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New 40Ar/39Ar ages for hornblende and muscovite from the Orange-Milford belt in southern Connecticut reflect cooling from Acadian amphibolite facies metamorphism between ∼380 to 360 Ma followed by retrograde recrystallization of fabric-forming muscovite and chlorite during lower greenschist facies Alleghanian transpression at ∼280 Ma. Reported field temperature and pressure gradients are improbably high for these rocks and a NW metamorphic field gradient climbing from chlorite-grade to staurolite-grade occurs over less than 5 km. Simple tilting cannot account for this compressed isograd spacing given the geothermal gradient of ∼20 °C/km present at the time of regional metamorphism. However, post-metamorphic transpression could effectively telescope the isograds by stretching the belt at an oblique angle to the isograd traces. Textures in the field and in thin section reveal several older prograde schistosities overprinted by lower greenschist facies fabrics. The late cleavages commonly occur at the scale of ∼100 μm and these samples contain multiple age populations of white mica. 40Ar/39Ar analysis of these poly-metamorphic samples with mixed muscovite populations yield climbing or U-shaped age spectra. The ages of the low temperature steps are late Paleozoic, while the ages of the older steps are late Devonian. These results support our petrologic interpretation that the younger cleavage developed under metamorphic conditions below the closure temperature for Ar diffusion in muscovite, that is, in the lower greenschist facies. The correlation of a younger regionally reproducible age population with a pervasive retrograde muscovite ± chlorite cleavage reveals an Alleghanian (∼280 Ma) overprint on the Acadian metamorphic gradient (∼380 Ma). Outcrop-scale structures including drag folds and imbricate boudins suggest that Alleghanian deformation and cleavage development occurred in response to dextral transpression along a northeast striking boundary. Alleghanian oblique collision of accreting terranes from the northeast would have resulted in northeast-southwest dextral transpression against the New York promontory. This deformation was responsible for crystallization of pervasive retrograde muscovite + chlorite cleavages and associated telescoping of the Acadian metamorphic isograds in southern Connecticut at ∼280 Ma.

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Austin","contributorId":18833,"corporation":false,"usgs":true,"family":"Reed","given":"Austin","email":"","affiliations":[],"preferred":false,"id":695910,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Derksen, Dirk V. dderksen@usgs.gov","contributorId":2269,"corporation":false,"usgs":true,"family":"Derksen","given":"Dirk","email":"dderksen@usgs.gov","middleInitial":"V.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":695911,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193800,"text":"70193800 - 2013 - Space use and movements of moose in Massachusetts: implications for conservation of large mammals in a fragmented environment","interactions":[],"lastModifiedDate":"2017-11-09T12:40:38","indexId":"70193800","displayToPublicDate":"2013-12-31T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":693,"text":"Alces","active":true,"publicationSubtype":{"id":10}},"title":"Space use and movements of moose in Massachusetts: implications for conservation of large mammals in a fragmented environment","docAbstract":"

Moose (Alces alces) have recently re-occupied a portion of their range in the temperate deciduous forest of the northeastern United States after a >200 year absence. In southern New England, moose encounter different forest types, more human development, and higher temperatures than in other parts of their geographic range in North America. We analyzed seasonal minimum convex polygon home ranges, utilization distributions, movement rates, and home range composition of GPS-collared moose in Massachusetts. Seasonal home range sizes were not different for males and females and were within the range reported for low latitudes elsewhere in North America. Seasonal movement patterns reflected the seasonal changes in metabolic rate and the influence of the species’ reproductive cycle and weather. Home ranges consisted almost entirely of forested habitat, included large amounts of conservation land, and had lower road densities as compared to the landscape as a whole, indicating that human development may be a limiting factor for moose in the region. The size and configuration of home ranges, seasonal movement patterns, and use relative to human development have implications for conservation of moose and other wide-ranging species in more highly developed portions of their ranges.

","language":"English","publisher":"Lakehead University","usgsCitation":"Wattles, D.W., and DeStefano, S., 2013, Space use and movements of moose in Massachusetts: implications for conservation of large mammals in a fragmented environment: Alces, v. 49, p. 65-81.","productDescription":"17 p.","startPage":"65","endPage":"81","ipdsId":"IP-043829","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348539,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"49","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a05771de4b09af898c7086e","contributors":{"authors":[{"text":"Wattles, David W.","contributorId":25012,"corporation":false,"usgs":true,"family":"Wattles","given":"David","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":721453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeStefano, Stephen 0000-0003-2472-8373 destef@usgs.gov","orcid":"https://orcid.org/0000-0003-2472-8373","contributorId":166706,"corporation":false,"usgs":true,"family":"DeStefano","given":"Stephen","email":"destef@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":720544,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192322,"text":"70192322 - 2013 - Use of fragile geologic structures as indicators of unexceeded ground motions and direct constraints on probabilistic seismic hazard analysis","interactions":[],"lastModifiedDate":"2017-10-25T10:31:13","indexId":"70192322","displayToPublicDate":"2013-12-31T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":960,"text":"BSSA","active":true,"publicationSubtype":{"id":10}},"title":"Use of fragile geologic structures as indicators of unexceeded ground motions and direct constraints on probabilistic seismic hazard analysis","docAbstract":"We present a quantitative procedure for constraining probabilistic seismic hazard analysis results at a given site, based on the existence of fragile geologic structures at that site. We illustrate this procedure by analyzing precarious rocks and undamaged lithophysae at Yucca Mountain, Nevada. The key metric is the probability that the feature would have survived to the present day, assuming that the hazard results are correct. If the fragile geologic structure has an extremely low probability of having survived (which would be inconsistent with the observed survival of the structure), then the calculations illustrate how much the hazard would have to be reduced to result in a nonnegligible survival probability. The calculations are able to consider structures the predicted failure probabilities of which are a function of one or more ground‐motion parameters, as well as structures that either rapidly or slowly evolved to their current state over time. These calculations are the only way to validate seismic hazard curves over long periods of time.","language":"English","publisher":"BSSA","doi":"10.1785/0120120202","usgsCitation":"Baker, J.W., Whitney, J.W., Hanks, T.C., Abramson, N.A., and Board, M.P., 2013, Use of fragile geologic structures as indicators of unexceeded ground motions and direct constraints on probabilistic seismic hazard analysis: BSSA, v. 103, no. 3, p. 1898-1911, https://doi.org/10.1785/0120120202.","productDescription":"14 p.","startPage":" 1898","endPage":"1911","ipdsId":"IP-038935","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":347318,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2013-06-07","publicationStatus":"PW","scienceBaseUri":"59f1a2a9e4b0220bbd9d9fb2","contributors":{"authors":[{"text":"Baker, J. 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It is believed that Lake Erie was the initial location of establishment for both species, and within 3 years, zebra mussels had been found in all the Great Lakes. Since 1986, the combined distribution of two dreissenids has expanded throughout the Great Lakes region and the St. Lawrence River in Canada and also in the United States from the Great Lakes to the Mississippi Basin including Arkansas, Cumberland, Illinois, Missouri, Ohio, and Tennessee river basins. The distribution of dreissenid mussels in the Atlantic drainage has been limited to the Hudson and Susquehanna rivers. In the western United States, the quagga mussel established a large population in the lower Colorado River and spread to reservoirs in Arizona, California, Colorado, Nevada, and Utah. Overall, dreissenid species have been documented in 131 river systems and 772 inland lakes, reservoirs, and impoundments in the United States.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Quagga and zebra mussels: biology, impacts, and control","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"CRC Press","doi":"10.1201/b15437-6","usgsCitation":"Benson, A.J., 2013, Chronological history of zebra and quagga mussels (Dreissenidae) in North America, 1988-2010, chap. of Quagga and zebra mussels: biology, impacts, and control, p. 9-32, https://doi.org/10.1201/b15437-6.","productDescription":"24 p.","startPage":"9","endPage":"32","ipdsId":"IP-025664","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":280713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280710,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1201/b15437-6"}],"edition":"Second","noUsgsAuthors":false,"publicationDate":"2013-10-07","publicationStatus":"PW","scienceBaseUri":"53cd5129e4b0b290850f3c1a","contributors":{"authors":[{"text":"Benson, Amy J. 0000-0002-4517-1466 abenson@usgs.gov","orcid":"https://orcid.org/0000-0002-4517-1466","contributorId":3836,"corporation":false,"usgs":true,"family":"Benson","given":"Amy","email":"abenson@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":486828,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70055519,"text":"70055519 - 2013 - Surveillance theory applied to virus detection: a case for targeted discovery","interactions":[],"lastModifiedDate":"2014-02-07T15:04:22","indexId":"70055519","displayToPublicDate":"2013-12-30T15:01:42","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1715,"text":"Future Virology","active":true,"publicationSubtype":{"id":10}},"title":"Surveillance theory applied to virus detection: a case for targeted discovery","docAbstract":"Virus detection and mathematical modeling have gone through rapid developments in the past decade. Both offer new insights into the epidemiology of infectious disease and characterization of future risk; however, modeling has not yet been applied to designing the best surveillance strategies for viral and pathogen discovery. We review recent developments and propose methods to integrate viral and pathogen discovery and mathematical modeling through optimal surveillance theory, arguing for a more targeted approach to novel virus detection guided by the principles of adaptive management and structured decision-making.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Future Virology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Future Medicine","doi":"10.2217/fvl.13.105","usgsCitation":"Bogich, T., Anthony, S.J., and Nichols, J., 2013, Surveillance theory applied to virus detection: a case for targeted discovery: Future Virology, v. 8, no. 12, p. 1201-1206, https://doi.org/10.2217/fvl.13.105.","productDescription":"6 p.","startPage":"1201","endPage":"1206","ipdsId":"IP-052119","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":282128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":282127,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2217/fvl.13.105"}],"volume":"8","issue":"12","noUsgsAuthors":false,"publicationDate":"2013-11-28","publicationStatus":"PW","scienceBaseUri":"53cd7605e4b0b2908510aa20","contributors":{"authors":[{"text":"Bogich, Tiffany L.","contributorId":40891,"corporation":false,"usgs":true,"family":"Bogich","given":"Tiffany L.","affiliations":[],"preferred":false,"id":486123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Simon J.","contributorId":34387,"corporation":false,"usgs":true,"family":"Anthony","given":"Simon","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":486122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":405,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":486121,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70059790,"text":"70059790 - 2013 - Conflation and aggregation of spatial data improve predictive models for species with limited habitats: a case of the threatened yellow-billed cuckoo in Arizona, USA","interactions":[],"lastModifiedDate":"2018-09-18T16:29:07","indexId":"70059790","displayToPublicDate":"2013-12-30T14:56:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":836,"text":"Applied Geography","active":true,"publicationSubtype":{"id":10}},"title":"Conflation and aggregation of spatial data improve predictive models for species with limited habitats: a case of the threatened yellow-billed cuckoo in Arizona, USA","docAbstract":"Riparian vegetation provides important wildlife habitat in the Southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeog.2013.12.003","usgsCitation":"Villarreal, M., van Riper, C., and Petrakis, R., 2013, Conflation and aggregation of spatial data improve predictive models for species with limited habitats: a case of the threatened yellow-billed cuckoo in Arizona, USA: Applied Geography, v. 47, p. 57-69, https://doi.org/10.1016/j.apgeog.2013.12.003.","productDescription":"13 p.","startPage":"57","endPage":"69","numberOfPages":"13","ipdsId":"IP-048880","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":280568,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280567,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.apgeog.2013.12.003"}],"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":"47","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52c29607e4b040b25da903da","contributors":{"authors":[{"text":"Villarreal, Miguel L.","contributorId":107012,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel L.","affiliations":[],"preferred":false,"id":487828,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":487826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petrakis, Roy E.","contributorId":46868,"corporation":false,"usgs":true,"family":"Petrakis","given":"Roy E.","affiliations":[],"preferred":false,"id":487827,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70059793,"text":"70059793 - 2013 - Advances and applications of occupancy models","interactions":[],"lastModifiedDate":"2014-12-12T14:42:58","indexId":"70059793","displayToPublicDate":"2013-12-30T14:37:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Advances and applications of occupancy models","docAbstract":"

Summary: The past decade has seen an explosion in the development and application of models aimed at estimating species occurrence and occupancy dynamics while accounting for possible non-detection or species misidentification. We discuss some recent occupancy estimation methods and the biological systems that motivated their development. Collectively, these models offer tremendous flexibility, but simultaneously place added demands on the investigator. Unlike many mark–recapture scenarios, investigators utilizing occupancy models have the ability, and responsibility, to define their sample units (i.e. sites), replicate sampling occasions, time period over which species occurrence is assumed to be static and even the criteria that constitute ‘detection’ of a target species. Subsequent biological inference and interpretation of model parameters depend on these definitions and the ability to meet model assumptions. We demonstrate the relevance of these definitions by highlighting applications from a single biological system (an amphibian–pathogen system) and discuss situations where the use of occupancy models has been criticized. Finally, we use these applications to suggest future research and model development.

","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Methods in Ecology and Evolution","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.12100","usgsCitation":"Bailey, L., MacKenzie, D.I., and Nichols, J., 2013, Advances and applications of occupancy models: Methods in Ecology and Evolution, v. 5, no. 12, p. 1269-1279, https://doi.org/10.1111/2041-210X.12100.","productDescription":"11 p.","startPage":"1269","endPage":"1279","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-050552","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":280566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280565,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/2041-210X.12100"}],"volume":"5","issue":"12","noUsgsAuthors":false,"publicationDate":"2013-09-04","publicationStatus":"PW","scienceBaseUri":"52c295dfe4b040b25da902eb","contributors":{"authors":[{"text":"Bailey, Larissa","contributorId":86059,"corporation":false,"usgs":true,"family":"Bailey","given":"Larissa","affiliations":[],"preferred":false,"id":487831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"MacKenzie, Darry I.","contributorId":15926,"corporation":false,"usgs":true,"family":"MacKenzie","given":"Darry","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":487830,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":405,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":487829,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70056564,"text":"sir20105070G - 2013 - Descriptive and geoenvironmental model for Co-Cu-Au deposits in metasedimentary rocks","interactions":[],"lastModifiedDate":"2022-12-12T23:19:59.000786","indexId":"sir20105070G","displayToPublicDate":"2013-12-30T13:46:00","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":"2010-5070","chapter":"G","title":"Descriptive and geoenvironmental model for Co-Cu-Au deposits in metasedimentary rocks","docAbstract":"

Introduction

This report is a revised model for a specific type of cobalt-copper-gold (Co-Cu-Au) deposit that will be evaluated in the next U.S. Geological Survey (USGS) assessment of undiscovered mineral resources in the United States (see Ferrero and others, 2012). Emphasis is on providing an up-to-date deposit model that includes both geologic and geoenvironmental aspects. The new model presented here supersedes previous USGS models by Earhart (1986) and Evans and others (1995), which are based solely on deposits in the Blackbird mining district of central Idaho. This report is a broader synthesis of information on 19 Co-Cu-Au deposits occurring in predominantly metasedimentary successions worldwide (table 1–1) that generally share common geologic, mineralogical, and geochemical features; preliminary summary versions were presented in Slack and others (2010) and Slack and others (2011), which are superseded by this report. As defined herein, the individual Co-Cu-Au deposits are located more than 500 meters from similar deposits and contain 0.1 percent or more by weight of Co in ore or mineralized rock; some deposits included in the database lack reported average Co grades, but they contain high Co concentrations, at least locally. Most of the deposits also have high As contents, present in Co arsenide and sulfarsenide minerals. Type examples of the Co-Cu-Au deposits are those in the Blackbird district, Skuterud in Norway, and Kouvervarra and Juomasuo in Finland. Some deposits in the database have low grades for Cu (for example, NICO in Canada) or Au (for example, Lemmonlampi in Finland), but these deposits are included because their geological, mineralogical, and alteration features are similar to those of the type examples. Several deposits included in the model are partly hosted by metavolcanic or metaigneous rocks (including granite), but regionally these deposits are within metasedimentary successions; no deposits are wholly within granite or other plutonic igneous intrusions.

Despite having a lower average Co grade, the Mt. Cobalt deposit in Australia is included here because it has past Co production from higher-grade ore zones (Nisbet and others, 1983). The Black Pine deposit in the Idaho cobalt belt is included because it contains mineable Co- and Au-rich lenses within Cu-rich mineralized zones (Formation Metals, Inc., 2012). Six deposits that lack data for average Co grades are also included because each reportedly contains abundant Co (>0.1 weight percent Co), at least locally. Many of the deposits are noteworthy as possible resources of Ag, Bi, W, Ni, Y, REE, and (or) U. Detailed data on the deposits listed in table 1–1, including references, are available in appendix 1. Significantly, the grouping in this report of Co-Cu-Au deposits in metasedimentary rocks into a single model includes deposits that other workers have previously classified in different ways. For background information, a global overview of different types of Co deposits worldwide is given in Smith (2001).

Additional geologically and compositionally similar deposits are known, but have average Co grades less than 0.1 percent. Most of these deposits contain cobalt-rich pyrite and lack appreciable amounts of distinct Co sulfide and (or) sulfarsenide minerals. Such deposits are not discussed in detail in the following sections, but these deposits may be relevant to the descriptive and genetic models presented below. Examples include the Scadding Au-Co-Cu deposit in Ontario, Canada; the Vähäjoki Co-Cu-Au deposit in Finland; the Tuolugou Co-Au deposit in Qinghai Province, China; the Lala Co-Cu-UREE deposit in Sichuan Province, China; the Guelb Moghrein Cu-Au-Co deposit in Mauritania; and the Great Australia Co-Cu, Greenmount Cu-Au-Co, and Monakoff Cu-Au-Co-UAg deposits in Queensland, Australia. Detailed information on these deposits is presented in appendix 2.

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Water-column and bed-sediment core samples were collected at each of the eight sites and analyzed for total mercury and methylmercury. Additional bed-sediment core samples, collected from three of the eight sites, were analyzed for pesticides and other organic compounds, trace metals, and physical characteristics, such as particle size.\n\nTotal mercury and methylmercury were detected in each of the water column and bed-sediment core samples. Only 17 of the 417 unique pesticide and organic compounds were detected in bed-sediment core samples. Concentrations of most organic wastewater compounds detected in bed sediment were less than the reporting level. Trace metals detected were greater than the reporting level in all the bed-sediment core samples submitted for analysis. 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Consequently, it is important that government policies and programs be adequate to protect these waters from invaders. 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Quagga and Zebra Mussels: Biology, Impacts, and Control, Second Edition provides a broad view of the zebra/quagga mussel issue, offering a historic perspective and up-to-date information on mussel research. Comprising 48 chapters, this second edition includes reviews of mussel morphology, physiology, and behavior. It details mussel distribution and spread in Europe and across North America, and examines policy and regulatory responses, management strategies, and mitigation efforts. In addition, this book provides extensive coverage of the impact of invasive mussel species on freshwater ecosystems, including effects on water clarity, phytoplankton, water quality, food web changes, and consequences to other aquatic fauna. It also reviews and offers new insights on how zebra and quagga mussels respond and adapt to varying environmental conditions. This new edition includes seven video clips that complement chapter text and, through visual documentation, provide a greater understanding of mussel behavior and distribution.

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These included the highly contaminated (Stege Marsh) and relatively clean (China Camp) marshes in San Francisco Bay and two reference marshes in Tomales Bay. Toxicity potential of contaminants and benthic macroinvertebrate survey showed significant differences between contaminated and reference marshes. Sublethal responses (e.g., apoptotic DNA fragmentation, lipid accumulation, and glycogen depletion) in livers of longjaw mudsucker (Gillichthys mirabilis) and embryo abnormality in lined shore crab (Pachygrapsus crassipes) also clearly distinguished contaminated and reference marshes, while other responses (e.g., cytochrome P450, metallothionein) did not. This study demonstrates that additional chronic sublethal responses in resident species under field exposure conditions can be readily combined with sediment quality triads for an expanded multiple lines of evidence approach. This confirmatory step may be warranted in environments like salt marshes in which natural variables may affect interpretation of toxicity test data. Qualitative and quantitative integration of the portfolio of responses in resident species and traditional approach can support a more comprehensive and informative sediment quality assessment in salt marshes and possibly other habitat types as well.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2013.02.039","usgsCitation":"Hwang, H., Carr, R.S., Cherr, G.N., Green, P.G., Grosholz, E.G., Judah, L., Morgan, S.G., Ogle, S., Rashbrook, V.K., Rose, W.L., Teh, S.J., Vines, C.A., and Anderson, S.L., 2013, Sediment quality assessment in tidal salt marshes in northern California, USA: An evaluation of multiple lines of evidence approach: Science of the Total Environment, v. 454-455, p. 189-198, https://doi.org/10.1016/j.scitotenv.2013.02.039.","productDescription":"10 p.","startPage":"189","endPage":"198","numberOfPages":"10","ipdsId":"IP-025328","costCenters":[{"id":192,"text":"Columbia Environmental Research 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Hyun-Min","contributorId":67798,"corporation":false,"usgs":true,"family":"Hwang","given":"Hyun-Min","email":"","affiliations":[],"preferred":false,"id":487780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carr, Robert S.","contributorId":9361,"corporation":false,"usgs":true,"family":"Carr","given":"Robert","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":487772,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cherr, Gary N.","contributorId":64990,"corporation":false,"usgs":true,"family":"Cherr","given":"Gary","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":487778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Green, Peter G.","contributorId":66592,"corporation":false,"usgs":true,"family":"Green","given":"Peter","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":487779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grosholz, Edwin G.","contributorId":13529,"corporation":false,"usgs":true,"family":"Grosholz","given":"Edwin","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":487773,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Judah, Linda","contributorId":35637,"corporation":false,"usgs":true,"family":"Judah","given":"Linda","email":"","affiliations":[],"preferred":false,"id":487776,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morgan, Steven G.","contributorId":26964,"corporation":false,"usgs":true,"family":"Morgan","given":"Steven","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":487774,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ogle, Scott","contributorId":76640,"corporation":false,"usgs":true,"family":"Ogle","given":"Scott","email":"","affiliations":[],"preferred":false,"id":487781,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rashbrook, Vanessa K.","contributorId":81008,"corporation":false,"usgs":true,"family":"Rashbrook","given":"Vanessa","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":487782,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rose, Wendy L.","contributorId":32076,"corporation":false,"usgs":true,"family":"Rose","given":"Wendy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":487775,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Teh, Swee J.","contributorId":104392,"corporation":false,"usgs":true,"family":"Teh","given":"Swee","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":487784,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Vines, Carol A.","contributorId":37634,"corporation":false,"usgs":true,"family":"Vines","given":"Carol","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":487777,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Anderson, Susan L.","contributorId":87062,"corporation":false,"usgs":true,"family":"Anderson","given":"Susan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":487783,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70059744,"text":"70059744 - 2013 - Preparation and characterization of nickel-spiked freshwater sediments for toxicity tests: toward more environmentally realistic nickel partitioning","interactions":[],"lastModifiedDate":"2017-05-23T11:34:30","indexId":"70059744","displayToPublicDate":"2013-12-30T10:16:00","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":"Preparation and characterization of nickel-spiked freshwater sediments for toxicity tests: toward more environmentally realistic nickel partitioning","docAbstract":"

Two spiking methods were compared and nickel (Ni) partitioning was evaluated during a series of toxicity tests with 8 different freshwater sediments having a range of physicochemical characteristics. A 2-step spiking approach with immediate pH adjustment by addition of NaOH at a 2:1 molar ratio to the spiked Ni was effective in producing consistent pH and other chemical characteristics across a range of Ni spiking levels. When Ni was spiked into sediment having a high acid-volatile sulfide and organic matter content, a total equilibration period of at least 10 wk was needed to stabilize Ni partitioning. However, highest spiking levels evidently exceeded sediment binding capacities; therefore, a 7-d equilibration in toxicity test chambers and 8 volume-additions/d of aerobic overlying water were used to avoid unrealistic Ni partitioning during toxicity testing. The 7-d pretest equilibration allowed excess spiked Ni and other ions from pH adjustment to diffuse from sediment porewater and promoted development of an environmentally relevant, 0.5- to 1-cm oxic/suboxic sediment layer in the test chambers. Among the 8 different spiked sediments, the logarithm of sediment/porewater distribution coefficient values (log Kd) for Ni during the toxicity tests ranged from 3.5 to 4.5. These Kd values closely match the range of values reported for various field Ni-contaminated sediments, indicating that testing conditions with our spiked sediments were environmentally realistic.

","language":"English","publisher":"Wiley","doi":"10.1002/etc.2272","usgsCitation":"Brumbaugh, W.G., Besser, J.M., Ingersoll, C.G., May, T.W., Ivey, C.D., Schlekat, C.E., and Garman, E.R., 2013, Preparation and characterization of nickel-spiked freshwater sediments for toxicity tests: toward more environmentally realistic nickel partitioning: Environmental Toxicology and Chemistry, v. 32, no. 11, p. 2482-2494, https://doi.org/10.1002/etc.2272.","productDescription":"13 p.","startPage":"2482","endPage":"2494","numberOfPages":"13","ipdsId":"IP-041903","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":280548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280531,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/etc.2272"}],"volume":"32","issue":"11","noUsgsAuthors":false,"publicationDate":"2013-05-08","publicationStatus":"PW","scienceBaseUri":"52c2960ae4b040b25da90401","contributors":{"authors":[{"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":487763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":487765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":487764,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"May, Thomas W. tmay@usgs.gov","contributorId":2598,"corporation":false,"usgs":true,"family":"May","given":"Thomas","email":"tmay@usgs.gov","middleInitial":"W.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":false,"id":487766,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":487767,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schlekat, Christian E.","contributorId":28519,"corporation":false,"usgs":true,"family":"Schlekat","given":"Christian","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":487769,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Garman, Emily R.","contributorId":19461,"corporation":false,"usgs":true,"family":"Garman","given":"Emily","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":487768,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70056529,"text":"sim3273 - 2013 - Characterization of hydrodynamic and sediment conditions in the lower Yampa River at Deerlodge Park, east entrance to Dinosaur National Monument, northwest Colorado, 2011","interactions":[],"lastModifiedDate":"2013-12-30T09:23:41","indexId":"sim3273","displayToPublicDate":"2013-12-30T09:07:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3273","title":"Characterization of hydrodynamic and sediment conditions in the lower Yampa River at Deerlodge Park, east entrance to Dinosaur National Monument, northwest Colorado, 2011","docAbstract":"The Yampa River in northwestern Colorado is the largest, relatively unregulated river system in the upper Colorado River Basin. Water from the Yampa River Basin continues to be sought for a number of municipal, industrial, and energy uses. It is anticipated that future water development within the Yampa River Basin above the amount of water development identified under the Upper Colorado River Endangered Fish Recovery Implementation Program and the Programmatic Biological Opinion may require additional analysis in order to understand the effects on habitat and river function. Water development in the Yampa River Basin could alter the streamflow regime and, consequently, could lead to changes in the transport and storage of sediment in the Yampa River at Deerlodge Park. These changes could affect the physical form of the reach and may impact aquatic and riparian habitat in and downstream from Deerlodge Park.\n\nThe U.S. Geological Survey, in cooperation with the Colorado Water Conservation Board, began a study in 2011 to characterize the current hydrodynamic and sediment-transport conditions for a 2-kilometer reach of the Yampa River in Deerlodge Park. Characterization of channel conditions in the Deerlodge Park reach was completed through topographic surveying, grain-size analysis of streambed sediment, and characterization of streamflow properties. This characterization provides (1) a basis for comparisons of current stream functions (channel geometry, sediment transport, and stream hydraulics) to future conditions and (2) a dataset that can be used to assess channel response to streamflow alteration scenarios indicated from computer modeling of streamflow and sediment-transport conditions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3273","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board","usgsCitation":"Williams, C.A., 2013, Characterization of hydrodynamic and sediment conditions in the lower Yampa River at Deerlodge Park, east entrance to Dinosaur National Monument, northwest Colorado, 2011: U.S. Geological Survey Scientific Investigations Map 3273, Map: 37.92 inches x 29.17 inches, https://doi.org/10.3133/sim3273.","productDescription":"Map: 37.92 inches x 29.17 inches","additionalOnlineFiles":"N","ipdsId":"IP-049562","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":280530,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3273/"},{"id":280546,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3273/pdf/sim3273.pdf"},{"id":280547,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim3273.jpg"}],"projection":"2011 Universal Transverse Mercator, Zone 12 North","datum":"North American Datum of 1983","country":"United States","state":"Colorado","otherGeospatial":"Dinosaur National Monument","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -108.519001,40.441199 ], [ -108.519001,40.453087 ], [ -108.499947,40.453087 ], [ -108.499947,40.441199 ], [ -108.519001,40.441199 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52c29607e4b040b25da903d3","contributors":{"authors":[{"text":"Williams, Cory A. 0000-0003-1461-7848 cawillia@usgs.gov","orcid":"https://orcid.org/0000-0003-1461-7848","contributorId":689,"corporation":false,"usgs":true,"family":"Williams","given":"Cory","email":"cawillia@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486588,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70058573,"text":"sir20135229 - 2013 - Evaporation from Lake Mead, Nevada and Arizona, March 2010 through February 2012","interactions":[],"lastModifiedDate":"2015-11-10T14:40:38","indexId":"sir20135229","displayToPublicDate":"2013-12-30T08:49:00","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":"2013-5229","title":"Evaporation from Lake Mead, Nevada and Arizona, March 2010 through February 2012","docAbstract":"

Evaporation from Lake Mead was measured using the eddy-covariance method for the 2-year period starting March 2010 and ending February 2012. When corrected for energy imbalances, annual eddy-covariance evaporation was 2,074 and 1,881 millimeters (81.65 and 74.07 inches), within the range of previous estimates. There was a 9-percent decrease in the evaporation rate and a 10-percent increase in the lake surface area during the second year of the study compared to the first. These offsetting factors resulted in a nearly identical 720 million cubic meters (584,000 acre feet) evaporation volume for both years. Monthly evaporation rates were best correlated with wind speed, vapor pressure difference, and atmospheric stability. Differences between individual monthly evaporation and mean monthly evaporation were as much as 20 percent. Net radiation provided most of the energy available for evaporative processes; however, advected heat from the Colorado River was an important energy source during the second year of the study. Peak evaporation lagged peak net radiation by 2 months because a larger proportion of the net radiation that reaches the lake goes to heating up the water column during the spring and summer months. As most of this stored energy is released, higher evaporation rates are sustained during fall months even though net radiation declines. The release of stored heat also fueled nighttime evaporation, which accounted for 37 percent of total evaporation. The annual energy-balance ratio was 0.90 on average and varied only 0.01 between the 2 years, thus implying that 90 percent of estimated available energy was accounted for by turbulent energy measured using the eddy-covariance method. More than 90 percent of the turbulent-flux source area represented the open-water surface, and 94 percent of 30-minute turbulent-flux measurements originated from wind directions where the fetch ranged from 2,000 to 16,000 meters. Evaporation uncertainties were estimated to be 5 to 7 percent. A secondary evaporation method, the Bowen ratio energy budget method, also was employed to measure evaporation from Lake Mead primarily as a validation of eddy-covariance evaporation measurements at annual timescales. There was good agreement between annual corrected eddy-covariance and Bowen ratio energy budget evaporation estimates, providing strong validation of these two largely independent methods. Annual Bowen ratio energy budget evaporation was 6 and 8 percent greater than eddy-covariance evaporation for the 2 study years, and both methods indicated there was a similar decrease in evaporation from the first to the second year. Both methods produced negative sensible heat fluxes during the same months, and there was a strong correlation between monthly Bowen ratios (R2 = 0.94). The correlation between monthly evaporation (R2 = 0.65), however, was not as strong. Monthly differences in evaporation were attributed primarily to heat storage estimate uncertainty.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135229","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Moreo, M.T., and Swancar, A., 2013, Evaporation from Lake Mead, Nevada and Arizona, March 2010 through February 2012: U.S. Geological Survey Scientific Investigations Report 2013-5229, Report: viii, 40 p.; Appendix A: 1 XLSX file, https://doi.org/10.3133/sir20135229.","productDescription":"Report: viii, 40 p.; Appendix A: 1 XLSX file","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-036635","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":280543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135229.jpg"},{"id":280536,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5229"},{"id":280545,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5229/pdf/sir2013-5229.pdf"},{"id":280544,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5229/downloads/20131210_appA.xlsx"},{"id":311171,"type":{"id":7,"text":"Companion Files"},"url":"https://dx.doi.org/10.5066/F79C6VG3","text":"Data Release"}],"projection":"Universal Transverse Mercator, Zone 11","datum":"North American Datum of 1983","country":"United States","state":"Arizona;Nevada","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -115.6448,35.8891 ], [ -115.6448,37.1822 ], [ -113.9722,37.1822 ], [ -113.9722,35.8891 ], [ -115.6448,35.8891 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52c29609e4b040b25da903f3","contributors":{"authors":[{"text":"Moreo, Michael T. 0000-0002-9122-6958 mtmoreo@usgs.gov","orcid":"https://orcid.org/0000-0002-9122-6958","contributorId":2363,"corporation":false,"usgs":true,"family":"Moreo","given":"Michael","email":"mtmoreo@usgs.gov","middleInitial":"T.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":487176,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swancar, Amy aswancar@usgs.gov","contributorId":450,"corporation":false,"usgs":true,"family":"Swancar","given":"Amy","email":"aswancar@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":487175,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188506,"text":"70188506 - 2013 - Sea-level change during the last 2500 years in New Jersey, USA","interactions":[],"lastModifiedDate":"2017-06-23T16:15:12","indexId":"70188506","displayToPublicDate":"2013-12-30T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Sea-level change during the last 2500 years in New Jersey, USA","docAbstract":"

Relative sea-level changes during the last ∼2500 years in New Jersey, USA were reconstructed to test if late Holocene sea level was stable or included persistent and distinctive phases of variability. Foraminifera and bulk-sediment δ13C values were combined to reconstruct paleomarsh elevation with decimeter precision from sequences of salt-marsh sediment at two sites using a multi-proxy approach. The additional paleoenvironmental information provided by bulk-sediment δ13C values reduced vertical uncertainty in the sea-level reconstruction by about one third of that estimated from foraminifera alone using a transfer function. The history of sediment deposition was constrained by a composite chronology. An age–depth model developed for each core enabled reconstruction of sea level with multi-decadal resolution. Following correction for land-level change (1.4 mm/yr), four successive and sustained (multi-centennial) sea-level trends were objectively identified and quantified (95% confidence interval) using error-in-variables change point analysis to account for age and sea-level uncertainties. From at least 500 BC to 250 AD, sea-level fell at 0.11 mm/yr. The second period saw sea-level rise at 0.62 mm/yr from 250 AD to 733 AD. Between 733 AD and 1850 AD, sea level fell at 0.12 mm/yr. The reconstructed rate of sea-level rise since ∼1850 AD was 3.1 mm/yr and represents the most rapid period of change for at least 2500 years. This trend began between 1830 AD and 1873 AD. Since this change point, reconstructed sea-level rise is in agreement with regional tide-gauge records and exceeds the global average estimate for the 20th century. These positive and negative departures from background rates demonstrate that the late Holocene sea level was not stable in New Jersey.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2013.09.024","usgsCitation":"Kemp, A.C., Horton, B.P., Vane, C.H., Bernhardt, C.E., Corbett, D.R., Engelhart, S.E., Anisfeld, S.C., Parnell, A.C., and Cahill, N., 2013, Sea-level change during the last 2500 years in New Jersey, USA: Quaternary Science Reviews, v. 81, p. 90-104, https://doi.org/10.1016/j.quascirev.2013.09.024.","productDescription":"15 p. ","startPage":"90","endPage":"104","ipdsId":"IP-051677","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":473395,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/geo_facpubs/32","text":"External Repository"},{"id":342493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.10827636718749,\n 40.15998434802335\n ],\n [\n -74.739990234375,\n 39.3279240176903\n ],\n [\n -74.608154296875,\n 39.25565142103588\n ],\n [\n -74.42962646484375,\n 39.308800296002914\n ],\n [\n -74.33624267578125,\n 39.42770738465604\n ],\n [\n -74.2236328125,\n 39.523110951240696\n ],\n [\n -74.17144775390625,\n 39.620499321968104\n ],\n [\n -74.11651611328125,\n 39.68182601089365\n ],\n [\n -74.06707763671875,\n 39.77054750039529\n ],\n [\n -74.03961181640625,\n 39.928694653732364\n ],\n [\n -74.02587890625,\n 40.0360265298117\n ],\n [\n -74.00115966796875,\n 40.15998434802335\n ],\n [\n -74.05334472656249,\n 40.22712123211294\n ],\n [\n -74.10827636718749,\n 40.15998434802335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59424b39e4b0764e6c65dc2c","contributors":{"authors":[{"text":"Kemp, Andrew C.","contributorId":192892,"corporation":false,"usgs":false,"family":"Kemp","given":"Andrew","email":"","middleInitial":"C.","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":698069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horton, Benjamin P.","contributorId":192807,"corporation":false,"usgs":false,"family":"Horton","given":"Benjamin","email":"","middleInitial":"P.","affiliations":[{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false},{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":698070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vane, Christopher H.","contributorId":192893,"corporation":false,"usgs":false,"family":"Vane","given":"Christopher","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":698071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bernhardt, Christopher E. 0000-0003-0082-4731 cbernhardt@usgs.gov","orcid":"https://orcid.org/0000-0003-0082-4731","contributorId":2131,"corporation":false,"usgs":true,"family":"Bernhardt","given":"Christopher","email":"cbernhardt@usgs.gov","middleInitial":"E.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":698068,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corbett, D. Reide","contributorId":192894,"corporation":false,"usgs":false,"family":"Corbett","given":"D.","email":"","middleInitial":"Reide","affiliations":[],"preferred":false,"id":698072,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Engelhart, Simon E.","contributorId":60104,"corporation":false,"usgs":false,"family":"Engelhart","given":"Simon","email":"","middleInitial":"E.","affiliations":[{"id":6923,"text":"University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":698073,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Anisfeld, Shimon C.","contributorId":173724,"corporation":false,"usgs":false,"family":"Anisfeld","given":"Shimon","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":698074,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Parnell, Andrew C.","contributorId":150753,"corporation":false,"usgs":false,"family":"Parnell","given":"Andrew","email":"","middleInitial":"C.","affiliations":[{"id":18091,"text":"University College Dublin","active":true,"usgs":false}],"preferred":false,"id":698075,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cahill, Niamh","contributorId":150754,"corporation":false,"usgs":false,"family":"Cahill","given":"Niamh","email":"","affiliations":[{"id":18091,"text":"University College Dublin","active":true,"usgs":false},{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":698076,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70058863,"text":"ofr20131295 - 2013 - Preliminary estimates of annual agricultural pesticide use for counties of the conterminous United States, 2010-11","interactions":[],"lastModifiedDate":"2013-12-30T08:25:24","indexId":"ofr20131295","displayToPublicDate":"2013-12-27T15:17:00","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":"2013-1295","subseriesTitle":"National Water-Quality Assessment Program","title":"Preliminary estimates of annual agricultural pesticide use for counties of the conterminous United States, 2010-11","docAbstract":"This report provides preliminary estimates of annual agricultural use of 374 pesticide compounds in counties of the conterminous United States in 2010 and 2011, compiled by means of methods described in Thelin and Stone (2013). U.S. Department of Agriculture (USDA) county-level data for harvested-crop acreage were used in conjunction with proprietary Crop Reporting District (CRD)-level pesticide-use data to estimate county-level pesticide use. Estimated pesticide use (EPest) values were calculated with both the EPest-high and EPest-low methods. The distinction between the EPest-high method and the EPest-low method is that there are more counties with estimated pesticide use for EPest-high compared to EPest-low, owing to differing assumptions about missing survey data (Thelin and Stone, 2013). Preliminary estimates in this report will be revised upon availability of updated crop acreages in the 2012 Agricultural Census, to be published by the USDA in 2014. In addition, estimates for 2008 and 2009 previously published by Stone (2013) will be updated subsequent to the 2012 Agricultural Census release. Estimates of annual agricultural pesticide use are provided as downloadable, tab-delimited files, which are organized by compound, year, state Federal Information Processing Standard (FIPS) code, county FIPS code, and kg (amount in kilograms).","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131295","usgsCitation":"Baker, N.T., and Stone, W.W., 2013, Preliminary estimates of annual agricultural pesticide use for counties of the conterminous United States, 2010-11: U.S. Geological Survey Open-File Report 2013-1295, Report: iii, 2 p.; Tables: 14 txt files, https://doi.org/10.3133/ofr20131295.","productDescription":"Report: iii, 2 p.; Tables: 14 txt files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-052139","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":280542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131295.jpg"},{"id":280539,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1295/"},{"id":280540,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1295/tables/of2013-1295_tables.zip"},{"id":280541,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1295/pdf/of2013-1295.pdf"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.8,24.5 ], [ -124.8,49.383333 ], [ -66.95,49.383333 ], [ -66.95,24.5 ], [ -124.8,24.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52bea162e4b052bfba83a2ed","contributors":{"authors":[{"text":"Baker, Nancy T. 0000-0002-7979-5744 ntbaker@usgs.gov","orcid":"https://orcid.org/0000-0002-7979-5744","contributorId":1955,"corporation":false,"usgs":true,"family":"Baker","given":"Nancy","email":"ntbaker@usgs.gov","middleInitial":"T.","affiliations":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":487407,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stone, Wesley W. 0000-0003-0239-2063 wwstone@usgs.gov","orcid":"https://orcid.org/0000-0003-0239-2063","contributorId":1496,"corporation":false,"usgs":true,"family":"Stone","given":"Wesley","email":"wwstone@usgs.gov","middleInitial":"W.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":487406,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70059316,"text":"ofr20131301 - 2013 - Monitoring of adult Lost River and shortnose suckers in Clear Lake Reservoir, California, 2008–2010","interactions":[],"lastModifiedDate":"2016-05-04T15:42:46","indexId":"ofr20131301","displayToPublicDate":"2013-12-23T14:53:00","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":"2013-1301","title":"Monitoring of adult Lost River and shortnose suckers in Clear Lake Reservoir, California, 2008–2010","docAbstract":"

Executive Summary

\n

In collaboration with the Bureau of Reclamation, the U.S. Geological Survey began a consistent monitoring program for endangered Lost River suckers (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Clear Lake Reservoir, California, in the fall of 2004. The program was intended to develop a more complete understanding of the Clear Lake Reservoir populations because they are important to the recovery efforts for these species. We report results from this ongoing program and include sampling efforts from fall 2008 to spring 2010. We summarize catches and passive integrated transponder (PIT) tagging efforts from trammel net sampling in fall 2008 and fall 2009, as well as detections of PIT-tagged suckers on remote antennas in the spawning tributary, Willow Creek, in spring 2009 and spring 2010.

\n

Trammel net sampling resulted in a relatively low catch of suckers in fall 2008 and a high catch of suckers in fall 2009. We attribute the high catch of suckers to low lake levels in 2009, which concentrated fish. As in previous years, shortnose suckers made up the vast majority of the sucker catch and recaptures of previously PIT-tagged suckers were relatively uncommon. Across the 2 years, we captured and tagged 389 new Lost River suckers and 2,874 new shortnose suckers. Since the program began, we have tagged a total of about 1,200 Lost River suckers and 5,900 shortnose suckers that can be detected on the remote antennas in Willow Creek. Detections of tagged suckers were low in both spring 2009 and spring 2010. The magnitude of the spawning migration was presumably small in both years because of low flows in Willow Creek; detections were similar to a previous low-flow year (spring 2007) and much lower than previous years with higher flows (spring 2006 and spring 2008).

\n

The size composition of fish captured in fall trammel net sampling over time suggests that the Lost River sucker population probably has decreased in abundance from what it was in the early 2000s. Shortnose suckers are smaller than Lost River suckers, and we are unable to infer any trend in abundance for shortnose suckers because it is impossible to separate recruitment of small fish from size selectivity of the trammel nets. Nonetheless, the substantial catch of small shortnose suckers in 2009, especially females, indicates that some new individuals recruited to the population.

\n

Problems with inferring status and population dynamics from size composition data can be overcome by a robust capture-recapture program that follows the histories of PIT-tagged individuals. Inferences from such a program are currently hindered by poor detection rates during spawning seasons with low flows in Willow Creek, which indicate that a key assumption of capture-recapture models is violated. We suggest that the most straightforward solution to this issue would be to collect detection data during the spawning season using remote PIT tag antennas in the strait between the west and east lobes of the lake.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131301","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hewitt, D.A., and Hayes, B., 2013, Monitoring of adult Lost River and shortnose suckers in Clear Lake Reservoir, California, 2008–2010: U.S. Geological Survey Open-File Report 2013-1301, iv, 18 p., https://doi.org/10.3133/ofr20131301.","productDescription":"iv, 18 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-051993","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":280526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131301.JPG"},{"id":280524,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1301/pdf/ofr2013-1301.pdf","text":"Report","size":"900 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":280525,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1301/"}],"country":"United States","state":"California, Oregon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.3831,41.78000 ], [ -122.3831,42.7534 ], [ -120.9161,42.7534 ], [ -120.9161,41.78000 ], [ -122.3831,41.78000 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52b95be1e4b0a747b3e7e7a1","contributors":{"authors":[{"text":"Hewitt, David A. 0000-0002-5387-0275 dhewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-5387-0275","contributorId":3767,"corporation":false,"usgs":false,"family":"Hewitt","given":"David","email":"dhewitt@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":487664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Brian S. 0000-0001-8229-4070","orcid":"https://orcid.org/0000-0001-8229-4070","contributorId":37022,"corporation":false,"usgs":true,"family":"Hayes","given":"Brian S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":487665,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70058790,"text":"pp1803 - 2013 - Selenium in ecosystems within the mountaintop coal mining and valley-fill region of southern West Virginia-assessment and ecosystem-scale modeling","interactions":[],"lastModifiedDate":"2013-12-23T14:47:58","indexId":"pp1803","displayToPublicDate":"2013-12-23T14:28:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1803","title":"Selenium in ecosystems within the mountaintop coal mining and valley-fill region of southern West Virginia-assessment and ecosystem-scale modeling","docAbstract":"Coal and associated waste rock are among environmental selenium (Se) sources that have the potential to affect reproduction in fish and aquatic birds. Ecosystems of southern West Virginia that are affected by drainage from mountaintop coal mines and valleys filled with waste rock in the Coal, Gauley, and Lower Guyandotte watersheds were assessed during 2010 and 2011. Sampling data from earlier studies in these watersheds (for example, Upper Mud River Reservoir) and other mining-affected watersheds also are included to assess additional hydrologic settings and food webs for comparison. Basin schematics give a comprehensive view of sampled species and Se concentration data specific to location and date. Food-web diagrams document the progression of Se trophic transfer across suspended particulate material, invertebrates, and fish for each site to serve as the basis for developing an ecosystem-scale model to predict Se exposure within the hydrologic conditions and food webs of southern West Virginia. This approach integrates a site-specific predator’s dietary exposure pathway into modeling to ensure an adequate link to Se toxicity and, thus, to species vulnerability.\n\nSite-specific fish abundance and richness data in streams documented various species of chub, shiner, dace, darters, bass, minnow, sunfish, sucker, catfish, and central stoneroller (Campostoma anomalum), mottled sculpin (Cottus bairdii), and least brook lamprey (Lampetra aepyptera). However, Se assessment species for streams, and hence, model species for streams, were limited to creek chub (Semotilus atromaculatus) and central stoneroller. Both of these species of fish are generally considered to have a high tolerance for environmental stress based on traditional comparative fish community assessment, with creek chub being present at all sites. Aquatic insects (mayfly, caddisfly, stonefly, dobsonfly, chironomid) were the main invertebrates sampled in streams. Collection of suspended particulate material acted as an integrator of organic-rich, fine-grained biomass present in streams.\n\nThe base-case food web modeled for streams was suspended particulate material to aquatic insect to creek chub, with comparative modeling of a direct particulate-to-stoneroller food web. Model species for a reservoir setting were based on an earlier study of bluegill sunfish (Lepomis macrochirus), green sunfish (Lepomis cyanellus), and largemouth bass (Micropterus salmoides). Several reservoir food webs were considered based on a variety of invertebrates (insect, snail, clam). For stream and reservoir settings, predicted Se concentrations in exposure scenarios showed a high degree of correlation (r2 = 0.91 for invertebrates and 0.75 for fish) with field observations of Se concentrations when modeling was initiated from suspended-particulate-material Se concentrations and model transfer parameters defined previously in the literature were used. These strong correlations validate the derived site-specific model and establish sufficient confidence that the predictions from the developed model can be quantitatively applied to the ecosystems in southern West Virginia.\n\nAn application of modeling used a metric describing the partitioning of Se between particulate material and dissolved phases (Kd) to allow determination of a dissolved Se concentration that would be necessary to attain a site-specific Se fish body burden. The operationally defined Kd quantifies the complex process of transformation at the base of a food web on a site-specific basis. The magnitude of this metric is known to vary with such factors as Se speciation, particulate-material type, and hydrology. This application (1) ties dissolved Se concentrations to fish tissue concentrations; (2) allows consideration of different choices for intervening site-specific exposure steps that set Se bioaccumulation, partitioning, and bioavailability; and (3) generates implications for management decisions that define protection through different regulatory pathways and guidelines. The range of model outcomes accounts for critical sources of variability and establishes whether site and food-web characterization were adequate to represent the dynamics of the system with certainty. This is especially true in terms of particulate-material phases at the base of the food web and utilization of Kd in different hydrologic settings. For streams, a range of field-derived Kdds were applied to food-web exposure scenarios within a framework of locational and hydrologic variables (area of stream basin; stream gradient and discharge) that may affect the magnitude of Kd. Overlaying even a coarse temporal scale that acknowledges variability in stream dissolved Se and Se speciation, such as through seasonal derivation of Kd, can substantially narrow model uncertainty.\n\nModeling that constrains the place and time of greatest ecosystem Se sensitivity within a specified food web gives insight into Se risk and identifies controlling management alternatives within a watershed or stream basin. If there is a range of hydrologic settings, specificity is needed to establish a hierarchy of in-stream and off-stream habitats for a watershed approach that takes into account Se-enriched water moving through different Kd and food web environments. If there is a range of predator vulnerabilities (measured as a combination of food-web Se biodynamics and response in Se toxicity tests) within the site-specific community of fish species to be protected, then choice of fish species is critical to protection because it determines the food web and, hence, the magnitude of biotransfer through which Se is modeled. Whether creek chub is representative of the vulnerability to Se of all fish species encountered within the study-site ecosystems will require additional species-specific data and analysis. A range of site-specific scenarios illustrated here set model outcomes, but the final quantitative evaluation of alternatives and their implications will be those generated through choices and guidance formulated by state and other agencies in their decisionmaking processes.\n\nProposed additions and refinements to the ecosystem-scale site-specific approach developed here include consideration of:\n\nmeasurement of temporally matched pairs of dissolved and suspended-particulate-material Se concentrations across a broader range of stream sites to expand the stream Kd database and to test the representativeness of a suspended-particulate-material sample within a stream;\ncharacterization of different phases of particulate material across seasons to better define the base of the food web and connect to invertebrate feeding;\nrefinement of model assumptions concerning dietary preferences and composition for fish to develop additional trophic transfer factors (TTFs) (for example, calculation of TTFinvertebrate composite for mixed diets);\nexpansion of modeling of fish species and their food webs to include Se-vulnerable species;\ntemporal characterization of a predator’s life cycle and habitat use as additional model layers to integrate with Se biodynamics in streams;\ninvestigation of the effect of stream gradient on Kd based on a finer scale than presented here in terms of such variables as residence time, watershed dilution, and physical habitat attributes (for example, amount of ponding versus run or riffle within a stream); and\nlinkage to discharge through use of stream gaging to record variability and enable model organization within water-year types and discharge seasons.\nInvestigating the presence and variability of prey and predator species in demographically open systems such as streams also is key to model outcomes given the overall environmental stressors (for example, general landscape change, food-web disruption, recolonization potential) imposed on the composition of biological communities in coal mining and valley-fill affected watersheds","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1803","usgsCitation":"Presser, T.S., 2013, Selenium in ecosystems within the mountaintop coal mining and valley-fill region of southern West Virginia-assessment and ecosystem-scale modeling: U.S. Geological Survey Professional Paper 1803, vi, 86 p., https://doi.org/10.3133/pp1803.","productDescription":"vi, 86 p.","numberOfPages":"96","additionalOnlineFiles":"N","ipdsId":"IP-051155","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":280523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/pp1803.jpg"},{"id":280521,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/pp/1803/"},{"id":280522,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1803/pdf/pp1803.pdf"}],"country":"United States","state":"West Virginia","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81.8207,37.4749 ], [ -81.8207,38.6340 ], [ -80.1453,38.6340 ], [ -80.1453,37.4749 ], [ -81.8207,37.4749 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52b95be2e4b0a747b3e7e7aa","contributors":{"authors":[{"text":"Presser, Theresa S. 0000-0001-5643-0147 tpresser@usgs.gov","orcid":"https://orcid.org/0000-0001-5643-0147","contributorId":2467,"corporation":false,"usgs":true,"family":"Presser","given":"Theresa","email":"tpresser@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":487377,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70057805,"text":"sir20135220 - 2013 - Evaluation of total phosphorus mass balance in the lower Boise River and selected tributaries, southwestern Idaho","interactions":[],"lastModifiedDate":"2013-12-23T14:21:17","indexId":"sir20135220","displayToPublicDate":"2013-12-23T13:59:00","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":"2013-5220","title":"Evaluation of total phosphorus mass balance in the lower Boise River and selected tributaries, southwestern Idaho","docAbstract":"he U.S. Geological Survey (USGS), in cooperation with Idaho Department of Environmental Quality, developed spreadsheet mass-balance models for total phosphorus using results from three synoptic sampling periods conducted in the lower Boise River watershed during August and October 2012, and March 2013. The modeling reach spanned 46.4 river miles (RM) along the Boise River from Veteran’s Memorial Parkway in Boise, Idaho (RM 50.2), to Parma, Idaho (RM 3.8). The USGS collected water-quality samples and measured streamflow at 14 main-stem Boise River sites, two Boise River north channel sites, two sites on the Snake River upstream and downstream of its confluence with the Boise River, and 17 tributary and return-flow sites. Additional samples were collected from treated effluent at six wastewater treatment plants and two fish hatcheries. The Idaho Department of Water Resources quantified diversion flows in the modeling reach.\n\nTotal phosphorus mass-balance models were useful tools for evaluating sources of phosphorus in the Boise River during each sampling period. The timing of synoptic sampling allowed the USGS to evaluate phosphorus inputs to and outputs from the Boise River during irrigation season, shortly after irrigation ended, and soon before irrigation resumed. Results from the synoptic sampling periods showed important differences in surface-water and groundwater distribution and phosphorus loading. In late August 2012, substantial streamflow gains to the Boise River occurred from Middleton (RM 31.4) downstream to Parma (RM 3.8). Mass-balance model results indicated that point and nonpoint sources (including groundwater) contributed phosphorus loads to the Boise River during irrigation season. Groundwater exchange within the Boise River in October 2012 and March 2013 was not as considerable as that measured in August 2012. However, groundwater discharge to agricultural tributaries and drains during non-irrigation season was a large source of discharge and phosphorus in the lower Boise River in October 2012 and March 2013. Model results indicate that point sources represent the largest contribution of phosphorus to the Boise River year round, but that reductions in point and nonpoint source phosphorus loads may be necessary to achieve seasonal total phosphorus concentration targets at Parma (RM 3.8) from May 1 through September 30, as set by the 2004 Snake River-Hells Canyon Total Maximum Daily Load document. The mass-balance models do not account for biological or depositional instream processes, but are useful indicators of locations where appreciable phosphorus uptake or release by aquatic plants may occur.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135220","collaboration":"Prepared in cooperation with the Idaho Department of Environmental Quality","usgsCitation":"Etheridge, A.B., 2013, Evaluation of total phosphorus mass balance in the lower Boise River and selected tributaries, southwestern Idaho: U.S. Geological Survey Scientific Investigations Report 2013-5220, Report: viii, 70 p.; 3 XLSM files, https://doi.org/10.3133/sir20135220.","productDescription":"Report: viii, 70 p.; 3 XLSM files","additionalOnlineFiles":"Y","ipdsId":"IP-039546","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":280515,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5220/pdf/sir20135220.pdf"},{"id":280516,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5220/"},{"id":280517,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5220/downloads/sir20135220_October2012.xlsm"},{"id":280518,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5220/downloads/sir20135220_August2012.xlsm"},{"id":280519,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5220/downloads/sir20135220_March2013.xlsm"},{"id":280520,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135220.jpg"}],"scale":"100000","projection":"Universal Transverse Mercator, Zone 10 North. 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2007 Energy Independence and Security Act (Public Law 110–140) directs the U.S. Geological Survey (USGS) to conduct a national assessment of potential geologic storage resources for carbon dioxide (CO2). The methodology used by the USGS for the national CO2 assessment follows that of previous USGS work. This methodology is non-economic and intended to be used at regional to subbasinal scales. This report identifies and contains geologic descriptions of three storage assessment units (SAUs) in Upper Cambrian to Mississippian sedimentary rocks within the Arkoma Basin study area, and two SAUs in Upper Cambrian to Mississippian sedimentary rocks within the Kansas Basins study area. The Arkoma Basin and Kansas Basins are adjacent with very similar geologic units; although the Kansas Basins area is larger, the Arkoma Basin is more structurally complex. The report focuses on the characteristics, specified in the methodology, that influence the potential CO2 storage resource in the SAUs. Specific descriptions of the SAU boundaries as well as their sealing and reservoir units are included. Properties for each SAU, such as depth to top, gross thickness, porosity, permeability, groundwater quality, and structural reservoir traps, are usually provided to illustrate geologic factors critical to the assessment. Although assessment results are not contained in this report, the geologic information herein was employed, as specified in the USGS methodology, to calculate a probabilistic distribution of potential storage resources in each SAU. The Midcontinent Rift Basin study area was not assessed, because no suitable storage formations meeting our size, depth, reservoir quality, and regional seal guidelines were found. Figures in this report show study area boundaries along with the SAU boundaries and cell maps of well penetrations through sealing units into the top of the storage formations. The cell maps show the number of penetrating wells within one-square mile and are derived from interpretations of incompletely attributed well data and from a digital compilation that is known not to include all drilling. The USGS does not expect to know the location of all wells and cannot guarantee the amount of drilling through specific formations in any given cell shown on the cell maps.

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