{"pageNumber":"512","pageRowStart":"12775","pageSize":"25","recordCount":40778,"records":[{"id":70176188,"text":"70176188 - 2016 - Unsteady flow in natural compound channel: Experiment and simulation","interactions":[],"lastModifiedDate":"2019-10-21T06:44:29","indexId":"70176188","displayToPublicDate":"2012-10-05T12:27:48","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Unsteady flow in natural compound channel: Experiment and simulation","docAbstract":"Phragmites Australis, or common reed, is an invasive plant species that has spread along channels of the Platte River (Nebraska, USA), adversely altering the biogeomorphology of the system. Of particular interest have been the impacts on riparian habitat, specifically the reduction of suitable areas for crane roosting and shorebird nesting. A program for managing and removing these and other invasive plant species has been in place to mitigate these effects. Spraying and mechanical removal of phragmites has been the established practice, but there is interest in evaluating other potential forms of maintenance, in particular the potential of uprooting seedlings using high velocity/high shear flows. For that purpose, in 2010 a high flow experiment was conducted on a return channel to the Platte River that feeds from the Thirty Mile Canal near Brady, Nebraska. As part of the experiment, a 250-meter long reach of the channel was monitored and data were collected for the set-up, testing, and evaluation of numerical models. This article presents a description of the experimental conditions, data-collection program, and application of an unsteady, depth-averaged numerical model to reproduce the event.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"River flow 2012: Proceedings of the International Conference on Fluvial Hydraulics","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Conference on Fluvial Hydraulics","conferenceDate":"September 5-7, 2012","conferenceLocation":"San José, Costa Rica","language":"English","publisher":"CRC Press","usgsCitation":"Simoes, F.J., and Kinzel, P.J., 2016, Unsteady flow in natural compound channel: Experiment and simulation, <i>in</i> River flow 2012: Proceedings of the International Conference on Fluvial Hydraulics, San José, Costa Rica, September 5-7, 2012, p. 271-278.","productDescription":"8 p.","startPage":"271","endPage":"278","ipdsId":"IP-038128","costCenters":[{"id":5044,"text":"National Research Program - 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Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":647664,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70038143,"text":"ds669 - 2016 - Tabulated Transmissivity and Storage Properties of the Floridan Aquifer System in Florida and Parts of Georgia, South Carolina, and Alabama","interactions":[],"lastModifiedDate":"2016-12-02T11:57:07","indexId":"ds669","displayToPublicDate":"2012-04-19T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"669","title":"Tabulated Transmissivity and Storage Properties of the Floridan Aquifer System in Florida and Parts of Georgia, South Carolina, and Alabama","docAbstract":"<p>A goal of the U.S. Geological Survey Groundwater Resources Program is to assess the availability of fresh water within each of the principal aquifers in the United States with the greatest groundwater withdrawals. The Floridan aquifer system (FAS), which covers an area of approximately 100,000 square miles in Florida and parts of Georgia, Alabama, Mississippi, and South Carolina, is one such principal aquifer, having the fifth largest groundwater withdrawals in the Nation, totaling 3.64 billion gallons per day in 2000. Compilation of FAS hydraulic properties is critical to the development and calibration of groundwater flow models that can be used to develop water budgets spatially and temporally, as well as to evaluate resource changes over time. Wells with aquifer test data were identified as Upper Floridan aquifer (UFA), Lower Floridan aquifer (LFA), Floridan aquifer system (FAS, Upper Floridan with some middle and/or Lower Floridan), or middle Floridan confining unit (MCU), based on the identification from the original database or report description, or comparison of the open interval of the well with previously published maps.</p><p>This report consolidates aquifer hydraulic property data obtained from multiple databases and reports of the U.S. Geological Survey, various State agencies, and the Water Management Districts of Florida, that are compiled into tables to provide a single information source for transmissivity and storage properties of the FAS as of October 2011. Transmissivity calculated from aquifer pumping tests and specific-capacity data are included. Values for transmissivity and storage coefficients are intended for use in regional or sub regional groundwater flow models; thus, any tests (aquifer pumping tests and specific capacity data) that were conducted with packers or for open intervals less than 30 feet in length are excluded from the summary statistics and tables of this report, but are included in the database.</p><p>The transmissivity distribution from the aquifer pumping tests is highly variable. The transmissivity based on aquifer pumping tests (from 1,045 values for the UFA and FAS) ranges from 8 to about 9,300,000 square feet per day (ft<sup>2</sup>/d) and values of storage coefficient (646 reported) range from 3x10<sup>-9</sup> to 0.41. The 64 transmissivity values for the LFA range from about 130 to 4,500,000 ft<sup>2</sup>/d, and the 17 storage coefficient values range from 7x10<sup>-8</sup> to 0.03. The 14 transmissivity values for the MCU range from 1 to about 600,000 ft<sup>2</sup>/d and the 10 storage coefficient values range from 8x10<sup>-8</sup> to 0.03. Transmissivity estimates for the UFA and FAS for 442 specific capacity tests range from approximately 200 to 1,000,000 ft<sup>2</sup>/d.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds669","collaboration":"Product of the U.S. Geological Survey Groundwater Resources Program","usgsCitation":"Kuniansky, E.L., and Bellino, J.C., 2016, Tabulated transmissivity and storage properties of the Floridan aquifer system in Florida and parts of Georgia, South Carolina, and Alabama (ver. 1.1, May 2016): U.S. Geological Survey Data Series 669, 37 p., https://pubs.usgs.gov/ds/669.","productDescription":"v, 16 p.; Appendix; 2 Tables; Table 1: 17 inches x 11 inches, Table 2: 17 inches x 11 inches; DS699 FAS AQT DS zip file","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":321206,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table1-v1.1.pdf","text":"Table 1 - Transmissivity and storage coefficients from aquifer pumping tests ","size":"321 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 669"},{"id":321207,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table1-v1.1.xls","text":"Table 1 - Excel ","size":"355 KB xls","description":"DS 669"},{"id":321208,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table2-v1.1.pdf","text":"Table 2 -Transmissivity estimates from specific capacity data","size":"157 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 669"},{"id":321209,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table2-v1.1.xls","text":"Table 2 - Excel ","size":"132 KB xls","description":"DS 669"},{"id":321211,"rank":8,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/ds/669/DS669_v1.1FAS_AQT_DATA_04192016.zip","text":"Aquifer Data","size":"1 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 669"},{"id":321205,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/669/versionHist.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"DS 669"},{"id":254562,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/669/","linkFileType":{"id":5,"text":"html"},"description":"DS 669"},{"id":254564,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/669/images/coverthb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia, South Carolina","otherGeospatial":"Floridan Aquifer System","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -90,25 ], [ -90,34 ], [ -79.5,34 ], [ -79.5,25 ], [ -90,25 ] ] ] } } ] }","edition":"Version 1.1 May 13, 2016","contact":"<p>Director, Caribbean-Florida Water Science Center<br> U.S. Geological Surveyr<br> 4446 Pet Lane, Suite 108r<br> Lutz, FL 33559r<br> Telephone: (813) 498–5000r<br> <a href=\"http://fl.water.usgs.gov/\" data-mce-href=\"http://fl.water.usgs.gov/\">http://fl.water.usgs.gov/</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Summary Statistics</li><li>References Cited</li><li>Appendix 1</li></ul>","publishedDate":"2012-04-19","revisedDate":"2016-05-13","noUsgsAuthors":false,"publicationDate":"2012-04-19","publicationStatus":"PW","scienceBaseUri":"505ba3a5e4b08c986b31fdbc","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":463509,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bellino, Jason C. 0000-0001-9046-9344 jbellino@usgs.gov","orcid":"https://orcid.org/0000-0001-9046-9344","contributorId":3724,"corporation":false,"usgs":true,"family":"Bellino","given":"Jason","email":"jbellino@usgs.gov","middleInitial":"C.","affiliations":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"preferred":true,"id":463510,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70178484,"text":"70178484 - 2016 - Integrated water flow model and modflow-farm process: A comparison of theory, approaches, and features of two integrated hydrologic models","interactions":[],"lastModifiedDate":"2016-12-19T16:50:28","indexId":"70178484","displayToPublicDate":"2011-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5239,"text":"California Natural Resources Agency","active":true,"publicationSubtype":{"id":2}},"title":"Integrated water flow model and modflow-farm process: A comparison of theory, approaches, and features of two integrated hydrologic models","docAbstract":"Effective modeling of conjunctive use of surface and subsurface water resources requires simulation of land use-based root zone and surface flow processes as well as groundwater flows, streamflows, and their interactions. Recently, two computer models developed for this purpose, the Integrated Water Flow Model (IWFM) from the California Department of Water Resources and the MODFLOW with Farm Process (MF-FMP) from the US Geological Survey, have been applied to complex basins such as the Central Valley of California. As both IWFM and MFFMP are publicly available for download and can be applied to other basins, there is a need to objectively compare the main approaches and features used in both models. This paper compares the concepts, as well as the method and simulation features of each hydrologic model pertaining to groundwater, surface water, and landscape processes. The comparison is focused on the integrated simulation of water demand and supply, water use, and the flow between coupled hydrologic processes. The differences in the capabilities and features of these two models could affect the outcome and types of water resource problems that can be simulated.","language":"English","publisher":"California Department of Water Resources","collaboration":"California Department of Water Resources","usgsCitation":"Dogrul, E.C., Schmid, W., Hanson, R.T., Kadir, T., and Chung, F., 2016, Integrated water flow model and modflow-farm process: A comparison of theory, approaches, and features of two integrated hydrologic models: California Natural Resources Agency, i-ix, 70 p. .","productDescription":"i-ix, 70 p. ","ipdsId":"IP-014776","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":332299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331154,"type":{"id":15,"text":"Index Page"},"url":"https://baydeltaoffice.water.ca.gov/modeling/hydrology/IWFM/Publications/downloadables/Reports/IWFM%20and%20MF-FMP%20TIR-1%20(DWR-USGS%20Nov2011).pdf"}],"publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58590009e4b03639a6025e2f","contributors":{"authors":[{"text":"Dogrul, Emin C.","contributorId":177560,"corporation":false,"usgs":false,"family":"Dogrul","given":"Emin","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":656220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmid, Wolfgang","contributorId":84020,"corporation":false,"usgs":false,"family":"Schmid","given":"Wolfgang","affiliations":[{"id":13040,"text":"Department of Hydrology and Water Resources, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":656221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanson, Randall T. 0000-0002-9819-7141 rthanson@usgs.gov","orcid":"https://orcid.org/0000-0002-9819-7141","contributorId":801,"corporation":false,"usgs":true,"family":"Hanson","given":"Randall","email":"rthanson@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":656222,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kadir, Tariq","contributorId":26208,"corporation":false,"usgs":true,"family":"Kadir","given":"Tariq","email":"","affiliations":[],"preferred":false,"id":656223,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chung, Francis","contributorId":54488,"corporation":false,"usgs":true,"family":"Chung","given":"Francis","email":"","affiliations":[],"preferred":false,"id":656224,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70005430,"text":"sir20115136 - 2016 - Determination of dilution factors for discharge of aluminum-containing wastes by public water-supply treatment facilities into lakes and reservoirs in Massachusetts","interactions":[],"lastModifiedDate":"2017-03-03T15:28:13","indexId":"sir20115136","displayToPublicDate":"2011-09-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2011-5136","title":"Determination of dilution factors for discharge of aluminum-containing wastes by public water-supply treatment facilities into lakes and reservoirs in Massachusetts","docAbstract":"<p>Dilution of aluminum discharged to reservoirs in filter-backwash effluents at water-treatment facilities in Massachusetts was investigated by a field study and computer simulation. Determination of dilution is needed so that permits for discharge ensure compliance with water-quality standards for aquatic life. The U.S. Environmental Protection Agency chronic standard for aluminum, 87 micrograms per liter (μg/L), rather than the acute standard, 750 μg/L, was used in this investigation because the time scales of chronic exposure (days) more nearly match rates of change in reservoir concentrations than do the time scales of acute exposure (hours).</p><p>Whereas dilution factors are routinely computed for effluents discharged to streams solely on the basis of flow of the effluent and flow of the receiving stream, dilution determination for effluents discharged to reservoirs is more complex because (1), compared to streams, additional water is available for dilution in reservoirs during low flows as a result of reservoir flushing and storage during higher flows, and (2) aluminum removal in reservoirs occurs by aluminum sedimentation during the residence time of water in the reservoir. Possible resuspension of settled aluminum was not considered in this investigation. An additional concern for setting discharge standards is the substantial concentration of aluminum that can be naturally present in ambient surface waters, usually in association with dissolved organic carbon (DOC), which can bind aluminum and keep it in solution.</p><p>A method for dilution determination was developed using a mass-balance equation for aluminum and considering sources of aluminum from groundwater, surface water, and filter-backwash effluents and losses caused by sedimentation, water withdrawal, and spill discharge from the reservoir. The method was applied to 13 reservoirs. Data on aluminum and DOC concentrations in reservoirs and influent water were collected during the fall of 2009. Complete reservoir volume was determined to be available for mixing on the basis of vertical and horizontal aluminum-concentration profiling. Losses caused by settling of aluminum were assumed to be proportional to aluminum concentration and reservoir area. The constant of proportionality, as a function of DOC concentration, was established by simulations in each of five reservoirs that differed in DOC concentration.</p><p>In addition to computing dilution factors, the project determined dilution factors that would be protective with the same statistical basis (frequency of exceedance of the chronic standard) as dilutions computed for streams at the 7-day-average 10-year-recurrence annual low flow (the 7Q10). Low-flow dilutions are used for permitting so that receiving waters are protected even at the worst-case flow levels. The low-flow dilution factors that give the same statistical protection are the lowest annual 7-day-average dilution factors with a recurrence of 10 years, termed 7DF10s. Determination of 7DF10 values for reservoirs required that long periods of record be simulated so that dilution statistics could be determined. Dilution statistics were simulated for 13 reservoirs from 1960 to 2004 using U.S. Geological Survey Firm-Yield Estimator software to model reservoir inputs and outputs and present-day values of filter-effluent discharge and aluminum concentration.</p><p>Computed settling velocities ranged from 0 centimeters per day (cm/d) at DOC concentrations of 15.5 milligrams per liter (mg/L) to 21.5 cm/d at DOC concentrations of 2.7 mg/L. The 7DF10 values were a function of aluminum effluent discharged. At current (2009) effluent discharge rates, the 7DF10 values varied from 1.8 to 115 among the 13 reservoirs. In most cases, the present-day (2009) discharge resulted in receiving water concentrations that did not exceed the standard at the 7DF10. Exceptions were one reservoir with a very small area and three reservoirs with high concentrations of DOC. Maximum permissible discharges were determined for water-treatment plants by adjusting discharges upward in simulations until the 7DF10 resulted in reservoir concentrations that just met the standard. In terms of aluminum flux, these discharges ranged from 0 to 28 kilograms of aluminum per day.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20115136","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Colman, J.A., Massey, A.J., and Levin, S.B., 2016, Determination of dilution factors for discharge of aluminum-containing wastes by public water-supply treatment facilities into lakes and reservoirs in Massachusetts (ver. 1.1, December 2016): U.S. Geological Survey Scientific Investigations Report 2011–5136, 36 p., https://pubs.usgs.gov/sir/2011/5136.","productDescription":"vi, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":376,"text":"Massachusetts Water Science 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 \"}}]}","edition":"Version 1.0: Originally posted September 16, 2011; Version 1.1: December 30, 2016","contact":"<p><a href=\"mailto:dc_ma@usgs.gov\" data-mce-href=\"mailto:dc_ma@usgs.gov\">Director</a>, Massachusetts-Rhode Island Water Science Center<br> U.S. Geological Survey<br> 10 Bearfoot Road<br> Northborough, MA 01532<br> (508) 490-5000<br> <a href=\"http://ma.water.usgs.gov\" data-mce-href=\"http://ma.water.usgs.gov\">http://ma.water.usgs.gov</a></p>","publishedDate":"2011-09-16","revisedDate":"2016-12-30","noUsgsAuthors":false,"publicationDate":"2011-09-16","publicationStatus":"PW","scienceBaseUri":"4f4e4aa8e4b07f02db6677d3","contributors":{"authors":[{"text":"Colman, John A. 0000-0001-9327-0779 jacolman@usgs.gov","orcid":"https://orcid.org/0000-0001-9327-0779","contributorId":2098,"corporation":false,"usgs":true,"family":"Colman","given":"John","email":"jacolman@usgs.gov","middleInitial":"A.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":352501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Massey, Andrew J. 0000-0003-3995-8657 ajmassey@usgs.gov","orcid":"https://orcid.org/0000-0003-3995-8657","contributorId":1862,"corporation":false,"usgs":true,"family":"Massey","given":"Andrew","email":"ajmassey@usgs.gov","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":352500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, Sara L.","contributorId":89240,"corporation":false,"usgs":true,"family":"Brandt","given":"Sara","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":352502,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70179395,"text":"70179395 - 2016 - Wildfire, climate, and invasive grass interactions negatively impact an indicator species by reshaping sagebrush ecosystems","interactions":[],"lastModifiedDate":"2017-05-25T12:32:52","indexId":"70179395","displayToPublicDate":"2007-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2982,"text":"PNAS","active":true,"publicationSubtype":{"id":10}},"title":"Wildfire, climate, and invasive grass interactions negatively impact an indicator species by reshaping sagebrush ecosystems","docAbstract":"Iconic sagebrush ecosystems of the American West are threatened by larger and more frequent wildfires that can kill sagebrush and facilitate invasion by annual grasses, creating a cycle that alters sagebrush ecosystem recovery post disturbance. Thwarting this accelerated grass–fire cycle is at the forefront of current national conservation efforts, yet its impacts on wildlife populations inhabiting these ecosystems have not been quantified rigorously. Within a Bayesian framework, we modeled 30 y of wildfire and climatic effects on population rates of change of a sagebrush-obligate species, the greater sage-grouse, across the Great Basin of western North America. Importantly, our modeling also accounted for variation in sagebrush recovery time post fire as determined by underlying soil properties that influence ecosystem resilience to disturbance and resistance to invasion. Our results demonstrate that the cumulative loss of sagebrush to direct and indirect effects of wildfire has contributed strongly to declining sage-grouse populations over the past 30 y at large spatial scales. Moreover, long-lasting effects from wildfire nullified pulses of sage-grouse population growth that typically follow years of higher precipitation. If wildfire trends continue unabated, model projections indicate sage-grouse populations will be reduced to 43% of their current numbers over the next three decades. Our results provide a timely example of how altered fire regimes are disrupting recovery of sagebrush ecosystems and leading to substantial declines of a widespread indicator species. Accordingly, we present scenario-based stochastic projections to inform conservation actions that may help offset the adverse effects of wildfire on sage-grouse and other wildlife populations.","language":"English","publisher":"HighWire Press","doi":"10.1073/pnas.1606898113","usgsCitation":"Coates, P.S., Ricca, M.A., Prochazka, B.G., Brooks, M.L., Doherty, K., Kroger, T., Blomberg, E.J., Hagen, C.A., and Casazza, M.L., 2016, Wildfire, climate, and invasive grass interactions negatively impact an indicator species by reshaping sagebrush ecosystems: PNAS, v. 113, no. 45, p. 12745-12750, https://doi.org/10.1073/pnas.1606898113.","productDescription":"6 p.","startPage":"12745","endPage":"12750","ipdsId":"IP-078711","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471482,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1606898113","text":"Publisher Index Page"},{"id":332700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"45","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-25","publicationStatus":"PW","scienceBaseUri":"586781f9e4b0cd2dabe7c71f","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657068,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657069,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657071,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doherty, Kevin E.","contributorId":177793,"corporation":false,"usgs":false,"family":"Doherty","given":"Kevin E.","affiliations":[],"preferred":false,"id":657072,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kroger, Travis 0000-0002-4168-6323 tkroger@usgs.gov","orcid":"https://orcid.org/0000-0002-4168-6323","contributorId":177794,"corporation":false,"usgs":true,"family":"Kroger","given":"Travis","email":"tkroger@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657073,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blomberg, Erik J.","contributorId":17543,"corporation":false,"usgs":false,"family":"Blomberg","given":"Erik","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":657074,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hagen, Christian A.","contributorId":177795,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":657075,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657076,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":75513,"text":"sir20055289 - 2016 - Development and evaluation of clear-water pier and contraction scour envelope curves in the Coastal Plain and Piedmont Provinces of South Carolina","interactions":[],"lastModifiedDate":"2017-06-01T08:57:09","indexId":"sir20055289","displayToPublicDate":"2006-03-13T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2005-5289","title":"Development and evaluation of clear-water pier and contraction scour envelope curves in the Coastal Plain and Piedmont Provinces of South Carolina","docAbstract":"<p>The U.S. Geological Survey in cooperation with the South Carolina Department of Transportation collected clear-water pier- and contraction-scour data at 116 bridges in the Coastal Plain and Piedmont Physiographic Provinces of South Carolina. Pier-scour depths collected in both provinces ranged from 0 to 8.0 feet. Contraction-scour depths collected in the Coastal Plain ranged from 0 to 3.9 feet. Using hydraulic data estimated with a one-dimensional flow model, predicted clear-water scour depths were computed with scour equations from the Federal Highway Administration Hydraulic Engineering Circular 18 and compared with measured scour. This comparison indicated that predicted clear-water scour depths, in general, exceeded measured scour depths and at times were excessive. Predicted clear-water contraction scour, however, was underpredicted approximately 30 percent of the time by as much as 7.1 feet. </p><p>The investigation focused on clear-water pier scour, comparing trends in the laboratory and field data. This comparison indicated that the range of dimensionless variables (relative depth, flow intensity, relative grain size) used in laboratory investigations of pier scour, were similar to the range for field data in South Carolina, further indicating that laboratory relations may have some applicability to field conditions in South Carolina. Variables determined to be important in developing pier scour in laboratory studies were investigated to understand their influence on the South Carolina field data, and many of these variables appeared to be insignificant under field conditions in South Carolina. The strongest explanatory variables were pier width and approach velocity. Envelope curves developed from the field data are useful tools for evaluating reasonable ranges of clear-water pier and contraction scour in South Carolina. A modified version of the Hydraulic Engineering Circular 18 pier-scour equation also was developed as a tool for evaluating clearwater pier scour. The envelope curves and modified equation offer an improvement over the current methods for predicting clear-water scour in South Carolina. </p><p>Data from this study were compiled into a database that includes photographs, measured scour depths, predicted scour depths, limited basin characteristics, limited soil data, and modeled hydraulic data. The South Carolina database can be used to compare studied sites with unstudied sites to evaluate the potential for scour at the unstudied sites. In addition, the database can be used to evaluate the performance of various methods for predicting clear-water pier and contraction scour.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20055289","collaboration":"Prepared in cooperation with the South Carolina Department of Transportaton","usgsCitation":"Benedict, S., and Caldwell, A.W., 2016, Development and evaluation of clear-water pier and contraction scour envelope curves in the Coastal Plain and Piedmont Provinces of South Carolina (Version 1.0: Originally posted 2006; Version 1.1: August 31, 2016): U.S. Geological Survey Scientific Investigations Report 2005-5289, Report: x, 98 p.; Database, https://doi.org/10.3133/sir20055289.","productDescription":"Report: x, 98 p.; Database","numberOfPages":"112","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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Carolina\",\"nation\":\"USA  \"}}]}","edition":"Version 1.0: Originally posted 2006; Version 1.1: August 31, 2016","contact":"<p>South Carolina Water Science Center<br /> U.S. Geological Survey<br />720 Gracern Road<br /> Stephenson Center, Suite 129<br /> Columbia, SC 29210<br /> <a href=\"http://sc.water.usgs.gov/\">http://sc.water.usgs.gov/</a></p>","tableOfContents":"<ul>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Approach</li>\n<li>Data Collection</li>\n<li>Development of the Predicted Bridge-Scour Database</li>\n<li>Development of the South Carolina Pier-Scour Envelope Curve</li>\n<li>Evaluation of Selected Methods for Predicting Clear-Water Pier Scour in South Carolina</li>\n<li>Guidance for Evaluating Pier-Scour Depth in South Carolina</li>\n<li>Development of the South Carolina Clear-Water Contraction-Scour Envelope Curves</li>\n<li>The South Carolina Clear-Water Pier- and Contraction-Scour Database</li>\n<li>Summary</li>\n<li>Selected References</li>\n<li>Appendix 1</li>\n<li>Appendix 2</li>\n</ul>","revisedDate":"2016-08-31","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4aa7e4b07f02db66728f","contributors":{"authors":[{"text":"Benedict, Stephen T. benedict@usgs.gov","contributorId":3198,"corporation":false,"usgs":true,"family":"Benedict","given":"Stephen T.","email":"benedict@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":286891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caldwell, Andral W. 0000-0003-1269-5463 acaldwel@usgs.gov","orcid":"https://orcid.org/0000-0003-1269-5463","contributorId":3228,"corporation":false,"usgs":true,"family":"Caldwell","given":"Andral","email":"acaldwel@usgs.gov","middleInitial":"W.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":286892,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70148551,"text":"sir20155080 - 2015 - Surface-water quality in agricultural watersheds of the North Carolina Coastal Plain associated with concentrated animal feeding operations","interactions":[],"lastModifiedDate":"2019-08-13T08:57:12","indexId":"sir20155080","displayToPublicDate":"2019-08-13T10:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5080","title":"Surface-water quality in agricultural watersheds of the North Carolina Coastal Plain associated with concentrated animal feeding operations","docAbstract":"<p>The effects of concentrated animal feeding operations (CAFOs) on water quality were investigated at 54 agricultural stream sites throughout the North Carolina Coastal Plain during 2012 and 2013. Three general watershed land-use types were examined during the study, including 18 background watersheds with no active CAFOs (BK sites), 18 watersheds with one or more active swine CAFOs but no poultry CAFOs (SW sites), and 18 watersheds with at least one active swine CAFO and one active dry-litter poultry CAFO (SP sites). The watershed drainage areas for these 54 stream sites ranged from 1.2 to 17.5 square miles. Conventional fertilizers used for crop production are the primary source of nutrients at the BK sites. Animal-waste manures represent an additional source of nutrients at the SW and SP study sites.</p>\n<p>Land cover, soil drainage, and CAFO attributes were compiled for each watershed. Water-quality field measurements were made and samples were collected at the 54 primary sites during 6 bimonthly sampling periods from June 2012 to April 2013. An additional 23 secondary sites were sampled once during April 2013 to provide supplemental data at stream locations directly adjacent or in close proximity to swine CAFOs and (or) background agricultural areas within 9 of the primary watersheds. The watershed drainage areas for the 23 secondary sites ranged from 0.2 to 8.9 square miles. Water temperature, specific conductance, dissolved-oxygen concentration, and pH were measured directly in the streams. Water samples were analyzed for major ions, nutrients, and stable isotopes, including delta hydrogen-2 (&delta;<sup>2</sup>H) and delta oxygen-18 (&delta;<sup>18</sup>O) of water and delta nitrogen-15 (&delta;<sup>15</sup>N) and &delta;<sup>18</sup>O of dissolved nitrate plus nitrite.</p>\n<p>Most of the water-quality properties and constituents varied significantly among the six sampling periods, changing both seasonally and in response to hydrologic conditions. The differences noted among the sampling periods indicate that the interactions between seasonal climatic differences, streamflow conditions, and instream biotic and abiotic processes are complex and their integrated effects can have varying degrees of influence on individual nutrients.</p>\n<p>Water-quality differences were noted for the SW and SP land-use groups relative to the BK group. Median values of specific conductance, several major ions (magnesium, sodium, potassium, and chloride), and nitrogen fractions (ammonia plus organic nitrogen, ammonia, nitrate plus nitrite, total nitrogen, and &delta;<sup>15</sup>N of nitrate plus nitrite) were higher for the SW and SP groups compared to the BK group. No significant differences in water temperature, dissolved oxygen, calcium, total organic nitrogen, orthophosphate, total phosphorus, or &delta;<sup>18</sup>O of nitrate plus nitrite were noted among the land-use groups. When compared on the basis of land-use type, there was an overall measurable effect of CAFO waste manures on stream water quality for the SW and SP watershed groups.</p>\n<p>Some individual sites within the SW and SP groups showed no measurable CAFO effects on water quality despite having CAFOs present upstream. An evaluation of sodium plus potassium concentrations coupled with &delta;<sup>15</sup>N values of nitrate plus nitrite proved valuable for distinguishing which SW and SP sites had a water-quality signature indicative of CAFO waste manures. Sites with CAFO manure effects were characterized by higher sodium plus potassium concentrations (commonly between 11 and 33 milligrams per liter) and &delta;<sup>15</sup>N values of nitrate plus nitrite (commonly between 11 and 26 parts per thousand) relative to sites reflecting background agricultural conditions, which commonly had sodium plus potassium concentrations between 6 and 14 milligrams per liter and &delta;<sup>15</sup>N values of nitrate plus nitrite between 6 and 15 parts per thousand. On the basis of the results of this study, land applications of waste manure at swine CAFOs influenced ion and nutrient chemistry in many of the North Carolina Coastal Plain streams that were studied.</p>\n<p>A classification tree model was developed to examine relations of watershed environmental attributes among the study sites with and without CAFO manure effects. Model results indicated that variations in swine barn density, percentage of wetlands, and total acres available for applying swine-waste manures had an important influence on those watersheds where CAFO effects on water quality were either evident or mitigated. Measurable effects of CAFO waste manures on stream water quality were most evident in those SW and SP watersheds having lower percentages of wetlands combined with higher swine barn densities and (or) higher total acres available for applying waste manure at the swine CAFOs. Stream water quality was similar to background agricultural conditions in SW and SP watersheds with lower swine barn densities coupled with higher percentages of wetlands or lower acres available for swine manure applications. The model provides a useful tool for exploring and identifying similar, unmonitored watersheds in the North Carolina Coastal Plain with potential CAFO manure influences on water quality that might warrant further examination.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155080","collaboration":"Prepared in cooperation with the North Carolina Department of Environment and Natural Resources, Division of Water Resources","usgsCitation":"Harden, S.L., 2015, Surface-water quality in agricultural watersheds of the North Carolina Coastal Plain associated with concentrated animal feeding operations: U.S. Geological Survey Scientific Investigations Report 2015-5080, Report: ix, 55 p.; 7 Appendices, https://doi.org/10.3133/sir20155080.","productDescription":"Report: ix, 55 p.; 7 Appendices","numberOfPages":"70","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2012-01-02","temporalEnd":"2013-12-31","ipdsId":"IP-060201","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":301512,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155080.jpg"},{"id":366493,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA2/sir20155080_appendixA2_2.xlsx","text":"Appendix A2-2","size":"28 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled hydrologic soil group data for each study site"},{"id":366494,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_2.xlsx","text":"Appendix A3-2","size":"13 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Inactive swine CAFO permits in sites"},{"id":366498,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_6.xlsx","text":"Appendix A3-6","size":"14 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Poultry CAFO attribute data by facility"},{"id":366495,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_3.xlsx","text":"Appendix A3-3","size":"37 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Active swine CAFO permits in sites"},{"id":366496,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_4.xlsx","text":"Appendix A3-4","size":"24 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Swine CAFO attribute data by permit"},{"id":366499,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_7.xlsx","text":"Appendix A3-7","size":"12 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Poultry CAFO attribute data by study site"},{"id":366500,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA4/sir20155080_appendixA4_2.xlsx","text":"Appendix A4-2","size":"13 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled water-quality data for precipitation samples"},{"id":366506,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_5.xlsx","text":"Appendix A3-5","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Summary of swine CAFO attribute data by watershed study site"},{"id":366501,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA4/sir20155080_appendixA4_3.xlsx","text":"Appendix A4-3","size":"14 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Results of field blanks"},{"id":366502,"rank":16,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA4/sir20155080_appendixA4_4.xlsx","text":"Appendix A4-4","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Results of replicate samples"},{"id":301478,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_1.xlsx","text":"Appendix A3-1","size":"13 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Permitted non-discharge facilities in sites"},{"id":301479,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA4/sir20155080_appendixA4_1.xlsx","text":"Appendix A4-1","size":"94 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled water-quality data for surface-water samples"},{"id":301481,"rank":19,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA6/sir20155080_appendixA6.xlsx","text":"Appendix A6","size":"26 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Data used for distinguishing sites with manure influences"},{"id":301480,"rank":17,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA5/sir2015-5080_appendixA5.pdf","text":"Appendix A5","size":"11.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Detailed Evaluations of the April 2013 Water-Quality Dataset"},{"id":301477,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA2/sir20155080_appendixA2_1.xlsx","text":"Appendix A2-1","size":"28 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled land-cover data for each study site"},{"id":301482,"rank":20,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA7/sir20155080_appendixA7.xlsx","text":"Appendix A7","size":"24 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled study site data used in classification tree analyses"},{"id":301476,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA1/sir20155080_appendixA1.pdf","text":"Appendix A1","size":"31.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Maps for the primary and secondary study sites"},{"id":301474,"rank":0,"type":{"id":15,"text":"Index 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,{"id":70147329,"text":"sir20155066 - 2015 - Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats","interactions":[{"subject":{"id":70147329,"text":"sir20155066 - 2015 - Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats","indexId":"sir20155066","publicationYear":"2015","noYear":false,"title":"Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats"},"predicate":"SUPERSEDED_BY","object":{"id":70200937,"text":"sir20185157 - 2019 - A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats","indexId":"sir20185157","publicationYear":"2019","noYear":false,"title":"A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats"},"id":1}],"supersededBy":{"id":70200937,"text":"sir20185157 - 2019 - A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats","indexId":"sir20185157","publicationYear":"2019","noYear":false,"title":"A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats"},"lastModifiedDate":"2019-07-23T13:11:00","indexId":"sir20155066","displayToPublicDate":"2019-07-23T14:11:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5066","title":"Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats","docAbstract":"<p>The U.S. Geological Survey has developed a methodology to assess the impacts of wind energy development on wildlife; it is a probabilistic, quantitative assessment methodology that can communicate to decision makers and the public the magnitude of these effects on species populations. The methodology is currently applicable to birds and bats, focuses primarily on the effects of collisions, and can be applied to any species that breeds in, migrates through, or otherwise uses any part of the United States. The methodology is intended to assess species at the national scale and is fundamentally different from existing methods focusing on impacts at individual facilities.</p>\n<p>Publicly available fatality information, population estimates, species range maps, turbine location data, biological characteristics, and generic population models are used to generate both a ranked list of species based on relative risk as well as quantitative measures of the magnitude of the effect on species' population trend and size. Three metrics are combined to determine direct and indirect relative risk to populations. A generic population model is used to estimate the expected change in population trend and includes additive mortality from collisions with wind turbines. Lastly, the methodology uses observed fatalities and an estimate of potential biological removal to assess the risk of a decline in population size. Data for six bird species have been processed through the entire methodology as a test case, and the results are presented in this report.</p>\n<p>Components of the methodology are based on simplifying assumptions and require information that, for many species, may be sparse or unreliable. These assumptions are presented in the report and should be carefully considered when using output from the methodology. In addition, this methodology can be used to recommend species for more intensive demographic modeling or highlight those species that may not require any additional protection because effects of wind energy development on their populations are projected to be small.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155066","usgsCitation":"Diffendorfer, J., Beston, J.A., Merrill, M., Stanton, J.C., Corum, M., Loss, S., Thogmartin, W.E., Johnson, D.H., Erickson, R.A., and Heist, K.W., 2015, Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats: U.S. Geological Survey Scientific Investigations Report 2015-5066, 51 p., https://doi.org/10.3133/sir20155066.","productDescription":"51 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063536","costCenters":[{"id":241,"text":"Eastern Energy Resources Science 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douglas_h_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":1387,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas","email":"douglas_h_johnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":545803,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":545804,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heist, Kevin W.","contributorId":83040,"corporation":false,"usgs":false,"family":"Heist","given":"Kevin","email":"","middleInitial":"W.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":545805,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70195581,"text":"70195581 - 2015 - Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus)","interactions":[],"lastModifiedDate":"2018-02-23T10:50:34","indexId":"70195581","displayToPublicDate":"2018-02-23T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Activity-specific ecological niche models for planning reintroductions of California condors (<i>Gymnogyps californianus</i>)","title":"Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus)","docAbstract":"<p><span>Ecological niche models can be a useful tool to identify candidate reintroduction sites for endangered species but have been infrequently used for this purpose. In this paper, we (1) develop activity-specific ecological niche models (nesting, roosting, and feeding) for the critically endangered California condor (</span><i>Gymnogyps californianus</i><span>) to aid in reintroduction planning in California, Oregon, and Washington, USA, (2) test the accuracy of these models using empirical data withheld from model development, and (3) integrate model results with information on condor movement ecology and biology to produce predictive maps of reintroduction site suitability. Our approach, which disentangles niche models into activity-specific components, has applications for other species where it is routinely assumed (often incorrectly) that individuals fulfill all requirements for life within a single environmental space. Ecological niche models conformed to our understanding of California condor ecology, had good predictive performance when tested with data withheld from model development, and aided in the identification of several candidate reintroduction areas outside of the current distribution of the species. Our results suggest there are large unoccupied regions of the California condor’s historical range that have retained ecological features similar to currently occupied habitats, and thus could be considered for future reintroduction efforts. Combining our activity-specific ENMs with ground reconnaissance and information on other threat factors that could not be directly incorporated into empirical ENMs will ultimately improve our ability to select successful reintroduction sites for the California condor.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2015.01.002","usgsCitation":"D'Elia, J., Haig, S.M., Johnson, M.J., Marcot, B.G., and Young, R., 2015, Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus): Biological Conservation, v. 184, p. 90-99, https://doi.org/10.1016/j.biocon.2015.01.002.","productDescription":"10 p.","startPage":"90","endPage":"99","ipdsId":"IP-061907","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":351901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -126.38671874999999,\n              32.54681317351514\n            ],\n            [\n              -116.89453125,\n              32.54681317351514\n            ],\n            [\n              -116.89453125,\n              49.26780455063753\n            ],\n            [\n              -126.38671874999999,\n              49.26780455063753\n            ],\n            [\n              -126.38671874999999,\n              32.54681317351514\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"184","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afeeb03e4b0da30c1bfc63e","contributors":{"authors":[{"text":"D'Elia, Jesse","contributorId":63152,"corporation":false,"usgs":true,"family":"D'Elia","given":"Jesse","affiliations":[],"preferred":false,"id":729364,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":729365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Matthew J. mjjohnson@usgs.gov","contributorId":3604,"corporation":false,"usgs":true,"family":"Johnson","given":"Matthew","email":"mjjohnson@usgs.gov","middleInitial":"J.","affiliations":[{"id":27989,"text":"Colorado Plateau Research Station, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":729366,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marcot, Bruce G.","contributorId":152612,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":18944,"text":"Pacific Northwest Research Station, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":729367,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, Richard","contributorId":202719,"corporation":false,"usgs":false,"family":"Young","given":"Richard","affiliations":[],"preferred":false,"id":729368,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70231261,"text":"70231261 - 2015 - Projected changes in diverse ecosystems from climate warming and biophysical drivers in northwest Alaska","interactions":[],"lastModifiedDate":"2022-05-04T14:07:39.294065","indexId":"70231261","displayToPublicDate":"2018-01-15T08:58:11","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Projected changes in diverse ecosystems from climate warming and biophysical drivers in northwest Alaska","docAbstract":"<p>Climate warming affects arctic and boreal ecosystems by interacting with numerous biophysical factors across heterogeneous landscapes. To assess potential effects of warming on diverse local-scale ecosystems (ecotypes) across northwest Alaska, we compiled data on historical areal changes over the last 25–50&nbsp;years. Based on historical rates of change relative to time and temperature, we developed three state-transition models to project future changes in area for 60 ecotypes involving 243 potential transitions during three 30-year periods (ending 2040, 2070, 2100). The time model, assuming changes over the past 30&nbsp;years continue at the same rate, projected a net change, or directional shift, of 6&nbsp;% by 2100. The temperature model, using past rates of change relative to the past increase in regional mean annual air temperatures (1&nbsp;°C/30&nbsp;year), projected a net change of 17&nbsp;% in response to expected warming of 2, 4, and 6&nbsp;°C at the end of the three periods. A rate-adjusted temperature model, which adjusted transition rates (±50&nbsp;%) based on assigned feedbacks associated with 23 biophysical drivers, estimated a net change of 13&nbsp;%, with 33 ecotypes gaining and 23 ecotypes losing area. Major drivers included shrub and tree expansion, fire, succession, and thermokarst. Overall, projected changes will be modest over the next century even though climate warming increased transition rates up to 9 fold. The strength of this state-transition modeling is that it used a large dataset of past changes to provide a comprehensive assessment of likely future changes associated with numerous drivers affecting the full diversity of ecosystems across a broad region.</p>","language":"English","publisher":"Springer","doi":"10.1007/s10584-014-1302-1","usgsCitation":"Jorgenson, M.T., Marcot, B.G., Swanson, D.K., Jorgenson, J.C., and DeGange, A.R., 2015, Projected changes in diverse ecosystems from climate warming and biophysical drivers in northwest Alaska: Climatic Change, v. 130, p. 131-144, https://doi.org/10.1007/s10584-014-1302-1.","productDescription":"14 p.","startPage":"131","endPage":"144","ipdsId":"IP-051465","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":471483,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10584-014-1302-1","text":"Publisher Index Page"},{"id":400129,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -168.4423828125,\n              64.32087157990324\n            ],\n            [\n              -149.326171875,\n              64.32087157990324\n            ],\n            [\n              -149.326171875,\n              71.63599288330609\n            ],\n            [\n              -168.4423828125,\n              71.63599288330609\n            ],\n            [\n              -168.4423828125,\n              64.32087157990324\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"130","noUsgsAuthors":false,"publicationDate":"2015-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Jorgenson, Mark Torre 0000-0002-9834-8851","orcid":"https://orcid.org/0000-0002-9834-8851","contributorId":169365,"corporation":false,"usgs":false,"family":"Jorgenson","given":"Mark","email":"","middleInitial":"Torre","affiliations":[{"id":13506,"text":"Alaska Ecoscience","active":true,"usgs":false}],"preferred":false,"id":842149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marcot, Bruce G.","contributorId":140456,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":842150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swanson, David K.","contributorId":178902,"corporation":false,"usgs":false,"family":"Swanson","given":"David","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":842151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jorgenson, Janet C.","contributorId":191903,"corporation":false,"usgs":false,"family":"Jorgenson","given":"Janet","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":842152,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeGange, Anthony R. tdegange@usgs.gov","contributorId":139765,"corporation":false,"usgs":true,"family":"DeGange","given":"Anthony","email":"tdegange@usgs.gov","middleInitial":"R.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":842153,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70184484,"text":"70184484 - 2015 - Demographic rates and population viability of black bears in Louisiana","interactions":[],"lastModifiedDate":"2017-03-10T11:39:39","indexId":"70184484","displayToPublicDate":"2017-03-10T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3773,"text":"Wildlife Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Demographic rates and population viability of black bears in Louisiana","docAbstract":"<p><span>The Louisiana black bear (</span><i>Ursus americanus luteolus</i><span>) was reduced to a few small, fragmented, and isolated subpopulations in the Lower Mississippi Alluvial Valley by the mid-twentieth century resulting from loss and fragmentation of habitat. In 1992, the United States Fish and Wildlife Service (USFWS) granted the Louisiana black bear threatened status under the United States Endangered Species Act of 1973. Since that time, a recovery plan was developed, a reintroduced population was established, and habitat recovery has occurred. The Recovery Plan states that a minimum of 2 populations must be viable (i.e., persistence probabilities over 100 years &gt;0.95), 1 in the Tensas River Basin and 1 in the Atchafalaya River Basin. Consequently, our objectives were to 1) estimate demographic rates of Louisiana black bear subpopulations, 2) develop data-driven stochastic population projection models, and 3) determine how different projection model assumptions affect population trajectories and predictions about long-term persistence. Our overall goal was to assess long-term persistence of the bear subpopulations in Louisiana, individually and as a whole. We collected data using varying combinations of non-invasive DNA sampling, live capture, winter den visits, and radio monitoring from 2002 to 2012 in the 4 areas currently supporting breeding subpopulations in Louisiana: Tensas River Basin (TRB), Upper Atchafalaya River Basin (UARB), Lower Atchafalaya River Basin (LARB), and a recently reintroduced population at the Three Rivers Complex (TRC). From 2002 to 2012, we radio monitored fates of 86 adult females within the TRB and 43 in the TRC. Mean estimates of annual adult survival for the TRB and TRC were 0.997 and 0.990, respectively, when unknown fates were assumed alive and 0.970 and 0.926 when unknown fates were assumed dead. From 2003 to 2013, we observed 130 cub litters from 74 females in the TRB, and 74 cub litters from 45 females in the TRC. During the same period, we observed 43 yearling litters for 33 females in the TRB and 21 yearling litters for 19 females in the TRC. The estimated number of cubs and number of yearlings produced per breeding adult female was 0.47 and 0.20, respectively, in the TRB and 0.32 and 0.18 in the TRC. On the basis of matrix projection models, asymptotic growth rates ranged from 1.053 to 1.078 for the TRB and from 1.005 to 1.062 for the TRC, depending on how we treated unresolved fates of adult females. Persistence probabilities estimated from stochastic population models based on telemetry data ranged from 0.997 to 0.998 for the TRC subpopulation depending on model assumptions and were &gt;0.999 for the TRB regardless of model assumptions. We extracted DNA from hair collected at baited, barbed-wire enclosures in the TRB, UARB, and LARB to determine individual identities for capture-mark-recapture (CMR) analysis. We used those detection histories to estimate apparent survival (</span><i>φ</i><span>), per-capita recruitment (</span><i>f</i><span>), abundance (</span><i>N</i><span>), realized growth rate (</span><i>λ</i><span>), and long-term viability, based on Bayesian hierarchical modeling methods that allowed estimation of temporal process variance and parameter uncertainty. Based on 23,312 hair samples, annual </span><i>N</i><span> for females in the TRB ranged from 133 to 164 during 2006–2012, depending on year and how detection heterogeneity was modeled. Geometric mean of </span><i>λ</i><span> ranged from 0.996 to 1.002. In the UARB, we collected 11,643 hair samples from 2007 to 2012, from which estimates of </span><i>N</i><span> for females ranged from 23 to 43 during the study period, depending on detection heterogeneity model. The geometric mean of </span><i>λ</i><span> ranged from 1.038 to 1.059. Estimated </span><i>N</i><span> for females in LARB ranged from 69 to 96, and annual </span><i>λ</i><span> ranged from 0.80 to 1.11 based on 3,698 hair samples collected during 2010–2012, also depending on year and heterogeneity model. Probabilities of persistence over 100 years for the TRC and TRB based on stochastic matrix projection models that used vital rate estimates from telemetry data were &gt;0.95 for all scenarios. Probability of persistence at the TRB and the UARB based on projection models that used vital rate estimates from CMR analyses ranged from 0.928 to 0.954 and from 0.906 to 0.959, respectively, depending on model assumptions. Data from the LARB were insufficient for a viability assessment. Thus, individual persistence probabilities for TRB and UARB did not meet the strict definition of viability (i.e., &gt;0.95) under some model assumptions. However, the joint probability of bears persisting either in the TRB or UARB was &gt;0.993 assuming individual population dynamics were independent and was &gt;0.958 assuming dynamics were perfectly correlated. Furthermore, including the TRC increased the joint probability of bears persisting somewhere in the TRB, UARB, or TRC to &gt;0.999 based on the most pessimistic individual persistence estimates from those subpopulations. Therefore, if the intent of specifying that 2 subpopulations should be viable was to ensure the persistence of Louisiana black bears somewhere within its historical range, then the viability threshold was met. © 2016 The Wildlife Society.</span></p>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wmon.1018","collaboration":"U.S. Fish and Wildlife Service; Louisiana Department of Wildlife and Fisheries","usgsCitation":"Laufenberg, J.S., Clark, J.D., Hooker, M.J., Lowe, C.L., O’Connell-Goode, K.C., Troxler, J.C., Davidson, M., Chamberlain, M.J., and Chandler, R.B., 2015, Demographic rates and population viability of black bears in Louisiana: Wildlife Monographs, v. 194, no. 1, p. 1-37, https://doi.org/10.1002/wmon.1018.","productDescription":"37 p.","startPage":"1","endPage":"37","ipdsId":"IP-069827","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science 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S.","contributorId":28899,"corporation":false,"usgs":false,"family":"Laufenberg","given":"Jared","email":"","middleInitial":"S.","affiliations":[{"id":7006,"text":"Department of Forestry, Wildlife and Fisheries, University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":681670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":681669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hooker, Michael J.","contributorId":187784,"corporation":false,"usgs":false,"family":"Hooker","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":681671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lowe, Carrie L.","contributorId":187785,"corporation":false,"usgs":false,"family":"Lowe","given":"Carrie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":681672,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Connell-Goode, Kaitlin C.","contributorId":187786,"corporation":false,"usgs":false,"family":"O’Connell-Goode","given":"Kaitlin","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":681673,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Troxler, Jesse C.","contributorId":187787,"corporation":false,"usgs":false,"family":"Troxler","given":"Jesse","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":681674,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davidson, Maria M.","contributorId":187788,"corporation":false,"usgs":false,"family":"Davidson","given":"Maria M.","affiliations":[],"preferred":false,"id":681675,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chamberlain, Michael J.","contributorId":179350,"corporation":false,"usgs":false,"family":"Chamberlain","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":681676,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chandler, Richard B. 0000-0003-4930-2790 rchandler@usgs.gov","orcid":"https://orcid.org/0000-0003-4930-2790","contributorId":187789,"corporation":false,"usgs":false,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","middleInitial":"B.","affiliations":[{"id":13267,"text":"Warnell School of Forestry and Natural Resources, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":681677,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70182768,"text":"70182768 - 2015 - Collapse risk of buildings in the Pacific Northwest region due to subduction earthquakes","interactions":[],"lastModifiedDate":"2017-03-06T11:56:42","indexId":"70182768","displayToPublicDate":"2017-02-28T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Collapse risk of buildings in the Pacific Northwest region due to subduction earthquakes","docAbstract":"<p><span>Subduction earthquakes similar to the 2011 Japan and 2010 Chile events will occur in the future in the Cascadia subduction zone in the Pacific Northwest. In this paper, nonlinear dynamic analyses are carried out on 24 buildings designed according to outdated and modern building codes for the cities of Seattle, Washington, and Portland, Oregon. The results indicate that the median collapse capacity of the ductile (post-1970) buildings is approximately 40% less when subjected to ground motions from subduction, as compared to crustal earthquakes. Buildings are more susceptible to earthquake-induced collapse when shaken by subduction records (as compared to crustal records of the same intensity) because the subduction motions tend to be longer in duration due to their larger magnitude and the greater source-to-site distance. As a result, subduction earthquakes are shown to contribute to the majority of the collapse risk of the buildings analyzed.</span></p>","language":"English","publisher":"Earthquake Engineering Research Institute","doi":"10.1193/012114EQS011M","usgsCitation":"Raghunandan, M., Liel, A.B., and Luco, N., 2015, Collapse risk of buildings in the Pacific Northwest region due to subduction earthquakes: Earthquake Spectra, v. 31, no. 4, p. 2087-2115, https://doi.org/10.1193/012114EQS011M.","productDescription":"29 p.","startPage":"2087","endPage":"2115","ipdsId":"IP-060810","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":336341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Pacific Northwest","volume":"31","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-01","publicationStatus":"PW","scienceBaseUri":"58b69a42e4b01ccd54ff3fa6","contributors":{"authors":[{"text":"Raghunandan, Meera","contributorId":184157,"corporation":false,"usgs":false,"family":"Raghunandan","given":"Meera","email":"","affiliations":[],"preferred":false,"id":673681,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liel, Abbie B.","contributorId":184158,"corporation":false,"usgs":false,"family":"Liel","given":"Abbie","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":673682,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":673680,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70159601,"text":"70159601 - 2015 - Management of aspen in a changing environment","interactions":[],"lastModifiedDate":"2017-11-22T15:36:39","indexId":"70159601","displayToPublicDate":"2016-12-31T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Management of aspen in a changing environment","docAbstract":"<ul><li>Aspen communities are biologically rich and ecologically valuable, yet they face myriad threats, including changing climate, altered fire regimes, and excessive browsing by domestic and wild ungulates.&nbsp;</li><li>Recognizing the different types of aspen communities that occur in the Great Basin, and being able to distinguish between seral and stable aspen stands, can help managers better identify restoration needs and objectives. </li><li>Identifying key threats to aspen regeneration and persistence in a given stand or landscape is important to designing restoration plans, and to selecting appropriate treatment types. </li><li>Although some aspen stands will need intensive treatment (e.g., use of fire) to persist or remain healthy, other stands may only require the modification of current management practices (e.g., reducing livestock browsing) or may not require any action at all (e.g., self-replacing stable aspen communities).<br></li></ul>","language":"English","publisher":"Sage Grouse Initiative","usgsCitation":"Shinneman, D.J., Halford, A.S., Howell, C., Krasnow, K., and Strand, E.K., 2015, Management of aspen in a changing environment, 8 p.","productDescription":"8 p.","ipdsId":"IP-070539","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":340728,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":340727,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sagegrouseinitiative.com/management-of-aspen-in-a-changing-environment/"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.7177734375,\n              36.08462129606931\n            ],\n            [\n              -112.82958984375,\n              36.08462129606931\n            ],\n            [\n              -112.82958984375,\n              42.924251753870685\n            ],\n            [\n              -120.7177734375,\n              42.924251753870685\n            ],\n            [\n              -120.7177734375,\n              36.08462129606931\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59099aaee4b0fc4e449157f2","contributors":{"editors":[{"text":"Chambers, Jeanne","contributorId":60141,"corporation":false,"usgs":true,"family":"Chambers","given":"Jeanne","affiliations":[],"preferred":false,"id":693916,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":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":579659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halford, Anne S.","contributorId":149808,"corporation":false,"usgs":false,"family":"Halford","given":"Anne","email":"","middleInitial":"S.","affiliations":[{"id":17831,"text":"USDI Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":579660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howell, Cheri","contributorId":149809,"corporation":false,"usgs":false,"family":"Howell","given":"Cheri","email":"","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":579661,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krasnow, Kevin","contributorId":127852,"corporation":false,"usgs":false,"family":"Krasnow","given":"Kevin","email":"","affiliations":[{"id":7170,"text":"Teton Science Schools, Jackson, WY","active":true,"usgs":false}],"preferred":false,"id":579662,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strand, Eva K.","contributorId":149810,"corporation":false,"usgs":false,"family":"Strand","given":"Eva","email":"","middleInitial":"K.","affiliations":[{"id":17832,"text":"University of Idaho Department of Forest, Rangeland, and Fire Sciences","active":true,"usgs":false}],"preferred":false,"id":579663,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70178118,"text":"70178118 - 2015 - Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition","interactions":[],"lastModifiedDate":"2018-02-21T14:08:18","indexId":"70178118","displayToPublicDate":"2016-11-03T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition","docAbstract":"<p><span>Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An “expedited” processing system called “eMODIS” operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1), the eMODIS Normalized Difference Vegetation Index (NDVI) maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra) or afternoon (Aqua) orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.</span></p>","language":"English","publisher":"Molecular Diversity Preservation International","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs71215825","usgsCitation":"Brown, J.F., Howard, D., Wylie, B.K., Friesz, A.M., Ji, L., and Gacke, C., 2015, Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition: Remote Sensing, v. 7, no. 12, p. 16226-16240, https://doi.org/10.3390/rs71215825.","productDescription":"15 p.","startPage":"16226","endPage":"16240","ipdsId":"IP-066456","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471489,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs71215825","text":"Publisher Index Page"},{"id":330712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"12","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-03","publicationStatus":"PW","scienceBaseUri":"581c4cc4e4b09688d6e90fd9","contributors":{"authors":[{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howard, Daniel M. 0000-0002-7563-7538 dhoward@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":139585,"corporation":false,"usgs":true,"family":"Howard","given":"Daniel M.","email":"dhoward@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":652852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friesz, Aaron M. 0000-0003-4096-3824 afriesz@usgs.gov","orcid":"https://orcid.org/0000-0003-4096-3824","contributorId":5943,"corporation":false,"usgs":true,"family":"Friesz","given":"Aaron","email":"afriesz@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652995,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652996,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gacke, Carolyn 0000-0001-6922-2814 cgacke@usgs.gov","orcid":"https://orcid.org/0000-0001-6922-2814","contributorId":152416,"corporation":false,"usgs":true,"family":"Gacke","given":"Carolyn","email":"cgacke@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652997,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70159593,"text":"70159593 - 2015 - The influence of food abundance, food dispersion and habitat structure on territory selection and size of an Afrotropical terrestrial insectivore","interactions":[],"lastModifiedDate":"2018-10-20T12:37:59","indexId":"70159593","displayToPublicDate":"2016-09-23T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2970,"text":"Ostrich","active":true,"publicationSubtype":{"id":10}},"title":"The influence of food abundance, food dispersion and habitat structure on territory selection and size of an Afrotropical terrestrial insectivore","docAbstract":"<p>Most tropical insectivorous birds, unlike their temperate counterparts, hold and defend a feeding and breeding territory year-around. However, our understanding of ecological factors influencing territory selection and size in tropical insectivores is limited. Here we examine three prominent hypotheses relating food abundance, food dispersion (spatial arrangement of food items), and habitat structure to territoriality in the Usambara Thrush <i>Turdus roehli</i>. We first compared leaf-litter macro-invertebrate abundance and dispersion, and habitat structure between territories and random sites. We then examined the relation between these same ecological factors and territory size. Invertebrate abundance and dispersion were sparsely and evenly distributed across our study system and did not vary between territories and random sites. In contrast, habitat structure did vary between territories and random sites indicating the Usambara Thrush selects territories with open understorey and closed overstorey habitat. Invertebrate abundance and dispersion within territories of the Usambara Thrush were not associated with habitat structure. We believe the most likely explanation for the Usambara Thrush’s preference for open understorey and closed overstorey habitat relates to foraging behavior. Using information-theoretic model selection we found that invertebrate abundance was the highest-ranked predictor of territory size and was inversely related, consistent with food value theory of territoriality.</p>","language":"English","publisher":"South African Ornithological Society","doi":"10.2989/00306525.2016.1216903","usgsCitation":"Stanley, T.R., and Newmark, W.D., 2015, The influence of food abundance, food dispersion and habitat structure on territory selection and size of an Afrotropical terrestrial insectivore: Ostrich, v. 87, no. 3, p. 199-207, https://doi.org/10.2989/00306525.2016.1216903.","productDescription":"9 p.","startPage":"199","endPage":"207","ipdsId":"IP-060994","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":488526,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/dataset/The_influence_of_food_abundance_food_dispersion_and_habitat_structure_on_territory_selection_and_size_of_an_Afrotropical_terrestrial_insectivore/3853491","text":"External Repository"},{"id":330898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-23","publicationStatus":"PW","scienceBaseUri":"582443f6e4b09065cdf30534","contributors":{"authors":[{"text":"Stanley, Thomas R. 0000-0002-8393-0005 stanleyt@usgs.gov","orcid":"https://orcid.org/0000-0002-8393-0005","contributorId":209928,"corporation":false,"usgs":true,"family":"Stanley","given":"Thomas","email":"stanleyt@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":579610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Newmark, William D.","contributorId":95783,"corporation":false,"usgs":true,"family":"Newmark","given":"William","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":579611,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70176171,"text":"70176171 - 2015 - Resolving bathymetry from airborne gravity along Greenland fjords","interactions":[],"lastModifiedDate":"2016-08-31T10:08:15","indexId":"70176171","displayToPublicDate":"2016-08-31T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Resolving bathymetry from airborne gravity along Greenland fjords","docAbstract":"<p><span>Recent glacier mass loss in Greenland has been attributed to encroaching warming waters, but knowledge of fjord bathymetry is required to investigate this mechanism. The bathymetry in many Greenland fjords is unmapped and difficult to measure. From 2010 to 2012, National Aeronautics and Space Administration's Operation IceBridge collected a unique set of airborne gravity, magnetic, radar, and lidar data along the major outlet glaciers and fjords in Greenland. We applied a consistent technique using the IceBridge gravity data to create 90 bathymetric profiles along 54 Greenland fjords. We also used this technique to recover subice topography where warm or crevassed ice prevents the radar system from imaging the bed. Here we discuss our methodology, basic assumptions and error analysis. We present the new bathymetry data and discuss observations in six major regions of Greenland covered by IceBridge. The gravity models provide a total of 1950 line kilometers of bathymetry, 875 line kilometers of subice topography, and 12 new grounding line depths.</span></p>","language":"English","publisher":"AGU Publications","doi":"10.1002/2015JB012129","usgsCitation":"Boghosian, A., Tinto, K., Cochran, J.R., Porter, D., Elieff, S., Burton, B.L., and Bell, R.E., 2015, Resolving bathymetry from airborne gravity along Greenland fjords: Journal of Geophysical Research B: Solid Earth, v. 120, no. 12, p. 8516-8533, https://doi.org/10.1002/2015JB012129.","productDescription":"18 p.","startPage":"8516","endPage":"8533","ipdsId":"IP-065813","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":471492,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012129","text":"Publisher Index Page"},{"id":328092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-02","publicationStatus":"PW","scienceBaseUri":"57c7f1ade4b0f2f0cebf11b1","contributors":{"authors":[{"text":"Boghosian, Alexandra","contributorId":174154,"corporation":false,"usgs":false,"family":"Boghosian","given":"Alexandra","email":"","affiliations":[{"id":27369,"text":"Lamont-Doherty Earth Observatory at Columbia University","active":true,"usgs":false}],"preferred":false,"id":647558,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinto, Kirsty","contributorId":174155,"corporation":false,"usgs":false,"family":"Tinto","given":"Kirsty","email":"","affiliations":[{"id":27369,"text":"Lamont-Doherty Earth Observatory at Columbia University","active":true,"usgs":false}],"preferred":false,"id":647559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochran, James R.","contributorId":127762,"corporation":false,"usgs":false,"family":"Cochran","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7135,"text":"Lamont Doherty Earth Observatory, Columbia University, Palisades, NY","active":true,"usgs":false}],"preferred":false,"id":647560,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Porter, David","contributorId":174156,"corporation":false,"usgs":false,"family":"Porter","given":"David","email":"","affiliations":[{"id":27369,"text":"Lamont-Doherty Earth Observatory at Columbia University","active":true,"usgs":false}],"preferred":false,"id":647561,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elieff, Stefan","contributorId":174157,"corporation":false,"usgs":false,"family":"Elieff","given":"Stefan","email":"","affiliations":[{"id":27370,"text":"Sander Geophysics Ltd.","active":true,"usgs":false}],"preferred":false,"id":647562,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burton, Bethany L. 0000-0001-5011-7862 blburton@usgs.gov","orcid":"https://orcid.org/0000-0001-5011-7862","contributorId":138925,"corporation":false,"usgs":true,"family":"Burton","given":"Bethany","email":"blburton@usgs.gov","middleInitial":"L.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":647557,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bell, Robin E.","contributorId":26902,"corporation":false,"usgs":true,"family":"Bell","given":"Robin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":647563,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70148677,"text":"sir20155087 - 2015 - Effects of groundwater pumping on agricultural drains in the Tule Lake subbasin, Oregon and California","interactions":[],"lastModifiedDate":"2016-08-08T09:08:08","indexId":"sir20155087","displayToPublicDate":"2016-07-28T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5087","title":"Effects of groundwater pumping on agricultural drains in the Tule Lake subbasin, Oregon and California","docAbstract":"<p class=\"p1\">Since 2001, irrigators in the upper Klamath Basin have increasingly turned to groundwater to compensate for reductions in surface-water allocation caused by shifts from irrigation use to instream flows for Endangered Species Act listed fishes. The largest increase in groundwater pumping has been in and around the Bureau of Reclamation&rsquo;s Klamath Irrigation Project, which includes the Tule Lake subbasin in the southern part of the upper Klamath Basin. Agricultural drains on the Klamath Project are an important source of water for downstream irrigators and for the Tule Lake and Lower Klamath Lake National Wildlife Refuges. U.S. Geological Survey regional groundwater-flow model simulations and records of irrigation-return flow pumped from the Tule Lake subbasin into the adjacent Lower Klamath Lake subbasin have indicated that water-level declines from pumping may be causing decreased flow of shallow groundwater to agricultural drains.&nbsp;</p>\n<p class=\"p1\">To better define the effect of increased pumping on drain flow and on the water balance of the groundwater system, the annual water volume pumped from drains in three subareas of the Tule Lake subbasin was estimated and a fine-grid, local groundwater model of the Tule Lake subbasin was constructed. Results of the agricultural-drain flow analysis indicate that groundwater discharge to drains has decreased such that flows in 2012 were approximately 32,400 acre-ft less than the 1997&ndash;2000 average flow. This decrease was concentrated in the northern and southeastern parts of the subbasin, which corresponds with the areas of greatest groundwater pumping. Model simulation results of the Tule Lake subbasin groundwater model indicate that increased supplemental pumping is the dominant stress to the groundwater system in the subbasin. Simulated supplemental pumping and decreased recharge from irrigation between 2000 and 2010 totaled 323,573 acre-ft, 234,800 acre-ft (73 percent) of which was from supplemental pumping. The response of the groundwater system to this change in stress included about 180,500 acre-ft (56 percent) of decreased groundwater discharge to drains and a 126,000 acre-ft (39 percent) reduction in aquifer storage. The remaining 5 percent came from reduced groundwater flow to other model boundaries, including the Lost River, the Tule Lake sumps, and interbasin flow.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155087","collaboration":"Prepared in cooperation with the Bureau of Reclamation and the U.S. Fish and Wildlife Service","usgsCitation":"Pischel, E.M., and Gannett, M.W., 2015, Effects of groundwater pumping on agricultural drains in the Tule Lake subbasin, Oregon and California: U.S. Geological Survey Scientific Investigations Report 2015–5087, 44 p.,\nhttps://dx.doi.org/10.3133/sir20155087.","productDescription":"vi, 44 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055376","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":305972,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5087/coverthb.jpg"},{"id":305973,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5087/sir20155087.pdf","text":"Report","size":"4.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5087"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Tule Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.58020019531249,\n              41.80817277478235\n            ],\n            [\n              -121.58020019531249,\n              42.04317376494972\n            ],\n            [\n              -121.3604736328125,\n              42.04317376494972\n            ],\n            [\n              -121.3604736328125,\n              41.80817277478235\n            ],\n            [\n              -121.58020019531249,\n              41.80817277478235\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_or@usgs.gov\">Director</a>, Oregon Water Science Center<br /> U.S. Geological Survey<br /> 2130 SW 5th Avenue<br /> Portland, Oregon 97201<br /> <a href=\"http://or.water.usgs.gov\">http://or.water.usgs.gov</a></p>","tableOfContents":"<ul>\n<li>Abstract</li>\n<li>Introduction&nbsp;</li>\n<li>Background</li>\n<li>Study Objectives</li>\n<li>Methods</li>\n<li>Agricultural Drain Flow Analysis</li>\n<li>Groundwater Model Results</li>\n<li>Potential Implications</li>\n<li>Study Limitations and Uncertainty</li>\n<li>Summary</li>\n<li>Acknowledgments</li>\n<li>References Cited</li>\n<li>Appendix A. Model Observation Wells</li>\n</ul>","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2015-07-24","noUsgsAuthors":false,"publicationDate":"2015-07-24","publicationStatus":"PW","scienceBaseUri":"579b1e9ee4b0589fa1c951c2","contributors":{"authors":[{"text":"Pischel, Esther M. 0000-0002-0393-6993 epischel@usgs.gov","orcid":"https://orcid.org/0000-0002-0393-6993","contributorId":5508,"corporation":false,"usgs":true,"family":"Pischel","given":"Esther","email":"epischel@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":548987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gannett, Marshall W. 0000-0003-2498-2427 mgannett@usgs.gov","orcid":"https://orcid.org/0000-0003-2498-2427","contributorId":2942,"corporation":false,"usgs":true,"family":"Gannett","given":"Marshall","email":"mgannett@usgs.gov","middleInitial":"W.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":548988,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70159402,"text":"70159402 - 2015 - Climate changes, shifting ranges","interactions":[],"lastModifiedDate":"2016-07-17T23:34:46","indexId":"70159402","displayToPublicDate":"2016-07-01T02:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Climate changes, shifting ranges","docAbstract":"<p>Even a fleeting mention of the Everglades conjures colorful images of alligators, panthers, flamingos, and manatees. Over the centuries, this familiar cast of characters has become synonymous with life in south Florida. But the workings of a changing climate have the potential to significantly alter the menagerie of animals that call this area home. Global projections suggest south Florida wildlife will need to contend with higher temperatures, drier conditions, and rising seas in the years ahead. Recent modeling efforts shed new light on the potential outcomes these changes may have for threatened and endangered species in the area.</p>","language":"English","publisher":"US DOI, South Florida National Parks","usgsCitation":"Romanach, S.S., 2015, Climate changes, shifting ranges.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057018","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":325343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":310652,"type":{"id":15,"text":"Index Page"},"url":"https://www.nps.gov/ever/learn/nature/upload/FINALShiftingRanges4Web.pdf"}],"publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"578cac2ee4b0c1aacabb4a55","contributors":{"authors":[{"text":"Romanach, Stephanie S. 0000-0003-0271-7825 sromanach@usgs.gov","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":140419,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","email":"sromanach@usgs.gov","middleInitial":"S.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":578412,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70159800,"text":"70159800 - 2015 - Dynamic reserve design in the face of climate change and urbanization","interactions":[],"lastModifiedDate":"2016-07-17T23:36:08","indexId":"70159800","displayToPublicDate":"2016-07-01T02:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Dynamic reserve design in the face of climate change and urbanization","docAbstract":"<p>Reserve design is a process that must address many ecological, social, and political factors to successfully identify parcels of land in need of protection to sustain wildlife populations and other natural resources. Making land acquisition choices for a large, terrestrial protected area is difficult because it occurs over a long timeframe and may involve consideration future conditions such as climate and urbanization changes. Decision makers need to consider factors including: order of parcel purchasing given budget constraints, future uncertainty, potential future landscape‐scale changes from urbanization and climate. In central Florida, two new refuges and the expansion of a third refuge are in various stages of USFWS planning. The Everglades Headwaters National Wildlife Refuge (EHNWR) has recently been established, is at the top of the Presidential Administration&rsquo;s priority conservation areas, and is cited by the Secretary of DOI routinely in the context of conservation. The new refuges were strategically located for both for species adaptation from climate change impacts as well as currently being host to a number of important threatened and endangered species and habitats. We plan to combine a structured decision making framework, optimal solution theory, and output from ecological and sociological models (these modeling efforts were previously funded by DOI partners) that incorporate climate change to provide guidance for EHNWR reserve design. Utilizing a SDM approach and optimal solution theory, decision support tools will be developed that will incorporate stakeholder and agency objectives into targeting conservation lands both through fee simple purchase and other incentives such as easements based on ecological and socioeconomic modeling outputs driven by climate change.</p>","language":"English","publisher":"Southeast Climate Science Center website","usgsCitation":"Romanach, S.S., Johnson, F.A., Stith, B., and Bonneau, M., 2015, Dynamic reserve design in the face of climate change and urbanization.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070665","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":325344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":311681,"type":{"id":15,"text":"Index Page"},"url":"https://globalchange.ncsu.edu/secsc/projects/dynamic-reserve-design-in-the-face-of-climate-change-and-urbanization/"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"578cac30e4b0c1aacabb4a60","contributors":{"authors":[{"text":"Romanach, Stephanie S. 0000-0003-0271-7825 sromanach@usgs.gov","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":140419,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","email":"sromanach@usgs.gov","middleInitial":"S.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":580508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Fred A. 0000-0002-5854-3695 fjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":2773,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","email":"fjohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":580509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stith, Bradley bstith@usgs.gov","contributorId":3596,"corporation":false,"usgs":true,"family":"Stith","given":"Bradley","email":"bstith@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":580510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonneau, Mathieu","contributorId":150041,"corporation":false,"usgs":false,"family":"Bonneau","given":"Mathieu","email":"","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":580511,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70173571,"text":"70173571 - 2015 - Factors influencing recruitment of walleye and white bass to three distinct early ontogenetic stages","interactions":[],"lastModifiedDate":"2016-09-06T13:59:58","indexId":"70173571","displayToPublicDate":"2016-06-13T16:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Factors influencing recruitment of walleye and white bass to three distinct early ontogenetic stages","docAbstract":"<p><span>Determining the factors that influence recruitment to sequential ontogenetic stages is critical for understanding recruitment dynamics of fish and for effective management of sportfish, particularly in dynamic and unpredictable environments. We sampled walleye (</span><i>Sander vitreus</i><span>) and white bass (</span><i>Morone chrysops</i><span>) at 3 ontogenetic stages (age 0 during spring: &lsquo;age-0 larval&rsquo;; age 0 during autumn: &lsquo;age-0 juvenile&rsquo;; and age 1 during autumn: &lsquo;age-1 juvenile&rsquo;) from 3 reservoirs. We developed multiple linear regression models to describe factors influencing age-0 larval, age-0 juvenile and age-1 juvenile walleye and white bass abundance indices. Our models explained 40&ndash;80% (68&nbsp;&plusmn;&nbsp;9%; mean&nbsp;&plusmn;&nbsp;SE) and 71%&ndash;97% (81&nbsp;&plusmn;&nbsp;6%) of the variability in catch for walleye and white bass respectively. For walleye, gizzard shad were present in the candidate model sets for all three ontogenetic stages we assessed. For white bass, there was no unifying variable in all three stage-specific candidate model sets, although walleye abundance was present in two of the three white bass candidate model sets. We were able to determine several factors affecting walleye and white bass year-class strength at multiple ontogenetic stages; comprehensive analyses of factors influencing recruitment to multiple early ontogenetic stages are seemingly rare in the literature. Our models demonstrate the interdependency among early ontogenetic stages and the complexities involved with sportfish recruitment.</span></p>","language":"English","publisher":"John Wiley & Sons","doi":"10.1111/eff.12229","usgsCitation":"DeBoer, J.A., and Pope, K.L., 2015, Factors influencing recruitment of walleye and white bass to three distinct early ontogenetic stages: Ecology of Freshwater Fish, v. 25, no. 4, p. 504-517, https://doi.org/10.1111/eff.12229.","productDescription":"14 p.","startPage":"504","endPage":"517","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057415","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":498941,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12229","text":"Publisher Index Page"},{"id":323513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-29","publicationStatus":"PW","scienceBaseUri":"575fcb1de4b04f417c2b266d","chorus":{"doi":"10.1111/eff.12229","url":"http://dx.doi.org/10.1111/eff.12229","publisher":"Wiley-Blackwell","authors":"DeBoer Jason A., Pope Kevin L.","journalName":"Ecology of Freshwater Fish","publicationDate":"5/29/2015","auditedOn":"11/12/2016"},"contributors":{"authors":[{"text":"DeBoer, Jason A.","contributorId":10272,"corporation":false,"usgs":true,"family":"DeBoer","given":"Jason","email":"","middleInitial":"A.","affiliations":[{"id":463,"text":"Nebraska Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":638584,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pope, Kevin L. 0000-0003-1876-1687 kpope@usgs.gov","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":1574,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"kpope@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":637353,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70159315,"text":"70159315 - 2015 - Final project memorandum: sea-level rise modeling handbook:  resource guide for resource managers, engineers, and scientists","interactions":[],"lastModifiedDate":"2016-07-11T15:43:49","indexId":"70159315","displayToPublicDate":"2016-06-06T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"Final project memorandum: sea-level rise modeling handbook:  resource guide for resource managers, engineers, and scientists","docAbstract":"<p>Coastal wetlands of the Southeastern United States are undergoing retreat and migration from increasing tidal inundation and saltwater intrusion attributed to climate variability and sea-level rise. Much of the literature describing potential sea-level rise projections and modeling predictions are found in peer-reviewed academic journals or government technical reports largely suited to reading by other Ph.D. scientists who are more familiar or engaged in the climate change debate. Various sea-level rise and coastal wetland models have been developed and applied of different designs and scales of spatial and temporal complexity for predicting habitat and environmental change that have not heretofore been synthesized to aid natural resource managers of their utility and limitations. Training sessions were conducted with Federal land managers with U.S. Fish and Wildlife Service, National Park Service, and NOAA National Estuarine Research Reserves as well as state partners and nongovernmental organizations across the northern Gulf Coast from Florida to Texas to educate and to evaluate user needs and understanding of concepts, data, and modeling tools for projecting sea-level rise and its impact on coastal habitats and wildlife. As a result, this handbook was constructed from these training and feedback sessions with coastal managers and biologists of published decision-support tools and simulation models for sea-level rise and climate change assessments. A simplified tabular context was developed listing the various kinds of decision-support tools and ecological models along with criteria to distinguish the source, scale, and quality of information input and geographic data sets, physical and biological constraints and relationships, datum characteristics of water and land elevation components, utility options for setting sea-level rise and climate change scenarios, and ease or difficulty of storing, displaying, or interpreting model output. The handbook is designed to be a primer to understanding sea-level rise and a practical synthesis of the current state of knowledge and modeling tools as a resource guide for DOl land management needs and facilitating Landscape Conservation Cooperative (LCC) research and conservation initiatives.</p>","language":"English","collaboration":"Southeast Climate Science Center","usgsCitation":"Doyle, T.W., 2015, Final project memorandum: sea-level rise modeling handbook:  resource guide for resource managers, engineers, and scientists.","productDescription":"8 p.","startPage":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065874","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":310250,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencebase.gov/catalog/item/560c29c2e4b058f706e540f9","text":"Final Memo for A Handbook for Resource Managers to Understand and Utilize Sea-Level Rise and Coastal Wetland Models for Ecosystem Management under Future Conditions","size":"120.61 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Final Memo for A Handbook for Resource Managers to Understand and Utilize Sea-Level Rise and Coastal Wetland Models for Ecosystem Management under Future Conditions"},{"id":322358,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5756909de4b023b96ec20aa0","contributors":{"authors":[{"text":"Doyle, Thomas W. 0000-0001-5754-0671 doylet@usgs.gov","orcid":"https://orcid.org/0000-0001-5754-0671","contributorId":703,"corporation":false,"usgs":true,"family":"Doyle","given":"Thomas","email":"doylet@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":577988,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70170559,"text":"70170559 - 2015 - Modeling the present and future geographic distribution of the Lone star tick, <i>Amblyomma americanum</i> (Ixodida: <i>Ixodidae</i>), in the continental United States","interactions":[],"lastModifiedDate":"2016-04-27T09:30:53","indexId":"70170559","displayToPublicDate":"2016-04-27T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":733,"text":"American Journal of Tropical Medicine and Hygiene","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the present and future geographic distribution of the Lone star tick, <i>Amblyomma americanum</i> (Ixodida: <i>Ixodidae</i>), in the continental United States","docAbstract":"<p><span>The Lone star tick (</span><i>Amblyomma americanum</i><span>&nbsp;L.) is the primary vector for pathogens of significant public health importance in North America, yet relatively little is known about its current and potential future distribution. Building on a published summary of tick collection records, we used an ensemble modeling approach to predict the present-day and future distribution of climatically suitable habitat for establishment of the Lone star tick within the continental United States. Of the nine climatic predictor variables included in our five present-day models, average vapor pressure in July was by far the most important determinant of suitable habitat. The present-day ensemble model predicted an essentially contiguous distribution of suitable habitat extending to the Atlantic coast east of the 100th western meridian and south of the 40th northern parallel, but excluding a high elevation region associated with the Appalachian Mountains. Future ensemble predictions for 2061&ndash;2080 forecasted a stable western range limit, northward expansion of suitable habitat into the Upper Midwest and western Pennsylvania, and range contraction along portions of the Gulf coast and the lower Mississippi river valley. These findings are informative for raising awareness of&nbsp;</span><i>A. americanum</i><span>-transmitted pathogens in areas where the Lone Star tick has recently or may become established.</span></p>","language":"English","publisher":"American Society of Tropical Medicine and Hygiene","doi":"10.4269/ajtmh.15-0330","usgsCitation":"Springer, Y.P., Jarnevich, C.S., Barnett, D., Monaghan, A., and Eisen, R., 2015, Modeling the present and future geographic distribution of the Lone star tick, <i>Amblyomma americanum</i> (Ixodida: <i>Ixodidae</i>), in the continental United States: American Journal of Tropical Medicine and Hygiene, v. 93, no. 4, p. 875-890, https://doi.org/10.4269/ajtmh.15-0330.","productDescription":"16 p.","startPage":"875","endPage":"890","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065942","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471493,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.4269/ajtmh.15-0330","text":"External Repository"},{"id":320587,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5721d4a4e4b0b13d39129149","contributors":{"authors":[{"text":"Springer, Yuri P.","contributorId":148010,"corporation":false,"usgs":false,"family":"Springer","given":"Yuri","email":"","middleInitial":"P.","affiliations":[{"id":16880,"text":"National Ecological Observatory Network (NEON), 1685 38th St., Boulder, CO 80301, USA","active":true,"usgs":false}],"preferred":false,"id":627651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":627650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnett, David T.","contributorId":86234,"corporation":false,"usgs":true,"family":"Barnett","given":"David T.","affiliations":[],"preferred":false,"id":627652,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Monaghan, Andrew J.","contributorId":61682,"corporation":false,"usgs":true,"family":"Monaghan","given":"Andrew J.","affiliations":[],"preferred":false,"id":627653,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eisen, Rebecca J.","contributorId":148027,"corporation":false,"usgs":false,"family":"Eisen","given":"Rebecca J.","affiliations":[{"id":16974,"text":"US Centers for Disease Control and Prevention (CDC)","active":true,"usgs":false}],"preferred":false,"id":627654,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70155806,"text":"70155806 - 2015 - Applications of genetic data to improve management and conservation of river fishes and their habitats","interactions":[],"lastModifiedDate":"2016-05-18T07:30:00","indexId":"70155806","displayToPublicDate":"2016-04-13T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1657,"text":"Fisheries","onlineIssn":"1548-8446","printIssn":"0363-2415","active":true,"publicationSubtype":{"id":10}},"title":"Applications of genetic data to improve management and conservation of river fishes and their habitats","docAbstract":"<div data-canvas-width=\"798.2826666666665\">Environmental variation and landscape features affect ecological processes in fluvial systems; however, assessing effects at management-relevant temporal and spatial scales is challenging. Genetic data can be used with landscape models and traditional ecological assessment data to identify biodiversity hotspots, predict ecosystem responses to anthropogenic effects, and detect impairments to underlying processes. We show that by combining taxonomic, demographic, and genetic data of species in complex riverscapes, managers can better understand the spatial and temporal scales over which environmental processes and disturbance influence biodiversity. We describe how population genetic models using empirical or simulated genetic data quantify effects of environmental processes affecting species diversity and distribution. Our summary shows that aquatic assessment initiatives that use standardized data sets to direct management actions can benefit from integration of genetic data to improve the predictability of disturbance–response relationships of river fishes and their habitats over a broad range of spatial and temporal scales.</div>","language":"English","publisher":"American Fisheries Society","doi":"10.1080/03632415.2016.1150838","usgsCitation":"Scribner, K.T., Lowe, W., Landguth, E.L., Luikart, G., Infante, D.M., Whelan, G., and Muhlfeld, C.C., 2015, Applications of genetic data to improve management and conservation of river fishes and their habitats: Fisheries, v. 41, no. 4, p. 174-188, https://doi.org/10.1080/03632415.2016.1150838.","productDescription":"15 p.","startPage":"174","endPage":"188","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066722","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":320020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-06","publicationStatus":"PW","scienceBaseUri":"570f5f9be4b0ef3b7ca3294b","contributors":{"authors":[{"text":"Scribner, Kim T.","contributorId":146113,"corporation":false,"usgs":false,"family":"Scribner","given":"Kim","email":"","middleInitial":"T.","affiliations":[{"id":135,"text":"Biological Resources Division","active":false,"usgs":true},{"id":16582,"text":"Department of Fisheries and Wildlife and Department of Zoology, 480 Wilson Rd. 13 Natural Resources Building, Michigan State University, East Lansing, MI 48824","active":true,"usgs":false}],"preferred":false,"id":566392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, Winsor H.","contributorId":64532,"corporation":false,"usgs":false,"family":"Lowe","given":"Winsor H.","affiliations":[{"id":5097,"text":"University of Montana, Division of Biological Sciences","active":true,"usgs":false}],"preferred":false,"id":566393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Landguth, Erin L.","contributorId":69002,"corporation":false,"usgs":true,"family":"Landguth","given":"Erin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":566394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Luikart, Gordon","contributorId":97409,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":6580,"text":"University of Montana, Flathead Lake Biological Station, Polson, Montana 59860, USA","active":true,"usgs":false}],"preferred":false,"id":566395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Infante, Dana M.","contributorId":146114,"corporation":false,"usgs":false,"family":"Infante","given":"Dana","email":"","middleInitial":"M.","affiliations":[{"id":16583,"text":"Department of Fisheries and Wildlife, 480 Wilson Rd. 13 Natural Resources Building, Michigan State University, East Lansing, MI 48824","active":true,"usgs":false}],"preferred":false,"id":566396,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whelan, Gary","contributorId":146115,"corporation":false,"usgs":false,"family":"Whelan","given":"Gary","email":"","affiliations":[{"id":16584,"text":"Fisheries Division, Michigan Department of Natural Resources, P.O. Box 30446, Lansing, MI 48909","active":true,"usgs":false}],"preferred":false,"id":566397,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":566391,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70173621,"text":"70173621 - 2015 - Using hierarchical Bayesian multi-species mixture models to estimate tandem hoop-net based habitat associations and detection probabilities of fishes in reservoirs","interactions":[],"lastModifiedDate":"2022-11-01T17:11:12.497163","indexId":"70173621","displayToPublicDate":"2016-04-11T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Using hierarchical Bayesian multi-species mixture models to estimate tandem hoop-net based habitat associations and detection probabilities of fishes in reservoirs","docAbstract":"<p><span>Species distribution models are useful tools to evaluate habitat relationships of fishes. We used hierarchical Bayesian multispecies mixture models to evaluate the relationships of both detection and abundance with habitat of reservoir fishes caught using tandem hoop nets. A total of 7,212 fish from 12 species were captured, and the majority of the catch was composed of Channel Catfish&nbsp;</span><i>Ictalurus punctatus</i><span>&nbsp;(46%), Bluegill&nbsp;</span><i>Lepomis macrochirus</i><span>(25%), and White Crappie&nbsp;</span><i>Pomoxis annularis</i><span>&nbsp;(14%). Detection estimates ranged from 8% to 69%, and modeling results suggested that fishes were primarily influenced by reservoir size and context, water clarity and temperature, and land-use types. Species were differentially abundant within and among habitat types, and some fishes were found to be more abundant in turbid, less impacted (e.g., by urbanization and agriculture) reservoirs with longer shoreline lengths; whereas, other species were found more often in clear, nutrient-rich impoundments that had generally shorter shoreline length and were surrounded by a higher percentage of agricultural land. Our results demonstrated that habitat and reservoir characteristics may differentially benefit species and assemblage structure. This study provides a useful framework for evaluating capture efficiency for not only hoop nets but other gear types used to sample fishes in reservoirs.</span></p>","language":"English","publisher":"American Fisheries Society","doi":"10.1080/00028487.2016.1143395","usgsCitation":"Stewart, D., and Long, J.M., 2015, Using hierarchical Bayesian multi-species mixture models to estimate tandem hoop-net based habitat associations and detection probabilities of fishes in reservoirs: Transactions of the American Fisheries Society, v. 145, no. 3, p. 450-461, https://doi.org/10.1080/00028487.2016.1143395.","productDescription":"12 p.","startPage":"450","endPage":"461","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058011","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":323420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70173493,"text":"70173493 - 2015 - The relationship between vessel traffic and noise levels received by killer whales (<i>Orcinus orca</i>)","interactions":[],"lastModifiedDate":"2016-06-17T12:17:45","indexId":"70173493","displayToPublicDate":"2016-03-07T09:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"The relationship between vessel traffic and noise levels received by killer whales (<i>Orcinus orca</i>)","docAbstract":"<p><span>Whale watching has become increasingly popular as an ecotourism activity around the globe and is beneficial for environmental education and local economies. Southern Resident killer whales (</span><i>Orcinus orca</i><span>) comprise an endangered population that is frequently observed by a large whale watching fleet in the inland waters of Washington state and British Columbia. One of the factors identified as a risk to recovery for the population is the effect of vessels and associated noise. An examination of the effects of vessels and associated noise on whale behavior utilized novel equipment to address limitations of previous studies. Digital acoustic recording tags (DTAGs) measured the noise levels the tagged whales received while laser positioning systems allowed collection of geo-referenced data for tagged whales and all vessels within 1000 m of the tagged whale. The objective of the current study was to compare vessel data and DTAG recordings to relate vessel traffic to the ambient noise received by tagged whales. Two analyses were conducted, one including all recording intervals, and one that excluded intervals when only the research vessel was present. For all data, significant predictors of noise levels were length (inverse relationship), number of propellers, and vessel speed, but only 15% of the variation in noise was explained by this model. When research-vessel-only intervals were excluded, vessel speed was the only significant predictor of noise levels, and explained 42% of the variation. Simple linear regressions (ignoring covariates) found that average vessel speed and number of propellers were the only significant correlates with noise levels. We conclude that vessel speed is the most important predictor of noise levels received by whales in this study. Thus, measures that reduce vessel speed in the vicinity of killer whales would reduce noise exposure in this population.</span></p>","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0140119","usgsCitation":"Houghton, J., Holt, M.M., Giles, D.A., Hanson, M.B., Emmons, C.K., Hogan, J.T., Branch, T., and VanBlaricom, G.R., 2015, The relationship between vessel traffic and noise levels received by killer whales (<i>Orcinus orca</i>): PLoS ONE, v. 10, no. 12, p. 1-20, https://doi.org/10.1371/journal.pone.0140119.","productDescription":"20 p.","startPage":"1","endPage":"20","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065949","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0140119","text":"Publisher Index Page"},{"id":323889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Vancouver, Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.67265319824217,\n              48.76343113791796\n            ],\n            [\n              -122.92602539062501,\n              48.772935170565056\n            ],\n            [\n              -123.25149536132811,\n              48.69232017824781\n            ],\n            [\n              -123.21098327636719,\n              48.569337856144415\n            ],\n            [\n              -123.14987182617188,\n              48.45653041501911\n            ],\n            [\n              -123.09219360351561,\n              48.41826449418743\n            ],\n            [\n              -123.16635131835938,\n              48.35442390123028\n            ],\n            [\n              -122.79968261718749,\n              48.28502057399577\n            ],\n            [\n              -122.70217895507811,\n              48.37449671682332\n            ],\n            [\n              -122.74063110351562,\n              48.4765629664158\n            ],\n            [\n              -122.684326171875,\n              48.521152504948994\n            ],\n            [\n              -122.64862060546875,\n              48.596592251456705\n            ],\n            [\n              -122.62527465820311,\n              48.63563125791999\n            ],\n            [\n              -122.56484985351561,\n              48.634723716904\n            ],\n            [\n              -122.67265319824217,\n              48.76343113791796\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-02","publicationStatus":"PW","scienceBaseUri":"57651f3be4b07657d19c793d","contributors":{"authors":[{"text":"Houghton, Juliana","contributorId":172082,"corporation":false,"usgs":false,"family":"Houghton","given":"Juliana","email":"","affiliations":[],"preferred":false,"id":639560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holt, Marla M.","contributorId":172083,"corporation":false,"usgs":false,"family":"Holt","given":"Marla","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":639561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giles, Deborah A.","contributorId":172084,"corporation":false,"usgs":false,"family":"Giles","given":"Deborah","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":639562,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanson, M. 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