Synchrotron-based X-ray absorption spectroscopy (XAS) is an in situ, minimally-destructive, element-specific, molecular-scale structural probe that has been employed to study the chemical forms (species) of arsenic (As) in solid and aqueous phases (including rocks, soils, sediment, synthetic compounds, and numerous types of biota including humans) for more than 20 years. Although several excellent reviews of As geochemistry and As speciation in the environment have been published previously (including recent contributions in this volume), the explosion of As-XAS studies over the past decade (especially studies employing microfocused X-ray beams) warrants this new review of the literature and of data analysis methods.
\nThis review has two main sections. The first is a presentation of methods for sample preparation and for the collection, processing and analysis of As-XAS spectra. Since several more comprehensive reviews of the X-ray absorption theory and data collection methodology exist, this section is brief and focused specifically on As. The second section is a critical review of the As-XAS literature, arranged by sample type and accompanied by summary tables (collected as appendices at the end of the chapter).
\nOne of the most important aims of this review is to clarify the different types of analysis that are performed on As-XAS spectra, and to describe the benefits, drawbacks, and limitations of each. Arsenic XAS spectra are analyzed to obtain one or more of the following types of information (in increasing order of sophistication):
","language":"English","publisher":"Mineralogical Society of America","publisherLocation":"Washington D.C.","doi":"10.2138/rmg.2014.79.5","usgsCitation":"Foster, A.L., and Kim, C.S., 2014, Arsenic speciation in solids using X-ray absorption spectroscopy: Reviews in Mineralogy and Geochemistry, v. 79, no. 1, p. 257-369, https://doi.org/10.2138/rmg.2014.79.5.","productDescription":"113 p.","startPage":"257","endPage":"369","numberOfPages":"113","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057070","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":313958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"79","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-09-05","publicationStatus":"PW","scienceBaseUri":"568e48e3e4b0e7a44bc41885","contributors":{"authors":[{"text":"Foster, Andrea L. 0000-0003-1362-0068 afoster@usgs.gov","orcid":"https://orcid.org/0000-0003-1362-0068","contributorId":1740,"corporation":false,"usgs":true,"family":"Foster","given":"Andrea","email":"afoster@usgs.gov","middleInitial":"L.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":587841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kim, Chris S.","contributorId":152089,"corporation":false,"usgs":false,"family":"Kim","given":"Chris","email":"","middleInitial":"S.","affiliations":[{"id":18864,"text":"Chapman University","active":true,"usgs":false}],"preferred":false,"id":587842,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70074738,"text":"70074738 - 2014 - Elk monitoring in Lewis and Clark National Historical Park: 2008-2012 synthesis report","interactions":[],"lastModifiedDate":"2014-04-09T15:00:12","indexId":"70074738","displayToPublicDate":"2014-01-01T14:51:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":54,"text":"Natural Resource Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NCCN/NRTR--2014/837","title":"Elk monitoring in Lewis and Clark National Historical Park: 2008-2012 synthesis report","docAbstract":"Maintaining elk (Cervus elaphus roosevelti) herds that frequent Lewis and Clark National Historical Park (NHP) is central to the park’s purpose of preserving the historic, cultural, scenic, and natural resources associated with the winter encampment of the Lewis and Clark expedition. Elk were critically important to the Lewis and Clark expedition in providing food and hides that sustained the expedition during the winter of 1805-06 and supplied them for their return east during 1806. Today, elk remain a key component of interpreting the Lewis and Clark story to over 200,000 park visitors each year at the Fort Clatsop visitor center.
\nIn 2008, the US Geological Survey (USGS) began collaborating with Lewis and Clark NHP and \nthe NPS North Coast and Cascades Network to develop a protocol for monitoring long-term \nchanges in the magnitude and spatial patterns of elk use within and adjacent to Lewis and Clark \nNHP (Griffin et al. 2011). Specific objectives of the monitoring program were to measure trends \nin (1) relative use of the Fort Clatsop unit by elk during winter; (2) the proportion of areas where \nelk sign is present in the Fort Clatsop unit in winter; and (3) the frequency of elk sightings from \nroads in and around the Fort Clatsop unit. This report synthesizes the results of the first four \nyears of monitoring elk distribution and use in Lewis and Clark NHP from 2008-2012. We also \npresent data from FY2012 (Appendix 1), in lieu of an annual report for that year.
\nWe used fecal pellet group surveys as the cornerstone for monitoring trends in both relative use \nof the Fort Clatsop Unit by elk and the proportion of areas where elk sign was present at the end \nof winter. We estimated pellet group density based on data collected from a network of fecal \npellet plots distributed systematically throughout the unit. We developed a double observer \nsampling scheme that enabled us to estimate detection biases and improve the accuracy of pellet \ngroup density estimates. We computed the estimated detection probability for any pellet group \nobserved; this probability was a function of the pellet group size and stage of decay, as well as \nlighting and vegetation conditions, and the number of observers (one or two) searching for \npellets in that subplot. We then used these estimated detection probabilities to adjust the raw \ncounts of the detected pellet groups to account for groups that likely went undetected under \nsimilar pellet and environmental conditions (each observed pellet group was weighted by the \ninverse of its estimated detection probability). We also used results from the late winter fecal \npellet surveys to quantify the proportion of areas where elk pellets occurred (PAO), which was \nbased on the presence of fecal pellet groups and estimation of detection biases (i.e., accounting \nfor pellet groups that likely went undetected by both observers). In this synthesis, we report \ntemporal trends in both pellet group density and PAO from 2008-2012, based on weighted linear \nregression analyses as well as spatial variation of pellet group densities over time.
\nWe completed late winter fecal pellet surveys at 61-66 plots annually, depending on yearly \nvariation in access. We cleared fecal pellets at survey points in late October / early November \neach year and returned in late February / early March to count pellet groups left by elk over the \nwinter. The estimated probability that a pellet group was detected by any one observer during \nlate winter was affected most by the pellet group size and was less affected by decay class and \nlighting conditions. Per-observer detection probabilities ranged from as low as ~10-15% for \nsingle pellets to ~85-90% for pellet groups with 50 pellets. Average pellet group density in the \nFort Clatsop unit ranged annually from 0.58 (+/- 1.43 standard error [SE]) to 0.93 (+/- 2.25 SE) \npellet groups per 3-m radius subplot. Pellet group density declined over time, at approximately 8.8% per year (+/- 2.5% SE), but that slope was not statistically distinguishable from zero (2-\ntailed P=0.16). Following correction for detection biases, the proportion of surveyed points used \nby elk (i.e., PAO) ranged from 0.44 (+/- 0.07 SE) to 0.53 (+/- 0.07 SE) during the 4 winters. The \nestimated proportion of areas where elk pellets occurred (PAO) declined at a rate of 2.6% per \nyear (+/- 1.2% per year SE), but that trend also was not statistically distinguishable from zero (2-\ntailed P=0.17). Statistical significance of a measure’s trend depends on both the magnitude (i.e., \nslope) of the observed trend and the number of years the trend continues in the same increasing \nor decreasing direction. Through simulation modeling we determined how many additional years \nof surveys would be required to reveal a statistically significant trend, based on the same trends \nin pellet group density and PAO, and associated variation, observed from 2009-2012. Assuming \nthe same trends persist in the future, simulations indicated that there is a 70% probability that a \nstatistically significant trend would be detected after two more years of conducting pellet group \nsurveys.
\nRelative use by elk during winter, as indexed by elk pellet group density, was generally greatest \nin the southeast region of the Fort Clatsop unit in or near the large freshwater marsh at the mouth \nof Colewort Creek and adjacent upland areas. Pellet group density was also higher than average \nin the north-central forested area, not far from a privately-owned pasture north of the park \nboundary. This spatial pattern in pellet group densities across the Fort Clatsop unit was \nconsistent across all four years, although specific pellet group densities varied from year to year. \nPellet group density declined significantly over time at two points in the southeast of the Fort \nClatsop unit, even though pellet group density at those points remained higher than the unit \naverage. Pellet group density increased significantly over time at one point in the north-central \nregion, and at one point in the south-central region of the unit, indicating a slight shift in the \ndistribution of elk use within the Fort Clatsop Unit over the four years.
\nAs an index of visitors’ opportunities to see elk in and around the Fort Clatsop Unit, we \nconducted replicated roadside elk surveys 3-5 times monthly during February, April, June, \nAugust, October and December 2008-2012. During each morning of survey, we searched for elk \nalong four routes that totaled 32 km. We examined bimonthly trends in the numbers of elk \ngroups seen, the total number of elk seen, and the observed composition ratios for those six \nmonths of the year. The average number of elk groups seen per survey ranged from 0.75 (+/- \n0.32 SE) during February to a peak of 1.95 (+/- 0.36 SE) during June. Despite this seasonal \nvariation in numbers of elk groups seen, the average total number of elk seen per morning was \nless variable. The average ratios of antlered elk to antlerless adult elk (i.e., bulls:cows) and \ncalves to antlerless adult elk (i.e. calves:cows) varied seasonally, with the highest of both \naverage ratios observed in August. We detected no significant trends in the average number of \nelk groups and total numbers of elk seen per survey from 2008-2012. Similarly, ratios of calves \nand antlered elk per antlerless elk did not differ over time.
\nElk groups were frequently seen from January to August in the southeast region of the Fort \nClatsop unit, in the vicinity of Colewort Creek. Outside of NPS lands, we observed elk most \nfrequently in open areas near the Astoria regional airport, in the pastures and forests immediately \nnorth of the Fort Clatsop unit and, prior to the construction of a residential development, in a \npasture northwest of the Fort Clatsop unit.
\nElk monitoring at Lewis and Clark NHP is still in its initial years and additional monitoring will \nbe required to verify trends that appear to be emerging. For example, the initial monitoring \nsuggested incipient declining trends in both pellet group density and proportion of plots with \npellets present, as well as, potentially, a small shift in elk distribution away from a new trail that \nwas recently constructed in the southeast portion of the Fort Clatsop unit. Continued monitoring \nwill aid in determining whether this local change in distribution persists (or, alternatively, \nresulted from short-term random variation), and whether there will be any positive or negative \neffect in the northern portion of the unit where a new trail has been constructed. High variability \nin road counts prevented our ability to find any clear trend in numbers or composition of elk \nobserved in and near Fort Clatsop, but changes in the patterns of observations of elk from \nroadways suggest that residential development outside the park has reduced the available habitat \nfor elk in some of the areas surveyed, and may have affected spatial use patterns of elk adjacent \nto some areas of the park. In addition to monitoring future effects of land use changes outside the \npark, continued monitoring may also prove useful for assessing elk responses to natural \nsuccession in forests disturbed by windthrow in December 2007 and to NPS vegetation \nmanagement activities such as variable density thinning in the forest, trail development, and \nrestoration at Otter Point tidal area and Colewort Creek Slough.
","language":"English","publisher":"National Park Service","usgsCitation":"Griffin, P., Jenkins, K.J., Cole, C., Clatterbuck, C., Boetsch, J., and Beirne, K., 2014, Elk monitoring in Lewis and Clark National Historical Park: 2008-2012 synthesis report: Natural Resource Technical Report NPS/NCCN/NRTR--2014/837, xii, 56 p.","productDescription":"xii, 56 p.","numberOfPages":"72","temporalStart":"2008-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-053359","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":286057,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":281885,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/App/Reference/Profile/2206594"}],"country":"United States","state":"Oregon;Washington","otherGeospatial":"Lewis And Clark National Historical Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.1,45.9 ], [ -124.1,46.3 ], [ -123.8,46.3 ], [ -123.8,45.9 ], [ -124.1,45.9 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53559432e4b0120853e8bf5d","contributors":{"authors":[{"text":"Griffin, Paul C.","contributorId":7802,"corporation":false,"usgs":true,"family":"Griffin","given":"Paul C.","affiliations":[],"preferred":false,"id":489768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkins, Kurt J. 0000-0003-1415-6607 kurt_jenkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1415-6607","contributorId":3415,"corporation":false,"usgs":true,"family":"Jenkins","given":"Kurt","email":"kurt_jenkins@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":489767,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cole, Carla","contributorId":44809,"corporation":false,"usgs":true,"family":"Cole","given":"Carla","email":"","affiliations":[],"preferred":false,"id":489769,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clatterbuck, Chris","contributorId":53697,"corporation":false,"usgs":true,"family":"Clatterbuck","given":"Chris","email":"","affiliations":[],"preferred":false,"id":489770,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boetsch, John","contributorId":57766,"corporation":false,"usgs":true,"family":"Boetsch","given":"John","affiliations":[],"preferred":false,"id":489771,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beirne, Katherine","contributorId":58754,"corporation":false,"usgs":true,"family":"Beirne","given":"Katherine","affiliations":[],"preferred":false,"id":489772,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70098947,"text":"70098947 - 2014 - Automated Cropland Classification Algorithm (ACCA) for California using multi-sensor remote sensing","interactions":[],"lastModifiedDate":"2024-07-09T13:43:41.863777","indexId":"70098947","displayToPublicDate":"2014-01-01T14:38:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5987,"text":"Photogrammetric Engineering & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Automated Cropland Classification Algorithm (ACCA) for California using multi-sensor remote sensing","docAbstract":"Increasing pressure to feed the growing population with scarce water resources requires accurate and routine cropland mapping. This paper develops and implements a rule-based automated cropland classification algorithm (ACCA) using multi-sensor remote sensing data. Pixel-by-pixel accuracy assessments showed that ACCA produced an overall accuracy of 96 percent (Khat = 0.8) when tested using independent data layers. Furthermore, ACCA-generated county cropland areas showed high agreement (R-square values 0.94) when compared with three independent data sources: (a) US Department of Agriculture (USDA) cropland data layer derived cropland areas, (b) county specific crop acreage data from the Farm Service Agency, and (c) the Census of Agriculture data for the 58 counties in California. Our results demonstrate the ability of ACCA to generate cropland extent and areas over space and time, in an automated fashion with high degree of accuracies year after year, greatly contributing to food and water security analysis and decision making.
","language":"English","publisher":"American Society for Photogrammetry and Remote Sensing","doi":"10.14358/PERS.80.1.81","usgsCitation":"Wu, Z., Thenkabail, P.S., and Verdin, J., 2014, Automated Cropland Classification Algorithm (ACCA) for California using multi-sensor remote sensing: Photogrammetric Engineering & Remote Sensing, v. 80, no. 1, p. 81-90, https://doi.org/10.14358/PERS.80.1.81.","productDescription":"10 p.","startPage":"81","endPage":"90","ipdsId":"IP-041735","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":473239,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.80.1.81","text":"Publisher Index Page"},{"id":284274,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Article"},"seriesTitle":{"id":7514,"text":"Journal of Geophysical Research - Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Source and progression of a submarine landslide and tsunami: The 1964 Great Alaska earthquake at Valdez","docAbstract":"Like many subduction zone earthquakes, the deadliest aspects of the 1964 M = 9.2 Alaska earthquake were the tsunamis it caused. The worst of these were generated by local submarine landslides induced by the earthquake. These caused high runups, engulfing several coastal towns in Prince William Sound. In this paper, we study one of these cases in detail, the Port Valdez submarine landslide and tsunami. We combine eyewitness reports, preserved film, and careful posttsunami surveys with new geophysical data to inform numerical models for landslide tsunami generation. We review the series of events as recorded at Valdez old town and then determine the corresponding subsurface events that led to the tsunami. We build digital elevation models of part of the pretsunami and posttsunami fjord‐head delta. Comparing them reveals a ~1500 m long region that receded 150 m to the east, which we interpret as the primary delta landslide source. Multibeam imagery and high‐resolution seismic reflection data identify a ~400 m wide chute with hummocky deposits at its terminus, which may define the primary slide path. Using these elements we run hydrodynamic models of the landslide‐driven tsunamis that match observations of current direction, maximum inundation, and wave height at Valdez old town. We speculate that failure conditions at the delta front may have been influenced by manmade changes in drainage patterns as well as the fast retreat of Valdez and other glaciers during the past century.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2014JB011514","usgsCitation":"Parsons, T.E., Geist, E.L., Ryan, H.F., Lee, H., Haeussler, P.J., Lynett, P., Hart, P.E., Sliter, R.W., and Roland, E.C., 2014, Source and progression of a submarine landslide and tsunami: The 1964 Great Alaska earthquake at Valdez: Journal of Geophysical Research - Solid Earth, v. 119, no. 11, p. 8502-8516, https://doi.org/10.1002/2014JB011514.","productDescription":"15 p.","startPage":"8502","endPage":"8516","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":473240,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2014jb011514","text":"Publisher Index Page"},{"id":358210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Valdez","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -146.964111328125,\n 60.85293796664351\n ],\n [\n -146.0137939453125,\n 60.85293796664351\n ],\n [\n -146.0137939453125,\n 61.22531306274158\n ],\n [\n -146.964111328125,\n 61.22531306274158\n ],\n [\n -146.964111328125,\n 60.85293796664351\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"119","issue":"11","noUsgsAuthors":false,"publicationDate":"2014-11-16","publicationStatus":"PW","scienceBaseUri":"5bc038ebe4b0fc368eb53b17","contributors":{"authors":[{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":747532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":747533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryan, Holly F. hryan@usgs.gov","contributorId":2375,"corporation":false,"usgs":true,"family":"Ryan","given":"Holly","email":"hryan@usgs.gov","middleInitial":"F.","affiliations":[],"preferred":false,"id":747534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, Homa J. hjlee@usgs.gov","contributorId":1021,"corporation":false,"usgs":true,"family":"Lee","given":"Homa J.","email":"hjlee@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":747535,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haeussler, Peter J. 0000-0002-1503-6247 pheuslr@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":503,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter","email":"pheuslr@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":747536,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lynett, Patrick","contributorId":24298,"corporation":false,"usgs":true,"family":"Lynett","given":"Patrick","affiliations":[],"preferred":false,"id":747537,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Patrick E. 0000-0002-5080-1426 hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5080-1426","contributorId":2879,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick","email":"hart@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":747538,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sliter, Ray W. 0000-0003-0337-3454 rsliter@usgs.gov","orcid":"https://orcid.org/0000-0003-0337-3454","contributorId":1992,"corporation":false,"usgs":true,"family":"Sliter","given":"Ray","email":"rsliter@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":747539,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Roland, Emily C. eroland@usgs.gov","contributorId":5075,"corporation":false,"usgs":true,"family":"Roland","given":"Emily","email":"eroland@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":false,"id":747540,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70047368,"text":"70047368 - 2014 - Integrating disparate lidar data at the national scale to assess the relationships between height above ground, land cover and ecoregions","interactions":[],"lastModifiedDate":"2024-06-13T16:33:39.373998","indexId":"70047368","displayToPublicDate":"2014-01-01T14:21:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Integrating disparate lidar data at the national scale to assess the relationships between height above ground, land cover and ecoregions","docAbstract":"With the acquisition of lidar data for over 30 percent of the US, it is now possible to assess the three-dimensional distribution of features at the national scale. This paper integrates over 350 billion lidar points from 28 disparate datasets into a national-scale database and evaluates if height above ground is an important variable in the context of other nationalscale layers, such as the US Geological Survey National Land Cover Database and the US Environmental Protection Agency ecoregions maps. While the results were not homoscedastic and the available data did not allow for a complete height census in any of the classes, it does appear that where lidar data were used, there were detectable differences in heights among many of these national classification schemes. This study supports the hypothesis that there were real, detectable differences in heights in certain national-scale classification schemes, despite height not being a variable used in any of the classification routines.","language":"English","publisher":"American Society for Photogrammetry and Remote Sensing","doi":"10.14358/PERS.80.1.59","usgsCitation":"Stoker, J.M., Cochrane, M.A., and Roy, D.P., 2014, Integrating disparate lidar data at the national scale to assess the relationships between height above ground, land cover and ecoregions: Photogrammetric Engineering and Remote Sensing, v. 80, no. 1, p. 59-70, https://doi.org/10.14358/PERS.80.1.59.","productDescription":"12 p.","startPage":"59","endPage":"70","numberOfPages":"12","ipdsId":"IP-049075","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473244,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.80.1.59","text":"Publisher Index Page"},{"id":280754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd62bbe4b0b290850fe5bf","contributors":{"authors":[{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":481852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cochrane, Mark A.","contributorId":20884,"corporation":false,"usgs":false,"family":"Cochrane","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":481850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, David P.","contributorId":71083,"corporation":false,"usgs":true,"family":"Roy","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":481851,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70148164,"text":"70148164 - 2014 - Reproductive ecology of American Oystercatchers nesting on shell rakes","interactions":[],"lastModifiedDate":"2015-05-26T12:57:05","indexId":"70148164","displayToPublicDate":"2014-01-01T14:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1318,"text":"Condor","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive ecology of American Oystercatchers nesting on shell rakes","docAbstract":"Degradation of nesting habitat for coastal birds has led to the use of nontraditional nesting habitat. The American Oystercatcher (Haematopus palliatus) is listed as a \"Species of High Concern'' by the U. S. Shorebird Conservation Plan and is declining in the southern portion of its U. S. breeding range, where ~ 50% of breeding oystercatchers nest on shell substrate instead of beachfront habitat. We measured daily survival rates during incubation and chick rearing in shell rake habitats over five breeding seasons in the Cape Romain region of South Carolina, USA. Of 354 nesting attempts monitored, 16.1% hatched at least one egg. During incubation, daily survival rate was 0.938, corresponding to 22.8% success to hatching (nest success). For broods, daily survival was 0.991, or 74.0% success from hatching to fledging. Productivity in the Cape Romain region is primarily being lost during the incubation phase, when nests are exposed to overwash and predation. Mobile chicks may, however, be able to avoid flood events or predators by relocating to higher or more protected portions of a shell rake. Based on comparative data for American Oystercatchers from elsewhere in their range, it does not appear that shell rakes in the Cape Romain region are inferior breeding habitat. Our data suggest that conservation actions targeting nest and chick loss from flooding and predation have the greatest opportunity to enhance reproductive success in this core breeding area, and that an assessment of the availability, structure, avian use, and protection status of shell rakes is warranted.
","language":"English","publisher":"Cooper Ornithological Club","publisherLocation":"Santa Clara, CA","doi":"10.1650/CONDOR-14-35.1","collaboration":"National Fish and Wildlife Foundation; South Carolina State Wildlife Grants Program; Cape Romain National Wildlife Refuge; U.S. Fish and Wildlife Service, U. S. Geological Survey South Carolina Cooperative Fish and Wildlife Research Unit, and Clemson University; South Carolina Department of Natural Resources, Clemson University, the U.S. Fish and Wildlife Service; U.S. Geological Survey","usgsCitation":"Jodice, P.G., Thibault, J.M., Collins, S., Spinks, M.D., and Sanders, F.J., 2014, Reproductive ecology of American Oystercatchers nesting on shell rakes: Condor, v. 116, no. 4, p. 588-598, https://doi.org/10.1650/CONDOR-14-35.1.","productDescription":"11 p.","startPage":"588","endPage":"598","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054897","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":473245,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-14-35.1","text":"Publisher Index Page"},{"id":300793,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55659953e4b0d9246a9eb63f","contributors":{"authors":[{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X pjodice@usgs.gov","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":1119,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","email":"pjodice@usgs.gov","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":547522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thibault, Janet M.","contributorId":140932,"corporation":false,"usgs":false,"family":"Thibault","given":"Janet","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":547623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, S.A.","contributorId":63947,"corporation":false,"usgs":true,"family":"Collins","given":"S.A.","email":"","affiliations":[],"preferred":false,"id":547624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spinks, Mark D.","contributorId":140933,"corporation":false,"usgs":false,"family":"Spinks","given":"Mark","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":547625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sanders, Felicia J.","contributorId":56574,"corporation":false,"usgs":false,"family":"Sanders","given":"Felicia","email":"","middleInitial":"J.","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":547626,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70097361,"text":"70097361 - 2014 - Status of pelagic prey fishes in Lake Michigan, 2013","interactions":[],"lastModifiedDate":"2014-05-27T13:31:37","indexId":"70097361","displayToPublicDate":"2014-01-01T13:24:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"title":"Status of pelagic prey fishes in Lake Michigan, 2013","docAbstract":"Acoustic surveys were conducted in late summer/early fall during the years 1992-1996 and 2001-2013 to estimate pelagic prey fish biomass in Lake Michigan. Midwater trawling during the surveys as well as target strength provided a measure of species and size composition of the fish community for use in scaling acoustic data and providing species-specific abundance estimates. The 2013 survey consisted of 27 acoustic transects (546 km total) and 31 midwater trawl tows. Mean prey fish biomass was 6.1 kg/ha (relative standard error, RSE = 11%) or 29.6 kilotonnes (kt = 1,000 metric tons), which was similar to the estimate in 2012 (31.1 kt) and 23.5% of the long-term (18 years) mean. The numeric density of the 2013 alewife year class was 6% of the time series average and this year-class contributed 4% of total alewife biomass (5.2 kg/ha, RSE = 12%). Alewife ≥age-1 comprised 96% of alewife biomass. In 2013, alewife comprised 86% of total prey fish biomass, while rainbow smelt and bloater were 4 and 10% of total biomass, respectively. Rainbow smelt biomass in 2013 (0.24 kg/ha, RSE = 17%) was essentially identical to the rainbow smelt biomass in 2012 and was 6% of the long term mean. Bloater biomass in 2013 was 0.6 kg/ha, only half the 2012 biomass, and 6% of the long term mean. Mean density of small bloater in 2013 (29 fish/ha, RSE = 29%) was lower than peak values observed in 2007-2009 and was 23% of the time series mean. In 2013, pelagic prey fish biomass in Lake Michigan was similar to Lake Huron, but pelagic community composition differs in the two lakes, with Lake Huron dominated by bloater.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70097361","usgsCitation":"Warner, D.M., Farha, S.A., O’Brien, T.P., Ogilvie, L., Claramunt, R., and Hanson, D., 2014, Status of pelagic prey fishes in Lake Michigan, 2013, 12 p., https://doi.org/10.3133/70097361.","productDescription":"12 p.","numberOfPages":"12","ipdsId":"IP-054851","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":287608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":287607,"type":{"id":11,"text":"Document"},"url":"https://www.in.gov/dnr/fishwild/files/fw-Lake_Michigan_acoustic_report_2014_final.pdf"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -88.0434,41.6089 ], [ -88.0434,46.1024 ], [ -84.7385,46.1024 ], [ -84.7385,41.6089 ], [ -88.0434,41.6089 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5385b403e4b09e18fc023aae","contributors":{"authors":[{"text":"Warner, David M. 0000-0003-4939-5368 dmwarner@usgs.gov","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":2986,"corporation":false,"usgs":true,"family":"Warner","given":"David","email":"dmwarner@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":491537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farha, Steven A.","contributorId":79026,"corporation":false,"usgs":true,"family":"Farha","given":"Steven","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":491541,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Brien, Timothy P. 0000-0003-4502-5204 tiobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-4502-5204","contributorId":2662,"corporation":false,"usgs":true,"family":"O’Brien","given":"Timothy","email":"tiobrien@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":491536,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ogilvie, Lynn","contributorId":60540,"corporation":false,"usgs":true,"family":"Ogilvie","given":"Lynn","affiliations":[],"preferred":false,"id":491540,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Claramunt, Randall M.","contributorId":19047,"corporation":false,"usgs":true,"family":"Claramunt","given":"Randall M.","affiliations":[],"preferred":false,"id":491538,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hanson, Dale","contributorId":43676,"corporation":false,"usgs":true,"family":"Hanson","given":"Dale","affiliations":[],"preferred":false,"id":491539,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201991,"text":"70201991 - 2014 - Spatially explicit modeling to evaluate regional stream water quality","interactions":[],"lastModifiedDate":"2019-02-05T13:09:51","indexId":"70201991","displayToPublicDate":"2014-01-01T13:09:44","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Spatially explicit modeling to evaluate regional stream water quality","docAbstract":"Spatially referenced regressions on watershed attributes (SPARROW) models have been developed and applied over the past two decades to address the need for large-scale, spatially explicit information on stream water quality conditions. The strength of SPARROW models is that they describe the primary environmental processes that affect the supply and transport of contaminant mass in watersheds, based on the use of stream monitoring and geospatial data to statistically estimate model parameters. SPARROW models were first applied at the scale of the conterminous US, but their use has grown through applications in many smaller regions of the US and in other countries. Recent developments include a web-based decision support system that provides open access to model results without the assistance of technical experts or special software. As it is highlighted in this chapter, SPARROW modeling provides a flexible framework for studying many aspects of water quality to support both research and resource management objectives.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Comprehensive water quality and purification","language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-382182-9.00013-X","usgsCitation":"Preston, S.D., Alexander, R.B., Schwarz, G., and Smith, R.A., 2014, Spatially explicit modeling to evaluate regional stream water quality, chap. of Comprehensive water quality and purification, v. 1, p. 221-244, https://doi.org/10.1016/B978-0-12-382182-9.00013-X.","productDescription":"24 p.","startPage":"221","endPage":"244","ipdsId":"IP-039865","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":361028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":756466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alexander, Richard B. 0000-0001-9166-0626 ralex@usgs.gov","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":541,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","email":"ralex@usgs.gov","middleInitial":"B.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":756464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarz, Gregory 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":208292,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":false,"id":756463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Richard A. 0000-0003-2117-2269 rsmith1@usgs.gov","orcid":"https://orcid.org/0000-0003-2117-2269","contributorId":580,"corporation":false,"usgs":true,"family":"Smith","given":"Richard","email":"rsmith1@usgs.gov","middleInitial":"A.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":756465,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70148663,"text":"70148663 - 2014 - Influence of sediment presence on freshwater mussel thermal tolerance","interactions":[],"lastModifiedDate":"2015-06-19T11:43:21","indexId":"70148663","displayToPublicDate":"2014-01-01T12:45:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Influence of sediment presence on freshwater mussel thermal tolerance","docAbstract":"Median lethal temperature (LT50) data from water-only exposures with the early life stages of freshwater mussels suggest that some species may be living near their upper thermal tolerances. However, evaluation of thermal sensitivity has never been conducted in sediment. Mussels live most of their lives burrowed in sediment, so understanding the effect of sediment on thermal sensitivity is a necessary step in evaluating the effectiveness of the water-only standard method, on which the regulatory framework for potential thermal criteria currently is based, as a test of thermal sensitivity. We developed a method for testing thermal sensitivity of juvenile mussels in sediment and used the method to assess thermal tolerance of 4 species across a range of temperatures common during summer. Stream beds may provide a thermal refuge in the wild, but we hypothesized that the presence of sediment alone does not alter thermal sensitivity. We also evaluated the effects of 2 temperature acclimation levels (22 and 27°C) and 2 water levels (watered and dewatered treatments). We then compared results from the sediment tests to those conducted using the water-only standard methods. We also conducted water-only LT tests with mussel larvae (glochidia) for comparison with the juvenile life stage. We found few consistent differences in thermal tolerance between sediment and water-only treatments, between acclimation temperatures, between waterlevel treatments, among species, or between juvenile and glochidial life stages (LT50 range = 33.3-37.2°C; mean = 35.6°C), supporting our hypothesis that the presence of sediment alone does not alter thermal sensitivity. The method we developed has potential for evaluating the role of other stressors (e.g., contaminants) in a more natural and complex environment.
","language":"English","publisher":"Society for Freshwater Science","publisherLocation":"Springfield, IL","doi":"10.1086/674141","collaboration":"US Geological Survey (USGS) National Climate Change and Wildlife Science Center through Researc","usgsCitation":"Archambault, J.M., Cope, W., and Kwak, T.J., 2014, Influence of sediment presence on freshwater mussel thermal tolerance: Freshwater Science, v. 33, no. 1, p. 56-65, https://doi.org/10.1086/674141.","productDescription":"10 p.","startPage":"56","endPage":"65","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-041347","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":301365,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55853d42e4b023124e8f5b11","contributors":{"authors":[{"text":"Archambault, Jennifer M.","contributorId":141248,"corporation":false,"usgs":false,"family":"Archambault","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":549042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cope, W. Gregory","contributorId":70353,"corporation":false,"usgs":true,"family":"Cope","given":"W. Gregory","affiliations":[],"preferred":false,"id":549043,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":548964,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70157438,"text":"70157438 - 2014 - Preliminary data used to assess the accuracy of estimating female white-tailed deer diel birthing-season home ranges using only daytime locations","interactions":[],"lastModifiedDate":"2017-09-08T10:19:08","indexId":"70157438","displayToPublicDate":"2014-01-01T12:15:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3767,"text":"Wildlife Biology in Practice","active":true,"publicationSubtype":{"id":10}},"title":"Preliminary data used to assess the accuracy of estimating female white-tailed deer diel birthing-season home ranges using only daytime locations","docAbstract":"Because many white-tailed deer (Odocoileus virginianus) home-range and habitat-use studies rely only on daytime radio-tracking data, we were interested in whether diurnal data sufficiently represented diel home ranges. We analyzed home-range and core-use size and overlap of 8 adult-female Global-Positioning-System-collared deer during May and June 2001 and 2002 in the Superior National Forest, Minnesota, USA. We used 2 traditional means of analysis: minimum-convex polygons (MCP) and fixed kernels (95% FK, home range and 50% FK, core use) and two methods to partition day and night location data: (1) daytime = 0800-2000 h versus nighttime = 2000-0800 h and (2) sunup versus sundown. We found no statistical difference in size of home-range and core-use areas across day and night comparisons; however, in terms of spatial overlap, approximately 30% of night-range areas on average were not accounted for using daytime locations, with even greater differences between core-use areas (on average approximately 50%). We conclude that diurnal data do not adequately describe diel adult-female-deer, May-June home-ranges due to differences in spatial overlap (location). We suggest research to determine (1) if our findings hold in other circumstances (e.g., exclusive of the parturition period, other age classes, etc.), (2) if our conclusions generalize under other conditions (e.g., across deer range, varying seasons, etc.), (3) if habitat-use conclusions are affected by the incomplete overlap between diurnal and diel data, (4) how many nocturnal locations must be included to generate sufficient overlap, and (5) the influence of using other kernel sizes (e.g., 75%, 90%).
","language":"English","publisher":"Portuguese Wildlife Society","publisherLocation":"Figueira da Foz, Portugal","doi":"10.2461/wbp.2014.10.8","usgsCitation":"Barber-Meyer, S., and Mech, L.D., 2014, Preliminary data used to assess the accuracy of estimating female white-tailed deer diel birthing-season home ranges using only daytime locations: Wildlife Biology in Practice, v. 10, no. 2, p. 62-68, https://doi.org/10.2461/wbp.2014.10.8.","productDescription":"7 p.","startPage":"62","endPage":"68","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-042753","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":308434,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.812744140625,\n 47.58764167941513\n ],\n [\n -90.0274658203125,\n 47.58764167941513\n ],\n [\n -90.0274658203125,\n 48.17707562779612\n ],\n [\n -91.812744140625,\n 48.17707562779612\n ],\n [\n -91.812744140625,\n 47.58764167941513\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-07-21","publicationStatus":"PW","scienceBaseUri":"5603cd56e4b03bc34f544b33","contributors":{"authors":[{"text":"Barber-Meyer, Shannon M. 0000-0002-3048-2616 sbarber-meyer@usgs.gov","orcid":"https://orcid.org/0000-0002-3048-2616","contributorId":147904,"corporation":false,"usgs":true,"family":"Barber-Meyer","given":"Shannon M.","email":"sbarber-meyer@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":573180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mech, L. David 0000-0003-3944-7769 david_mech@usgs.gov","orcid":"https://orcid.org/0000-0003-3944-7769","contributorId":2518,"corporation":false,"usgs":true,"family":"Mech","given":"L.","email":"david_mech@usgs.gov","middleInitial":"David","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":573179,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154845,"text":"70154845 - 2014 - Influence of riparian and watershed alterations on sandbars in a Great Plains river","interactions":[],"lastModifiedDate":"2015-11-09T11:26:25","indexId":"70154845","displayToPublicDate":"2014-01-01T12:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Influence of riparian and watershed alterations on sandbars in a Great Plains river","docAbstract":"Anthropogenic alterations have caused sandbar habitats in rivers and the biota dependent on them to decline. Restoring large river sandbars may be needed as these habitats are important components of river ecosystems and provide essential habitat to terrestrial and aquatic organisms. We quantified factors within the riparian zone of the Kansas River, USA, and within its tributaries that influenced sandbar size and density using aerial photographs and land use/land cover (LULC) data. We developed, a priori, 16 linear regression models focused on LULC at the local, adjacent upstream river bend, and the segment (18–44 km upstream) scales and used an information theoretic approach to determine what alterations best predicted the size and density of sandbars. Variation in sandbar density was best explained by the LULC within contributing tributaries at the segment scale, which indicated reduced sandbar density with increased forest cover within tributary watersheds. Similarly, LULC within contributing tributary watersheds at the segment scale best explained variation in sandbar size. These models indicated that sandbar size increased with agriculture and forest and decreased with urban cover within tributary watersheds. Our findings suggest that sediment supply and delivery from upstream tributary watersheds may be influential on sandbars within the Kansas River and that preserving natural grassland and reducing woody encroachment within tributary watersheds in Great Plains rivers may help improve sediment delivery to help restore natural river function.
","language":"English","publisher":"John Wiley & Sons","publisherLocation":"Chichester, West Sussex, UK","doi":"10.1002/rra.2811","usgsCitation":"Fischer, J., Paukert, C.P., and Daniels, M., 2014, Influence of riparian and watershed alterations on sandbars in a Great Plains river: River Research and Applications, v. 31, no. 9, p. 1140-1150, https://doi.org/10.1002/rra.2811.","productDescription":"11 p.","startPage":"1140","endPage":"1150","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044525","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":305651,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"9","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2014-08-01","publicationStatus":"PW","scienceBaseUri":"55a0ecb2e4b0183d66e43043","contributors":{"authors":[{"text":"Fischer, Jeffrey M. 0000-0003-2996-9272 fischer@usgs.gov","orcid":"https://orcid.org/0000-0003-2996-9272","contributorId":573,"corporation":false,"usgs":true,"family":"Fischer","given":"Jeffrey M.","email":"fischer@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":564597,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paukert, Craig P. 0000-0002-9369-8545 cpaukert@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":879,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","email":"cpaukert@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564259,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daniels, M.L.","contributorId":52689,"corporation":false,"usgs":true,"family":"Daniels","given":"M.L.","email":"","affiliations":[],"preferred":false,"id":564598,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70148654,"text":"70148654 - 2014 - Density and abundance of Wilson's snipe Gallinago delicata in winter in the Lower Mississippi Flyway, USA","interactions":[],"lastModifiedDate":"2015-07-13T11:03:41","indexId":"70148654","displayToPublicDate":"2014-01-01T12:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Density and abundance of Wilson's snipe Gallinago delicata in winter in the Lower Mississippi Flyway, USA","docAbstract":"Wilson's snipe Gallinago delicata is one of the least studied North American game birds, and information on snipe populations and abundance is mostly unknown. We conducted roadside surveys stratified at the township level in the lower Mississippi Alluvial Valley (LMAV) in Arkansas, Mississippi and Louisiana, as well as the Red River Region, and the Gulf Coastal Plain of Louisiana during winters of 2009 and 2010. We identified observer, vegetation cover, and water cover as important covariates in estimating snipe densities. We detected 2915 snipe along 814 line transects (1450 km) for 2009 and 2010 combined. We estimated snipe densities of 8.05 individuals km-2 (95% CI: 4.57-14.17) in 2009, and 2.13 individuals km-2 (95% CI: 1.47-3.08) in 2010. We used the resulting snipe density estimates within the study area to calculate abundance estimates of 1 026 431 (95% CI: 582 707-1 806 774) in 2009, and 271 590 (95% CI: 187 435-392 722) in 2010 for the LMAV. Our data indicate that a road transect survey method is effective for estimating wintering snipe density and abundance in the lower Mississippi Flyway.
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","language":"English","publisher":"Springer","publisherLocation":"New York, NY","doi":"10.1007/978-3-319-14717-8_19","usgsCitation":"Wang, Y., Luo, Z., Baoping, Y., Takekawa, J.Y., Prosser, D.J., and Newman, S.H., 2014, Mining continuous activity patterns from animal trajectory data: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8933, p. 239-252, https://doi.org/10.1007/978-3-319-14717-8_19.","productDescription":"14 p.","startPage":"239","endPage":"252","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060339","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":296927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":296896,"type":{"id":15,"text":"Index Page"},"url":"https://link.springer.com/chapter/10.1007%2F978-3-319-14717-8_19"}],"volume":"8933","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54dd2bfee4b08de9379b35d4","contributors":{"authors":[{"text":"Wang, Y.","contributorId":64213,"corporation":false,"usgs":true,"family":"Wang","given":"Y.","affiliations":[],"preferred":false,"id":537377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luo, Ze","contributorId":41307,"corporation":false,"usgs":true,"family":"Luo","given":"Ze","affiliations":[],"preferred":false,"id":537378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baoping, Yan","contributorId":86670,"corporation":false,"usgs":true,"family":"Baoping","given":"Yan","email":"","affiliations":[],"preferred":false,"id":537379,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Takekawa, John Y. 0000-0003-0217-5907 john_takekawa@usgs.gov","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":176168,"corporation":false,"usgs":true,"family":"Takekawa","given":"John","email":"john_takekawa@usgs.gov","middleInitial":"Y.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":537380,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prosser, Diann J. 0000-0002-5251-1799 dprosser@usgs.gov","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":2389,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","email":"dprosser@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":537281,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newman, Scott H.","contributorId":101372,"corporation":false,"usgs":true,"family":"Newman","given":"Scott","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":537381,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70135676,"text":"70135676 - 2014 - A Global Lake Ecological Observatory Network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models","interactions":[],"lastModifiedDate":"2014-12-16T11:02:08","indexId":"70135676","displayToPublicDate":"2014-01-01T11:15:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1999,"text":"Inland Waters","active":true,"publicationSubtype":{"id":10}},"title":"A Global Lake Ecological Observatory Network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models","docAbstract":"A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. 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China","active":true,"usgs":false}],"preferred":false,"id":536749,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Trolle, Dennis","contributorId":38023,"corporation":false,"usgs":true,"family":"Trolle","given":"Dennis","email":"","affiliations":[],"preferred":false,"id":536750,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Brookes, Justin D","contributorId":130984,"corporation":false,"usgs":false,"family":"Brookes","given":"Justin","email":"","middleInitial":"D","affiliations":[{"id":7196,"text":"Water Research Centre, The Environment Institute, School of Earth and Environmental Science, University of Adelaide, South Australia, Australia","active":true,"usgs":false}],"preferred":false,"id":536751,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70148188,"text":"70148188 - 2014 - Reconstructing historical habitat data with predictive models Read More: http://www.esajournals.org/doi/abs/10.1890/13-0327.1","interactions":[],"lastModifiedDate":"2015-05-26T10:03:40","indexId":"70148188","displayToPublicDate":"2014-01-01T11:15:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing historical habitat data with predictive models Read More: http://www.esajournals.org/doi/abs/10.1890/13-0327.1","docAbstract":"Historical vegetation data are important to ecological studies, as many structuring processes operate at long time scales, from decades to centuries. Capturing the pattern of variability within a system (enough to declare a significant change from past to present) relies on correct assumptions about the temporal scale of the processes involved. Sufficient long-term data are often lacking, and current techniques have their weaknesses. To address this concern, we constructed multistate and artificial neural network models (ANN) to provide fore- and hindcast vegetation communities considered critical foraging habitat for an endangered bird, the Florida Snail Kite (Rostrhamus sociabilis). Multistate models were not able to hindcast due to our data not satisfying a detailed balance requirement for time reversibility in Markovian dynamics. Multistate models were useful for forecasting and providing environmental variables for the ANN. Results from our ANN hindcast closely mirrored the population collapse of the Snail Kite population using only environmental data to inform the model. The parallel between the two gives us confidence in the hindcasting results and their use in future demographic models.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Tempe, AZ","doi":"10.1890/13-0327.1","usgsCitation":"Zweig, C.L., and Kitchens, W.M., 2014, Reconstructing historical habitat data with predictive models Read More: http://www.esajournals.org/doi/abs/10.1890/13-0327.1: Ecological Applications, v. 24, no. 1, p. 196-203, https://doi.org/10.1890/13-0327.1.","productDescription":"8 p.","startPage":"196","endPage":"203","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045474","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":300772,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55659951e4b0d9246a9eb63c","contributors":{"authors":[{"text":"Zweig, Christa L.","contributorId":99767,"corporation":false,"usgs":true,"family":"Zweig","given":"Christa","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":547585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kitchens, Wiley M. kitchensw@usgs.gov","contributorId":2851,"corporation":false,"usgs":true,"family":"Kitchens","given":"Wiley","email":"kitchensw@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":547547,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70156878,"text":"70156878 - 2014 - Native plant recovery in study plots after fennel (Foeniculum vulgare) control on Santa Cruz Island","interactions":[],"lastModifiedDate":"2020-12-31T19:28:28.636247","indexId":"70156878","displayToPublicDate":"2014-01-01T11:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2785,"text":"Monographs of the Western North American Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Native plant recovery in study plots after fennel (Foeniculum vulgare) control on Santa Cruz Island","title":"Native plant recovery in study plots after fennel (Foeniculum vulgare) control on Santa Cruz Island","docAbstract":"Santa Cruz Island is the largest of the California Channel Islands and supports a diverse and unique flora which includes 9 federally listed species. Sheep, cattle, and pigs, introduced to the island in the mid-1800s, disturbed the soil, browsed native vegetation, and facilitated the spread of exotic invasive plants. Recent removal of introduced herbivores on the island led to the release of invasive fennel (Foeniculum vulgare), which expanded to become the dominant vegetation in some areas and has impeded the recovery of some native plant communities. In 2007, Channel Islands National Park initiated a program to control fennel using triclopyr on the eastern 10% of the island. We established replicate paired plots (seeded and nonseeded) at Scorpion Anchorage and Smugglers Cove, where notably dense fennel infestations (>10% cover) occurred, to evaluate the effectiveness of native seed augmentation following fennel removal. Five years after fennel removal, vegetative cover increased as litter and bare ground cover decreased significantly (P < 0.0001) on both plot types. Vegetation cover of both native and other (nonfennel) exotic species increased at Scorpion Anchorage in both seeded and nonseeded plots. At Smugglers Cove, exotic cover decreased significantly (P = 0.0001) as native cover comprised of Eriogonum arborescens and Leptosyne gigantea increased significantly (P < 0.0001) in seeded plots only. Nonseeded plots at Smugglers Cove were dominated by exotic annual grasses, primarily Avena barbata. The data indicate that seeding with appropriate native seed is a critical step in restoration following fennel control in areas where the native seed bank is depauperate.
","language":"English","publisher":"Monte L. Bean Life Science Museum","publisherLocation":"Provo, UT","doi":"10.3398/042.007.0136","usgsCitation":"Power, P., Stanley, T.R., Cowan, C., and Robertson, J.R., 2014, Native plant recovery in study plots after fennel (Foeniculum vulgare) control on Santa Cruz Island: Monographs of the Western North American Naturalist, v. 7, no. 1, p. 465-476, https://doi.org/10.3398/042.007.0136.","productDescription":"12 p.","startPage":"465","endPage":"476","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058375","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":473258,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3398/042.007.0136","text":"Publisher Index Page"},{"id":307811,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Santa Cruz Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.94117736816406,\n 33.94222067051576\n ],\n [\n -119.50721740722655,\n 33.94222067051576\n ],\n [\n -119.50721740722655,\n 34.093610452768715\n ],\n [\n -119.94117736816406,\n 34.093610452768715\n ],\n [\n -119.94117736816406,\n 33.94222067051576\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55e81dbde4b0dacf699e6688","contributors":{"authors":[{"text":"Power, Paula","contributorId":38253,"corporation":false,"usgs":true,"family":"Power","given":"Paula","affiliations":[],"preferred":false,"id":570928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanley, Thomas R. 0000-0002-8393-0005 stanleyt@usgs.gov","orcid":"https://orcid.org/0000-0002-8393-0005","contributorId":209928,"corporation":false,"usgs":true,"family":"Stanley","given":"Thomas","email":"stanleyt@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":570927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cowan, Clark","contributorId":147264,"corporation":false,"usgs":false,"family":"Cowan","given":"Clark","email":"","affiliations":[{"id":7237,"text":"NPS, Olympic National Park","active":true,"usgs":false}],"preferred":false,"id":570929,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robertson, James R.","contributorId":13892,"corporation":false,"usgs":true,"family":"Robertson","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":570930,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70141682,"text":"70141682 - 2014 - Protection benefits desert tortoise (Gopherus agassizii) abundance: the influence of three management strategies on a threatened species","interactions":[],"lastModifiedDate":"2016-07-08T13:43:24","indexId":"70141682","displayToPublicDate":"2014-01-01T11:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1896,"text":"Herpetological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Protection benefits desert tortoise (Gopherus agassizii) abundance: the influence of three management strategies on a threatened species","docAbstract":"We surveyed an area of ∼260 km2 in the western Mojave Desert to evaluate relationships between condition of Agassiz's Desert Tortoise populations (Gopherus agassizii) and habitat on lands that have experienced three different levels of management and protection. We established 240 1-ha plots using random sampling, with 80 plots on each of the three types of managed lands. We conducted surveys in spring 2011 and collected data on live tortoises, shell-skeletal remains, other signs of tortoises, perennial vegetation, predators, and evidence of human use. Throughout the study area and regardless of management area, tortoise abundance was positively associated with one of the more diverse associations of perennial vegetation. The management area with the longest history of protection, a fence, and legal exclusion of livestock and vehicles had significantly more live tortoises and lower death rates than the other two areas. Tortoise presence and abundance in this protected area had no significant positive or negative associations with predators or human-related impacts. In contrast, the management area with a more recent exclusion of livestock, limited vehicular traffic, and with a recent, partial fence had lower tortoise densities and high death rates. Tortoise abundance here was negatively associated with vehicle tracks and positively associated with mammalian predators and debris from firearms. The management area with the least protection—unfenced, with uncontrolled vehicle use, sheep grazing, and high trash counts—also had low tortoise densities and high death rates. Tortoise abundance was negatively associated with sheep grazing and positively associated with trash and mammalian predator scat.cat.
","language":"English","publisher":"Herpetologists' League","publisherLocation":"Washington, D.C.","doi":"10.1655/HERPMONOGRAPHS-D-14-00002","usgsCitation":"Berry, K.H., Lyren, L.M., Yee, J.L., and Bailey, T.Y., 2014, Protection benefits desert tortoise (Gopherus agassizii) abundance: the influence of three management strategies on a threatened species: Herpetological Monographs, v. 28, no. 1, p. 66-92, https://doi.org/10.1655/HERPMONOGRAPHS-D-14-00002.","productDescription":"27 p.","startPage":"66","endPage":"92","numberOfPages":"27","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052915","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":298098,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.00689697265625,\n 35.099686964274724\n ],\n [\n -118.00689697265625,\n 35.47185482057798\n ],\n [\n -117.65533447265626,\n 35.47185482057798\n ],\n [\n -117.65533447265626,\n 35.099686964274724\n ],\n [\n -118.00689697265625,\n 35.099686964274724\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54ec5d48e4b02d776a67dab3","contributors":{"authors":[{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":540979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyren, Lisa M. llyren@usgs.gov","contributorId":2398,"corporation":false,"usgs":true,"family":"Lyren","given":"Lisa","email":"llyren@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":540980,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":540981,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bailey, Tracy Y.","contributorId":139383,"corporation":false,"usgs":false,"family":"Bailey","given":"Tracy","email":"","middleInitial":"Y.","affiliations":[{"id":12758,"text":"independent, 619 Pinon Court, Ridgecrest, CA","active":true,"usgs":false}],"preferred":false,"id":540982,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70048391,"text":"70048391 - 2014 - Trends in groundwater quality in principal aquifers of the United States, 1988-2012","interactions":[],"lastModifiedDate":"2014-07-03T12:37:07","indexId":"70048391","displayToPublicDate":"2014-01-01T10:49:00","publicationYear":"2014","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":12,"text":"Conference publication"},"title":"Trends in groundwater quality in principal aquifers of the United States, 1988-2012","docAbstract":"The U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Program analyzed trends in groundwater quality throughout the nation for the sampling period of 1988-2012. Trends were determined for networks (sets of wells routinely monitored by the USGS) for a subset of constituents by statistical analysis of paired water-quality measurements collected on a near-decadal time scale. The data set for chloride, dissolved solids, and nitrate consisted of 1,511 wells in 67 networks, whereas the data set for methyl tert-butyl ether (MTBE) consisted of 1, 013 wells in 46 networks. The 25 principal aquifers represented by these networks account for about 75 percent of withdrawals of groundwater used for drinking-water supply for the nation.
\nStatistically significant changes in chloride, dissolved-solids, or nitrate concentrations were found in many well networks over a decadal period. Concentrations increased significantly in 48 percent of networks for chloride, 42 percent of networks for dissolved solids, and 21 percent of networks for nitrate. Chloride, dissolved solids, and nitrate concentrations decreased significantly in 3, 3, and 10 percent of the networks, respectively. The magnitude of change in concentrations was typically small in most networks; however, the magnitude of change in networks with statistically significant increases was typically much larger than the magnitude of change in networks with statistically significant decreases. The largest increases of chloride concentrations were in urban areas in the northeastern and north central United States. The largest increases of nitrate concentrations were in networks in agricultural areas.
\nStatistical analysis showed 42 or the 46 networks had no statistically significant changes in MTBE concentrations. The four networks with statistically significant changes in MTBE concentrations were in the northeastern United States, where MTBE was widely used. Two networks had increasing concentrations, and two networks had decreasing concentrations. Production and use of MTBE peaked in about 2000 and has been effectively banned in many areas since about 2006. The two networks that had increasing concentrations were sampled for the second time close to the peak of MTBE production, whereas the two networks that had decreasing concentrations were sampled for the second time 10 years after the peak of MTBE production.
","largerWorkTitle":"9th National Monitoring Conference","conferenceTitle":"9th National Monitoring Conference","conferenceDate":"2014-04-28T00:00:00","conferenceLocation":"Cincinnati, OH","language":"English","usgsCitation":"Lindsey, B., and Rupert, M.G., 2014, Trends in groundwater quality in principal aquifers of the United States, 1988-2012.","ipdsId":"IP-051703","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":289428,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b67b85e4b014fc094d5479","contributors":{"authors":[{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":434,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce D.","email":"blindsey@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":484515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rupert, Michael G. mgrupert@usgs.gov","contributorId":1194,"corporation":false,"usgs":true,"family":"Rupert","given":"Michael","email":"mgrupert@usgs.gov","middleInitial":"G.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484516,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70125308,"text":"70125308 - 2014 - Survival of adult Red-throated Loons (Gavia stellata) may be linked to marine conditions","interactions":[],"lastModifiedDate":"2018-03-29T11:18:42","indexId":"70125308","displayToPublicDate":"2014-01-01T10:48:41","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Survival of adult Red-throated Loons (Gavia stellata) may be linked to marine conditions","title":"Survival of adult Red-throated Loons (Gavia stellata) may be linked to marine conditions","docAbstract":"Large variations in the summering population size of Red-throated Loons (Gavia stellata) have occurred in recent decades in Alaska. Little information exists about annual or seasonal survival rates of adult Red-throated Loons. This study used tracking data from satellite transmitters implanted into 33 Red-throated Loons captured on breeding areas in Alaska to estimate annual survival with the sampling effort split between two study periods: 2000–2002 and 2008–2010. Mortality was inferred from transmitted sensor data that indicated body temperature of the Red-throated Loon and voltage of the transmitter's battery. Two definitive mortalities occurred, resulting in an annual survival estimate of 0.920 (SE = 0.054). The fates of two additional Red-throated Loons were ambiguous and, when treated as mortalities, the annual survival estimate was 0.838 (SE = 0.074). All four putative mortalities occurred during the non-breeding season in the early study period. Oceanic conditions, indexed by the Pacific Decadal Oscillation, appeared to differ between the study periods with higher Pacific Decadal Oscillation values associated with the early study period. Given that high values for Pacific Decadal Oscillation were also associated with the large decline of Red-throated Loons observed in Alaska during 1977–1993, this study suggests that survival of adult Red-throated Loons may vary in relation to the state of the marine ecosystem and thus contribute to long-term variation in population trends.
","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.037.sp114","usgsCitation":"Schmutz, J.A., 2014, Survival of adult Red-throated Loons (Gavia stellata) may be linked to marine conditions: Waterbirds, v. 37, no. SP1, p. 118-124, https://doi.org/10.1675/063.037.sp114.","productDescription":"7 p.","startPage":"118","endPage":"124","ipdsId":"IP-045417","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":488259,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1675/063.037.sp114","text":"Publisher Index Page"},{"id":293922,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"SP1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54195156e4b091c7ffc8e859","contributors":{"authors":[{"text":"Schmutz, Joel A. 0000-0002-6516-0836 jschmutz@usgs.gov","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":1805,"corporation":false,"usgs":true,"family":"Schmutz","given":"Joel","email":"jschmutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":501215,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70199220,"text":"70199220 - 2014 - Generalisation operators","interactions":[],"lastModifiedDate":"2018-09-11T10:49:09","indexId":"70199220","displayToPublicDate":"2014-01-01T10:46:34","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Generalisation operators","docAbstract":"This chapter summarises cartographic generalisation operators used to generalise geospatial data. It includes a review of recent approaches that have been tested or implemented to generalise networks, points, or groups. Emphasis is placed on recent advances that permit additional flexibility to tailor generalisation processing in particular geographic contexts, and to permit more advanced types of reasoning about spatial conflicts, preservation of specific feature characteristics, and local variations in geometry, content and enriched attribution. Rather than an exhaustive review of generalisation operators, the chapter devotes more attention to operators associated with network generalisation, which illustrates well the logic behind map generalisation developments. Three case studies demonstrate the application of operators to road thinning, to river network and braid pruning, and to hierarchical point elimination. The chapter closes with some summary comments and future directions.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Abstracting geographic information in a data rich world","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-00203-3_6","usgsCitation":"Stanislawski, L.V., Buttenfield, B.P., Bereuter, P., Savino, S., and Brewer, C.A., 2014, Generalisation operators, chap. of Abstracting geographic information in a data rich world, p. 157-195, https://doi.org/10.1007/978-3-319-00203-3_6.","productDescription":"39 p.","startPage":"157","endPage":"195","ipdsId":"IP-101340","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":357222,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2014-02-15","publicationStatus":"PW","scienceBaseUri":"5b98ab88e4b0702d0e843144","contributors":{"editors":[{"text":"Burghardt, Dirk","contributorId":207788,"corporation":false,"usgs":false,"family":"Burghardt","given":"Dirk","email":"","affiliations":[],"preferred":false,"id":744729,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Duchene, Cecile","contributorId":207789,"corporation":false,"usgs":false,"family":"Duchene","given":"Cecile","email":"","affiliations":[],"preferred":false,"id":744730,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Mackaness, William","contributorId":207790,"corporation":false,"usgs":false,"family":"Mackaness","given":"William","email":"","affiliations":[],"preferred":false,"id":744731,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":744724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buttenfield, Barbara P. 0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":744725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bereuter, Pia 0000-0001-8127-2654","orcid":"https://orcid.org/0000-0001-8127-2654","contributorId":207785,"corporation":false,"usgs":false,"family":"Bereuter","given":"Pia","email":"","affiliations":[{"id":27368,"text":"University of Zurich","active":true,"usgs":false}],"preferred":false,"id":744726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Savino, Sandro 0000-0003-0184-7580","orcid":"https://orcid.org/0000-0003-0184-7580","contributorId":207786,"corporation":false,"usgs":false,"family":"Savino","given":"Sandro","email":"","affiliations":[{"id":17793,"text":"University of Padova, Italy","active":true,"usgs":false}],"preferred":false,"id":744727,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brewer, Cynthia A.","contributorId":207787,"corporation":false,"usgs":false,"family":"Brewer","given":"Cynthia","email":"","middleInitial":"A.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":744728,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70113031,"text":"70113031 - 2014 - Presence of the Corexit component dioctyl sodium sulfosuccinate in Gulf of Mexico waters after the 2010 Deepwater Horizon oil spill","interactions":[],"lastModifiedDate":"2018-09-18T16:04:37","indexId":"70113031","displayToPublicDate":"2014-01-01T10:44:10","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1226,"text":"Chemosphere","active":true,"publicationSubtype":{"id":10}},"title":"Presence of the Corexit component dioctyl sodium sulfosuccinate in Gulf of Mexico waters after the 2010 Deepwater Horizon oil spill","docAbstract":"Between April 22 and July 15, 2010, approximately 4.9 million barrels of oil were released into the Gulf of Mexico from the Deepwater Horizon oil well. Approximately 16% of the oil was chemically dispersed, at the surface and at 1500 m depth, using Corexit 9527 and Corexit 9500, which contain dioctyl sodium sulfosuccinate (DOSS) as a major surfactant component. This was the largest documented release of oil in history at substantial depth, and the first time large quantities of dispersant (0.77 million gallons of approximately 1.9 million gallons total) were applied to a subsurface oil plume. During two cruises in late May and early June, water samples were collected at the surface and at depth for DOSS analysis. Real-time fluorimetry data was used to infer the presence of oil components to select appropriate sampling depths. Samples were stored frozen and in the dark for approximately 6 months prior to analysis by liquid chromatography/tandem mass spectrometry with isotope-dilution quantification. The blank-limited method detection limit (0.25 μg L−1) was substantially less than the U.S. Environmental Protection Agency’s (USEPA) aquatic life benchmark of 40 μg L−1. Concentrations of DOSS exceeding 200 μg L−1 were observed in one surface sample near the well site; in subsurface samples DOSS did not exceed 40 μg L−1. Although DOSS was present at high concentration in the immediate vicinity of the well where it was being continuously applied, a combination of biodegradation, photolysis, and dilution likely reduced persistence at concentrations exceeding the USEPA aquatic life benchmark beyond this immediate area.","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemosphere.2013.08.049","usgsCitation":"Gray, J.L., Kanagy, L.K., Furlong, E.T., Kanagy, C., McCoy, J.W., Mason, A., and Lauenstein, G., 2014, Presence of the Corexit component dioctyl sodium sulfosuccinate in Gulf of Mexico waters after the 2010 Deepwater Horizon oil spill: Chemosphere, v. 95, p. 124-130, https://doi.org/10.1016/j.chemosphere.2013.08.049.","productDescription":"7 p.","startPage":"124","endPage":"130","numberOfPages":"7","ipdsId":"IP-037292","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":288890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288889,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.chemosphere.2013.08.049"}],"country":"United States","otherGeospatial":"Gulf Of Mexico","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -91.5,28.0 ], [ -91.5,31.0 ], [ -87.0,31.0 ], [ -87.0,28.0 ], [ -91.5,28.0 ] ] ] } } ] }","volume":"95","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae77f1e4b0abf75cf2c5c2","contributors":{"authors":[{"text":"Gray, James L. 0000-0002-0807-5635 jlgray@usgs.gov","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":1253,"corporation":false,"usgs":true,"family":"Gray","given":"James","email":"jlgray@usgs.gov","middleInitial":"L.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":494989,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kanagy, Leslie K. 0000-0001-5073-8538 lkkanagy@usgs.gov","orcid":"https://orcid.org/0000-0001-5073-8538","contributorId":4543,"corporation":false,"usgs":true,"family":"Kanagy","given":"Leslie","email":"lkkanagy@usgs.gov","middleInitial":"K.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":494990,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Furlong, Edward T. 0000-0002-7305-4603 efurlong@usgs.gov","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":740,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","email":"efurlong@usgs.gov","middleInitial":"T.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":494988,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanagy, Chris J.","contributorId":81616,"corporation":false,"usgs":true,"family":"Kanagy","given":"Chris J.","affiliations":[],"preferred":false,"id":494993,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCoy, Jeff W. 0000-0002-9817-6711 jefmccoy@usgs.gov","orcid":"https://orcid.org/0000-0002-9817-6711","contributorId":738,"corporation":false,"usgs":true,"family":"McCoy","given":"Jeff","email":"jefmccoy@usgs.gov","middleInitial":"W.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":494987,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mason, Andrew","contributorId":10334,"corporation":false,"usgs":true,"family":"Mason","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":494991,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lauenstein, Gunnar","contributorId":50080,"corporation":false,"usgs":true,"family":"Lauenstein","given":"Gunnar","affiliations":[],"preferred":false,"id":494992,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70199502,"text":"70199502 - 2014 - Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation","interactions":[],"lastModifiedDate":"2018-09-20T10:39:42","indexId":"70199502","displayToPublicDate":"2014-01-01T10:39:14","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2165,"text":"Journal of Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation","docAbstract":"Prediction of potential methane emission pathways from various sources into active mine workings or sealed gobs from longwall overburden is important for controlling methane and for improving mining safety. The aim of this paper is to infer strata separation intervals and thus gas emission pathways from standard well log data. The proposed technique was applied to well logs acquired through the Mary Lee/Blue Creek coal seam of the Upper Pottsville Formation in the Black Warrior Basin, Alabama, using well logs from a series of boreholes aligned along a nearly linear profile.
For this purpose, continuous wavelet transform (CWT) of digitized gamma well logs was performed by using Mexican hat and Morlet, as the mother wavelets, to identify potential discontinuities in the signal. Pointwise Hölder exponents (PHE) of gamma logs were also computed using the generalized quadratic variations (GQV) method to identify the location and strength of singularities of well log signals as a complementary analysis. PHEs and wavelet coefficients were analyzed to find the locations of singularities along the logs.
Using the well logs in this study, locations of predicted singularities were used as indicators in single normal equation simulation (SNESIM) to generate equi-probable realizations of potential strata separation intervals. Horizontal and vertical variograms of realizations were then analyzed and compared with those of indicator data and training image (TI) data using the Kruskal–Wallis test. A sum of squared differences was employed to select the most probable realization representing the locations of potential strata separations and methane flow paths.
Results indicated that singularities located in well log signals reliably correlated with strata transitions or discontinuities within the strata. Geostatistical simulation of these discontinuities provided information about the location and extents of the continuous channels that may form during mining. If there is a gas source within their zone of influence, paths may develop and allow methane movement towards sealed or active gobs under pressure differentials. Knowledge gained from this research will better prepare mine operations for potential methane inflows, thus improving mine safety.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jappgeo.2014.03.019","usgsCitation":"Karacan, C.O., and Olea, R., 2014, Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation: Journal of Applied Geophysics, v. 105, p. 147-158, https://doi.org/10.1016/j.jappgeo.2014.03.019.","productDescription":"12 p.","startPage":"147","endPage":"158","ipdsId":"IP-054149","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":357539,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"105","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc038fae4b0fc368eb53b1f","contributors":{"authors":[{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":208012,"corporation":false,"usgs":false,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[],"preferred":false,"id":745610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":120616,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":745609,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045681,"text":"70045681 - 2014 - A bootstrap estimation scheme for chemical compositional data with nondetects","interactions":[],"lastModifiedDate":"2016-07-01T11:01:48","indexId":"70045681","displayToPublicDate":"2014-01-01T10:35:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2210,"text":"Journal of Chemometrics","active":true,"publicationSubtype":{"id":10}},"title":"A bootstrap estimation scheme for chemical compositional data with nondetects","docAbstract":"The bootstrap method is commonly used to estimate the distribution of estimators and their associated uncertainty when explicit analytic expressions are not available or are difficult to obtain. It has been widely applied in environmental and geochemical studies, where the data generated often represent parts of whole, typically chemical concentrations. This kind of constrained data is generically called compositional data, and they require specialised statistical methods to properly account for their particular covariance structure. On the other hand, it is not unusual in practice that those data contain labels denoting nondetects, that is, concentrations falling below detection limits. Nondetects impede the implementation of the bootstrap and represent an additional source of uncertainty that must be taken into account. In this work, a bootstrap scheme is devised that handles nondetects by adding an imputation step within the resampling process and conveniently propagates their associated uncertainly. In doing so, it considers the constrained relationships between chemical concentrations originated from their compositional nature. Bootstrap estimates using a range of imputation methods, including new stochastic proposals, are compared across scenarios of increasing difficulty. They are formulated to meet compositional principles following the log-ratio approach, and an adjustment is introduced in the multivariate case to deal with nonclosed samples. Results suggest that nondetect bootstrap based on model-based imputation is generally preferable. A robust approach based on isometric log-ratio transformations appears to be particularly suited in this context. Computer routines in the R statistical programming language are provided.
","language":"English","publisher":"Wiley","doi":"10.1002/cem.2621","usgsCitation":"Palarea-Albaladejo, J., Martin-Fernandez, J., and Olea, R., 2014, A bootstrap estimation scheme for chemical compositional data with nondetects: Journal of Chemometrics, v. 28, no. 7, p. 585-599, https://doi.org/10.1002/cem.2621.","productDescription":"15 p.","startPage":"585","endPage":"599","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044452","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":324712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"UNITED STATES","volume":"28","issue":"7","noUsgsAuthors":false,"publicationDate":"2014-04-02","publicationStatus":"PW","scienceBaseUri":"5777942ee4b07dd077c905be","contributors":{"authors":[{"text":"Palarea-Albaladejo, Javier","contributorId":120518,"corporation":false,"usgs":true,"family":"Palarea-Albaladejo","given":"Javier","email":"","affiliations":[],"preferred":false,"id":517798,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin-Fernandez, J.A","contributorId":116812,"corporation":false,"usgs":true,"family":"Martin-Fernandez","given":"J.A","email":"","affiliations":[],"preferred":false,"id":517796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olea, Ricardo A. 0000-0003-4308-0808 rolea@usgs.gov","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":1401,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","email":"rolea@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":641512,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70107378,"text":"70107378 - 2014 - Distribution of soil organic carbon in the conterminous United States","interactions":[],"lastModifiedDate":"2014-07-02T10:34:40","indexId":"70107378","displayToPublicDate":"2014-01-01T10:30:03","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Distribution of soil organic carbon in the conterminous United States","docAbstract":"The U.S. Soil Survey Geographic (SSURGO) database provides detailed soil mapping for most of the conterminous United States (CONUS). These data have been used to formulate estimates of soil carbon stocks, and have been useful for environmental models, including plant productivity models, hydrologic models, and ecological models for studies of greenhouse gas exchange. The data were compiled by the U.S. Department of Agriculture Natural Resources Conservation Service (NRCS) from 1:24,000-scale or 1:12,000-scale maps. It was found that the total soil organic carbon stock in CONUS to 1 m depth is 57 Pg C and for the total profile is 73 Pg C, as estimated from SSURGO with data gaps filled from the 1:250,000-scale Digital General Soil Map. We explore the non-linear distribution of soil carbon on the landscape and with depth in the soil, and the implications for sampling strategies that result from the observed soil carbon variability.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Soil Carbon","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Springer International Publishing","publisherLocation":"New York, NY","doi":"10.1007/978-3-319-04084-4_9","isbn":"978-3-319-04083-7","usgsCitation":"Bliss, N.B., Waltman, S., West, L.T., Neale, A., and Mehaffey, M., 2014, Distribution of soil organic carbon in the conterminous United States, chap. of Soil Carbon, p. 85-93, https://doi.org/10.1007/978-3-319-04084-4_9.","productDescription":"p. 85-93","numberOfPages":"9","ipdsId":"IP-054429","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":289366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":287316,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/978-3-319-04084-4_9"}],"noUsgsAuthors":false,"publicationDate":"2014-03-03","publicationStatus":"PW","scienceBaseUri":"53b7b101e4b0388651d916cb","contributors":{"editors":[{"text":"Hartemink, Alfred E.","contributorId":111952,"corporation":false,"usgs":true,"family":"Hartemink","given":"Alfred","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":509850,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"McSweeney, Kevin M.","contributorId":113219,"corporation":false,"usgs":true,"family":"McSweeney","given":"Kevin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":509851,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Bliss, Norman B. 0000-0003-2409-5211 bliss@usgs.gov","orcid":"https://orcid.org/0000-0003-2409-5211","contributorId":1921,"corporation":false,"usgs":true,"family":"Bliss","given":"Norman","email":"bliss@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":493903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waltman, Sharon","contributorId":96596,"corporation":false,"usgs":true,"family":"Waltman","given":"Sharon","affiliations":[],"preferred":false,"id":493907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"West, Larry T.","contributorId":18681,"corporation":false,"usgs":true,"family":"West","given":"Larry","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":493904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Neale, Anne","contributorId":43275,"corporation":false,"usgs":true,"family":"Neale","given":"Anne","email":"","affiliations":[],"preferred":false,"id":493905,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mehaffey, Megan","contributorId":58568,"corporation":false,"usgs":true,"family":"Mehaffey","given":"Megan","affiliations":[],"preferred":false,"id":493906,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70056519,"text":"70056519 - 2014 - Holocene and latest Pleistocene paleoseismology of the Salt Lake City segment of the Wasatch Fault Zone, Utah, at the Penrose Drive Trench Site","interactions":[],"lastModifiedDate":"2014-10-02T15:51:23","indexId":"70056519","displayToPublicDate":"2014-01-01T10:25:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesNumber":"149","title":"Holocene and latest Pleistocene paleoseismology of the Salt Lake City segment of the Wasatch Fault Zone, Utah, at the Penrose Drive Trench Site","docAbstract":"The Salt Lake City segment (SLCS) of the Wasatch fault zone (WFZ) and the West Valley fault zone (WVFZ) compromise Holocene-active normal faults that bound a large intrabasin graben in northern Salt Lake Valley and have evidence of recurrent, large-magnitude (M ~6-7) surface-faulting earthquakes. However, at the time of this investigation, questions remained regarding the timing, displacement, and recurrence of latest Pleistocene and Holocene earthquakes on the northern SLCS and WVFZ , and whether the WVFZ is seismically independent of, or moves coseismically with, the SLCS.
\nTo improve paleoseismic data for the SLCS, we conducted a fault-trench investigation at the Penrose Drive site on the northern SLCS. Two trenches, excavated across an 11-m-high scarp near the northern end of the East Bench fault, exposed colluvial-wedge evidence for fize of six (preferred) surface-faulting earthquakes postdating to Provo-phase shoreline of Lake Bonneville (~14-18 ka). Radiocarbon and luminescence ages support earthquake times at 4.0 ± 0.5 ka (2σ) (PD1), 5.9 ± 0.7 ka (PD2), 7.5 ± 0.8 ka (PD3a), 9.7 ± 1.1 ka (PD3b), 10.9 ± 0.2 ka (PD4), and 12.1 ± 1.6 ka (PD5). At least one additional earthquake occurred at 16.5 ± 1.9 ka (PD6) based on an erosional unconformity that separates deformed Lake Bonneville sily and flat-lying Provo-phase shoreline gravel. Earthquakes PD5-PD1 yield latest Pleistocene (post-Provo) and Holocene mean recurrence intervals of ~1.6 kyr and ~1.7-1.9 kyr, respectively. Using 1.0-1.4 m of per-event vertical displacement for PD5-PD3b corroborate previously identified SLCS earthquakes at 4-10 ka. PD4 and PD5 occurred within an ~8-kyr *17-9 ka) time interval on the SLCS previously interpreted as a period of seismic quiescence, and PD6 possibly corresponds with a previously identified earthquake at ~17 ka (although both events have large timing uncertainties).
\nThe Penrose data, when combined with previous paleoseismic results, improve the latest Pleistocene-Holocene earthquake chronology of the SLCS, and demonstrate that the SLCS has been a consistently active source of large-magnitude earthquakes since the latest Pleistocene. At least nine surface-faulting earthquakes (S1-S9) have occurred since the highstand of Lake Bonneville (~18 ka). Where the SLCS earthquake record is most complete (since ~14 ka), per-site estimates of mean recurrence are similar for the latest Pleistocene (post-Provo) (~1.6 kyr), Holocene (~1.6-1.9 kyr), and late Holocene (~1.2-1.4 kyr). These SLCS paleoearthquake data indicate an essentially stable rate of earthquake recurrence since the latest Pleistocene and are important for understanding the earthquake potential of the SLCS, clarifying the seismogenic relation between the SLCS and WVFZ, and forecasting the probabilities of future large-magnitude earthquake in the Wasatch Front region.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Evaluating surface faulting chronologies of Graben-Bounding Faults in Salt Lake Valley, Utah: new paleoseismic data from the Salt Lake City segment of the Wasatch Fault Zone and the West Valley Fault Zone","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"Utah Geological Survey","publisherLocation":"Salt Lake City, UT","usgsCitation":"DuRoss, C., Hylland, M., McDonald, G., Crone, A.J., Personius, S.F., Gold, R.D., and Mahan, S., 2014, Holocene and latest Pleistocene paleoseismology of the Salt Lake City segment of the Wasatch Fault Zone, Utah, at the Penrose Drive Trench Site, v. 24, 39 p.","productDescription":"39 p.","numberOfPages":"39","ipdsId":"IP-051371","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":294884,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294883,"type":{"id":11,"text":"Document"},"url":"https://geology.utah.gov/online/ss/ss-149/SS-149_PenroseDrive_report.pdf"}],"country":"United States","state":"Utah","otherGeospatial":"Wasatch Fault Zone","volume":"24","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"542e6963e4b092f17df5a8a2","contributors":{"authors":[{"text":"DuRoss, Christopher B.","contributorId":100764,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher B.","affiliations":[],"preferred":false,"id":486582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hylland, Michael D.","contributorId":106031,"corporation":false,"usgs":true,"family":"Hylland","given":"Michael D.","affiliations":[],"preferred":false,"id":486583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McDonald, Greg N.","contributorId":55362,"corporation":false,"usgs":true,"family":"McDonald","given":"Greg N.","affiliations":[],"preferred":false,"id":486581,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crone, Anthony J. 0000-0002-3006-406X crone@usgs.gov","orcid":"https://orcid.org/0000-0002-3006-406X","contributorId":790,"corporation":false,"usgs":true,"family":"Crone","given":"Anthony","email":"crone@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":486577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Personius, Stephen F. personius@usgs.gov","contributorId":1214,"corporation":false,"usgs":true,"family":"Personius","given":"Stephen","email":"personius@usgs.gov","middleInitial":"F.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":486578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":486580,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mahan, Shannon 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":1215,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":486579,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}