{"pageNumber":"440","pageRowStart":"10975","pageSize":"25","recordCount":46642,"records":[{"id":70191979,"text":"70191979 - 2016 - Estimating black bear density in New Mexico using noninvasive genetic sampling coupled with spatially explicit capture-recapture methods","interactions":[],"lastModifiedDate":"2018-01-26T14:15:01","indexId":"70191979","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-120-2016","title":"Estimating black bear density in New Mexico using noninvasive genetic sampling coupled with spatially explicit capture-recapture methods","docAbstract":"<p>During the 2004–2005 to 2015–2016 hunting seasons, the New Mexico Department of Game and Fish (NMDGF) estimated black bear abundance (Ursus americanus) across the state by coupling density estimates with the distribution of primary habitat generated by Costello et al. (2001). These estimates have been used to set harvest limits. For example, a density of 17 bears/100 km2 for the Sangre de Cristo and Sacramento Mountains and 13.2 bears/100 km2 for the Sandia Mountains were used to set harvest levels. The advancement and widespread acceptance of non-invasive sampling and mark-recapture methods, prompted the NMDGF to collaborate with the New Mexico Cooperative Fish and Wildlife Research Unit and New Mexico State University to update their density estimates for black bear populations in select mountain ranges across the state.</p><p>We established 5 study areas in 3 mountain ranges: the northern (NSC; sampled in 2012) and southern Sangre de Cristo Mountains (SSC; sampled in 2013), the Sandia Mountains (Sandias; sampled in 2014), and the northern (NSacs) and southern Sacramento Mountains (SSacs; both sampled in 2014). We collected hair samples from black bears using two concurrent non-invasive sampling methods, hair traps and bear rubs. We used a gender marker and a suite of microsatellite loci to determine the individual identification of hair samples that were suitable for genetic analysis. We used these data to generate mark-recapture encounter histories for each bear and estimated density in a spatially explicit capture-recapture framework (SECR). We constructed a suite of SECR candidate models using sex, elevation, land cover type, and time to model heterogeneity in detection probability and the spatial scale over which detection probability declines. We used Akaike’s Information Criterion corrected for small sample size (AICc) to rank and select the most supported model from which we estimated density.</p><p>We set 554 hair traps, 117 bear rubs and collected 4,083 hair samples. We identified 725 (367 M, 358 F) individuals; the sex ratio for each study area was approximately equal. Our density estimates varied within and among mountain ranges with an estimated density of 21.86 bears/100 km2 (95% CI: 17.83 – 26.80) for the NSC, 19.74 bears/100 km2 (95% CI: 13.77 – 28.30) in the SSC, 25.75 bears/100 km2 (95% CI: 13.22 – 50.14) in the Sandias, 21.86 bears/100 km2 (95% CI: 17.83 – 26.80) in the NSacs, and 16.55 bears/100 km2 (95% CI: 11.64 – 23.53) in the SSacs. Overall detection probability for hair traps and bear rubs, combined, was low across all study areas and ranged from 0.00001 to 0.02. We speculate that detection probabilities were affected by failure of some hair samples to produce a complete genotype due to UV degradation of DNA, and our inability to set and check some sampling devices due to wildfires in the SSC. Ultraviolet radiation levels are particularly high in New Mexico compared to other states where NGS methods have been used because New Mexico receives substantial amounts of sunshine, is relatively high in elevation (1,200 m – 4,000 m), and is at a lower latitude. Despite these sampling difficulties, we were able to produce density estimates for New Mexico black bear populations with levels of precision comparable to estimated black bear densities made elsewhere in the U.S.</p><p>Our ability to generate reliable black bear density estimates for 3 New Mexico mountain ranges is attributable to our use of a statistically robust study design and analytical method. There are multiple factors that need to be considered when developing future SECR-based density estimation projects. First, the spatial extent of the population of interest and the smallest average home range size must be determined; these will dictate size of the trapping array and spacing necessary between hair traps. The number of technicians needed and access to the study areas will also influence configuration of the trapping array. We believe shorter sampling occasions could be implemented to reduce degradation of DNA due to UV radiation; this might help increase amplification rates and thereby increase both the number of unique individuals identified and the number of recaptures, improving the precision of the density estimates. A pilot study may be useful to determine the length of time hair samples can remain in the field prior to collection. In addition, researchers may consider setting hair traps and bear rubs in more shaded areas (e.g., north facing slopes) to help reduce exposure to UV radiation. To reduce the sampling interval it will be necessary to either hire more field personnel or decrease the number of hair traps per sampling session. Both of these will enhance detection of long-range movement events by individual bears, increase initial capture and recapture rates, and improve precision of the parameter estimates. We recognize that all studies are constrained by limited resources, however, increasing field personnel would also allow a larger study area to be sampled or enable higher trap density.</p><p>In conclusion, we estimated the density of black bears in 5 study areas within 3 mountains ranges of New Mexico. Our estimates will aid the NMDGF in setting sustainable harvest limits. Along with estimates of density, information on additional demographic rates (e.g., survival rates and reproduction) and the potential effects that climate change and future land use may have on the demography of black bears may also help inform management of black bears in New Mexico, and may be considered as future areas for research.</p>","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Gould, M.J., Cain, J.W., Roemer, G.W., and Gould, W., 2016, Estimating black bear density in New Mexico using noninvasive genetic sampling coupled with spatially explicit capture-recapture methods: Cooperator Science Series FWS/CSS-120-2016, ii, 41 p.","productDescription":"ii, 41 p.","numberOfPages":"43","ipdsId":"IP-074771","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":350702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350701,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalmedia.fws.gov/cdm/ref/collection/document/id/2132"}],"country":"United States","state":"New Mexico","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6c4c96e4b06e28e9cabb0a","contributors":{"authors":[{"text":"Gould, Matthew J.","contributorId":201504,"corporation":false,"usgs":false,"family":"Gould","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":725970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":713806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roemer, Gary W.","contributorId":95355,"corporation":false,"usgs":true,"family":"Roemer","given":"Gary","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":725971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gould, William R.","contributorId":63780,"corporation":false,"usgs":true,"family":"Gould","given":"William R.","affiliations":[],"preferred":false,"id":725972,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192005,"text":"70192005 - 2016 - Prioritizing landscapes for longleaf pine conservation","interactions":[],"lastModifiedDate":"2018-01-25T13:33:39","indexId":"70192005","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-119-2016","title":"Prioritizing landscapes for longleaf pine conservation","docAbstract":"<p>We developed a spatially explicit model and map, as a decision support tool (DST), to aid conservation agencies creating or maintaining open pine ecosystems. The tool identified areas that are likely to provide the greatest benefit to focal bird populations based on a comprehensive landscape analysis. We used NLCD 2011, SSURGO, and SEGAP data to map the density of desired resources for open pine ecosystems and six focal species of birds and 2 reptiles within the historic range of longleaf pine east of the Mississippi River. Binary rasters were created of sites with desired characteristics such as land form, hydrology, land use and land cover, soils, potential habitat for focal species, and putative source populations of focal species. Each raster was smoothed using a kernel density estimator. Rasters were combined and scaled to map priority locations for the management of each focal species. Species’ rasters were combined and scaled to provide maps of overall priority for birds and for birds and reptiles. The spatial data can be used to identify high priority areas for conservation or to compare areas under consideration for maintenance or creation of open pine ecosystems.</p>","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Grand, J.B., and Kleiner, K.J., 2016, Prioritizing landscapes for longleaf pine conservation: Cooperator Science Series FWS/CSS-119-2016, ii, 50 p.","productDescription":"ii, 50 p.","numberOfPages":"52","ipdsId":"IP-071312","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":350618,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350617,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalmedia.fws.gov/cdm/ref/collection/document/id/2131"}],"publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6afac6e4b06e28e9c9a8ff","contributors":{"authors":[{"text":"Grand, J. Barry 0000-0002-3576-4567 barry_grand@usgs.gov","orcid":"https://orcid.org/0000-0002-3576-4567","contributorId":579,"corporation":false,"usgs":true,"family":"Grand","given":"J.","email":"barry_grand@usgs.gov","middleInitial":"Barry","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":713832,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleiner, Kevin J.","contributorId":200004,"corporation":false,"usgs":false,"family":"Kleiner","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":725822,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70162557,"text":"70162557 - 2016 - Improved geomagnetic referencing in the Arctic environment","interactions":[],"lastModifiedDate":"2020-07-13T14:45:53.629471","indexId":"70162557","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Improved geomagnetic referencing in the Arctic environment","docAbstract":"<p>Geomagnetic referencing uses the Earth&rsquo;s magnetic field to determine accurate wellbore positioning essential for success in today's complex drilling programs, either as an alternative or a complement to north-seeking gyroscopic referencing. However, fluctuations in the geomagnetic field, especially at high latitudes, make the application of geomagnetic referencing in those areas more challenging. Precise crustal mapping and the monitoring of real-time variations by nearby magnetic observatories is crucial to achieving the required geomagnetic referencing accuracy. The Deadhorse Magnetic Observatory (DED), located at Prudhoe Bay, Alaska, has already played a vital role in the success of several commercial ventures in the area, providing essential, accurate, real-time data to the oilfield drilling industry. Geomagnetic referencing is enhanced with real-time data from DED and other observatories, and has been successfully used for accurate wellbore positioning. The availability of real-time geomagnetic measurements leads to significant cost and time savings in wellbore surveying, improving accuracy and alleviating the need for more expensive surveying techniques. The correct implementation of geomagnetic referencing is particularly critical as we approach the increased activity associated with the upcoming maximum of the 11-year solar cycle. The DED observatory further provides an important service to scientific communities engaged in studies of ionospheric, magnetospheric and space weather phenomena.</p>","conferenceTitle":"SPE Arctic and Extreme Environments Technical Conference and Exhibition","conferenceDate":"October 15, 2015","conferenceLocation":"Moscow, Russia","language":"English","publisher":"Society of Petroleum Engineers","doi":"10.2118/166850-MS","usgsCitation":"Poedjono, B., Beck, N., Buchanan, A.C., Borri, L., Maus, S., Finn, C., Worthington, E.W., and White, T., 2016, Improved geomagnetic referencing in the Arctic environment, SPE Arctic and Extreme Environments Technical Conference and Exhibition, Moscow, Russia, October 15, 2015, 21 p., https://doi.org/10.2118/166850-MS.","productDescription":"21 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049377","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":314916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2013-10-15","publicationStatus":"PW","scienceBaseUri":"56a9f847e4b012c193aa3ed0","contributors":{"authors":[{"text":"Poedjono, B.","contributorId":56465,"corporation":false,"usgs":true,"family":"Poedjono","given":"B.","affiliations":[],"preferred":false,"id":589850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beck, N.","contributorId":18995,"corporation":false,"usgs":true,"family":"Beck","given":"N.","email":"","affiliations":[],"preferred":false,"id":589849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buchanan, A. C.","contributorId":11027,"corporation":false,"usgs":true,"family":"Buchanan","given":"A.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":589848,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Borri, L.","contributorId":152590,"corporation":false,"usgs":false,"family":"Borri","given":"L.","email":"","affiliations":[{"id":18940,"text":"SPE, Eni Petroleum Co.","active":true,"usgs":false}],"preferred":false,"id":589847,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maus, S.","contributorId":104315,"corporation":false,"usgs":true,"family":"Maus","given":"S.","email":"","affiliations":[],"preferred":false,"id":589846,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Finn, Carol 0000-0003-3144-1645","orcid":"https://orcid.org/0000-0003-3144-1645","contributorId":13201,"corporation":false,"usgs":true,"family":"Finn","given":"Carol","affiliations":[],"preferred":false,"id":589845,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Worthington, E. William 0000-0002-5879-0477 bworth@usgs.gov","orcid":"https://orcid.org/0000-0002-5879-0477","contributorId":2570,"corporation":false,"usgs":true,"family":"Worthington","given":"E.","email":"bworth@usgs.gov","middleInitial":"William","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":589843,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"White, Tim 0000-0002-3563-0649 ttwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-3563-0649","contributorId":2010,"corporation":false,"usgs":true,"family":"White","given":"Tim","email":"ttwhite@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":589844,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70160493,"text":"70160493 - 2016 - Integrated groundwater data management","interactions":[],"lastModifiedDate":"2017-04-17T14:45:30","indexId":"70160493","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Integrated groundwater data management","docAbstract":"<p><span>The goal of a data manager is to ensure that data is safely stored, adequately described, discoverable and easily accessible. However, to keep pace with the evolution of groundwater studies in the last decade, the associated data and data management requirements have changed significantly. In particular, there is a growing recognition that management questions cannot be adequately answered by single discipline studies. This has led a push towards the paradigm of integrated modeling, where diverse parts of the hydrological cycle and its human connections are included. This chapter describes groundwater data management practices, and reviews the current state of the art with enterprise groundwater database management systems. It also includes discussion on commonly used data management models, detailing typical data management lifecycles. We discuss the growing use of web services and open standards such as GWML and WaterML2.0 to exchange groundwater information and knowledge, and the need for national data networks. We also discuss cross-jurisdictional interoperability issues, based on our experience sharing groundwater data across the US/Canadian border. Lastly, we present some future trends relating to groundwater data management.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Integrated groundwater management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-319-23576-9_26","isbn":"978-3-319-23575-2","usgsCitation":"Fitch, P., Brodaric, B., Stenson, M., and Booth, N., 2016, Integrated groundwater data management, chap. <i>of</i> Integrated groundwater management, p. 667-692, https://doi.org/10.1007/978-3-319-23576-9_26.","productDescription":"26 p.","startPage":"667","endPage":"692","ipdsId":"IP-057014","costCenters":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"links":[{"id":488592,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-319-23576-9_26","text":"Publisher Index Page"},{"id":339814,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f5d440e4b0f2e20545e413","contributors":{"editors":[{"text":"Jakeman, Anthony J. 0000-0001-5282-2215","orcid":"https://orcid.org/0000-0001-5282-2215","contributorId":173848,"corporation":false,"usgs":false,"family":"Jakeman","given":"Anthony","email":"","middleInitial":"J.","affiliations":[{"id":17939,"text":"The Australian National University","active":true,"usgs":false}],"preferred":false,"id":691268,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Barreteau, Olivier","contributorId":173849,"corporation":false,"usgs":false,"family":"Barreteau","given":"Olivier","email":"","affiliations":[{"id":27301,"text":"IRSTEA - UMR G-EAU (France)","active":true,"usgs":false}],"preferred":false,"id":691269,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Hunt, Randall J. 0000-0001-6465-9304 rjhunt@usgs.gov","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":1129,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall","email":"rjhunt@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":691270,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Rinaudo, Jean-Daniel","contributorId":173850,"corporation":false,"usgs":false,"family":"Rinaudo","given":"Jean-Daniel","email":"","affiliations":[{"id":27302,"text":"BRGM (France)","active":true,"usgs":false}],"preferred":false,"id":691271,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Ross, Andrew","contributorId":173851,"corporation":false,"usgs":false,"family":"Ross","given":"Andrew","email":"","affiliations":[{"id":13328,"text":"UNESCO-IHE","active":true,"usgs":false}],"preferred":false,"id":691272,"contributorType":{"id":2,"text":"Editors"},"rank":5}],"authors":[{"text":"Fitch, Peter","contributorId":150765,"corporation":false,"usgs":false,"family":"Fitch","given":"Peter","email":"","affiliations":[{"id":18100,"text":"Commonwealth Scientific Research Organisation","active":true,"usgs":false}],"preferred":false,"id":583002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brodaric, Boyan","contributorId":80341,"corporation":false,"usgs":true,"family":"Brodaric","given":"Boyan","affiliations":[],"preferred":false,"id":583003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stenson, Matt","contributorId":150766,"corporation":false,"usgs":false,"family":"Stenson","given":"Matt","email":"","affiliations":[{"id":18100,"text":"Commonwealth Scientific Research Organisation","active":true,"usgs":false}],"preferred":false,"id":583004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Booth, Nathaniel 0000-0001-6040-1031 nlbooth@usgs.gov","orcid":"https://orcid.org/0000-0001-6040-1031","contributorId":140641,"corporation":false,"usgs":true,"family":"Booth","given":"Nathaniel","email":"nlbooth@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":583001,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70187256,"text":"70187256 - 2016 - Louisiana waterthrush and benthic macroinvertebrate response to shale gas development","interactions":[],"lastModifiedDate":"2017-04-27T11:39:11","indexId":"70187256","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Louisiana waterthrush and benthic macroinvertebrate response to shale gas development","docAbstract":"<p><span>Because shale gas development is occurring over large landscapes and consequently is affecting many headwater streams, an understanding of its effects on headwater-stream faunal communities is needed. We examined effects of shale gas development (well pads and associated infrastructure) on Louisiana waterthrush </span><i>Parkesia motacilla</i><span> and benthic macroinvertebrate communities in 12 West Virginia headwater streams in 2011. Streams were classed as impacted (</span><i>n</i><span> = 6) or unimpacted (</span><i>n</i><span> = 6) by shale gas development. We quantified waterthrush demography (nest success, clutch size, number of fledglings, territory density), a waterthrush Habitat Suitability Index, a Rapid Bioassessment Protocol habitat index, and benthic macroinvertebrate metrics including a genus-level stream-quality index for each stream. We compared each benthic metric between impacted and unimpacted streams with a Student's </span><i>t</i><span>-test that incorporated adjustments for normalizing data. Impacted streams had lower genus-level stream-quality index scores; lower overall and Ephemeroptera, Plecoptera, and Trichoptera richness; fewer intolerant taxa, more tolerant taxa, and greater density of 0–3-mm individuals (</span><i>P</i><span> ≤ 0.10). We then used Pearson correlation to relate waterthrush metrics to benthic metrics across the 12 streams. Territory density (no. of territories/km of stream) was greater on streams with higher genus-level stream-quality index scores; greater density of all taxa and Ephemeroptera, Plecoptera, and Trichoptera taxa; and greater biomass. Clutch size was greater on streams with higher genus-level stream-quality index scores. Nest survival analyses (</span><i>n</i><span> = 43 nests) completed with Program MARK suggested minimal influence of benthic metrics compared with nest stage and Habitat Suitability Index score. Although our study spanned only one season, our results suggest that shale gas development affected waterthrush and benthic communities in the headwater streams we studied. Thus, these ecological effects of shale gas development warrant closer examination.</span></p>","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/092015-JFWM-084","usgsCitation":"Wood, P., Frantz, M.W., and Becker, D.A., 2016, Louisiana waterthrush and benthic macroinvertebrate response to shale gas development: Journal of Fish and Wildlife Management, v. 7, no. 2, p. 423-433, https://doi.org/10.3996/092015-JFWM-084.","productDescription":"11 p.","startPage":"423","endPage":"433","ipdsId":"IP-066368","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":490023,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/092015-jfwm-084","text":"Publisher Index Page"},{"id":340503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":" Lewis Wetzel Wildlife Management Area","volume":"7","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-01","publicationStatus":"PW","scienceBaseUri":"59030326e4b0e862d230f72b","contributors":{"authors":[{"text":"Wood, Petra pbwood@usgs.gov","contributorId":169812,"corporation":false,"usgs":true,"family":"Wood","given":"Petra","email":"pbwood@usgs.gov","affiliations":[{"id":34541,"text":"West Virginia Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":693114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frantz, Mack W.","contributorId":191486,"corporation":false,"usgs":false,"family":"Frantz","given":"Mack","email":"","middleInitial":"W.","affiliations":[{"id":34541,"text":"West Virginia Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false},{"id":34542,"text":"Department of Biology. Indiana University of Pennsylvania","active":true,"usgs":false}],"preferred":false,"id":693208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Becker, Douglas A.","contributorId":169852,"corporation":false,"usgs":false,"family":"Becker","given":"Douglas","email":"","middleInitial":"A.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":693209,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70187148,"text":"70187148 - 2016 - An evaluation of methods for estimating decadal stream loads","interactions":[],"lastModifiedDate":"2018-03-15T10:26:32","indexId":"70187148","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of methods for estimating decadal stream loads","docAbstract":"<p><span>Effective management of water resources requires accurate information on the mass, or load of water-quality constituents transported from upstream watersheds to downstream receiving waters. Despite this need, no single method has been shown to consistently provide accurate load estimates among different water-quality constituents, sampling sites, and sampling regimes. We evaluate the accuracy of several load estimation methods across a broad range of sampling and environmental conditions. This analysis uses random sub-samples drawn from temporally-dense data sets of total nitrogen, total phosphorus, nitrate, and suspended-sediment concentration, and includes measurements of specific conductance which was used as a surrogate for dissolved solids concentration. Methods considered include linear interpolation and ratio estimators, regression-based methods historically employed by the U.S. Geological Survey, and newer flexible techniques including Weighted Regressions on Time, Season, and Discharge (WRTDS) and a generalized non-linear additive model. No single method is identified to have the greatest accuracy across all constituents, sites, and sampling scenarios. Most methods provide accurate estimates of specific conductance (used as a surrogate for total dissolved solids or specific major ions) and total nitrogen – lower accuracy is observed for the estimation of nitrate, total phosphorus and suspended sediment loads. Methods that allow for flexibility in the relation between concentration and flow conditions, specifically Beale’s ratio estimator and WRTDS, exhibit greater estimation accuracy and lower bias. Evaluation of methods across simulated sampling scenarios indicate that (1) high-flow sampling is necessary to produce accurate load estimates, (2) extrapolation of sample data through time or across more extreme flow conditions reduces load estimate accuracy, and (3) WRTDS and methods that use a Kalman filter or smoothing to correct for departures between individual modeled and observed values benefit most from more frequent water-quality sampling.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2016.08.059","usgsCitation":"Lee, C.J., Hirsch, R.M., Schwarz, G., Holtschlag, D.J., Preston, S.D., Crawford, C.G., and Vecchia, A.V., 2016, An evaluation of methods for estimating decadal stream loads: Journal of Hydrology, v. 542, p. 185-203, https://doi.org/10.1016/j.jhydrol.2016.08.059.","productDescription":"19 p.","startPage":"185","endPage":"203","ipdsId":"IP-070870","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":471371,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2016.08.059","text":"Publisher Index Page"},{"id":340405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"542","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59006063e4b0e85db3a5ddd9","contributors":{"authors":[{"text":"Lee, Casey J. 0000-0002-5753-2038 cjlee@usgs.gov","orcid":"https://orcid.org/0000-0002-5753-2038","contributorId":2627,"corporation":false,"usgs":true,"family":"Lee","given":"Casey","email":"cjlee@usgs.gov","middleInitial":"J.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":692771,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":692772,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":543,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":692773,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holtschlag, David J. 0000-0001-5185-4928 dholtschlag@usgs.gov","orcid":"https://orcid.org/0000-0001-5185-4928","contributorId":5447,"corporation":false,"usgs":true,"family":"Holtschlag","given":"David","email":"dholtschlag@usgs.gov","middleInitial":"J.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":692774,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Preston, Stephen D. 0000-0003-1515-6692 spreston@usgs.gov","orcid":"https://orcid.org/0000-0003-1515-6692","contributorId":1463,"corporation":false,"usgs":true,"family":"Preston","given":"Stephen","email":"spreston@usgs.gov","middleInitial":"D.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":692775,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crawford, Charles G. 0000-0003-1653-7841 cgcrawfo@usgs.gov","orcid":"https://orcid.org/0000-0003-1653-7841","contributorId":1064,"corporation":false,"usgs":true,"family":"Crawford","given":"Charles","email":"cgcrawfo@usgs.gov","middleInitial":"G.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":692776,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vecchia, Aldo V. 0000-0002-2661-4401 avecchia@usgs.gov","orcid":"https://orcid.org/0000-0002-2661-4401","contributorId":1173,"corporation":false,"usgs":true,"family":"Vecchia","given":"Aldo","email":"avecchia@usgs.gov","middleInitial":"V.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":692777,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70176570,"text":"70176570 - 2016 - A linear relationship between wave power and erosion determines salt-marsh resilience to violent storms and hurricanes","interactions":[],"lastModifiedDate":"2016-09-21T16:20:15","indexId":"70176570","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"A linear relationship between wave power and erosion determines salt-marsh resilience to violent storms and hurricanes","docAbstract":"Salt marsh losses have been documented worldwide because of land use change, wave erosion, and sea-level rise. It is still unclear how resistant salt marshes are to extreme storms and whether they can survive multiple events without collapsing. Based on a large dataset of salt marsh lateral erosion rates collected around the world, here, we determine the general response of salt marsh boundaries to wave action under normal and extreme weather conditions. As wave energy increases, salt marsh response to wind waves remains linear, and there is not a critical threshold in wave energy above which salt marsh erosion drastically accelerates. We apply our general formulation for salt marsh erosion to historical wave climates at eight salt marsh locations affected by hurricanes in the United States. Based on the analysis of two decades of data, we find that violent storms and hurricanes contribute less than 1% to long-term salt marsh erosion rates. In contrast, moderate storms with a return period of 2.5 mo are those causing the most salt marsh deterioration. Therefore, salt marshes seem more susceptible to variations in mean wave energy rather than changes in the extremes. The intrinsic resistance of salt marshes to violent storms and their predictable erosion rates during moderate events should be taken into account by coastal managers in restoration projects and risk management plans.","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.1510095112","usgsCitation":"Leonardi, N., Ganju, N., and Fagherazzi, S., 2016, A linear relationship between wave power and erosion determines salt-marsh resilience to violent storms and hurricanes: Proceedings of the National Academy of Sciences of the United States of America, v. 113, no. 1, p. 64-68, https://doi.org/10.1073/pnas.1510095112.","productDescription":"5 p.","startPage":"64","endPage":"68","ipdsId":"IP-065113","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1510095112","text":"Publisher Index Page"},{"id":328837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"1","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-22","publicationStatus":"PW","scienceBaseUri":"57f7c6e6e4b0bc0bec09cbdf","contributors":{"authors":[{"text":"Leonardi, Nicoletta","contributorId":174783,"corporation":false,"usgs":false,"family":"Leonardi","given":"Nicoletta","affiliations":[{"id":27508,"text":"Dept of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":649263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465 nganju@usgs.gov","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":140088,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","email":"nganju@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":649264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagherazzi, Sergio","contributorId":89282,"corporation":false,"usgs":true,"family":"Fagherazzi","given":"Sergio","affiliations":[],"preferred":false,"id":649265,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70180371,"text":"70180371 - 2016 - Potentiometric surface and water-level difference maps of selected confined aquifers in Southern Maryland and Maryland’s Eastern Shore, 1975-2015","interactions":[],"lastModifiedDate":"2017-02-16T15:41:14","indexId":"70180371","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Potentiometric surface and water-level difference maps of selected confined aquifers in Southern Maryland and Maryland’s Eastern Shore, 1975-2015","docAbstract":"Key Results\r\n\r\nThis report presents potentiometric-surface maps of the Aquia and Magothy aquifers and the Upper Patapsco, Lower Patapsco, and Patuxent aquifer systems using water levels measured during September 2015. Water-level difference maps are also presented for these aquifers. The water-level differences in the Aquia aquifer are shown using groundwater-level data from 1982 and 2015, while the water-level differences are shown for the Magothy aquifer using data from 1975 and 2015. Water-level difference maps for both the Upper Patapsco and Lower Patapsco aquifer systems are shown using data from 1990 and 2015. The water-level differences in the Patuxent aquifer system are shown using groundwater-level data from 2007 and 2015.\r\n\r\nThe potentiometric surface maps show water levels ranging from 53 feet above sea level to 164 feet below sea level in the Aquia aquifer, from 86 feet above sea level to 106 feet below sea level in the Magothy aquifer, from 115 feet above sea level to 115 feet below sea level in the Upper Patapsco aquifer system, from 106 feet above sea level to 194 feet below sea level in the Lower Patapsco aquifer system, and from 165 feet above sea level to 171 feet below sea level in the Patuxent aquifer system. Water levels have declined by as much as 116 feet in the Aquia aquifer since 1982, 99 feet in the Magothy aquifer since 1975, 66 and 83 feet in the Upper Patapsco and Lower Patapsco aquifer systems, respectively, since 1990, and 80 feet in the Patuxent aquifer system since 2007.","language":"English","publisher":"Maryland Geological Survey","collaboration":"Maryland Geological Survey; Maryland Department of Natural Resources","usgsCitation":"Curtin, S.E., Staley, A.W., and Andreasen, D.C., 2016, Potentiometric surface and water-level difference maps of selected confined aquifers in Southern Maryland and Maryland’s Eastern Shore, 1975-2015, iii., 30 p. .","productDescription":"iii., 30 p. ","ipdsId":"IP-077192","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":335793,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":334236,"type":{"id":15,"text":"Index Page"},"url":"https://www.mgs.md.gov/publications/report_pages/OFR_16-02-02.html"}],"publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a6c833e4b025c464286294","contributors":{"authors":[{"text":"Curtin, Stephen E. securtin@usgs.gov","contributorId":3703,"corporation":false,"usgs":true,"family":"Curtin","given":"Stephen","email":"securtin@usgs.gov","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":661415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Staley, Andrew W.","contributorId":178867,"corporation":false,"usgs":false,"family":"Staley","given":"Andrew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":661416,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andreasen, David C.","contributorId":178868,"corporation":false,"usgs":false,"family":"Andreasen","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":661417,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70168829,"text":"70168829 - 2016 - Water-quality response to a high-elevation wildfire in the Colorado Front Range","interactions":[],"lastModifiedDate":"2021-04-20T13:20:37.020538","indexId":"70168829","displayToPublicDate":"2015-12-29T15:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Water-quality response to a high-elevation wildfire in the Colorado Front Range","docAbstract":"<p><span>Water quality of the Big Thompson River in the Front Range of Colorado was studied for 2 years following a high‐elevation wildfire that started in October 2012 and burned 15% of the watershed. A combination of fixed‐interval sampling and continuous water‐quality monitors was used to examine the timing and magnitude of water‐quality changes caused by the wildfire. Prefire water quality was well characterized because the site has been monitored at least monthly since the early 2000s. Major ions and nitrate showed the largest changes in concentrations; major ion increases were greatest in the first postfire snowmelt period, but nitrate increases were greatest in the second snowmelt period. The delay in nitrate release until the second snowmelt season likely reflected a combination of factors including fire timing, hydrologic regime, and rates of nitrogen transformations. Despite the small size of the fire, annual yields of dissolved constituents from the watershed increased 20–52% in the first 2 years following the fire. Turbidity data from the continuous sensor indicated high‐intensity summer rain storms had a much greater effect on sediment transport compared to snowmelt. High‐frequency sensor data also revealed that weekly sampling missed the concentration peak during snowmelt and short‐duration spikes during rain events, underscoring the challenge of characterizing postfire water‐quality response with fixed‐interval sampling.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/hyp.10755","usgsCitation":"Mast, M.A., Murphy, S.F., Clow, D.W., Penn, C.A., and Sexstone, G.A., 2016, Water-quality response to a high-elevation wildfire in the Colorado Front Range: Hydrological Processes, v. 30, no. 12, p. 1811-1823, https://doi.org/10.1002/hyp.10755.","productDescription":"13 p.","startPage":"1811","endPage":"1823","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065060","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":318577,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Alisa 0000-0001-6253-8162 mamast@usgs.gov","orcid":"https://orcid.org/0000-0001-6253-8162","contributorId":827,"corporation":false,"usgs":true,"family":"Mast","given":"M.","email":"mamast@usgs.gov","middleInitial":"Alisa","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":621895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":621896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":621897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Penn, Colin A. 0000-0002-5195-2744 cpenn@usgs.gov","orcid":"https://orcid.org/0000-0002-5195-2744","contributorId":5336,"corporation":false,"usgs":true,"family":"Penn","given":"Colin","email":"cpenn@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":621898,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sexstone, Graham A. 0000-0001-8913-0546 sexstone@usgs.gov","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":5159,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham","email":"sexstone@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":621899,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70161737,"text":"70161737 - 2016 - Identification of groundwater nitrate contamination from explosives used in road construction: Isotopic, chemical, and hydrologic evidence","interactions":[],"lastModifiedDate":"2023-03-28T16:35:56.038705","indexId":"70161737","displayToPublicDate":"2015-12-28T11:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Identification of groundwater nitrate contamination from explosives used in road construction: Isotopic, chemical, and hydrologic evidence","docAbstract":"<p><span>Explosives used in construction have been implicated as sources of NO</span><sub><span>3</span></sub><span>&ndash;</span><span>&nbsp;contamination in groundwater, but direct forensic evidence is limited. Identification of blasting-related NO</span><sub><span>3</span></sub><span>&ndash;</span><span>&nbsp;can be complicated by other NO</span><sub><span>3</span></sub><span>&ndash;</span><span>&nbsp;sources, including agriculture and wastewater disposal, and by hydrogeologic factors affecting NO</span><sub><span>3</span></sub><span>&ndash;</span><span>&nbsp;transport and stability. Here we describe a study that used hydrogeology, chemistry, stable isotopes, and mass balance calculations to evaluate groundwater NO</span><sub><span>3</span></sub><span>&ndash;</span><span>&nbsp;sources and transport in areas surrounding a highway construction site with documented blasting in New Hampshire. Results indicate various groundwater responses to contamination: (1) rapid breakthrough and flushing of synthetic NO</span><sub><span>3</span></sub><span>&ndash;</span><span>&nbsp;(low &delta;</span><span>15</span><span>N, high &delta;</span><span>18</span><span>O) from dissolution of unexploded NH</span><sub><span>4</span></sub><span>NO</span><sub><span>3</span></sub><span>&nbsp;blasting agents in oxic groundwater; (2) delayed and reduced breakthrough of synthetic NO</span><sub><span>3</span></sub><span>&ndash;</span><span>&nbsp;subjected to partial denitrification (high &delta;</span><sup><span>15</span></sup><span>N, high &delta;</span><sup><span>18</span></sup><span>O); (3) relatively persistent concentrations of blasting-related biogenic NO</span><sub><span>3</span></sub><span>&ndash;</span><span>&nbsp;derived from nitrification of NH</span><sub><span>4</span></sub><span>+</span><span>&nbsp;(low &delta;</span><sup><span>15</span></sup><span>N, low &delta;</span><sup><span>18</span></sup><span>O); and (4) stable but spatially variable biogenic NO</span><sub><span>3</span></sub><span>&ndash;</span><span>&nbsp;concentrations, consistent with recharge from septic systems (high &delta;</span><sup><span>15</span></sup><span>N, low &delta;</span><sup><span>18</span></sup><span>O), variably affected by denitrification. Source characteristics of denitrified samples were reconstructed from dissolved-gas data (Ar, N</span><sub><span>2</span></sub><span>) and isotopic fractionation trends associated with denitrification (&Delta;&delta;</span><sup><span>15</span></sup><span>N/&Delta;&delta;</span><sup><span>18</span></sup><span>O &asymp; 1.31). Methods and data from this study are expected to be applicable in studies of other aquifers affected by explosives used in construction.</span></p>","language":"English","publisher":"American Chemical Society","publisherLocation":"Easton, PA","doi":"10.1021/acs.est.5b03671","usgsCitation":"Degnan, J.R., Bohlke, J.K., Pelham, K., Langlais, D.M., and Walsh, G.J., 2016, Identification of groundwater nitrate contamination from explosives used in road construction: Isotopic, chemical, and hydrologic evidence: Environmental Science & Technology, v. 50, no. 2, p. 593-603, https://doi.org/10.1021/acs.est.5b03671.","productDescription":"11 p.","startPage":"593","endPage":"603","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067263","costCenters":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":313910,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-28","publicationStatus":"PW","scienceBaseUri":"568e4912e4b0e7a44bc419dd","chorus":{"doi":"10.1021/acs.est.5b03671","url":"http://dx.doi.org/10.1021/acs.est.5b03671","publisher":"American Chemical Society (ACS)","authors":"Degnan James R., Böhlke J. K., Pelham Krystle, Langlais David M., Walsh Gregory J.","journalName":"Environmental Science & Technology","publicationDate":"1/19/2016"},"contributors":{"authors":[{"text":"Degnan, James R. 0000-0002-5665-9010 jrdegnan@usgs.gov","orcid":"https://orcid.org/0000-0002-5665-9010","contributorId":498,"corporation":false,"usgs":true,"family":"Degnan","given":"James","email":"jrdegnan@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":587712,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohlke, John Karl 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":127841,"corporation":false,"usgs":true,"family":"Bohlke","given":"John","email":"jkbohlke@usgs.gov","middleInitial":"Karl","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":587713,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pelham, Krystle","contributorId":152027,"corporation":false,"usgs":false,"family":"Pelham","given":"Krystle","email":"","affiliations":[{"id":18856,"text":"NH Department of Transportation","active":true,"usgs":false}],"preferred":false,"id":587714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langlais, David M.","contributorId":152028,"corporation":false,"usgs":false,"family":"Langlais","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":18857,"text":"Hoyle, Tanner & Associates, Inc.","active":true,"usgs":false}],"preferred":false,"id":587715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Gregory J. 0000-0003-4264-8836 gwalsh@usgs.gov","orcid":"https://orcid.org/0000-0003-4264-8836","contributorId":873,"corporation":false,"usgs":true,"family":"Walsh","given":"Gregory","email":"gwalsh@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":587716,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70160573,"text":"70160573 - 2016 - Quantifying watershed-scale groundwater loading and in-stream fate of nitrate using high-frequency water quality data","interactions":[],"lastModifiedDate":"2018-02-04T13:28:33","indexId":"70160573","displayToPublicDate":"2015-12-28T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying watershed-scale groundwater loading and in-stream fate of nitrate using high-frequency water quality data","docAbstract":"<p><span>We describe a new approach that couples hydrograph separation with high-frequency nitrate data to quantify time-variable groundwater and runoff loading of nitrate to streams, and the net in-stream fate of nitrate at the watershed-scale. The approach was applied at three sites spanning gradients in watershed size and land use in the Chesapeake Bay watershed. Results indicate that 58-73% of the annual nitrate load to the streams was groundwater-discharged nitrate. Average annual first order nitrate loss rate constants (k) were similar to those reported in both modelling and in-stream process-based studies, and were greater at the small streams (0.06 and 0.22 d<sup>-1</sup></span><span>) than at the large river (0.05 d</span><sup><span>-1</span></sup><span>), but 11% of the annual loads were retained/lost in the small streams, compared with 23% in the large river. Larger streambed area to water volume ratios in small streams result in greater loss rates, but shorter residence times in small streams result in a smaller fraction of nitrate loads being removed than in larger streams. A seasonal evaluation of k values suggests that nitrate was retained/lost at varying rates during the growing season. Consistent with previous studies, streamflow and nitrate concentration were inversely related to k. This new approach for interpreting high-frequency nitrate data and the associated findings furthers our ability to understand, predict, and mitigate nitrate impacts on streams and receiving waters by providing insights into temporal nitrate dynamics that would be difficult to obtain using traditional field-based studies.</span></p>","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2015WR017753","usgsCitation":"Miller, M.P., Tesoriero, A., Capel, P.D., Pellerin, B.A., Hyer, K., and Burns, D.A., 2016, Quantifying watershed-scale groundwater loading and in-stream fate of nitrate using high-frequency water quality data: Water Resources Research, v. 52, no. 1, p. 330-347, https://doi.org/10.1002/2015WR017753.","productDescription":"18 p.","startPage":"330","endPage":"347","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062753","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":471396,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015wr017753","text":"Publisher Index Page"},{"id":314157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Pennsylvania, Maryland, New York, Virginia, West Virginia","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.0478515625,\n              42.956422511073335\n            ],\n            [\n              -74.619140625,\n              42.334184385939416\n            ],\n            [\n              -74.81689453125,\n              41.87774145109676\n            ],\n            [\n              -74.739990234375,\n              41.590796851056005\n            ],\n            [\n              -74.739990234375,\n              41.343824581185686\n            ],\n            [\n              -75.201416015625,\n              40.93011520598305\n            ],\n            [\n              -75.3662109375,\n              40.588928169693745\n            ],\n            [\n              -75.38818359375,\n              40.22082997283284\n            ],\n            [\n              -75.5419921875,\n              39.842286020743394\n            ],\n            [\n              -75.6298828125,\n              39.46164364205549\n            ],\n            [\n              -75.12451171875,\n              37.883524980871336\n            ],\n            [\n              -75.6298828125,\n              36.949891786813296\n            ],\n            [\n              -75.73974609375,\n              36.659606226479696\n            ],\n            [\n              -77.36572265625,\n              36.54494944148322\n            ],\n            [\n              -79.12353515625,\n              36.721273880045004\n            ],\n            [\n              -79.661865234375,\n              36.914764288955936\n            ],\n            [\n              -80.782470703125,\n              37.31775185163688\n            ],\n            [\n              -81.375732421875,\n              37.883524980871336\n            ],\n            [\n              -80.5517578125,\n              39.07890809706475\n            ],\n            [\n              -79.46411132812499,\n              39.51251701659638\n            ],\n            [\n              -79.024658203125,\n              40.22082997283284\n            ],\n            [\n              -78.92578124999999,\n              40.91351257612758\n            ],\n            [\n              -78.44238281249999,\n              41.6154423246811\n            ],\n            [\n              -76.9482421875,\n              42.83569550641454\n            ],\n            [\n              -76.37695312499999,\n              43.14909399920127\n            ],\n            [\n              -75.904541015625,\n              43.34914966389313\n            ],\n            [\n              -75.443115234375,\n              43.46089378008257\n            ],\n            [\n              -74.432373046875,\n              43.42898792344157\n            ],\n            [\n              -74.11376953125,\n              43.28520334369384\n            ],\n            [\n              -74.0478515625,\n              42.956422511073335\n            ]\n          ]\n        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Center","active":true,"usgs":true}],"preferred":false,"id":588269,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":588270,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pellerin, Brian A. bpeller@usgs.gov","contributorId":1451,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian","email":"bpeller@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":588271,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hyer, Kenneth E. kenhyer@usgs.gov","contributorId":152108,"corporation":false,"usgs":true,"family":"Hyer","given":"Kenneth E.","email":"kenhyer@usgs.gov","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":false,"id":588272,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":588273,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70173768,"text":"70173768 - 2016 - Fish assemblage shifts in the Powder River of Wyoming: an unregulated prairie river system previously considered to be relatively pristine.","interactions":[],"lastModifiedDate":"2016-06-09T14:22:50","indexId":"70173768","displayToPublicDate":"2015-12-23T17:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Fish assemblage shifts in the Powder River of Wyoming: an unregulated prairie river system previously considered to be relatively pristine.","docAbstract":"<p><span class=\"ff1 fs6\"><span class=\"current-selection\">W</span><span class=\"current-selection\">y</span><span class=\"current-selection\">oming</span><span class=\"ff5\"><span class=\"current-selection\">&rsquo;</span><span class=\"ff1\"><span class=\"current-selection\">s</span> <span class=\"current-selection\">P</span><span class=\"current-selection\">owder</span> <span class=\"current-selection\">River</span> <span class=\"current-selection\">is</span> <span class=\"current-selection\">considered</span> <span class=\"current-selection\">an</span> <span class=\"current-selection\">example</span> <span class=\"current-selection\">of</span> <span class=\"current-selection\">a</span> <span class=\"current-selection\">pristine</span> <span class=\"current-selection\">prairie</span> <span class=\"current-selection\">river</span> <span class=\"current-selection\">system.</span> <span class=\"current-selection\">While </span></span></span></span><span class=\"current-selection\">the</span> <span class=\"current-selection\">river</span> <span class=\"current-selection\">hosts</span> <span class=\"current-selection\">a</span> <span class=\"current-selection\">largely</span> <span class=\"current-selection\">native</span> <span class=\"current-selection\">fish</span> <span class=\"current-selection\">assemblage</span> <span class=\"current-selection\">and</span> <span class=\"current-selection\">remains</span> <span class=\"current-selection\">unimpounded</span> <span class=\"current-selection\">over</span> <span class=\"current-selection\">its</span> <span class=\"current-selection\">1,146-km</span> <span class=\"current-selection\">course</span> <span class=\"current-selection\">to</span> <span class=\"current-selection\">the </span><span class=\"current-selection\">Y</span><span class=\"current-selection\">ellowstone</span> <span class=\"current-selection\">River</span> <span class=\"current-selection\">confluence,</span> <span class=\"current-selection\">the</span> <span class=\"current-selection\">hydrologic</span> <span class=\"current-selection\">regime</span> <span class=\"current-selection\">has</span> <span class=\"current-selection\">been</span> <span class=\"current-selection\">altered</span> <span class=\"current-selection\">through</span> <span class=\"current-selection\">w</span><span class=\"current-selection\">ater</span> <span class=\"current-selection\">dive</span><span class=\"current-selection\">rsion</span> <span class=\"current-selection\">for </span><span class=\"current-selection\">agriculture</span> <span class=\"current-selection\">and</span> <span class=\"current-selection\">natural</span> <span class=\"current-selection\">gas</span> <span class=\"current-selection\">extraction</span> <span class=\"current-selection\">and</span> <span class=\"current-selection\">there</span> <span class=\"current-selection\">has</span> <span class=\"current-selection\">been</span> <span class=\"current-selection\">limited</span> <span class=\"current-selection\">study</span> <span class=\"current-selection\">of</span> <span class=\"current-selection\">fish</span> <span class=\"current-selection\">assemblage</span> <span class=\"current-selection\">structure.</span> <span class=\"current-selection\">W</span><span class=\"current-selection\">e </span><span class=\"current-selection\">analyzed</span> <span class=\"current-selection\">fish</span> <span class=\"current-selection\">data</span> <span class=\"current-selection\">collected</span> <span class=\"current-selection\">from</span> <span class=\"current-selection\">the</span> <span class=\"current-selection\">mainstem</span> <span class=\"current-selection\">P</span><span class=\"current-selection\">owder</span> <span class=\"current-selection\">River</span> <span class=\"current-selection\">in</span> <span class=\"current-selection\">W</span><span class=\"current-selection\">yoming</span> <span class=\"current-selection\">between</span> <span class=\"current-selection\">1896</span> <span class=\"current-selection\">and</span> <span class=\"current-selection\">2008.</span> <span class=\"current-selection\">Shifts </span><span class=\"current-selection\">in</span> <span class=\"current-selection\">presence/absence</span> <span class=\"current-selection\">and</span> <span class=\"current-selection\">relative</span> <span class=\"current-selection\">abundance</span> <span class=\"current-selection\">of</span> <span class=\"current-selection\">fish</span> <span class=\"current-selection\">species,</span> <span class=\"current-selection\">as</span> <span class=\"current-selection\">well</span> <span class=\"current-selection\">as</span> <span class=\"current-selection\">fish</span> <span class=\"current-selection\">assemblage</span> <span class=\"current-selection\">composition,</span> <span class=\"current-selection\">were </span><span class=\"current-selection\">assessed</span> <span class=\"current-selection\">among</span> <span class=\"current-selection\">historical</span> <span class=\"current-selection\">and</span> <span class=\"current-selection\">recent</span> <span class=\"current-selection\">samples.</span> <span class=\"current-selection\">The</span> <span class=\"current-selection\">recent</span> <span class=\"current-selection\">P</span><span class=\"current-selection\">owder</span> <span class=\"current-selection\">Rive</span><span class=\"current-selection\">r</span> <span class=\"current-selection\">fish</span> <span class=\"current-selection\">assemblage</span> <span class=\"current-selection\">w</span><span class=\"current-selection\">as</span> <span class=\"current-selection\">characterized </span><span class=\"current-selection\">by</span> <span class=\"current-selection\">increased</span> <span class=\"current-selection\">relative</span> <span class=\"current-selection\">abundances</span> <span class=\"current-selection\">of</span> <span class=\"current-selection\">sand</span> <span class=\"current-selection\">shiner</span> <span class=\"ff3\"><span class=\"current-selection\">Notr</span><span class=\"current-selection\">opis</span> <span class=\"current-selection\">stramineus</span> <span class=\"ff1\"><span class=\"current-selection\">and</span> <span class=\"current-selection\">plains</span> <span class=\"current-selection\">killifish</span> </span><span class=\"current-selection\">Fundulus</span> <span class=\"current-selection\">zebrinus</span><span class=\"ff1 current-selection\">, </span></span><span class=\"current-selection\">and</span> <span class=\"current-selection\">decreases</span> <span class=\"current-selection\">in</span> <span class=\"current-selection\">sturgeon</span> <span class=\"current-selection\">chub</span> <span class=\"ff3\"><span class=\"current-selection\">Macrhybopsis</span> <span class=\"current-selection\">gelida</span></span><span class=\"current-selection\">.</span> <span class=\"current-selection\">Shifts</span> <span class=\"current-selection\">in</span> <span class=\"current-selection\">fish</span> <span class=\"current-selection\">species</span> <span class=\"current-selection\">relative</span> <span class=\"current-selection\">abundance</span> <span class=\"current-selection\">are</span> <span class=\"current-selection\">linked</span> <span class=\"current-selection\">to </span><span class=\"current-selection\">their</span> <span class=\"current-selection\">reproductive</span> <span class=\"current-selection\">ecology</span> <span class=\"current-selection\">with</span> <span class=\"current-selection\">species</span> <span class=\"current-selection\">with</span> <span class=\"current-selection\">adhesive</span> <span class=\"current-selection\">eggs</span> <span class=\"current-selection\">generally</span> <span class=\"current-selection\">increasing</span> <span class=\"current-selection\">in</span> <span class=\"current-selection\">relative</span> <span class=\"current-selection\">abundance </span><span class=\"current-selection\">while</span> <span class=\"current-selection\">those</span> <span class=\"current-selection\">with</span> <span class=\"current-selection\">buoy</span><span class=\"current-selection\">ant</span> <span class=\"current-selection\">drifting</span> <span class=\"current-selection\">eggs</span> <span class=\"current-selection\">are</span> <span class=\"current-selection\">decreasing.</span> <span class=\"current-selection\">Assemblage</span> <span class=\"current-selection\">shifts</span> <span class=\"current-selection\">could</span> <span class=\"current-selection\">be</span> <span class=\"current-selection\">the</span> <span class=\"current-selection\">result</span> <span class=\"current-selection\">of</span> <span class=\"current-selection\">landscape </span><span class=\"current-selection\">level</span> <span class=\"current-selection\">changes,</span> <span class=\"current-selection\">such</span> <span class=\"current-selection\">as</span> <span class=\"current-selection\">the</span> <span class=\"current-selection\">loss</span> <span class=\"current-selection\">of</span> <span class=\"current-selection\">extreme</span> <span class=\"current-selection\">high</span> <span class=\"current-selection\">and</span> <span class=\"current-selection\">low</span> <span class=\"current-selection\">flow</span> <span class=\"current-selection\">events</span> <span class=\"current-selection\">and</span> <span class=\"current-selection\">changing</span> <span class=\"current-selection\">land</span> <span class=\"current-selection\">use</span> <span class=\"current-selection\">practices.</span></p>","language":"English","publisher":"Wiley","doi":"10.1890/ES14-00361.1","usgsCitation":"Senecal, A.C., Walters, A.W., and Hubert, W.A., 2016, Fish assemblage shifts in the Powder River of Wyoming: an unregulated prairie river system previously considered to be relatively pristine.: Ecosphere, v. 6, no. 12, p. 1-13, https://doi.org/10.1890/ES14-00361.1.","productDescription":"13 p.","startPage":"1","endPage":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055924","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/es14-00361.1","text":"Publisher Index Page"},{"id":323391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Powder River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -105.47973632812499,\n              46.7774927637683\n            ],\n            [\n              -108.30322265624999,\n              42.771211138625894\n            ],\n            [\n              -106.578369140625,\n              41.75492216766298\n            ],\n            [\n              -104.293212890625,\n              46.29381556233369\n            ],\n            [\n              -105.47973632812499,\n              46.7774927637683\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"6","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-21","publicationStatus":"PW","scienceBaseUri":"575a9332e4b04f417c27514c","contributors":{"authors":[{"text":"Senecal, Anna C.","contributorId":171649,"corporation":false,"usgs":false,"family":"Senecal","given":"Anna","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":638234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":638145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hubert, Wayne A.","contributorId":9325,"corporation":false,"usgs":true,"family":"Hubert","given":"Wayne","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":638235,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70160346,"text":"70160346 - 2016 - Oil slick morphology derived from AVIRIS measurements of the Deepwater Horizon oil spill: Implications for spatial resolution requirements of remote sensors","interactions":[],"lastModifiedDate":"2016-12-14T12:39:17","indexId":"70160346","displayToPublicDate":"2015-12-22T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Oil slick morphology derived from AVIRIS measurements of the Deepwater Horizon oil spill: Implications for spatial resolution requirements of remote sensors","docAbstract":"<p>Using fine spatial resolution (~ 7.6 m) hyperspectral AVIRIS data collected over the <i>Deepwater Horizon</i> oil spill in the Gulf of Mexico, we statistically estimated slick lengths, widths and length/width ratios to characterize oil slick morphology for different thickness classes. For all AVIRIS-detected oil slicks (N = 52,100 continuous features) binned into four thickness classes (&le; 50 &mu;m but thicker than sheen, 50&ndash;200 &mu;m, 200&ndash;1000 &mu;m, and &gt; 1000 &mu;m), the median lengths, widths, and length/width ratios of these classes ranged between 22 and 38 m, 7&ndash;11 m, and 2.5&ndash;3.3, respectively. The AVIRIS data were further aggregated to 30-m (Landsat resolution) and 300-m (MERIS resolution) spatial bins to determine the fractional oil coverage in each bin. Overall, if 50% fractional pixel coverage were to be required to detect oil with thickness greater than sheen for most oil containing pixels, a 30-m resolution sensor would be needed.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpolbul.2015.12.003","usgsCitation":"Sun, S., Hu, C., Feng, L., Swayze, G.A., Holmes, J., Graettinger, G., MacDonald, I.R., Garcia, O., and Leifer, I., 2016, Oil slick morphology derived from AVIRIS measurements of the Deepwater Horizon oil spill: Implications for spatial resolution requirements of remote sensors: Marine Pollution Bulletin, v. 103, no. 1-2, p. 276-285, https://doi.org/10.1016/j.marpolbul.2015.12.003.","productDescription":"10 p.","startPage":"276","endPage":"285","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061904","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":312797,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -85.31982421875,\n              29.630771207229\n            ],\n            [\n              -85.75927734375,\n              30.088107753367257\n            ],\n            [\n              -86.41845703124999,\n              30.278044377800153\n            ],\n            [\n              -87.01171875,\n              30.467614102257855\n            ],\n            [\n              -87.6708984375,\n              30.240086360983426\n            ],\n            [\n              -88.39599609375,\n              30.334953881988564\n          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Shaojie","contributorId":150679,"corporation":false,"usgs":false,"family":"Sun","given":"Shaojie","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":582642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hu, Chuanmin","contributorId":24696,"corporation":false,"usgs":true,"family":"Hu","given":"Chuanmin","affiliations":[],"preferred":false,"id":582643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Feng, Lian","contributorId":150680,"corporation":false,"usgs":false,"family":"Feng","given":"Lian","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":582644,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swayze, Gregg A. 0000-0002-1814-7823 gswayze@usgs.gov","orcid":"https://orcid.org/0000-0002-1814-7823","contributorId":518,"corporation":false,"usgs":true,"family":"Swayze","given":"Gregg","email":"gswayze@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":582641,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holmes, Jamie","contributorId":150681,"corporation":false,"usgs":false,"family":"Holmes","given":"Jamie","email":"","affiliations":[{"id":18067,"text":"Stratus Consulting, Inc","active":true,"usgs":false}],"preferred":false,"id":582645,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Graettinger, George","contributorId":69595,"corporation":false,"usgs":true,"family":"Graettinger","given":"George","email":"","affiliations":[],"preferred":false,"id":582646,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"MacDonald, Ian R.","contributorId":150517,"corporation":false,"usgs":false,"family":"MacDonald","given":"Ian","email":"","middleInitial":"R.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":582647,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Garcia, Oscar","contributorId":150682,"corporation":false,"usgs":false,"family":"Garcia","given":"Oscar","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":582648,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Leifer, Ira","contributorId":57988,"corporation":false,"usgs":true,"family":"Leifer","given":"Ira","email":"","affiliations":[],"preferred":false,"id":582649,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70156787,"text":"70156787 - 2016 - Multi-scale predictions of massive conifer mortality due to chronic temperature rise","interactions":[],"lastModifiedDate":"2018-01-12T15:44:21","indexId":"70156787","displayToPublicDate":"2015-12-21T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Multi-scale predictions of massive conifer mortality due to chronic temperature rise","docAbstract":"<p>Global temperature rise and extremes accompanying drought threaten forests<font size=\"1\">&nbsp;</font>and their associated climatic feedbacks. Our&nbsp;ability to accurately simulate drought-induced forest impacts remains highly uncertain&nbsp;in part owing to our failure to integrate physiological measurements, regional-scale models, and dynamic global vegetation models (DGVMs). Here we show consistent predictions of widespread mortality of needleleaf evergreen trees (NET) within Southwest USA by 2100 using state-of-the-art models evaluated against empirical data sets. Experimentally, dominant Southwest USA NET species died when they fell below predawn water potential (<i><span class=\"mb\">Ψ</span></i><sub>pd</sub>) thresholds (April–August mean) beyond which photosynthesis, hydraulic and stomatal conductance, and carbohydrate availability approached zero. The evaluated regional models accurately predicted NET <i><span class=\"mb\">Ψ</span></i><sub>pd</sub>, and 91% of predictions (10 out of 11) exceeded mortality thresholds within the twenty-first century due to temperature rise. The independent DGVMs predicted ≥50% loss of Northern Hemisphere NET by 2100, consistent with the NET findings for Southwest USA. Notably, the global models underestimated future mortality within Southwest USA, highlighting that predictions of future mortality within global models may be underestimates. Taken together, the validated regional predictions and the global simulations predict widespread conifer loss in coming decades under projected global warming.</p>","language":"English","publisher":"Nature Publishing Group","publisherLocation":"London, UK","doi":"10.1038/nclimate2873","usgsCitation":"McDowell, N., Williams, A., Xu, C., Pockman, W., Dickman, L., Sevanto, S., Pangle, R., Limousin, J., Plaut, J., Mackay, D., Ogee, J., Domec, J., Allen, C.D., Fisher, R.A., Jiang, X., Muss, J., Breshears, D., Rauscher, S.A., and Koven, C., 2016, Multi-scale predictions of massive conifer mortality due to chronic temperature rise: Nature Climate Change, v. 6, p. 295-300, https://doi.org/10.1038/nclimate2873.","productDescription":"6 p.","startPage":"295","endPage":"300","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058464","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471401,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1492529","text":"External Repository"},{"id":312936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-21","publicationStatus":"PW","scienceBaseUri":"56826b46e4b0a04ef4925b86","contributors":{"authors":[{"text":"McDowell, Nathan G.","contributorId":9176,"corporation":false,"usgs":true,"family":"McDowell","given":"Nathan G.","affiliations":[],"preferred":false,"id":583294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, A.P.","contributorId":70226,"corporation":false,"usgs":true,"family":"Williams","given":"A.P.","email":"","affiliations":[],"preferred":false,"id":583295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xu, C.","contributorId":9781,"corporation":false,"usgs":true,"family":"Xu","given":"C.","email":"","affiliations":[],"preferred":false,"id":583296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pockman, W. T.","contributorId":57260,"corporation":false,"usgs":false,"family":"Pockman","given":"W. T.","affiliations":[{"id":7164,"text":"Department of Biology, University of New Mexico, Albuquerque, NM 87131 USA","active":true,"usgs":false}],"preferred":false,"id":583297,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dickman, L. T.","contributorId":150844,"corporation":false,"usgs":false,"family":"Dickman","given":"L. T.","affiliations":[],"preferred":false,"id":583298,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sevanto, Sanna","contributorId":150845,"corporation":false,"usgs":false,"family":"Sevanto","given":"Sanna","email":"","affiliations":[],"preferred":false,"id":583299,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pangle, R.","contributorId":150846,"corporation":false,"usgs":false,"family":"Pangle","given":"R.","email":"","affiliations":[],"preferred":false,"id":583300,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Limousin, J.","contributorId":150892,"corporation":false,"usgs":false,"family":"Limousin","given":"J.","affiliations":[],"preferred":false,"id":583491,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Plaut, J.J.","contributorId":6982,"corporation":false,"usgs":true,"family":"Plaut","given":"J.J.","email":"","affiliations":[],"preferred":false,"id":583302,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mackay, D.S.","contributorId":150893,"corporation":false,"usgs":false,"family":"Mackay","given":"D.S.","email":"","affiliations":[],"preferred":false,"id":583492,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ogee, J.","contributorId":150847,"corporation":false,"usgs":false,"family":"Ogee","given":"J.","affiliations":[],"preferred":false,"id":583301,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Domec, Jean-Christophe","contributorId":146460,"corporation":false,"usgs":false,"family":"Domec","given":"Jean-Christophe","email":"","affiliations":[],"preferred":false,"id":583305,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Allen, Craig D. 0000-0002-8777-5989 craig_allen@usgs.gov","orcid":"https://orcid.org/0000-0002-8777-5989","contributorId":2597,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"craig_allen@usgs.gov","middleInitial":"D.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":570546,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Fisher, Rosie A.","contributorId":147090,"corporation":false,"usgs":false,"family":"Fisher","given":"Rosie","email":"","middleInitial":"A.","affiliations":[{"id":16785,"text":"National Center for Atmospheric Research, Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":583306,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Jiang, X.","contributorId":150848,"corporation":false,"usgs":false,"family":"Jiang","given":"X.","email":"","affiliations":[],"preferred":false,"id":583307,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Muss, J.D.","contributorId":31954,"corporation":false,"usgs":true,"family":"Muss","given":"J.D.","affiliations":[],"preferred":false,"id":583308,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Breshears, D.D.","contributorId":17952,"corporation":false,"usgs":false,"family":"Breshears","given":"D.D.","email":"","affiliations":[{"id":12625,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA","active":true,"usgs":false}],"preferred":false,"id":583309,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Rauscher, Sara A.","contributorId":47653,"corporation":false,"usgs":true,"family":"Rauscher","given":"Sara","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":583310,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Koven, C.","contributorId":39655,"corporation":false,"usgs":true,"family":"Koven","given":"C.","email":"","affiliations":[],"preferred":false,"id":583311,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}}
,{"id":70160337,"text":"70160337 - 2016 - Spatial capture-recapture models allowing Markovian transience or dispersal","interactions":[],"lastModifiedDate":"2016-01-11T11:10:49","indexId":"70160337","displayToPublicDate":"2015-12-17T15:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3103,"text":"Population Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial capture-recapture models allowing Markovian transience or dispersal","docAbstract":"<p><span>Spatial capture&ndash;recapture (SCR) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, DNA sampling and other methods which produce spatially explicit individual encounter information. One of the core assumptions of SCR models is that individuals possess home ranges that are spatially stationary during the sampling period. For many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. In this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary SCR models when dispersal or transience is present in the population. Then, using both simulated and real data, we demonstrate that such models can easily be described in the BUGS language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture&ndash;recapture data. We find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. Thus, use of ordinary SCR models to make inferences about density is feasible, but interpretation of SCR model parameters in relation to movement should be avoided. Instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10144-015-0524-z","usgsCitation":"Royle, J., Fuller, A.K., and Sutherland, C., 2016, Spatial capture-recapture models allowing Markovian transience or dispersal: Population Ecology, v. 58, no. 1, p. 53-62, https://doi.org/10.1007/s10144-015-0524-z.","productDescription":"10 p.","startPage":"53","endPage":"62","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069359","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471404,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s10144-015-0524-z","text":"External Repository"},{"id":312465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-21","publicationStatus":"PW","scienceBaseUri":"5673dcb3e4b0da412f4f81fd","contributors":{"authors":[{"text":"Royle, J. Andrew aroyle@usgs.gov","contributorId":138860,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":582604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":582623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutherland, Chris","contributorId":150670,"corporation":false,"usgs":false,"family":"Sutherland","given":"Chris","affiliations":[],"preferred":false,"id":582624,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70159869,"text":"ofr20151230 - 2016 - Water use in Georgia by county for 2010 and water-use trends, 1985–2010","interactions":[],"lastModifiedDate":"2016-12-08T17:04:41","indexId":"ofr20151230","displayToPublicDate":"2015-12-16T13:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1230","title":"Water use in Georgia by county for 2010 and water-use trends, 1985–2010","docAbstract":"<p>Water use and water withdrawals and returns in 2010 are estimated for each major river basin, principal aquifer, water-planning region, and county in Georgia using data obtained from various Federal and State agencies and local sources. Offstream water use in 2010 is estimated for the categories of public supply, domestic, commercial, industrial, mining, irrigation, livestock, aquaculture, and thermoelectric power. Water-use trends for 1985 to 2010 are also shown.</p>\n<p>The period between 2007 and 2010 was a challenging time economically and climatologically in Georgia. During that period, the United States was in the midst of a major recession, resulting in decreases in the manufacturing and construction industries and large increases in unemployment. During 2007, 2008, and the latter half of 2010, precipitation in Georgia was substantially below the 30-year norm.</p>\n<p>According to the 2010 Census of Population and Housing, nearly 9.7 million people lived in Georgia. The water for about 85 percent of that population was provided by public water suppliers. Estimated total water withdrawals from ground-water and surface-water sources were about 4,670 million gallons per day (Mgal/d) in 2010, about a 15-percent reduction from 2005 (5,471 Mgal/d). In 2010, thermoelectric-power facilities (2,046 Mgal/d) and public-supply uses (1,121 Mgal/d) accounted for 68 percent of all water withdrawn in Georgia. Surface-water withdrawals were greatest for thermoelectric-power generation (2,043 Mgal/d), whereas irrigation used the largest amount of groundwater (599 Mgal/d). Surface water provided 78 percent of the 1,121 Mgal/day withdrawn for public supply in 2010. Typically, counties in northern Georgia withdraw a larger percentage of water from surface water than groundwater sources; whereas, counties in the southern part of the State withdraw more water from groundwater sources.</p>\n<p>Historically, water withdrawals in Georgia were highest in 1980 (6,725 Mgal/d). By 1990, water use had decreased by 20 percent to 5,353 Mgal/d, but increased to 6,487 Mgal/d in 2000. By 2005, water use had decreased to an estimated 5,471 Mgal/d, and declined further to 4,670 Mgal/d in 2010&mdash;a 30-percent decrease since 1980. This decline was evident across all water-use categories, but was greatest for surface-water withdrawals by thermoelectric-power facilities. The estimated total water use per capita in 1985 (total withdrawals for all categories divided by total population) was about 850 gallons per day (gal/d), steadily decreasing to about 798 gal/d in 2000, and decreasing further to 460 gal/d in 2010. Although water use declined among all use categories during that 10-year period, most of the decline in per capita water use was caused by the large decrease in water used for thermoelectric-power generation.</p>\n<p>Throughout 1985&ndash;2010 water withdrawn for thermoelectric-power generation has constituted the largest volume of offstream water use in Georgia. Total withdrawals for thermoelectric-power generation declined about 37 percent between 2000 and 2010, mostly due to the decommissioning of power plants in the State. Also during this period, several power plants were shut down and re-tooled to use natural gas-powered generators; thus, water withdrawals for cooling were substantially reduced.</p>\n<p>The decline in water withdrawals and use between 2005 and 2010 can probably be attributed to several factors working together during this period: (1) water conservation laws and policies along with advances in water-conservation technology; (2) the onset of a major recession in 2007; and (3) below average rainfall in 2007, 2008, and the latter half of 2010. Because of these factors, water withdrawn by public suppliers decreased by 4.8 percent (despite a nearly 11-percent increase in population served) and per capita use decreased by 19 percent between 2005 and 2010.</p>\n<p>About 2,225 Mgal/d of water was returned to Georgia streams and lakes in 2010 under the National Pollutant Discharge Elimination System program administered by the Georgia Environmental Protection Division. This amount is about 48 percent of the total water withdrawn from all sources in 2010. Water returns declined 39 percent between 1995 and 2010, mirroring the decline in water withdrawals during that period. In addition, land applications of treated wastewater increased steadily between 1995 and 2010.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151230","collaboration":"Prepared in cooperation with the Georgia Department of Natural Resources, Environmental Protection Division","usgsCitation":"Lawrence, S.J., 2016, Water use in Georgia by county for 2010 and water-use trends, 1985–2010 (ver. 1.1, January 2016): U.S. Geological Survey Open-File Report 2015–1230, 206 p., https://dx.doi.org/10.3133/ofr20151230.","productDescription":"viii, 206 p.","numberOfPages":"218","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-01-01","temporalEnd":"2010-12-31","ipdsId":"IP-037442","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":312330,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1230/ofr20151230.pdf","text":"Report","size":"19.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1230"},{"id":312329,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1230/coverthbn.jpg"},{"id":314402,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2015/1230/verHist.txt","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2015-1230"}],"country":"United States","state":"Georgia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -85.594482421875,\n              34.994003757575776\n            ],\n            [\n              -84.990234375,\n              32.222095840502334\n            ],\n            [\n              -85.166015625,\n              31.83089906339438\n            ],\n            [\n              -85.05615234375,\n              31.541089879585808\n            ],\n            [\n              -85.1220703125,\n              31.21280145833882\n            ],\n            [\n              -84.91333007812499,\n              30.72294882477251\n            ],\n            [\n              -82.24365234375,\n              30.5717205651999\n            ],\n            [\n              -82.188720703125,\n              30.363396239603716\n            ],\n            [\n              -82.0458984375,\n              30.334953881988564\n            ],\n            [\n              -82.034912109375,\n              30.732392734006083\n            ],\n            [\n              -81.89208984375,\n              30.86451022625836\n            ],\n            [\n              -81.45263671875,\n              30.62845887475364\n            ],\n            [\n              -81.10107421874999,\n              31.690781806136822\n            ],\n            [\n              -80.9033203125,\n              31.942839972853083\n            ],\n            [\n              -81.27685546875,\n              32.55607364492029\n            ],\n            [\n              -81.5625,\n              33.08233672856376\n            ],\n            [\n              -81.9580078125,\n              33.46810795527896\n            ],\n            [\n              -82.518310546875,\n              33.93424531117312\n            ],\n            [\n              -82.880859375,\n              34.4793919710481\n            ],\n            [\n              -83.045654296875,\n              34.470335121217495\n            ],\n            [\n              -83.375244140625,\n              34.732584206123626\n            ],\n            [\n              -83.08959960937499,\n              35.0120020431607\n            ],\n            [\n              -85.594482421875,\n              34.994003757575776\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: Originally posted December 16, 2015; Version 1.1: January 15, 2016","contact":"<p>Director, South Atlantic Water Science Center<br /> U.S. Geological Survey<br /> 720 Gracern Road<br /> Columbia, SC 29210<br /> <a href=\"http://www.usgs.gov/water/southatlantic/\">http://www.usgs.gov/water/southatlantic/</a></p>","tableOfContents":"<ul>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Data Sources and Methodology</li>\n<li>Water Withdrawals, Estimated Water Use, and Surface-Water Returns</li>\n<li>Water-Use Trends, 1985&ndash;2010</li>\n<li>Summary</li>\n<li>References Cited</li>\n<li>Glossary</li>\n<li>Appendix 1. North American Industrial Classification Codes</li>\n<li>Appendix 2. Population, Water Withdrawals, and Water Use by Source of Water for Each County in Georgia, 2010</li>\n</ul>","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"publishedDate":"2015-12-16","revisedDate":"2016-01-15","noUsgsAuthors":false,"publicationDate":"2015-12-16","publicationStatus":"PW","scienceBaseUri":"56728b29e4b01a7f82451d87","contributors":{"authors":[{"text":"Lawrence, Stephen J. slawrenc@usgs.gov","contributorId":1885,"corporation":false,"usgs":true,"family":"Lawrence","given":"Stephen","email":"slawrenc@usgs.gov","middleInitial":"J.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":580832,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70207052,"text":"70207052 - 2016 - Climate change and water resources in a tropical island system: Propagation of uncertainty from statistically downscaled climate models to hydrologic models","interactions":[],"lastModifiedDate":"2019-12-04T15:05:24","indexId":"70207052","displayToPublicDate":"2015-12-15T14:59:21","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2032,"text":"International Journal of Climatology","active":true,"publicationSubtype":{"id":10}},"title":"Climate change and water resources in a tropical island system: Propagation of uncertainty from statistically downscaled climate models to hydrologic models","docAbstract":"<p><span>Many tropical islands have limited water resources with historically increasing demand, all potentially affected by a changing climate. The effects of climate change on island hydrology are difficult to model due to steep local precipitation gradients and sparse data. This work uses 10 statistically downscaled general circulation models (GCMs) under two greenhouse gas emission scenarios to evaluate the uncertainty propagated from GCMs in projecting the effects of climate change on water resources in a tropical island system. The assessment is conducted using a previously configured hydrologic model, the Precipitation Runoff Modelling System (PRMS) for Puerto Rico. Projected climate data and their modelled hydrologic variables versus historical measurements and their modelled hydrologic variables are found to have empirical distribution functions that are statistically different with less than 1 year of daily data aggregation. Thus, only annual averages of the projected hydrologic variables are employed as completely bias‐corrected model outputs. The magnitude of the projected total flow decreases in the four regions covering Puerto Rico, but with a large range of uncertainty depending on the makeup of the GCM ensemble. The multi‐model mean projected total flow decreases by 49–88% of historical amounts from the 1960s to the 2090s for the high emissions scenarios and by 39–79% for the low emissions scenarios. Subsurface flow contributions decreased the least and groundwater flow contributions decreased the most across the island. At locations critical to water supply for human use, projected streamflow is shown to decrease substantially below projected withdrawals by 2099.</span></p>","language":"English","publisher":"Royal Meteorological Society","doi":"10.1002/joc.4560","usgsCitation":"Van Beusekom, A.E., Gould, W.A., Terando, A.J., and Collazo, J.A., 2016, Climate change and water resources in a tropical island system: Propagation of uncertainty from statistically downscaled climate models to hydrologic models: International Journal of Climatology, v. 36, no. 9, p. 3370-3383, https://doi.org/10.1002/joc.4560.","productDescription":"14 p.","startPage":"3370","endPage":"3383","ipdsId":"IP-062479","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":369912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -67.4066162109375,\n              17.814071002942764\n            ],\n            [\n              -65.56915283203125,\n              17.814071002942764\n            ],\n            [\n              -65.56915283203125,\n              18.609807415471877\n            ],\n            [\n              -67.4066162109375,\n              18.609807415471877\n            ],\n            [\n              -67.4066162109375,\n              17.814071002942764\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"36","issue":"9","noUsgsAuthors":false,"publicationDate":"2015-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Beusekom, Ashley E. 0000-0002-6996-978X beusekom@usgs.gov","orcid":"https://orcid.org/0000-0002-6996-978X","contributorId":3992,"corporation":false,"usgs":true,"family":"Van Beusekom","given":"Ashley","email":"beusekom@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":776637,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gould, William A.","contributorId":103535,"corporation":false,"usgs":true,"family":"Gould","given":"William","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":776638,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terando, Adam J. 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":173447,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":776639,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":776640,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70160291,"text":"70160291 - 2016 - Transforming growth factor-β1 expression in endangered age-0 shortnose suckers (<i>Chasmistes brevirostris</i>) from Upper Klamath Lake, OR relative to histopathology, meristic, spatial, and temporal data","interactions":[],"lastModifiedDate":"2016-12-14T12:34:20","indexId":"70160291","displayToPublicDate":"2015-12-15T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1644,"text":"Fish & Shellfish Immunology","active":true,"publicationSubtype":{"id":10}},"title":"Transforming growth factor-β1 expression in endangered age-0 shortnose suckers (<i>Chasmistes brevirostris</i>) from Upper Klamath Lake, OR relative to histopathology, meristic, spatial, and temporal data","docAbstract":"<p>During July &ndash; September of 2008, 2009, and 2010 endangered age-0 juvenile shortnose suckers were sampled from Upper Klamath Lake, OR in a health evaluation that included the measurement of transforming growth factor &ndash; beta (TGF-&beta;) expression in spleen in combination with a histopathology assessment. This analysis was performed to determine if the expression of this immuno-regulator could be used as a component of a larger health evaluation intended to identify potential risk-factors that may help to explain why very few of these fish survive to age-1. Potential associations between TGF-&beta;1 expression, histopathological findings, meristic data as well as temporal and spatial data were evaluated using analysis-of-variance. In this analysis, the absence or presence of opercula deformity and hepatic cell necrosis were identified as significant factors in accounting for the variance in TGF-&beta;1 expression observed in age-0 shortnose suckers (n = 122, squared multiple R = 0.989). Location of sample collection and the absence or presence of anchor worms (<i>Lernaea</i> spp.) were identified as significant cofactors. The actual mechanisms involved with these relationships have yet to be determined. The strength, however, of our findings support the concept of using TGF-&beta;1 expression as part of a broader fish health assessment and suggests the potential for using additional immunologic measures in future studies. Specifically, our results indicate that the measure of TGF-&beta;1 expression in age-0 shortnose sucker health assessments can facilitate the process of identifying disease risks that are associated with the documented lack of recruitment into the adult population.</p>","language":"English","publisher":"Academic Press","publisherLocation":"London, UK","doi":"10.1016/j.fsi.2015.12.019","usgsCitation":"Ottinger, C.A., Densmore, C.L., Robertson, L.S., Iwanowicz, D.D., and Vanderkooi, S.P., 2016, Transforming growth factor-β1 expression in endangered age-0 shortnose suckers (<i>Chasmistes brevirostris</i>) from Upper Klamath Lake, OR relative to histopathology, meristic, spatial, and temporal data: Fish & Shellfish Immunology, v. 49, p. 1-6, https://doi.org/10.1016/j.fsi.2015.12.019.","productDescription":"6 p.","startPage":"1","endPage":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068548","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":471406,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fsi.2015.12.019","text":"Publisher Index Page"},{"id":312804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.92764282226564,\n              42.582410848693954\n            ],\n            [\n              -121.92832946777345,\n              42.521711682043765\n            ],\n            [\n              -121.95785522460936,\n              42.50754004948742\n            ],\n            [\n              -121.97021484374999,\n          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,{"id":70160100,"text":"70160100 - 2016 - The effects of habitat, climate, and Barred Owls on long-term demography of Northern Spotted Owls","interactions":[],"lastModifiedDate":"2017-10-07T08:55:53","indexId":"70160100","displayToPublicDate":"2015-12-15T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1318,"text":"Condor","active":true,"publicationSubtype":{"id":10}},"title":"The effects of habitat, climate, and Barred Owls on long-term demography of Northern Spotted Owls","docAbstract":"<p><span>Estimates of species' vital rates and an understanding of the factors affecting those parameters over time and space can provide crucial information for management and conservation. We used mark&ndash;recapture, reproductive output, and territory occupancy data collected during 1985&ndash;2013 to evaluate population processes of Northern Spotted Owls (</span><i><i>Strix occidentalis</i>&nbsp;caurina</i><span>) in 11 study areas in Washington, Oregon, and northern California, USA. We estimated apparent survival, fecundity, recruitment, rate of population change, and local extinction and colonization rates, and investigated relationships between these parameters and the amount of suitable habitat, local and regional variation in meteorological conditions, and competition with Barred Owls (</span><i><i>Strix varia</i></i><span>). Data were analyzed for each area separately and in a meta-analysis of all areas combined, following a strict protocol for data collection, preparation, and analysis. We used mixed effects linear models for analyses of fecundity, Cormack-Jolly-Seber open population models for analyses of apparent annual survival (ϕ), and a reparameterization of the Jolly-Seber capture&ndash;recapture model (i.e. reverse Jolly-Seber; RJS) to estimate annual rates of population change (&lambda;</span><i><sub>RJS</sub></i><span>) and recruitment. We also modeled territory occupancy dynamics of Northern Spotted Owls and Barred Owls in each study area using 2-species occupancy models. Estimated mean annual rates of population change (&lambda;) suggested that Spotted Owl populations declined from 1.2% to 8.4% per year depending on the study area. The weighted mean estimate of &lambda; for all study areas was 0.962 (&plusmn; 0.019 SE; 95% CI: 0.925&ndash;0.999), indicating an estimated range-wide decline of 3.8% per year from 1985 to 2013. Variation in recruitment rates across the range of the Spotted Owl was best explained by an interaction between total winter precipitation and mean minimum winter temperature. Thus, recruitment rates were highest when both total precipitation (29 cm) and minimum winter temperature (&minus;9.5&deg;C) were lowest. Barred Owl presence was associated with increased local extinction rates of Spotted Owl pairs for all 11 study areas. Habitat covariates were related to extinction rates for Spotted Owl pairs in 8 of 11 study areas, and a greater amount of suitable owl habitat was generally associated with decreased extinction rates. We observed negative effects of Barred Owl presence on colonization rates of Spotted Owl pairs in 5 of 11 study areas. The total amount of suitable Spotted Owl habitat was positively associated with colonization rates in 5 areas, and more habitat disturbance was associated with lower colonization rates in 2 areas. We observed strong declines in derived estimates of occupancy in all study areas. Mean fecundity of females was highest for adults (0.309 &plusmn; 0.027 SE), intermediate for 2-yr-olds (0.179 &plusmn; 0.040 SE), and lowest for 1-yr-olds (0.065 &plusmn; 0.022 SE). The presence of Barred Owls and habitat covariates explained little of the temporal variation in fecundity in most study areas. Climate covariates occurred in competitive fecundity models in 8 of 11 study areas, but support for these relationships was generally weak. The fecundity meta-analysis resulted in 6 competitive models, all of which included the additive effects of geographic region and annual time variation. The 2 top-ranked models also weakly supported the additive negative effects of the amount of suitable core area habitat, Barred Owl presence, and the amount of edge habitat on fecundity. We found strong support for a negative effect of Barred Owl presence on apparent survival of Spotted Owls in 10 of 11 study areas, but found few strong effects of habitat on survival at the study area scale. Climate covariates occurred in top or competitive survival models for 10 of 11 study areas, and in most cases the relationships were as predicted; however, there was little consistency among areas regarding the relative importance of specific climate covariates. In contrast, meta-analysis results suggested that Spotted Owl survival was higher across all study areas when the Pacific Decadal Oscillation (PDO) was in a warming phase and the Southern Oscillation Index (SOI) was negative, with a strongly negative SOI indicative of El Ni&ntilde;o events. The best model that included the Barred Owl covariate (BO) was ranked 4</span><sup>th</sup><span>&nbsp;and also included the PDO covariate, but the BO effect was strongly negative. Our results indicated that Northern Spotted Owl populations were declining throughout the range of the subspecies and that annual rates of decline were accelerating in many areas. We observed strong evidence that Barred Owls negatively affected Spotted Owl populations, primarily by decreasing apparent survival and increasing local territory extinction rates. However, the amount of suitable owl habitat, local weather, and regional climatic patterns also were related to survival, occupancy (via colonization rate), recruitment, and, to a lesser extent, fecundity, although there was inconsistency in regard to which covariates were important for particular demographic parameters or across study areas. In the study areas where habitat was an important source of variation for Spotted Owl demographics, vital rates were generally positively associated with a greater amount of suitable owl habitat. However, Barred Owl densities may now be high enough across the range of the Northern Spotted Owl that, despite the continued management and conservation of suitable owl habitat on federal lands, the long-term prognosis for the persistence of Northern Spotted Owls may be in question without additional management intervention. Based on our study, the removal of Barred Owls from the Green Diamond Resources (GDR) study area had rapid, positive effects on Northern Spotted Owl survival and the rate of population change, supporting the hypothesis that, along with habitat conservation and management, Barred Owl removal may be able to slow or reverse Northern Spotted Owl population declines on at least a localized scale.</span></p>","language":"English","publisher":"Cooper Ornithological Society","doi":"10.1650/CONDOR-15-24.1","usgsCitation":"Dugger, K., Forsman, E.D., Franklin, A.B., Davis, R.J., White, G.C., Schwarz, C.J., Burnham, K.P., Nichols, J., Hines, J., Yackulic, C.B., Doherty, P., Bailey, L., Clark, D.A., Ackers, S.H., Andrews, L.S., Augustine, B., Biswell, B.L., Blakesley, J., Carlson, P., Clement, M.J., Diller, L.V., Glenn, E.M., Green, A., Gremel, S.A., Herter, D.R., Higley, J.M., Hobson, J., Horn, R.B., Huyvaert, K., McCafferty, C., McDonald, T., McDonnell, K., Olson, G.S., Reid, J.A., Rockweit, J., Ruiz, V., Saenz, J., and Sovern, S.G., 2016, The effects of habitat, climate, and Barred Owls on long-term demography of Northern Spotted Owls: Condor, v. 118, no. 1, p. 57-116, https://doi.org/10.1650/CONDOR-15-24.1.","productDescription":"59 p.","startPage":"57","endPage":"116","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063590","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-15-24.1","text":"Publisher Index Page"},{"id":312301,"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\": 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,{"id":70160226,"text":"70160226 - 2016 - The current status of mapping karst areas and availability of public sinkhole-risk resources in karst terrains of the United States","interactions":[],"lastModifiedDate":"2017-04-07T13:53:44","indexId":"70160226","displayToPublicDate":"2015-12-15T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"The current status of mapping karst areas and availability of public sinkhole-risk resources in karst terrains of the United States","docAbstract":"<p><span>Subsidence from sinkhole collapse is a common occurrence in areas underlain by water-soluble rocks such as carbonate and evaporite rocks, typical of karst terrain. Almost all 50 States within the United States (excluding Delaware and Rhode Island) have karst areas, with sinkhole damage highest in Florida, Texas, Alabama, Missouri, Kentucky, Tennessee, and Pennsylvania. A conservative estimate of losses to all types of ground subsidence was $125 million per year in 1997. This estimate may now be low, as review of cost reports from the last 15&nbsp;years indicates that the cost of karst collapses in the United States averages more than $300 million per year. Knowing when a catastrophic event will occur is not possible; however, understanding where such occurrences are likely is possible. The US Geological Survey has developed and maintains national-scale maps of karst areas and areas prone to sinkhole formation. Several States provide additional resources for their citizens; Alabama, Colorado, Florida, Indiana, Iowa, Kentucky, Minnesota, Missouri, Ohio, and Pennsylvania maintain databases of sinkholes or karst features, with Florida, Kentucky, Missouri, and Ohio providing sinkhole reporting mechanisms for the public.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10040-015-1333-3","usgsCitation":"Kuniansky, E.L., Weary, D.J., and Kaufmann, J.E., 2016, The current status of mapping karst areas and availability of public sinkhole-risk resources in karst terrains of the United States: Hydrogeology Journal, v. 24, no. 3, p. 613-624, https://doi.org/10.1007/s10040-015-1333-3.","productDescription":"12 p.","startPage":"613","endPage":"624","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064145","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":471407,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-015-1333-3","text":"Publisher Index Page"},{"id":312295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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States\"}}]}","volume":"24","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-25","publicationStatus":"PW","scienceBaseUri":"567139b0e4b09cfe53ca7d5a","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":582103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weary, David J. 0000-0002-6115-6397 dweary@usgs.gov","orcid":"https://orcid.org/0000-0002-6115-6397","contributorId":545,"corporation":false,"usgs":true,"family":"Weary","given":"David","email":"dweary@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":582104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kaufmann, James E. jkaufmann@usgs.gov","contributorId":2312,"corporation":false,"usgs":true,"family":"Kaufmann","given":"James","email":"jkaufmann@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":582105,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70164524,"text":"70164524 - 2016 - Radiometric dating of marine-influenced coal using Re–Os geochronology","interactions":[],"lastModifiedDate":"2016-02-09T13:23:41","indexId":"70164524","displayToPublicDate":"2015-12-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Radiometric dating of marine-influenced coal using Re–Os geochronology","docAbstract":"<div class=\"abstract svAbstract \" data-etype=\"ab\">\n<p id=\"sp0110\">Coal deposits are integral to understanding the structural evolution and thermal history of sedimentary basins and correlating contemporeous estuarine and fluvial delatic strata with marine sections. While marine shales may readily lend themselves to Re&ndash;Os dating due to the dominance of hydrogenous Re and Os, the lack of a chronometer for near-shore sedimentary environments hampers basinwide correlations in absolute time. Here, we employ the Re&ndash;Os geochronometer, along with total organic carbon (TOC) and Rock&ndash;Eval data, to determine the timing and conditions of a marine incursion at the top of the Matewan coal bed, Kanawha Formation, Pottsville Group, West Virginia, USA. The observed range for hydrogen index (HI: 267&ndash;290 mg hydrocarbon/gram total organic carbon) for these coal samples suggests dominance of aliphatic hydrocarbons with low carbon (&lt;C<sub>19</sub>) chain length. Average Re (<span id=\"mmlsi1\" class=\"mathmlsrc\"><span class=\"formulatext stixSupport mathImg\" title=\"Click to view the MathML source\" data-mathurl=\"/science?_ob=MathURL&amp;_method=retrieve&amp;_eid=1-s2.0-S0012821X15006056&amp;_mathId=si1.gif&amp;_user=111111111&amp;_pii=S0012821X15006056&amp;_rdoc=1&amp;_issn=0012821X&amp;md5=bc2944291302252512cfdefae42b865b\">107.6&plusmn;16.4&nbsp;ng/g</span></span>) and Os (<span id=\"mmlsi2\" class=\"mathmlsrc\"><span class=\"formulatext stixSupport mathImg\" title=\"Click to view the MathML source\" data-mathurl=\"/science?_ob=MathURL&amp;_method=retrieve&amp;_eid=1-s2.0-S0012821X15006056&amp;_mathId=si2.gif&amp;_user=111111111&amp;_pii=S0012821X15006056&amp;_rdoc=1&amp;_issn=0012821X&amp;md5=8dec0d0a6e0498d3d756ca94a204da46\">0.52&plusmn;0.09&nbsp;ng/g</span></span>) concentrations of the marine-influenced Matewan coal are higher by few orders of magnitude than published data for terrestrial coal. A Re&ndash;Os isochron for the Matewan coal provides an age of&nbsp;<span id=\"mmlsi3\" class=\"mathmlsrc\"><span class=\"formulatext stixSupport mathImg\" title=\"Click to view the MathML source\" data-mathurl=\"/science?_ob=MathURL&amp;_method=retrieve&amp;_eid=1-s2.0-S0012821X15006056&amp;_mathId=si3.gif&amp;_user=111111111&amp;_pii=S0012821X15006056&amp;_rdoc=1&amp;_issn=0012821X&amp;md5=c75685db623fd2b8eb6fe1a800af416d\">325&plusmn;14&nbsp;Ma</span></span>&nbsp;(Model 3; MSWD = 12;&nbsp;<span id=\"mmlsi4\" class=\"mathmlsrc\"><span class=\"formulatext stixSupport mathImg\" title=\"Click to view the MathML source\" data-mathurl=\"/science?_ob=MathURL&amp;_method=retrieve&amp;_eid=1-s2.0-S0012821X15006056&amp;_mathId=si4.gif&amp;_user=111111111&amp;_pii=S0012821X15006056&amp;_rdoc=1&amp;_issn=0012821X&amp;md5=82cb9a0cbbb37111e82dde71ca23285d\">n=19</span></span>; 2<i>&sigma; </i>). This is the first Re&ndash;Os age derived from coal samples; the age overlaps a new composite Re&ndash;Os age of&nbsp;<span id=\"mmlsi26\" class=\"mathmlsrc\"><span class=\"formulatext stixSupport mathImg\" title=\"Click to view the MathML source\" data-mathurl=\"/science?_ob=MathURL&amp;_method=retrieve&amp;_eid=1-s2.0-S0012821X15006056&amp;_mathId=si26.gif&amp;_user=111111111&amp;_pii=S0012821X15006056&amp;_rdoc=1&amp;_issn=0012821X&amp;md5=11d71f446454cdf5c83161c90c00d583\">317&plusmn;2&nbsp;Ma</span></span>&nbsp;for the immediately overlying Betsie Shale Member.</p>\n<p id=\"sp0120\">External precision for replicate Os analyses carried out for several Matewan coal samples shows a positive correlation with their HI. The HI, which is low in terrestrial organic matter, reflects the degree of marine influence. Thus, samples with the most profound marine influence also have the best analytical reproducibility. Equilibration of Os isotopes with seawater under marine conditions overwhelms variability inherited from terrestrial plant debris, decreasing scatter on the isochron. The&nbsp;<sup>187</sup>Re/<sup>188</sup>Os ratios of the Matewan coal (&sim;3300&ndash;5135) are higher than most of those previously published for Phanerozoic black shale (mostly &lt;2000). Mass balance calculations based on Re/TOC and Os/TOC ratios for the Matewan coal indicate that both Re and Os are primarily marine in origin, and their high&nbsp;<sup>187</sup>Re/<sup>188</sup>Os ratios confirm efficient removal of both elements from a sulfidic water column into the coal. We show that Re&ndash;Os geochronology of marine-influenced coal can be a viable tool for constraining depositional ages.</p>\n<p>&nbsp;</p>\n</div>","language":"English","publisher":"North-Holland Pub. 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,{"id":70170964,"text":"70170964 - 2016 - Impacts of climatic variation on trout: A global synthesis and path forward","interactions":[],"lastModifiedDate":"2017-11-27T10:28:22","indexId":"70170964","displayToPublicDate":"2015-12-10T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of climatic variation on trout: A global synthesis and path forward","docAbstract":"<p><span>Despite increasing concern that climate change may negatively impact trout&mdash;a globally distributed group of fish with major economic, ecological, and cultural value&mdash;a synthetic assessment of empirical data quantifying relationships between climatic variation and trout ecology does not exist. We conducted a systematic review to describe how temporal variation in temperature and streamflow influences trout ecology in freshwater ecosystems. Few studies (</span><i class=\"EmphasisTypeItalic \">n</i><span>&nbsp;=&nbsp;42) have quantified relationships between temperature or streamflow and trout demography, growth, or phenology, and nearly all estimates (96&nbsp;%) were for&nbsp;</span><i class=\"EmphasisTypeItalic \">Salvelinus fontinalis</i><span>&nbsp;and&nbsp;</span><i class=\"EmphasisTypeItalic \">Salmo trutta</i><span>. Only seven studies used temporal data to quantify climate-driven changes in trout ecology. Results from these studies were beset with limitations that prohibited quantitatively rigorous meta-analysis, a concerning inadequacy given major investment in trout conservation and management worldwide. Nevertheless, consistent patterns emerged from our synthesis, particularly a positive effect of summer streamflow on trout demography and growth; 64&nbsp;% of estimates were positive and significant across studies, age classes, species, and locations, highlighting that climate-induced changes in hydrology may have numerous consequences for trout. To a lesser degree, summer and fall temperatures were negatively related to population demography (51 and 53&nbsp;% of estimates, respectively), but temperature was rarely related to growth. To address limitations and uncertainties, we recommend: (1) systematically improving data collection, description, and sharing; (2) appropriately integrating climate impacts with other intrinsic and extrinsic drivers over the entire lifecycle; (3) describing indirect consequences of climate change; and (4) acknowledging and describing intrinsic resiliency.</span></p>","language":"English","publisher":"Chapman & Hall","publisherLocation":"Andover, UK","doi":"10.1007/s11160-015-9414-x","usgsCitation":"Kovach, R., Muhlfeld, C.C., Al-Chokhachy, R.K., Dunham, J.B., Letcher, B., and Kershner, J.L., 2016, Impacts of climatic variation on trout: A global synthesis and path forward: Reviews in Fish Biology and Fisheries, v. 26, no. 2, p. 135-151, https://doi.org/10.1007/s11160-015-9414-x.","productDescription":"17 p.","startPage":"135","endPage":"151","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064125","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":321217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-10","publicationStatus":"PW","scienceBaseUri":"5736fad0e4b0dae0d5e03dde","contributors":{"authors":[{"text":"Kovach, Ryan 0000-0001-5402-2123 rkovach@usgs.gov","orcid":"https://orcid.org/0000-0001-5402-2123","contributorId":145914,"corporation":false,"usgs":true,"family":"Kovach","given":"Ryan","email":"rkovach@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":629259,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":629260,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Al-Chokhachy, Robert K. 0000-0002-2136-5098 ral-chokhachy@usgs.gov","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":1674,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","email":"ral-chokhachy@usgs.gov","middleInitial":"K.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":629261,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":629262,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Letcher, Benjamin 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":169305,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":629263,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kershner, Jeffrey L. 0000-0002-7093-9860 jkershner@usgs.gov","orcid":"https://orcid.org/0000-0002-7093-9860","contributorId":310,"corporation":false,"usgs":true,"family":"Kershner","given":"Jeffrey","email":"jkershner@usgs.gov","middleInitial":"L.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":629264,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70160011,"text":"70160011 - 2016 - High and dry: high elevations disproportionately exposed to regional climate change in Mediterranean-climate landscapes","interactions":[],"lastModifiedDate":"2016-04-28T12:59:29","indexId":"70160011","displayToPublicDate":"2015-12-08T15:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"High and dry: high elevations disproportionately exposed to regional climate change in Mediterranean-climate landscapes","docAbstract":"<div id=\"ASec1\" class=\"AbstractSection\">\n<h3 class=\"Heading\">Context</h3>\n<p id=\"Par1\" class=\"Para\">Predicting climate-driven species&rsquo; range shifts depends substantially on species&rsquo; exposure to climate change. Mountain landscapes contain a wide range of topoclimates and soil characteristics that are thought to mediate range shifts and buffer species&rsquo; exposure. Quantifying fine-scale patterns of exposure across mountainous terrain is a key step in understanding vulnerability of species to regional climate change.</p>\n</div>\n<div id=\"ASec2\" class=\"AbstractSection\">\n<h3 class=\"Heading\">Objectives</h3>\n<p id=\"Par2\" class=\"Para\">We demonstrated a transferable, flexible approach for mapping climate change exposure in a moisture-limited, mountainous California landscape across 4 climate change projections under phase 5 of the Coupled Model Intercomparison Project (CMIP5) for mid-(2040&ndash;2069) and end-of-century (2070&ndash;2099).</p>\n</div>\n<div id=\"ASec3\" class=\"AbstractSection\">\n<h3 class=\"Heading\">Methods</h3>\n<p id=\"Par3\" class=\"Para\">We produced a 149-year dataset (1951&ndash;2099) of modeled climatic water deficit (CWD), which is strongly associated with plant distributions, at 30-m resolution to map climate change exposure in the Tehachapi Mountains, California, USA. We defined climate change exposure in terms of departure from the 1951&ndash;1980 mean and historical range of variability in CWD in individual years and 3-year moving windows.</p>\n</div>\n<div id=\"ASec4\" class=\"AbstractSection\">\n<h3 class=\"Heading\">Results</h3>\n<p id=\"Par4\" class=\"Para\">Climate change exposure was generally greatest at high elevations across all future projections, though we encountered moderate topographic buffering on poleward-facing slopes. Historically dry lowlands demonstrated the least exposure to climate change.</p>\n</div>\n<div id=\"ASec5\" class=\"AbstractSection\">\n<h3 class=\"Heading\">Conclusions</h3>\n<p id=\"Par5\" class=\"Para\">In moisture-limited, Mediterranean-climate landscapes, high elevations may experience the greatest exposure to climate change in the 21st century. High elevation species may thus be especially vulnerable to continued climate change as habitats shrink and historically energy-limited locations become increasingly moisture-limited in the future.</p>\n</div>","language":"English","publisher":"Springer","doi":"10.1007/s10980-015-0318-x","usgsCitation":"McCullough, I.M., Davis, F.W., Dingman, J.R., Flint, L.E., Flint, A.L., Serra-Diaz, J.M., Syphard, A.D., Moritz, M., Hannah, L., and Franklin, J., 2016, High and dry: high elevations disproportionately exposed to regional climate change in Mediterranean-climate landscapes: Landscape Ecology, v. 31, no. 5, p. 1063-1075, https://doi.org/10.1007/s10980-015-0318-x.","productDescription":"13 p.","startPage":"1063","endPage":"1075","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070738","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":471411,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/7bk1x7rp","text":"External Repository"},{"id":312043,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"5","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-30","publicationStatus":"PW","scienceBaseUri":"5667ff3be4b06a3ea36c8e0e","contributors":{"authors":[{"text":"McCullough, Ian M.","contributorId":150407,"corporation":false,"usgs":false,"family":"McCullough","given":"Ian","email":"","middleInitial":"M.","affiliations":[{"id":13549,"text":"UC Santa Barbara Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":581551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Frank W.","contributorId":150406,"corporation":false,"usgs":false,"family":"Davis","given":"Frank","email":"","middleInitial":"W.","affiliations":[{"id":18015,"text":"Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":581550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dingman, John R.","contributorId":150408,"corporation":false,"usgs":false,"family":"Dingman","given":"John","email":"","middleInitial":"R.","affiliations":[{"id":18016,"text":"CA Air Resources Board","active":true,"usgs":false}],"preferred":false,"id":581552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":581549,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":581553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Serra-Diaz, Josep M.","contributorId":149950,"corporation":false,"usgs":false,"family":"Serra-Diaz","given":"Josep","email":"","middleInitial":"M.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":581554,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Syphard, Alexandra D.","contributorId":8977,"corporation":false,"usgs":false,"family":"Syphard","given":"Alexandra","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":581555,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moritz, Max A.","contributorId":57586,"corporation":false,"usgs":false,"family":"Moritz","given":"Max A.","affiliations":[],"preferred":false,"id":581556,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hannah, Lee","contributorId":147796,"corporation":false,"usgs":false,"family":"Hannah","given":"Lee","affiliations":[{"id":16938,"text":"Conservation International","active":true,"usgs":false}],"preferred":false,"id":581557,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Franklin, Janet","contributorId":90833,"corporation":false,"usgs":true,"family":"Franklin","given":"Janet","affiliations":[],"preferred":false,"id":581558,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70160004,"text":"70160004 - 2016 - Comparative demographics of a Hawaiian forest bird community","interactions":[],"lastModifiedDate":"2018-01-04T12:40:53","indexId":"70160004","displayToPublicDate":"2015-12-08T11:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2190,"text":"Journal of Avian Biology","active":true,"publicationSubtype":{"id":10}},"title":"Comparative demographics of a Hawaiian forest bird community","docAbstract":"<p><span>Estimates of demographic parameters such as survival and reproductive success are critical for guiding management efforts focused on species of conservation concern. Unfortunately, reliable demographic parameters are difficult to obtain for any species, but especially for rare or endangered species. Here we derived estimates of adult survival and recruitment in a community of Hawaiian forest birds, including eight native species (of which three are endangered) and two introduced species at Hakalau Forest National Wildlife Refuge, Hawaiʻi. Integrated population models (IPM) were used to link mark&ndash;recapture data (1994&ndash;1999) with long-term population surveys (1987&ndash;2008). To our knowledge, this is the first time that IPM have been used to characterize demographic parameters of a whole avian community, and provides important insights into the life history strategies of the community. The demographic data were used to test two hypotheses: 1) arthropod specialists, such as the &lsquo;Akiapōlā&lsquo;au&nbsp;</span><i>Hemignathus munroi</i><span>, are &lsquo;slower&rsquo; species characterized by a greater relative contribution of adult survival to population growth, i.e. lower fecundity and increased adult survival; and 2) a species&rsquo; susceptibility to environmental change, as reflected by its conservation status, can be predicted by its life history traits. We found that all species were characterized by a similar population growth rate around one, independently of conservation status, origin (native vs non-native), feeding guild, or life history strategy (as measured by &lsquo;slowness&rsquo;), which suggested that the community had reached an equilibrium. However, such stable dynamics were achieved differently across feeding guilds, as demonstrated by a significant increase of adult survival and a significant decrease of recruitment along a gradient of increased insectivory, in support of hypothesis 1. Supporting our second hypothesis, we found that slower species were more vulnerable species at the global scale than faster ones. The possible causes and conservation implications of these patterns are discussed.</span></p>","language":"English","publisher":"Wiley-Blackwell","doi":"10.1111/jav.00756","usgsCitation":"Guillaumet, A., Woodworth, B., Camp, R., and Paxton, E., 2016, Comparative demographics of a Hawaiian forest bird community: Journal of Avian Biology, v. 47, no. 2, p. 185-196, https://doi.org/10.1111/jav.00756.","productDescription":"12 p.","startPage":"185","endPage":"196","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068685","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":312034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -160.46630859375,\n              21.69826549685252\n            ],\n            [\n              -158.04931640625,\n              21.238182425982313\n            ],\n            [\n              -156.016845703125,\n              20.004322295998723\n            ],\n            [\n              -156.236572265625,\n              19.590844152960933\n            ],\n            [\n              -155.797119140625,\n              18.760712758499565\n            ],\n            [\n              -154.698486328125,\n              19.46659223220761\n            ],\n            [\n              -155.819091796875,\n              20.80747157680652\n            ],\n            [\n              -157.1484375,\n              21.493963563064455\n            ],\n            [\n              -159.697265625,\n              22.411028521558706\n            ],\n            [\n              -160.59814453125,\n              21.800308050972603\n            ],\n            [\n              -160.46630859375,\n              21.69826549685252\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"47","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-22","publicationStatus":"PW","scienceBaseUri":"5667ff38e4b06a3ea36c8e06","chorus":{"doi":"10.1111/jav.00756","url":"http://dx.doi.org/10.1111/jav.00756","publisher":"Wiley-Blackwell","authors":"Guillaumet Alban, Woodworth Bethany L., Camp Richard J., Paxton Eben H.","journalName":"Journal of Avian Biology","publicationDate":"11/22/2015","auditedOn":"3/28/2016"},"contributors":{"authors":[{"text":"Guillaumet, Alban","contributorId":150397,"corporation":false,"usgs":false,"family":"Guillaumet","given":"Alban","email":"","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":581523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodworth, Bethany L.","contributorId":66797,"corporation":false,"usgs":true,"family":"Woodworth","given":"Bethany L.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":581524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Camp, Richard J.","contributorId":27392,"corporation":false,"usgs":true,"family":"Camp","given":"Richard J.","affiliations":[],"preferred":false,"id":581525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paxton, Eben H. 0000-0001-5578-7689 epaxton@usgs.gov","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":438,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben H.","email":"epaxton@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":581522,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70160002,"text":"70160002 - 2016 - Evidence of estrogenic endocrine disruption in smallmouth and largemouth bass inhabiting Northeast U.S. National Wildlife Refuge waters: A reconnaissance study","interactions":[],"lastModifiedDate":"2018-08-07T12:44:08","indexId":"70160002","displayToPublicDate":"2015-12-08T11:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1480,"text":"Ecotoxicology and Environmental Safety","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of estrogenic endocrine disruption in smallmouth and largemouth bass inhabiting Northeast U.S. National Wildlife Refuge waters: A reconnaissance study","docAbstract":"<p><span>Intersex as the manifestation of testicular oocytes (TO) in male gonochoristic fishes has been used as an indicator of estrogenic exposure. Here we evaluated largemouth bass (</span><i>Micropterus salmoides</i><span>) or smallmouth bass (</span><i>Micropterus dolomieu</i><span>) form 19 National Wildlife Refuges (NWRs) in the Northeast U.S. inhabiting waters on or near NWR lands for evidence of estrogenic endocrine disruption. Waterbodies sampled included rivers, lakes, impoundments, ponds, and reservoirs. Here we focus on evidence of endocrine disruption in male bass evidenced by gonad histopathology including intersex or abnormal plasma vitellogenin (Vtg) concentrations. During the fall seasons of 2008&ndash;2010, we collected male smallmouth bass (</span><i>n</i><span>=118) from 12 sites and largemouth bass (</span><i>n</i><span>=173) from 27 sites. Intersex in male smallmouth bass was observed at all sites and ranged from 60% to 100%; in male largemouth bass the range was 0&ndash;100%. Estrogenicity, as measured using a bioluminescent yeast reporter, was detected above the probable no effects concentration (0.73&nbsp;ng/L) in ambient water samples from 79% of the NWR sites. Additionally, the presence of androgen receptor and glucocorticoid receptor ligands were noted as measured via novel nuclear receptor translocation assays. Mean plasma Vtg was elevated (&gt;0.2&nbsp;mg/ml) in male smallmouth bass at four sites and in male largemouth bass at one site. This is the first reconnaissance survey of this scope conducted on US National Wildlife Refuges. The baseline data collected here provide a necessary benchmark for future monitoring and justify more comprehensive NWR-specific studies.</span></p>","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.ecoenv.2015.09.035","usgsCitation":"Iwanowicz, L., Blazer, V., Pinkney, A., Guy, C., Major, A., Munney, K., Mierzykowski, S., Lingenfelser, S., Secord, A., Patnode, K., Kubiak, T., Stern, C., Hahn, C.M., Iwanowicz, D.D., Walsh, H.L., and Sperry, A.J., 2016, Evidence of estrogenic endocrine disruption in smallmouth and largemouth bass inhabiting Northeast U.S. National Wildlife Refuge waters: A reconnaissance study: Ecotoxicology and Environmental Safety, v. 124, p. 50-59, https://doi.org/10.1016/j.ecoenv.2015.09.035.","productDescription":"10 p.","startPage":"50","endPage":"59","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059732","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":312035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Ohio, Pennsylvannia, Vermont, Virginia, West Virginia","otherGeospatial":"Assabet River, Back Bay, Blackwater, Cherry Valley, Erie, Great Bay, Great Meadows, Great Swamp, John Heinzat Tinicum, Mason Neck, Missisquoi, Montezuma, Moosehorn, Ohio River Islands, Patuxent, Rappahannock, Sunkhaze, Umbagog, Wallkill","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -71.103515625,\n              45.321254361171476\n            ],\n            [\n              -67.43408203124999,\n              45.583289756006316\n            ],\n            [\n              -67.39013671875,\n              45.321254361171476\n            ],\n            [\n              -67.1044921875,\n              45.182036837015886\n            ],\n            [\n              -66.8408203125,\n              44.66865287227321\n            ],\n            [\n              -68.48876953125,\n              44.10336537791152\n            ],\n            [\n              -69.76318359375,\n              43.67581809328344\n            ],\n            [\n              -70.46630859375,\n              43.42100882994726\n            ],\n            [\n              -70.7080078125,\n              42.553080288955826\n            ],\n            [\n              -71.103515625,\n              42.04929263868686\n            ],\n            [\n              -71.8505859375,\n              41.27780646738183\n            ],\n            [\n              -74.02587890625,\n              40.58058466412764\n            ],\n            [\n              -75.5419921875,\n              39.436192999314095\n            ],\n            [\n              -75.95947265625,\n              38.993572058209466\n            ],\n            [\n              -75.87158203125,\n              37.996162679728116\n            ],\n            [\n              -75.65185546874999,\n              36.82687474287728\n            ],\n            [\n              -75.89355468749999,\n              36.38591277287651\n            ],\n            [\n              -77.36572265625,\n              36.56260003738548\n            ],\n            [\n              -79.0576171875,\n              38.993572058209466\n            ],\n            [\n              -80.79345703125,\n              39.67337039176558\n            ],\n            [\n              -81.49658203125,\n              40.27952566881291\n            ],\n            [\n              -81.32080078125,\n              41.492120839687786\n            ],\n            [\n              -80.61767578124999,\n              42.04929263868686\n            ],\n            [\n              -77.49755859375,\n              43.34116005412307\n            ],\n            [\n              -76.13525390624999,\n              43.50075243569041\n            ],\n            [\n              -73.32275390625,\n              45.213003555993964\n            ],\n            [\n              -71.103515625,\n              45.321254361171476\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"124","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5667ff39e4b06a3ea36c8e0a","contributors":{"authors":[{"text":"Iwanowicz, Luke R.  0000-0002-1197-6178 liwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":150383,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R. 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C.P.","contributorId":22983,"corporation":false,"usgs":true,"family":"Guy","given":"C.P.","email":"","affiliations":[],"preferred":false,"id":581500,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Major, A.M.","contributorId":150387,"corporation":false,"usgs":false,"family":"Major","given":"A.M.","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":581501,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Munney, K.","contributorId":150388,"corporation":false,"usgs":false,"family":"Munney","given":"K.","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":581502,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mierzykowski, S.","contributorId":150389,"corporation":false,"usgs":false,"family":"Mierzykowski","given":"S.","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife 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System","active":true,"usgs":false}],"preferred":false,"id":581506,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kubiak, T.J.","contributorId":150393,"corporation":false,"usgs":false,"family":"Kubiak","given":"T.J.","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":581507,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Stern, C.","contributorId":150394,"corporation":false,"usgs":false,"family":"Stern","given":"C.","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":581508,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hahn, Cassidy M. cmhahn@usgs.gov","contributorId":5321,"corporation":false,"usgs":true,"family":"Hahn","given":"Cassidy","email":"cmhahn@usgs.gov","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science 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0000-0002-4815-3730 asperry@usgs.gov","orcid":"https://orcid.org/0000-0002-4815-3730","contributorId":5872,"corporation":false,"usgs":true,"family":"Sperry","given":"Adam","email":"asperry@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":581512,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
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