Coral disease is a global problem. Diseases are typically named or described based on macroscopic changes, but broad signs of coral distress such as tissue loss or discoloration are unlikely to be specific to a particular pathogen. For example, there appear to be multiple diseases that manifest the rapid tissue loss that characterizes ‘white plague.’ PhyloChip™ G3 microarrays were used to compare the bacterial community composition of both healthy and white plague-like diseased corals. Samples of lobed star coral (Orbicella annularis, formerly of the genus Montastraea [1]) were collected from two geographically distinct areas, Dry Tortugas National Park and Virgin Islands National Park, to determine if there were biogeographic differences between the diseases. In fact, all diseased samples clustered together, however there was no consistent link to Aurantimonas coralicida, which has been described as the causative agent of white plague type II. The microarrays revealed a large amount of bacterial heterogeneity within the healthy corals and less diversity in the diseased corals. Gram-positive bacterial groups (Actinobacteria, Firmicutes) comprised a greater proportion of the operational taxonomic units (OTUs) unique to healthy samples. Diseased samples were enriched in OTUs from the families Corynebacteriaceae, Lachnospiraceae, Rhodobacteraceae, and Streptococcaceae. Much previous coral disease work has used clone libraries, which seem to be methodologically biased toward recovery of Gram-negative bacterial sequences and may therefore have missed the importance of Gram-positive groups. The PhyloChip™ data presented here provide a broader characterization of the bacterial community changes that occur within Orbicella annularis during the shift from a healthy to diseased state.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0079801","usgsCitation":"Kellogg, C.A., Piceno, Y., Tom, L.M., DeSantis, T.Z., Gray, M.A., Zawada, D., and Andersen, G.L., 2013, Comparing bacterial community composition between healthy and white plague-like disease states in Orbicella annularis using PhyloChip™ G3 microarrays: PLoS ONE, v. 8, no. 11, e79801; 10 p., https://doi.org/10.1371/journal.pone.0079801.","productDescription":"e79801; 10 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-050897","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":473464,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0079801","text":"Publisher Index Page"},{"id":279514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Dry Tortugas National Park, St. John, Virgin Island","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -82.93,18.3 ], [ -82.93,24.64 ], [ -64.66,24.64 ], [ -64.66,18.3 ], [ -82.93,18.3 ] ] ] } } ] }","volume":"8","issue":"11","noUsgsAuthors":false,"publicationDate":"2013-11-20","publicationStatus":"PW","scienceBaseUri":"52908b00e4b0bbdcf23f08d9","contributors":{"authors":[{"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":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":486642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Piceno, Yvette M.","contributorId":66977,"corporation":false,"usgs":true,"family":"Piceno","given":"Yvette M.","affiliations":[],"preferred":false,"id":486645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tom, Lauren M.","contributorId":92938,"corporation":false,"usgs":true,"family":"Tom","given":"Lauren","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":486647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeSantis, Todd Z.","contributorId":101158,"corporation":false,"usgs":true,"family":"DeSantis","given":"Todd","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":486648,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gray, Michael A. 0000-0002-3856-5037 mgray@usgs.gov","orcid":"https://orcid.org/0000-0002-3856-5037","contributorId":3532,"corporation":false,"usgs":true,"family":"Gray","given":"Michael","email":"mgray@usgs.gov","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":486644,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zawada, David G. 0000-0003-4547-4878 dzawada@usgs.gov","orcid":"https://orcid.org/0000-0003-4547-4878","contributorId":1898,"corporation":false,"usgs":true,"family":"Zawada","given":"David G.","email":"dzawada@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":486643,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Andersen, Gary L.","contributorId":68610,"corporation":false,"usgs":true,"family":"Andersen","given":"Gary","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":486646,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70140927,"text":"70140927 - 2013 - Improving sediment-quality guidelines for nickel: development and application of predictive bioavailability models to assess chronic toxicity of nickel in freshwater sediments","interactions":[],"lastModifiedDate":"2016-12-02T14:59:21","indexId":"70140927","displayToPublicDate":"2013-11-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Improving sediment-quality guidelines for nickel: development and application of predictive bioavailability models to assess chronic toxicity of nickel in freshwater sediments","docAbstract":"Within the framework of European Union chemical legislations an extensive data set on the chronic toxicity of sediment nickel has been generated. In the initial phase of testing, tests were conducted with 8 taxa of benthic invertebrates in 2 nickel-spiked sediments, including 1 reasonable worst-case sediment with low concentrations of acid-volatile sulfide (AVS) and total organic carbon. The following species were tested: amphipods (Hyalella azteca, Gammarus pseudolimnaeus), mayflies (Hexagenia sp.), oligochaetes (Tubifex tubifex, Lumbriculus variegatus), mussels (Lampsilis siliquoidea), and midges (Chironomus dilutus, Chironomus riparius). In the second phase, tests were conducted with the most sensitive species in 6 additional spiked sediments, thus generating chronic toxicity data for a total of 8 nickel-spiked sediments. A species sensitivity distribution was elaborated based on 10% effective concentrations yielding a threshold value of 94 mg Ni/kg dry weight under reasonable worst-case conditions. Data from all sediments were used to model predictive bioavailability relationships between chronic toxicity thresholds (20% effective concentrations) and AVS and Fe, and these models were used to derive site-specific sediment-quality criteria. Normalization of toxicity values reduced the intersediment variability in toxicity values significantly for the amphipod species Hyalella azteca and G. pseudolimnaeus, but these relationships were less clearly defined for the mayfly Hexagenia sp. Application of the models to prevailing local conditions resulted in threshold values ranging from 126 mg to 281 mg Ni/kg dry weight, based on the AVS model, and 143 mg to 265 mg Ni/kg dry weight, based on the Fe model
","language":"English","publisher":"Wiley","doi":"10.1002/etc.2373","usgsCitation":"Vangheluwe, M.L., Verdonck, F.A., Besser, J.M., Brumbaugh, W.G., Ingersoll, C.G., Schlekat, C.E., and Rogevich Garman, E., 2013, Improving sediment-quality guidelines for nickel: development and application of predictive bioavailability models to assess chronic toxicity of nickel in freshwater sediments: Environmental Toxicology and Chemistry, v. 32, no. 11, p. 2507-2519, https://doi.org/10.1002/etc.2373.","productDescription":"13 p.","startPage":"2507","endPage":"2519","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045453","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":297915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"11","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2013-08-24","publicationStatus":"PW","scienceBaseUri":"54dd2bd2e4b08de9379b34f8","contributors":{"authors":[{"text":"Vangheluwe, Marnix L. U.","contributorId":139229,"corporation":false,"usgs":false,"family":"Vangheluwe","given":"Marnix","email":"","middleInitial":"L. U.","affiliations":[{"id":12706,"text":"ARCHE (Assessing Risks of Chemicals), Ghent, Belgium","active":true,"usgs":false}],"preferred":false,"id":540440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Verdonck, Frederik A. M.","contributorId":139230,"corporation":false,"usgs":false,"family":"Verdonck","given":"Frederik","email":"","middleInitial":"A. M.","affiliations":[{"id":12706,"text":"ARCHE (Assessing Risks of Chemicals), Ghent, Belgium","active":true,"usgs":false}],"preferred":false,"id":540441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Besser, John M. 0000-0002-9464-2244 jbesser@usgs.gov","orcid":"https://orcid.org/0000-0002-9464-2244","contributorId":2073,"corporation":false,"usgs":true,"family":"Besser","given":"John","email":"jbesser@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":540437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brumbaugh, William G. 0000-0003-0081-375X bbrumbaugh@usgs.gov","orcid":"https://orcid.org/0000-0003-0081-375X","contributorId":493,"corporation":false,"usgs":true,"family":"Brumbaugh","given":"William","email":"bbrumbaugh@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":540435,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ingersoll, Christopher G. 0000-0003-4531-5949 cingersoll@usgs.gov","orcid":"https://orcid.org/0000-0003-4531-5949","contributorId":2071,"corporation":false,"usgs":true,"family":"Ingersoll","given":"Christopher","email":"cingersoll@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":540436,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schlekat, Christan E.","contributorId":139228,"corporation":false,"usgs":false,"family":"Schlekat","given":"Christan","email":"","middleInitial":"E.","affiliations":[{"id":12705,"text":"Nickel Producers Environmental Research Association, Durham, Nor","active":true,"usgs":false}],"preferred":false,"id":540439,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rogevich Garman, Emily","contributorId":139227,"corporation":false,"usgs":false,"family":"Rogevich Garman","given":"Emily","email":"","affiliations":[{"id":12705,"text":"Nickel Producers Environmental Research Association, Durham, Nor","active":true,"usgs":false}],"preferred":false,"id":540438,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70042648,"text":"70042648 - 2013 - Estimating animal resource selection from telemetry data using point process models","interactions":[],"lastModifiedDate":"2013-11-07T14:21:56","indexId":"70042648","displayToPublicDate":"2013-11-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating animal resource selection from telemetry data using point process models","docAbstract":"Analyses of animal resource selection functions (RSF) using data collected from relocations of individuals via remote telemetry devices have become commonplace. Increasing technological advances, however, have produced statistical challenges in analysing such highly autocorrelated data. Weighted distribution methods have been proposed for analysing RSFs with telemetry data. However, they can be computationally challenging due to an intractable normalizing constant and cannot be aggregated (i.e. collapsed) over time to make space-only inference.\nIn this study, we take a conceptually different approach to modelling animal telemetry data for making RSF inference. We consider the telemetry data to be a realization of a space–time point process. Under the point process paradigm, the times of the relocations are also considered to be random rather than fixed.\nWe show the point process models we propose are a generalization of the weighted distribution telemetry models. By generalizing the weighted model, we can access several numerical techniques for evaluating point process likelihoods that make use of common statistical software. Thus, the analysis methods can be readily implemented by animal ecologists.\nIn addition to ease of computation, the point process models can be aggregated over time by marginalizing over the temporal component of the model. This allows a full range of models to be constructed for RSF analysis at the individual movement level up to the study area level.\nTo demonstrate the analysis of telemetry data with the point process approach, we analysed a data set of telemetry locations from northern fur seals (Callorhinus ursinus) in the Pribilof Islands, Alaska. Both a space–time and an aggregated space-only model were fitted. At the individual level, the space–time analysis showed little selection relative to the habitat covariates. However, at the study area level, the space-only model showed strong selection relative to the covariates.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Animal Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/1365-2656.12087","usgsCitation":"Johnson, D., Hooten, M., and Kuhn, C.E., 2013, Estimating animal resource selection from telemetry data using point process models: Journal of Animal Ecology, v. 82, no. 6, p. 1155-1164, https://doi.org/10.1111/1365-2656.12087.","productDescription":"10 p.","startPage":"1155","endPage":"1164","numberOfPages":"10","ipdsId":"IP-039994","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":473466,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.12087","text":"Publisher Index Page"},{"id":278937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278936,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/1365-2656.12087"}],"country":"United States","state":"Alaska","otherGeospatial":"Pribilof Islands","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -170.42,56.53 ], [ -170.42,57.25 ], [ -169.46,57.25 ], [ -169.46,56.53 ], [ -170.42,56.53 ] ] ] } } ] }","volume":"82","issue":"6","noUsgsAuthors":false,"publicationDate":"2013-06-25","publicationStatus":"PW","scienceBaseUri":"527cc48de4b0850ea050ce5d","contributors":{"authors":[{"text":"Johnson, Devin S.","contributorId":47524,"corporation":false,"usgs":true,"family":"Johnson","given":"Devin S.","affiliations":[],"preferred":false,"id":471984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":471982,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuhn, Carey E.","contributorId":42128,"corporation":false,"usgs":true,"family":"Kuhn","given":"Carey","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":471983,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048745,"text":"ds799 - 2013 - Baseline coastal oblique aerial photographs collected from Pensacola, Florida, to Breton Islands, Louisiana, February 7, 2012","interactions":[],"lastModifiedDate":"2015-02-02T15:11:40","indexId":"ds799","displayToPublicDate":"2013-10-31T16:06:00","publicationYear":"2013","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":"799","title":"Baseline coastal oblique aerial photographs collected from Pensacola, Florida, to Breton Islands, Louisiana, February 7, 2012","docAbstract":"The U.S. Geological Survey (USGS) conducts baseline and storm response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On February 7, 2012, the USGS conducted an oblique aerial photographic survey from Pensacola, Fla., to Breton Islands, La., aboard a Piper Navajo Chieftain at an altitude of 500 feet (ft) and approximately 1,000 ft offshore. This mission was flown to collect baseline data for assessing incremental changes since the last survey, and the data can be used in the assessment of future coastal change. The photographs provided here are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the position of the aircraft and do not indicate the location of the feature in the images (see the Navigation Data page). These photos document the configuration of the barrier islands and other coastal features at the time of the survey. The header of each photo is populated with time of collection, Global Positioning System (GPS) latitude, GPS longitude, GPS position (latitude and longitude), keywords, credit, artist (photographer), caption, copyright, and contact information using EXIFtools (Subino and others, 2012). Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet. Table 1 provides detailed information about the assigned location, name, data, and time the photograph was taken along with links to the photograph. In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files (see the Photos and Maps page).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds799","usgsCitation":"Morgan, K., Krohn, M.D., Doran, K., and Guy, K.K., 2013, Baseline coastal oblique aerial photographs collected from Pensacola, Florida, to Breton Islands, Louisiana, February 7, 2012: U.S. Geological Survey Data Series 799, HTML Document, https://doi.org/10.3133/ds799.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":278623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds799.gif"},{"id":278621,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0799/"},{"id":278622,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0799/title.html"}],"country":"United States","state":"Alabama; Florida; Louisiana; Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.93408203124999,\n 28.998531814051795\n ],\n [\n -89.93408203124999,\n 30.751277776257812\n ],\n [\n -86.781005859375,\n 30.751277776257812\n ],\n [\n -86.781005859375,\n 28.998531814051795\n ],\n [\n -89.93408203124999,\n 28.998531814051795\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52736dd3e4b097f32ac3dadd","contributors":{"authors":[{"text":"Morgan, Karen L.M. 0000-0002-2994-5572","orcid":"https://orcid.org/0000-0002-2994-5572","contributorId":95553,"corporation":false,"usgs":true,"family":"Morgan","given":"Karen L.M.","affiliations":[],"preferred":false,"id":485535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krohn, M. Dennis dkrohn@usgs.gov","contributorId":3378,"corporation":false,"usgs":true,"family":"Krohn","given":"M.","email":"dkrohn@usgs.gov","middleInitial":"Dennis","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":485532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doran, Kara 0000-0001-8050-5727","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":56550,"corporation":false,"usgs":true,"family":"Doran","given":"Kara","affiliations":[],"preferred":false,"id":485534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guy, Kristy K. kguy@usgs.gov","contributorId":3546,"corporation":false,"usgs":true,"family":"Guy","given":"Kristy","email":"kguy@usgs.gov","middleInitial":"K.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":485533,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70048715,"text":"sir20135174 - 2013 - Refinement of regression models to estimate real-time concentrations of contaminants in the Menomonee River drainage basin, southeast Wisconsin, 2008-11","interactions":[],"lastModifiedDate":"2018-02-06T12:25:47","indexId":"sir20135174","displayToPublicDate":"2013-10-31T09:36:00","publicationYear":"2013","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":"2013-5174","title":"Refinement of regression models to estimate real-time concentrations of contaminants in the Menomonee River drainage basin, southeast Wisconsin, 2008-11","docAbstract":"In 2008, the U.S. Geological Survey and the Milwaukee Metropolitan Sewerage District initiated a study to develop regression models to estimate real-time concentrations and loads of chloride, suspended solids, phosphorus, and bacteria in streams near Milwaukee, Wisconsin. To collect monitoring data for calibration of models, water-quality sensors and automated samplers were installed at six sites in the Menomonee River drainage basin. The sensors continuously measured four potential explanatory variables: water temperature, specific conductance, dissolved oxygen, and turbidity. Discrete water-quality samples were collected and analyzed for five response variables: chloride, total suspended solids, total phosphorus, Escherichia coli bacteria, and fecal coliform bacteria. Using the first year of data, regression models were developed to continuously estimate the response variables on the basis of the continuously measured explanatory variables. Those models were published in a previous report. In this report, those models are refined using 2 years of additional data, and the relative improvement in model predictability is discussed. In addition, a set of regression models is presented for a new site in the Menomonee River Basin, Underwood Creek at Wauwatosa.\n\nThe refined models use the same explanatory variables as the original models. The chloride models all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity. Total suspended solids and total phosphorus models used turbidity as the only explanatory variable, and bacteria models used water temperature and turbidity as explanatory variables.\n\nAn analysis of covariance (ANCOVA), used to compare the coefficients in the original models to those in the refined models calibrated using all of the data, showed that only 3 of the 25 original models changed significantly. Root-mean-squared errors (RMSEs) calculated for both the original and refined models using the entire dataset showed a median improvement in RMSE of 2.1 percent, with a range of 0.0–13.9 percent. Therefore most of the original models did almost as well at estimating concentrations during the validation period (October 2009–September 2011) as the refined models, which were calibrated using those data.\n\nApplication of these refined models can produce continuously estimated concentrations of chloride, total suspended solids, total phosphorus, E. coli bacteria, and fecal coliform bacteria that may assist managers in quantifying the effects of land-use changes and improvement projects, establish total maximum daily loads, and enable better informed decision making in the future.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135174","collaboration":"Prepared in cooperation with the Milwaukee Metropolitan Sewerage District","usgsCitation":"Baldwin, A.K., Robertson, D.M., Saad, D.A., and Magruder, C., 2013, Refinement of regression models to estimate real-time concentrations of contaminants in the Menomonee River drainage basin, southeast Wisconsin, 2008-11: U.S. Geological Survey Scientific Investigations Report 2013-5174, vii, 113 p., https://doi.org/10.3133/sir20135174.","productDescription":"vii, 113 p.","numberOfPages":"125","onlineOnly":"Y","temporalStart":"2008-01-01","temporalEnd":"2011-12-31","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":278596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135174.gif"},{"id":278594,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5174/"},{"id":278595,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5174/pdf/sir2013-5174.pdf"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Menomonee River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -88.25,42.833333 ], [ -88.25,43.333333 ], [ -87.833333,43.333333 ], [ -87.833333,42.833333 ], [ -88.25,42.833333 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52736dfee4b097f32ac3dae6","contributors":{"authors":[{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robertson, Dale M. 0000-0001-6799-0596 dzrobert@usgs.gov","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":150760,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"dzrobert@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saad, David A. dasaad@usgs.gov","contributorId":121,"corporation":false,"usgs":true,"family":"Saad","given":"David","email":"dasaad@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485476,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Magruder, Christopher","contributorId":35995,"corporation":false,"usgs":true,"family":"Magruder","given":"Christopher","affiliations":[],"preferred":false,"id":485478,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70048712,"text":"sir20135051 - 2013 - Groundwater and surface-water interaction within the upper Smith River Watershed, Montana 2006-2010","interactions":[],"lastModifiedDate":"2014-01-30T14:30:20","indexId":"sir20135051","displayToPublicDate":"2013-10-31T08:34:00","publicationYear":"2013","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":"2013-5051","title":"Groundwater and surface-water interaction within the upper Smith River Watershed, Montana 2006-2010","docAbstract":"The 125-mile long Smith River, a tributary of the Missouri River, is highly valued as an agricultural resource and for its many recreational uses. During a drought starting in about 1999, streamflow was insufficient to meet all of the irrigation demands, much less maintain streamflow needed for boating and viable fish habitat. In 2006, the U.S. Geological Survey, in cooperation with the Meagher County Conservation District, initiated a multi-year hydrologic investigation of the Smith River watershed. This investigation was designed to increase understanding of the water resources of the upper Smith River watershed and develop a detailed description of groundwater and surface-water interactions. A combination of methods, including miscellaneous and continuous groundwater-level, stream-stage, water-temperature, and streamflow monitoring was used to assess the hydrologic system and the spatial and temporal variability of groundwater and surface-water interactions. Collectively, data are in agreement and show: (1) the hydraulic connectedness of groundwater and surface water, (2) the presence of both losing and gaining stream reaches, (3) dynamic changes in direction and magnitude of water flow between the stream and groundwater with time, (4) the effects of local flood irrigation on groundwater levels and gradients in the watershed, and (5) evidence and timing of irrigation return flows to area streams.
\nGroundwater flow within the alluvium and older (Tertiary) basin-fill sediments generally followed land-surface topography from the uplands to the axis of alluvial valleys of the Smith River and its tributaries. Groundwater levels were typically highest in the monitoring wells located within and adjacent to streams in late spring or early summer, likely affected by recharge from snowmelt and local precipitation, leakage from losing streams and canals, and recharge from local flood irrigation. The effects of flood irrigation resulted in increased hydraulic gradients (increased groundwater levels relative to stream stage) or even reversed gradient direction at several monitoring sites coincident with the onset of nearby flood irrigation. Groundwater-level declines in mid-summer were due to groundwater withdrawals and reduced recharge from decreased precipitation, increased evapotranspiration, and reduced leakage in some area streams during periods of low flow. Groundwater levels typically rebounded in late summer, a result of decreased evapotranspiration, decreased groundwater use for irrigation, increased flow in losing streams, and the onset of late-season flood irrigation at some sites.
\nThe effect of groundwater and surface-water interactions is most apparent along the North and South Forks of the Smith River where the magnitude of streamflow losses and gains can be greater than the magnitude of flow within the stream. Net gains consistently occurred over the lower 15 miles of the South Fork Smith River. A monitoring site near the mouth of the South Fork Smith River gained (flow from the groundwater to the stream) during all seasons, with head gradients towards the stream. Two upstream sites on the South Fork Smith River exhibited variable conditions that ranged from gaining during the spring, losing (flowing from the stream to the groundwater) during most of the summer as groundwater levels declined, and then approached or returned to gaining conditions in late summer. Parts of the South Fork Smith River became dry during periods of losing conditions, thus classifying this tributary as intermittent. The North Fork Smith River is highly managed at times through reservoir releases. The North Fork Smith River was perennial throughout the study period although irrigation diversions removed a large percentage of streamflow at times and losing conditions persisted along a lower reach. The lowermost reach of the North Fork Smith River near its mouth transitioned from a losing reach to a gaining reach throughout the study period.
\nGroundwater and surface-water interactions occur downstream from the confluence of the North and South Fork Smith Rivers, but are less discernible compared to the overall magnitude of the main-stem streamflow. The Smith River was perennial throughout the study. Monitoring sites along the Smith River generally displayed small head gradients between the stream and the groundwater, while one site consistently showed strongly gaining conditions. Synoptic streamflow measurements during periods of limited irrigation diversion in 2007 and 2008 consistently showed gains over the upper 41.4 river miles of the main stem Smith River where net gains ranged from 13.0 to 28.9 cubic feet per second. Continuous streamflow data indicated net groundwater discharge and small-scale tributary inflow contributions of around 25 cubic feet per second along the upper 10-mile reach of the Smith River for most of the 2010 record. A period of intense irrigation withdrawal during the last two weeks in May was followed by a period (early June 2010 to mid-July 2010) with the largest net increase (an average of 71.1 cubic feet per second) in streamflow along this reach of the Smith River. This observation is likely due to increased groundwater discharge to the Smith River resulting from irrigation return flow. By late July, the apparent effects of return flows receded, and the net increase in streamflow returned to about 25 cubic feet per second.
\nTwo-dimensional heat and solute transport VS2DH models representing selected stream cross sections were used to constrain the hydraulic properties of the Quaternary alluvium and estimate temporal water-flux values through model boundaries. Hydraulic conductivity of the Quaternary alluvium of the modeled sections ranged from 3x10-6 to 4x10-5 feet per second. The models showed reasonable approximations of the streambed and shallow aquifer environment, and the dynamic changes in water flux between the stream and the groundwater through different model boundaries.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135051","collaboration":"Prepared in cooperation with Meagher County Conservation District","usgsCitation":"Caldwell, R.R., and Eddy-Miller, C., 2013, Groundwater and surface-water interaction within the upper Smith River Watershed, Montana 2006-2010: U.S. Geological Survey Scientific Investigations Report 2013-5051, xi, 88 p., https://doi.org/10.3133/sir20135051.","productDescription":"xi, 88 p.","numberOfPages":"104","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":278592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135051.gif"},{"id":278591,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5051/pdf/sir2013-5051.pdf"},{"id":279219,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5051/"}],"scale":"100000","projection":"Lambert Conformal Conic Projection","datum":"North American Datum of 1983","country":"United States","state":"Montana","otherGeospatial":"Smith River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -112.0,46.0 ], [ -112.0,47.5 ], [ -110.5,47.5 ], [ -110.5,46.0 ], [ -112.0,46.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52736dfce4b097f32ac3dae0","contributors":{"authors":[{"text":"Caldwell, Rodney R. 0000-0002-2588-715X caldwell@usgs.gov","orcid":"https://orcid.org/0000-0002-2588-715X","contributorId":2577,"corporation":false,"usgs":true,"family":"Caldwell","given":"Rodney","email":"caldwell@usgs.gov","middleInitial":"R.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":485472,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eddy-Miller, Cheryl A.","contributorId":86755,"corporation":false,"usgs":true,"family":"Eddy-Miller","given":"Cheryl A.","affiliations":[],"preferred":false,"id":485473,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70048699,"text":"70048699 - 2013 - Nitrate Trends in Minnesota Rivers","interactions":[],"lastModifiedDate":"2013-10-30T13:37:31","indexId":"70048699","displayToPublicDate":"2013-10-30T13:15:00","publicationYear":"2013","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Nitrate Trends in Minnesota Rivers","docAbstract":"The objective of this study was to assess long-term trends (30 to 35 years) of flow-adjusted concentrations of nitrite+nitrate-N (hereinafter referred to as nitrate) in a way that would allow us to discern changing trends. Recognizing that these trends are commonly different from one river to another river and from one part of the state to another, our objective was to examine as many river monitoring sites across the state as possible for which sufficient long term streamflow and concentration data were available.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Nitrogen in Minnesota surface waters:","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"Minnesota Pollution Control Agency","usgsCitation":"Wall, D., Christopherson, D., Lorenz, D., and Martin, G., 2013, Nitrate Trends in Minnesota Rivers, chap. of Nitrogen in Minnesota surface waters:, 48 p.","productDescription":"48 p.","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":278585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278584,"type":{"id":11,"text":"Document"},"url":"https://www.pca.state.mn.us/index.php/view-document.html?gid=19844"}],"country":"United States","state":"Minnesota","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -97.24,43.50 ], [ -97.24,49.38 ], [ -89.48,49.38 ], [ -89.48,43.50 ], [ -97.24,43.50 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52721c77e4b0ce70249c6307","contributors":{"authors":[{"text":"Wall, Dave","contributorId":63296,"corporation":false,"usgs":true,"family":"Wall","given":"Dave","email":"","affiliations":[],"preferred":false,"id":485461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christopherson, Dave","contributorId":48471,"corporation":false,"usgs":true,"family":"Christopherson","given":"Dave","email":"","affiliations":[],"preferred":false,"id":485459,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorenz, Dave","contributorId":66162,"corporation":false,"usgs":true,"family":"Lorenz","given":"Dave","email":"","affiliations":[],"preferred":false,"id":485462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Gary","contributorId":53687,"corporation":false,"usgs":true,"family":"Martin","given":"Gary","affiliations":[],"preferred":false,"id":485460,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70074147,"text":"70074147 - 2013 - Creating potentiometric surfaces from combined water well and oil well data in the midcontinent of the United States","interactions":[],"lastModifiedDate":"2014-07-02T10:52:38","indexId":"70074147","displayToPublicDate":"2013-10-30T10:47:38","publicationYear":"2013","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":12,"text":"Conference publication"},"title":"Creating potentiometric surfaces from combined water well and oil well data in the midcontinent of the United States","docAbstract":"For years, hydrologists have defined potentiometric surfaces using measured hydraulic-head values in water wells from aquifers. Down-dip, the oil and gas industry is also interested in the formation pressures of many of the same geologic formations for the purpose of hydrocarbon recovery. In oil and gas exploration, drillstem tests (DSTs) provide the formation pressure for a given depth interval in a well. These DST measurements can be used to calculate hydraulic-head values in deep hydrocarbon-bearing formations in areas where water wells do not exist. Unlike hydraulic-head measurements in water wells, which have a low number of problematic data points (outliers), only a small subset of the DST data measure true formation pressures.
\nUsing 3D imaging capabilities to view and clean the data, we have developed a process to estimate potentiometric surfaces from erratic DST data sets of hydrocarbon-bearing formations in the midcontinent of the U.S. The analysis indicates that the potentiometric surface is more readily defined through human interpretation of the chaotic DST data sets rather than through the application of filtering and geostatistical analysis. The data are viewed as a series of narrow, 400-mile-long swaths and a 2D viewer is used to select a subset of hydraulic-head values that represent the potentiometric surface. The user-selected subsets for each swath are then combined into one data set for each formation. These data are then joined with the hydraulic-head values from water wells to define the 3D potentiometric surfaces. The final product is an interactive, 3D digital display containing: (1) the subsurface structure of the formation, (2) the cluster of DST-derived hydraulic head values, (3) the user-selected subset of hydraulic-head values that define the potentiometric surface, (4) the hydraulic-head measurements from the corresponding shallow aquifer, (5) the resulting potentiometric surface encompassing both oil and gas and water wells, and (6) the land surface elevation of the region. Examples from the midcontinent of the United States, specifically Kansas, Oklahoma, and parts of adjacent states illustrate the process.
","largerWorkTitle":"125th Anniversary Annual Meeting & Expo: The Geological Society of America","conferenceTitle":"125th Anniversary Annual Meeting & Expo: The Geological Society of America","conferenceDate":"2013-10-27T00:00:00","conferenceLocation":"Denver, CO","language":"English","publisher":"The Geological Society of America 2013 Annual Meeting","publisherLocation":"New York, NY","usgsCitation":"Gianoutsos, N.J., and Nelson, P.H., 2013, Creating potentiometric surfaces from combined water well and oil well data in the midcontinent of the United States, 14 p.","productDescription":"14 p.","numberOfPages":"14","ipdsId":"IP-053110","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":289368,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":281598,"type":{"id":15,"text":"Index Page"},"url":"https://gsa.confex.com/gsa/2013AM/webprogram/Paper226579.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b7b0dde4b0388651d916a8","contributors":{"authors":[{"text":"Gianoutsos, Nicholas J. 0000-0002-6510-6549 ngianoutsos@usgs.gov","orcid":"https://orcid.org/0000-0002-6510-6549","contributorId":3607,"corporation":false,"usgs":true,"family":"Gianoutsos","given":"Nicholas","email":"ngianoutsos@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":489426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Philip H. pnelson@usgs.gov","contributorId":862,"corporation":false,"usgs":true,"family":"Nelson","given":"Philip","email":"pnelson@usgs.gov","middleInitial":"H.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":489425,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70048693,"text":"ofr20131156 - 2013 - Characterization of cyanophyte biomass in a Bureau of Reclamation reservoir","interactions":[],"lastModifiedDate":"2013-11-14T16:17:18","indexId":"ofr20131156","displayToPublicDate":"2013-10-30T09:07:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1156","title":"Characterization of cyanophyte biomass in a Bureau of Reclamation reservoir","docAbstract":"The purpose of this study was to characterize the cyanophyte Aphanizomenon flos-aquae (AFA) from Upper Klamath Lake, Oregon, (UKL) and, based on this description, explore uses for AFA, which would have commercial value. AFA collected from UKL in 2010 from eight sites during a period of approximately 2 weeks were similar in composition spatially and temporally. 31P nuclear magnetic resonance analysis of the samples indicated that the AFA samples contained a broad range of phosphorus-containing compounds. The largest variation in organic phosphorus compounds was found in a sample collected from Howard Bay compared with samples collected the sites at Pelican Marina, North Buck Island, Eagle Ridge, Eagle Ridge South, Shoalwater Bay, and Agency Lake South. 31P Nuclear Magnetic Resonance data indicated that the average ratio of inorganic phosphorus (orthophosphate) to organic phosphorus in the AFA samples was approximately 60:40 in extraction solutions of either water or a more rigorous solution of sodium hydroxide plus ethylenediaminetetraacetic acid. This indicates that when AFA cells senesce, die and lyse, cell contents added to the water column contain a broad spectrum of phosphorus-containing compounds approximately 50 percent of which are organic phosphorus compounds. The organic phosphorus content of AFA is directly and significantly related to the total carbon content of AFA. Total concentrations of the elements Al, Ca, Fe, Mg, Ti and Zn were similar in all samples with the exception of elevated iron in the July 27, 2010, sample from Pelican Marina. Iron concentration in the July 27, 2010, Pelican Marina sample was elevated; the concentration of iron in the August 9, 2010, sample from Pelican Marina was indistinguishable from iron in the other AFA samples that were collected. The carbon to nitrogen ratio in all AFA samples that were analyzed was 5.4 plus or minus 0.04 as compared with the Redfield ratio of carbon to nitrogen ratio of 6.6, which could be attributed to the large concentrations of nitrogen (protein) in AFA or to optimal growth rate. In UKL there is a concern that microcystin, the toxin produced by microcystis, might be present in what appears to be predominantly AFA in the lake water. Experiments preformed as part of this study identified a process that reduces the toxicity of microcystin when it is present in water slurry containing AFA. The process combines (1) the inhibition of the α, ß-unsaturated carbonyl in microcystin with (2) the breakdown of proteins in AFA using the protease activity of plant enzymes. Protease enzymes can break peptide bonds in microcystin, which results in destruction of the cyclic structure of the microcystin polypeptide. Laboratory conditions used in this study resulted in the inactivation of approximately 60 percent of the activity of microcystin.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131156","collaboration":"Prepared in cooperation with the U.S. Bureau of Reclamation","usgsCitation":"Simon, N.S., Ali, A.A., Samperton, K.M., Korson, C.S., Fischer, K., and Hughes, M.L., 2013, Characterization of cyanophyte biomass in a Bureau of Reclamation reservoir: U.S. Geological Survey Open-File Report 2013-1156, ix, 59 p., https://doi.org/10.3133/ofr20131156.","productDescription":"ix, 59 p.","numberOfPages":"68","onlineOnly":"Y","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":278577,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131156.gif"},{"id":278575,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1156/"},{"id":278576,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1156/of2013-1156.pdf"}],"country":"United States","state":"Oregon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.106,42.233 ], [ -122.106,42.599 ], [ -121.802,42.599 ], [ -121.802,42.233 ], [ -122.106,42.233 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52721c52e4b0ce70249c6262","contributors":{"authors":[{"text":"Simon, Nancy S. 0000-0003-2706-7611 nssimon@usgs.gov","orcid":"https://orcid.org/0000-0003-2706-7611","contributorId":838,"corporation":false,"usgs":true,"family":"Simon","given":"Nancy","email":"nssimon@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":true,"id":485442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ali, Ahmad Abdul","contributorId":25853,"corporation":false,"usgs":true,"family":"Ali","given":"Ahmad","email":"","middleInitial":"Abdul","affiliations":[],"preferred":false,"id":485444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Samperton, Kyle Michael","contributorId":11926,"corporation":false,"usgs":true,"family":"Samperton","given":"Kyle","email":"","middleInitial":"Michael","affiliations":[],"preferred":false,"id":485443,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Korson, Charles S.","contributorId":85494,"corporation":false,"usgs":true,"family":"Korson","given":"Charles","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":485447,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fischer, Kris","contributorId":54101,"corporation":false,"usgs":true,"family":"Fischer","given":"Kris","email":"","affiliations":[],"preferred":false,"id":485446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hughes, Michael L.","contributorId":43265,"corporation":false,"usgs":true,"family":"Hughes","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":485445,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70144456,"text":"70144456 - 2013 - Improving regression-model-based streamwater constituent load estimates derived from serially correlated data","interactions":[],"lastModifiedDate":"2015-03-30T14:05:44","indexId":"70144456","displayToPublicDate":"2013-10-30T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Improving regression-model-based streamwater constituent load estimates derived from serially correlated data","docAbstract":"A regression-model based approach is a commonly used, efficient method for estimating streamwater constituent load when there is a relationship between streamwater constituent concentration and continuous variables such as streamwater discharge, season and time. A subsetting experiment using a 30-year dataset of daily suspended sediment observations from the Mississippi River at Thebes, Illinois, was performed to determine optimal sampling frequency, model calibration period length, and regression model methodology, as well as to determine the effect of serial correlation of model residuals on load estimate precision. Two regression-based methods were used to estimate streamwater loads, the Adjusted Maximum Likelihood Estimator (AMLE), and the composite method, a hybrid load estimation approach. While both methods accurately and precisely estimated loads at the model’s calibration period time scale, precisions were progressively worse at shorter reporting periods, from annually to monthly. Serial correlation in model residuals resulted in observed AMLE precision to be significantly worse than the model calculated standard errors of prediction. The composite method effectively improved upon AMLE loads for shorter reporting periods, but required a sampling interval of at least 15-days or shorter, when the serial correlations in the observed load residuals were greater than 0.15. AMLE precision was better at shorter sampling intervals and when using the shortest model calibration periods, such that the regression models better fit the temporal changes in the concentration–discharge relationship. The models with the largest errors typically had poor high flow sampling coverage resulting in unrepresentative models. Increasing sampling frequency and/or targeted high flow sampling are more efficient approaches to ensure sufficient sampling and to avoid poorly performing models, than increasing calibration period length.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2013.09.001","usgsCitation":"Aulenbach, B.T., 2013, Improving regression-model-based streamwater constituent load estimates derived from serially correlated data: Journal of Hydrology, v. 503, p. 55-66, https://doi.org/10.1016/j.jhydrol.2013.09.001.","productDescription":"12 p.","startPage":"55","endPage":"66","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"1980-10-01","temporalEnd":"2010-09-30","ipdsId":"IP-050633","costCenters":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"links":[{"id":299141,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","city":"Thebes","otherGeospatial":"Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.46922302246094,\n 37.18609994167537\n ],\n [\n -89.46922302246094,\n 37.229303292139896\n ],\n [\n -89.44785118103027,\n 37.229303292139896\n ],\n [\n -89.44785118103027,\n 37.18609994167537\n ],\n [\n -89.46922302246094,\n 37.18609994167537\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"503","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551a75f8e4b03238427835b0","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":543628,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70048667,"text":"70048667 - 2013 - Crustal structure and fault geometry of the 2010 Haiti earthquake from temporary seismometer deployments","interactions":[],"lastModifiedDate":"2018-03-23T14:04:05","indexId":"70048667","displayToPublicDate":"2013-10-29T09:07:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Crustal structure and fault geometry of the 2010 Haiti earthquake from temporary seismometer deployments","docAbstract":"Haiti has been the locus of a number of large and damaging historical earthquakes. The recent 12 January 2010 Mw 7.0 earthquake affected cities that were largely unprepared, which resulted in tremendous losses. It was initially assumed that the earthquake ruptured the Enriquillo Plantain Garden fault (EPGF), a major active structure in southern Haiti, known from geodetic measurements and its geomorphic expression to be capable of producing M 7 or larger earthquakes. Global Positioning Systems (GPS) and Interferometric Synthetic Aperture Radar (InSAR) data, however, showed that the event ruptured a previously unmapped fault, the Léogâne fault, a north‐dipping oblique transpressional fault located immediately north of the EPGF. Following the earthquake, several groups installed temporary seismic stations to record aftershocks, including ocean‐bottom seismometers on either side of the EPGF. We use data from the complete set of stations deployed after the event, on land and offshore, to relocate all aftershocks from 10 February to 24 June 2010, determine a 1D regional crustal velocity model, and calculate focal mechanisms. The aftershock locations from the combined dataset clearly delineate the Léogâne fault, with a geometry close to that inferred from geodetic data. Its strike and dip closely agree with the global centroid moment tensor solution of the mainshock but with a steeper dip than inferred from previous finite fault inversions. The aftershocks also delineate a structure with shallower southward dip offshore and to the west of the rupture zone, which could indicate triggered seismicity on the offshore Trois Baies reverse fault. We use first‐motion focal mechanisms to clarify the relationship of the fault geometry to the triggered aftershocks.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120120303","usgsCitation":"Douilly, R., Haase, J.S., Ellsworth, W.L., Bouin, M., Calais, E., Symithe, S.J., Armbruster, J.G., Mercier de Lepinay, B., Deschamps, A., Mildor, S., Meremonte, M.E., and Hough, S.E., 2013, Crustal structure and fault geometry of the 2010 Haiti earthquake from temporary seismometer deployments: Bulletin of the Seismological Society of America, v. 103, no. 4, p. 2305-2325, https://doi.org/10.1785/0120120303.","productDescription":"21 p.","startPage":"2305","endPage":"2325","numberOfPages":"21","ipdsId":"IP-044476","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":278499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278495,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1785/0120120303"}],"country":"Haiti","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -72.793426,18.351113 ], [ -72.793426,18.671113 ], [ -72.473426,18.671113 ], [ -72.473426,18.351113 ], [ -72.793426,18.351113 ] ] ] } } ] }","volume":"103","issue":"4","noUsgsAuthors":false,"publicationDate":"2013-07-31","publicationStatus":"PW","scienceBaseUri":"5270caf9e4b0f7a10664c75e","contributors":{"authors":[{"text":"Douilly, Roby","contributorId":68173,"corporation":false,"usgs":true,"family":"Douilly","given":"Roby","email":"","affiliations":[],"preferred":false,"id":485359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haase, Jennifer S.","contributorId":81238,"corporation":false,"usgs":true,"family":"Haase","given":"Jennifer","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":485360,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellsworth, William L. ellsworth@usgs.gov","contributorId":787,"corporation":false,"usgs":true,"family":"Ellsworth","given":"William","email":"ellsworth@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":485351,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouin, Marie-Paule","contributorId":49697,"corporation":false,"usgs":true,"family":"Bouin","given":"Marie-Paule","email":"","affiliations":[],"preferred":false,"id":485357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Calais, Eric","contributorId":98838,"corporation":false,"usgs":true,"family":"Calais","given":"Eric","email":"","affiliations":[],"preferred":false,"id":485361,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Symithe, Steeve J.","contributorId":32818,"corporation":false,"usgs":true,"family":"Symithe","given":"Steeve","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":485356,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Armbruster, John G.","contributorId":51195,"corporation":false,"usgs":true,"family":"Armbruster","given":"John","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":485358,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mercier de Lepinay, Bernard","contributorId":10322,"corporation":false,"usgs":true,"family":"Mercier de Lepinay","given":"Bernard","email":"","affiliations":[],"preferred":false,"id":485353,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Deschamps, Anne","contributorId":24269,"corporation":false,"usgs":true,"family":"Deschamps","given":"Anne","email":"","affiliations":[],"preferred":false,"id":485354,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mildor, Saint‐Louis","contributorId":26217,"corporation":false,"usgs":true,"family":"Mildor","given":"Saint‐Louis","affiliations":[],"preferred":false,"id":485355,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Meremonte, Mark E. meremonte@usgs.gov","contributorId":4664,"corporation":false,"usgs":true,"family":"Meremonte","given":"Mark","email":"meremonte@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":485352,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hough, Susan E. 0000-0002-5980-2986 hough@usgs.gov","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":587,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"hough@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":485350,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70047215,"text":"70047215 - 2013 - Linking movement and reproductive history of brook trout to assess habitat connectivity in a heterogeneous stream network","interactions":[],"lastModifiedDate":"2013-12-16T09:51:09","indexId":"70047215","displayToPublicDate":"2013-10-28T11:36:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Linking movement and reproductive history of brook trout to assess habitat connectivity in a heterogeneous stream network","docAbstract":"1. Defining functional connectivity between habitats in spatially heterogeneous landscapes is a particular challenge for small-bodied aquatic species. Traditional approaches (e.g. mark–recapture studies) preclude an assessment of animal movement over the life cycle (birth to reproduction), and movement of individuals may not represent the degree of gene movement for fecund species.\nWe investigated the degree of habitat connectivity (defined as the exchange of individuals and genes between mainstem and tributary habitats) in a stream brook trout (Salvelinus fontinalis) population using mark–recapture [passive integrated transponder (PIT) tags], stationary PIT-tag antennae and genetic pedigree data collected over 4 years (3425 marked individuals). We hypothesised that: (i) a combination of these data would reveal higher estimates of animal movement over the life cycle (within a generation), relative to more temporally confined approaches, and (ii) movement estimates of individuals within a generation would differ from between-generation movement of genes because of spatial variation in reproductive success associated with high fecundity of this species.\nOver half of PIT-tagged fish (juveniles and adults) were recaptured within 20 m during periodic sampling, indicating restricted movement. However, continuous monitoring with stationary PIT-tag antennae revealed distinct peaks in trout movements in June and October–November, and sibship data inferred post-emergence movements of young-of-year trout that were too small to be tagged physically. A combination of these methods showed that a moderate portion of individuals (28–33%) moved between mainstem and tributary habitats over their life cycle.\nPatterns of reproductive success varied spatially and temporally. The importance of tributaries as spawning habitat was discovered by accounting for reproductive history. When individuals born in the mainstem reproduced successfully, over 50% of their surviving offspring were inferred to have been born in tributaries. This high rate of gene movement to tributaries was cryptic, and it would have been missed by estimates based only on movement of individuals.\nThis study highlighted the importance of characterising animal movement over the life cycle for inferring habitat connectivity accurately. Such movements of individuals can contribute to substantial gene movements in a fecund species characterised by high variation in reproductive success.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Freshwater Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/fwb.12254","usgsCitation":"Kanno, Y., Letcher, B., Coombs, J.A., Nislow, K.H., and Whiteley, A.R., 2013, Linking movement and reproductive history of brook trout to assess habitat connectivity in a heterogeneous stream network: Freshwater Biology, v. 59, no. 1, p. 142-154, https://doi.org/10.1111/fwb.12254.","productDescription":"13 p.","startPage":"142","endPage":"154","numberOfPages":"13","ipdsId":"IP-048823","costCenters":[],"links":[{"id":278473,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278472,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/fwb.12254"}],"volume":"59","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-10-18","publicationStatus":"PW","scienceBaseUri":"526f7970e4b0493c992e9954","contributors":{"authors":[{"text":"Kanno, Yoichiro ykanno@usgs.gov","contributorId":4876,"corporation":false,"usgs":true,"family":"Kanno","given":"Yoichiro","email":"ykanno@usgs.gov","affiliations":[],"preferred":true,"id":481414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Letcher, Benjamin H. 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":24774,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":481415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coombs, Jason A.","contributorId":77039,"corporation":false,"usgs":true,"family":"Coombs","given":"Jason","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":481417,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nislow, Keith H.","contributorId":103564,"corporation":false,"usgs":true,"family":"Nislow","given":"Keith","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":481418,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whiteley, Andrew R.","contributorId":52072,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":481416,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70048637,"text":"70048637 - 2013 - Genetic structure, diversity and subspecies status of Gull-billed Terns (Gelochelidon nilotica) from the United States","interactions":[],"lastModifiedDate":"2013-10-25T13:02:08","indexId":"70048637","displayToPublicDate":"2013-10-25T12:52:55","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"Genetic structure, diversity and subspecies status of Gull-billed Terns (Gelochelidon nilotica) from the United States","docAbstract":"Gull-billed Terns (Gelochelidon nilotica) are among the most widespread, yet scarce, Charadriiformes in the world. Two subspecies are recognized in the United States: G. n. aranea breeds along the U.S. Atlantic and Gulf coasts and G. n. vanrossemi breeds in the Salton Sea and San Diego Bay of California. Conservation concerns exist for the species due to its low abundance in the United States and apparent declines in some parts of its North American range. We used nuclear microsatellite markers and mitochondrial DNA sequences to assess genetic diversity and differentiation patterns among Gull-billed Tern populations from Virginia, Texas, and California. We also tested for evidence of population bottlenecks, and evaluated the support our data provide for the North American subspecies. Genetic diversity was highest in Texas and underscored the importance of habitat in that large population. Significant population differentiation existed, but could not be consistently identified using various analytical approaches and suggested that the magnitude of differentiation was low. No evidence for bottlenecks was identified. Our data could not distinguish individuals from different subspecies and therefore do not support the current intraspecific taxonomy. Tenable explanations for many findings are related to the low site tenacity demonstrated by the species.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Waterbirds","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"BioOne","doi":"10.1675/063.036.0308","usgsCitation":"Miller, M.P., Mullins, T., and Haig, S.M., 2013, Genetic structure, diversity and subspecies status of Gull-billed Terns (Gelochelidon nilotica) from the United States: Waterbirds, v. 36, no. 3, p. 310-318, https://doi.org/10.1675/063.036.0308.","productDescription":"9 p.","startPage":"310","endPage":"318","ipdsId":"IP-044929","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":278449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278442,"type":{"id":15,"text":"Index Page"},"url":"https://www.bioone.org/doi/full/10.1675/063.036.0308"},{"id":278441,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1675/063.036.0308"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.8,24.5 ], [ -124.8,49.383 ], [ -66.95,49.383 ], [ -66.95,24.5 ], [ -124.8,24.5 ] ] ] } } ] }","volume":"36","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"526b84d1e4b058918d0a9872","contributors":{"authors":[{"text":"Miller, Mark P. 0000-0003-1045-1772 mpmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-1045-1772","contributorId":1967,"corporation":false,"usgs":true,"family":"Miller","given":"Mark","email":"mpmiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":485236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullins, Thomas D.","contributorId":12819,"corporation":false,"usgs":true,"family":"Mullins","given":"Thomas D.","affiliations":[],"preferred":false,"id":485237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","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":485235,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048638,"text":"70048638 - 2013 - Has the time come for big science in wildlife health?","interactions":[],"lastModifiedDate":"2023-10-18T20:38:30.358709","indexId":"70048638","displayToPublicDate":"2013-10-25T12:41:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1443,"text":"EcoHealth","active":true,"publicationSubtype":{"id":10}},"title":"Has the time come for big science in wildlife health?","docAbstract":"The consequences of wildlife emerging diseases are global and profound with increased burden on the public health system, negative impacts on the global economy, declines and extinctions of wildlife species, and subsequent loss of ecological integrity. Examples of health threats to wildlife include Batrachochytrium dendrobatidis, which causes a cutaneous fungal infection of amphibians and is linked to declines of amphibians globally; and the recently discovered Pseudogymnoascus (Geomyces) destructans, the etiologic agent of white nose syndrome which has caused precipitous declines of North American bat species. Of particular concern are the novel pathogens that have emerged as they are particularly devastating and challenging to manage. A big science approach to wildlife health research is needed if we are to make significant and enduring progress in managing these diseases. The advent of new analytical models and bench assays will provide us with the mathematical and molecular tools to identify and anticipate threats to wildlife, and understand the ecology and epidemiology of these diseases. Specifically, new molecular diagnostic techniques have opened up avenues for pathogen discovery, and the application of spatially referenced databases allows for risk assessments that can assist in targeting surveillance. Long-term, systematic collection of data for wildlife health and integration with other datasets is also essential. Multidisciplinary research programs should be expanded to increase our understanding of the drivers of emerging diseases and allow for the development of better disease prevention and management tools, such as vaccines. Finally, we need to create a National Fish and Wildlife Health Network that provides the operational framework (governance, policies, procedures, etc.) by which entities with a stake in wildlife health cooperate and collaborate to achieve optimal outcomes for human, animal, and ecosystem health.
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States","interactions":[],"lastModifiedDate":"2025-05-14T19:12:13.211576","indexId":"ds801","displayToPublicDate":"2013-10-25T11:54:00","publicationYear":"2013","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":"801","title":"Geochemical and mineralogical data for soils of the conterminous United States","docAbstract":"In 2007, the U.S. Geological Survey initiated a low-density (1 site per 1,600 square kilometers, 4,857 sites) geochemical and mineralogical survey of soils of the conterminous United States as part of the North American Soil Geochemical Landscapes Project. Sampling and analytical protocols were developed at a workshop in 2003, and pilot studies were conducted from 2004 to 2007 to test and refine these recommended protocols. The final sampling protocol for the national-scale survey included, at each site, a sample from a depth of 0 to 5 centimeters, a composite of the soil A horizon, and a deeper sample from the soil C horizon or, if the top of the C horizon was at a depth greater than 1 meter, from a depth of approximately 80–100 centimeters. The <2-millimeter fraction of each sample was analyzed for a suite of 45 major and trace elements by methods that yield the total or near-total elemental content. The major mineralogical components in the samples from the soil A and C horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. Sampling in the conterminous United States was completed in 2010, with chemical and mineralogical analyses completed in May 2013. The resulting dataset provides an estimate of the abundance and spatial distribution of chemical elements and minerals in soils of the conterminous United States and represents a baseline for soil geochemistry and mineralogy against which future changes may be recognized and quantified. This report (1) describes the sampling, sample preparation, and analytical methods used; (2) gives details of the quality control protocols used to monitor the quality of chemical and mineralogical analyses over approximately six years; and (3) makes available the soil geochemical and mineralogical data in downloadable tables.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds801","usgsCitation":"Smith, D., Cannon, W.F., Woodruff, L.G., Solano, F., Kilburn, J.E., and Fey, D.L., 2013, Geochemical and mineralogical data for soils of the conterminous United States: U.S. Geological Survey Data Series 801, Report: iv, 19 p.; Downloads Directory, https://doi.org/10.3133/ds801.","productDescription":"Report: iv, 19 p.; Downloads Directory","numberOfPages":"26","onlineOnly":"Y","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and 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}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"526b852de4b058918d0a99a7","contributors":{"authors":[{"text":"Smith, David B. 0000-0001-8396-9105 dsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8396-9105","contributorId":1274,"corporation":false,"usgs":true,"family":"Smith","given":"David B.","email":"dsmith@usgs.gov","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":false,"id":485230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cannon, William F. 0000-0002-2699-8118 wcannon@usgs.gov","orcid":"https://orcid.org/0000-0002-2699-8118","contributorId":1883,"corporation":false,"usgs":true,"family":"Cannon","given":"William","email":"wcannon@usgs.gov","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":485231,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodruff, Laurel G. 0000-0002-2514-9923 woodruff@usgs.gov","orcid":"https://orcid.org/0000-0002-2514-9923","contributorId":2224,"corporation":false,"usgs":true,"family":"Woodruff","given":"Laurel","email":"woodruff@usgs.gov","middleInitial":"G.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":485232,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Solano, Federico 0000-0002-0308-5850 fsolanoc@usgs.gov","orcid":"https://orcid.org/0000-0002-0308-5850","contributorId":4302,"corporation":false,"usgs":true,"family":"Solano","given":"Federico","email":"fsolanoc@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":485233,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kilburn, James E.","contributorId":40189,"corporation":false,"usgs":true,"family":"Kilburn","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":485234,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fey, David L. dfey@usgs.gov","contributorId":713,"corporation":false,"usgs":true,"family":"Fey","given":"David","email":"dfey@usgs.gov","middleInitial":"L.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":485229,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70048622,"text":"fs20133093 - 2013 - US Topo: topographic maps for the nation","interactions":[{"subject":{"id":70048622,"text":"fs20133093 - 2013 - US Topo: topographic maps for the nation","indexId":"fs20133093","publicationYear":"2013","noYear":false,"title":"US Topo: topographic maps for the nation"},"predicate":"SUPERSEDED_BY","object":{"id":70188457,"text":"fs20173045 - 2017 - US Topo—Topographic maps for the Nation","indexId":"fs20173045","publicationYear":"2017","noYear":false,"title":"US Topo—Topographic maps for the Nation"},"id":1}],"supersededBy":{"id":70188457,"text":"fs20173045 - 2017 - US Topo—Topographic maps for the Nation","indexId":"fs20173045","publicationYear":"2017","noYear":false,"title":"US Topo—Topographic maps for the Nation"},"lastModifiedDate":"2017-06-23T13:59:00","indexId":"fs20133093","displayToPublicDate":"2013-10-25T11:01:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-3093","title":"US Topo: topographic maps for the nation","docAbstract":"US Topo is the next generation of topographic maps from the U.S. Geological Survey (USGS). Arranged in the familiar 7.5-minute quadrangle format, digital US Topo maps are designed to look and feel (and perform) like the traditional paper topographic maps for which the USGS is so well known. In contrast to paper-based maps, US Topo maps provide modern technical advantages that support faster, wider public distribution and enable basic, on-screen geographic analysis for all users.
\nThe US Topo quadrangle map has been redesigned so that map elements are visually distinguishable with the imagery turned on and off, while keeping the file size as small as possible. The US Topo map redesign includes improvements to various display factors, including symbol definitions (color, line thickness, line symbology, area fills), layer order, and annotation fonts. New features for 2013 include the following: a raster shaded relief layer, military boundaries, cemeteries and post offices, and a US Topo cartographic symbols legend as an attachment.
\nUS Topo quadrangle maps are available free on the Web. Each map quadrangle is constructed in GeoPDF® format using key layers of geographic data (orthoimagery, roads, geographic names, topographic contours, and hydrographic features) from The National Map databases.
\nUS Topo quadrangle maps can be printed from personal computers or plotters as complete, full-sized, maps or in customized sections, in a user-desired specific format. Paper copies of the maps can also be purchased from the USGS Store. Download links and a users guide are featured on the US Topo Web site.
\nUS Topo users can turn geographic data layers on and off as needed; they can zoom in and out to highlight specific features or see a broader area. File size for each digital 7.5-minute quadrangle, about 30 megabytes. Associated electronic tools for geographic analysis are available free for download. The US Topo provides the Nation with a topographic product that users can quickly incorporate into decisionmaking, operational or recreational activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133093","usgsCitation":"Carswell, W., 2013, US Topo: topographic maps for the nation: U.S. Geological Survey Fact Sheet 2013-3093, 2 p., https://doi.org/10.3133/fs20133093.","productDescription":"2 p.","numberOfPages":"2","additionalOnlineFiles":"N","ipdsId":"IP-049142","costCenters":[{"id":425,"text":"National Geospatial Technical Operations Center","active":false,"usgs":true}],"links":[{"id":278424,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3093/"},{"id":278427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20133093.gif"},{"id":278426,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3093/pdf/fs2013-3093.pdf"}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"526b8532e4b058918d0a99cf","contributors":{"authors":[{"text":"Carswell, William J. Jr. carswell@usgs.gov","contributorId":1787,"corporation":false,"usgs":true,"family":"Carswell","given":"William J.","suffix":"Jr.","email":"carswell@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":false,"id":485217,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70048591,"text":"ofr20131259 - 2013 - Postwildfire debris-flow hazard assessment of the area burned by the 2013 West Fork Fire Complex, southwestern Colorado","interactions":[],"lastModifiedDate":"2013-11-14T18:01:35","indexId":"ofr20131259","displayToPublicDate":"2013-10-25T08:03:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1259","title":"Postwildfire debris-flow hazard assessment of the area burned by the 2013 West Fork Fire Complex, southwestern Colorado","docAbstract":"This report presents a preliminary emergency assessment of the debris-flow hazards from drainage basins burned by the 2013 West Fork Fire Complex near South Fork in southwestern Colorado. Empirical models derived from statistical evaluation of data collected from recently burned basins throughout the intermountain western United States were used to estimate the probability of debris-flow occurrence, potential volume of debris flows, and the combined debris-flow hazard ranking along the drainage network within and just downstream from the burned area, and to estimate the same for 54 drainage basins of interest within the perimeter of the burned area. Input data for the debris-flow models included topographic variables, soil characteristics, burn severity, and rainfall totals and intensities for a (1) 2-year-recurrence, 1-hour-duration rainfall, referred to as a 2-year storm; (2) 10-year-recurrence, 1-hour-duration rainfall, referred to as a 10-year storm; and (3) 25-year-recurrence, 1-hour-duration rainfall, referred to as a 25-year storm.\n \nEstimated debris-flow probabilities at the pour points of the 54 drainage basins of interest ranged from less than 1 to 65 percent in response to the 2-year storm; from 1 to 77 percent in response to the 10-year storm; and from 1 to 83 percent in response to the 25-year storm. Twelve of the 54 drainage basins of interest have a 30-percent probability or greater of producing a debris flow in response to the 25-year storm. Estimated debris-flow volumes for all rainfalls modeled range from a low of 2,400 cubic meters to a high of greater than 100,000 cubic meters. Estimated debris-flow volumes increase with basin size and distance along the drainage network, but some smaller drainages also were predicted to produce substantial debris flows. One of the 54 drainage basins of interest had the highest combined hazard ranking, while 9 other basins had the second highest combined hazard ranking. Of these 10 basins with the 2 highest combined hazard rankings, 7 basins had predicted debris-flow volumes exceeding 100,000 cubic meters, while 3 had predicted probabilities of debris flows exceeding 60 percent. The 10 basins with high combined hazard ranking include 3 tributaries in the headwaters of Trout Creek, four tributaries to the West Fork San Juan River, Hope Creek draining toward a county road on the eastern edge of the burn, Lake Fork draining to U.S. Highway 160, and Leopard Creek on the northern edge of the burn. The probabilities and volumes for the modeled storms indicate a potential for debris-flow impacts on structures, reservoirs, roads, bridges, and culverts located within and immediately downstream from the burned area. U.S. Highway 160, on the eastern edge of the burn area, also is susceptible to impacts from debris flows.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131259","collaboration":"Prepared in cooperation with Hinsdale County, Colorado","usgsCitation":"Verdin, K.L., Dupree, J.A., and Stevens, M.R., 2013, Postwildfire debris-flow hazard assessment of the area burned by the 2013 West Fork Fire Complex, southwestern Colorado: U.S. Geological Survey Open-File Report 2013-1259, Report: iv, 30 p.; 3 Plates: 34 x 22.31 inches or smaller, https://doi.org/10.3133/ofr20131259.","productDescription":"Report: iv, 30 p.; 3 Plates: 34 x 22.31 inches or smaller","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-050942","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":278394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131259.gif"},{"id":278398,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1259/pdf/of2013-1259.pdf"},{"id":278399,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1259/pdf/of2013-1259_plate1.pdf"},{"id":278400,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1259/pdf/of2013-1259_plate2.pdf"},{"id":278401,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1259/pdf/of2013-1259_plate3.pdf"},{"id":278392,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1259/"}],"country":"United States","state":"Colorado","otherGeospatial":"West Fork Complex","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -107.1052,37.1977 ], [ -107.1052,38.1408 ], [ -106.1574,38.1408 ], [ -106.1574,37.1977 ], [ -107.1052,37.1977 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"526b852fe4b058918d0a99b7","contributors":{"authors":[{"text":"Verdin, Kristine L. 0000-0002-6114-4660 kverdin@usgs.gov","orcid":"https://orcid.org/0000-0002-6114-4660","contributorId":3070,"corporation":false,"usgs":true,"family":"Verdin","given":"Kristine","email":"kverdin@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dupree, Jean A. dupree@usgs.gov","contributorId":2563,"corporation":false,"usgs":true,"family":"Dupree","given":"Jean","email":"dupree@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":485152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stevens, Michael R. 0000-0002-9476-6335 mrsteven@usgs.gov","orcid":"https://orcid.org/0000-0002-9476-6335","contributorId":769,"corporation":false,"usgs":true,"family":"Stevens","given":"Michael","email":"mrsteven@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485151,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048578,"text":"fs20133099 - 2013 - Hurricane Sandy science plan: coastal topographic and bathymetric data to support hurricane impact assessment and response","interactions":[],"lastModifiedDate":"2017-07-05T09:30:44","indexId":"fs20133099","displayToPublicDate":"2013-10-24T10:13:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-3099","title":"Hurricane Sandy science plan: coastal topographic and bathymetric data to support hurricane impact assessment and response","docAbstract":"Hurricane Sandy devastated some of the most heavily populated eastern coastal areas of the Nation. With a storm surge peaking at more than 19 feet, the powerful landscape-altering destruction of Hurricane Sandy is a stark reminder of why the Nation must become more resilient to coastal hazards. In response to this natural disaster, the U.S. Geological Survey (USGS) received a total of $41.2 million in supplemental appropriations from the Department of the Interior (DOI) to support response, recovery, and rebuilding efforts. These funds support a science plan that will provide critical scientific information necessary to inform management decisions for recovery of coastal communities, and aid in preparation for future natural hazards. This science plan is designed to coordinate continuing USGS activities with stakeholders and other agencies to improve data collection and analysis that will guide recovery and restoration efforts. The science plan is split into five distinct themes:
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\n• Impacts to coastal beaches and barriers
\n• Impacts of storm surge, including disturbed estuarine and bay hydrology
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\n• Impacts to coastal ecosystems, habitats, and fish and wildlife This fact sheet focuses on coastal topography and bathymetry.
This fact sheet focuses on coastal topography and bathymetry.
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With a storm surge peaking at more than 19 feet, the powerful landscape-altering destruction of Hurricane Sandy is a stark reminder of why the Nation must become more resilient to coastal hazards. In response to this natural disaster, the U.S. Geological Survey (USGS) received a total of $41.2 million in supplemental appropriations from the Department of the Interior (DOI) to support response, recovery, and rebuilding efforts. These funds support a science plan that will provide critical scientific information necessary to inform management decisions for recovery of coastal communities, and aid in preparation for future natural hazards. This science plan is designed to coordinate continuing USGS activities with stakeholders and other agencies to improve data collection and analysis that will guide recovery and restoration efforts. The science plan is split into five distinct themes:
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This fact sheet focuses on assessing impacts on environmental quality and persisting contaminant exposures.
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With a storm surge peaking at more than 19 feet, the powerful landscape-altering destruction of Hurricane Sandy is a stark reminder of why the Nation must become more resilient to coastal hazards. In response to this natural disaster, the U.S. Geological Survey (USGS) received a total of $41.2 million in supplemental appropriations from the Department of the Interior (DOI) to support response, recovery, and rebuilding efforts. These funds support a science plan that will provide critical scientific information necessary to inform management decisions for recovery of coastal communities, and aid in preparation for future natural hazards. This science plan is designed to coordinate continuing USGS activities with stakeholders and other agencies to improve data collection and analysis that will guide recovery and restoration efforts. The science plan is split into five distinct themes:\n\n• Coastal topography and bathymetry\n• Impacts to coastal beaches and barriers\n• Impacts of storm surge, including disturbed estuarine and bay hydrology\n• Impacts on environmental quality and persisting contaminant exposures\n• Impacts to coastal ecosystems, habitats, and fish and wildlife\n\nThis fact sheet focuses on impacts to coastal ecosystems, habitats, and fish and wildlife.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133096","usgsCitation":"Campbell, W.H., 2013, Hurricane Sandy science plan: impacts to coastal ecosystems, habitats, and fish and wildlife: U.S. Geological Survey Fact Sheet 2013-3096, 2 p., https://doi.org/10.3133/fs20133096.","productDescription":"2 p.","numberOfPages":"2","additionalOnlineFiles":"Y","costCenters":[{"id":459,"text":"Natural Hazards Mission Area","active":false,"usgs":true}],"links":[{"id":278364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20133096.gif"},{"id":278361,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3096/"},{"id":278359,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3096/pdf/fs2013-3096.pdf"}],"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 -97.91015624999999,\n 27.955591004642553\n ],\n [\n -97.91015624999999,\n 26.23430203240673\n ],\n [\n -94.1748046875,\n 29.11377539511439\n ],\n [\n -92.197265625,\n 29.152161283318915\n ],\n [\n -90.43945312500001,\n 28.57487404744697\n ],\n [\n -89.1650390625,\n 28.65203063036226\n ],\n [\n -88.330078125,\n 29.11377539511439\n ],\n [\n -88.1982421875,\n 29.7643773751631\n ],\n [\n -88.02246093750001,\n 29.99300228455108\n ],\n [\n -87.0556640625,\n 29.99300228455108\n ],\n [\n -86.044921875,\n 29.72622231939548\n ],\n [\n -85.25390625,\n 29.305561325527698\n ],\n [\n -84.55078125,\n 28.998531814051795\n ],\n [\n -83.8037109375,\n 29.267232865200853\n ],\n [\n -83.27636718749999,\n 28.921631282421266\n ],\n [\n -83.3642578125,\n 27.994401411046148\n ],\n [\n -82.177734375,\n 24.206889622398023\n ],\n [\n -80.8154296875,\n 24.44714958973082\n ],\n [\n -79.7607421875,\n 25.20494115356912\n ],\n [\n -79.541015625,\n 27.137368359795584\n ],\n [\n -79.8486328125,\n 28.14950321154457\n ],\n [\n -80.15625000000001,\n 28.921631282421266\n ],\n [\n -80.8154296875,\n 30.29701788337205\n ],\n [\n -80.63964843750001,\n 31.653381399664\n ],\n [\n -77.95898437499999,\n 33.54139466898275\n ],\n [\n -75.498046875,\n 34.95799531086792\n ],\n [\n -74.92675781249999,\n 35.746512259918504\n ],\n [\n -75.5419921875,\n 36.914764288955936\n ],\n [\n -74.267578125,\n 39.027718840211605\n ],\n [\n -71.19140625,\n 41.31082388091818\n ],\n [\n -72.59765625,\n 41.50857729743935\n ],\n [\n -74.00390625,\n 41.1455697310095\n ],\n [\n -75.1025390625,\n 39.977120098439634\n ],\n [\n -76.5087890625,\n 39.605688178320804\n ],\n [\n -77.0361328125,\n 38.61687046392973\n ],\n [\n -76.9482421875,\n 37.50972584293751\n ],\n [\n -76.9921875,\n 35.31736632923788\n ],\n [\n -79.5849609375,\n 33.578014746143985\n ],\n [\n -80.85937499999999,\n 32.54681317351514\n ],\n [\n -82.001953125,\n 31.278550858946506\n ],\n [\n -80.33203125,\n 26.43122806450644\n ],\n [\n -80.9033203125,\n 25.997549919572084\n ],\n [\n -81.34277343749999,\n 26.470573022375056\n ],\n [\n -83.408203125,\n 30.637912028341123\n ],\n [\n -88.1982421875,\n 30.864510226258336\n ],\n [\n -90.8349609375,\n 30.713503990354965\n ],\n [\n -91.845703125,\n 30.221101852485987\n ],\n [\n -93.0322265625,\n 30.41078179084589\n ],\n [\n -95.9326171875,\n 29.53522956294847\n ],\n [\n -97.91015624999999,\n 27.955591004642553\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"526a3364e4b0c0d229f9bdda","contributors":{"authors":[{"text":"Campbell, Warren H. wcampbell@usgs.gov","contributorId":5374,"corporation":false,"usgs":true,"family":"Campbell","given":"Warren","email":"wcampbell@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":485115,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70048574,"text":"fs20133090 - 2013 - Hurricane Sandy science plan: coastal impact assessments","interactions":[],"lastModifiedDate":"2013-11-14T17:38:38","indexId":"fs20133090","displayToPublicDate":"2013-10-24T09:55:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-3090","title":"Hurricane Sandy science plan: coastal impact assessments","docAbstract":"Hurricane Sandy devastated some of the most heavily populated eastern coastal areas of the Nation. With a storm surge peaking at more than 19 feet, the powerful landscape-altering destruction of Hurricane Sandy is a stark reminder of why the Nation must become more resilient to coastal hazards. In response to this natural disaster, the U.S. Geological Survey (USGS) received a total of $41.2 million in supplemental appropriations from the Department of the Interior (DOI) to support response, recovery, and rebuilding efforts. These funds support a science plan that will provide critical scientific information necessary to inform management decisions for recovery of coastal communities, and aid in preparation for future natural hazards. This science plan is designed to coordinate continuing USGS activities with stakeholders and other agencies to improve data collection and analysis that will guide recovery and restoration efforts. The science plan is split into five distinct themes: coastal topography and bathymetry, impacts to coastal beaches and barriers, impacts of storm surge, including disturbed estuarine and bay hydrology, impacts on environmental quality and persisting contaminant exposures, impacts to coastal ecosystems, habitats, and fish and wildlife.\n\nThis fact sheet focuses assessing impacts to coastal beaches and barriers.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133090","usgsCitation":"Stronko, J.M., 2013, Hurricane Sandy science plan: coastal impact assessments: U.S. Geological Survey Fact Sheet 2013-3090, 2 p., https://doi.org/10.3133/fs20133090.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","costCenters":[{"id":459,"text":"Natural Hazards Mission Area","active":false,"usgs":true}],"links":[{"id":278363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20133090.gif"},{"id":278360,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3090/"},{"id":278362,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3090/pdf/fs2013-3090.pdf"}],"country":"United States","otherGeospatial":"East Coast","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81.39,32.28 ], [ -81.39,45.91 ], [ -66.84,45.91 ], [ -66.84,32.28 ], [ -81.39,32.28 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"526a3363e4b0c0d229f9bdd1","contributors":{"authors":[{"text":"Stronko, Jakob M. jstronko@usgs.gov","contributorId":5372,"corporation":false,"usgs":true,"family":"Stronko","given":"Jakob","email":"jstronko@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":485114,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70048573,"text":"fs20133092 - 2013 - Hurricane Sandy science plan: impacts of storm surge, including disturbed estuarine and bay hydrology","interactions":[],"lastModifiedDate":"2017-07-05T09:34:32","indexId":"fs20133092","displayToPublicDate":"2013-10-24T09:44:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-3092","title":"Hurricane Sandy science plan: impacts of storm surge, including disturbed estuarine and bay hydrology","docAbstract":"Hurricane Sandy devastated some of the most heavily populated eastern coastal areas of the Nation. With a storm surge peaking at more than 19 feet, the powerful landscape-altering destruction of Hurricane Sandy is a stark reminder of why the Nation must become more resilient to coastal hazards. In response to this natural disaster, the U.S. Geological Survey (USGS) received a total of $41.2 million in supplemental appropriations from the Department of the Interior (DOI) to support response, recovery, and rebuilding efforts. These funds support a science plan that will provide critical scientific information necessary to inform management decisions for recovery of coastal communities, and aid in preparation for future natural hazards. This science plan is designed to coordinate continuing USGS activities with stakeholders and other agencies to improve data collection and analysis that will guide recovery and restoration efforts. The science plan is split into five distinct themes:
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This fact sheet focuses on assessing impacts of storm surge, including disturbed estuarine and bay hydrology.
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The decline of water levels in the aquifer was noted soon after the development of the Wichita well field began. Development of irrigation wells began in the 1960s. City and agricultural withdrawals led to substantial water-level declines. Water-level declines enhanced movement of brines from past oil and gas activities near Burrton, Kansas and enhanced movement of natural saline water from the Arkansas River into the well field area. Large chloride concentrations may limit use or require the treatment of water from the well field for irrigation or public supply. In 1993, the city of Wichita adopted the Integrated Local Water Supply Program (ILWSP) to ensure an adequate water supply for the city through 2050 and as part of its effort to effectively manage the part of the Equus Beds aquifer it uses. ILWSP uses several strategies to do this including the Equus Beds Aquifer Storage and Recovery (ASR) project. The purpose of the ASR project is to store water in the aquifer for later recovery and to help protect the aquifer from encroachment of a known oilfield brine plume near Burrton and saline water from the Arkansas River.\n\nAs part of Wichita’s ASR permits, Wichita is prohibited from artificially recharging water into the aquifer in a Basin Storage area (BSA) grid cell if water levels in that cell are above the January 1940 water levels or are less than 10 feet below land surface. The map previously used for this purpose did not provide an accurate representation of the shallow water table. The revised predevelopment water-level altitude map of the shallow part of the aquifer is presented in this report.\n\nThe city of Wichita’s ASR permits specify that the January 1993 water-level altitudes will be used as a lower baseline for regulating the withdrawal of artificial rechage credits from the Equus Beds aquifer by the city of Wichita. The 1993 water levels correspond to the lowest recorded levels and largest storage declines since 1940. Revised and new water-level maps of shallow and deep layers were developed to better represent the general condition of the aquifer. Only static water levels were used to better represent the general condition of the aquifer and comply with Wichita’s ASR permits. To ensure adequate data density, the January 1993 period was expanded to October 1992 through February 1993. Static 1993 water levels from the deep aquifer layer of the Equus Beds aquifer possibly could be used as the lower baseline for regulatory purposes.\n\nPreviously, maps of water-level changes used to estimate the storage-volume changes included a combination of static (unaffected by pumping or nearby pumping) and stressed (affected by pumping or nearby pumping) water levels from wells. Some of these wells were open to the shallow aquifer layer and some were open to the deep aquifer layer of the Equus Beds aquifer. In this report, only static water levels in the shallow aquifer layer were used to determine storage-volume changes.\n\nThe effects on average water-level and storage-volume change from the use of mixed, stressed water levels and a specific yield of 0.20 were compared to the use of static water levels in the shallow aquifer and a specific yield of 0.15. This comparison indicates that the change in specific yield causes storage-volume changes to decrease about 25 percent, whereas the use of static water levels in the shallow aquifer layer causes an increase of less than 4 percent. Use of a specific yield of 0.15 will result in substantial decreases in the amount of storage-volume change compared to those reported previously that were calculated using a specific yield of 0.20. Based on these revised water-level maps and computations, the overall decline and change in storage from predevelopment to 1993 represented a loss in storage of about 6 percent (-202,000 acre-feet) of the overall storage volume within the newly defined study area.","language":"English","publisher":"U.S Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135170","usgsCitation":"Hansen, C.V., Lanning-Rush, J., and Ziegler, A., 2013, Revised shallow and deep water-level and storage-volume changes in the Equus Beds Aquifer near Wichita, Kansas, predevelopment to 1993: U.S. Geological Survey Scientific Investigations Report 2013-5170, v.; 18 p., https://doi.org/10.3133/sir20135170.","productDescription":"v.; 18 p.","numberOfPages":"23","onlineOnly":"Y","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":278347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135170.gif"},{"id":278346,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5170/"},{"id":278345,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5170/pdf/sir2013_5170.pdf"}],"country":"United States","state":"Kansas","city":"Wichita","otherGeospatial":"Equus Beds Aquifer","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -97.68355,37.73379 ], [ -97.68355,38.181032 ], [ -97.396098,38.181032 ], [ -97.396098,37.73379 ], [ -97.68355,37.73379 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"526a3365e4b0c0d229f9bde0","contributors":{"authors":[{"text":"Hansen, Cristi V. chansen@usgs.gov","contributorId":435,"corporation":false,"usgs":true,"family":"Hansen","given":"Cristi","email":"chansen@usgs.gov","middleInitial":"V.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":485108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lanning-Rush, Jennifer L. jlanning@usgs.gov","contributorId":5809,"corporation":false,"usgs":true,"family":"Lanning-Rush","given":"Jennifer L.","email":"jlanning@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":485109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ziegler, Andrew C. aziegler@usgs.gov","contributorId":433,"corporation":false,"usgs":true,"family":"Ziegler","given":"Andrew C.","email":"aziegler@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":485107,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048530,"text":"70048530 - 2013 - Influence of monsoon-related riparian phenology on yellow-billed cuckoo habitat selection in Arizona","interactions":[],"lastModifiedDate":"2017-11-25T13:36:41","indexId":"70048530","displayToPublicDate":"2013-10-22T15:19:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Influence of monsoon-related riparian phenology on yellow-billed cuckoo habitat selection in Arizona","docAbstract":"Aim: The western yellow-billed cuckoo (Coccyzus americanus occidentalis), a Neotropical migrant bird, is facing steep population declines in its western breeding grounds owing primarily to loss of native habitat. The favoured esting habitat for the cuckoo in the south-western United States is low-elevation riparian forests and woodlands. Our aim was to explore relationships between vegetation phenology patterns captured by satellite phenometrics and the distribution of the yellow-billed cuckoo, and to use this information to map cuckoo habitat. Location: Arizona, USA. Methods: Land surface phenometrics were derived from satellite Advanced Very High-Resolution Radiometer (AVHRR), bi-weekly time-composite, ormalized difference vegetation index (NDVI) data for 1998 and 1999 at a resolution of 1 km. Fourier harmonics were used to analyse the waveform of the annual NDVI profile in each pixel. To create the models, we coupled 1998 satellite phenometrics with 1998 field survey data of cuckoo presence or absence and with point data that sampled riparian and cottonwood–willow vegetation types. Our models were verified and refined using field and satellite data collected in 1999. Results: The models reveal that cuckoos prefer areas that experience peak greenness 29 days later, are 36% more dynamic and slightly (< 1%) more productive than their average cottonwood–willow habitat. The results support a scenario in which cuckoos migrate northwards, following the greening of riparian corridors and surrounding landscapes in response to monsoon precipitation, but then select a nesting site based on optimizing the near-term foraging potential of the neighbourhood. Main conclusions: The identification of preferred phenotypes within recognized habitat can be used to refine future habitat models, inform habitat response to climate change, and suggest adaptation strategies. For example, models of phenotype preferences can guide management actions by identifying and prioritizing for conservation those landscapes that reliably exhibit highly preferred phenometrics on a consistent basis.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Biogeography","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/jbi.12167","usgsCitation":"Wallace, C., Villarreal, M.L., and van Riper, C., 2013, Influence of monsoon-related riparian phenology on yellow-billed cuckoo habitat selection in Arizona: Journal of Biogeography, v. 40, no. 11, p. 2094-2107, https://doi.org/10.1111/jbi.12167.","productDescription":"14 p.","startPage":"2094","endPage":"2107","numberOfPages":"14","ipdsId":"IP-013518","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":473475,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jbi.12167","text":"Publisher Index Page"},{"id":278336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278334,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/jbi.12167"}],"country":"United States","state":"Arizona","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -114.8184,31.3322 ], [ -114.8184,37.0043 ], [ -109.0452,37.0043 ], [ -109.0452,31.3322 ], [ -114.8184,31.3322 ] ] ] } } ] }","volume":"40","issue":"11","noUsgsAuthors":false,"publicationDate":"2013-07-19","publicationStatus":"PW","scienceBaseUri":"52679067e4b0c24c90856d8a","contributors":{"authors":[{"text":"Wallace, Cynthia S.A. cwallace@usgs.gov","contributorId":3335,"corporation":false,"usgs":true,"family":"Wallace","given":"Cynthia S.A.","email":"cwallace@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":484978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":484977,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":484976,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}