This report, prepared by the U.S. Geological Survey in cooperation with the Kansas Department of Agriculture, Division of Water Resources, presents derivative statistics of 2013 irrigation water use in Kansas. The published regional and county-level statistics from the previous 4 years (2009–12) are shown with the 2013 statistics and are used to calculate a 5-year average. An overall Kansas average and regional averages also are calculated and presented. Total reported irrigation water use in 2013 was 3.3 million acre-feet of water applied to 3.0 million irrigated acres.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds981","collaboration":"Prepared in cooperation with the Kansas Department of Agriculture, Division of Water Resources","usgsCitation":"Lanning-Rush, J.L., 2016, Irrigation water use in Kansas, 2013: U.S. Geological Survey Data Series 981, 12 p., https://dx.doi.org/10.3133/ds981.","productDescription":"Report: iv, 12 p.; Tables 6-12; Appendix: 2 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070156","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":318889,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0981/ds981.pdf","text":"Report","size":"617 kB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 981"},{"id":318890,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/0981/downloads/ds981_tables6to12.xlsx","text":"Tables 6–12","size":"80 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 981 Tables "},{"id":318888,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0981/coverthb_new.jpg"},{"id":318893,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0981/downloads/ds981_appendix.pdf","text":"Water-Use Report","size":"1.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 981 Appendix"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.29541015625,\n 39.99395569397331\n ],\n [\n -102.06298828125,\n 40.01078714046552\n ],\n [\n -102.052001953125,\n 36.98500309285596\n ],\n [\n -94.603271484375,\n 36.99377838872517\n ],\n [\n -94.6142578125,\n 39.13006024213511\n ],\n [\n -94.76806640624999,\n 39.172658670429946\n ],\n [\n -94.82299804687499,\n 39.2407625100131\n ],\n [\n -94.910888671875,\n 39.29179704377487\n ],\n [\n -94.8779296875,\n 39.38526381099777\n ],\n [\n -94.976806640625,\n 39.436192999314095\n ],\n [\n -95.086669921875,\n 39.53793974517628\n ],\n [\n -95.042724609375,\n 39.57182223734374\n ],\n [\n -95.042724609375,\n 39.63953756436671\n ],\n [\n -94.9658203125,\n 39.73253798438173\n ],\n [\n -94.910888671875,\n 39.73253798438173\n ],\n [\n -94.888916015625,\n 39.78321267821705\n ],\n [\n -94.910888671875,\n 39.842286020743394\n ],\n [\n -94.95483398437499,\n 39.8928799002948\n ],\n [\n -95.03173828125,\n 39.90973623453719\n ],\n [\n -95.06469726562499,\n 39.87601941962116\n ],\n [\n -95.174560546875,\n 39.926588421909436\n ],\n [\n -95.29541015625,\n 39.99395569397331\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
Digital flood-inundation maps for a 12.5-mile reach of the Big Papillion Creek from 0.6 mile upstream from the State Street Bridge to the 72nd Street Bridge in Omaha, Nebraska, were created by the U.S. Geological Survey (USGS) in cooperation with the Papio-Missouri River Natural Resources District. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Big Papillion Creek at Fort Street at Omaha, Nebraska (station 06610732). Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at this site.
\nFlood profiles were computed for the 12.5-mile reach by means of a one-dimensional step-backwater model. The model was calibrated by using the current (2015) stage-discharge relation at streamgages for the Big Papillion Creek at Fort Street at Omaha, Nebraska, and the Big Papillion Creek at Q Street at Omaha, Nebraska. The hydraulic model was then used to compute 15 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum for the Big Papillion Creek at Fort Street and ranging from 18 ft (or near bankfull) to 32 ft, which exceeds the “major flood stage” as defined by the National Weather Service. The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging data having a 1.18-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each flood stage (water level).
\nThe availability of these flood-inundation maps, along with Internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155152","collaboration":"Prepared in cooperation with the Papio-Missouri River Natural Resources District","usgsCitation":"Strauch, K.R., Dietsch, B.J., and Anderson, K.J., 2016, Flood-inundation maps for a 12.5-mile reach of Big Papillion Creek at Omaha, Nebraska: U.S. Geological Survey Scientific Investigations Report 2015–5152, 11 p., https://dx.doi.org/10.3133/sir20155152.","productDescription":"Report: v, 11 p.; Datasets; Metadata","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066029","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":314625,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_river_areas_GRIDS.zip","text":"River area GRIDS","size":"52.9 MB","description":"SIR 2015–5152 River area GRIDS"},{"id":314626,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_river_areas_shapefiles.zip","text":"River area shapefiles","size":"2.2 MB","description":"SIR 2015–5152 River area shapefiles"},{"id":314628,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_metadata_BigPapio_at_Fort_FIM_GRID.txt","text":"GRID metadata","size":"20.0 kb","description":"SIR 2015–5152 GRID metadata"},{"id":314629,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_metadata_BigPapio_at_Fort_FIM_shapefile.txt","text":"Shapefile metadata","size":"20.0 kb","description":"SIR 2015–5152 Shapefile metadata"},{"id":314466,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152.pdf","text":"Report","size":"2.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015–5152"},{"id":314622,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_levee_areas_GRIDS.zip","text":"Levee area GRIDS","size":"1.6 MB","description":"SIR 2015–5152 Levee area GRIDS"},{"id":314623,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_levee_areas_shapefiles.zip","text":"Levee area shapefiles","size":"144 kb","description":"SIR 2015–5152 Levee area shapefiles"},{"id":314465,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5152/coverthb.jpg"}],"country":"United States","state":"Nebraska","city":"Omaha","otherGeospatial":"Big Papillion Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.16676330566406,\n 41.23702755320388\n ],\n [\n -96.16676330566406,\n 41.35052580597025\n ],\n [\n -96.01020812988281,\n 41.35052580597025\n ],\n [\n -96.01020812988281,\n 41.23702755320388\n ],\n [\n -96.16676330566406,\n 41.23702755320388\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Non-native fish introductions are a major threat to biodiversity and fisheries, and occur through numerous pathways that vary regionally in importance. A key strategy for managing invasions is to focus prevention efforts on pathways posing the greatest risk of future introductions. We identified high-risk pathways for fish establishment in the Mid-Atlantic region of the United States based on estimates of probability of establishment and records of previous introductions, which were considered in the context of emerging socioeconomic trends. We used estimates of propagule pressure, species’ environmental tolerance, and size of species pool to assess the risk of establishment by pathway. Pathways varied considerably in historic importance and species composition, with the majority of species introduced intentionally via stocking (primarily for sport, forage, or biocontrol) or bait release. Bait release, private stocking, illegal introductions intended to establish reproducing populations (e.g., of sport fish), aquaculture, and the sale of live organisms all create risks for future invasions in the Mid-Atlantic region. Of these pathways, bait release probably poses the greatest risk of introductions for the Mid-Atlantic region because propagule pressure is moderate, most released species are tolerant of local environmental conditions, and the pool of species available for transplantation is large. Our findings differ considerably from studies in other regions (e.g., bait release is a dominant pathway in the Mid-Atlantic region, whereas illegal introduction of sport fish is dominant in the western US and aquarium releases are dominant in Florida), demonstrating the need for regional-scale assessments of, and management strategies for, introduction pathways.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Management of Biological Invasions","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"REABIC : Regional Euro-Asian Biological Invasions Centre","doi":"10.3391/mbi.2016.7.3.02","usgsCitation":"Lapointe, N.W., Fuller, P., Neilson, M.E., Murphy, B., and Angermeier, P.L., 2016, Pathways of fish invasions in the Mid-Atlantic region of the United States: Management of Biological Invasions, v. 7, no. 3, p. 212-220, https://doi.org/10.3391/mbi.2016.7.3.02.","productDescription":"13 p.","startPage":"212","endPage":"220","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-032121","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":471133,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2016.7.3.02","text":"Publisher Index Page"},{"id":319091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New York, North Carolina, Pennsylvania, Virginia","otherGeospatial":"Chesapeake Bay subregion, Mid-Atlantic region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.4208984375,\n 34.75966612466248\n ],\n [\n -77.16796875,\n 34.786739162702524\n ],\n [\n -77.574462890625,\n 34.82282272723702\n ],\n [\n -78.057861328125,\n 34.912962495216966\n ],\n [\n -78.57421875,\n 35.02099970111467\n ],\n [\n -78.79394531249999,\n 35.31736632923788\n ],\n [\n -78.9697265625,\n 35.60371874069731\n ],\n [\n -79.024658203125,\n 35.96022296929667\n ],\n [\n -79.727783203125,\n 36.12900165569652\n ],\n [\n -80.079345703125,\n 36.09349937380574\n ],\n [\n -80.650634765625,\n 36.155617833818525\n ],\n [\n -80.782470703125,\n 36.421282443649496\n ],\n [\n -80.771484375,\n 36.721273880045004\n ],\n [\n -80.7275390625,\n 36.83566824724438\n ],\n [\n -80.88134765625,\n 36.949891786813296\n ],\n [\n -81.002197265625,\n 37.15156050223665\n ],\n [\n -80.804443359375,\n 37.396346133189255\n ],\n [\n -80.39794921875,\n 37.483576550426996\n ],\n [\n -80.233154296875,\n 37.60552821745789\n ],\n [\n -80.277099609375,\n 37.727280276860036\n ],\n [\n -80.2001953125,\n 37.86618078529668\n ],\n [\n -79.87060546875,\n 38.16911413556086\n ],\n [\n -79.749755859375,\n 38.33303882235456\n ],\n [\n -79.683837890625,\n 38.565347844885466\n ],\n [\n -79.530029296875,\n 38.8225909761771\n ],\n [\n -79.365234375,\n 39.07890809706475\n ],\n [\n -79.453125,\n 39.198205348894795\n ],\n [\n -79.29931640625,\n 39.487084981687495\n ],\n [\n -79.002685546875,\n 39.740986355883564\n ],\n [\n -78.782958984375,\n 40.17047886718109\n ],\n [\n -78.695068359375,\n 40.27952566881291\n ],\n [\n -78.892822265625,\n 40.43858586704331\n ],\n [\n -78.9697265625,\n 40.6306300839918\n ],\n [\n -78.936767578125,\n 40.82212357516945\n ],\n [\n -78.760986328125,\n 41.17038447781618\n ],\n [\n -78.68408203124999,\n 41.36856413680967\n ],\n [\n -78.475341796875,\n 41.60722821271717\n ],\n [\n -78.046875,\n 41.80407814427237\n ],\n [\n -77.860107421875,\n 42.04113400940809\n ],\n [\n -77.9150390625,\n 42.25291778330197\n ],\n [\n -77.6513671875,\n 42.45588764197166\n ],\n [\n -77.442626953125,\n 42.47209690919285\n ],\n [\n -77.135009765625,\n 42.261049162113856\n ],\n [\n -76.607666015625,\n 42.342305278572816\n ],\n [\n -76.212158203125,\n 42.54498667313236\n ],\n [\n -76.102294921875,\n 42.78733853172001\n ],\n [\n -75.992431640625,\n 43.02071359427862\n ],\n [\n -75.47607421875,\n 43.11702412135048\n ],\n [\n -74.520263671875,\n 43.07691312608711\n ],\n [\n -74.322509765625,\n 42.706659563510385\n ],\n [\n -74.212646484375,\n 42.439674178149424\n ],\n [\n -74.498291015625,\n 42.374778361114195\n ],\n [\n -75.11352539062499,\n 42.12267315117259\n ],\n [\n -75.34423828125,\n 41.95949009892465\n ],\n [\n -75.289306640625,\n 41.73852846935917\n ],\n [\n -75.245361328125,\n 41.44272637767212\n ],\n [\n -75.245361328125,\n 41.3108238809182\n ],\n [\n -75.531005859375,\n 41.062786068733026\n ],\n [\n -75.640869140625,\n 40.896905775860006\n ],\n [\n -75.82763671875,\n 40.51379915504413\n ],\n [\n -75.91552734375,\n 40.212440718286466\n ],\n [\n -75.794677734375,\n 39.884450178234395\n ],\n [\n -75.772705078125,\n 39.68182601089365\n ],\n [\n -75.56396484375,\n 39.08743603215884\n ],\n [\n -75.35522460937499,\n 38.71123253895224\n ],\n [\n -75.21240234375,\n 38.38472766885085\n ],\n [\n -75.552978515625,\n 37.93553306183642\n ],\n [\n -75.794677734375,\n 37.52715361723378\n ],\n [\n -75.970458984375,\n 37.13404537126446\n ],\n [\n -76.036376953125,\n 37.309014074275915\n ],\n [\n -75.904541015625,\n 37.61423141542417\n ],\n [\n -75.9375,\n 37.83148014503288\n ],\n [\n -76.058349609375,\n 38.108627664321276\n ],\n [\n -76.102294921875,\n 38.26406296833964\n ],\n [\n -76.300048828125,\n 38.436379603\n ],\n [\n -76.3330078125,\n 38.676933444637925\n ],\n [\n -76.343994140625,\n 38.93377552819722\n ],\n [\n -76.22314453125,\n 39.198205348894795\n ],\n [\n -76.014404296875,\n 39.436192999314095\n ],\n [\n -76.04736328125,\n 39.52099229357195\n ],\n [\n -76.1572265625,\n 39.36827914916014\n ],\n [\n -76.4208984375,\n 39.095962936305504\n ],\n [\n -76.5087890625,\n 38.762650338334154\n ],\n [\n -76.39892578125,\n 38.37611542403604\n ],\n [\n -76.32202148437499,\n 38.09998264736481\n ],\n [\n -76.2451171875,\n 37.883524980871336\n ],\n [\n -76.2890625,\n 37.63163475580643\n ],\n [\n -76.2451171875,\n 37.43997405227057\n ],\n [\n -76.256103515625,\n 37.28279464911045\n ],\n [\n -76.37695312499999,\n 37.35269280367274\n ],\n [\n -76.32202148437499,\n 37.18657859524883\n ],\n [\n -76.2451171875,\n 37.055177106660814\n ],\n [\n -76.2451171875,\n 36.958671131530316\n ],\n [\n -75.904541015625,\n 36.914764288955936\n ],\n [\n -75.76171875,\n 36.2176871222506\n ],\n [\n -75.465087890625,\n 35.67514743608467\n ],\n [\n -75.5419921875,\n 35.209721645221386\n ],\n [\n -76.08032226562499,\n 35.0120020431607\n ],\n [\n -76.4208984375,\n 34.75966612466248\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56f10d1de4b0f59b85dd6841","contributors":{"authors":[{"text":"Lapointe, Nicolas W. R.","contributorId":166959,"corporation":false,"usgs":false,"family":"Lapointe","given":"Nicolas","email":"","middleInitial":"W. R.","affiliations":[],"preferred":false,"id":623095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Pam 0000-0002-9389-9144 pfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9389-9144","contributorId":167676,"corporation":false,"usgs":true,"family":"Fuller","given":"Pam","email":"pfuller@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":623092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Neilson, Matthew E. 0000-0002-5139-5677 mneilson@usgs.gov","orcid":"https://orcid.org/0000-0002-5139-5677","contributorId":167677,"corporation":false,"usgs":true,"family":"Neilson","given":"Matthew","email":"mneilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":623094,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murphy, Brian R.","contributorId":71433,"corporation":false,"usgs":true,"family":"Murphy","given":"Brian R.","affiliations":[],"preferred":false,"id":623096,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Angermeier, Paul L. 0000-0003-2864-170X biota@usgs.gov","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":166679,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul","email":"biota@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":623093,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170288,"text":"70170288 - 2016 - Quasi-extinction risk and population targets for the Eastern, migratory population of monarch butterflies (Danaus plexippus)","interactions":[],"lastModifiedDate":"2017-02-13T14:20:40","indexId":"70170288","displayToPublicDate":"2016-03-21T01:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Quasi-extinction risk and population targets for the Eastern, migratory population of monarch butterflies (Danaus plexippus)","docAbstract":"The Eastern, migratory population of monarch butterflies (Danaus plexippus), an iconic North American insect, has declined by ~80% over the last decade. The monarch’s multi-generational migration between overwintering grounds in central Mexico and the summer breeding grounds in the northern U.S. and southern Canada is celebrated in all three countries and creates shared management responsibilities across North America. Here we present a novel Bayesian multivariate auto-regressive state-space model to assess quasi-extinction risk and aid in the establishment of a target population size for monarch conservation planning. We find that, given a range of plausible quasi-extinction thresholds, the population has a substantial probability of quasi-extinction, from 11–57% over 20 years, although uncertainty in these estimates is large. Exceptionally high population stochasticity, declining numbers, and a small current population size act in concert to drive this risk. An approximately 5-fold increase of the monarch population size (relative to the winter of 2014–15) is necessary to halve the current risk of quasi-extinction across all thresholds considered. Conserving the monarch migration thus requires active management to reverse population declines, and the establishment of an ambitious target population size goal to buffer against future environmentally driven variability.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/srep23265","usgsCitation":"Semmens, B.X., Semmens, D.J., Thogmartin, W.E., Wiederholt, R., Lopez-Hoffman, L., Diffendorfer, J., Pleasants, J., Oberhauser, K.S., and Taylor, O.R., 2016, Quasi-extinction risk and population targets for the Eastern, migratory population of monarch butterflies (Danaus plexippus): Scientific Reports, v. 6, Article number 23265; 7 p., https://doi.org/10.1038/srep23265.","productDescription":"Article number 23265; 7 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066835","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":471134,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/srep23265","text":"Publisher Index Page"},{"id":320161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-21","publicationStatus":"PW","scienceBaseUri":"571756e6e4b0ef3b7caa62a9","contributors":{"authors":[{"text":"Semmens, Brice X.","contributorId":149775,"corporation":false,"usgs":false,"family":"Semmens","given":"Brice","email":"","middleInitial":"X.","affiliations":[{"id":17820,"text":"Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":626766,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Semmens, Darius J. 0000-0001-7924-6529 dsemmens@usgs.gov","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":1714,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius","email":"dsemmens@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":626765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":626767,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiederholt, Ruscena","contributorId":149125,"corporation":false,"usgs":false,"family":"Wiederholt","given":"Ruscena","affiliations":[{"id":17653,"text":"School of Natural Resources & the Environment, The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":626768,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lopez-Hoffman, Laura","contributorId":149127,"corporation":false,"usgs":false,"family":"Lopez-Hoffman","given":"Laura","affiliations":[{"id":17654,"text":"School of Natural Resources & the Environment and Udall Center for Studies in Public Policy, The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":626769,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":626770,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pleasants, John M.","contributorId":168616,"corporation":false,"usgs":false,"family":"Pleasants","given":"John M.","affiliations":[{"id":25341,"text":"Department of Ecology, Evolution, and Organismal Biology, Iowa State University","active":true,"usgs":false}],"preferred":false,"id":626771,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oberhauser, Karen S.","contributorId":27737,"corporation":false,"usgs":true,"family":"Oberhauser","given":"Karen","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":626772,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Taylor, Orley R.","contributorId":168617,"corporation":false,"usgs":false,"family":"Taylor","given":"Orley","email":"","middleInitial":"R.","affiliations":[{"id":25342,"text":"Department of Ecology and Evolutionary Biology, University of Kansas","active":true,"usgs":false}],"preferred":false,"id":626773,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70156258,"text":"sir20155071 - 2016 - Arsenic and radionuclide occurrence and relation to geochemistry in groundwater of the Gulf Coast Aquifer System in Houston, Texas, 2007–11","interactions":[],"lastModifiedDate":"2016-03-22T08:40:17","indexId":"sir20155071","displayToPublicDate":"2016-03-21T00:00:00","publicationYear":"2016","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":"2015-5071","title":"Arsenic and radionuclide occurrence and relation to geochemistry in groundwater of the Gulf Coast Aquifer System in Houston, Texas, 2007–11","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the City of Houston, began a study in 2007 to determine concentrations, spatial extent, and associated geochemical conditions that might be conducive for mobility and transport of selected naturally occurring trace elements and radionuclides in the Gulf Coast aquifer system in Houston, Texas. Water samples were collected from 91 municipal supply wells completed in the Evangeline and Chicot aquifers of the Gulf Coast aquifer system in northeastern, northwestern, and southwestern Houston; hereinafter referred to as northeast, northwest and southwest Houston areas. Wells were sampled in three phases: (1) 28 municipal supply wells were sampled during 2007–8, (2) 60 municipal supply wells during 2010, and (3) 3 municipal supply wells during December 2011. During each phase of sampling, samples were analyzed for major ions, selected trace elements, and radionuclides. At a subset of wells, concentrations of arsenic species and other radionuclides (carbon-14, radium-226, radium-228, radon-222, and tritium) also were analyzed. Selected physicochemical properties were measured in the field at the time each sample was collected, and oxidation-reduction potential and unfiltered sulfides also were measured at selected wells. The source-water (the raw, ambient water withdrawn from municipal supply wells prior to water treatment) samples were collected for assessment of aquifer conditions in order to provide community water-system operators information that could be important when they make decisions about which treatment processes to apply before distributing finished drinking water.
\nGeochemical conditions of groundwater of the Gulf Coast aquifer system are suitable in some instances for release of arsenic and radionuclides from aquifer materials. Recent changes to the U.S. Environmental Protection Agency (EPA) primary drinking-water regulations for arsenic and a selected number of natural radionuclides have highlighted the necessity for municipal supply system managers to be aware of the occurrence and distribution of these constituents in their source water. Concentrations of arsenic ranged from 0.58 to 23.5 micrograms per liter (μg/L), with relatively low median and 75th percentile concentrations (2.7 and 3.6 μg/L, respectively). The gross alpha-particle activity completed within 72 hours after sample collection ranged from R-1.1 (nondetect where the result was below the sample specific critical level) to 39.7 picocuries per liter (pCi/L), with a median of 10.3 pCi/L. After 30 days, the gross alpha-particle activities in the 91 samples ranged from R-0.94 to 25.5 pCi/L, with a median of 5.60 pCi/L. Concentrations of uranium ranged from less than 0.02 to 42.7 μg/L, with a median value of 1.69 μg/L and a 75th-percentile value of 6.48 μg/L. The maximum concentrations of radium-226 and combined radium (sum of radium-226 plus radium-228) were 4.34 pCi/L and 3.23 pCi/L, respectively.
\nAquifer major-ion geochemistry was characterized and shown to contain three chemical types of water as grouped by a simplified predominant cation and anion classification system: (1) calcium- bicarbonate type, (2) sodium-bicarbonate type, and (3) sodium-chloride type. Aquifer geochemistry also was characterized into four reduction-oxidation (redox) categories: (1) oxic, (2) suboxic, (3) mixed, and (4) anoxic. Within the anoxic category, groundwater was further characterized into four presumed predominant reduction processes: (1) iron or sulfate or both [Fe(III)/SO4] reducing, (2) iron [Fe(III)] reducing, (3) iron and sulfate [Fe(III)-SO4] reducing, or (4) methanogenic, as defined by composition of redox species. The oxic category was associated with calcium-bicarbonate-type water, and the methanogenic-anoxic process was associated exclusively with the sodium-bicarbonate-type water. The species of arsenic and the dominant radionuclide present were associated with specific redox categories. Arsenate was associated primarily with oxic water and did not exceed 3.5 µg/L, whereas arsenite was associated with iron-reducing, anoxic water samples and, at the highest concentrations, occurred in sulfate-reducing, anoxic; methanogenic-anoxic; or both water samples. Uranium was associated exclusively with the oxic water, whereas the highest concentrations of combined radium were associated with the iron-reducing, anoxic water. The gross alpha-particle activity was greatest in the oxic waters where the source of the radioactivity was the uranium.
\nAssociated geochemical conditions conducive for mobility of arsenic and radionuclides and their spatial and vertical extent in the Gulf Coast aquifer system in Houston are important aspects to the areal management of the municipal groundwater supplies in Houston. Ongoing research is seeking to define chemical or geological factors that are the optimal indicators for elevated concentrations of these naturally occurring constituents.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155071","collaboration":"Prepared in cooperation with the City of Houston","usgsCitation":"Oden, J.H., and Szabo, Zoltan, 2015, Arsenic and radionuclide occurrence and relation to geochemistry in groundwater of the Gulf Coast Aquifer System in Houston, Texas, 2007–11: U.S. Geological Scientific Investigations Report 2015–5071, 105 p., 4 apps., https://dx.doi.org/10.3133/sir20155071.","productDescription":"Report: xi, 105 p.; Appendixes: 29 p. ","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055471","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":318941,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5071/coverthb.jpg"},{"id":318942,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5071/sir20155071.pdf","text":"Report","size":"3.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015–5071"},{"id":318943,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5071/sir20155071_appendix1to4.pdf","text":"Appendixes 1–4","size":"428 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015–5071 Appendixes 1–4"}],"country":"United States","state":"Texas","city":"Houston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.35333251953125,\n 29.5232805008286\n ],\n [\n -94.515380859375,\n 29.441989466225706\n ],\n [\n -94.64996337890625,\n 29.351057685705033\n ],\n [\n -94.84222412109375,\n 29.216904948184734\n ],\n [\n -95.05645751953124,\n 29.048966726566885\n ],\n [\n -95.3118896484375,\n 28.86632376895912\n ],\n [\n -95.53436279296875,\n 28.878349647602047\n ],\n [\n -95.75958251953125,\n 29.07297469064491\n ],\n [\n -95.877685546875,\n 29.27202470909843\n ],\n [\n -95.92987060546874,\n 29.34148119149953\n ],\n [\n -95.98480224609374,\n 29.420460341013133\n ],\n [\n -96.03973388671875,\n 29.44916482692468\n ],\n [\n -96.06170654296875,\n 29.49698759653577\n ],\n [\n -96.02325439453124,\n 29.508939763268394\n ],\n [\n -96.0589599609375,\n 29.554345125748267\n ],\n [\n -96.08367919921875,\n 29.587788659909958\n ],\n [\n -96.0205078125,\n 29.633158589140564\n ],\n [\n -96.05072021484375,\n 29.680894340704334\n ],\n [\n -96.0040283203125,\n 29.702368038541767\n ],\n [\n -96.01776123046875,\n 29.716681287231072\n ],\n [\n -96.04522705078125,\n 29.728607435707517\n ],\n [\n -96.05072021484375,\n 29.790600550959457\n ],\n [\n -96.1029052734375,\n 29.81205076752506\n ],\n [\n -96.119384765625,\n 29.87637380707133\n ],\n [\n -96.12487792968749,\n 29.938275329718987\n ],\n [\n -96.10015869140625,\n 29.99775972557893\n ],\n [\n -96.12487792968749,\n 30.035811042667792\n ],\n [\n -96.1578369140625,\n 30.083354648756153\n ],\n [\n -96.17980957031249,\n 30.128499916692782\n ],\n [\n -96.1688232421875,\n 30.171250084367482\n ],\n [\n -96.119384765625,\n 30.225848323247707\n ],\n [\n -96.09466552734375,\n 30.28041626667403\n ],\n [\n -96.14410400390625,\n 30.334953881988564\n ],\n [\n -96.16333007812499,\n 30.37761431777479\n ],\n [\n -96.185302734375,\n 30.4060442699695\n ],\n [\n -96.2347412109375,\n 30.441570071519443\n ],\n [\n -95.7623291015625,\n 30.68988785772121\n ],\n [\n -95.30914306640625,\n 30.904581367044212\n ],\n [\n -95.240478515625,\n 30.871582821957446\n ],\n [\n -95.22125244140625,\n 30.831497881307943\n ],\n [\n -95.16357421875,\n 30.7937555812177\n ],\n [\n -95.13885498046875,\n 30.741835717889792\n ],\n [\n -95.108642578125,\n 30.69697330439986\n ],\n [\n -95.00976562499999,\n 30.652090026760014\n ],\n [\n -95.00976562499999,\n 30.62373195163005\n ],\n [\n -95.0372314453125,\n 30.58354378627138\n ],\n [\n -94.976806640625,\n 30.576450026618076\n ],\n [\n -94.9273681640625,\n 30.56462594065098\n ],\n [\n -94.85321044921875,\n 30.545704405480997\n ],\n [\n -94.866943359375,\n 30.517315187761888\n ],\n [\n -94.83673095703125,\n 30.479449996609706\n ],\n [\n -94.8834228515625,\n 30.462879341709886\n ],\n [\n -94.8394775390625,\n 30.439202087235607\n ],\n [\n -94.7955322265625,\n 30.38472258144958\n ],\n [\n -94.7735595703125,\n 30.344435586368462\n ],\n [\n -94.78729248046875,\n 30.299389313522617\n ],\n [\n -94.61151123046875,\n 30.097613277217132\n ],\n [\n -94.52636718749999,\n 29.971590978566113\n ],\n [\n -94.44122314453124,\n 29.816816857649936\n ],\n [\n -94.37805175781249,\n 29.676121784724305\n ],\n [\n -94.35333251953125,\n 29.5232805008286\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Extreme rainfall in September 2013 caused destructive floods in part of the Front Range in Boulder County, Colorado. Erosion from these floods cut roads and isolated mountain communities for several weeks, and large volumes of eroded sediment were deposited downstream, which caused further damage of property and infrastructures. Estimates of peak discharge for these floods and the associated rainfall characteristics will aid land and emergency managers in the future. Several methods (an ensemble) were used to estimate peak discharge at 21 measurement sites, and the ensemble average and standard deviation provided a final estimate of peak discharge and its uncertainty. Because of the substantial erosion and deposition of sediment, an additional estimate of peak discharge was made based on the flow resistance caused by sediment transport effects.
Although the synoptic-scale rainfall was extreme (annual exceedance probability greater than 1,000 years, about 450 millimeters in 7 days) for these mountains, the resulting peak discharges were not. Ensemble average peak discharges per unit drainage area (unit peak discharge, [Qu]) for the floods were 1–2 orders of magnitude less than those for the maximum worldwide floods with similar drainage areas and had a wide range of values (0.21–16.2 cubic meters per second per square kilometer [m3 s-1 km-2]). One possible explanation for these differences was that the band of high-accumulation, high-intensity rainfall was narrow (about 50 kilometers wide), oriented nearly perpendicular to the predominant drainage pattern of the mountains, and therefore entire drainage areas were not subjected to the same range of extreme rainfall. A linear relation (coefficient of determination [R2]=0.69) between Qu and the rainfall intensity (ITc, computed for a time interval equal to the time-of-concentration for the drainage area upstream from each site), had the form: Qu=0.26(ITc-8.6), where the coefficient 0.26 can be considered to be an area-averaged peak runoff coefficient for the September 2013 rain storms in Boulder County, and the 8.6 millimeters per hour to be the rainfall intensity corresponding to a soil moisture threshold that controls the soil infiltration rate. Peak discharge estimates based on the sediment transport effects were generally less than the ensemble average and indicated that sediment transport may be a mechanism that limits velocities in these types of mountain streams such that the Froude number fluctuates about 1 suggesting that this type of floodflow can be approximated as critical flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165003","usgsCitation":"Moody, J.A., 2016, Estimates of peak flood discharge for 21 sites in the Front Range in Colorado in response to extreme rainfall in September 2013: U.S. Geological Survey Scientific Investigations Report 2016–5003, 64 p., https://dx.doi.org/10.3133/sir20165003.","productDescription":"vi, 65p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068930","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":319140,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5003/sir20165003.pdf","text":"Report","size":"14.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5003"},{"id":319139,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5003/coverthb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.833333333333,\n 40.33333333333333\n ],\n [\n -105,\n 40.33333333333333\n ],\n [\n -105,\n 39.83333333333333\n ],\n [\n -105.833333333333,\n 39.83333333333333\n ],\n [\n -105.833333333333,\n 40.33333333333333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Office of Surface Water
U.S. Geological Survey
415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Cold-water corals, similar to tropical corals, contain diverse and complex microbial assemblages. These bacteria provide essential biological functions within coral holobionts, facilitating increased nutrient utilization and production of antimicrobial compounds. To date, few cold-water octocoral species have been analyzed to explore the diversity and abundance of their microbial associates. For this study, 23 samples of the family Anthothelidae were collected from Norfolk (n = 12) and Baltimore Canyons (n = 11) from the western Atlantic in August 2012 and May 2013. Genetic testing found that these samples comprised two Anthothela species (Anthothela grandiflora and Anthothela sp.) and Alcyonium grandiflorum. DNA was extracted and sequenced with primers targeting the V4-V5 variable region of the 16S rRNA gene using 454 pyrosequencing with GS FLX Titanium chemistry. Results demonstrated that the coral host was the primary driver of bacterial community composition. Al. grandiflorum, dominated by Alteromonadales and Pirellulales had much higher species richness, and a distinct bacterial community compared to Anthothela samples. Anthothela species (A. grandiflora and Anthothela sp.) had very similar bacterial communities, dominated by Oceanospirillales and Spirochaetes. Additional analysis of core-conserved bacteria at 90% sample coverage revealed genus level conservation across Anthothela samples. This core included unclassified Oceanospirillales, Kiloniellales, Campylobacterales, and genus Spirochaeta. Members of this core were previously recognized for their functional capabilities in nitrogen cycling and suggest the possibility of a nearly complete nitrogen cycle within Anthothela species. Overall, many of the bacterial associates identified in this study have the potential to contribute to the acquisition and cycling of nutrients within the coral holobiont.
","language":"English","publisher":"Frontiers in Microbiology","doi":"10.3389/fmicb.2016.00458","usgsCitation":"Lawler, S.N., Kellogg, C.A., France, S.C., Clostio, R.W., Brooke, S.D., and Ross, S., 2016, Coral-associated bacterial diversity is conserved across two deep-sea Anthothela species: Frontiers in Microbiology, v. 7, art458: 18 p., https://doi.org/10.3389/fmicb.2016.00458.","productDescription":"art458: 18 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070380","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471135,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2016.00458","text":"Publisher Index Page"},{"id":319379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Norfolk Canyon; Baltimore Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.124755859375,\n 37.60552821745789\n ],\n [\n -74.1357421875,\n 37.322120359451766\n ],\n [\n -73.729248046875,\n 37.31338308990806\n ],\n [\n -73.7567138671875,\n 37.64468458716586\n ],\n [\n -74.124755859375,\n 37.60552821745789\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.7841796875,\n 38.55246141354153\n ],\n [\n -73.8720703125,\n 38.33303882235456\n ],\n [\n -73.54248046875,\n 38.302869955150044\n ],\n [\n -73.4326171875,\n 38.50519140240354\n ],\n [\n -73.7841796875,\n 38.55246141354153\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-05","publicationStatus":"PW","scienceBaseUri":"56f50fb6e4b0f59b85e1ead5","contributors":{"authors":[{"text":"Lawler, Stephanie N.","contributorId":149424,"corporation":false,"usgs":false,"family":"Lawler","given":"Stephanie","email":"","middleInitial":"N.","affiliations":[{"id":17733,"text":"University of South Florida, St. Petersburg, FL","active":true,"usgs":false}],"preferred":false,"id":623799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kellogg, Christina A. 0000-0002-6492-9455 ckellogg@usgs.gov","orcid":"https://orcid.org/0000-0002-6492-9455","contributorId":391,"corporation":false,"usgs":true,"family":"Kellogg","given":"Christina","email":"ckellogg@usgs.gov","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":623800,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"France, Scott C","contributorId":167845,"corporation":false,"usgs":false,"family":"France","given":"Scott","email":"","middleInitial":"C","affiliations":[{"id":7155,"text":"University of Louisiana at Lafayette","active":true,"usgs":false}],"preferred":false,"id":623801,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clostio, Rachel W","contributorId":167846,"corporation":false,"usgs":false,"family":"Clostio","given":"Rachel","email":"","middleInitial":"W","affiliations":[{"id":7155,"text":"University of Louisiana at Lafayette","active":true,"usgs":false}],"preferred":false,"id":623802,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brooke, Sandra D.","contributorId":167844,"corporation":false,"usgs":false,"family":"Brooke","given":"Sandra","email":"","middleInitial":"D.","affiliations":[{"id":24836,"text":"Coastal and Marine Laboratory, Florida State University","active":true,"usgs":false}],"preferred":false,"id":623803,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ross, Steve W.","contributorId":41134,"corporation":false,"usgs":false,"family":"Ross","given":"Steve W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":623804,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70170076,"text":"70170076 - 2016 - Identifying the origin of waterbird carcasses in Lake Michigan using a neural network source tracking model","interactions":[],"lastModifiedDate":"2016-12-16T11:32:48","indexId":"70170076","displayToPublicDate":"2016-03-19T13:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Identifying the origin of waterbird carcasses in Lake Michigan using a neural network source tracking model","docAbstract":"Avian botulism type E is responsible for extensive waterbird mortality on the Great Lakes, yet the actual site of toxin exposure remains unclear. Beached carcasses are often used to describe the spatial aspects of botulism mortality outbreaks, but lack specificity of offshore toxin source locations. We detail methodology for developing a neural network model used for predicting waterbird carcass motions in response to wind, wave, and current forcing, in lieu of a complex analytical relationship. This empirically trained model uses current velocity, wind velocity, significant wave height, and wave peak period in Lake Michigan simulated by the Great Lakes Coastal Forecasting System. A detailed procedure is further developed to use the model for back-tracing waterbird carcasses found on beaches in various parts of Lake Michigan, which was validated using drift data for radiomarked common loon (Gavia immer) carcasses deployed at a variety of locations in northern Lake Michigan during September and October of 2013. The back-tracing model was further used on 22 non-radiomarked common loon carcasses found along the shoreline of northern Lake Michigan in October and November of 2012. The model-estimated origins of those cases pointed to some common source locations offshore that coincide with concentrations of common loons observed during aerial surveys. The neural network source tracking model provides a promising approach for identifying locations of botulinum neurotoxin type E intoxication and, in turn, contributes to developing an understanding of the dynamics of toxin production and possible trophic transfer pathways.
","language":"English","publisher":"International Association for Great Lakes Research","doi":"10.1016/j.jglr.2016.02.014","usgsCitation":"Kenow, K.P., Ge, Z., Fara, L.J., Houdek, S.C., and Lubinski, B.R., 2016, Identifying the origin of waterbird carcasses in Lake Michigan using a neural network source tracking model: Journal of Great Lakes Research, v. 42, no. 3, p. 637-648, https://doi.org/10.1016/j.jglr.2016.02.014.","productDescription":"12 p.","startPage":"637","endPage":"648","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067361","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":471136,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2016.02.014","text":"Publisher Index Page"},{"id":319840,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.737548828125,\n 45.82879925192134\n ],\n [\n -84.74853515625,\n 45.775186183521036\n ],\n [\n -84.891357421875,\n 45.72152152227954\n ],\n [\n -85.01220703125,\n 45.60635207711834\n ],\n [\n -85.067138671875,\n 45.537136680398596\n ],\n [\n -85.0341796875,\n 45.460130637921004\n ],\n [\n -84.78149414062499,\n 45.43700828867389\n ],\n [\n -85.01220703125,\n 45.336701909968106\n ],\n [\n -85.1220703125,\n 45.36758436884978\n ],\n [\n -85.23193359375,\n 45.31352900692261\n ],\n [\n -85.352783203125,\n 45.251688256117646\n ],\n [\n -85.352783203125,\n 45.18978009667531\n ],\n [\n -85.25390625,\n 45.042478050891546\n ],\n [\n -85.25390625,\n 44.933696389694674\n ],\n [\n -85.26489257812499,\n 44.86365630540611\n ],\n [\n -85.3857421875,\n 44.793530904744074\n ],\n [\n -85.49560546875,\n 44.73892994307368\n ],\n [\n -85.60546875,\n 44.77793589631623\n ],\n [\n -85.63842773437499,\n 44.88701247981298\n ],\n [\n -85.60546875,\n 44.99588261816546\n ],\n [\n -85.616455078125,\n 45.1433047394883\n ],\n [\n -85.75927734375,\n 44.98034238084973\n ],\n [\n -85.814208984375,\n 44.902577996288876\n ],\n [\n -85.902099609375,\n 44.902577996288876\n ],\n [\n -85.98999023437499,\n 44.84808025602074\n ],\n [\n -86.077880859375,\n 44.73892994307368\n ],\n [\n -86.19873046875,\n 44.68427737181225\n ],\n [\n -86.187744140625,\n 44.6061127451739\n ],\n [\n -86.187744140625,\n 44.449467536006935\n ],\n [\n -86.275634765625,\n 44.26093725039923\n ],\n [\n -86.36352539062499,\n 44.11125397357153\n ],\n [\n -86.46240234375,\n 44.04811573082351\n ],\n [\n -86.41845703124999,\n 43.929549935614595\n ],\n [\n -86.37451171875,\n 43.8028187190472\n ],\n [\n -86.47338867187499,\n 43.644025847699496\n ],\n [\n -86.407470703125,\n 43.44494295526125\n ],\n [\n -86.2646484375,\n 43.15710884095329\n ],\n [\n -86.165771484375,\n 42.85985981506279\n ],\n [\n -86.209716796875,\n 42.5611728553181\n ],\n [\n -86.275634765625,\n 42.309815415686664\n ],\n [\n -86.4404296875,\n 42.12267315117259\n ],\n [\n -86.572265625,\n 41.86137915587359\n ],\n [\n -86.8798828125,\n 41.68932225997044\n ],\n [\n -87.099609375,\n 41.64007838467894\n ],\n [\n -87.275390625,\n 41.599013054830216\n ],\n [\n -87.440185546875,\n 41.64007838467894\n ],\n [\n -87.5830078125,\n 41.77131167976407\n ],\n [\n -87.703857421875,\n 42.01665183556825\n ],\n [\n -87.791748046875,\n 42.17154633452751\n ],\n [\n -87.8466796875,\n 42.285437007491545\n ],\n [\n -87.81372070312499,\n 42.382894009614056\n ],\n [\n -87.835693359375,\n 42.553080288955826\n ],\n [\n -87.802734375,\n 42.74701217318067\n ],\n [\n -87.879638671875,\n 42.867912483915305\n ],\n [\n -87.890625,\n 43.03677585761058\n ],\n [\n -87.923583984375,\n 43.22118973298753\n ],\n [\n -87.9345703125,\n 43.31718491566708\n ],\n [\n -87.835693359375,\n 43.48481212891603\n ],\n [\n -87.791748046875,\n 43.58039085560786\n ],\n [\n -87.703857421875,\n 43.691707903073805\n ],\n [\n -87.78076171875,\n 43.84245116699036\n ],\n [\n -87.659912109375,\n 44.11914151643737\n ],\n [\n -87.550048828125,\n 44.18220395771566\n ],\n [\n -87.56103515625,\n 44.276671273775186\n ],\n [\n -87.5390625,\n 44.402391829093915\n ],\n [\n -87.506103515625,\n 44.56699093657141\n ],\n [\n -87.418212890625,\n 44.72332018895825\n ],\n [\n -87.286376953125,\n 44.81691551782855\n ],\n [\n -87.176513671875,\n 45.02695045318546\n ],\n [\n -87.07763671875,\n 45.213003555993964\n ],\n [\n -87.242431640625,\n 45.058001435398296\n ],\n [\n -87.34130859375,\n 44.902577996288876\n ],\n [\n -87.451171875,\n 44.84029065139799\n ],\n [\n -87.56103515625,\n 44.80132682904856\n ],\n [\n -87.6708984375,\n 44.715513732021336\n ],\n [\n -87.7587890625,\n 44.6061127451739\n ],\n [\n -87.91259765625,\n 44.49650533109348\n ],\n [\n -88.08837890625,\n 44.55916341529184\n ],\n [\n -88.033447265625,\n 44.692088041727814\n ],\n [\n -87.8466796875,\n 44.88701247981298\n ],\n [\n -87.86865234374999,\n 44.94924926661153\n ],\n [\n -87.64892578125,\n 44.98811302615805\n ],\n [\n -87.593994140625,\n 45.1510532655634\n ],\n [\n -87.4072265625,\n 45.36758436884978\n ],\n [\n -87.308349609375,\n 45.49094569262732\n ],\n [\n -87.12158203125,\n 45.69850658738848\n ],\n [\n -87.022705078125,\n 45.85176048817254\n ],\n [\n -86.956787109375,\n 45.92822950933618\n ],\n [\n -86.978759765625,\n 45.84410779560204\n ],\n [\n -86.923828125,\n 45.72152152227954\n ],\n [\n -86.824951171875,\n 45.79816953017265\n ],\n [\n -86.8359375,\n 45.882360730184025\n ],\n [\n -86.693115234375,\n 45.85941212790755\n ],\n [\n -86.539306640625,\n 45.935870621190546\n ],\n [\n -86.517333984375,\n 45.897654534346884\n ],\n [\n -86.63818359375,\n 45.706179285330855\n ],\n [\n -86.46240234375,\n 45.79816953017265\n ],\n [\n -86.3525390625,\n 45.867062714815475\n ],\n [\n -86.341552734375,\n 45.9511496866914\n ],\n [\n -86.0888671875,\n 45.98932892799953\n ],\n [\n -85.9130859375,\n 45.95878764035642\n ],\n [\n -85.836181640625,\n 46.01985337287634\n ],\n [\n -85.682373046875,\n 45.98169518512228\n ],\n [\n -85.53955078125,\n 46.126556302418514\n ],\n [\n -85.440673828125,\n 46.11894150610708\n ],\n [\n -85.23193359375,\n 46.10370875598026\n ],\n [\n -85.0341796875,\n 46.01985337287634\n ],\n [\n -84.83642578125,\n 45.9511496866914\n ],\n [\n -84.737548828125,\n 45.82879925192134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57248643e4b0b13d3915958b","contributors":{"authors":[{"text":"Kenow, Kevin P. 0000-0002-3062-5197 kkenow@usgs.gov","orcid":"https://orcid.org/0000-0002-3062-5197","contributorId":3339,"corporation":false,"usgs":true,"family":"Kenow","given":"Kevin","email":"kkenow@usgs.gov","middleInitial":"P.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":626034,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ge, Zhongfu","contributorId":139463,"corporation":false,"usgs":false,"family":"Ge","given":"Zhongfu","email":"","affiliations":[{"id":12773,"text":"American Bureau of Shipping, Corporate Marine Technology","active":true,"usgs":false}],"preferred":false,"id":626035,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fara, Luke J. 0000-0002-1143-4395 lfara@usgs.gov","orcid":"https://orcid.org/0000-0002-1143-4395","contributorId":5248,"corporation":false,"usgs":true,"family":"Fara","given":"Luke","email":"lfara@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":626036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Houdek, Steven C. 0000-0001-9452-6596 shoudek@usgs.gov","orcid":"https://orcid.org/0000-0001-9452-6596","contributorId":4423,"corporation":false,"usgs":true,"family":"Houdek","given":"Steven","email":"shoudek@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":626037,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lubinski, Brian R.","contributorId":177523,"corporation":false,"usgs":false,"family":"Lubinski","given":"Brian","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":626038,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178254,"text":"70178254 - 2016 - A Unified Cropland Layer at 250-m for global agriculture monitoring","interactions":[],"lastModifiedDate":"2016-11-09T15:52:09","indexId":"70178254","displayToPublicDate":"2016-03-19T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5226,"text":"Data","active":true,"publicationSubtype":{"id":10}},"title":"A Unified Cropland Layer at 250-m for global agriculture monitoring","docAbstract":"Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products.
","language":"English","publisher":"MDPI","doi":"10.3390/data1010003","usgsCitation":"Waldner, F., Fritz, S., Di Gregorio, A., Plotnikov, D., Bartalev, S., Kussul, N., Gong, P., Thenkabail, P.S., Hazeu, G., Klein, I., Low, F., Miettinen, J., Dadhwal, V.K., Lamarche, C., Bontemps, S., and Defourny, P., 2016, A Unified Cropland Layer at 250-m for global agriculture monitoring: Data, v. 1, no. 3, 1010003: 13 p., https://doi.org/10.3390/data1010003.","productDescription":"1010003: 13 p.","ipdsId":"IP-072621","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471137,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/data1010003","text":"Publisher Index Page"},{"id":330907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-19","publicationStatus":"PW","scienceBaseUri":"582443f6e4b09065cdf3052e","contributors":{"authors":[{"text":"Waldner, Francois","contributorId":176747,"corporation":false,"usgs":false,"family":"Waldner","given":"Francois","email":"","affiliations":[],"preferred":false,"id":653410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fritz, Steffen","contributorId":149670,"corporation":false,"usgs":false,"family":"Fritz","given":"Steffen","email":"","affiliations":[],"preferred":false,"id":653411,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Di Gregorio, Antonio","contributorId":176748,"corporation":false,"usgs":false,"family":"Di Gregorio","given":"Antonio","email":"","affiliations":[],"preferred":false,"id":653412,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plotnikov, Dmitry","contributorId":176749,"corporation":false,"usgs":false,"family":"Plotnikov","given":"Dmitry","email":"","affiliations":[],"preferred":false,"id":653413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bartalev, Sergey","contributorId":176750,"corporation":false,"usgs":false,"family":"Bartalev","given":"Sergey","email":"","affiliations":[],"preferred":false,"id":653414,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kussul, Nataliia","contributorId":176751,"corporation":false,"usgs":false,"family":"Kussul","given":"Nataliia","email":"","affiliations":[],"preferred":false,"id":653415,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gong, Peng","contributorId":102393,"corporation":false,"usgs":true,"family":"Gong","given":"Peng","affiliations":[],"preferred":false,"id":653416,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":653417,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hazeu, Gerard","contributorId":176757,"corporation":false,"usgs":false,"family":"Hazeu","given":"Gerard","email":"","affiliations":[],"preferred":false,"id":653418,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Klein, Igor","contributorId":176758,"corporation":false,"usgs":false,"family":"Klein","given":"Igor","email":"","affiliations":[],"preferred":false,"id":653419,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Low, Fabian","contributorId":176759,"corporation":false,"usgs":false,"family":"Low","given":"Fabian","email":"","affiliations":[],"preferred":false,"id":653420,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Miettinen, Jukka","contributorId":176760,"corporation":false,"usgs":false,"family":"Miettinen","given":"Jukka","email":"","affiliations":[],"preferred":false,"id":653421,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Dadhwal, Vinay Kumar","contributorId":176761,"corporation":false,"usgs":false,"family":"Dadhwal","given":"Vinay","email":"","middleInitial":"Kumar","affiliations":[],"preferred":false,"id":653422,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lamarche, Celine","contributorId":176762,"corporation":false,"usgs":false,"family":"Lamarche","given":"Celine","email":"","affiliations":[],"preferred":false,"id":653423,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Bontemps, Sophie","contributorId":176764,"corporation":false,"usgs":false,"family":"Bontemps","given":"Sophie","email":"","affiliations":[],"preferred":false,"id":653424,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Defourny, Pierre","contributorId":146809,"corporation":false,"usgs":false,"family":"Defourny","given":"Pierre","email":"","affiliations":[],"preferred":false,"id":653425,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70160629,"text":"70160629 - 2016 - Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model","interactions":[],"lastModifiedDate":"2016-03-18T16:11:26","indexId":"70160629","displayToPublicDate":"2016-03-18T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model","docAbstract":"For decision support, the insights and predictive power of numerical process models can be hampered by insufficient expertise and computational resources required to evaluate system response to new stresses. An alternative is to emulate the process model with a statistical “metamodel.” Built on a dataset of collocated numerical model input and output, a groundwater flow model was emulated using a Bayesian Network, an Artificial neural network, and a Gradient Boosted Regression Tree. The response of interest was surface water depletion expressed as the source of water-to-wells. The results have application for managing allocation of groundwater. Each technique was tuned using cross validation and further evaluated using a held-out dataset. A numerical MODFLOW-USG model of the Lake Michigan Basin, USA, was used for the evaluation. The performance and interpretability of each technique was compared pointing to advantages of each technique. The metamodel can extend to unmodeled areas.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Modelling & Software","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Oxford","doi":"10.1016/j.envsoft.2015.11.023","usgsCitation":"Fienen, M., Nolan, B.T., and Feinstein, D.T., 2016, Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model: Environmental Modelling and Software, v. 77, p. 95-107, https://doi.org/10.1016/j.envsoft.2015.11.023.","productDescription":"13 p.","startPage":"95","endPage":"107","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066378","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":319030,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Michigan, Ohio, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.210205078125,\n 46.354510837365254\n ],\n [\n -84.232177734375,\n 45.98169518512228\n ],\n [\n -84.3310546875,\n 45.94351068030587\n ],\n [\n -84.605712890625,\n 46.00459325574482\n ],\n [\n -84.67163085937499,\n 45.82879925192134\n ],\n [\n -84.847412109375,\n 45.85941212790755\n ],\n [\n -84.979248046875,\n 45.97406038956237\n ],\n [\n -85.308837890625,\n 46.06560846138691\n ],\n [\n -85.49560546875,\n 46.06560846138691\n ],\n [\n -85.770263671875,\n 45.92822950933618\n ],\n [\n -85.93505859374999,\n 45.92822950933618\n ],\n [\n -86.11083984375,\n 45.95878764035642\n ],\n [\n -86.2646484375,\n 45.836454050187726\n ],\n [\n -86.60522460937499,\n 45.706179285330855\n ],\n [\n -86.68212890625,\n 45.57560020947799\n ],\n [\n -86.72607421875,\n 45.805828539928356\n ],\n [\n -86.98974609375,\n 45.68315803253308\n ],\n [\n -87.022705078125,\n 45.79050946752472\n ],\n [\n -87.25341796875,\n 45.521743896993634\n ],\n [\n -87.506103515625,\n 45.213003555993964\n ],\n [\n -87.64892578125,\n 44.99588261816546\n ],\n [\n -87.82470703125,\n 44.95702412512118\n ],\n [\n -87.857666015625,\n 44.77793589631623\n ],\n [\n -88.00048828124999,\n 44.61393394730626\n ],\n [\n -87.95654296875,\n 44.55133484083592\n ],\n [\n -87.6708984375,\n 44.731125592643274\n ],\n [\n -87.528076171875,\n 44.879228141635274\n ],\n [\n -87.308349609375,\n 45.11230010229608\n ],\n [\n -87.132568359375,\n 45.259422036351694\n ],\n [\n -86.84692382812499,\n 45.44471679159555\n ],\n [\n -87.03369140625,\n 45.1510532655634\n ],\n [\n -87.2314453125,\n 44.84029065139799\n ],\n [\n -87.47314453125,\n 44.4808302785626\n ],\n [\n -87.5390625,\n 44.308126684886126\n ],\n [\n -87.506103515625,\n 44.213709909702054\n ],\n [\n -87.6708984375,\n 44.06390660801779\n ],\n [\n -87.73681640625,\n 43.874138181474734\n ],\n [\n -87.69287109375,\n 43.74728909225906\n ],\n [\n -87.7587890625,\n 43.58039085560786\n ],\n [\n -87.81372070312499,\n 43.42898792344157\n ],\n [\n -87.901611328125,\n 43.213183300738876\n ],\n [\n -87.8466796875,\n 42.98053954751642\n ],\n [\n -87.769775390625,\n 42.779275360241904\n ],\n [\n -87.802734375,\n 42.58544425738491\n ],\n [\n -87.81372070312499,\n 42.342305278572816\n ],\n [\n -87.78076171875,\n 42.17968819665961\n ],\n [\n -87.637939453125,\n 42.05745022024682\n ],\n [\n -87.593994140625,\n 41.78769700539063\n ],\n [\n -87.36328125,\n 41.63186741069748\n ],\n [\n -87.0556640625,\n 41.64828831259535\n ],\n [\n -86.60522460937499,\n 41.88592102814744\n ],\n [\n -86.517333984375,\n 42.13082130188811\n ],\n [\n -86.36352539062499,\n 42.3016903282445\n ],\n [\n -86.23168945312499,\n 42.62587560259137\n ],\n [\n -86.23168945312499,\n 42.94033923363183\n ],\n [\n -86.28662109375,\n 43.1811470593997\n ],\n [\n -86.495361328125,\n 43.48481212891603\n ],\n [\n -86.539306640625,\n 43.6599240747891\n ],\n [\n -86.4404296875,\n 43.8028187190472\n ],\n [\n -86.495361328125,\n 44.07969327425713\n ],\n [\n -86.341552734375,\n 44.276671273775186\n ],\n [\n -86.220703125,\n 44.465151013519616\n ],\n [\n -86.2646484375,\n 44.66865287227321\n ],\n [\n -86.099853515625,\n 44.793530904744074\n ],\n [\n -85.9130859375,\n 44.98034238084973\n ],\n [\n -85.62744140625,\n 45.22848059584359\n ],\n [\n -85.53955078125,\n 45.11230010229608\n ],\n [\n -85.528564453125,\n 44.81691551782855\n ],\n [\n -85.418701171875,\n 45.02695045318546\n ],\n [\n -85.418701171875,\n 45.27488643704894\n ],\n [\n -85.111083984375,\n 45.40616374516014\n ],\n [\n -84.96826171874999,\n 45.42158812329091\n ],\n [\n -85.10009765625,\n 45.51404592560424\n ],\n [\n -85.05615234375,\n 45.66780526567164\n ],\n [\n -84.79248046875,\n 45.767522962149904\n ],\n [\n -84.52880859375,\n 45.706179285330855\n ],\n [\n -84.26513671875,\n 45.65244828675087\n ],\n [\n -84.1552734375,\n 45.49094569262732\n ],\n [\n -84.144287109375,\n 43.08493742707592\n ],\n [\n -83.95751953125,\n 43.03677585761058\n ],\n [\n -83.748779296875,\n 42.93229601903058\n ],\n [\n -83.7158203125,\n 42.5611728553181\n ],\n [\n -83.69384765625,\n 42.342305278572816\n ],\n [\n -83.75976562499999,\n 42.24478535602799\n ],\n [\n -84.012451171875,\n 42.114523952464246\n ],\n [\n -84.122314453125,\n 42.09822241118974\n ],\n [\n -84.111328125,\n 41.236511201246216\n ],\n [\n -88.857421875,\n 41.261291493919856\n ],\n [\n -88.824462890625,\n 43.54058479482877\n ],\n [\n -89.033203125,\n 43.476840397778915\n ],\n [\n -89.56054687499999,\n 43.51668853502909\n ],\n [\n -89.89013671875,\n 43.6599240747891\n ],\n [\n -89.967041015625,\n 43.77902662160831\n ],\n [\n -89.75830078125,\n 43.97700467496408\n ],\n [\n -89.49462890625,\n 44.190082025040525\n ],\n [\n -89.49462890625,\n 44.36313311380771\n ],\n [\n -89.571533203125,\n 44.55133484083592\n ],\n [\n -89.56054687499999,\n 44.77013681219717\n ],\n [\n -89.49462890625,\n 45.01141864227728\n ],\n [\n -89.31884765624999,\n 45.24395342262324\n ],\n [\n -89.044189453125,\n 45.321254361171476\n ],\n [\n -89.20898437499999,\n 45.43700828867389\n ],\n [\n -89.219970703125,\n 45.66012730272194\n ],\n [\n -89.02221679687499,\n 45.78284835197676\n ],\n [\n -88.96728515624999,\n 45.935870621190546\n ],\n [\n -88.78051757812499,\n 46.01222384063238\n ],\n [\n -88.8134765625,\n 46.263442671779885\n ],\n [\n -88.604736328125,\n 46.32417161725694\n ],\n [\n -88.253173828125,\n 46.33175800051563\n ],\n [\n -88.1982421875,\n 46.521075663842836\n ],\n [\n -88.00048828124999,\n 46.64189395892874\n ],\n [\n -87.593994140625,\n 46.430285240839964\n ],\n [\n -86.36352539062499,\n 46.392411189814645\n ],\n [\n -86.30859375,\n 46.49839225859763\n ],\n [\n -86.099853515625,\n 46.604167162931844\n ],\n [\n -85.71533203125,\n 46.5739667965278\n ],\n [\n -85.60546875,\n 46.37725420510028\n ],\n [\n -84.210205078125,\n 46.354510837365254\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ed18a1e4b0f59b85da9eaa","contributors":{"authors":[{"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":583409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":583410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530 dtfeinst@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":1907,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"dtfeinst@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":583411,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70168953,"text":"ds982 - 2016 - Archive of ground penetrating radar data collected during USGS field activity 13BIM01—Dauphin Island, Alabama, April 2013","interactions":[],"lastModifiedDate":"2025-05-14T13:46:44.90138","indexId":"ds982","displayToPublicDate":"2016-03-18T14:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"982","title":"Archive of ground penetrating radar data collected during USGS field activity 13BIM01—Dauphin Island, Alabama, April 2013","docAbstract":"From April 13 to 20, 2013, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) conducted geophysical and sediment sampling surveys on Dauphin Island, Alabama, as part of Field Activity 13BIM01. The objectives of the study were to quantify inorganic and organic accretion rates in back-barrier and mainland marsh and estuarine environments. Various field and laboratory methods were used to achieve these objectives, including subsurface imaging using Ground Penetrating Radar (GPR), sediment sampling, lithologic and microfossil analyses, and geochronology techniques to produce barrier island stratigraphic cross sections to help interpret the recent (last 2000 years) geologic evolution of the island.
This data series report is an archive of GPR and associated Global Positioning System (GPS) data collected in April 2013 from Dauphin Island and adjacent barrier-island environments. In addition to GPR data, marsh core and vibracore data were also collected collected but are not reported (or included) in the current report. Data products, including elevation-corrected subsurface profile images of the processed GPR data, unprocessed digital GPR trace data, post-processed GPS data, Geographic Information System (GIS) files and accompanying Federal Geographic Data Committee (FGDC) metadata, can be downloaded from the Data Downloads page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds982","usgsCitation":"Forde, A.S., Smith, C.G., and Reynolds, B.J., 2016, Archive of ground penetrating radar data collected during USGS field activity 13BIM01—Dauphin Island, Alabama, April 2013: U.S. Geological Survey Data Series 982, https://dx.doi.org/10.3133/ds982.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-068738","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438631,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H70DSZ","text":"USGS data release","linkHelpText":"Sedimentary Data Collected in April 2013 From Dauphin Island and Salt Marshes of Coastal Alabama"},{"id":318974,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":318878,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0982/index.html"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.32832628252302,\n 30.222222538874064\n ],\n [\n -88.0647962871504,\n 30.23742743361879\n ],\n [\n -88.07755483450525,\n 30.261370373972056\n ],\n [\n -88.12286967511226,\n 30.286827097172534\n ],\n [\n -88.13694807219358,\n 30.27124988630632\n ],\n [\n -88.34636422878367,\n 30.235907049955017\n ],\n [\n -88.32832628252302,\n 30.222222538874064\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
(727) 502-8000
http://coastal.er.usgs.gov
Detection of West Nile virus (WNV) in ducks has been reported in North America in isolated cases of mortality in wild waterbirds and following outbreaks in farmed ducks. Although the virus has been noted as an apparent incidental finding in several species of ducks, little is known about the prevalence of exposure or the outcome of infection with WNV in wild ducks in North America. From 2004–06, we collected sera from 1,406 wild-caught American Wigeon (Anas americana), Mallard (Anas platyrhynchos), and Northern Pintail (Anas acuta) ducks at national wildlife refuges (NWRs) in North Dakota and Wood Ducks (Aix sponsa) at NWRs in South Carolina and Tennessee. We measured the prevalence of previous exposure to WNV in these ducks by measuring WNV antibodies and evaluated variation in exposure among species, age, and year. Additionally, we evaluated the performance of a commercial antibody to wild bird immunoglobulin in duck species that varied in their phylogenetic relatedness to the bird species the antibody was directed against. As determined by a screening immunoassay and a confirmatory plaque reduction neutralization assay, the prevalence of WNV antibody was 10%. In light of experimental studies that show ducks to be relatively resistant to mortality caused by WNV, the antibody prevalence we detected suggests that wild ducks may be less-frequently exposed to WNV than expected for birds inhabiting wetlands where they may acquire infection from mosquitoes.
","language":"English","publisher":"Wildlife Disease Association","publisherLocation":"Lawrence, KS","doi":"10.7589/2015-06-137","usgsCitation":"Hofmeister, E.K., Jankowski, M.D., Goldberg, D.R., and Franson, J., 2016, Survey for West Nile virus antibodies in wild ducks, 2004-06, USA: Journal of Wildlife Diseases, v. 52, no. 2, 10 p., https://doi.org/10.7589/2015-06-137.","productDescription":"10 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069694","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":318954,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota, South Carolina, Tennessee","otherGeospatial":"J. Clark Salyer National Wildlife Refuge, Santee National Wildlife Refuge, Savannah National Wildlife Refuge, Tennessee 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 -80.26235,\n 33.33145\n ],\n [\n -80.26235,\n 33.33135\n ],\n [\n -80.26225,\n 33.33135\n ],\n [\n -80.26225,\n 33.33145\n ],\n [\n -80.26235,\n 33.33145\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.06195,\n 32.11255\n ],\n [\n -81.06195,\n 32.11245\n ],\n [\n -81.06185,\n 32.11245\n ],\n [\n -81.06185,\n 32.11255\n ],\n [\n -81.06195,\n 32.11255\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.58115,\n 35.57095\n ],\n [\n -87.58115,\n 35.57085\n ],\n [\n -87.58105,\n 35.57085\n ],\n [\n -87.58105,\n 35.57095\n ],\n [\n -87.58115,\n 35.57095\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.31315,\n 48.32215\n ],\n [\n -100.31315,\n 48.32205\n ],\n [\n -100.31305,\n 48.32205\n ],\n [\n -100.31305,\n 48.32215\n ],\n [\n -100.31315,\n 48.32215\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ed18a1e4b0f59b85da9eb1","contributors":{"authors":[{"text":"Hofmeister, Erik K. 0000-0002-6360-3912 ehofmeister@usgs.gov","orcid":"https://orcid.org/0000-0002-6360-3912","contributorId":3230,"corporation":false,"usgs":true,"family":"Hofmeister","given":"Erik","email":"ehofmeister@usgs.gov","middleInitial":"K.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":578735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jankowski, Mark","contributorId":149535,"corporation":false,"usgs":false,"family":"Jankowski","given":"Mark","affiliations":[{"id":17765,"text":"Present address: Minnesota Pollution Control Agency, 520 Lafayette Road N., St. Paul, MN 55155","active":true,"usgs":false}],"preferred":false,"id":578736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldberg, Diana R. 0000-0001-8540-8512 dgoldberg@usgs.gov","orcid":"https://orcid.org/0000-0001-8540-8512","contributorId":5739,"corporation":false,"usgs":true,"family":"Goldberg","given":"Diana","email":"dgoldberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":578737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Franson, J. Christian jfranson@usgs.gov","contributorId":149318,"corporation":false,"usgs":true,"family":"Franson","given":"J. Christian","email":"jfranson@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":578738,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70184175,"text":"70184175 - 2016 - Determination of polydimethylsiloxane–water partition coefficients for ten 1-chloro-4-[2,2,2-trichloro-1-(4-chlorophenyl)ethyl]benzene-related compounds and twelve polychlorinated biphenyls using gas chromatography/mass spectrometry","interactions":[],"lastModifiedDate":"2017-03-01T14:22:27","indexId":"70184175","displayToPublicDate":"2016-03-18T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2214,"text":"Journal of Chromatography A","active":true,"publicationSubtype":{"id":10}},"title":"Determination of polydimethylsiloxane–water partition coefficients for ten 1-chloro-4-[2,2,2-trichloro-1-(4-chlorophenyl)ethyl]benzene-related compounds and twelve polychlorinated biphenyls using gas chromatography/mass spectrometry","docAbstract":"Polymer-water partition coefficients (Kpw) of ten DDT-related compounds were determined in pure water at 25 °C using commercial polydimethylsiloxane-coated optical fiber. Analyte concentrations were measured by thermal desorption-gas chromatography/full scan mass spectrometry (TD–GC/MSFS; fibers) and liquid injection-gas chromatography/selected ion monitoring mass spectrometry (LI–GC/MSSIM; water). Equilibrium was approached from two directions (fiber uptake and depletion) as a means of assessing data concordance. Measured compound-specific log Kpw values ranged from 4.8 to 6.1 with an average difference in log Kpw between the two approaches of 0.05 log units (∼12% of Kpw). Comparison of the experimentally-determined log Kpw values with previously published data confirmed the consistency of the results and the reliability of the method. A second experiment was conducted with the same ten DDT-related compounds and twelve selected PCB (polychlorinated biphenyl) congeners under conditions characteristic of a coastal marine field site (viz., seawater, 11 °C) that is currently under investigation for DDT and PCB contamination. Equilibration at lower temperature and higher ionic strength resulted in an increase in log Kpw for the DDT-related compounds of 0.28–0.49 log units (61–101% of Kpw), depending on the analyte. The increase in Kpw would have the effect of reducing by approximately half the calculated freely dissolved pore-water concentrations (Cfree). This demonstrates the importance of determining partition coefficients under conditions as they exist in the field.","language":"English","publisher":"Elsevier","doi":"10.1016/j.chroma.2016.02.038","usgsCitation":"Eganhouse, R., 2016, Determination of polydimethylsiloxane–water partition coefficients for ten 1-chloro-4-[2,2,2-trichloro-1-(4-chlorophenyl)ethyl]benzene-related compounds and twelve polychlorinated biphenyls using gas chromatography/mass spectrometry: Journal of Chromatography A, v. 1438, p. 226-235, https://doi.org/10.1016/j.chroma.2016.02.038.","productDescription":"10 p.","startPage":"226","endPage":"235","ipdsId":"IP-072715","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":471138,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chroma.2016.02.038","text":"Publisher Index Page"},{"id":336774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":336715,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0021967316301376"}],"volume":"1438","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b7eba8e4b01ccd5500bb23","contributors":{"authors":[{"text":"Eganhouse, Robert P. eganhous@usgs.gov","contributorId":2031,"corporation":false,"usgs":true,"family":"Eganhouse","given":"Robert P.","email":"eganhous@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":680342,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70169093,"text":"70169093 - 2016 - Groundwater data network interoperability","interactions":[],"lastModifiedDate":"2016-03-24T13:02:36","indexId":"70169093","displayToPublicDate":"2016-03-17T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2340,"text":"Journal of Hydroinformatics","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater data network interoperability","docAbstract":"Water data networks are increasingly being integrated to answer complex scientific questions that often span large geographical areas and cross political borders. Data heterogeneity is a major obstacle that impedes interoperability within and between such networks. It is resolved here for groundwater data at five levels of interoperability, within a Spatial Data Infrastructure architecture. The result is a pair of distinct national groundwater data networks for the United States and Canada, and a combined data network in which they are interoperable. This combined data network enables, for the first time, transparent public access to harmonized groundwater data from both sides of the shared international border.
","language":"English","publisher":"IWA Publishing","doi":"10.2166/hydro.2015.242","usgsCitation":"Brodaric, B., Booth, N., Boisvert, E., and Lucido, J., 2016, Groundwater data network interoperability: Journal of Hydroinformatics, v. 18, no. 1, p. 210-225, https://doi.org/10.2166/hydro.2015.242.","productDescription":"16 p.","startPage":"210","endPage":"225","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064679","costCenters":[],"links":[{"id":471140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2166/hydro.2015.242","text":"Publisher Index Page"},{"id":318936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-28","publicationStatus":"PW","scienceBaseUri":"56ebc71de4b0f59b85d99422","contributors":{"authors":[{"text":"Brodaric, Boyan","contributorId":80341,"corporation":false,"usgs":true,"family":"Brodaric","given":"Boyan","affiliations":[],"preferred":false,"id":622902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Booth, Nathaniel 0000-0001-6040-1031 nlbooth@usgs.gov","orcid":"https://orcid.org/0000-0001-6040-1031","contributorId":140641,"corporation":false,"usgs":true,"family":"Booth","given":"Nathaniel","email":"nlbooth@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":622903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boisvert, Eric","contributorId":167613,"corporation":false,"usgs":false,"family":"Boisvert","given":"Eric","email":"","affiliations":[{"id":24780,"text":"Geological Survey of Canada, Quebec, QC, Canada","active":true,"usgs":false}],"preferred":false,"id":622904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lucido, Jessica M. jlucido@usgs.gov","contributorId":4695,"corporation":false,"usgs":true,"family":"Lucido","given":"Jessica M.","email":"jlucido@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":622901,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169063,"text":"70169063 - 2016 - Ecological resistance in urban streams: the role of natural and legacy attributes","interactions":[],"lastModifiedDate":"2016-03-17T10:45:06","indexId":"70169063","displayToPublicDate":"2016-03-17T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Ecological resistance in urban streams: the role of natural and legacy attributes","docAbstract":"Urbanization substantially changes the physicochemical and biological characteristics of streams. The trajectory of negative effect is broadly similar around the world, but the nature and magnitude of ecological responses to urban growth differ among locations. Some heterogeneity in response arises from differences in the level of urban development and attributes of urban water management. However, the heterogeneity also may arise from variation in hydrologic, biological, and physicochemical templates that shaped stream ecosystems before urban development. We present a framework to develop hypotheses that predict how natural watershed and channel attributes in the pre-urban-development state may confer ecological resistance to urbanization. We present 6 testable hypotheses that explore the expression of such attributes under our framework: 1) greater water storage capacity mitigates hydrologic regime shifts, 2) coarse substrates and a balance between erosive forces and sediment supply buffer morphological changes, 3) naturally high ionic concentrations and pH pre-adapt biota to water-quality stress, 4) metapopulation connectivity results in retention of species richness, 5) high functional redundancy buffers trophic function from species loss, and 6) landuse history mutes or reverses the expected trajectory of eutrophication. Data from past comparative analyses support these hypotheses, but rigorous testing will require targeted investigations that account for confounding or interacting factors, such as diversity in urban infrastructure attributes. Improved understanding of the susceptibility or resistance of stream ecosystems could substantially strengthen conservation, management, and monitoring efforts in urban streams. We hope that these preliminary, conceptual hypotheses will encourage others to explore these ideas further and generate additional explanations for the heterogeneity observed in urban streams.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/684839","usgsCitation":"Utz, R.M., Hopkins, K., Beesley, L., Booth, D.B., Hawley, R.J., Baker, M.E., Freeman, M., and Jones, K.L., 2016, Ecological resistance in urban streams: the role of natural and legacy attributes: Freshwater Science, v. 35, no. 1, p. 380-397, https://doi.org/10.1086/684839.","productDescription":"18 p.","startPage":"380","endPage":"397","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066391","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":318934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ebc71be4b0f59b85d99410","contributors":{"authors":[{"text":"Utz, Ryan M.","contributorId":167572,"corporation":false,"usgs":false,"family":"Utz","given":"Ryan","email":"","middleInitial":"M.","affiliations":[{"id":24756,"text":"Chatham University, Pittsburg PA","active":true,"usgs":false}],"preferred":false,"id":622742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hopkins, Kristina G.","contributorId":167573,"corporation":false,"usgs":false,"family":"Hopkins","given":"Kristina G.","affiliations":[{"id":24757,"text":"University of Maryland, Annapolis MD","active":true,"usgs":false}],"preferred":false,"id":622743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beesley, Leah","contributorId":146250,"corporation":false,"usgs":false,"family":"Beesley","given":"Leah","email":"","affiliations":[{"id":16644,"text":"Centre of Excellence in Natural Resource Management, University of Western Australia,","active":true,"usgs":false}],"preferred":false,"id":622744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Booth, Derek B.","contributorId":100873,"corporation":false,"usgs":false,"family":"Booth","given":"Derek","email":"","middleInitial":"B.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":622745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hawley, Robert J.","contributorId":167574,"corporation":false,"usgs":false,"family":"Hawley","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":24758,"text":"Sustainable Streams, LLC, Louisville, KY","active":true,"usgs":false}],"preferred":false,"id":622746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baker, Matthew E.","contributorId":149189,"corporation":false,"usgs":false,"family":"Baker","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":17665,"text":"Department of Geography and Environmental Systems, University of Maryland, Baltimore County, Baltimore, Maryland, US","active":true,"usgs":false}],"preferred":false,"id":622747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":622741,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jones, Krista L. 0000-0002-0301-4497 kljones@usgs.gov","orcid":"https://orcid.org/0000-0002-0301-4497","contributorId":4550,"corporation":false,"usgs":true,"family":"Jones","given":"Krista","email":"kljones@usgs.gov","middleInitial":"L.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":622748,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70169087,"text":"70169087 - 2016 - Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood","interactions":[],"lastModifiedDate":"2017-04-06T17:10:58","indexId":"70169087","displayToPublicDate":"2016-03-17T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood","docAbstract":"Sea-level rise (SLR) poses a range of threats to natural and built environments1, 2, making assessments of SLR-induced hazards essential for informed decision making3. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30 × 30 m resolution predictions for more than 38,000 km2 of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/NCLIMATE2957","usgsCitation":"Lentz, E.E., Thieler, E.R., Plant, N.G., Stippa, S., Horton, R., and Gesch, D.B., 2016, Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood: Nature Climate Change, v. 6, p. 696-700, https://doi.org/10.1038/NCLIMATE2957.","productDescription":"5 p.","startPage":"696","endPage":"700","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062287","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471141,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/8260","text":"External Repository"},{"id":318931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-14","publicationStatus":"PW","scienceBaseUri":"56ebc71ce4b0f59b85d99418","contributors":{"authors":[{"text":"Lentz, Erika E. elentz@usgs.gov","contributorId":167611,"corporation":false,"usgs":true,"family":"Lentz","given":"Erika","email":"elentz@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":622852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":622853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":622854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stippa, Sawyer R. sstippa@usgs.gov","contributorId":139266,"corporation":false,"usgs":true,"family":"Stippa","given":"Sawyer R.","email":"sstippa@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":622855,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horton, Radley M.","contributorId":100407,"corporation":false,"usgs":true,"family":"Horton","given":"Radley M.","affiliations":[],"preferred":false,"id":622856,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","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},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":622857,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70169086,"text":"70169086 - 2016 - Detecting grizzly bear use of ungulate carcasses using global positioning system telemetry and activity data","interactions":[],"lastModifiedDate":"2016-06-24T11:09:17","indexId":"70169086","displayToPublicDate":"2016-03-17T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Detecting grizzly bear use of ungulate carcasses using global positioning system telemetry and activity data","docAbstract":"Global positioning system (GPS) wildlife collars have revolutionized wildlife research. Studies of predation by free-ranging carnivores have particularly benefited from the application of location clustering algorithms to determine when and where predation events occur. These studies have changed our understanding of large carnivore behavior, but the gains have concentrated on obligate carnivores. Facultative carnivores, such as grizzly/brown bears (Ursus arctos), exhibit a variety of behaviors that can lead to the formation of GPS clusters. We combined clustering techniques with field site investigations of grizzly bear GPS locations (n = 732 site investigations; 2004–2011) to produce 174 GPS clusters where documented behavior was partitioned into five classes (large-biomass carcass, small-biomass carcass, old carcass, non-carcass activity, and resting). We used multinomial logistic regression to predict the probability of clusters belonging to each class. Two cross-validation methods—leaving out individual clusters, or leaving out individual bears—showed that correct prediction of bear visitation to large-biomass carcasses was 78–88%, whereas the false-positive rate was 18–24%. As a case study, we applied our predictive model to a GPS data set of 266 bear-years in the Greater Yellowstone Ecosystem (2002–2011) and examined trends in carcass visitation during fall hyperphagia (September–October). We identified 1997 spatial GPS clusters, of which 347 were predicted to be large-biomass carcasses. We used the clustered data to develop a carcass visitation index, which varied annually, but more than doubled during the study period. Our study demonstrates the effectiveness and utility of identifying GPS clusters associated with carcass visitation by a facultative carnivore.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-016-3594-5","usgsCitation":"Ebinger, M.R., Haroldson, M.A., van Manen, F.T., Costello, C., Bjornlie, D., Thompson, D.J., Gunther, K.A., Fortin, J., Teisberg, J.E., Pils, S.R., White, P.J., Cain, S.L., and Cross, P.C., 2016, Detecting grizzly bear use of ungulate carcasses using global positioning system telemetry and activity data: Oecologia, v. 181, no. 3, p. 695-708, https://doi.org/10.1007/s00442-016-3594-5.","productDescription":"14 p.","startPage":"695","endPage":"708","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065281","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":318930,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Greater Yellowstone Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.895751953125,\n 42.49640294093708\n ],\n [\n -111.895751953125,\n 45.767522962149904\n ],\n [\n -108.5888671875,\n 45.767522962149904\n ],\n [\n -108.5888671875,\n 42.49640294093708\n ],\n [\n -111.895751953125,\n 42.49640294093708\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"181","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-14","publicationStatus":"PW","scienceBaseUri":"56ebc71ae4b0f59b85d9940b","chorus":{"doi":"10.1007/s00442-016-3594-5","url":"http://dx.doi.org/10.1007/s00442-016-3594-5","publisher":"Springer Nature","authors":"Ebinger Michael R., Haroldson Mark A., van Manen Frank T., Costello Cecily M., Bjornlie Daniel D., Thompson Daniel J., Gunther Kerry A., Fortin Jennifer K., Teisberg Justin E., Pils Shannon R., White P. J., Cain Steven L., Cross Paul C.","journalName":"Oecologia","publicationDate":"3/14/2016","auditedOn":"8/1/2016","publiclyAccessibleDate":"3/14/2016"},"contributors":{"authors":[{"text":"Ebinger, Michael R. mebinger@usgs.gov","contributorId":5771,"corporation":false,"usgs":true,"family":"Ebinger","given":"Michael","email":"mebinger@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":622840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":622839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":622841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Costello, Cecily M.","contributorId":145510,"corporation":false,"usgs":false,"family":"Costello","given":"Cecily M.","affiliations":[{"id":5117,"text":"University of Montana, College of Forestry and Conservation, University Hall, Room 309, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":622842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bjornlie, Daniel D.","contributorId":145512,"corporation":false,"usgs":false,"family":"Bjornlie","given":"Daniel D.","affiliations":[{"id":16140,"text":"Wyoming Game & Fish Department, Large Carnivore Section, Lander, Wyoming 82520, USA","active":true,"usgs":false}],"preferred":false,"id":622843,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, Daniel J.","contributorId":149795,"corporation":false,"usgs":false,"family":"Thompson","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":5116,"text":"Large Carnivore Section, Wyoming Game & Fish Department, 260 Buena Vista, Lander, WY 82520, USA","active":true,"usgs":false}],"preferred":false,"id":622844,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gunther, Kerry A.","contributorId":84621,"corporation":false,"usgs":false,"family":"Gunther","given":"Kerry","email":"","middleInitial":"A.","affiliations":[{"id":5118,"text":"Yellowstone National Park, Yellowstone Center for Resources, Bear Management Office, P.O. Box 168, Yellowstone National Park, WY 82190","active":true,"usgs":false}],"preferred":false,"id":622845,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fortin, Jennifer K.","contributorId":99030,"corporation":false,"usgs":true,"family":"Fortin","given":"Jennifer K.","affiliations":[],"preferred":false,"id":622846,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Teisberg, Justin E.","contributorId":124582,"corporation":false,"usgs":false,"family":"Teisberg","given":"Justin","email":"","middleInitial":"E.","affiliations":[{"id":5127,"text":"Washington State University, P.O. Box 644236, Pullman, WA 99164","active":true,"usgs":false}],"preferred":false,"id":622847,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pils, Shannon R","contributorId":167609,"corporation":false,"usgs":false,"family":"Pils","given":"Shannon","email":"","middleInitial":"R","affiliations":[{"id":24778,"text":"US Forest Service, Shoshone National Forest, Wapiti Ranger District, 203A Yellowstone Avenue, Cody, WY 82414,USA","active":true,"usgs":false}],"preferred":false,"id":622848,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"White, P J","contributorId":167610,"corporation":false,"usgs":false,"family":"White","given":"P","email":"","middleInitial":"J","affiliations":[{"id":24779,"text":"National Park Service, Yellowstone Center for Resources, P.O. Box 168, Yellowstone National Park, WY 82190, USA","active":true,"usgs":false}],"preferred":false,"id":622849,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Cain, Steven L.","contributorId":145511,"corporation":false,"usgs":false,"family":"Cain","given":"Steven","email":"","middleInitial":"L.","affiliations":[{"id":16139,"text":"National Park Service, Grand Teton National Park, Moose, Wyoming 83012, USA","active":true,"usgs":false}],"preferred":false,"id":622850,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":622851,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70174417,"text":"70174417 - 2016 - Contact heterogeneities in feral swine: implications for disease management and future research","interactions":[],"lastModifiedDate":"2016-07-12T12:39:51","indexId":"70174417","displayToPublicDate":"2016-03-17T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Contact heterogeneities in feral swine: implications for disease management and future research","docAbstract":"Contact rates vary widely among individuals in socially structured wildlife populations. Understanding the interplay of factors responsible for this variation is essential for planning effective disease management. Feral swine (Sus scrofa) are a socially structured species which pose an increasing threat to livestock and human health, and little is known about contact structure. We analyzed 11 GPS data sets from across the United States to understand the interplay of ecological and demographic factors on variation in co-location rates, a proxy for contact rates. Between-sounder contact rates strongly depended on the distance among home ranges (less contact among sounders separated by >2 km; negligible between sounders separated by >6 km), but other factors causing high clustering between groups of sounders also seemed apparent. Our results provide spatial parameters for targeted management actions, identify data gaps that could lead to improved management and provide insight on experimental design for quantitating contact rates and structure.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1230","usgsCitation":"Pepin, K., Davis, A.J., Beasley, J., Boughton, R., Campbell, T., Cooper, S., Gaston, W., Hartley, S.B., Kilgo, J.C., Wisely, S., Wyckoff, C., and VerCauteren, K., 2016, Contact heterogeneities in feral swine: implications for disease management and future research: Ecosphere, v. 7, no. 3, Article e01230; 11 p., https://doi.org/10.1002/ecs2.1230.","productDescription":"Article e01230; 11 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065400","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":471143,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1230","text":"Publisher Index Page"},{"id":325097,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-17","publicationStatus":"PW","scienceBaseUri":"579dcfcbe4b0589fa1cbd683","contributors":{"authors":[{"text":"Pepin, Kim","contributorId":172813,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim","affiliations":[{"id":27093,"text":"National Wildlife Research Center, United States Department of Agriculture, 4101 Laporte Ave., Fort Collins, CO, 80526","active":true,"usgs":false}],"preferred":false,"id":642166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Amy J.","contributorId":149854,"corporation":false,"usgs":false,"family":"Davis","given":"Amy","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":642167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beasley, James","contributorId":172814,"corporation":false,"usgs":false,"family":"Beasley","given":"James","affiliations":[{"id":27094,"text":"University of Georgia, Savannah River Ecology Laboratory, Warnell School of Forestry and Natural Resources, PO Drawer E, Aiken, SC 29802","active":true,"usgs":false}],"preferred":false,"id":642168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boughton, Raoul","contributorId":172817,"corporation":false,"usgs":false,"family":"Boughton","given":"Raoul","affiliations":[{"id":27096,"text":"Wildlife Ecology and Conservation, Range Cattle Research and Education Center, University of Florida, 3401 Experiment Station, Ona, Florida 33865 USA","active":true,"usgs":false}],"preferred":false,"id":642171,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell, Tyler","contributorId":172818,"corporation":false,"usgs":false,"family":"Campbell","given":"Tyler","affiliations":[{"id":27097,"text":"East Foundation, 200 Concord Plaza Drive, Suite 410, San Antonio, Texas 78216 USA","active":true,"usgs":false}],"preferred":false,"id":642172,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cooper, Susan","contributorId":172819,"corporation":false,"usgs":false,"family":"Cooper","given":"Susan","affiliations":[{"id":27098,"text":"Texas A&M AgriLife Research, 1619 Garner Field Road, Uvalde, Texas 78801 USA","active":true,"usgs":false}],"preferred":false,"id":642173,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gaston, Wes","contributorId":172816,"corporation":false,"usgs":false,"family":"Gaston","given":"Wes","email":"","affiliations":[{"id":27095,"text":"USDA/APHIS/Wildlife Services, 602 Duncan Drive, Auburn, Alabama 36849 USA","active":true,"usgs":false}],"preferred":false,"id":642170,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hartley, Stephen B. 0000-0003-1380-2769 hartleys@usgs.gov","orcid":"https://orcid.org/0000-0003-1380-2769","contributorId":4164,"corporation":false,"usgs":true,"family":"Hartley","given":"Stephen","email":"hartleys@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":642165,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kilgo, John C.","contributorId":112955,"corporation":false,"usgs":true,"family":"Kilgo","given":"John","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":642174,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wisely, Samantha","contributorId":172820,"corporation":false,"usgs":false,"family":"Wisely","given":"Samantha","affiliations":[{"id":27099,"text":"Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida 32611 USA","active":true,"usgs":false}],"preferred":false,"id":642175,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wyckoff, Christy","contributorId":172821,"corporation":false,"usgs":false,"family":"Wyckoff","given":"Christy","email":"","affiliations":[{"id":27100,"text":"Santa Lucia Conservancy, 26700 Rancho San Carlos Road, Carmel, California 93923 USA","active":true,"usgs":false}],"preferred":false,"id":642176,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"VerCauteren, Kurt","contributorId":172815,"corporation":false,"usgs":false,"family":"VerCauteren","given":"Kurt","affiliations":[{"id":27093,"text":"National Wildlife Research Center, United States Department of Agriculture, 4101 Laporte Ave., Fort Collins, CO, 80526","active":true,"usgs":false}],"preferred":false,"id":642169,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70169116,"text":"70169116 - 2016 - Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation","interactions":[],"lastModifiedDate":"2017-11-22T17:32:53","indexId":"70169116","displayToPublicDate":"2016-03-17T09:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation","title":"Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation","docAbstract":"Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS ONE","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0151811","usgsCitation":"Shinneman, D.J., Means, R.E., Potter, K.M., and Hipkins, V.D., 2016, Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation: PLoS ONE, v. 11, no. 3, https://doi.org/10.1371/journal.pone.0151811.","productDescription":"24 p.","startPage":"e0151811","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-062671","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":471144,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0151811","text":"Publisher Index Page"},{"id":319044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.26855468749999,\n 32.76880048488168\n ],\n [\n -115.400390625,\n 34.415973384481866\n ],\n [\n -116.4111328125,\n 35.137879119634185\n ],\n [\n -116.6748046875,\n 36.527294814546245\n ],\n [\n -116.806640625,\n 37.64903402157866\n ],\n [\n -117.94921874999999,\n 38.788345355085625\n ],\n [\n -118.69628906249999,\n 39.63953756436671\n ],\n [\n -118.91601562499999,\n 41.11246878918086\n ],\n [\n -118.95996093749999,\n 41.80407814427237\n ],\n [\n -119.44335937499999,\n 42.61779143282346\n ],\n [\n -119.66308593749999,\n 43.32517767999296\n ],\n [\n -118.3447265625,\n 43.35713822211053\n ],\n [\n -117.333984375,\n 43.8028187190472\n ],\n [\n -116.93847656250001,\n 44.05601169578525\n ],\n [\n -116.1474609375,\n 43.54854811091288\n ],\n [\n -115.57617187499999,\n 43.03677585761058\n ],\n [\n -114.7412109375,\n 43.26120612479979\n ],\n [\n -114.12597656249999,\n 43.866218006556394\n ],\n [\n -113.64257812499999,\n 44.5278427984555\n ],\n [\n -113.51074218749999,\n 45.182036837015886\n ],\n [\n -112.67578124999999,\n 45.82879925192134\n ],\n [\n -112.587890625,\n 46.22545288226939\n ],\n [\n -111.9287109375,\n 46.34692761055676\n ],\n [\n -110.56640625,\n 45.9511496866914\n ],\n [\n -109.951171875,\n 45.398449976304086\n ],\n [\n -109.6875,\n 44.62175409623324\n ],\n [\n -108.7646484375,\n 44.465151013519616\n ],\n [\n -107.8857421875,\n 43.77109381775651\n ],\n [\n -108.017578125,\n 42.87596410238254\n ],\n [\n -108.2373046875,\n 42.42345651793833\n ],\n [\n -108.5009765625,\n 41.57436130598913\n ],\n [\n -108.544921875,\n 41.37680856570233\n ],\n [\n -110.91796875,\n 41.244772343082104\n ],\n [\n -112.7197265625,\n 40.212440718286466\n ],\n [\n -114.12597656249999,\n 39.842286020743394\n ],\n [\n -114.697265625,\n 39.26628442213066\n ],\n [\n -115.09277343749999,\n 38.34165619279593\n ],\n [\n -114.873046875,\n 37.09023980307208\n ],\n [\n -113.90625,\n 35.31736632923788\n ],\n [\n -112.5,\n 33.97980872872457\n ],\n [\n -111.57714843749999,\n 33.61461929233378\n ],\n [\n -111.005859375,\n 31.952162238024975\n ],\n [\n -111.005859375,\n 31.27855085894653\n ],\n [\n -108.2373046875,\n 31.353636941500987\n ],\n [\n -108.1494140625,\n 31.728167146023935\n ],\n [\n -106.74316406249999,\n 31.80289258670676\n ],\n [\n -105.64453124999999,\n 31.203404950917395\n ],\n [\n -104.765625,\n 30.524413269923986\n ],\n [\n -104.4580078125,\n 29.726222319395504\n ],\n [\n -103.4033203125,\n 28.9600886880068\n ],\n [\n -102.83203125,\n 29.19053283229458\n ],\n [\n -103.1396484375,\n 30.44867367928756\n ],\n [\n -103.623046875,\n 31.80289258670676\n ],\n [\n -103.9306640625,\n 32.02670629333614\n ],\n [\n -103.974609375,\n 32.879587173066305\n ],\n [\n -104.58984375,\n 34.56085936708384\n ],\n [\n -103.6669921875,\n 35.746512259918504\n ],\n [\n -102.5244140625,\n 37.020098201368114\n ],\n [\n -102.919921875,\n 38.134556577054134\n ],\n [\n -103.22753906249999,\n 39.198205348894795\n ],\n [\n -103.5791015625,\n 39.90973623453719\n ],\n [\n -102.6123046875,\n 40.74725696280421\n ],\n [\n -101.7333984375,\n 41.37680856570233\n ],\n [\n -98.96484375,\n 41.244772343082104\n ],\n [\n -97.42675781249999,\n 41.541477666790286\n ],\n [\n -97.3828125,\n 42.16340342422401\n ],\n [\n -98.4375,\n 42.84375132629021\n ],\n [\n -98.8330078125,\n 43.58039085560786\n ],\n [\n -100.1513671875,\n 43.89789239125797\n ],\n [\n -101.42578124999999,\n 44.308126684886126\n ],\n [\n -101.6455078125,\n 45.213003555993964\n ],\n [\n -101.77734374999999,\n 46.13417004624326\n ],\n [\n -101.9970703125,\n 47.040182144806664\n ],\n [\n -102.568359375,\n 47.45780853075031\n ],\n [\n -103.4912109375,\n 47.724544549099676\n ],\n [\n -105.205078125,\n 47.81315451752768\n ],\n [\n -106.69921875,\n 48.019324184801185\n ],\n [\n -107.40234375,\n 47.78363463526376\n ],\n [\n -108.10546875,\n 47.45780853075031\n ],\n [\n -109.951171875,\n 47.57652571374621\n ],\n [\n -111.005859375,\n 48.1367666796927\n ],\n [\n -111.4013671875,\n 49.009050809382046\n ],\n [\n -123.1787109375,\n 48.980216985374994\n ],\n [\n -122.958984375,\n 48.777912755501845\n ],\n [\n -123.26660156249999,\n 48.69096039092549\n ],\n [\n -123.134765625,\n 48.42920055556841\n ],\n [\n -123.57421875,\n 48.19538740833338\n ],\n [\n -124.67285156250001,\n 48.42920055556841\n ],\n [\n -124.67285156250001,\n 47.98992166741417\n ],\n [\n -124.3212890625,\n 47.249406957888446\n ],\n [\n -123.96972656249999,\n 46.13417004624326\n ],\n [\n -124.18945312500001,\n 44.74673324024678\n ],\n [\n -124.45312499999999,\n 43.42100882994726\n ],\n [\n -124.49707031249999,\n 42.90816007196054\n ],\n [\n -124.3212890625,\n 42.261049162113856\n ],\n [\n -124.23339843749999,\n 41.409775832009565\n ],\n [\n -124.49707031249999,\n 40.41349604970198\n ],\n [\n -123.96972656249999,\n 39.87601941962116\n ],\n [\n -123.8818359375,\n 38.95940879245423\n ],\n [\n -123.134765625,\n 38.238180119798635\n ],\n [\n -123.00292968749999,\n 37.64903402157866\n ],\n [\n -121.86035156249999,\n 36.87962060502676\n ],\n [\n -121.9921875,\n 36.421282443649496\n ],\n [\n -120.84960937499999,\n 35.28150065789119\n ],\n [\n -120.673828125,\n 34.34343606848294\n ],\n [\n -120.05859375,\n 34.30714385628804\n ],\n [\n -118.65234374999999,\n 34.052659421375964\n ],\n [\n -117.7734375,\n 33.54139466898275\n ],\n [\n -117.24609374999999,\n 32.58384932565662\n ],\n [\n -115.26855468749999,\n 32.76880048488168\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-17","publicationStatus":"PW","scienceBaseUri":"56f11b4de4b0f59b85ddc3c9","contributors":{"authors":[{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":623045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Means, Robert E.","contributorId":105215,"corporation":false,"usgs":true,"family":"Means","given":"Robert","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":623046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Potter, Kevin M.","contributorId":167660,"corporation":false,"usgs":false,"family":"Potter","given":"Kevin","email":"","middleInitial":"M.","affiliations":[{"id":24794,"text":"Department of Forestry and Environmental Resources, North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":623047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hipkins, Valerie D.","contributorId":167661,"corporation":false,"usgs":false,"family":"Hipkins","given":"Valerie","email":"","middleInitial":"D.","affiliations":[{"id":24795,"text":"National Forest Genetics Laboratory, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":623048,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209112,"text":"70209112 - 2016 - Report A: Fish and habitat assessment in Rock Creek, Klickitat County, Washington, June 2013-December 2015","interactions":[],"lastModifiedDate":"2020-03-18T06:43:44","indexId":"70209112","displayToPublicDate":"2016-03-17T07:26:46","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Report A: Fish and habitat assessment in Rock Creek, Klickitat County, Washington, June 2013-December 2015","docAbstract":"The U.S. Geological Survey (USGS) and the Yakama Nation have collaborated in the Rock Creek subbasin, southeastern, Washington since 2009 to assess steelhead (Oncorynchus mykiss) populations and habitat conditions. Rock Creek, flows south to the Columbia River at river kilometer (rkm) 368. During 2015, a habitat survey was conducted, and monitoring of Passive Integrated Transponder (PIT)-tagged salmonids in the Rock Creek subbasin continued. Two multiplexing PIT-tag interrogation systems (PTISs) were installed in Rock Creek in the fall of 2009 to evaluate timing and degree of salmonid movement, smolting success, stray rates, and other life history attributes. These have been monitored every year since, during the spring, fall, and winter months. Returning adult steelhead detection histories are summarized from past Rock Creek PIT-tagging efforts. Detection histories and detection efficiencies were used to estimate a smolt-to-adult return rate (SAR) for Rock Creek PIT-tagged steelhead (tagged from 2009 to 2012) that ranged from 2.2% to 5.5%. Additionally, a SAR was also estimated for Rock Creek PIT-tagged steelhead returning to Bonneville Dam, Columbia River (rkm 235). The SAR rate to Bonneville Dam was always higher (2.4% to 10.4%), indicating straying of adults to other sites for spawning potentially further upstream or in other tributaries, or pre-spawn mortality. Twenty-two Rock Creek PIT-tagged steelhead were detected returning to Rock Creek and 35 were detected at Bonneville Dam from past tagging efforts (2009 – 2012). Monitoring of the Rock Creek PTISs [Rock Creek Lower (RCL) and Rock Creek Squaw (RCS)] provide evidence for PIT-tagged salmonid use from fish tagged outside of Rock Creek subbasin (out-of-basin) origins. A total of 82 out-of-basin PIT-tagged fish have been detected at the Rock Creek PTISs since installation.\n\nThe habitat survey was conducted in Rock Creek from September 16 to October 7, 2015. The survey started at rkm 2 and continued upstream to rkm 29.3, and included portions of the major tributaries, ranging from 1 to 9 rkm survey length upstream from their confluence. During the survey, we measured the lengths of all dry and non-pool wet sections, and for pools: the length, wetted width, average residual depth, maximum residual depth, and temperature. During the 2015 survey of Rock Creek, 38% of the river between rkm 2 and 29 was classified as dry, with a higher relative proportion (57%) of dry being in the lower river section (rkm 2-13) than the upper river section (33%, rkm 14-22). This was higher than during survey years 2010 to 2012, which ranged from 29% to 43% in the lower river. As a result of the increase in dry area the percent of non-pool wet habitat was less (22%) than previous years (range 34% to 43%), as well as the percent of pools (21%). However, more river kilometers were surveyed in the lower river section (rkm 2-13) than previous years. For the 2015 survey length (rkm 2 to 29), 19% was classified as pools and 43% was non-pool wet. This work informs potential restoration actions by identifying the persistent pools across years and in years of low water flow (i.e., 2015). This work also provides a baseline to evaluate effectiveness of future restoration actions. Potential restoration actions could include headwater and upland restoration to improve base flows and pool habitat enhancement, through increased structure and vegetation plantings for increased cover.\n\nThe Rock Creek steelhead population remains to be an important cultural resource for the Rock Creek Band of the Yakama Nation Tribe. The Rock Creek steelhead population is part of the Cascades Eastern Slope major population group (MPG), one of four MPGs contributing to the Middle Columbia steelhead distinct population segment (DPS). The National Marine Fisheries Service recovery plan for Rock Creek (NMFS 2009a) identifies an overall biological recovery goal for Rock Creek steelhead to contribute to recovery of the Mid-Columbia D","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Rock Creek Fish and Habitat Assessment for Prioritization of Restoration and Protection Actions","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Bonneville Power Administration","collaboration":"Bonneville Power Administration; Yakama Nation","usgsCitation":"Hardiman, J.M., and Harvey, E., 2016, Report A: Fish and habitat assessment in Rock Creek, Klickitat County, Washington, June 2013-December 2015, 41 p.","productDescription":"41 p.","startPage":"A-1","endPage":"A-41","ipdsId":"IP-087695","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373308,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P160751"}],"country":"United States","state":"Washington","county":"Klickitat County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-121.5188,46.0428],[-121.4474,46.0429],[-121.4133,46.0431],[-121.1152,46.043],[-120.9841,46.0421],[-120.3681,46.0415],[-120.3072,46.041],[-120.1152,46.0397],[-119.9088,46.039],[-119.8662,46.0388],[-119.8677,45.9536],[-119.8679,45.8676],[-119.8688,45.8376],[-119.8839,45.8331],[-119.893,45.8311],[-119.9022,45.8291],[-119.9081,45.8275],[-119.9205,45.8285],[-119.9316,45.8278],[-119.9407,45.8288],[-119.9531,45.8288],[-119.9616,45.8284],[-119.9682,45.8273],[-119.978,45.8253],[-119.9819,45.8225],[-119.9905,45.8178],[-119.997,45.8148],[-119.9983,45.8142],[-120.0075,45.8117],[-120.0154,45.808],[-120.0219,45.8044],[-120.0272,45.8008],[-120.0363,45.7978],[-120.0461,45.7944],[-120.0586,45.7906],[-120.0716,45.7902],[-120.0749,45.7881],[-120.0827,45.7861],[-120.0945,45.7829],[-120.101,45.7827],[-120.1102,45.7823],[-120.1239,45.7794],[-120.1344,45.7759],[-120.1435,45.7732],[-120.15,45.7709],[-120.1638,45.7653],[-120.1703,45.7626],[-120.1769,45.7587],[-120.1828,45.7459],[-120.1893,45.742],[-120.1939,45.7363],[-120.2005,45.7308],[-120.209,45.7281],[-120.2142,45.7267],[-120.2188,45.7261],[-120.2233,45.7254],[-120.2299,45.7249],[-120.2416,45.7236],[-120.2494,45.725],[-120.2579,45.7245],[-120.267,45.7241],[-120.2762,45.7236],[-120.2853,45.7227],[-120.2951,45.7209],[-120.3049,45.7182],[-120.3147,45.7168],[-120.3212,45.7155],[-120.3342,45.7118],[-120.3486,45.7109],[-120.359,45.7091],[-120.3695,45.7076],[-120.3819,45.7064],[-120.3858,45.7066],[-120.391,45.7062],[-120.4008,45.7048],[-120.4066,45.7035],[-120.4145,45.7023],[-120.4255,45.7005],[-120.4321,45.6998],[-120.4444,45.6989],[-120.4516,45.6987],[-120.4594,45.6978],[-120.466,45.6968],[-120.4738,45.6959],[-120.4809,45.6955],[-120.492,45.6971],[-120.5018,45.7023],[-120.5096,45.7069],[-120.5162,45.7101],[-120.5259,45.7156],[-120.5331,45.7213],[-120.5409,45.7277],[-120.5475,45.7341],[-120.554,45.7401],[-120.5625,45.7435],[-120.5723,45.7462],[-120.5808,45.7471],[-120.5912,45.7489],[-120.5984,45.7489],[-120.6095,45.7485],[-120.6186,45.748],[-120.631,45.7478],[-120.6401,45.7455],[-120.6479,45.7423],[-120.6544,45.7394],[-120.6557,45.7387],[-120.6629,45.735],[-120.6714,45.731],[-120.6779,45.7264],[-120.6837,45.7214],[-120.6896,45.7173],[-120.6955,45.7155],[-120.7046,45.7141],[-120.7124,45.7116],[-120.7222,45.7104],[-120.7281,45.7076],[-120.7365,45.7051],[-120.7463,45.7021],[-120.7567,45.6996],[-120.7645,45.697],[-120.7736,45.6945],[-120.788,45.6904],[-120.7951,45.6883],[-120.8134,45.6837],[-120.8251,45.6804],[-120.8348,45.6777],[-120.8492,45.6744],[-120.8576,45.6712],[-120.8667,45.6662],[-120.8725,45.662],[-120.8784,45.6577],[-120.8829,45.6542],[-120.8862,45.6515],[-120.892,45.649],[-120.9011,45.6453],[-120.9076,45.6439],[-120.9128,45.6443],[-120.9148,45.645],[-120.9187,45.6463],[-120.9239,45.6504],[-120.9324,45.6562],[-120.9383,45.6589],[-120.9442,45.6598],[-120.952,45.6584],[-120.9644,45.6553],[-120.9709,45.6533],[-120.9754,45.6516],[-120.9793,45.6507],[-120.9832,45.651],[-120.9878,45.653],[-120.995,45.6562],[-121.0022,45.6584],[-121.0061,45.6593],[-121.01,45.6579],[-121.0158,45.6556],[-121.0204,45.6554],[-121.0289,45.6554],[-121.0393,45.6549],[-121.0484,45.6548],[-121.0549,45.6571],[-121.0699,45.6561],[-121.079,45.6538],[-121.092,45.6455],[-121.0971,45.6368],[-121.1036,45.6338],[-121.1094,45.6301],[-121.1133,45.6269],[-121.1152,45.6232],[-121.1191,45.6198],[-121.1236,45.6161],[-121.1288,45.6133],[-121.1327,45.6126],[-121.1366,45.6117],[-121.1444,45.6101],[-121.1561,45.6077],[-121.1665,45.6067],[-121.1763,45.6064],[-121.1867,45.6117],[-121.1933,45.6194],[-121.1934,45.631],[-121.1941,45.6352],[-121.1962,45.6447],[-121.2002,45.6539],[-121.2035,45.6591],[-121.2094,45.6669],[-121.2167,45.6719],[-121.2232,45.673],[-121.2415,45.6774],[-121.2539,45.679],[-121.2591,45.6817],[-121.2735,45.6832],[-121.2794,45.6855],[-121.2912,45.6925],[-121.3004,45.6988],[-121.3063,45.7008],[-121.3141,45.7022],[-121.322,45.7017],[-121.3272,45.7021],[-121.333,45.7032],[-121.3396,45.7048],[-121.3455,45.7066],[-121.3553,45.7065],[-121.3631,45.706],[-121.3709,45.705],[-121.3806,45.7013],[-121.3845,45.699],[-121.3916,45.6964],[-121.3975,45.695],[-121.4111,45.6929],[-121.4183,45.6928],[-121.4294,45.6948],[-121.4367,45.6998],[-121.4412,45.7008],[-121.4484,45.7025],[-121.4576,45.7035],[-121.4628,45.7062],[-121.4707,45.7096],[-121.4799,45.7141],[-121.4852,45.7173],[-121.4976,45.7215],[-121.5029,45.7233],[-121.5146,45.7246],[-121.5251,45.7262],[-121.5245,45.728],[-121.5239,45.733],[-121.5233,45.738],[-121.524,45.7412],[-121.5247,45.7458],[-121.5255,45.7504],[-121.5268,45.754],[-121.5295,45.7568],[-121.5321,45.759],[-121.5361,45.7622],[-121.5374,45.7658],[-121.5362,45.7727],[-121.5324,45.7746],[-121.5259,45.7778],[-121.5168,45.7797],[-121.5136,45.7852],[-121.5978,45.785],[-121.607,45.7863],[-121.6068,45.8156],[-121.6073,45.8454],[-121.6071,45.8737],[-121.611,45.8737],[-121.6111,45.9588],[-121.6113,46.0435],[-121.5188,46.0428]]]},\"properties\":{\"name\":\"Klickitat\",\"state\":\"WA\"}}]}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hardiman, Jill M. 0000-0002-3661-9695 jhardiman@usgs.gov","orcid":"https://orcid.org/0000-0002-3661-9695","contributorId":2672,"corporation":false,"usgs":true,"family":"Hardiman","given":"Jill","email":"jhardiman@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":784965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Elaine","contributorId":203907,"corporation":false,"usgs":false,"family":"Harvey","given":"Elaine","email":"","affiliations":[{"id":36750,"text":"Yakama Nation Fisheries, 4 Bickleton Hwy, Goldendale, WA 98620","active":true,"usgs":false}],"preferred":false,"id":784966,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168418,"text":"sir20165021 - 2016 - Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10","interactions":[],"lastModifiedDate":"2016-03-18T08:13:50","indexId":"sir20165021","displayToPublicDate":"2016-03-17T00:00:00","publicationYear":"2016","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":"2016-5021","title":"Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10","docAbstract":"The Albuquerque area of New Mexico has two principal sources of water: (1) groundwater from the Santa Fe Group aquifer system, and (2) surface water from the Rio Grande. From 1960 to 2002, pumping from the Santa Fe Group aquifer system caused groundwater levels to decline more than 120 feet while water-level declines along the Rio Grande in Albuquerque were generally less than 40 feet. These differences in water-level declines in the Albuquerque area have resulted in a great deal of interest in quantifying the river-aquifer interaction associated with the Rio Grande.
In 2003, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, acting as fiscal agent for the Middle Rio Grande Endangered Species Collaborative Program, and the U.S. Army Corps of Engineers, began a study to characterize the hydrogeology of the Rio Grande inner valley alluvial aquifer in the Albuquerque area of New Mexico. The study provides hydrologic data in order to enhance the understanding of rates of water leakage from the Rio Grande to the alluvial aquifer, groundwater flow through the aquifer, and discharge of water from the aquifer to riverside drains. The study area extends about 20 miles along the Rio Grande in the Albuquerque area. Piezometers and surface-water gages were installed in paired transects at eight locations. Nested piezometers, completed at various depths in the alluvial aquifer, and surface-water gages, installed in the Rio Grande and riverside drains, were instrumented with pressure transducers. Water-level and water-temperature data were collected from 2009 to 2010.
Water levels from the piezometers indicated that groundwater movement was usually away from the river towards the riverside drains. Annual mean horizontal groundwater gradients in the inner valley alluvial aquifer ranged from 0.0024 (I-25 East) to 0.0144 (Pajarito East). The median hydraulic conductivity values of the inner valley alluvial aquifer, determined from slug tests, ranged from 30 feet per day (ft/d) (Montaño) to 120 ft/d (Central) for paired transects, with a median hydraulic conductivity for all transects of 50 ft/d. Daily mean groundwater fluxes from the river through the inner valley alluvial aquifer computed using Darcy’s Law and the slug test results ranged from about 0.01 ft/d (Montaño West) to between 1.0 and 2.0 ft/d (Central East). Median annual groundwater fluxes from the river through the inner valley alluvial aquifer determined using the Suzuki-Stallman method was greatest at Alameda East (0.50 ft/d) and lowest at Alameda West (0.25 ft/d). The results from both methods agreed reasonably well.
Seepage investigations conducted by measuring discharge in the east and west riverside drains provided information for computing changes in flow within the drains and for evaluating results from Darcy’s Law and Suzuki-Stallman method flux calculations. Discharge measured in the east riverside drain between the Barelas Bridge and the I-25 bridge indicated that the flow in the east riverside drain increased by an average of 56.5 cubic feet per day per linear foot (ft3/d/ft) of drain. Discharge measured in the west riverside drain between the Central bridge and the I-25 bridge indicated that flow increased between west drain miles 0 and 4, an average of 53.8 ft3/d/ft of drain, and that flow increased between west drain miles 7 and 10, an average of 44.9 ft3/d/ft of drain. In comparison to the seepage measurement results, the groundwater fluxes from the river through the inner valley alluvial aquifer calculated from Darcy’s Law (qslug) and by the Suzuki-Stallman method (qheat) would account for 20–36 percent or 53–95 percent, respectively, of the total flow in the east riverside drain and 22–31 percent or 19–26 percent, respectively, of the total flow in the west drain. These results indicate that the drains likely also receive water from outside the inner valley.
The spatial variability of horizontal hydraulic gradients and groundwater fluxes can be primarily attributed to variability in the distances between the river and riverside drains throughout the study area and geologic heterogeneities in the alluvial aquifer. Temporal variability in the water levels, which control the horizontal hydraulic gradients and fluxes between the Rio Grande and the riverside drains, can be primarily attributed to seasonal fluctuations in river stage and irrigation practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165021","collaboration":"Prepared in cooperation with Bureau of Reclamation acting as fiscal agent for the Middle Rio Grande Endangered Species Collaborative Program","usgsCitation":"Rankin, D.R., Oelsner, G.P., McCoy, K.J., Moret, G.J.M., Worthington, J.A., and Bandy-Baldwin, K.M., 2016, Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10: U.S. Geological Survey Scientific Investigations Report 2016–5021, 89 p., https://dx.doi.org/10.3133/sir20165021.","productDescription":"viii, 89 p.","numberOfPages":"101","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-033038","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":318926,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5021/sir20165021.pdf","text":"Report","size":"5.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5021"},{"id":318925,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5021/coverthb.jpg"}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.7266845703125,\n 34.95011635301367\n ],\n [\n -106.7266845703125,\n 35.290468565908775\n ],\n [\n -106.56051635742188,\n 35.290468565908775\n ],\n [\n -106.56051635742188,\n 34.95011635301367\n ],\n [\n -106.7266845703125,\n 34.95011635301367\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
5338 Montogmery Blvd., NE Suite 400
Albuquerque, NM 87109–1311
The biogeochemical cycling of metals and other contaminants in river-floodplain corridors is controlled by microbial activity responding to dynamic redox conditions. Riverine flooding thus has the potential to affect speciation of redox-sensitive metals such as mercury (Hg). Therefore, inundation history over a period of decades potentially holds information on past production of bioavailable Hg. We investigate this within a Northern California river system with a legacy of landscape-scale 19th century hydraulic gold mining. We combine hydraulic modeling, Hg measurements in sediment and biota, and first-order calculations of mercury transformation to assess the potential role of river floodplains in producing monomethylmercury (MMHg), a neurotoxin which accumulates in local and migratory food webs. We identify frequently inundated floodplain areas, as well as floodplain areas inundated for long periods. We quantify the probability of MMHg production potential (MPP) associated with hydrology in each sector of the river system as a function of the spatial patterns of overbank inundation and drainage, which affect long-term redox history of contaminated sediments. Our findings identify river floodplains as periodic, temporary, yet potentially important, loci of biogeochemical transformation in which contaminants may undergo change during limited periods of the hydrologic record. We suggest that inundation is an important driver of MPP in river corridors and that the entire flow history must be analyzed retrospectively in terms of inundation magnitude and frequency in order to accurately assess biogeochemical risks, rather than merely highlighting the largest floods or low-flow periods. MMHg bioaccumulation within the aquatic food web in this system may pose a major risk to humans and waterfowl that eat migratory salmonids, which are being encouraged to come up these rivers to spawn. There is a long-term pattern of MPP under the current flow regime that is likely to be accentuated by increasingly common large floods with extended duration.
","language":"English","publisher":"ScienceDirect","doi":"10.1016/j.scitotenv.2016.03.005","usgsCitation":"Singer, M.B., Harrison, L.R., Donovan, P.M., Blum, J.D., and Marvin-DiPasquale, M.C., 2016, Hydrologic indicators of hot spots and hot moments of mercury methylation potential along river corridors: Science of the Total Environment, v. 568, p. 697-711, https://doi.org/10.1016/j.scitotenv.2016.03.005.","productDescription":"15 p.","startPage":"697","endPage":"711","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071066","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":471145,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.03.005","text":"Publisher Index Page"},{"id":328234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Bear River, Feather River, Sacramento River, Yuba River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.01965332031249,\n 38.13887716726548\n ],\n [\n -122.01965332031249,\n 39.317300373271024\n ],\n [\n -121.2451171875,\n 39.317300373271024\n ],\n [\n -121.2451171875,\n 38.13887716726548\n ],\n [\n -122.01965332031249,\n 38.13887716726548\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"568","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57cd45abe4b0f2f0cec4cb4e","contributors":{"authors":[{"text":"Singer, Michael B.","contributorId":168369,"corporation":false,"usgs":false,"family":"Singer","given":"Michael","email":"","middleInitial":"B.","affiliations":[{"id":25268,"text":"University of St Andrews, UK","active":true,"usgs":false}],"preferred":false,"id":647993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":647994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donovan, Patrick M.","contributorId":168368,"corporation":false,"usgs":false,"family":"Donovan","given":"Patrick","email":"","middleInitial":"M.","affiliations":[{"id":25267,"text":"Univ. of Michigan","active":true,"usgs":false}],"preferred":false,"id":647995,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blum, Joel D.","contributorId":83657,"corporation":false,"usgs":true,"family":"Blum","given":"Joel","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":647996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marvin-DiPasquale, Mark C. 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":1485,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":647992,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70175153,"text":"70175153 - 2016 - Non-invasive genetic sampling of Southern Mule Deer (Odocoileus hemionus fuliginatus) reveals limited movement across California State Route 67 in San Diego County","interactions":[],"lastModifiedDate":"2016-08-01T14:29:20","indexId":"70175153","displayToPublicDate":"2016-03-16T15:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5156,"text":"Western Wildlife","active":true,"publicationSubtype":{"id":10}},"title":"Non-invasive genetic sampling of Southern Mule Deer (Odocoileus hemionus fuliginatus) reveals limited movement across California State Route 67 in San Diego County","docAbstract":"—The Southern Mule Deer is a mobile but non-migratory large mammal found throughout southern California and is a covered species in the San Diego Multi-Species Conservation Plan. We assessed deer movement and population connectivity across California State Route 67 and two smaller roads in eastern San Diego County using non-invasive genetic sampling. We collected deer scat pellets between April and November 2015, and genotyped pellets at 15 microsatellites and a sex determination marker. We successfully genotyped 71 unique individuals from throughout the study area and detected nine recapture events. Recaptures were generally found close to original capture locations (within 1.5 km). We did not detect recaptures across roads; however, pedigree analysis detected 21 first order relative pairs, of which approximately 20% were found across State Route 67. Exact tests comparing allele frequencies between groups of individuals in pre-defined geographic clusters detected significant genetic differentiation across State Route 67. In contrast, the assignment-based algorithm of STRUCTURE supported a single genetic cluster across the study area. Our data suggest that State Route 67 may reduce, but does not preclude, movement and gene flow of Southern Mule Deer.
","language":"English","publisher":"The Journal of the Western Section of The Wildlife Society","publisherLocation":"Albany, CA","usgsCitation":"Mitelberg, A., and Vandergast, A.G., 2016, Non-invasive genetic sampling of Southern Mule Deer (Odocoileus hemionus fuliginatus) reveals limited movement across California State Route 67 in San Diego County: Western Wildlife, v. 3, p. 8-18.","productDescription":"11 p.","startPage":"8","endPage":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-076907","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":325878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":325873,"type":{"id":15,"text":"Index Page"},"url":"https://wwjournal.org/index.php/current-volume"}],"country":"United States","state":"California","city":"San Diego","volume":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a072b9e4b060ce18fb2dda","contributors":{"authors":[{"text":"Mitelberg, Anna amitelberg@usgs.gov","contributorId":173293,"corporation":false,"usgs":true,"family":"Mitelberg","given":"Anna","email":"amitelberg@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":644117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571 avandergast@usgs.gov","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":3963,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"avandergast@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":644116,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168374,"text":"sir20165024 - 2016 - Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012","interactions":[],"lastModifiedDate":"2022-09-15T18:41:32.475293","indexId":"sir20165024","displayToPublicDate":"2016-03-16T14:00:00","publicationYear":"2016","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":"2016-5024","title":"Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012","docAbstract":"Estimates of the magnitude and frequency of floods are needed across Alaska for engineering design of transportation and water-conveyance structures, flood-insurance studies, flood-plain management, and other water-resource purposes. This report updates methods for estimating flood magnitude and frequency in Alaska and conterminous basins in Canada. Annual peak-flow data through water year 2012 were compiled from 387 streamgages on unregulated streams with at least 10 years of record. Flood-frequency estimates were computed for each streamgage using the Expected Moments Algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows. A multiple Grubbs-Beck test was used to identify potentially influential low floods in the time series of peak flows for censoring in the flood frequency analysis.
For two new regional skew areas, flood-frequency estimates using station skew were computed for stations with at least 25 years of record for use in a Bayesian least-squares regression analysis to determine a regional skew value. The consideration of basin characteristics as explanatory variables for regional skew resulted in improvements in precision too small to warrant the additional model complexity, and a constant model was adopted. Regional Skew Area 1 in eastern-central Alaska had a regional skew of 0.54 and an average variance of prediction of 0.45, corresponding to an effective record length of 22 years. Regional Skew Area 2, encompassing coastal areas bordering the Gulf of Alaska, had a regional skew of 0.18 and an average variance of prediction of 0.12, corresponding to an effective record length of 59 years. Station flood-frequency estimates for study sites in regional skew areas were then recomputed using a weighted skew incorporating the station skew and regional skew. In a new regional skew exclusion area outside the regional skew areas, the density of long-record streamgages was too sparse for regional analysis and station skew was used for all estimates. Final station flood frequency estimates for all study streamgages are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities.
Regional multiple-regression analysis was used to produce equations for estimating flood frequency statistics from explanatory basin characteristics. Basin characteristics, including physical and climatic variables, were updated for all study streamgages using a geographical information system and geospatial source data. Screening for similar-sized nested basins eliminated hydrologically redundant sites, and screening for eligibility for analysis of explanatory variables eliminated regulated peaks, outburst peaks, and sites with indeterminate basin characteristics. An ordinary least‑squares regression used flood-frequency statistics and basin characteristics for 341 streamgages (284 in Alaska and 57 in Canada) to determine the most suitable combination of basin characteristics for a flood-frequency regression model and to explore regional grouping of streamgages for explaining variability in flood-frequency statistics across the study area. The most suitable model for explaining flood frequency used drainage area and mean annual precipitation as explanatory variables for the entire study area as a region. Final regression equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability discharge in Alaska and conterminous basins in Canada were developed using a generalized least-squares regression. The average standard error of prediction for the regression equations for the various annual exceedance probabilities ranged from 69 to 82 percent, and the pseudo-coefficient of determination (pseudo-R2) ranged from 85 to 91 percent.
The regional regression equations from this study were incorporated into the U.S. Geological Survey StreamStats program for a limited area of the State—the Cook Inlet Basin. StreamStats is a national web-based geographic information system application that facilitates retrieval of streamflow statistics and associated information. StreamStats retrieves published data for gaged sites and, for user-selected ungaged sites, delineates drainage areas from topographic and hydrographic data, computes basin characteristics, and computes flood frequency estimates using the regional regression equations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165024","collaboration":"Prepared in cooperation with the Alaska Department of Transportation and Public Facilities, Alaska Department of Natural Resources, and U.S. Army Corps of Engineers","usgsCitation":"Curran, J.H., Barth, N.A., Veilleux, A.G., and Ourso, R.T., 2016, Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012: U.S. Geological Survey Scientific Investigations Report 2016–5024, 47 p., https://dx.doi.org/10.3133/sir20165024.","productDescription":"Report: vi, 47 p.; 3 Tables; 1 Appendix; Companion File; Database","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2011-10-01","ipdsId":"IP-068358","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":438636,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P143KNEJ","text":"USGS data release","linkHelpText":"Selected Basin Boundaries for USGS Streamgages in Alaska Through 2014"},{"id":438635,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WANH3M","text":"USGS data release","linkHelpText":"Flood Frequency Data and 2022 Observed Flood Probability for Two Streamgages in the Delta River Basin, Alaska, 1963-2022"},{"id":438634,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IZB7HH","text":"USGS data release","linkHelpText":"Flood Frequency Data Collection, Alaska and Conterminous Basins in Canada"},{"id":438633,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YGWNDR","text":"USGS data release","linkHelpText":"Flood Frequency Data for a Streamgage in the Hulahula River Basin, Alaska, 2011-2021"},{"id":438632,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JEL0UU","text":"USGS data release","linkHelpText":"Flood Frequency Data and 2020 Observed Flood Probability for Selected Streamgages in the Fortymile River Basin, Alaska, 1911-2020"},{"id":318921,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_table09.xlsx","text":"Table 9","size":"78 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":318920,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_appendixa.xlsx","text":"Appendix A","size":"99 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":318919,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_table04.xlsx","text":"Table 4","size":"88 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":318918,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_table01.xlsx","text":"Table 1","size":"68 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":318917,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_Applications-of-Methods-Tool-version-1.2.xlsx","text":"Application of Methods Tool version 1.2","size":"48 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":318922,"rank":8,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5024/coverthb.jpg"},{"id":318915,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5024 Report PDF"},{"id":318916,"rank":2,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_geodatabase_BasinChar.gdb.zip","text":"Geodatabase of basin characteristics","size":"106 KB","linkFileType":{"id":6,"text":"zip"}}],"country":"Canada, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.517578125,\n 54.316523240258284\n ],\n [\n -127.79296875,\n 55.727110085045986\n ],\n [\n -126.826171875,\n 57.657157596582984\n ],\n [\n -127.705078125,\n 59.489726035537075\n ],\n [\n -131.396484375,\n 60.54377524118842\n ],\n [\n -132.802734375,\n 61.98026726504402\n ],\n [\n -131.044921875,\n 63.194018438087845\n ],\n [\n -130.693359375,\n 64.35893097894458\n ],\n [\n -131.396484375,\n 65.18303007291382\n ],\n [\n -134.033203125,\n 65.5129625532949\n ],\n [\n -136.23046875,\n 65.87472467098549\n ],\n [\n -137.8125,\n 66.40795547978848\n ],\n [\n -136.23046875,\n 67.16995497083367\n ],\n [\n -136.845703125,\n 68.00757101804004\n ],\n [\n -138.8671875,\n 68.942606818121\n ],\n [\n -141.15234374999997,\n 69.16255790810501\n ],\n [\n -141.328125,\n 69.7181066990676\n ],\n [\n -143.525390625,\n 70.1403642720717\n ],\n [\n -144.755859375,\n 70.08056215839737\n ],\n [\n -148.53515625,\n 70.4367988185464\n ],\n [\n -151.69921875,\n 70.46620742226558\n ],\n [\n -152.841796875,\n 70.9883492241249\n ],\n [\n -155.478515625,\n 71.27259471233448\n ],\n [\n -157.58789062499997,\n 71.30079291637452\n ],\n [\n -158.994140625,\n 70.8734913192635\n ],\n [\n -161.630859375,\n 70.34831755984781\n ],\n [\n -162.68554687499997,\n 69.93030017617484\n ],\n [\n -163.65234374999997,\n 69.16255790810501\n ],\n [\n -166.11328125,\n 68.87935761076949\n ],\n [\n -166.728515625,\n 68.49604022839505\n ],\n [\n -165.146484375,\n 67.80924450600008\n ],\n [\n -163.037109375,\n 66.687783861162\n ],\n [\n -161.455078125,\n 66.33750501996518\n ],\n [\n -163.4765625,\n 66.16051056018838\n ],\n [\n -163.916015625,\n 66.86108230224612\n ],\n [\n -166.55273437499997,\n 66.08936427047088\n ],\n [\n -168.310546875,\n 65.58572002329473\n ],\n [\n -166.81640625,\n 65.18303007291382\n ],\n [\n -166.376953125,\n 64.35893097894458\n ],\n [\n -164.35546875,\n 64.35893097894458\n ],\n [\n -161.982421875,\n 64.47279382008166\n ],\n [\n -161.279296875,\n 64.66151739623564\n ],\n [\n -160.6640625,\n 63.82128765261384\n ],\n [\n -163.388671875,\n 63.27318217465046\n ],\n [\n -164.794921875,\n 63.31268278043484\n ],\n [\n -166.11328125,\n 61.81466389468391\n ],\n [\n -165.322265625,\n 60.673178565817715\n ],\n [\n -164.267578125,\n 59.84481485969105\n ],\n [\n -161.806640625,\n 59.130863097255904\n ],\n [\n -161.806640625,\n 58.44773280389084\n ],\n [\n -159.345703125,\n 58.81374171570782\n ],\n [\n -157.236328125,\n 58.722598828043374\n ],\n [\n -159.08203125,\n 57.18390185831188\n ],\n [\n -161.806640625,\n 56.022948079627454\n ],\n [\n -164.443359375,\n 55.27911529201561\n ],\n [\n -164.70703125,\n 54.265224078605655\n ],\n [\n -160.400390625,\n 54.826007999094955\n ],\n [\n -156.4453125,\n 55.677584411089505\n ],\n [\n -152.75390624999997,\n 57.136239319177434\n ],\n [\n -151.083984375,\n 58.309488840677645\n ],\n [\n -149.4140625,\n 59.84481485969105\n ],\n [\n -146.513671875,\n 60.28340847828243\n ],\n [\n -144.31640625,\n 59.84481485969105\n ],\n [\n -141.50390625,\n 60.10867046303597\n ],\n [\n -138.603515625,\n 58.99531118795094\n ],\n [\n -135.791015625,\n 56.992882804633986\n ],\n [\n -133.330078125,\n 54.87660665410869\n ],\n [\n -131.66015625,\n 54.11094294272432\n ],\n [\n -130.517578125,\n 54.316523240258284\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560
http://alaska.usgs.gov