{"pageNumber":"394","pageRowStart":"9825","pageSize":"25","recordCount":46619,"records":[{"id":70176711,"text":"70176711 - 2017 - Golden Eagle fatalities and the continental-scale consequences of local wind-energy generation","interactions":[],"lastModifiedDate":"2017-11-22T17:05:33","indexId":"70176711","displayToPublicDate":"2016-10-03T14:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Golden Eagle fatalities and the continental-scale consequences of local wind-energy generation","docAbstract":"<p><span>Renewable energy production is expanding rapidly despite mostly unknown environmental effects on wildlife and habitats. We used genetic and stable isotope data collected from Golden Eagles </span><i>(Aquila chrysaetos)</i><span> killed at the Altamont Pass Wind Resource Area (APWRA) in California in demographic models to test hypotheses about the geographic extent and demographic consequences of fatalities caused by renewable energy facilities. Geospatial analyses of δ</span><sup>2</sup><span>H values obtained from feathers showed that ≥25% of these APWRA-killed eagles were recent immigrants to the population, most from long distances away (&gt;100 km). Data from nuclear genes indicated this subset of immigrant eagles was genetically similar to birds identified as locals from the δ</span><sup>2</sup><span>H data. Demographic models implied that in the face of this mortality, the apparent stability of the local Golden Eagle population was maintained by continental-scale immigration. These analyses demonstrate that ecosystem management decisions concerning the effects of local-scale renewable energy can have continental-scale consequences.</span></p>","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.12836","usgsCitation":"Katzner, T., Nelson, D.M., Braham, M., Doyle, J.M., Fernandez, N.B., Duerr, A.E., Bloom, P., Fitzpatrick, M.C., Miller, T., Culver, R.C., Braswell, L., and DeWoody, J.A., 2017, Golden Eagle fatalities and the continental-scale consequences of local wind-energy generation: Conservation Biology, v. 31, no. 2, p. 406-415, https://doi.org/10.1111/cobi.12836.","productDescription":"10 p.","startPage":"406","endPage":"415","ipdsId":"IP-068630","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":329239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-27","publicationStatus":"PW","scienceBaseUri":"57f7c63ae4b0bc0bec09c826","chorus":{"doi":"10.1111/cobi.12836","url":"http://dx.doi.org/10.1111/cobi.12836","publisher":"Wiley-Blackwell","authors":"Katzner Todd E., Nelson David M., Braham Melissa A., Doyle Jacqueline M., Fernandez Nadia B., Duerr Adam E., Bloom Peter H., Fitzpatrick Matthew C., Miller Tricia A., Culver Renee C. E., Braswell Loan, DeWoody J. Andrew","journalName":"Conservation Biology","publicationDate":"9/27/2016","publiclyAccessibleDate":"9/27/2016"},"contributors":{"authors":[{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":5979,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":649961,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, David M.","contributorId":175098,"corporation":false,"usgs":false,"family":"Nelson","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":13479,"text":"University of Maryland Center for Environmental Science, Appalachian Laboratory,  301 Braddock Road, Frostburg, Maryland","active":true,"usgs":false}],"preferred":false,"id":649962,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Braham, Melissa A.","contributorId":140127,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa A.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":649963,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Doyle, Jacqueline M.","contributorId":175099,"corporation":false,"usgs":false,"family":"Doyle","given":"Jacqueline","email":"","middleInitial":"M.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":649964,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fernandez, Nadia B.","contributorId":175100,"corporation":false,"usgs":false,"family":"Fernandez","given":"Nadia","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":649965,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duerr, Adam E.","contributorId":102324,"corporation":false,"usgs":true,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":649966,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bloom, Peter H.","contributorId":42829,"corporation":false,"usgs":true,"family":"Bloom","given":"Peter H.","affiliations":[],"preferred":false,"id":649967,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fitzpatrick, Matthew C.","contributorId":53299,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":649968,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Miller, Tricia A.","contributorId":64790,"corporation":false,"usgs":true,"family":"Miller","given":"Tricia A.","affiliations":[],"preferred":false,"id":649969,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Culver, Renee C. E.","contributorId":175101,"corporation":false,"usgs":false,"family":"Culver","given":"Renee","email":"","middleInitial":"C. E.","affiliations":[],"preferred":false,"id":649970,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Braswell, Loan","contributorId":175102,"corporation":false,"usgs":false,"family":"Braswell","given":"Loan","email":"","affiliations":[],"preferred":false,"id":649971,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"DeWoody, J. Andrew","contributorId":175103,"corporation":false,"usgs":false,"family":"DeWoody","given":"J.","email":"","middleInitial":"Andrew","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":649972,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70178522,"text":"70178522 - 2017 - Disentangling density-dependent dynamics using full annual cycle models and Bayesian model weight updating","interactions":[],"lastModifiedDate":"2017-03-22T14:56:18","indexId":"70178522","displayToPublicDate":"2016-09-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Disentangling density-dependent dynamics using full annual cycle models and Bayesian model weight updating","docAbstract":"<ol id=\"jpe12761-list-0001\" class=\"o-list--numbered o-list--paragraph\"><li>Density dependence regulates populations of many species across all taxonomic groups. Understanding density dependence is vital for predicting the effects of climate, habitat loss and/or management actions on wild populations. Migratory species likely experience seasonal changes in the relative influence of density dependence on population processes such as survival and recruitment throughout the annual cycle. These effects must be accounted for when characterizing migratory populations via population models.</li><li>To evaluate effects of density on seasonal survival and recruitment of a migratory species, we used an existing full annual cycle model framework for American black ducks <i>Anas rubripes</i>, and tested different density effects (including no effects) on survival and recruitment. We then used a Bayesian model weight updating routine to determine which population model best fit observed breeding population survey data between 1990 and 2014.</li><li>The models that best fit the survey data suggested that survival and recruitment were affected by density dependence and that density effects were stronger on adult survival during the breeding season than during the non-breeding season.</li><li>Analysis also suggests that regulation of survival and recruitment by density varied over time. Our results showed that different characterizations of density regulations changed every 8–12&nbsp;years (three times in the 25-year period) for our population.</li><li><i>Synthesis and applications</i>. Using a full annual cycle, modelling framework and model weighting routine will be helpful in evaluating density dependence for migratory species in both the short and long term. We used this method to disentangle the seasonal effects of density on the continental American black duck population which will allow managers to better evaluate the effects of habitat loss and potential habitat management actions throughout the annual cycle. The method here may allow researchers to hone in on the proper form and/or strength of density dependence for use in models for conservation recommendations.</li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.12761","usgsCitation":"Robinson, O.J., McGowan, C.P., and Devers, P.K., 2017, Disentangling density-dependent dynamics using full annual cycle models and Bayesian model weight updating: Journal of Applied Ecology, v. 54, no. 2, p. 670-678, https://doi.org/10.1111/1365-2664.12761.","productDescription":"9 p.","startPage":"670","endPage":"678","ipdsId":"IP-073168","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":470222,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.12761","text":"Publisher Index Page"},{"id":331202,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-26","publicationStatus":"PW","scienceBaseUri":"5835672ae4b0070c0abfb6d6","contributors":{"authors":[{"text":"Robinson, Orin J.","contributorId":167172,"corporation":false,"usgs":false,"family":"Robinson","given":"Orin","email":"","middleInitial":"J.","affiliations":[{"id":33694,"text":"School of Forestry and Wildlife Sciences, Auburn University","active":true,"usgs":false}],"preferred":false,"id":654244,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":654225,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Devers, Patrick K.","contributorId":167173,"corporation":false,"usgs":false,"family":"Devers","given":"Patrick","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":654245,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70176524,"text":"70176524 - 2017 - Sea-level rise and coastal groundwater inundation and shoaling at select sites in California, USA","interactions":[],"lastModifiedDate":"2017-07-25T12:50:36","indexId":"70176524","displayToPublicDate":"2016-09-20T16:20:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Sea-level rise and coastal groundwater inundation and shoaling at select sites in California, USA","docAbstract":"<h4 id=\"absSec_1\">Study region</h4><p id=\"spar0065\">The study region spans coastal California, USA, and focuses on three primary sites: Arcata, Stinson Beach, and Malibu Lagoon.</p><h4 id=\"absSec_2\">Study focus</h4><p id=\"spar0070\">1&nbsp;m and 2&nbsp;m sea-level rise (SLR) projections were used to assess vulnerability to SLR-driven groundwater emergence and shoaling at select low-lying, coastal sites in California. Separate and combined inundation scenarios for SLR and groundwater emergence were developed using digital elevation models of study site topography and groundwater surfaces constructed from well data or published groundwater level contours.</p><h4 id=\"absSec_3\">New hydrological insights for the region</h4><p id=\"spar0075\">SLR impacts are a serious concern in coastal California which has a long (∼1800&nbsp;km) and populous coastline. Information on the possible importance of SLR-driven groundwater inundation in California is limited. In this study, the potential for SLR-driven groundwater inundation at three sites (Arcata, Stinson Beach, and Malibu Lagoon) was investigated under 1&nbsp;m and 2&nbsp;m SLR scenarios. These sites provide insight into the vulnerability of Northern California coastal plains, coastal developments built on beach sand or sand spits, and developed areas around coastal lagoons associated with seasonal streams and berms. Northern California coastal plains with abundant shallow groundwater likely will see significant and widespread groundwater emergence, while impacts along the much drier central and southern California coast may be less severe due to the absence of shallow groundwater in many areas. Vulnerability analysis is hampered by the lack of data on shallow coastal aquifers, which commonly are not studied because they are not suitable for domestic or agricultural use. Shallow saline aquifers may be present in many areas along coastal California, which would dramatically increase vulnerability to SLR-driven groundwater emergence and shoaling. Improved understanding of the extent and response of California coastal aquifers to SLR will help in preparing for mitigation and adaptation.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2015.12.055","usgsCitation":"Hoover, D.J., Odigie, K., Swarzenski, P.W., and Barnard, P., 2017, Sea-level rise and coastal groundwater inundation and shoaling at select sites in California, USA: Journal of Hydrology: Regional Studies, v. 11, p. 234-249, https://doi.org/10.1016/j.ejrh.2015.12.055.","productDescription":"16 p.","startPage":"234","endPage":"249","ipdsId":"IP-068144","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470225,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2015.12.055","text":"Publisher Index Page"},{"id":328775,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Center","active":true,"usgs":true}],"preferred":true,"id":649097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swarzenski, Peter W. 0000-0003-0116-0578 pswarzen@usgs.gov","orcid":"https://orcid.org/0000-0003-0116-0578","contributorId":1070,"corporation":false,"usgs":true,"family":"Swarzenski","given":"Peter","email":"pswarzen@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":649095,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":649098,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70176589,"text":"70176589 - 2017 - Lineage diversification of fringe-toed lizards (Phrynosomatidae: <i>Uma notata</i> complex) in the Colorado Desert: Delimiting species in the presence of gene flow","interactions":[],"lastModifiedDate":"2016-11-10T09:12:39","indexId":"70176589","displayToPublicDate":"2016-09-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2779,"text":"Molecular Phylogenetics and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Lineage diversification of fringe-toed lizards (Phrynosomatidae: <i>Uma notata</i> complex) in the Colorado Desert: Delimiting species in the presence of gene flow","docAbstract":"<p><span>Multi-locus nuclear DNA data were used to delimit species of fringe-toed lizards of the</span><i>Uma notata</i><span> complex, which are specialized for living in wind-blown sand habitats in the deserts of southwestern North America, and to infer whether Quaternary glacial cycles or Tertiary geological events were important in shaping the historical biogeography of this group. We analyzed ten nuclear loci collected using Sanger sequencing and genome-wide sequence and single-nucleotide polymorphism (SNP) data collected using restriction-associated DNA (RAD) sequencing. A combination of species discovery methods (concatenated phylogenies, parametric and non-parametric clustering algorithms) and species validation approaches (coalescent-based species tree/isolation-with-migration models) were used to delimit species, infer phylogenetic relationships, and to estimate effective population sizes, migration rates, and speciation times. </span><i>Uma notata</i><span>, </span><i>U. inornata</i><span>, </span><i>U. cowlesi</i><span>, and an undescribed species from Mohawk Dunes, Arizona (</span><i>U.</i><span> sp.) were supported as distinct in the concatenated analyses and by clustering algorithms, and all operational taxonomic units were decisively supported as distinct species by ranking hierarchical nested speciation models with Bayes factors based on coalescent-based species tree methods. However, significant unidirectional gene flow (2</span><i>NM</i><span> &gt;1) from </span><i>U. cowlesi</i><span> and </span><i>U. notata</i><span> into </span><i>U. rufopunctata</i><span> was detected under the isolation-with-migration model. Therefore, we conservatively delimit four species-level lineages within this complex (</span><i>U. inornata</i><span>, </span><i>U. notata</i><span>, </span><i>U. cowlesi</i><span>, and </span><i>U.</i><span> sp.), treating </span><i>U. rufopunctata</i><span> as a hybrid population (</span><i>U. notata x cowlesi</i><span>). Both concatenated and coalescent-based estimates of speciation times support the hypotheses that speciation within the complex occurred during the late Pleistocene, and that the geological evolution of the Colorado River delta during this period was an important process shaping the observed phylogeographic patterns.</span></p>","language":"English","publisher":"Academic Press","publisherLocation":"Orlando, FL","doi":"10.1016/j.ympev.2016.09.008","usgsCitation":"Gottscho, A.D., Wood, D.A., Vandergast, A.G., Lemos Espinal, J.A., Gatesy, J., and Reeder, T., 2017, Lineage diversification of fringe-toed lizards (Phrynosomatidae: <i>Uma notata</i> complex) in the Colorado Desert: Delimiting species in the presence of gene flow: Molecular Phylogenetics and Evolution, v. 106, p. 103-117, https://doi.org/10.1016/j.ympev.2016.09.008.","productDescription":"15 p.","startPage":"103","endPage":"117","ipdsId":"IP-073166","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470227,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ympev.2016.09.008","text":"Publisher Index Page"},{"id":328848,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.88281249999999,\n              30.097613277217132\n            ],\n            [\n              -119.88281249999999,\n              36.87962060502676\n            ],\n            [\n              -110.8740234375,\n              36.87962060502676\n            ],\n            [\n              -110.8740234375,\n              30.097613277217132\n            ],\n            [\n              -119.88281249999999,\n              30.097613277217132\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"106","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c63ce4b0bc0bec09c868","contributors":{"authors":[{"text":"Gottscho, Andrew D.","contributorId":49284,"corporation":false,"usgs":true,"family":"Gottscho","given":"Andrew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":649285,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":649286,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandergast, Amy G. 0000-0002-7835-6571 avandergast@usgs.gov","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":3963,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"avandergast@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":649287,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lemos Espinal, Julio A.","contributorId":167616,"corporation":false,"usgs":false,"family":"Lemos Espinal","given":"Julio","email":"","middleInitial":"A.","affiliations":[{"id":24782,"text":"Avenida De Los Barrios No. 1, Col. Los Reyes Iztacala, Tlalnepantla, Edo. De Mexico, 54090 Mexico","active":true,"usgs":false}],"preferred":false,"id":649288,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gatesy, John","contributorId":174796,"corporation":false,"usgs":false,"family":"Gatesy","given":"John","email":"","affiliations":[],"preferred":false,"id":649289,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reeder, Tod","contributorId":169070,"corporation":false,"usgs":false,"family":"Reeder","given":"Tod","affiliations":[{"id":25405,"text":"San Diego State U.","active":true,"usgs":false}],"preferred":false,"id":649290,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70176437,"text":"70176437 - 2017 - Custom map projections for regional groundwater models","interactions":[],"lastModifiedDate":"2017-03-22T15:07:24","indexId":"70176437","displayToPublicDate":"2016-09-14T12:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Custom map projections for regional groundwater models","docAbstract":"<p><span>For regional groundwater flow models (areas greater than 100,000 km</span><sup>2</sup><span>), improper choice of map projection parameters can result in model error for boundary conditions dependent on area (recharge or evapotranspiration simulated by application of a rate using cell area from model discretization) and length (rivers simulated with head-dependent flux boundary). Smaller model areas can use local map coordinates, such as State Plane (United States) or Universal Transverse Mercator (correct zone) without introducing large errors. Map projections vary in order to preserve one or more of the following properties: area, shape, distance (length), or direction. Numerous map projections are developed for different purposes as all four properties cannot be preserved simultaneously. Preservation of area and length are most critical for groundwater models. The Albers equal-area conic projection with custom standard parallels, selected by dividing the length north to south by 6 and selecting standard parallels 1/6th above or below the southern and northern extent, preserves both area and length for continental areas in mid latitudes oriented east-west. Custom map projection parameters can also minimize area and length error in non-ideal projections. Additionally, one must also use consistent vertical and horizontal datums for all geographic data. The generalized polygon for the Floridan aquifer system study area (306,247.59 km</span><sup>2</sup><span>) is used to provide quantitative examples of the effect of map projections on length and area with different projections and parameter choices. Use of improper map projection is one model construction problem easily avoided.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12450","usgsCitation":"Kuniansky, E.L., 2017, Custom map projections for regional groundwater models: Groundwater, v. 55, no. 2, p. 255-260, https://doi.org/10.1111/gwat.12450.","productDescription":"6 p.","startPage":"255","endPage":"260","ipdsId":"IP-071775","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":470228,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.12450","text":"Publisher Index Page"},{"id":328638,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-09","publicationStatus":"PW","scienceBaseUri":"57da66a2e4b090824ffb1646","contributors":{"authors":[{"text":"Kuniansky, Eve L. 0000-0002-5581-0225 elkunian@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-0225","contributorId":932,"corporation":false,"usgs":true,"family":"Kuniansky","given":"Eve","email":"elkunian@usgs.gov","middleInitial":"L.","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"preferred":true,"id":648761,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70176315,"text":"70176315 - 2017 - Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates","interactions":[],"lastModifiedDate":"2017-02-24T11:04:33","indexId":"70176315","displayToPublicDate":"2016-09-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates","docAbstract":"<p><span>How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (</span><i>Ochotona princeps</i><span>) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5&nbsp;°C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect: climate-imposed restrictions on activity. This more complete understanding is necessary to inform climate adaptation actions, management strategies, and conservation plans.</span></p>","language":"English","publisher":"Blackwell Science","doi":"10.1111/gcb.13454","usgsCitation":"Mathewson, P., Moyer-Horner, L., Beever, E., Briscoe, N., Kearney, M.T., Yahn, J., and Porter, W.P., 2017, Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates: Global Change Biology, v. 23, no. 3, p. 1048-1064, https://doi.org/10.1111/gcb.13454.","productDescription":"17 p.","startPage":"1048","endPage":"1064","ipdsId":"IP-075458","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":470231,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarworks.montana.edu/xmlui/handle/1/14639","text":"External Repository"},{"id":328347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.053249,41.001406],[-102.124972,41.002338],[-102.051292,40.749591],[-102.04192,37.035083],[-102.979613,36.998549],[-103.002247,36.911587],[-103.064423,32.000518],[-106.565142,32.000736],[-106.577244,31.810406],[-106.750547,31.783706],[-108.208394,31.783599],[-108.208573,31.333395],[-111.000643,31.332177],[-114.813613,32.494277],[-114.722746,32.713071],[-117.118868,32.534706],[-117.50565,33.334063],[-118.088896,33.729817],[-118.428407,33.774715],[-118.519514,34.027509],[-119.159554,34.119653],[-119.616862,34.420995],[-120.441975,34.451512],[-120.608355,34.556656],[-120.644311,35.139616],[-120.873046,35.225688],[-120.884757,35.430196],[-121.851967,36.277831],[-121.932508,36.559935],[-121.788278,36.803994],[-121.880167,36.950151],[-122.140578,36.97495],[-122.419113,37.24147],[-122.511983,37.77113],[-122.425942,37.810979],[-122.168449,37.504143],[-122.144396,37.581866],[-122.385908,37.908136],[-122.301804,38.105142],[-122.484411,38.11496],[-122.492474,37.82484],[-122.972378,38.020247],[-123.103706,38.415541],[-123.725367,38.917438],[-123.851714,39.832041],[-124.373599,40.392923],[-124.063076,41.439579],[-124.536073,42.814175],[-124.150267,43.91085],[-123.962887,45.280218],[-123.996766,46.20399],[-123.548194,46.248245],[-124.029924,46.308312],[-124.06842,46.601397],[-123.97083,46.47537],[-123.84621,46.716795],[-124.022413,46.708973],[-124.108078,46.836388],[-123.86018,46.948556],[-124.138035,46.970959],[-124.425195,47.738434],[-124.672427,47.964414],[-124.727022,48.371101],[-123.981032,48.164761],[-122.748911,48.117026],[-122.637425,47.889945],[-123.15598,47.355745],[-122.527593,47.905882],[-122.578211,47.254804],[-122.725738,47.33047],[-122.691771,47.141958],[-122.796646,47.341654],[-122.863732,47.270221],[-122.67813,47.103866],[-122.364168,47.335953],[-122.429841,47.658919],[-122.230046,47.970917],[-122.425572,48.232887],[-122.358375,48.056133],[-122.512031,48.133931],[-122.424102,48.334346],[-122.689121,48.476849],[-122.425271,48.599522],[-122.796887,48.975026],[-104.048736,48.999877],[-104.053249,41.001406]]],[[[-119.789798,34.05726],[-119.5667,34.053452],[-119.795938,33.962929],[-119.916216,34.058351],[-119.789798,34.05726]]],[[[-118.524531,32.895488],[-118.573522,32.969183],[-118.369984,32.839273],[-118.524531,32.895488]]],[[[-118.500212,33.449592],[-118.32446,33.348782],[-118.593969,33.467198],[-118.500212,33.449592]]],[[[-122.519535,48.288314],[-122.66921,48.240614],[-122.400628,48.036563],[-122.419274,47.912125],[-122.744612,48.20965],[-122.664928,48.374823],[-122.519535,48.288314]]],[[[-122.800217,48.60169],[-122.883759,48.418793],[-123.173061,48.579086],[-122.949116,48.693398],[-122.743049,48.661991],[-122.800217,48.60169]]]]},\"properties\":{\"name\":\"Arizona\",\"nation\":\"USA  \"}}]}","volume":"23","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-04","publicationStatus":"PW","scienceBaseUri":"57d27d9fe4b0571647d0d8fa","contributors":{"authors":[{"text":"Mathewson, Paul","contributorId":174452,"corporation":false,"usgs":false,"family":"Mathewson","given":"Paul","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":648301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moyer-Horner, Lucas","contributorId":174453,"corporation":false,"usgs":false,"family":"Moyer-Horner","given":"Lucas","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":648302,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beever, Erik A. 0000-0002-9369-486X ebeever@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-486X","contributorId":147685,"corporation":false,"usgs":true,"family":"Beever","given":"Erik A.","email":"ebeever@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"preferred":true,"id":648300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briscoe, Natalie","contributorId":174454,"corporation":false,"usgs":false,"family":"Briscoe","given":"Natalie","email":"","affiliations":[{"id":13336,"text":"University of Melbourne","active":true,"usgs":false}],"preferred":false,"id":648303,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kearney, Michael T.","contributorId":149971,"corporation":false,"usgs":false,"family":"Kearney","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":17867,"text":"Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, Louisiana, USA 70803","active":true,"usgs":false}],"preferred":false,"id":648304,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yahn, Jeremiah","contributorId":174455,"corporation":false,"usgs":false,"family":"Yahn","given":"Jeremiah","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":648305,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Porter, Warren P.","contributorId":87281,"corporation":false,"usgs":true,"family":"Porter","given":"Warren","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":648306,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70181004,"text":"70181004 - 2017 - Evaluation of physicochemical and physical habitat associations for Cambarus callainus (Big Sandy crayfish), an imperilled crayfish endemic to the Central Appalachians","interactions":[],"lastModifiedDate":"2018-03-16T15:29:22","indexId":"70181004","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":862,"text":"Aquatic Conservation: Marine and Freshwater Ecosystems","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evaluation of physicochemical and physical habitat associations for <i>Cambarus callainus</i> (Big Sandy crayfish), an imperilled crayfish endemic to the Central Appalachians","title":"Evaluation of physicochemical and physical habitat associations for Cambarus callainus (Big Sandy crayfish), an imperilled crayfish endemic to the Central Appalachians","docAbstract":"<p>1. Crayfish represent one of the most imperilled animal groups on the planet. Habitat degradation, destruction and fragmentation, introduction of invasive crayfishes, and a lack of applied biological information have all been identified as agents thwarting crayfish conservation.</p><p>2.<span>&nbsp;</span><i>Cambarus callainus</i><span>&nbsp;</span>was warranted federal protection by the United States Fish and Wildlife Service (USFWS) in April, 2016. As part of the USFWS listing procedure, a survey for<span>&nbsp;</span><i>C. callainus</i><span>&nbsp;</span>in the Big Sandy River catchment was conducted to determine points of occurrence with a secondary objective of determining reach level physical habitat and physicochemical correlates of<span>&nbsp;</span><i>C. callainus</i><span>&nbsp;</span>presence and absence.</p><p>3. At each site, physicochemical and physical habitat data were collected to determine the influence of abiotic covariates on the presence of<span>&nbsp;</span><i>C. callainus</i>.<span>&nbsp;</span><i>Cambarus callainus</i><span>&nbsp;</span>presence or absence and associated site covariates were modelled using logistic regression.</p><p>4. Survey results recorded<span>&nbsp;</span><i>C. callainus</i><span>&nbsp;</span>at 39 sites in the Upper Levisa Fork (ULF) and Tug Fork (TF) drainages of the Big Sandy River; no<span>&nbsp;</span><i>C. callainus</i><span>&nbsp;</span>were collected in the Lower Levisa Fork (LLF). An additive effects model of physical habitat quality (Basin + Boulder presence/embeddedness) was the only model selected, supporting an association of<span>&nbsp;</span><i>C. callainus</i><span>&nbsp;</span>with slab boulders, open interstitial spaces, and moderate to no sedimentation. All sites lacking<span>&nbsp;</span><i>C. callainus</i><span>&nbsp;</span>were experiencing some degree of sedimentation. Physicochemical covariates were not supported by the data.</p><p>5. Results indicated that good quality habitat was lacking in the LLF, but was present in the ULF and TF catchments, with ULF supporting the most robust populations and most suitable habitat. Effective conservation for<span>&nbsp;</span><i>C. callainus</i><span>&nbsp;</span>should focus on efforts that limit sedimentation as well as restore good quality instream habitat in the greater Big Sandy catchment.</p>","language":"English","publisher":"Wiley","doi":"10.1002/aqc.2746","usgsCitation":"Loughman, Z.J., Welsh, S., Sadecky, N., Dillard, Z.W., and Scott, R.K., 2017, Evaluation of physicochemical and physical habitat associations for Cambarus callainus (Big Sandy crayfish), an imperilled crayfish endemic to the Central Appalachians: Aquatic Conservation: Marine and Freshwater Ecosystems, v. 27, no. 4, p. 755-763, https://doi.org/10.1002/aqc.2746.","productDescription":"9 p.","startPage":"755","endPage":"763","ipdsId":"IP-078756","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":335188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky, Virginia, West Virginia","otherGeospatial":"Big Sandy river catchment, Levisa Fork river, Tug Fork river ","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -82.6007080078125,\n              37.223767535823576\n            ],\n            [\n              -81.5350341796875,\n              37.223767535823576\n            ],\n            [\n              -81.5350341796875,\n              37.81195385919268\n            ],\n            [\n              -82.6007080078125,\n              37.81195385919268\n            ],\n            [\n              -82.6007080078125,\n              37.223767535823576\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"27","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-16","publicationStatus":"PW","scienceBaseUri":"589fff3be4b099f50d3e0455","contributors":{"authors":[{"text":"Loughman, Zachary J.","contributorId":76157,"corporation":false,"usgs":false,"family":"Loughman","given":"Zachary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":663411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":663162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadecky, Nicole M.","contributorId":179375,"corporation":false,"usgs":false,"family":"Sadecky","given":"Nicole M.","affiliations":[],"preferred":false,"id":663412,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dillard, Zachary W.","contributorId":179376,"corporation":false,"usgs":false,"family":"Dillard","given":"Zachary","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":663413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scott, R. Katie","contributorId":179377,"corporation":false,"usgs":false,"family":"Scott","given":"R.","email":"","middleInitial":"Katie","affiliations":[],"preferred":false,"id":663414,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70182775,"text":"70182775 - 2017 - Biogeography of boreal passerine range dynamics in western North America: past, present, and future","interactions":[],"lastModifiedDate":"2017-09-05T12:59:52","indexId":"70182775","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1446,"text":"Ecography: Pattern and Diversity in Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Biogeography of boreal passerine range dynamics in western North America: past, present, and future","docAbstract":"<p><span>Many of the Neotropical migrant bird species that breed throughout the Canadian boreal region are not found in the Alaskan boreal region, separated by the northwestern cordilleran mountains, despite the presence of climatically suitable habitat. We asked whether biological or climatic factors constrain certain species from crossing this geographic barrier. Analyzing a comprehensive dataset for 80 boreal passerine species, we used phylogenetic logistic regression to evaluate the relative importance of physical, migratory and competition metrics versus current and paleoclimatic suitability factors. Controlling for current climatic suitability within boreal Alaska, we found that species with the greatest climatic suitability across the northwestern cordillera, presently and also during the mid-Holocene period, were most likely to be regular breeders in the Alaskan boreal region. Migratory strategy also played a role, but could not be disentangled from its strong phylogenetic basis. Our analysis suggests that the perceived barrier of the northwestern cordillera may be easily weakened as climate change improves conditions there for many forest species. The weakening of this barrier may lead to relatively rapid range expansions and the reshuffling of species communities. Species’ realized distributional shifts will be a function of the interplay between a changing climate and static topographic features.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/ecog.02393","usgsCitation":"Stralberg, D., Matsuoka, S.M., Handel, C.M., Schmiegelow, F.K., Hamann, A., and Bayne, E.M., 2017, Biogeography of boreal passerine range dynamics in western North America: past, present, and future: Ecography: Pattern and Diversity in Ecology, v. 40, no. 9, p. 1050-1066, https://doi.org/10.1111/ecog.02393.","productDescription":"17 p.","startPage":"1050","endPage":"1066","ipdsId":"IP-076123","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":336723,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"9","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-26","publicationStatus":"PW","scienceBaseUri":"58b7eba7e4b01ccd5500bb0d","contributors":{"authors":[{"text":"Stralberg, Diana","contributorId":187413,"corporation":false,"usgs":false,"family":"Stralberg","given":"Diana","email":"","affiliations":[],"preferred":false,"id":680372,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matsuoka, Steven M. 0000-0001-6415-1885 smatsuoka@usgs.gov","orcid":"https://orcid.org/0000-0001-6415-1885","contributorId":184173,"corporation":false,"usgs":true,"family":"Matsuoka","given":"Steven","email":"smatsuoka@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":673712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Handel, Colleen M. 0000-0002-0267-7408 cmhandel@usgs.gov","orcid":"https://orcid.org/0000-0002-0267-7408","contributorId":3067,"corporation":false,"usgs":true,"family":"Handel","given":"Colleen","email":"cmhandel@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":673711,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmiegelow, Fiona K.A.","contributorId":187414,"corporation":false,"usgs":false,"family":"Schmiegelow","given":"Fiona","email":"","middleInitial":"K.A.","affiliations":[],"preferred":false,"id":680373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hamann, Andreas","contributorId":187415,"corporation":false,"usgs":false,"family":"Hamann","given":"Andreas","email":"","affiliations":[],"preferred":false,"id":680374,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bayne, Erin M.","contributorId":140675,"corporation":false,"usgs":false,"family":"Bayne","given":"Erin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":680375,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70175520,"text":"70175520 - 2017 - Evaluating alternative methods for biophysical and cultural ecosystem services hotspot mapping in natural resource planning","interactions":[],"lastModifiedDate":"2017-01-19T14:11:04","indexId":"70175520","displayToPublicDate":"2016-08-16T11:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating alternative methods for biophysical and cultural ecosystem services hotspot mapping in natural resource planning","docAbstract":"<p>Context</p>\n<p>Data for biophysically modeled and Public Participatory GIS (PPGIS)-derived cultural ecosystem services have potential to identify natural resource management synergies and conflicts, but have rarely been combined. Ecosystem service hot/coldspots generated using different methods vary in their spatial extent and connectivity, with important implications.</p>\n<p>Objectives</p>\n<p>We map biophysically modeled and PPGIS-derived cultural services for six U.S. national forests using six hot/coldspot delineation methods. We evaluate the implications of hotspot methods for management within and outside of designated wilderness areas.</p>\n<p>Methods</p>\n<p>We used the ARIES and SolVES modeling tools to quantify four biophysically modeled and 11 largely cultural ecosystem services for six national forests in Colorado and Wyoming, USA. We mapped hot/coldspots using two quantile methods (top and bottom 10 and 33 % of values), two area-based methods (top and bottom 10 and 33 % of area), and two statistical methods (Getis-Ord Gi* at &alpha; = 0.05 and 0.10 significance level) and compare results within and outside wilderness areas.</p>\n<p>Results</p>\n<p>Delineation methods vary in their degree of conservatism for hot/coldspot extents and spatial clustering. Hotspots were more common in wilderness areas in national forests near the more densely populated Colorado Front Range, while coldspots were more common in wilderness areas in more urban-distant forests in northwest Wyoming.</p>\n<p>Conclusions</p>\n<p>Statistical hotspot methods of intermediate conservatism (i.e., Getis-Ord Gi*, &alpha; = 0.10 significance) may be most useful for ecosystem service hot/coldspot mapping to inform landscape scale planning. We also found spatially explicit evidence in support of past findings about public attitudes toward wilderness areas.</p>","language":"English","publisher":"International Association of Landscape Ecology","doi":"10.1007/s10980-016-0430-6","usgsCitation":"Bagstad, K.J., Semmens, D.J., Ancona, Z.H., and Sherrouse, B.C., 2017, Evaluating alternative methods for biophysical and cultural ecosystem services hotspot mapping in natural resource planning: Landscape Ecology, v. 32, no. 1, p. 77-97, https://doi.org/10.1007/s10980-016-0430-6.","productDescription":"21 p.","startPage":"77","endPage":"97","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065847","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":326546,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-11","publicationStatus":"PW","scienceBaseUri":"57b42b30e4b03bcb01039f8c","chorus":{"doi":"10.1007/s10980-016-0430-6","url":"http://dx.doi.org/10.1007/s10980-016-0430-6","publisher":"Springer Nature","authors":"Bagstad Kenneth J., Semmens Darius J., Ancona Zachary H., Sherrouse Benson C.","journalName":"Landscape Ecology","publicationDate":"8/11/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"8/11/2016"},"contributors":{"authors":[{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":645553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Semmens, Darius J. 0000-0001-7924-6529 dsemmens@usgs.gov","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":1714,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius","email":"dsemmens@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":645554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":645555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherrouse, Benson C. 0000-0002-5102-5895 bcsherrouse@usgs.gov","orcid":"https://orcid.org/0000-0002-5102-5895","contributorId":2445,"corporation":false,"usgs":true,"family":"Sherrouse","given":"Benson","email":"bcsherrouse@usgs.gov","middleInitial":"C.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":645556,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70175488,"text":"70175488 - 2017 - Habitat drives dispersal and survival of translocated juvenile desert tortoises","interactions":[],"lastModifiedDate":"2017-03-22T15:08:53","indexId":"70175488","displayToPublicDate":"2016-08-15T14:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Habitat drives dispersal and survival of translocated juvenile desert tortoises","docAbstract":"<p>1.In spite of growing reliance on translocations in wildlife conservation, translocation efficacy remains inconsistent. One factor that can contribute to failed translocations is releasing animals into poor quality or otherwise inadequate habitat.</p>\n<p>2.Here we used a targeted approach to test the relationship of habitat features to post-translocation dispersal and survival of juvenile Mojave desert tortoises&nbsp;<i>Gopherus agassizii</i>.</p>\n<p>3.We selected three habitat characteristics&mdash;rodent burrows, substrate texture (prevalence and size of rocks), and washes (ephemeral river beds)&ndash;that are tied to desert tortoise ecology. At the point of release, we documented rodent burrow abundance, substrate texture, and wash presence and analysed their relationship to maximum dispersal. We also documented relative use by each individual for each habitat characteristic and analysed their relationships with survival and fatal encounters with a predator in the first year after release.</p>\n<p>4.In general, the presence of refugia or other areas that enabled animals to avoid detection, such as burrows and substrate, decreased overall mortality as well as predator-mediated mortality. The presence of washes and substrate that enhanced the tortoises&rsquo; ability to avoid detection also associated with reduced dispersal away from the release site. These results indicate an important role for all three measured habitat characteristics in driving dispersal, survival, or fatal encounters with a predator in the first year after translocation.</p>\n<p>5.<i>Synthesis and applications</i>. Resource managers using translocations as a conservation tool should prioritize acquiring data linking habitat to fitness. In particular, for species that depend on avoiding detection, refuges such as burrows and habitat that improved concealment had notable ability to improve survival and dispersal. Our study on juvenile Mojave desert tortoises showed that refuge availability or the distributions of habitat appropriate for concealment are important considerations for identifying translocation sites for species highly dependent on crypsis, camouflage, or other forms of habitat matching.</p>","language":"English","publisher":"Wiley","doi":"10.1111/1365-2664.12774","usgsCitation":"Nafus, A., Esque, T., Averill-Murray, R., Nussear, K.E., and Swaisgood, R.R., 2017, Habitat drives dispersal and survival of translocated juvenile desert tortoises: Journal of Applied Ecology, v. 54, no. 2, p. 430-438, https://doi.org/10.1111/1365-2664.12774.","productDescription":"9 p.","startPage":"430","endPage":"438","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-077837","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470237,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.12774","text":"Publisher Index Page"},{"id":326485,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-19","publicationStatus":"PW","scienceBaseUri":"57b2d9a7e4b03bcb010287bc","contributors":{"authors":[{"text":"Nafus, Aleta","contributorId":167781,"corporation":false,"usgs":false,"family":"Nafus","given":"Aleta","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":true,"id":645443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esque, Todd C. 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":168763,"corporation":false,"usgs":true,"family":"Esque","given":"Todd C.","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":645442,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Averill-Murray, Roy C.","contributorId":173687,"corporation":false,"usgs":false,"family":"Averill-Murray","given":"Roy C.","affiliations":[{"id":27274,"text":"US Fish and Wildlife Service, Desert Tortoise Recovery Office, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":645444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nussear, Kenneth E. knussear@usgs.gov","contributorId":2695,"corporation":false,"usgs":true,"family":"Nussear","given":"Kenneth","email":"knussear@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":645445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swaisgood, Ronald R.","contributorId":69490,"corporation":false,"usgs":false,"family":"Swaisgood","given":"Ronald","email":"","middleInitial":"R.","affiliations":[{"id":12762,"text":"San Diego Zoo Institure for Conservation Research","active":true,"usgs":false}],"preferred":false,"id":645446,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70175425,"text":"70175425 - 2017 - A modeling approach to compare ΣPCB concentrations between congener-specific analyses","interactions":[],"lastModifiedDate":"2018-08-09T12:10:41","indexId":"70175425","displayToPublicDate":"2016-08-09T05:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2006,"text":"Integrated Environmental Assessment and Management","active":true,"publicationSubtype":{"id":10}},"title":"A modeling approach to compare ΣPCB concentrations between congener-specific analyses","docAbstract":"<p><span>Changes in analytical methods over time pose problems for assessing long-term trends in environmental contamination by polychlorinated biphenyls (PCBs). Congener-specific analyses vary widely in the number and identity of the 209 distinct PCB chemical configurations (congeners) that are quantified, leading to inconsistencies among summed PCB concentrations (&Sigma;PCB) reported by different studies. Here we present a modeling approach using linear regression to compare &Sigma;PCB concentrations derived from different congener-specific analyses measuring different co-eluting groups. The approach can be used to develop a specific conversion model between any two sets of congener-specific analytical data from similar samples (similar matrix and geographic origin). We demonstrate the method by developing a conversion model for an example data set that includes data from two different analytical methods, a low resolution method quantifying 119 congeners and a high resolution method quantifying all 209 congeners. We used the model to show that the 119-congener set captured most (93%) of the total PCB concentration (i.e., &Sigma;</span><sub><span>209</span></sub><span>PCB) in sediment and biological samples. &Sigma;PCB concentrations estimated using the model closely matched measured values (mean relative percent difference&thinsp;=&thinsp;9.6). General applications of the modeling approach include (a) generating comparable &Sigma;PCB concentrations for samples that were analyzed for different congener sets; and (b) estimating the proportional contribution of different congener sets to &Sigma;PCB. This approach may be especially valuable for enabling comparison of long-term remediation monitoring results even as analytical methods change over time.&nbsp;</span></p>","language":"English","publisher":"SETAC","doi":"10.1002/ieam.1821","usgsCitation":"Gibson, P., Mills, M.A., Kraus, J.M., and Walters, D., 2017, A modeling approach to compare ΣPCB concentrations between congener-specific analyses: Integrated Environmental Assessment and Management, v. 13, no. 2, p. 227-232, https://doi.org/10.1002/ieam.1821.","productDescription":"6 p.","startPage":"227","endPage":"232","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071506","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":326369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-18","publicationStatus":"PW","scienceBaseUri":"57ac5033e4b0d1835674a983","contributors":{"authors":[{"text":"Gibson, Polly P.","contributorId":173584,"corporation":false,"usgs":true,"family":"Gibson","given":"Polly P.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":645142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mills, Marc A.","contributorId":141085,"corporation":false,"usgs":false,"family":"Mills","given":"Marc","email":"","middleInitial":"A.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":645143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":645139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, David M. 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":4444,"corporation":false,"usgs":true,"family":"Walters","given":"David M.","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":645144,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70162581,"text":"70162581 - 2017 - Shapes on a plane: Evaluating the impact of projection distortion on spatial binning","interactions":[],"lastModifiedDate":"2017-07-24T09:50:40","indexId":"70162581","displayToPublicDate":"2016-06-30T12:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1191,"text":"Cartography and Geographic Information Science","active":true,"publicationSubtype":{"id":10}},"title":"Shapes on a plane: Evaluating the impact of projection distortion on spatial binning","docAbstract":"<p><span>One method for working with large, dense sets of spatial point data is to aggregate the measure of the data into polygonal containers, such as political boundaries, or into regular spatial bins such as triangles, squares, or hexagons. When mapping these aggregations, the map projection must inevitably distort relationships. This distortion can impact the reader&rsquo;s ability to compare count and density measures across the map. Spatial binning, particularly via hexagons, is becoming a popular technique for displaying aggregate measures of point data sets. Increasingly, we see questionable use of the technique without attendant discussion of its hazards. In this work, we discuss when and why spatial binning works and how mapmakers can better understand the limitations caused by distortion from projecting to the plane. We introduce equations for evaluating distortion&rsquo;s impact on one common projection (Web Mercator) and discuss how the methods used generalize to other projections. While we focus on hexagonal binning, these same considerations affect spatial bins of any shape, and more generally, any analysis of geographic data performed in planar space.</span></p>","language":"English","publisher":"Taylor and Francis","doi":"10.1080/15230406.2016.1180263","usgsCitation":"Battersby, S.E., Strebe, D., and Finn, M.P., 2017, Shapes on a plane: Evaluating the impact of projection distortion on spatial binning: Cartography and Geographic Information Science, v. 44, p. 410-421, https://doi.org/10.1080/15230406.2016.1180263.","productDescription":"12 p.","startPage":"410","endPage":"421","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070632","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":324674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-16","publicationStatus":"PW","scienceBaseUri":"5776349de4b07dd077c829d1","contributors":{"authors":[{"text":"Battersby, Sarah E.","contributorId":138943,"corporation":false,"usgs":false,"family":"Battersby","given":"Sarah","email":"","middleInitial":"E.","affiliations":[{"id":12589,"text":"University of South Carolina/ Department of Geography","active":true,"usgs":false}],"preferred":false,"id":589875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strebe, Daniel","contributorId":152596,"corporation":false,"usgs":false,"family":"Strebe","given":"Daniel","email":"","affiliations":[{"id":18941,"text":"Tableau Inc.","active":true,"usgs":false}],"preferred":false,"id":589876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finn, Michael P. 0000-0003-0415-2194 mfinn@usgs.gov","orcid":"https://orcid.org/0000-0003-0415-2194","contributorId":2657,"corporation":false,"usgs":true,"family":"Finn","given":"Michael","email":"mfinn@usgs.gov","middleInitial":"P.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":589874,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70174052,"text":"sir20165073 - 2017 - Baseline assessment of groundwater quality in Wayne County, Pennsylvania, 2014","interactions":[],"lastModifiedDate":"2017-03-14T09:49:07","indexId":"sir20165073","displayToPublicDate":"2016-06-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5073","title":"Baseline assessment of groundwater quality in Wayne County, Pennsylvania, 2014","docAbstract":"<p>The Devonian-age Marcellus Shale and the Ordovician-age Utica Shale, geologic formations which have potential for natural gas development, underlie Wayne County and neighboring counties in northeastern Pennsylvania. In 2014, the U.S. Geological Survey, in cooperation with the Wayne Conservation District, conducted a study to assess baseline shallow groundwater quality in bedrock aquifers in Wayne County prior to potential extensive shale-gas development. The 2014 study expanded on previous, more limited studies that included sampling of groundwater from 2 wells in 2011 and 32 wells in 2013 in Wayne County. Eighty-nine water wells were sampled in summer 2014 to provide data on the presence of methane and other aspects of existing groundwater quality throughout the county, including concentrations of inorganic constituents commonly present at low levels in shallow, fresh groundwater but elevated in brines associated with fluids extracted from geologic formations during shale-gas development. Depths of sampled wells ranged from 85 to 1,300 feet (ft) with a median of 291 ft. All of the groundwater samples collected in 2014 were analyzed for bacteria, major ions, nutrients, selected inorganic trace constituents (including metals and other elements), radon-222, gross alpha- and gross beta-particle activity, selected man-made organic compounds (including volatile organic compounds and glycols), dissolved gases (methane, ethane, and propane), and, if sufficient methane was present, the isotopic composition of methane.</p><p>Results of the 2014 study show that groundwater quality generally met most drinking-water standards, but some well-water samples had one or more constituents or properties, including arsenic, iron, pH, bacteria, and radon-222, that exceeded primary or secondary maximum contaminant levels (MCLs). Arsenic concentrations were higher than the MCL of 10 micrograms per liter (µg/L) in 4 of 89 samples (4.5 percent) with concentrations as high as 20 µg/L; arsenic concentrations were higher than the Health Advisory level of 2 µg/L in 27 of 89 samples (30 percent). Total iron concentrations exceeded the secondary maximum contaminant level (SMCL) of 300 µg/L in 9 of 89 samples (10 percent). The pH ranged from 5.4 to 9.3 and did not meet the SMCL range of greater than 6.5 to less than 8.5 in 27 samples (30 percent); 22 samples had pH values less than 6.5, and 5 samples had pH values greater than 8.5. Total coliform bacteria were detected in 22 of 89 samples (25 percent); <i>Escherichia coli</i> were detected in only 2 of those 22 samples. Radon-222 activities ranged from 25 to 7,400 picocuries per liter (pCi/L), with a median of 2,120 pCi/L, and exceeded the proposed drinking-water standard of 300 pCi/L in 86 of 89 samples (97 percent); radon-222 activities were higher than the alternative proposed standard of 4,000 pCi/L in 12 of 89 samples (13.5 percent).</p><p>Water from 8 of the 89 wells (9 percent) had concentrations of methane greater than the reporting level of 0.24 milligrams per liter (mg/L) with the detectable methane concentrations ranging from 0.74 to 9.6 mg/L. Of 16 replicate samples submitted to another laboratory with a lower reporting level of 0.0002 mg/L, 15 samples had detectable methane concentrations that ranged from 0.0011 to 9.7 mg/L. Of these 15 samples, low levels of ethane (0.00032 to 0.0017 mg/L) were detected in 6 of 7 samples with methane concentrations greater than 0.75 mg/L. The isotopic composition of methane in 6 of 8 samples with sufficient dissolved methane (about 1 mg/L) for isotopic analysis is consistent with a predominantly thermogenic methane source (sample carbon isotopic ratio δ13C<sub>CH4</sub> values ranging from -56.36 to -45.97 parts per thousand (‰) and hydrogen isotopic ratio δD<sub>CH4</sub> values ranging from -233.1 to -141.1 ‰). However, the low levels of ethane relative to methane indicate that the methane may be of microbial origin and subsequently underwent oxidation. Isotopic compositions indicated a possibly mixed thermogenic and microbial source (carbon dioxide reduction process) for the methane in 1 of the 8 samples (δ<sup>13</sup>C<sub>CH4</sub> of -63.72 and δD<sub>CH4</sub> of -192.3 ‰) and potential oxidation of microbial and (or) thermogenic methane in the remaining sample (δ<sup>13</sup>C<sub>CH4</sub> of -46.56 and δD<sub>CH4</sub> of -79.7 ‰).</p><p>Groundwater samples with relatively elevated methane concentrations (near or greater than 1 mg/L) had a chemical composition that differed in some respects (pH, selected major ions, and inorganic trace constituents) from groundwater with relatively low methane concentrations (less than 0.75 mg/L). The seven well-water samples with the highest methane concentrations (from about 1 to 9.6 mg/L) also had among the highest pH values (8.1 to 9.3, respectively) and the highest concentrations of sodium, lithium, boron, fluoride, arsenic, and bromide. Relatively elevated concentrations of some other constituents, such as barium, strontium, and chloride, commonly were present in, but not limited to, those well-water samples with elevated methane.</p><p>Groundwater samples with the highest methane concentrations had chloride/bromide ratios that indicate mixing with a small amount of brine (0.02 percent or less, by volume) similar in composition to that reported for gas and oil well brines in Pennsylvania. Most other samples with low methane concentrations (less than about 1 mg/L) had chloride/bromide ratios that indicate predominantly man-made sources of chloride, such as road salt, septic systems, and (or) animal waste. Although naturally occurring brines may originate from deeper parts of the aquifer system, the man-made sources are likely to affect shallow groundwater.</p><p>Geochemical modeling showed that the water chemistry of samples with elevated pH, sodium, lithium, bromide, and alkalinity could result from dissolution of calcite (calcium carbonate) combined with cation exchange and mixing with a small amount of brine. Through cation exchange reactions (which are equivalent to processes in a water softener) calcium ions released by calcite dissolution are exchanged for sodium ions on clay minerals. The spatial distribution of groundwater compositions generally shows that (1) relatively dilute, slightly acidic, oxygenated, calcium-carbonate type waters tend to occur in the uplands along the western border of Wayne County; (2) waters of near neutral pH with the highest amounts of hardness (calcium and magnesium) generally occur in areas of intermediate altitudes; and (3) waters with pH values greater than 8, low oxygen concentrations, and the highest arsenic, sodium, lithium, bromide, and methane concentrations can occur in deep wells in uplands but most frequently occur in stream valleys, especially at low elevations (less than about 1,200 ft above North American Vertical Datum of 1988) where groundwater may be discharging regionally, such as to the Delaware River. Thus, the baseline assessment of groundwater quality in Wayne County prior to gas-well development shows that shallow (less than about 1,000 ft deep) groundwater is generally of good quality, but methane and some constituents present in high concentrations in brine (and produced waters from gas and oil wells) may be present at low to moderate concentrations in some parts of Wayne County.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165073","collaboration":"Prepared in cooperation with the Wayne Conservation District","usgsCitation":"Senior, L.A., Cravotta, C.A., III, and Sloto, R.A., 2017, Baseline assessment of groundwater quality in Wayne County, Pennsylvania, 2014 (ver. 1.1, March 2017): U.S. Geological Survey Scientific Investigations Report 2016–5073, 136 p.,  https://dx.doi.org/10.3133/sir20165073.","productDescription":"xi, 136 p.","numberOfPages":"152","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075428","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":324374,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5073/sir20165073.pdf","text":"Report","size":"7.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5073"},{"id":336925,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5073/versionHist.txt"},{"id":324373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5073/coverthb2.jpg"}],"country":"United States","state":"Pennsylvania","county":"Wayne 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yne\",\"state\":\"PA\"}}]}","edition":"Version 1.0: Originally posted June 30, 2016; Version 1.1: March 9, 2017","contact":"<p><a href=\"mailto:dc_pa@usgs.gov\">Director</a>, Pennsylvania Water Science Center<br /> U.S. Geological Survey<br /> 215 Limekiln Road<br /> New Cumberland, PA 17070</p>\n<p>Or visit our Web site at: <a href=\"http://pa.water.usgs.gov\">http://pa.water.usgs.gov</a></p>","tableOfContents":"<ul>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Methods of Sample Collection and Analysis</li>\n<li>Baseline Groundwater Quality in Wayne County</li>\n<li>Geochemical Modeling and Analysis of Water Quality Data</li>\n<li>Summary and Conclusions&nbsp;</li>\n<li>Acknowledgments</li>\n<li>References Cited</li>\n<li>Appendixes 1&ndash;4</li>\n</ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2016-06-30","revisedDate":"2017-03-09","noUsgsAuthors":false,"publicationDate":"2016-06-30","publicationStatus":"PW","scienceBaseUri":"577642ace4b07dd077c873e8","contributors":{"authors":[{"text":"Senior, Lisa A. 0000-0003-2629-1996 lasenior@usgs.gov","orcid":"https://orcid.org/0000-0003-2629-1996","contributorId":2150,"corporation":false,"usgs":true,"family":"Senior","given":"Lisa","email":"lasenior@usgs.gov","middleInitial":"A.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":640709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, III cravotta@usgs.gov","contributorId":149319,"corporation":false,"usgs":true,"family":"Cravotta","given":"III","email":"cravotta@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":640710,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sloto, Ronald A. rasloto@usgs.gov","contributorId":424,"corporation":false,"usgs":true,"family":"Sloto","given":"Ronald","email":"rasloto@usgs.gov","middleInitial":"A.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":640711,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70170461,"text":"sir20165047 - 2017 - Response of selenium concentrations in groundwater to seasonal canal leakage, lower Gunnison River Basin, Colorado, 2013","interactions":[],"lastModifiedDate":"2017-01-17T13:32:56","indexId":"sir20165047","displayToPublicDate":"2016-05-23T14:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5047","title":"Response of selenium concentrations in groundwater to seasonal canal leakage, lower Gunnison River Basin, Colorado, 2013","docAbstract":"<p>Selenium is a water-quality concern in the lower Gunnison River Basin because irrigation water interacting with seleniferous soils derived from the Mancos Shale Formation has mobilized selenium and increased its concentrations in surface water. Understanding the occurrence of elevated selenium concentrations in groundwater is necessary because groundwater discharge is an important source of selenium in surface water in the basin. In 2013, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation and the Colorado Water Conservation Board, began a study to understand how changes in groundwater levels attributed to canal leakage affected the concentrations and speciation of dissolved selenium in groundwater. The purpose of this report is to characterize the groundwater adjacent to an unlined leaky canal. Two locations, near the East Canal (W-N1 and W-N2) and farther from the East Canal (W-M1 and W-M2), were selected for nested monitoring well installations. The pressure exerted by changes in canal stage was more readily transferred to the deep groundwater measured in the W-N1 near the canal than the shallow groundwater at the W-N2 well. No definitive relation could be made between canal water-level elevation and water-level elevations in monitoring wells farther from the canal (W-M1 and W-M2).&nbsp;</p>\n<p>Water flowing through the East Canal before the irrigation season had much higher selenium concentrations (140 micrograms per liter) than water in the canal during the irrigation season (3.02 micrograms per liter). Total selenium concentrations in the monitoring wells near the canal initially increased to 51.8 micrograms per liter in W-N1 and 1.66 micrograms per liter in W-N2. The initial increase in groundwater selenium concentrations presumably resulted from the dissolution of salts in the unsaturated zone by rising groundwater levels associated with canal leakage. The subsequent decrease in total selenium concentrations resulted from a combination of dilution by canal leakage and selenium reduction processes. Total selenium concentrations in monitoring wells located farther from the canal were not directly affected by canal leakage.</p>\n<p>Selenite/total selenium mass ratios in the East Canal samples ranged from about 0.02 to 0.13, indicating that&nbsp;about 2 to 13 percent of the total selenium in canal samples was composed of selenite. The increase in total selenium at W-N1 from before the irrigation season to the early irrigation season was accompanied by a decrease in the percentage of selenite from about 10 to 1 percent, indicating that selenate was added to the groundwater. A nitrate pulse occurred with the selenate pulse in W-N1 at the beginning of the irrigation season but apparently dissipated to a low enough concentration during the irrigation season to allow for selenate reduction to occur, as indicated by the relatively high percentages of selenite in W-N1 during the late irrigation season. W-N2 generally contained higher percentages of selenite than W-N1.</p>\n<p>Percentages of selenite in W-M1 did not change in response to filling the canal and generally composed less than 1 percent of the total selenium in that well. The predominance of selenate in W-M1, and apparent lack of selenate reduction, cannot be explained by a lack of anoxic conditions in the groundwater because all the available dissolved-oxygen data indicate that concentrations were less than 0.5 milligrams per liter. The most likely explanation for the lack of selenate reduction in W-M1 is that the exceptionally high concentrations of nitrate in the groundwater (about 340 to 390 milligrams per liter as nitrogen) inhibited selenate reduction. These high nitrate concentrations presumably come from the Mancos Shale and its weathering products because there was no evidence for a human source of nitrate at the lower Gunnison River Basin wetland. The high concentrations of selenate in W-M1 may persist and eventually discharge to surface water unless nitrate concentrations are reduced to low enough levels to permit substantial selenate reduction to occur. Well W-M2 contained relatively low concentrations of total selenium and high percentages of selenite before and at the onset of the irrigation season. An increase in total selenium concentration associated with a drying and wetting period later in the summer was accompanied by a decrease in the percentage of selenite to near 0 percent, indicating that selenate was added to the groundwater. This pattern is consistent with the examples of increasing concentrations of total selenium in the other wells and presumably resulted from the dissolution of selenate-bearing salts in the unsaturated zone by rising water levels in W-M2.</p>\n<p>&nbsp;</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165047","collaboration":"Prepared in cooperation with the Bureau of Reclamation and Colorado Water Conservation Board","usgsCitation":"Linard, J.I., McMahon, P.B., Arnold, L.R., and Thomas, J.C., 2017, Response of selenium concentrations in groundwater to seasonal canal leakage, lower Gunnison River Basin, Colorado, 2013 (ver. 1.1, January 2017): U.S. Geological Survey Scientific Investigations Report 2016–5047, 30 p., https://dx.doi.org/10.3133/sir20165047.","productDescription":"v, 30 p.","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2013-01-01","ipdsId":"IP-067265","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":333218,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5047/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016-5047 Version History"},{"id":321476,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5047/sir20165047.pdf","text":"Report","size":"28.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5047"},{"id":321475,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5047/coverthb2.jpg"}],"country":"United States","state":"Colorado","county":"Montrose County","otherGeospatial":"Gunnison River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.9583,\n              38.65\n            ],\n            [\n              -107.9583,\n              38.6542\n            ],\n            [\n              -107.9514,\n             38.6542\n            ],\n            [\n              -107.9514,\n              38.65\n            ],\n            [\n              -107.9583,\n              38.65\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: Originally posted May 23, 2016; Version 1.1: January 13, 2017","contact":"<p>Director, USGS Colorado Water Science Center<br>Box 25046, Mail Stop 415<br>Denver, CO 80225</p><p><a href=\"http://co.water.usgs.gov/\" data-mce-href=\"http://co.water.usgs.gov/\">http://co.water.usgs.gov/</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Study Methods</li><li>Seasonality of Water Levels</li><li>Seasonality of Total Selenium Concentrations</li><li>Seasonality of Selenium Species</li><li>Summary</li><li>References</li><li>Appendix 1. Water-quality data collected between September 1978 and October 1983 at Bureau of Reclamation observation well E263133, Montrose County, Colorado</li><li>Appendix 2. Lithologic logs</li><li>Appendix 3. Well-construction diagrams</li><li>Appendix 4. Methods for calculating initial nitrate concentration, initial nitrogen isotopic composition of nitrate, and ratio of measured to initial nitrate in well W-M1</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2016-05-23","revisedDate":"2017-01-13","noUsgsAuthors":false,"publicationDate":"2016-05-23","publicationStatus":"PW","scienceBaseUri":"574d5671e4b07e28b667f7a5","contributors":{"authors":[{"text":"Linard, J.I.","contributorId":64376,"corporation":false,"usgs":true,"family":"Linard","given":"J.I.","email":"","affiliations":[],"preferred":false,"id":627305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McMahon, P.B. 0000-0001-7452-2379","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":10762,"corporation":false,"usgs":true,"family":"McMahon","given":"P.B.","affiliations":[],"preferred":false,"id":627306,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arnold, L. R.","contributorId":92738,"corporation":false,"usgs":true,"family":"Arnold","given":"L.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":627307,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thomas, J.C.","contributorId":95435,"corporation":false,"usgs":true,"family":"Thomas","given":"J.C.","affiliations":[],"preferred":false,"id":627308,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70170894,"text":"70170894 - 2017 - Climate adaption and post-fire restoration of a foundational perennial in cold desert: Insights from intraspecific variation in response to weather","interactions":[],"lastModifiedDate":"2017-11-22T17:05:45","indexId":"70170894","displayToPublicDate":"2016-05-11T12:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Climate adaption and post-fire restoration of a foundational perennial in cold desert: Insights from intraspecific variation in response to weather","docAbstract":"<div class=\"para\">\n<p>1.The loss of foundational but fire-intolerant perennials such as sagebrush due to increases in fire size and frequency in semiarid regions has motivated efforts to restore them, often with mixed or even no success. Seeds of sagebrush&nbsp;<i>Artemisia tridentata</i>&nbsp;and related species must be moved considerable distances from seed source to planting sites, but such transfers have not been guided by an understanding of local climate adaptation. Initial seedling establishment and its response to weather are a key demographic bottleneck that likely varies among subspecies and populations of sagebrush.</p>\n</div>\n<div class=\"para\">\n<p>2.We assessed differences in survival, growth, and physiological responses of sagebrush to weather among eleven seed sources that varied in subspecies, cytotype, and climates-of-origin over 18 months following outplanting. Diploid or polyploid populations of mountain, Wyoming, and basin big sagebrush (<i>A.tridentata</i>&nbsp;ssp.&nbsp;<i>vaseyana, A.tridentata</i>&nbsp;ssp.&nbsp;<i>wyomingensis,</i>&nbsp;and&nbsp;<i>A.tridentata</i>&nbsp;ssp.&nbsp;<i>tridentata</i>, respectively) were planted onto five burned sites that normally support&nbsp;<i>A.t.wyomingensis</i>&nbsp;with some&nbsp;<i>A.t.tridentata</i>.</p>\n</div>\n<div class=\"para\">\n<p>3.A.t.wyomingensis had the most growth and survival, and tetraploid populations had greater survival and height than diploids. Seasonal timing of mortality varied among the subspecies/cytotypes and was more closely related to minimum temperatures than water deficit.</p>\n</div>\n<div class=\"para\">\n<p>4.Temperatures required to induce ice formation were up to 6&deg;C more negative in 4n-<i>A.t.tridentata</i>&nbsp;and&nbsp;<i>A.t.wyomingensis</i>&nbsp;than other subspecies/cytotypes, indicating greater freezing avoidance. In contrast, freezing resistance of photosynthesis varied only 1&deg;C among subspecies/cytotypes, being greatest in&nbsp;<i>A.t.wyomingensis</i>&nbsp;and least in the subspecies normally considered most cold-adapted,<i>A.t.vaseyana</i>. A large spectrum of reliance on freezing-avoidance vs. freezing-tolerance was observed and corresponded to differences in post-fire survivorship among subspecies/cytotypes. Differences in water deficit responses among subspecies/cytotypes were not as strong and did not relate to survival patterns.</p>\n</div>\n<div class=\"para\">\n<p><strong>5.</strong>Synthesis and applications. Low temperature responses are a key axis defining climate adaptation in young sagebrush seedlings and vary more with cytotype than with subspecies, which contrasts with the traditional emphases on (i) water limitations to explain establishment in these deserts, and (ii) subspecies in selecting restoration seedings. These important and novel insights on climate adaptation are critical for seed selection and parameterizing seed transfer zones, and were made possible by incorporating weather data with survival statistics. The survival/weather statistics used here could be applied to any restoration planting or seeding to help elucidate factors contributing to success and enable adaptive management.</p>\n</div>","language":"English","publisher":"Wiley","doi":"10.1111/1365-2664.12679","usgsCitation":"Brabec, M., Germino, M., and Richardson, B., 2017, Climate adaption and post-fire restoration of a foundational perennial in cold desert: Insights from intraspecific variation in response to weather: Journal of Applied Ecology, v. 54, no. 1, p. 293-302, https://doi.org/10.1111/1365-2664.12679.","productDescription":"10 p.","startPage":"293","endPage":"302","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060367","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":470242,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.12679","text":"Publisher Index Page"},{"id":321119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-12","publicationStatus":"PW","scienceBaseUri":"5734499ae4b0dae0d5dd68ee","contributors":{"authors":[{"text":"Brabec, Martha M. mbrabec@usgs.gov","contributorId":147542,"corporation":false,"usgs":true,"family":"Brabec","given":"Martha M.","email":"mbrabec@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":629144,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":628952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richardson, Bryce A.","contributorId":37249,"corporation":false,"usgs":true,"family":"Richardson","given":"Bryce A.","affiliations":[],"preferred":false,"id":629145,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70168447,"text":"ofr20161020 - 2017 - Surface-water quality-assurance plan for the U.S. Geological Survey Washington Water Science Center","interactions":[],"lastModifiedDate":"2017-08-28T10:57:41","indexId":"ofr20161020","displayToPublicDate":"2016-02-19T15:00:00","publicationYear":"2017","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":"2016-1020","title":"Surface-water quality-assurance plan for the U.S. Geological Survey Washington Water Science Center","docAbstract":"<p>This Surface-Water Quality-Assurance Plan documents the standards, policies, and procedures used by the U.S. Geological Survey Washington Water Science Center (WAWSC) for activities related to the collection, processing, storage, analysis, and publication of surface-water data. This plan serves as a guide to all WAWSC personnel involved in surface-water data activities, and changes as the needs and requirements of the WAWSC change. Regular updates to this plan represent an integral part of the quality-assurance process. In the WAWSC, direct oversight and responsibility by the hydrographer(s) assigned to a surface-water station, combined with team approaches in all work efforts, assure highquality data, analyses, reviews, and reports for cooperating agencies and the public. </p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161020","usgsCitation":"Mastin, M.C., 2017, Surface-water quality-assurance plan for the U.S. Geological Survey Washington Water Science Center (ver. 1.1, August 2017): U.S. Geological Survey Open-File Report 2016-1020, 85 p., https://dx.doi.org/10.3133/ofr20161020.","productDescription":"vi, 85 p.","numberOfPages":"98","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069496","costCenters":[{"id":622,"text":"Washington Water Science 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 \"}}]}","edition":"Verison 1.0: Originally posted February 19, 2017; Version 1.1: August 2017","contact":"<p><a href=\"mailto:dc_wa@usgs.gov\" data-mce-href=\"mailto:dc_wa@usgs.gov\">Director</a>, Washington Water Science Center<br>U.S. Geological Survey<br>934 Broadway, Suite 300<br>Tacoma, Washington 98402<br><a href=\"http://wa.water.usgs.gov\" data-mce-href=\"http://wa.water.usgs.gov\">http://wa.water.usgs.gov</a><br></p>","tableOfContents":"<ul>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Collection of Surface-Water Data</li>\n<li>Processing and Analysis of Surface-Water Data</li>\n<li>Safety</li>\n<li>Training</li>\n<li>Summary</li>\n<li>Acknowledgments</li>\n<li>References Cited</li>\n<li>Appendixes A-E</li>\n</ul>\n<p>&nbsp;</p>","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2016-02-19","revisedDate":"2017-08-18","noUsgsAuthors":false,"publicationDate":"2016-02-19","publicationStatus":"PW","scienceBaseUri":"56c83cabe4b0b3c9ae37b205","contributors":{"authors":[{"text":"Mastin, Mark C. 0000-0003-4018-7861 mcmastin@usgs.gov","orcid":"https://orcid.org/0000-0003-4018-7861","contributorId":1652,"corporation":false,"usgs":true,"family":"Mastin","given":"Mark","email":"mcmastin@usgs.gov","middleInitial":"C.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620204,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70168334,"text":"70168334 - 2017 - Monitoring for the management of disease risk in animal translocation programmes","interactions":[],"lastModifiedDate":"2017-03-22T15:11:54","indexId":"70168334","displayToPublicDate":"2016-02-10T11:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1443,"text":"EcoHealth","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring for the management of disease risk in animal translocation programmes","docAbstract":"<p><span>Monitoring is best viewed as a component of some larger programme focused on science or conservation. The value of monitoring is determined by the extent to which it informs the parent process. Animal translocation programmes are typically designed to augment or establish viable animal populations without changing the local community in any detrimental way. Such programmes seek to minimize disease risk to local wild animals, to translocated animals, and in some cases to humans. Disease monitoring can inform translocation decisions by (1) providing information for state-dependent decisions, (2) assessing progress towards programme objectives, and (3) permitting learning in order to make better decisions in the future. Here we discuss specific decisions that can be informed by both pre-release and post-release disease monitoring programmes. We specify state variables and vital rates needed to inform these decisions. We then discuss monitoring data and analytic methods that can be used to estimate these state variables and vital rates. Our discussion is necessarily general, but hopefully provides a basis for tailoring disease monitoring approaches to specific translocation programmes.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10393-015-1094-4","usgsCitation":"Nichols, J., Hollmen, T.E., and Grand, J.B., 2017, Monitoring for the management of disease risk in animal translocation programmes: EcoHealth, v. 14, no. 1, p. 156-166, https://doi.org/10.1007/s10393-015-1094-4.","productDescription":"11 p.","startPage":"156","endPage":"166","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069177","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":317902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-14","publicationStatus":"PW","scienceBaseUri":"56bc5f35e4b08d617f660022","contributors":{"authors":[{"text":"Nichols, James D. jnichols@usgs.gov","contributorId":139087,"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":619729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hollmen, Tuula E.","contributorId":106077,"corporation":false,"usgs":true,"family":"Hollmen","given":"Tuula","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":619730,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grand, J. Barry 0000-0002-3576-4567 barry_grand@usgs.gov","orcid":"https://orcid.org/0000-0002-3576-4567","contributorId":579,"corporation":false,"usgs":true,"family":"Grand","given":"J.","email":"barry_grand@usgs.gov","middleInitial":"Barry","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":619706,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70162135,"text":"70162135 - 2017 - Low incidence of clonality in cold water corals revealed through the novel use of standardized protocol adapted to deep sea sampling","interactions":[],"lastModifiedDate":"2017-11-29T16:44:42","indexId":"70162135","displayToPublicDate":"2016-01-14T10:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1371,"text":"Deep-Sea Research Part II: Topical Studies in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Low incidence of clonality in cold water corals revealed through the novel use of standardized protocol adapted to deep sea sampling","docAbstract":"<p>Sampling in the deep sea is a technical challenge, which has hindered the acquisition of robust datasets that are necessary to determine the fine-grained biological patterns and processes that may shape genetic diversity. Estimates of the extent of clonality in deep-sea species, despite the importance of clonality in shaping the local dynamics and evolutionary trajectories, have been largely obscured by such limitations. Cold-water coral reefs along European margins are formed mainly by two reef-building species, <i>Lophelia pertusa</i> and<i> Madrepora oculata</i>. Here we present a fine-grained analysis of the genotypic and genetic composition of reefs occurring in the Bay of Biscay, based on an innovative deep-sea sampling protocol. This strategy was designed to be standardized, random, and allowed the georeferencing of all sampled colonies. Clonal lineages discriminated through their Multi-Locus Genotypes (MLG) at 6–7 microsatellite markers could thus be mapped to assess the level of clonality and the spatial spread of clonal lineages. High values of clonal richness were observed for both species across all sites suggesting a limited occurrence of clonality, which likely originated through fragmentation. Additionally, spatial autocorrelation analysis underlined the possible occurrence of fine-grained genetic structure in several populations of both<i> L. pertusa</i> and <i>M. oculata</i>. The two cold-water coral species examined had contrasting patterns of connectivity among canyons, with among-canyon genetic structuring detected in <i>M. oculata</i>, whereas <i>L. pertusa</i> was panmictic at the canyon scale. This study exemplifies that a standardized, random and georeferenced sampling strategy, while challenging, can be applied in the deep sea, and associated benefits outlined here include improved estimates of fine grained patterns of clonality and dispersal that are comparable across sites and among species.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2015.11.013","usgsCitation":"Becheler, R., Cassone, A., Noel, P., Mouchel, O., Morrison, C.L., and Arnaud-Haond, S., 2017, Low incidence of clonality in cold water corals revealed through the novel use of standardized protocol adapted to deep sea sampling: Deep-Sea Research Part II: Topical Studies in Oceanography, v. 145, p. 120-130, https://doi.org/10.1016/j.dsr2.2015.11.013.","productDescription":"11 p.","startPage":"120","endPage":"130","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055976","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":470247,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1016/j.dsr2.2015.11.013","text":"External Repository"},{"id":314314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Bay of Biscay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -9.82177734375,\n              43.27720532212024\n            ],\n            [\n              -9.82177734375,\n              48.574789910928864\n            ],\n            [\n              -0.15380859375,\n              48.574789910928864\n            ],\n            [\n              -0.15380859375,\n              43.27720532212024\n            ],\n            [\n              -9.82177734375,\n              43.27720532212024\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"145","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5698c6b2e4b0fbd3f7fa4be4","contributors":{"authors":[{"text":"Becheler, Ronan","contributorId":152249,"corporation":false,"usgs":false,"family":"Becheler","given":"Ronan","email":"","affiliations":[{"id":18891,"text":"Ifremer, Centre de Brest, France","active":true,"usgs":false}],"preferred":false,"id":588650,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cassone, Anne-Laure","contributorId":152250,"corporation":false,"usgs":false,"family":"Cassone","given":"Anne-Laure","email":"","affiliations":[{"id":18891,"text":"Ifremer, Centre de Brest, France","active":true,"usgs":false}],"preferred":false,"id":588651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Noel, Philippe","contributorId":152251,"corporation":false,"usgs":false,"family":"Noel","given":"Philippe","email":"","affiliations":[{"id":18891,"text":"Ifremer, Centre de Brest, France","active":true,"usgs":false}],"preferred":false,"id":588652,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mouchel, Olivier","contributorId":152252,"corporation":false,"usgs":false,"family":"Mouchel","given":"Olivier","email":"","affiliations":[{"id":18891,"text":"Ifremer, Centre de Brest, France","active":true,"usgs":false}],"preferred":false,"id":588653,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morrison, Cheryl L. 0000-0001-9425-691X cmorrison@usgs.gov","orcid":"https://orcid.org/0000-0001-9425-691X","contributorId":146488,"corporation":false,"usgs":true,"family":"Morrison","given":"Cheryl","email":"cmorrison@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":588649,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Arnaud-Haond, Sophie","contributorId":152253,"corporation":false,"usgs":false,"family":"Arnaud-Haond","given":"Sophie","email":"","affiliations":[{"id":18892,"text":"Ifremer, Centre of Brest, and Ifremer, Centre de Sete, France","active":true,"usgs":false}],"preferred":false,"id":588654,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70190718,"text":"70190718 - 2017 - Surface slip during large Owens Valley earthquakes","interactions":[],"lastModifiedDate":"2019-03-27T10:08:47","indexId":"70190718","displayToPublicDate":"2016-01-08T10:08:29","publicationYear":"2017","noYear":false,"publicationType":{"id":4,"text":"Book"},"title":"Surface slip during large Owens Valley earthquakes","docAbstract":"<div class=\"article-section__content en main\"><p>The 1872 Owens Valley earthquake is the third largest known historical earthquake in California. Relatively sparse field data and a complex rupture trace, however, inhibited attempts to fully resolve the slip distribution and reconcile the total moment release. We present a new, comprehensive record of surface slip based on lidar and field investigation, documenting 162 new measurements of laterally and vertically displaced landforms for 1872 and prehistoric Owens Valley earthquakes. Our lidar analysis uses a newly developed analytical tool to measure fault slip based on cross‐correlation of sublinear topographic features and to produce a uniquely shaped probability density function (PDF) for each measurement. Stacking PDFs along strike to form cumulative offset probability distribution plots (COPDs) highlights common values corresponding to single and multiple‐event displacements. Lateral offsets for 1872 vary systematically from ∼1.0 to 6.0 m and average 3.3 ± 1.1 m (2σ). Vertical offsets are predominantly east‐down between ∼0.1 and 2.4 m, with a mean of 0.8 ± 0.5 m. The average lateral‐to‐vertical ratio compiled at specific sites is ∼6:1. Summing displacements across subparallel, overlapping rupture traces implies a maximum of 7–11 m and net average of 4.4 ± 1.5 m, corresponding to a geologic M<sub><i>w</i></sub><span>&nbsp;</span>∼7.5 for the 1872 event. We attribute progressively higher‐offset lateral COPD peaks at 7.1 ± 2.0 m, 12.8 ± 1.5 m, and 16.6 ± 1.4 m to three earlier large surface ruptures. Evaluating cumulative displacements in context with previously dated landforms in Owens Valley suggests relatively modest rates of fault slip, averaging between ∼0.6 and 1.6 mm/yr (1σ) over the late Quaternary.</p></div>","language":"English","publisher":"AGU","doi":"10.1002/2015GC006033","usgsCitation":"Haddon, E., Amos, C., Zielke, O., Jayko, A.S., and Burgmann, R., 2017, Surface slip during large Owens Valley earthquakes, v. 17, no. 6, 31 p., https://doi.org/10.1002/2015GC006033.","productDescription":"31 p.","startPage":"2239","endPage":"2269","ipdsId":"IP-088576","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":362369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haddon, Elizabeth 0000-0001-7601-7755 ehaddon@usgs.gov","orcid":"https://orcid.org/0000-0001-7601-7755","contributorId":196407,"corporation":false,"usgs":true,"family":"Haddon","given":"Elizabeth","email":"ehaddon@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":710260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amos, Colin","contributorId":196408,"corporation":false,"usgs":false,"family":"Amos","given":"Colin","affiliations":[],"preferred":false,"id":710261,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zielke, Olaf","contributorId":174026,"corporation":false,"usgs":false,"family":"Zielke","given":"Olaf","affiliations":[{"id":24561,"text":"KAUST","active":true,"usgs":false}],"preferred":false,"id":710262,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jayko, Angela S. 0000-0002-7378-0330 ajayko@usgs.gov","orcid":"https://orcid.org/0000-0002-7378-0330","contributorId":2531,"corporation":false,"usgs":true,"family":"Jayko","given":"Angela","email":"ajayko@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":710263,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burgmann, Roland","contributorId":192700,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","affiliations":[],"preferred":false,"id":710264,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70161826,"text":"70161826 - 2017 - An alternative framework for responding to the amphibian crisis","interactions":[],"lastModifiedDate":"2017-03-03T11:01:19","indexId":"70161826","displayToPublicDate":"2016-01-07T10:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2968,"text":"Oryx","active":true,"publicationSubtype":{"id":10}},"title":"An alternative framework for responding to the amphibian crisis","docAbstract":"<p><span>Volumes of data illustrate the severity of the crisis affecting amphibians, where &gt; 32% of amphibians worldwide are threatened with declining populations. Although there have been isolated victories, the current approach to the issue is unsuccessful. We suggest that a radically different approach, something akin to human emergency response management (i.e. the Incident Command System), is one alternative to addressing the inertia and lack of cohesion in responding to amphibian issues. We acknowledge existing efforts and the useful research that has been conducted, but we suggest that a change is warranted and that the identification of a new amphibian chytrid provides the impetus for such a change. Our goal is to recognize that without a centralized effort we (collectively) are likely to fail in responding to this challenge.</span></p>","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0030605315001131","usgsCitation":"Muths, E.L., and Fisher, R.N., 2017, An alternative framework for responding to the amphibian crisis: Oryx, v. 51, no. 2, p. 210-213, https://doi.org/10.1017/S0030605315001131.","productDescription":"4 p.","startPage":"210","endPage":"213","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064687","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":461851,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/s0030605315001131","text":"Publisher Index Page"},{"id":313996,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-16","publicationStatus":"PW","scienceBaseUri":"568f8c2ee4b0e7a44bc5ec76","contributors":{"authors":[{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":587863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":587864,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70228805,"text":"70228805 - 2017 - An Arctic and Subarctic ostracode database: Biogeographic and paleoceanographic applications","interactions":[],"lastModifiedDate":"2022-02-22T15:28:27.560491","indexId":"70228805","displayToPublicDate":"2015-12-10T09:23:26","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"An Arctic and Subarctic ostracode database: Biogeographic and paleoceanographic applications","docAbstract":"<p><span>A new Arctic Ostracode Database-2015 (AOD-2015) provides census data for 96 species of benthic marine Ostracoda from 1340 modern surface sediments from the Arctic Ocean and subarctic seas. Ostracoda is a meiofaunal, Crustacea group that secretes a bivalved calcareous (CaCO</span><sub>3</sub><span>) shell commonly preserved in sediments. Arctic and subarctic ostracode species have ecological limits controlled by temperature, salinity, oxygen, sea ice, food, and other habitat-related factors. Unique species ecology, shell chemistry (Mg/Ca ratios, stable isotopes), and limited stratigraphic ranges make them a useful tool for paleoceanographic reconstructions and biostratigraphy. The database, described here, will facilitate the investigation of modern ostracode biogeography, regional community structure, and ecology. These data, when compared to downcore faunal data from sediment cores, will provide a better understanding of how the Arctic has been affected by climatic and oceanographic change during the Quaternary. Images of all species and biogeographic distribution maps for selected species are presented, with brief discussion of representative species’ biogeographic and ecological significance. Publication of AOD-2015 is open-sourced and will be available online at several public websites with latitude, longitude, water depth, and bottom water temperature for most samples. It includes material from Arctic abyssal plains and submarine ridges, continental slopes, and shelves of the Kara, Laptev, East Siberian, Chukchi, Beaufort Seas, and several subarctic regions.</span></p>","language":"English","publisher":"Springer Link","doi":"10.1007/s10750-015-2587-4","usgsCitation":"Gemery, L., Cronin, T.M., Briggs, W.M., Brouwers, E.M., Schornikov, E.I., Stepanova, A., Wood, A.M., and Yasuhara, M., 2017, An Arctic and Subarctic ostracode database: Biogeographic and paleoceanographic applications: Hydrobiologia, v. 786, p. 59-95, https://doi.org/10.1007/s10750-015-2587-4.","productDescription":"37 p.","startPage":"59","endPage":"95","ipdsId":"IP-070287","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":396243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic, Subarctic","volume":"786","noUsgsAuthors":false,"publicationDate":"2015-12-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Gemery, Laura 0000-0003-1966-8732 lgemery@usgs.gov","orcid":"https://orcid.org/0000-0003-1966-8732","contributorId":5402,"corporation":false,"usgs":true,"family":"Gemery","given":"Laura","email":"lgemery@usgs.gov","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":835560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":835561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, William M.","contributorId":279853,"corporation":false,"usgs":false,"family":"Briggs","given":"William","email":"","middleInitial":"M.","affiliations":[{"id":57377,"text":"INSTAAR","active":true,"usgs":false}],"preferred":false,"id":835562,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brouwers, Elisabeth M. brouwers@usgs.gov","contributorId":279854,"corporation":false,"usgs":true,"family":"Brouwers","given":"Elisabeth","email":"brouwers@usgs.gov","middleInitial":"M.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":835563,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schornikov, Eugene I.","contributorId":279855,"corporation":false,"usgs":false,"family":"Schornikov","given":"Eugene","email":"","middleInitial":"I.","affiliations":[{"id":57378,"text":"Zhirmunsky Institute of Marine Biology","active":true,"usgs":false}],"preferred":false,"id":835564,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stepanova, Anna","contributorId":147368,"corporation":false,"usgs":false,"family":"Stepanova","given":"Anna","email":"","affiliations":[{"id":16831,"text":"Borissiak Paleontological Institute","active":true,"usgs":false}],"preferred":false,"id":835565,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wood, Adrian M.","contributorId":279856,"corporation":false,"usgs":false,"family":"Wood","given":"Adrian","email":"","middleInitial":"M.","affiliations":[{"id":57379,"text":"Coventry University","active":true,"usgs":false}],"preferred":false,"id":835566,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yasuhara, Moriaki","contributorId":178705,"corporation":false,"usgs":false,"family":"Yasuhara","given":"Moriaki","email":"","affiliations":[],"preferred":false,"id":835567,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70072607,"text":"ds817 - 2017 -  Onshore industrial wind turbine locations for the United States","interactions":[],"lastModifiedDate":"2017-02-06T09:48:58","indexId":"ds817","displayToPublicDate":"2014-02-11T11:39:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"817","title":" Onshore industrial wind turbine locations for the United States","docAbstract":"<p>This dataset provides industrial-scale onshore wind turbine locations in the United States, corresponding facility information, and turbine technical specifications. The database has wind turbine records that have been collected, digitized, locationally verified, and internally quality controlled. Turbines from the Federal Aviation Administration Digital Obstacles File, through product release date July 22, 2013, were used as the primary source of turbine data points. The dataset was subsequently revised and reposted as described in the revision histories for the report. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in Environmental Systems Research Institute (Esri) ArcGIS Desktop. Turbines without Federal Aviation Administration Obstacles Repository System numbers were visually identified and point locations were added to the collection. We estimated a locational error of plus or minus 10 meters for turbine locations. Wind farm facility names were identified from publicly available facility datasets. Facility names were then used in a Web search of additional industry publications and press releases to attribute additional turbine information (such as manufacturer, model, and technical specifications of wind turbines). Wind farm facility location data from various wind and energy industry sources were used to search for and digitize turbines not in existing databases. Technical specifications for turbines were assigned based on the wind turbine make and model as described in literature, specifications listed in the Federal Aviation Administration Digital Obstacles File, and information on the turbine manufacturer’s Web site. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. 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Roger","contributorId":9961,"corporation":false,"usgs":true,"family":"Compton","given":"Roger","affiliations":[],"preferred":false,"id":488523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kramer, Louisa lkramer@usgs.gov","contributorId":5579,"corporation":false,"usgs":true,"family":"Kramer","given":"Louisa","email":"lkramer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":488521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ancona, Zach","contributorId":53292,"corporation":false,"usgs":true,"family":"Ancona","given":"Zach","affiliations":[],"preferred":false,"id":488524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Norton, Donna dnorton@usgs.gov","contributorId":5580,"corporation":false,"usgs":true,"family":"Norton","given":"Donna","email":"dnorton@usgs.gov","affiliations":[],"preferred":true,"id":488522,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70190114,"text":"70190114 - 2017 - Chemical tracer methods","interactions":[],"lastModifiedDate":"2021-04-26T17:27:03.084879","indexId":"70190114","displayToPublicDate":"2013-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"7","title":"Chemical tracer methods","docAbstract":"<p>Tracers have a wide variety of uses in hydrologic studies: providing quantitative or qualitative estimates of recharge, identifying sources of recharge, providing information on velocities and travel times of water movement, assessing the importance of preferential flow paths, providing information on hydrodynamic dispersion, and providing data for calibration of water flow and solute-transport models (Walker, 1998; Cook and Herczeg, 2000; Scanlon<span>&nbsp;</span><span class=\"italic\">et al</span>., 2002b). Tracers generally are ions, isotopes, or gases that move with water and that can be detected in the atmosphere, in surface waters, and in the subsurface. Heat also is transported by water; therefore, temperatures can be used to trace water movement. This chapter focuses on the use of chemical and isotopic tracers in the subsurface to estimate recharge. Tracer use in surface-water studies to determine groundwater discharge to streams is addressed in Chapter 4; the use of temperature as a tracer is described in Chapter 8.</p><p>Following the nomenclature of Scanlon<span>&nbsp;</span><span class=\"italic\">et al</span>. (2002b), tracers are grouped into three categories: natural environmental tracers, historical tracers, and applied tracers. Natural environmental tracers are those that are transported to or created within the atmosphere under natural processes; these tracers are carried to the Earth’s surface as wet or dry atmospheric deposition. The most commonly used natural environmental tracer is chloride (Cl) (Allison and Hughes, 1978). Ocean water, through the process of evaporation, is the primary source of atmospheric Cl. Other tracers in this category include chlorine-36 (<sup><span class=\"sup\">36</span></sup>Cl) and tritium (<sup><span class=\"sup\">3</span></sup>H); these two isotopes are produced naturally in the Earth’s atmosphere; however, there are additional anthropogenic sources of them.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Estimating groundwater recharge","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Cambridge University Press","publisherLocation":"Cambridge, UK","doi":"10.1017/CBO9780511780745.008","usgsCitation":"Healy, R.W., 2017, Chemical tracer methods, chap. 7 <i>of</i> Estimating groundwater recharge, p. 136-165, https://doi.org/10.1017/CBO9780511780745.008.","productDescription":"30 p.","startPage":"136","endPage":"165","ipdsId":"IP-014174","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344807,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59b76f73e4b08b1644ddfb03","contributors":{"authors":[{"text":"Healy, Richard W. 0000-0002-0224-1858 rwhealy@usgs.gov","orcid":"https://orcid.org/0000-0002-0224-1858","contributorId":658,"corporation":false,"usgs":true,"family":"Healy","given":"Richard","email":"rwhealy@usgs.gov","middleInitial":"W.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":707545,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70191533,"text":"70191533 - 2017 - Micro-seismicity within the Coso Geothermal field, California, from 1996-2012","interactions":[],"lastModifiedDate":"2018-01-05T14:56:42","indexId":"70191533","displayToPublicDate":"2013-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Micro-seismicity within the Coso Geothermal field, California, from 1996-2012","docAbstract":"<p>We extend our previous catalog of seismicity within the Coso Geothermal field by adding over two and a half years of additional data to prior results. In total, we locate over 16 years of seismicity spanning from April 1996 to May of 2012 using a refined velocity model, apply it to all events and utilize differential travel times in relocations to improve the accuracy of event locations. The improved locations elucidate major structural features within the reservoir that we interpret to be faults that contribute to heat and fluid flow within the reservoir. Much of the relocated seismicity remains diffuse between these major structural features, suggesting that a large volume of accessible and distributed fracture porosity is maintained within the geothermal reservoir through ongoing brittle failure. We further track changes in b value and seismic moment release within the reservoir as a whole through time. We find that b values decrease significantly during 2009 and 2010, coincident with the occurrence of a greater number of moderate magnitude earthquakes (3.0 ≤ ML &lt; 4.5). Analysis of spatial variations in seismic moment release between years reveals that localized seismicity tends to spread from regions of high moment release into regions with previously low moment release, akin to aftershock sequences. These results indicate that the Coso reservoir is comprised of a network of fractures at a variety of spatial scales that evolves dynamically over time, with progressive changes in characteristics of microseismicity and inferred fractures and faults that are only evident from a long period of seismic monitoring analyzed using self-consistent methods.</p>","largerWorkTitle":"Proceedings, Thirty-Eighth Workshop on Geothermal Reservoir Engineering","conferenceTitle":"Thirty-Eighth Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 11-13, 2013","conferenceLocation":"Stanford, California","language":"English","publisher":"Stanford University","usgsCitation":"Kaven, J., Hickman, S.H., and Weber, L.C., 2017, Micro-seismicity within the Coso Geothermal field, California, from 1996-2012, <i>in</i> Proceedings, Thirty-Eighth Workshop on Geothermal Reservoir Engineering, Stanford, California, February 11-13, 2013, 10 p.","productDescription":"10 p.","ipdsId":"IP-043946","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":350339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Coso Geothermal Field","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.84,\n              35.95\n            ],\n            [\n              -117.76,\n              35.95\n            ],\n            [\n              -117.76,\n              36.1\n            ],\n            [\n              -117.84,\n              36.1\n            ],\n            [\n              -117.84,\n              35.95\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc5ae4b06e28e9c23da0","contributors":{"authors":[{"text":"Kaven, J. Ole 0000-0003-2625-2786 okaven@usgs.gov","orcid":"https://orcid.org/0000-0003-2625-2786","contributorId":3993,"corporation":false,"usgs":true,"family":"Kaven","given":"J. Ole","email":"okaven@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":712666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hickman, Stephen H. 0000-0003-2075-9615 hickman@usgs.gov","orcid":"https://orcid.org/0000-0003-2075-9615","contributorId":2705,"corporation":false,"usgs":true,"family":"Hickman","given":"Stephen","email":"hickman@usgs.gov","middleInitial":"H.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":712665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weber, Lisa C.","contributorId":124586,"corporation":false,"usgs":true,"family":"Weber","given":"Lisa","email":"","middleInitial":"C.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":712664,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70039731,"text":"sir20125171 - 2017 - Methods for estimating selected low-flow frequency statistics and harmonic mean flows for streams in Iowa","interactions":[],"lastModifiedDate":"2017-11-30T18:31:02","indexId":"sir20125171","displayToPublicDate":"2012-08-27T00:00:00","publicationYear":"2017","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":"2012-5171","title":"Methods for estimating selected low-flow frequency statistics and harmonic mean flows for streams in Iowa","docAbstract":"A statewide study was conducted to develop regression equations for estimating six selected low-flow frequency statistics and harmonic mean flows for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include: the annual 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years, the annual 30-day mean low flow for a recurrence interval of 5 years, and the seasonal (October 1 through December 31) 1- and 7-day mean low flows for a recurrence interval of 10 years. Estimation equations also were developed for the harmonic-mean-flow statistic. Estimates of these seven selected statistics are provided for 208 U.S. Geological Survey continuous-record streamgages using data through September 30, 2006. The study area comprises streamgages located within Iowa and 50 miles beyond the State's borders. Because trend analyses indicated statistically significant positive trends when considering the entire period of record for the majority of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. The median number of years of record used to compute each of these seven selected statistics was 35. Geographic information system software was used to measure 54 selected basin characteristics for each streamgage. Following the removal of two streamgages from the initial data set, data collected for 206 streamgages were compiled to investigate three approaches for regionalization of the seven selected statistics. Regionalization, a process using statistical regression analysis, provides a relation for efficiently transferring information from a group of streamgages in a region to ungaged sites in the region. The three regionalization approaches tested included statewide, regional, and region-of-influence regressions. For the regional regression, the study area was divided into three low-flow regions on the basis of hydrologic characteristics, landform regions, and soil regions. A comparison of root mean square errors and average standard errors of prediction for the statewide, regional, and region-of-influence regressions determined that the regional regression provided the best estimates of the seven selected statistics at ungaged sites in Iowa. Because a significant number of streams in Iowa reach zero flow as their minimum flow during low-flow years, four different types of regression analyses were used: left-censored, logistic, generalized-least-squares, and weighted-least-squares regression. A total of 192 streamgages were included in the development of 27 regression equations for the three low-flow regions. For the northeast and northwest regions, a censoring threshold was used to develop 12 left-censored regression equations to estimate the 6 low-flow frequency statistics for each region. For the southern region a total of 12 regression equations were developed; 6 logistic regression equations were developed to estimate the probability of zero flow for the 6 low-flow frequency statistics and 6 generalized least-squares regression equations were developed to estimate the 6 low-flow frequency statistics, if nonzero flow is estimated first by use of the logistic equations. A weighted-least-squares regression equation was developed for each region to estimate the harmonic-mean-flow statistic. Average standard errors of estimate for the left-censored equations for the northeast region range from 64.7 to 88.1 percent and for the northwest region range from 85.8 to 111.8 percent. Misclassification percentages for the logistic equations for the southern region range from 5.6 to 14.0 percent. Average standard errors of prediction for generalized least-squares equations for the southern region range from 71.7 to 98.9 percent and pseudo coefficients of determination for the generalized-least-squares equations range from 87.7 to 91.8 percent. Average standard errors of prediction for weighted-least-squares equations developed for estimating the harmonic-mean-flow statistic for each of the three regions range from 66.4 to 80.4 percent. The regression equations are applicable only to stream sites in Iowa with low flows not significantly affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. If the equations are used at ungaged sites on regulated streams, or on streams affected by water-supply and agricultural withdrawals, then the estimates will need to be adjusted by the amount of regulation or withdrawal to estimate the actual flow conditions if that is of interest. Caution is advised when applying the equations for basins with characteristics near the applicable limits of the equations and for basins located in karst topography. A test of two drainage-area ratio methods using 31 pairs of streamgages, for the annual 7-day mean low-flow statistic for a recurrence interval of 10 years, indicates a weighted drainage-area ratio method provides better estimates than regional regression equations for an ungaged site on a gaged stream in Iowa when the drainage-area ratio is between 0.5 and 1.4. These regression equations will be implemented within the U.S. Geological Survey StreamStats web-based geographic-information-system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the seven selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these seven selected statistics are provided for the streamgage.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125171","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Eash, D.A., and Barnes, K., 2017, Methods for estimating selected low-flow frequency statistics and harmonic mean flows for streams in Iowa (Version 1.0: Originally posted 2012; Version 1.1: November 21, 2017): U.S. Geological Survey Scientific Investigations Report 2012-5171, viii, 94 p., https://doi.org/10.3133/sir20125171.","productDescription":"viii, 94 p.","numberOfPages":"106","onlineOnly":"Y","costCenters":[{"id":351,"text":"Iowa Water Science 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