microbial processing due to low soil temperature and relatively short flow path lengths and transport times, contributed to a higher overall terrestrial and freshwater DOC loss. Additionally, we found that the fraction of BDOC decreased moving down the fluvial network in continuous permafrost regions, i.e. from streams to large rivers, suggesting that highly biodegradable DOC is lost in headwater streams. We also observed a seasonal (January–December) decrease in BDOC in large streams and rivers, but saw no apparent change in smaller streams or soil leachates. We attribute this seasonal change to a combination of factors including shifts in carbon source, changing DOC residence time related to increasing thaw-depth, increasing water temperatures later in the summer, as well as decreasing hydrologic connectivity between soils and surface water as the thaw season progresses. Our results suggest that future climate warming-induced shifts of continuous permafrost into discontinuous permafrost regions could affect the degradation potential of thaw-released DOC, the amount of BDOC, as well as its variability throughout the Arctic summer. We lastly recommend a standardized BDOC protocol to facilitate the comparison of future work and improve our knowledge of processing and transport of DOC in a changing Arctic.
The development of robust modelling techniques to derive inferences from large-scale migratory bird monitoring data at appropriate scales has direct relevance to their management. The Integrated Waterbird Management and Monitoring programme (IWMM) represents one of the few attempts to monitor migrating waterbirds across entire flyways using targeted local surveys. This dataset included 13,208,785 waterfowl (eight Anas species) counted during 28,000 surveys at nearly 1,000 locations across the eastern United States between autumn 2010 and spring 2013 and was used to evaluate potential predictors of waterfowl abundance at the wetland scale. Mixed-effects, log-linear models of local abundance were built for the Atlantic and Mississippi flyways during spring and autumn migration to identify factors relating to habitat structure, forage availability, and migration timing that influence target dabbling duck species abundance. Results indicated that migrating dabbling ducks responded differently to environmental factors. While the factors identified demonstrated a high degree of importance, they were inconsistent across species, flyways and seasons. Furthermore, the direction and magnitude of the importance of each covariate group considered here varied across species. Given our results, actionable policy recommendations are likely to be most effective if they consider species-level variation within targeted taxonomic units and across management areas. The methods implemented here can easily be applied to other contexts, and serve as a novel investigation into local-level population patterns using data from broad-scale monitoring programmes.
","language":"English","publisher":"Wildfowl & Wetlands Trust","publisherLocation":"Slimbridge","collaboration":"The Nature Conservancy; U.S. Fish and Wildlife Service","usgsCitation":"Aagaard, K., Crimmins, S.M., Thogmartin, W.E., Tavernia, B., and Lyons, J., 2015, Evaluating predictors of local dabbling duck abundance during migration: Managing the spectrum of conditions faced by migrants: Wildfowl, v. 65, p. 100-120.","productDescription":"21 p.","startPage":"100","endPage":"120","numberOfPages":"21","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066133","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":312659,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":312658,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://wildfowl.wwt.org.uk/index.php/wildfowl/article/view/2628/1750","linkFileType":{"id":1,"text":"pdf"}}],"country":"United 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Data were collected monthly from mid-July to late October of 2013, with an additional dataset collected in June of 2014. Inversion of the ER data shows the development through time of multiple resistive anomalies in the subsurface, which corroborating data suggest are driven by changes in total dissolved solids (TDS) localized in preferential flow pathways. Sensitivity analyses on a synthetic model of the site suggest that the anomalies would need to be at least several meters in diameter to be adequately resolved by the inversions. The existence of preferential flow paths would have a critical impact on the extent of attenuation mechanisms at the site, and their further characterization could be used to parameterize reactive transport models in developing quantitative predictions of remediation strategies.
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A consideration of data limitations associated with settlement patterns increases the range to 6.3–6.7, with a preferred estimate of 6.5. Although magnitude estimates for historical earthquakes are inevitably uncertain, we conclude that, at a minimum, a lower-magnitude estimate represents a credible alternative interpretation of available data. We further discuss implications of our results for probabilistic seismic-hazard assessment from partially creeping faults.
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