Geochemical data for more than 120,000 oil and natural gas wells from the major sedimentary basins in the USA are listed in the USGS National Produced Waters Geochemical Database [1]. In this summary, we report and discuss the geochemical data on produced waters obtained from published literature and the Colorado Oil and Gas Conservation Commission (COGCC) from close to 4,000 new oil and gas wells in Colorado. We emphasize geochemical data of produced waters from shale and tight reservoirs that have increased dramatically in Colorado since 2011, due to deep horizontal drilling, downhole telemetry and massive multi-stage hydraulic fracturing. These operations require large volumes of fresh water, but contamination of groundwater is the major environmental concern. Also, induced seismicity caused by water injection has been reported from several areas in Colorado, including Trinidad, Raton basin, and Greely, Denver basin. Produced water salinities in Colorado obtained from unconventional oil and gas wells are relatively low, generally less than 30,000 mg/L TDS. Produced water salinities from conventional oil and gas wells overlap those from unconventional wells, but many wells have higher salinities (up to 90,000 mg/L TDS) and different chemical compositions.
From 1997 to the present, the U.S. Geological Survey and other agencies have been collecting water samples for chemical analyses on Mount Emmons in central Colorado, USA. The geology of Mount Emmons is dominated by Upper Cretaceous to Paleogene sediments of marine to continental origin, with felsic intrusive rocks interrupting the sedimentary block. Extensive sulphide-rich alteration accompanied the intrusive events and forms an alteration halo in the sediments. Weathering of these sulphide minerals has led to numerous springs and seeps with a naturally low pH and high concentrations of metals, especially Fe and Zn. Superimposed on the natural geochemical signature are acid, metal-rich drainages from several mines and drill holes. Thus, streams on Mt. Emmons have a mix of natural and anthropogenic metal sources. Nearly 450 samples compose the database, with numerous sample locations replicated from the late 1990s to the present. Although there does not appear to be any temporal pattern in the data, consistent spatial variations are observed that allow us to characterize the natural and anthropogenic water sources.
Submarine canyons are conduits for episodic and powerful sediment density flows (commonly called turbidity currents) that move globally significant amounts of terrestrial sediment and organic carbon into the deep sea, forming some of the largest sedimentary deposits on Earth. The only record available for most turbidity currents is the deposit they leave behind. Therefore, to understand turbidity current processes, we need to determine the degree to which these flows are represented by their deposits. However, linking flows and deposits is a major long-standing scientific challenge. There are few detailed measurements from submarine turbidity currents in action, and even fewer direct measurements that can be compared to resulting seabed deposits. Recently, an extensive array of moorings along Monterey Canyon, offshore California, took measurements and samples during sediment density flow events, providing the most comprehensive dataset to date of turbidity current flows and their deposits. Here, we use sediment trap samples, velocity measurements, and seafloor cores to document how sand is transported through a submarine canyon, and how the transported sediment is represented in seafloor deposits. Sediment trap samples from events contain primarily fine to medium-grained sand with sharp bases, normal grading, and muddy tops. Sediment captured from the water column during the flow shows normal grading, which is broadly consistent with the initial peak and waning of flow velocities measured at a single height within the flow, and may be enhanced by collapsing flows. Flow events contain coarser sand concentrated toward the seafloor and larger grain sizes on the seafloor or in the dense near-bed layer, possibly representative of stratified flows. Although flow velocity varies, sand grain sizes in sediment traps are similar over distances of 50 km down-canyon, suggesting that grain size is an unfaithful record of down-canyon changes in maximum flow speeds. Sand transported within flow events and sampled in sediment traps is similar to sand sampled from the seafloor shortly after the events, but traps do not contain pebbles and gravel common in seabed deposits. Seabed deposits thus appear to faithfully record the sand component that is transported in the water column during sub-annual turbidity currents.
","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2019.00144","usgsCitation":"Maier, K.L., Gales, J., Paull, C.K., Rosenberger, K.J., Talling, P.J., Simmons, S., Gwiazda, R., McGann, M., Cartigny, M.J., Lundsten, E.M., Anderson, K., Clare, M., Xu, J., Parsons, D., Barry, J., Wolfson-Schwher, M., Nieminski, N.M., and Sumner, E., 2019, Linking direct measurements of turbidity currents to submarine canyon-floor deposits: Frontiers in Earth Science, v. 7, 144; 18 p., https://doi.org/10.3389/feart.2019.00144.","productDescription":"144; 18 p.","ipdsId":"IP-104483","costCenters":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467553,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2019.00144","text":"Publisher Index 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UK","active":true,"usgs":false}],"preferred":false,"id":764915,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Barry, James P.","contributorId":140935,"corporation":false,"usgs":false,"family":"Barry","given":"James P.","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":764916,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wolfson-Schwher, Monica","contributorId":216509,"corporation":false,"usgs":false,"family":"Wolfson-Schwher","given":"Monica","email":"","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":764917,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Nieminski, Nora M.","contributorId":216510,"corporation":false,"usgs":false,"family":"Nieminski","given":"Nora","email":"","middleInitial":"M.","affiliations":[{"id":6986,"text":"Stanford 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long-term gas hydrate testing site confirmed on the Alaska north slope","docAbstract":"In December 2018, data acquired in a Stratigraphic Test Well drilled from the 7-11-12 pad in the western part of the Prudhoe Bay Unit, Alaska North Slope confirmed the occurrence of two high-quality reservoirs fully saturated with gas hydrate. The drilling was the initial phase of a planned, three-well program designed to conduct an extended duration test of the response to gas hydrate reservoirs to controlled depressurization. The Stratigraphic Test Well (formally “PBU Hydrate-01”) was operated by the PBU Operator BP Exploration, (Alaska), Inc. (BPXA) using the Parker 272 drilling rig (Figure 1) through a Drilling Services Agreement executed with Petrotechnical Resources of Alaska (PRA) in association with a contract between NETL and PRA. The science program executed by BPXA was developed over a two-year period through extensive discussions and scientific evaluation undertaken by NETL, the Japan, Oil, Gas, and Metals, National Corporation (JOGMEC), the U.S. Geological Survey (USGS), and PRA. The effort also benefitted greatly from the support of the Alaska Department of Natural Resources (ADNR) and the PBU Working Interest Owners (WIOs).
","language":"English","publisher":"Department of Energy","usgsCitation":"Boswell, R., Marsteller, S., Nori Okinaka, Wakatsuki, M., Collett, T.S., Hunter, R., Tom Walsh, David Itter, and Crumley, S., 2019, Viable long-term gas hydrate testing site confirmed on the Alaska north slope: Fire in the Ice: NETL Methane Hydrate Newsletter, v. 19, no. 1, p. 1-5.","productDescription":"5 p.","startPage":"1","endPage":"5","ipdsId":"IP-106571","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":364480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364463,"type":{"id":15,"text":"Index Page"},"url":"https://www.netl.doe.gov/sites/default/files/publication/MHNews_2019_Spring.pdf"}],"country":"United States","state":"Alaska","otherGeospatial":"Prudhoe Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.55615234375,\n 69.31055846850984\n ],\n [\n -148.82080078125,\n 69.31055846850984\n ],\n [\n -148.82080078125,\n 71.05266461121374\n ],\n [\n -152.55615234375,\n 71.05266461121374\n ],\n [\n -152.55615234375,\n 69.31055846850984\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boswell, Ray","contributorId":173139,"corporation":false,"usgs":false,"family":"Boswell","given":"Ray","email":"","affiliations":[{"id":17887,"text":"National Energy Technology Laboratory, Department of Energy","active":true,"usgs":false}],"preferred":false,"id":763825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marsteller, Scott","contributorId":216073,"corporation":false,"usgs":false,"family":"Marsteller","given":"Scott","email":"","affiliations":[{"id":34152,"text":"US Department of Energy","active":true,"usgs":false}],"preferred":false,"id":763826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nori Okinaka","contributorId":216074,"corporation":false,"usgs":false,"family":"Nori Okinaka","affiliations":[{"id":39359,"text":"JOGMEC","active":true,"usgs":false}],"preferred":false,"id":763827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wakatsuki, Motoi","contributorId":216075,"corporation":false,"usgs":false,"family":"Wakatsuki","given":"Motoi","email":"","affiliations":[{"id":39359,"text":"JOGMEC","active":true,"usgs":false}],"preferred":false,"id":763828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":763824,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hunter, Robert","contributorId":216076,"corporation":false,"usgs":false,"family":"Hunter","given":"Robert","email":"","affiliations":[{"id":39360,"text":"PRA","active":true,"usgs":false}],"preferred":false,"id":763829,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tom Walsh","contributorId":216077,"corporation":false,"usgs":false,"family":"Tom Walsh","affiliations":[{"id":39360,"text":"PRA","active":true,"usgs":false}],"preferred":false,"id":763830,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"David Itter","contributorId":216078,"corporation":false,"usgs":false,"family":"David Itter","affiliations":[{"id":39361,"text":"BP Alaska","active":true,"usgs":false}],"preferred":false,"id":763831,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Crumley, Stephen","contributorId":216079,"corporation":false,"usgs":false,"family":"Crumley","given":"Stephen","email":"","affiliations":[{"id":39361,"text":"BP Alaska","active":true,"usgs":false}],"preferred":false,"id":763832,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70203721,"text":"70203721 - 2019 - Statistical power of dynamic occupancy models to identify temporal change: Informing the North American Bat Monitoring Program","interactions":[],"lastModifiedDate":"2019-06-18T12:21:17","indexId":"70203721","displayToPublicDate":"2019-06-06T15:14:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Statistical power of dynamic occupancy models to identify temporal change: Informing the North American Bat Monitoring Program","docAbstract":"Dynamic occupancy models provide a flexible framework for estimating and mapping species occupancy patterns\nover space and time for large-scale monitoring programs (e.g., the North American Bat Monitoring Program\n(NABat), the Amphibian Research and Monitoring Initiative). Challenges for designing surveys using the dynamic\noccupancy modeling framework include defining appropriate derived trend parameters, and providing\nusable tools for researchers to conduct project-specific sample size investigations. We present a simulation-based\npower analysis framework for dynamic occupancy models that allows for the incorporation of the underlying\nenvironmental space (i.e., as covariates) within a specific study region to inform sample size estimation. We\ninvestigate two definitions of temporal trend: (1) a gradual, sustained (linear or nonlinear) change over a period\nof many years, and (2) an abrupt increase or decrease between two time periods. We draw upon pilot data\ncollected following NABat protocols to inform assumed data generating values in a demonstration of our approach.\nDue to the complicated parameter structure of dynamic occupancy models, we emphasize the importance\nof visualizing simulated changes over time based on different parameter settings prior to conducting a\npower analysis. Our simulations revealed that the linearity of short-term trends (five years in our investigation)\nconferred higher power with lower sample size than longer trends where occupancy probabilities approached\nzero (ten years in our investigation). We provide an example of how to use our tools to conduct customized\ninvestigations using questions posed by NABat, and in doing so, we shed light on general guidelines that can be\napplied to programs monitoring species occupancy for other taxa. Importantly, we created an R package to\nexecute our approach for informing program-, species-, and study-specific investigations aimed at identifying\nchanges in species occupancy.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2019.05.047","usgsCitation":"Banner, K., Irvine, K., Rodhouse, T.J., Donner, D.M., and Litt, A.R., 2019, Statistical power of dynamic occupancy models to identify temporal change: Informing the North American Bat Monitoring Program: Ecological Indicators, v. 105, p. 166-176, https://doi.org/10.1016/j.ecolind.2019.05.047.","productDescription":"11 p.","startPage":"166","endPage":"176","ipdsId":"IP-103005","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":460363,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2019.05.047","text":"Publisher Index Page"},{"id":437428,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WHOH6D","text":"USGS data release","linkHelpText":"Online supporting information for "Statistical power of dynamic occupancy models to identify temporal change: informing the North American Bat Monitoring Program""},{"id":364476,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"105","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Banner, Katherine","contributorId":216067,"corporation":false,"usgs":false,"family":"Banner","given":"Katherine","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":763807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":214591,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":763806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodhouse, Tom J","contributorId":176228,"corporation":false,"usgs":false,"family":"Rodhouse","given":"Tom","email":"","middleInitial":"J","affiliations":[],"preferred":false,"id":763808,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donner, Deahn M.","contributorId":171823,"corporation":false,"usgs":false,"family":"Donner","given":"Deahn","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":763809,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Litt, Andrea R.","contributorId":208358,"corporation":false,"usgs":false,"family":"Litt","given":"Andrea","email":"","middleInitial":"R.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":763810,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202987,"text":"70202987 - 2019 - Screen-printed soft capacitive sensors for spatial mapping of both positive and negative pressures","interactions":[],"lastModifiedDate":"2019-07-23T13:27:45","indexId":"70202987","displayToPublicDate":"2019-06-06T13:39:32","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5827,"text":"Advanced Functional Materials","active":true,"publicationSubtype":{"id":10}},"title":"Screen-printed soft capacitive sensors for spatial mapping of both positive and negative pressures","docAbstract":"Soft pressure sensors are one class of the essential devices for robotics and wearable device applications. Despite the tremendous progress, sensors that can reliably detect both positive and negative pressures have not yet been demonstrated. In this paper, we report a soft capacitive pressure sensor made using a convenient and low-cost screen-printing process that can reliably detect both positive and negative pressures from −60 kPa to 20 kPa. The sensor is made with an Ecoflex-0030 dielectric layer, conductive and stretchable poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) (with ionic additives) electrodes and polydimethylsiloxane (PDMS) encapsulation layers. Air gaps are designed and incorporated into the dielectric layer to significantly enhance the sample deformation and pressure response especially to negative pressure. The sensor exhibits repeatable response for thousands of cycles, even under bending or stretching conditions. Lastly, to demonstrate the practical application, a 12×12-pixel sensor array that can automatically measure both positive and negative pressure distributions has been reported under −20 kPa and 10 kPa.","language":"English","publisher":"Wiley","doi":"10.1002/adfm.201809116","usgsCitation":"Shi, H., Al-Rubaiai, M., Holbrook, C., Miao, J., Pinto, T., Wang, C., and Tan, X., 2019, Screen-printed soft capacitive sensors for spatial mapping of both positive and negative pressures: Advanced Functional Materials, v. 29, no. 23, Article 1809116, https://doi.org/10.1002/adfm.201809116.","productDescription":"Article 1809116","ipdsId":"IP-106197","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":362916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"23","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hongyang 0000-0003-4135-3673","orcid":"https://orcid.org/0000-0003-4135-3673","contributorId":214760,"corporation":false,"usgs":false,"family":"Shi","given":"Hongyang","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":760711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Al-Rubaiai, Mohammed","contributorId":214761,"corporation":false,"usgs":false,"family":"Al-Rubaiai","given":"Mohammed","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":760712,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":760710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miao, Jinshui","contributorId":214762,"corporation":false,"usgs":false,"family":"Miao","given":"Jinshui","email":"","affiliations":[{"id":16979,"text":"University of Pennsylvania","active":true,"usgs":false}],"preferred":false,"id":760713,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pinto, Thassyo","contributorId":214763,"corporation":false,"usgs":false,"family":"Pinto","given":"Thassyo","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":760714,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Chuan","contributorId":214764,"corporation":false,"usgs":false,"family":"Wang","given":"Chuan","email":"","affiliations":[{"id":35028,"text":"Washington University in St. Louis","active":true,"usgs":false}],"preferred":false,"id":760715,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tan, Xiaobo 0000-0002-5542-6266","orcid":"https://orcid.org/0000-0002-5542-6266","contributorId":214765,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":760716,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203899,"text":"70203899 - 2019 - Operationalizing resilience and resistance concepts to address invasive grass-fire cycles","interactions":[],"lastModifiedDate":"2019-06-20T13:01:59","indexId":"70203899","displayToPublicDate":"2019-06-06T13:01:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Operationalizing resilience and resistance concepts to address invasive grass-fire cycles","docAbstract":"Plant invasions can affect fuel characteristics, fire behavior, and fire regimes resulting in invasive plant-fire cycles and alternative, self-perpetuating states that can be difficult, if not impossible, to reverse. Concepts related to general resilience to disturbance and resistance to invasive plants provide the basis for managing landscapes to increase their capacity to reorganize and adjust following fire, while concepts related to spatial resilience provide the basis for managing landscapes to conserve resources and habitats and maintain connectivity. New, spatially explicit approaches and decision-tools enable managers to understand and evaluate general and spatial resilience to fire and resistance to invasive grasses across large landscapes in arid and semi-arid shrublands and woodlands. These approaches and tools provide the capacity to locate management actions strategically to prevent development of invasive grass-fire cycles and maintain or improve resources and habitats. In this review, we discuss the factors that influence fire regimes, general and spatial resilience to fire, resistance to invasive annual grasses, and thus invasive grass-fire cycles in global arid and semi-arid shrublands and woodlands. The Cold Deserts, Mediterranean Ecoregion, and Warm Deserts of North America are used as model systems to describe how and why resilience to disturbance and resistance to invasive annuals differ over large landscapes. The Cold Deserts are used to illustrate an approach and decision tools for prioritizing areas on the landscape for management actions to prevent development of invasive grass-fire cycles and protect high value resources and habitats and for determining effective management strategies. The concepts and approach herein represent a paradigm shift in the management of these ecosystems, which allows managers to use geospatial tools to identify resilience to disturbance and resistance to invasive plants in order to target conservation and restoration actions where they will provide the greatest benefits.","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2019.00185","usgsCitation":"Chambers, J.C., Brooks, M.L., Germino, M., Maestas, J.D., Board, D.I., Jones, M.O., and Allred, B.W., 2019, Operationalizing resilience and resistance concepts to address invasive grass-fire cycles: Frontiers in Ecology and Evolution, v. 7, no. 185, https://doi.org/10.3389/fevo.2019.00185.","ipdsId":"IP-106949","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":467554,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00185","text":"Publisher Index Page"},{"id":364839,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364821,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00185"}],"volume":"7","issue":"185","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Chambers, Jeanne C.","contributorId":178256,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":764646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":764645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","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":764647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maestas, Jeremy D","contributorId":191086,"corporation":false,"usgs":false,"family":"Maestas","given":"Jeremy","email":"","middleInitial":"D","affiliations":[],"preferred":false,"id":764648,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Board, David I","contributorId":216377,"corporation":false,"usgs":false,"family":"Board","given":"David","email":"","middleInitial":"I","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":764649,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, Matthew O.","contributorId":169805,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew","email":"","middleInitial":"O.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":764650,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Allred, Brady W","contributorId":216378,"corporation":false,"usgs":false,"family":"Allred","given":"Brady","email":"","middleInitial":"W","affiliations":[{"id":39397,"text":"W.A. Franke College of Forestry and Conservation University of Montana, Missoula","active":true,"usgs":false}],"preferred":false,"id":764651,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70204697,"text":"70204697 - 2019 - Estimating domestic well locations and populations served in the contiguous U.S. for years 2000 and 2010","interactions":[],"lastModifiedDate":"2019-08-09T12:10:34","indexId":"70204697","displayToPublicDate":"2019-06-06T12:02:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Estimating domestic well locations and populations served in the contiguous U.S. for years 2000 and 2010","docAbstract":"Domestic wells provide drinking water supply for approximately 40 million people in the United States. Knowing the location of these wells, and the populations they serve, is important for identifying heavily used aquifers, locations susceptible to contamination, and populations potentially impacted by poor-quality groundwater. The 1990 census was the last nationally consistent survey of a home’s source of water, and has not been surveyed since. This paper presents a method for projecting the population dependent on domestic wells for years after 1990, using information from the 1990 census along with population data from subsequent censuses. The method is based on the “domestic ratio” at the census block-group level, defined here as the number of households dependent on domestic wells divided by the total population. Analysis of 1990 data (>220,000 block-groups) indicates that the domestic ratio is a function of the household density. As household density increases, the domestic ratio decreases, once a household density threshold is met. The 1990 data were used to develop a relationship between household density and the domestic ratio. The fitted model, along with household density data from 2000 and 2010, was used to estimate domestic ratios for each decadal year. In turn, the number of households dependent on domestic wells was estimated at the block-group level for 2000 and 2010. High-resolution census-block population data were used to refine the spatial distribution of domestic-well usage and to convert the data into population numbers. The results are presented in two downloadable raster datasets for each decadal year. It is estimated that the total population using domestic-well water in the contiguous U.S. increased 1.5% from 1990 to 2000 to a total of 37.25 million people and increased slightly from 2000 to 2010 to 37.29 million people.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.06.036","usgsCitation":"Johnson, T., Belitz, K., and Lombard, M.A., 2019, Estimating domestic well locations and populations served in the contiguous U.S. for years 2000 and 2010: Science of the Total Environment, v. 687, p. 1261-1273, https://doi.org/10.1016/j.scitotenv.2019.06.036.","productDescription":"13 p.","startPage":"1261","endPage":"1273","ipdsId":"IP-101767","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science 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0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lombard, Melissa A. 0000-0001-5924-6556 mlombard@usgs.gov","orcid":"https://orcid.org/0000-0001-5924-6556","contributorId":198254,"corporation":false,"usgs":true,"family":"Lombard","given":"Melissa","email":"mlombard@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768107,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70250179,"text":"70250179 - 2019 - The unprecedented loss of Florida's reef-building corals and the emergence of a novel coral-reef assemblage","interactions":[],"lastModifiedDate":"2023-11-27T16:53:33.19877","indexId":"70250179","displayToPublicDate":"2019-06-06T10:46:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The unprecedented loss of Florida's reef-building corals and the emergence of a novel coral-reef assemblage","docAbstract":"Over the last half century, climate change, coral disease, and other anthropogenic disturbances have restructured coral-reef ecosystems on a global scale. The disproportionate loss of once-dominant, reef-building taxa has facilitated relative increases in the abundance of “weedy” or stress-tolerant coral species. Although the recent transformation of coral-reef assemblages is unprecedented on ecological timescales, determining whether modern coral reefs have truly reached a novel ecosystem state requires evaluating the dynamics of reef composition over much longer periods of time. Here, we provide a geologic perspective on the shifting composition of Florida's reefs by reconstructing the millennial-scale spatial and temporal variability in reef assemblages using 59 Holocene reef cores collected throughout the Florida Keys Reef Tract (FKRT). We then compare the relative abundances of reef-building species in the Holocene reef framework to data from contemporary reef surveys to determine how much Florida's modern reef assemblages have diverged from long-term baselines. We show that the composition of Florida's reefs was, until recently, remarkably stable over the last 8000 yr. The same corals that have dominated shallow-water reefs throughout the western Atlantic for hundreds of thousands of years, Acropora palmata, Orbicella spp., and other massive coral taxa, accounted for nearly 90% of Florida's Holocene reef framework. In contrast, the species that now have the highest relative abundances on the FKRT, primarily Porites astreoides and Siderastrea siderea, were rare in the reef framework, suggesting that recent shifts in species assemblages are unprecedented over millennial timescales. Although it may not be possible to return coral reefs to pre-Anthropocene states, our results suggest that coral-reef management focused on the conservation and restoration of the reef-building species of the past, will optimize efforts to preserve coral reefs, and the valuable ecosystem services they provide into the future.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2781","usgsCitation":"Toth, L., Stathakopoulos, A., Kuffner, I.B., Ruzicka, R.R., Colella, M.A., and Shinn, E.A., 2019, The unprecedented loss of Florida's reef-building corals and the emergence of a novel coral-reef assemblage: Ecology, v. 100, no. 9, e02781, 14 p., https://doi.org/10.1002/ecy.2781.","productDescription":"e02781, 14 p.","ipdsId":"IP-104540","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467556,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.2781","text":"Publisher Index Page"},{"id":437430,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93XXXA0","text":"USGS data release","linkHelpText":"The Absolute and Relative Composition of Holocene Reef Cores From the Florida Keys Reef Tract"},{"id":422972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys Reef Tract","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.52594320175321,\n 24.764514561822665\n ],\n [\n -83.03873267817458,\n 24.764514561822665\n ],\n [\n -83.08197998341461,\n 24.427458630027616\n ],\n [\n -81.47565150305881,\n 24.41058205242757\n ],\n [\n -80.33268700742039,\n 24.92709848702593\n ],\n [\n -80.06702498951552,\n 25.525116607289604\n ],\n [\n -80.25854876986541,\n 25.53069165144869\n ],\n [\n -80.598349025326,\n 25.03910062993201\n ],\n [\n -81.3644441467264,\n 24.826209468694913\n ],\n [\n -82.52594320175321,\n 24.764514561822665\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"100","issue":"9","noUsgsAuthors":false,"publicationDate":"2019-07-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888681,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888682,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888683,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruzicka, Robert R.","contributorId":204569,"corporation":false,"usgs":false,"family":"Ruzicka","given":"Robert","email":"","middleInitial":"R.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":888684,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Colella, Michael A.","contributorId":139979,"corporation":false,"usgs":false,"family":"Colella","given":"Michael","email":"","middleInitial":"A.","affiliations":[{"id":13340,"text":"Fish & Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":888685,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shinn, Eugene A.","contributorId":210858,"corporation":false,"usgs":false,"family":"Shinn","given":"Eugene","email":"","middleInitial":"A.","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":888686,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203718,"text":"70203718 - 2019 - Biota dose assessment of small rodents sampled near breccia pipe uranium mines in the Grand Canyon watershed","interactions":[],"lastModifiedDate":"2019-06-07T16:35:05","indexId":"70203718","displayToPublicDate":"2019-06-06T10:16:34","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1884,"text":"Health Physics","active":true,"publicationSubtype":{"id":10}},"title":"Biota dose assessment of small rodents sampled near breccia pipe uranium mines in the Grand Canyon watershed","docAbstract":"The biotic exposure and uptake of radionuclides and potential health effects due to breccia pipe uranium mining in the Grand Canyon watershed are largely unknown. This paper describes the use of the RESRAD-BIOTA dose model to assess exposure of small rodents (n = 11) sampled at three uranium mine sites in different stages of ore production (active and postproduction). Rodent tissue and soil concentrations of naturally occurring uranium (234U, 235U, and 238U), thorium (228Th, 230Th, and 232Th), and radium (226Ra) radioisotopes were used in the dose model. The dose assessment results indicated that the potential internal, external, and total doses to rodents were below the US Department of Energy’s biota dose standard of 1 mGy d−1. As expected, tissue concentrations of 238U, 234U, and 230Th were in approximate equilibrium; however, 226Ra results in tissue were 1.25 to 5.75 times greater than 238U, 234U, and 230Th tissue results for 10 out of 11 samples. Soil at the three sites also displayed 226Ra enrichment, so it is likely that the 226Ra enrichment in the rodents was from soil via typical activities (i.e., burrowing, incidental ingestion, bathing, etc.) or by dietary uptake of translocated 226Ra. The results suggest that 226Ra is more mobile in this environment and bioaccumulates in these rodent species (e.g., in bones via the bloodstream). Internal dose accounting suggests that 226Ra is the radionuclide of most concern for rodent exposure and health.","language":"English","publisher":"Kluwer","doi":"10.1097/HP.0000000000001041","usgsCitation":"Minter, K.M., Jannik, T., Hinck, J.E., Cleveland, D.M., Kubilius, W.P., and Kuhne, W.W., 2019, Biota dose assessment of small rodents sampled near breccia pipe uranium mines in the Grand Canyon watershed: Health Physics, v. 117, no. 1, p. 20-27, https://doi.org/10.1097/HP.0000000000001041.","productDescription":"8 p.","startPage":"20","endPage":"27","ipdsId":"IP-099488","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":364427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Grand Canyon ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.0106201171875,\n 35.70414710206052\n ],\n [\n -111.50848388671875,\n 35.70414710206052\n ],\n [\n -111.50848388671875,\n 36.89499795802219\n ],\n [\n -114.0106201171875,\n 36.89499795802219\n ],\n [\n -114.0106201171875,\n 35.70414710206052\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"117","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Minter, Kelsey M.","contributorId":216055,"corporation":false,"usgs":false,"family":"Minter","given":"Kelsey","email":"","middleInitial":"M.","affiliations":[{"id":39358,"text":"Savannah River National Laboratory, Savannah River Site, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":763784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jannik, Timothy","contributorId":216056,"corporation":false,"usgs":false,"family":"Jannik","given":"Timothy","email":"","affiliations":[{"id":39358,"text":"Savannah River National Laboratory, Savannah River Site, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":763785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hinck, Jo Ellen 0000-0002-4912-5766 jhinck@usgs.gov","orcid":"https://orcid.org/0000-0002-4912-5766","contributorId":2743,"corporation":false,"usgs":true,"family":"Hinck","given":"Jo","email":"jhinck@usgs.gov","middleInitial":"Ellen","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":763786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":763783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kubilius, Walter P.","contributorId":216057,"corporation":false,"usgs":false,"family":"Kubilius","given":"Walter","email":"","middleInitial":"P.","affiliations":[{"id":39358,"text":"Savannah River National Laboratory, Savannah River Site, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":763787,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kuhne, Wendy W.","contributorId":216058,"corporation":false,"usgs":false,"family":"Kuhne","given":"Wendy","email":"","middleInitial":"W.","affiliations":[{"id":39358,"text":"Savannah River National Laboratory, Savannah River Site, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":763788,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215386,"text":"70215386 - 2019 - Incorporating citizen science data in spatially explicit integrated population models","interactions":[],"lastModifiedDate":"2020-10-18T14:13:58.668729","indexId":"70215386","displayToPublicDate":"2019-06-06T09:05:04","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating citizen science data in spatially explicit integrated population models","docAbstract":"Information about population abundance, distribution, and demographic rates is critical for understanding a species’ ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence–absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture–recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi‐season simulations with parameters informed by North American black bear (Ursus americanus) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population‐level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost‐efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.
Information concerning the availability, use, and quality of water in West Carroll Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, 21.27 million gallons per day (Mgal/d) of water were withdrawn in West Carroll Parish, including 17.91 Mgal/d from groundwater sources and 3.37 Mgal/d from surface-water sources. Withdrawals for agricultural use, composed of general irrigation, rice irrigation, and livestock, accounted for 93 percent (19.76 Mgal/d) of the total water withdrawn. Other use categories included public supply and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2000 at 31.7 Mgal/d. The large decreases in water use from 1985 to 1990 and again from 2005 to 2010 are primarily attributable to declines in groundwater withdrawals for rice irrigation from 10 Mgal/d in 1985 to 2.22 Mgal/d in 1990 and from 10.52 Mgal/d in 2005 to 5.14 Mgal/d in 2010. Surface-water withdrawals for general irrigation declined from 2.44 Mgal/d in 1985 to 0.42 Mgal/d in 1990 and from 2.2 Mgal/d in 2005 to 1.1 Mgal/d in 2010. Surface-water withdrawals for rice irrigation declined from 1.41 Mgal/d in 1985 to 0.66 Mgal/d in 1990 and from 2.06 Mgal/d in 2005 to 1.01 Mgal/d in 2010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183069","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2019, Water resources of West Carroll Parish, Louisiana: U.S. Geological Survey Fact Sheet 2018–3069, 6 p., https://doi.org/10.3133/fs20183069.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-081704","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":362840,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 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U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Morehouse Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, 109.84 million gallons per day (Mgal/d) of water were withdrawn in Morehouse Parish: 78.05 Mgal/d from groundwater sources and 31.79 Mgal/d from surface-water sources. Withdrawals for agricultural use—including general irrigation, rice irrigation, and livestock—accounted for about 97 percent (106.29 Mgal/d) of the total water withdrawn. Other categories of use included public supply, rural domestic, and industrial. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 1975 at 167.82 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183068","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2019, Water resources of Morehouse Parish, Louisiana: U.S. Geological Survey Fact Sheet 2018–3068, 6 p., https://doi.org/10.3133/fs20183068. 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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Within most resource management agencies, fish ages assigned by multiple readers are used to estimate age-based population metrics and to develop state or regional growth standards under the assumption that among-reader precision and accuracy are high. A cursory evaluation suggested that precision of age estimates among seven individuals who routinely estimate Walleye Sander vitreus age for the Wisconsin Department of Natural Resources was remarkably low (otolith mean coefficient of variation [CV] = 37%; dorsal spine mean CV = 35%), which prompted concern and interest in whether among-reader precision could be improved with a minimal level of training. Consequently, we compared among-reader precision and accuracy before and after a 1-d training exercise. We distributed images of sectioned otoliths and sectioned dorsal spines from a random sample of 50 Walleye, along with images of structures from 25 known-age Walleye, to 21 readers grouped into beginner, intermediate, and advanced experience levels based on responses to a pretraining survey. Among-reader precision was substantially higher after training (otolith mean CV = 16%; dorsal spine mean CV = 15%) than before (otolith mean CV = 27%; dorsal spine mean CV = 26%). Accuracy of age estimates also improved after training, but greater improvements were observed for otoliths (mean difference between estimated and known ages before training = 0.80 year; after training = 0.15 year) than for dorsal spines (mean difference between estimated and known ages before training = 0.38 year; after training = 0.22 year). Similar improvements in precision and accuracy were evident for all experience levels. Our results suggest that a simple, low-cost age estimation training can substantially increase precision and accuracy of age estimates among a large group of readers. However, additional training and quality control measures may be required to achieve higher levels of precision and accuracy.
Despite increasing interest in determining the population-level effects of emerging infectious diseases on wildlife, estimating effects of disease on survival rates remains difficult. Even for a well-studied disease such as amphibian chytridiomycosis (caused by the fungus Batrachochytrium dendrobatidis [Bd]), there are few estimates of how survival of wild hosts is affected. We applied hierarchical models to long-term capture-mark-recapture data (mean = 10.6 yrs, range = 6–15 yrs) from >5500 uniquely-marked individuals to estimate the effect of Bd on apparent survival of four threatened or endangered ranid frog species (Rana draytonii, R. muscosa, R. pretiosa, R. sierrae) at 14 study sites in California and Oregon (USA) and one bufonid toad (Anaxyrus boreas) at two study sites in Wyoming and Montana. Our models indicated that the presence of Bd on an individual reduced apparent survival of ranid frogs by ~6–15% depending on species and sex. The estimated difference between toads with and without Bd was 19% for the Montana population and 55% for the Wyoming population; however, the 95% Credible Interval of these estimates included zero. These results provide evidence for negative effects of Bd on survival in wild populations even in the absence of obvious die-offs. Determining what factors influence the magnitude of the effects of Bd on wildlife populations is an important next step toward identifying management actions. These estimates of Bd effects are important for understanding the extent and severity of disease, whether disease effects have changed over time, and for informing management actions.
The importance of long-distance dispersal (LDD) in shaping geographical distributions has been debated since the nineteenth century. In terrestrial vertebrates, LDD events across large water bodies are considered highly improbable, but organismal traits affecting dispersal capacity are generally not taken into account. Here, we focus on a recent lizard radiation and combine a summary-coalescent species tree based on 1225 exons with a probabilistic model that links dispersal capacity to an evolving trait, to investigate whether ecological specialization has influenced the probability of trans-oceanic dispersal. Cryptoblepharus species that occur in coastal habitats have on average dispersed 13 to 14 times more frequently than non-coastal species and coastal specialization has, therefore, led to an extraordinarily widespread distribution that includes multiple continents and distant island archipelagoes. Furthermore, their presence across the Pacific substantially predates the age of human colonization and we can explicitly reject the possibility that these patterns are solely shaped by human-mediated dispersal. Overall, by combining new analytical methods with a comprehensive phylogenomic dataset, we use a quantitative framework to show how coastal specialization can influence dispersal capacity and eventually shape geographical distributions at a macroevolutionary scale.
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California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
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Coastal development and climate change are dramatically increasing the risks of flooding, erosion, and extreme weather events. Coral reefs and other coastal ecosystems act as natural defenses against coastal hazards, but their degradation increases risk to people and property. Environmental degradation, however, has rarely been quantified as a driver of coastal risk. In Quintana Roo, Mexico, a region on the Mexican Caribbean coast with an annual tourism economy of 10 billion USD, coral reefs constitute a natural barrier against flooding from hurricanes. This study spatially quantifies the risk reduction benefits of the Mesoamerican Reef in Quintana Roo for people, buildings, and hotel infrastructure. The risk reduction benefits are substantial. For example, the reefs prevented 43% additional damage during Hurricane Dean in (2007) and provide nowadays hazard risk reduction for 4.3% of the people, 1.9% of the built capital, and 2.4% of the hotel infrastructure, per year. The annual benefits are estimated in 4,600 people, 42 million USD damage prevention for buildings, and 20.8 million USD for hotel infrastructure. The study also compares the risk reduction of coral reefs with (i) the protection offered by dunes and (ii) the increase in coastal risk from sea-level rise (SLR). The risk reduction of dunes is more critical where there are no coral reefs offshore and for small return-periods storms. Sea-level rise, however, will make the more frequent storms more impactful and will drive significant increases in annual expected damages across the region. However, we demonstrate that, in coral reef environments, the contribution of reef degradation to coastal risk is larger than the expected increase in risk from SLR. However, the spatial distribution of the risk reduction benefits from reefs differs for people and infrastructure, and in particular for hotels, which receive the most protection from reefs. Furthermore, many sections present larger benefits than the typical costs of restoration. This valuation makes a compelling case for protecting and maintaining this natural infrastructure for its risk reduction service, but also allows the development of piloting innovative strategies, such as risk finance and insurance strategies, that can align environmental and risk management goals.
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