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Chinook Salmon Oncorhynchus tshawytscha are the primary apex predators in pelagic Lake Michigan and patterns in their prey selection (by species and size) may strongly influence pelagic prey fish communities in any given year. In 1994–1996, there were larger Alewives, relatively more abundant alternative prey species, fewer Chinook Salmon, and fewer invasive species in Lake Michigan than in 2009–2010. The years 2009–2010 were instead characterized by smaller, leaner Alewives, fewer alternative prey species, higher abundance of Chinook Salmon, a firmly established nonnative benthic community, and reduced abundance of Diporeia, an important food of Lake Michigan prey fish. We characterized Chinook Salmon diets, prey species selectivity, and prey size selectivity between 1994–1996 and 2009–2010 time periods. In 1994–1996, Alewife as prey represented a smaller percentage of Chinook Salmon diets than in 2009–2010, when alewife comprised over 90% of Chinook Salmon diets, possibly due to declines in alternative prey fish populations. The size of Alewives eaten by Chinook Salmon also decreased between these two time periods. For the largest Chinook Salmon in 2009–2010, the average size of Alewife prey was nearly 50 mm total length shorter than in 1994–1996. We suggest that changes in the Lake Michigan food web, such as the decline in Diporeia, may have contributed to the relatively low abundance of large Alewives during the late 2000s by heightening the effect of predation from top predators like Chinook Salmon, which have retained a preference for Alewife and now forage with greater frequency on smaller Alewives.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Transactions of the American Fisheries Society","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Taylor & Francis","publisherLocation":"Philadelphia, PA","doi":"10.1080/00028487.2012.739981","usgsCitation":"Jacobs, G.R., Madenjian, C.P., Bunnell, D., Warner, D.M., and Claramunt, R., 2013, Chinook salmon foraging patterns in a changing Lake Michigan: Transactions of the American Fisheries Society, v. 142, no. 2, p. 362-372, https://doi.org/10.1080/00028487.2012.739981.","productDescription":"11 p.","startPage":"362","endPage":"372","ipdsId":"IP-039307","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":267139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":267138,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/00028487.2012.739981"}],"country":"United States","volume":"142","issue":"2","noUsgsAuthors":false,"publicationDate":"2013-01-22","publicationStatus":"PW","scienceBaseUri":"5114cd02e4b0ca7af0743ad7","contributors":{"authors":[{"text":"Jacobs, Gregory R.","contributorId":68189,"corporation":false,"usgs":true,"family":"Jacobs","given":"Gregory","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":473141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":473137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunnell, David B.","contributorId":14360,"corporation":false,"usgs":true,"family":"Bunnell","given":"David B.","affiliations":[],"preferred":false,"id":473139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":473138,"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":473140,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70043200,"text":"ds709P - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Baghlan mineral district in Afghanistan: Chapter P in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-07T13:47:41","indexId":"ds709P","displayToPublicDate":"2013-02-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"P","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Baghlan mineral district in Afghanistan: Chapter P in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Baghlan mineral district, which has industrial clay and gypsum deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA, 2006, 2007, 2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 315-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Baghlan) and the WGS84 datum. The final image mosaics were subdivided into two overlapping tiles or quadrants because of the large size of the target area. The two image tiles (or quadrants) for the Baghlan area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709P","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter P in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709","usgsCitation":"Davis, P.A., and Cagney, L.E., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Baghlan mineral district in Afghanistan: Chapter P in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, HTML Document; Readme; 2 Maps; 4 Image Files; 4 Metadata; 1 Shapefile, https://doi.org/10.3133/ds709P.","productDescription":"HTML Document; Readme; 2 Maps; 4 Image Files; 4 Metadata; 1 Shapefile","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":267118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_p.png"},{"id":267113,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/p/index_maps/Baghlan_Area-of-Interest_Index_Map.pdf"},{"id":267111,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/p/"},{"id":267112,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/p/1_readme.txt"},{"id":267114,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/p/index_maps/Baghlan_Image_Index_Map.pdf"},{"id":267115,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/p/image_files/image_files.html"},{"id":267116,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/p/metadata/metadata.html"},{"id":267117,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/p/shapefiles/shapefiles.html"}],"country":"Afghanistan","otherGeospatial":"Baghlan Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.52,29.38 ], [ 60.52,38.49 ], [ 74.89,38.49 ], [ 74.89,29.38 ], [ 60.52,29.38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5114cd05e4b0ca7af0743adf","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":473151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":473152,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70043205,"text":"sir20125284 - 2013 - Assessment of macroinvertebrate communities in adjacent urban stream basins, Kansas City, Missouri, metropolitan area, 2007 through 2011","interactions":[],"lastModifiedDate":"2013-02-07T14:09:37","indexId":"sir20125284","displayToPublicDate":"2013-02-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5284","title":"Assessment of macroinvertebrate communities in adjacent urban stream basins, Kansas City, Missouri, metropolitan area, 2007 through 2011","docAbstract":"Macroinvertebrates were collected as part of two separate urban water-quality studies from adjacent basins, the Blue River Basin (Kansas City, Missouri), the Little Blue River and Rock Creek Basins (Independence, Missouri), and their tributaries. Consistent collection and processing procedures between the studies allowed for statistical comparisons. Seven Blue River Basin sites, nine Little Blue River Basin sites, including Rock Creek, and two rural sites representative of Missouri ecological drainage units and the area’s ecoregions were used in the analysis. Different factors or levels of urban intensity may affect the basins and macroinvertebrate community metrics differently, even though both basins are substantially developed above their downstream streamgages (Blue River, 65 percent; Little Blue River, 52 percent). The Blue River has no flood control reservoirs and receives wastewater effluent and stormflow from a combined sewer system. The Little Blue River has flood control reservoirs, receives no wastewater effluent, and has a separate stormwater sewer system. Analysis of macroinvertebrate community structure with pollution-tolerance metrics and water-quality parameters indicated differences between the Blue River Basin and the Little Blue River Basin.\nA four-metric score (total taxa richness, Ephemeroptera plus Plecoptera plus Trichoptera taxa richness, Macroinvertebrate Biotic Index, and Shannon Diversity Index) for richest-targeted habitat was used to calculate a Stream Condition Index (SCI) in order to evaluate the aquatic-life status of the streams. About 80 percent of all samples combined were determined to be less than fully biologically supporting, and about 11 percent of spring samples were fully biologically supporting. No sites within the Blue River Basin had a fully supporting score. The aquatic-life status scores for the Little Blue River and its tributaries were higher (indicating more optimal conditions) than for the Blue River and its tributaries. Fall samples scored higher than spring samples. However, fall samples were collected at the Little Blue River Basin and rural sites only. The Little Blue River sites scored higher for fall samples than spring samples; about 39 percent fully biologically supporting and 61 percent partially biologically supporting; more similar to the rural comparison sites, 40 percent fully biologically supporting and 60 percent partially biologically supporting.\nThe SCI was compared to other multimetric indices with more or other component metrics to determine if the SCI effectively described differences among sites. Environmental variables (streamflow, water quality, land use, impervious cover, and population density) were used in statistical analyses to evaluate relations to macroinvertebrate metrics. Multimetric indices (MMIs) were modeled using step regression with a simple urban intensity index (SUII) based on percentage of impervious cover, population density, and forest cover in a 30-meter stream-buffer zone, and two were selected for further analysis. Three other multimetric indices composed of metrics common to local and national studies show results similar to the two modeled MMIs. A common Benthic Index of Biotic Integrity (R2 equals 0.71) developed for a national study had the highest correlation with urban intensity as measured with the SUII, followed by a modeled 6-metric index (R2 equals 0.61). The other MMIs and the SCI explained less than a half of the variability in macroinvertebrate communities in relation to the SUII.\nWastewater-treatment plant discharges during base flow, which elevated specific conductance and nutrient concentrations, combined sewer overflows, and nonpoint sources likely contributed to water-quality impairment and lower aquatic-life status at the Blue River Basin sites. Releases from upstream reservoirs to the Little Blue River likely decreased specific conductance, suspended-sediment, and dissolved constituent concentrations and may have benefitted water quality and aquatic life of main-stem sites. Chloride concentrations in base-flow samples, attributable to winter road salt application, had the highest correlation with the SUII (Spearman’s ρ equals 0.87), were negatively correlated with the SCI (Spearman’s ρ equals -0.53) and several pollution sensitive Ephemeroptera plus Plecoptera plus Trichoptera abundance and percent richness metrics, and were positively correlated with pollution tolerant Oligochaeta abundance and percent richness metrics. Study results show that the easily calculated SUII and the selected modeled multimetric indices are effective for comparing urban basins and for evaluation of water quality in the Kansas City metropolitan area.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125284","collaboration":"Prepared in cooperation with the City of Independence, Missouri Water Pollution Control Department","usgsCitation":"Christensen, E.D., and Krempa, H., 2013, Assessment of macroinvertebrate communities in adjacent urban stream basins, Kansas City, Missouri, metropolitan area, 2007 through 2011: U.S. Geological Survey Scientific Investigations Report 2012-5284, viii, 45 p.; Tables, https://doi.org/10.3133/sir20125284.","productDescription":"viii, 45 p.; Tables","startPage":"i","endPage":"45","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2007-01-01","temporalEnd":"2011-12-31","ipdsId":"IP-037625","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":267130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5284.gif"},{"id":267127,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5284/"},{"id":267128,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5284/sir2012-5284.pdf"},{"id":267129,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2012/5284/downloads/tables.xlsx"}],"country":"United States","state":"Missouri","city":"Kansas City","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.7659,38.8272 ], [ -94.7659,39.3567 ], [ -94.3855,39.3567 ], [ -94.3855,38.8272 ], [ -94.7659,38.8272 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5114cd01e4b0ca7af0743ad3","contributors":{"authors":[{"text":"Christensen, Eric D. echriste@usgs.gov","contributorId":4230,"corporation":false,"usgs":true,"family":"Christensen","given":"Eric","email":"echriste@usgs.gov","middleInitial":"D.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":473167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krempa, Heather M.","contributorId":35612,"corporation":false,"usgs":true,"family":"Krempa","given":"Heather M.","affiliations":[],"preferred":false,"id":473168,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70128265,"text":"70128265 - 2013 - Assessing the risk of nitrogen deposition to natural resources in the Four Corners area","interactions":[],"lastModifiedDate":"2017-06-13T13:34:10","indexId":"70128265","displayToPublicDate":"2013-02-06T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Assessing the risk of nitrogen deposition to natural resources in the Four Corners area","docAbstract":"

Nitrogen (N) deposition in the western U.S. is on the rise and is already dramatically affecting terrestrial ecosystems. For example, N deposition has repeatedly been shown to lower air and water quality, increase greenhouse gas emissions, alter plant community composition, and significantly modify fire regimes. Accordingly, the effects of N deposition represent one of our largest environmental challenges and make difficult the National Park Service’s (NPS) important mission to “preserve the scenery and the natural and historic objects and the wildlife… unimpaired for the enjoyment of future generations”. Due to increased population growth and energy development (e.g., natural gas wells), the Four Corners region has become a notable ‘hotspot’ for N deposition. However, our understanding of how increased N deposition will affect these unique ecosystems, as well as how much deposition is actually occurring, remains notably poor. Here we used a multi-disciplinary approach to gathering information in an effort to help NPS safeguard the Four Corners national parks, both now and into the future. We applied modeling, field, and laboratory techniques to clarify current N deposition gradients and to help elucidate the ecosystem consequences of N deposition to the national parks of the Four Corners area. Our results suggest that NOx deposition does indeed represent a significant source of N to Mesa Verde National Park and, as expected, N deposition significantly affects coupled biogeochemical cycling (N, carbon, and phosphorus) of these landscapes. We also found some surprising results. For example, perhaps due to the low nutrient availability in these (and other) dryland ecosystems, although most other research suggests that adding N reduces N fixation rates, N additions did not consistently reduce natural N inputs via biological N2 fixation at our dryland sites. While the timeline of this pilot project is too brief to elucidate all the potential insight from the approach utilized here (e.g., we have fertilization plots to explore how N deposition affects Bromus tectorum invasion that will surely yield provoking results), we plan to continue this exciting line of questioning and expect further insight to be forthcoming.

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The group addressed the challenge of reconciling current geodetic measurements, which show low present-day surface strain rates, with paleoseismic evidence of recent, relatively frequent, major earthquakes in the region. Several researchers were invited by the organizing committee to give overview presentations while all participants were encouraged to present their most recent ideas. 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Historically, the USGS Chesapeake Bay studies have relied on national USGS databases to store only major nationally available sources of data such as streamflow and water-quality data collected through local monitoring programs and projects, leaving a multitude of other important project data out of the data management process. This practice has led to inefficient methods of finding Chesapeake Bay studies data and underutilization of data resources. Data management by definition is “the business functions that develop and execute plans, policies, practices and projects that acquire, control, protect, deliver and enhance the value of data and information.” (Mosley, 2008a). In other words, data management is a way to preserve, integrate, and share data to address the needs of the Chesapeake Bay studies to better manage data resources, work more efficiently with partners, and facilitate holistic watershed science. 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the Florida Everglades. Since the 1970s, substantial focus has been given to the restoration of the Everglades ecosystem. The U.S. Geological Survey through its Greater Everglades Priority Ecosystem Science and National Water-Quality Assessment Programs has been providing scientific information to resource managers to assist in the Everglades restoration efforts. The current investigation included development of a simple method to identify and quantify changes in historical hydrologic behavior within the Everglades that could be used by researchers to identify responses of ecological communities to those changes. Such information then could be used by resource managers to develop appropriate water-management practices within the Everglades to promote restoration. The identification of changes in historical hydrologic behavior within the Everglades was accomplished by analyzing historical time-series water-level data from selected gages in the Everglades using (1) break-point analysis of cumulative Z-scores to identify hydrologic changes and (2) cumulative water-level frequency distribution curves to evaluate the magnitude of those changes. This analytical technique was applied to six long-term water-level gages in the Florida Everglades. The break-point analysis for the concurrent period of record (1978–2011) identified 10 common periods of changes in hydrologic behavior at the selected gages. The water-level responses at each gage for the 10 periods displayed similarity in fluctuation patterns, highlighting the interconnectedness of the Florida Everglades hydrologic system. While the patterns were similar, the analysis also showed that larger fluctuations in water levels between periods occurred in Water Conservation Areas 2 and 3 in contrast to those in Water Conservation Area 1 and the Everglades National Park. Results from the analysis indicate that the cumulative Z-score curve, in conjunction with cumulative water-level frequency distribution curves, can be a useful tool in identifying and quantifying changes in historical hydrologic behavior within the Everglades. In addition to the analysis, a spreadsheet application was developed to assist in applying these techniques to time-series water-level data at gages within the Everglades and is included with this report.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125286","collaboration":"Prepared as part of the U.S. Geological Survey Greater Everglades Priority Ecosystem Science","usgsCitation":"Conrads, P., and Benedict, S., 2013, Analysis of changes in water-level dynamics at selected sites in the Florida Everglades: U.S. Geological Survey Scientific Investigations Report 2012-5286, v, 36 p.; ZEBRA Spreadsheet, https://doi.org/10.3133/sir20125286.","productDescription":"v, 36 p.; ZEBRA Spreadsheet","startPage":"i","endPage":"36","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"links":[{"id":267083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5286.gif"},{"id":267082,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2012/5286/ZEBRA_(Beta-Version).xlsx"},{"id":267080,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5286/"},{"id":267081,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5286/pdf/sir2012-5286.pdf"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -87.63,24.52 ], [ -87.63,31.0 ], [ -80.03,31.0 ], [ -80.03,24.52 ], [ -87.63,24.52 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51137b5fe4b0a9ee4115b9f8","contributors":{"authors":[{"text":"Conrads, Paul 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":764,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":473103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benedict, Stephen T. benedict@usgs.gov","contributorId":3198,"corporation":false,"usgs":true,"family":"Benedict","given":"Stephen T.","email":"benedict@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":473104,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70043135,"text":"70043135 - 2013 - Hydrography change detection: the usefulness of surface channels derived From LiDAR DEMs for updating mapped hydrography","interactions":[],"lastModifiedDate":"2017-05-26T12:58:01","indexId":"70043135","displayToPublicDate":"2013-02-06T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Hydrography change detection: the usefulness of surface channels derived From LiDAR DEMs for updating mapped hydrography","docAbstract":"The 1:24,000-scale high-resolution National Hydrography Dataset (NHD) mapped hydrography flow lines require regular updating because land surface conditions that affect surface channel drainage change over time. Historically, NHD flow lines were created by digitizing surface water information from aerial photography and paper maps. Using these same methods to update nationwide NHD flow lines is costly and inefficient; furthermore, these methods result in hydrography that lacks the horizontal and vertical accuracy needed for fully integrated datasets useful for mapping and scientific investigations. Effective methods for improving mapped hydrography employ change detection analysis of surface channels derived from light detection and ranging (LiDAR) digital elevation models (DEMs) and NHD flow lines. In this article, we describe the usefulness of surface channels derived from LiDAR DEMs for hydrography change detection to derive spatially accurate and time-relevant mapped hydrography. The methods employ analyses of horizontal and vertical differences between LiDAR-derived surface channels and NHD flow lines to define candidate locations of hydrography change. These methods alleviate the need to analyze and update the nationwide NHD for time relevant hydrography, and provide an avenue for updating the dataset where change has occurred.","language":"English","publisher":"American Water Resources Association","publisherLocation":"Middleburg, VA","doi":"10.1111/jawr.12027","usgsCitation":"Poppenga, S.K., Gesch, D.B., and Worstell, B.B., 2013, Hydrography change detection: the usefulness of surface channels derived From LiDAR DEMs for updating mapped hydrography: Journal of the American Water Resources Association, v. 49, no. 2, p. 371-389, https://doi.org/10.1111/jawr.12027.","productDescription":"19 p.","startPage":"371","endPage":"389","ipdsId":"IP-035009","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473953,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jawr.12027","text":"Publisher Index Page"},{"id":267066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":267042,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/jawr.12027"}],"volume":"49","issue":"2","noUsgsAuthors":false,"publicationDate":"2013-01-28","publicationStatus":"PW","scienceBaseUri":"51137b6ae4b0a9ee4115ba00","contributors":{"authors":[{"text":"Poppenga, Sandra K. 0000-0002-2846-6836","orcid":"https://orcid.org/0000-0002-2846-6836","contributorId":84465,"corporation":false,"usgs":true,"family":"Poppenga","given":"Sandra","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":473029,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":473028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Worstell, Bruce B. 0000-0001-8927-3336 worstell@usgs.gov","orcid":"https://orcid.org/0000-0001-8927-3336","contributorId":1815,"corporation":false,"usgs":true,"family":"Worstell","given":"Bruce","email":"worstell@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":473027,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70043133,"text":"70043133 - 2013 - Vegetation impoverishment despite greening: a case study from central Senegal","interactions":[],"lastModifiedDate":"2013-02-06T13:46:13","indexId":"70043133","displayToPublicDate":"2013-02-06T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Vegetation impoverishment despite greening: a case study from central Senegal","docAbstract":"Recent remote sensing studies have documented a greening trend in the semi-arid Sahel and Sudan zones of West Africa since the early 1980s, which challenges the mainstream paradigm of irreversible land degradation in this region. What the greening trend means on the ground, however, has not yet been explored. This research focuses on a region in central Senegal to examine changes in woody vegetation abundance and composition in selected sites by means of a botanical inventory of woody vegetation species, repeat photography, and perceptions of local land users. Despite the greening, an impoverishment of the woody vegetation cover was observed in the studied sites, indicated by an overall reduction in woody species richness, a loss of large trees, an increasing dominance of shrubs, and a shift towards more arid-tolerant, Sahelian species since 1983. Thus, interpretation of the satellite-derived greening trend as an improvement or recovery is not always justified. The case of central Senegal represents only one of several possible pathways of greening throughout the region, all of which result in similar satellite-derived greening signals.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Arid Environments","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.jaridenv.2012.10.020","usgsCitation":"Herrmann, S.M., and Tappan, G.G., 2013, Vegetation impoverishment despite greening: a case study from central Senegal: Journal of Arid Environments, v. 90, p. 55-66, https://doi.org/10.1016/j.jaridenv.2012.10.020.","productDescription":"12 p.","startPage":"55","endPage":"66","ipdsId":"IP-032790","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":267069,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":267041,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.jaridenv.2012.10.020"}],"country":"Senegal","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -17.5298,12.3073 ], [ -17.5298,16.6931 ], [ -11.3486,16.6931 ], [ -11.3486,12.3073 ], [ -17.5298,12.3073 ] ] ] } } ] }","volume":"90","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51137b6ce4b0a9ee4115ba08","contributors":{"authors":[{"text":"Herrmann, Stefanie M. 0000-0002-4069-2019","orcid":"https://orcid.org/0000-0002-4069-2019","contributorId":20234,"corporation":false,"usgs":true,"family":"Herrmann","given":"Stefanie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":473026,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tappan, G. 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Sampling sites were selected near or within agricultural research farms using prophylactic fungicides at rates and types typical of their geographic location. At least two fungicides were detected in 55% of the bed and 83% of the suspended solid samples and were detected in conjunction with herbicides and insecticides. Six fungicides were detected in all samples including pyraclostrobin (75%), boscalid (53%), chlorothalonil (41%) and zoxamide (22%). Pyraclostrobin, a strobilurin fungicide, used frequently in the United States on a variety of crops, was detected more frequently than p,p′-DDE, the primary degradate of p,p′-DDT, which is typically one of the most frequently occurring pesticides in sediments collected within highly agricultural areas. Maximum fungicide concentrations in bed sediments and suspended solids were 198 and 56.7 μg/kg dry weight, respectively. There is limited information on the occurrence, fate, and persistence of many fungicides in sediment and the environmental impacts are largely unknown. The results of this study indicate the importance of documenting the persistence of fungicides in the environment and the need for a better understanding of off-site transport mechanisms, particularly in areas where crops are grown that require frequent treatments to prevent fungal diseases.","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.scitotenv.2013.01.021","usgsCitation":"Smalling, K., Reilly, T.J., Sandstrom, M.W., and Kuivila, K., 2013, Occurrence and persistence of fungicides in bed sediments and suspended solids from three targeted use areas in the United States: Science of the Total Environment, v. 447, p. 179-185, https://doi.org/10.1016/j.scitotenv.2013.01.021.","productDescription":"7 p.","startPage":"179","endPage":"185","ipdsId":"IP-036904","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":452,"text":"National Water Quality 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Center","active":true,"usgs":true}],"preferred":false,"id":473071,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70111686,"text":"70111686 - 2013 - Partial least squares for efficient models of fecal indicator bacteria on Great Lakes beaches","interactions":[],"lastModifiedDate":"2014-06-06T10:53:51","indexId":"70111686","displayToPublicDate":"2013-02-05T10:49:03","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Partial least squares for efficient models of fecal indicator bacteria on Great Lakes beaches","docAbstract":"At public beaches, it is now common to mitigate the impact of water-borne pathogens by posting a swimmer's advisory when the concentration of fecal indicator bacteria (FIB) exceeds an action threshold. Since culturing the bacteria delays public notification when dangerous conditions exist, regression models are sometimes used to predict the FIB concentration based on readily-available environmental measurements. It is hard to know which environmental parameters are relevant to predicting FIB concentration, and the parameters are usually correlated, which can hurt the predictive power of a regression model. Here the method of partial least squares (PLS) is introduced to automate the regression modeling process. Model selection is reduced to the process of setting a tuning parameter to control the decision threshold that separates predicted exceedances of the standard from predicted non-exceedances. The method is validated by application to four Great Lakes beaches during the summer of 2010. Performance of the PLS models compares favorably to that of the existing state-of-the-art regression models at these four sites.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Environmental Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2012.09.033","usgsCitation":"Brooks, W.R., Fienen, M., and Corsi, S., 2013, Partial least squares for efficient models of fecal indicator bacteria on Great Lakes beaches: Journal of Environmental Management, v. 114, p. 470-475, https://doi.org/10.1016/j.jenvman.2012.09.033.","productDescription":"6 p.","startPage":"470","endPage":"475","ipdsId":"IP-030717","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":288141,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288140,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.jenvman.2012.09.033"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -88,41.5 ], [ -88,46.5 ], [ -78,46.5 ], [ -78,41.5 ], [ -88,41.5 ] ] ] } } ] }","volume":"114","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae77a1e4b0abf75cf2c18c","contributors":{"authors":[{"text":"Brooks, Wesley R. wrbrooks@usgs.gov","contributorId":4217,"corporation":false,"usgs":true,"family":"Brooks","given":"Wesley","email":"wrbrooks@usgs.gov","middleInitial":"R.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494429,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":893,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","email":"mnfienen@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":494428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Corsi, Steven R. srcorsi@usgs.gov","contributorId":511,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":494427,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70044216,"text":"70044216 - 2013 - Population genetic structure of rare and endangered plants using molecular markers","interactions":[],"lastModifiedDate":"2018-01-05T12:37:57","indexId":"70044216","displayToPublicDate":"2013-02-05T05:15:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":414,"text":"Technical Report","active":false,"publicationSubtype":{"id":9}},"seriesNumber":"HCSU-036","title":"Population genetic structure of rare and endangered plants using molecular markers","docAbstract":"
\n
\n
\n

This study was initiated to assess the levels of genetic diversity and differentiation in the remaining populations of Phyllostegia stachyoides and Melicope zahlbruckneri in Hawai`i Volcanoes National Park and determine the extent of gene flow to identify genetically distinct individuals or groups for conservation purposes. Thirty-six Amplified Fragment Length Polymorphic (AFLP) primer combinations generated a total of 3,242 polymorphic deoxyribonucleic acid (DNA) fragments in the P. stachyoides population with a percentage of polymorphic bands (PPB) ranging from 39.3 to 65.7% and 2,780 for the M. zahlbruckneri population with a PPB of 18.8 to 64.6%. Population differentiation (Fst) of AFLP loci between subpopulations of P. stachyoides was low (0.043) across populations. Analysis of molecular variance of P. stachyoides showed that 4% of the observed genetic differentiation occurred between populations in different kīpuka and 96% when individuals were pooled from all kīpuka. Moderate genetic diversity was detected within the M. zahlbruckneri population. Bayesian and multivariate analyses both classified the P. stachyoides and M. zahlbruckneri populations into genetic groups with considerable sub-structuring detected in the P. stachyoides population. The proportion of genetic differentiation among populations explained by geographical distance was estimated by Mantel tests. No spatial correlation was found between genetic and geographic distances in both populations. Finally, a moderate but significant gene flow that could be attributed to insect or bird-mediated dispersal of pollen across the different kīpuka was observed. The results of this study highlight the utility of a multi-allelic DNA-based marker in screening a large number of polymorphic loci in small and closely related endangered populations and revealed the presence of genetically unique groups of individuals in both M. zahlbruckneri and P. stachyoides populations. Based on these findings, approaches that can assist conservation efforts of these species are proposed. 

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","language":"English","publisher":"University of Hawi'i at Hilo","publisherLocation":"Hilo, HI","usgsCitation":"Raji, J., and Atkinson, C.T., 2013, Population genetic structure of rare and endangered plants using molecular markers: Technical Report HCSU-036, iv, 42 p.","productDescription":"iv, 42 p.","numberOfPages":"48","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-042186","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":325134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai`i","otherGeospatial":"Hawai`i Volcanoes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.401611328125,\n 19.241143039165962\n ],\n [\n -155.401611328125,\n 19.535201464574232\n ],\n [\n -155.1324462890625,\n 19.535201464574232\n ],\n [\n -155.1324462890625,\n 19.241143039165962\n ],\n [\n -155.401611328125,\n 19.241143039165962\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"579dd01ce4b0589fa1cbdc3c","contributors":{"authors":[{"text":"Raji, Jennifer","contributorId":172853,"corporation":false,"usgs":false,"family":"Raji","given":"Jennifer","email":"","affiliations":[{"id":13357,"text":"Hawaiʻi Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":517235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atkinson, Carter T. 0000-0002-4232-5335 catkinson@usgs.gov","orcid":"https://orcid.org/0000-0002-4232-5335","contributorId":1124,"corporation":false,"usgs":true,"family":"Atkinson","given":"Carter","email":"catkinson@usgs.gov","middleInitial":"T.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":642273,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70043164,"text":"ds709O - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the South Helmand mineral district in Afghanistan: Chapter O in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-06T14:05:56","indexId":"ds709O","displayToPublicDate":"2013-02-05T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"O","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the South Helmand mineral district in Afghanistan: Chapter O in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the South Helmand mineral district, which has travertine deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA, 2008, 2010), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (41 for South Helmand) and the WGS84 datum. The final image mosaics were subdivided into eight overlapping tiles or quadrants because of the large size of the target area. The eight image tiles (or quadrants) for the South Helmand area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709O","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter O in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. 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This report includes nearly 53 hours of digital underwater video footage collected during four USGS cruises and more than 10,200 still images extracted from the videos, including still frames from every 10 seconds along transect lines, and still frames showing both an overview and a near-bottom view from fixed stations. Environmental Systems Research Institute (ESRI) shapefiles of individual video and still-image locations, and Google Earth kml files with explanatory text and links to the video and still images, are included. 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The basin is bounded by the Washakie Range and Owl Creek and southern Bighorn Mountains on the north, the Casper arch on the east and northeast, and the Granite Mountains on the south, and Wind River Range on the west. The purpose of this report is to present new vitrinite reflectance data collected mainly from Cretaceous marine shales in the Wind River Basin to better characterize their thermal maturity and hydrocarbon potential.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131002","usgsCitation":"Pawlewicz, M.J., and Finn, T.M., 2013, New vitrinite reflectance data for the Wind River Basin, Wyoming: U.S. Geological Survey Open-File Report 2013-1002, iii, 11 p., https://doi.org/10.3133/ofr20131002.","productDescription":"iii, 11 p.","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-040666","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":266975,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2013_1002.gif"},{"id":266973,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1002/"},{"id":266974,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1002/OF13-1002.pdf"}],"country":"United States","state":"Arizona;Colorado;Idaho;Montana;Nebraska;New Mexico;North Dakota;South Dakota;Wyoming","otherGeospatial":"Wind River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -112.37,35.87 ], [ -112.37,46.07 ], [ -102.46,46.07 ], [ -102.46,35.87 ], [ -112.37,35.87 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"511229fee4b0ebe69d7eb604","contributors":{"authors":[{"text":"Pawlewicz, Mark J. pawlewicz@usgs.gov","contributorId":752,"corporation":false,"usgs":true,"family":"Pawlewicz","given":"Mark","email":"pawlewicz@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":472943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finn, Thomas M. 0000-0001-6396-9351 finn@usgs.gov","orcid":"https://orcid.org/0000-0001-6396-9351","contributorId":778,"corporation":false,"usgs":true,"family":"Finn","given":"Thomas","email":"finn@usgs.gov","middleInitial":"M.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":472944,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70043143,"text":"tm7C10 - 2013 - Computing ordinary least-squares parameter estimates for the National Descriptive Model of Mercury in Fish","interactions":[],"lastModifiedDate":"2013-02-05T16:04:45","indexId":"tm7C10","displayToPublicDate":"2013-02-05T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C10","title":"Computing ordinary least-squares parameter estimates for the National Descriptive Model of Mercury in Fish","docAbstract":"A specialized technique is used to compute weighted ordinary least-squares (OLS) estimates of the parameters of the National Descriptive Model of Mercury in Fish (NDMMF) in less time using less computer memory than general methods. The characteristics of the NDMMF allow the two products X'X and X'y in the normal equations to be filled out in a second or two of computer time during a single pass through the N data observations. As a result, the matrix X does not have to be stored in computer memory and the computationally expensive matrix multiplications generally required to produce X'X and X'y do not have to be carried out. The normal equations may then be solved to determine the best-fit parameters in the OLS sense. The computational solution based on this specialized technique requires O(8p2+16p) bytes of computer memory for p parameters on a machine with 8-byte double-precision numbers. This publication includes a reference implementation of this technique and a Gaussian-elimination solver in preliminary custom software.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer programs in Book 7 Automated Data Processing and Computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C10","collaboration":"This report is Chapter 10 of Section C: Computer programs in Book 7 Automated Data Processing and Computations","usgsCitation":"Donato, D.I., 2013, Computing ordinary least-squares parameter estimates for the National Descriptive Model of Mercury in Fish: U.S. Geological Survey Techniques and Methods 7-C10, iii, 9 p.; Appendix, https://doi.org/10.3133/tm7C10.","productDescription":"iii, 9 p.; Appendix","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":267063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm_7_c10.gif"},{"id":267060,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/07/c10/"},{"id":267062,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/07/c10/Ols.zip"},{"id":267061,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c10/pdf/tm7-c10.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"511229e3e4b0ebe69d7eb5fc","contributors":{"authors":[{"text":"Donato, David I. 0000-0002-5412-0249 didonato@usgs.gov","orcid":"https://orcid.org/0000-0002-5412-0249","contributorId":2234,"corporation":false,"usgs":true,"family":"Donato","given":"David","email":"didonato@usgs.gov","middleInitial":"I.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":473038,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045219,"text":"70045219 - 2013 - Temporal shifts in top-down vs. bottom-up control of epiphytic algae in a seagrass ecosystem","interactions":[],"lastModifiedDate":"2024-04-19T21:10:24.024228","indexId":"70045219","displayToPublicDate":"2013-02-01T16:09:09","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Temporal shifts in top-down vs. bottom-up control of epiphytic algae in a seagrass ecosystem","docAbstract":"

In coastal marine food webs, small invertebrate herbivores (mesograzers) have long been hypothesized to occupy an important position facilitating dominance of habitat-forming macrophytes by grazing competitively superior epiphytic algae. Because of the difficulty of manipulating mesograzers in the field, however, their impacts on community organization have rarely been rigorously documented. Understanding mesograzer impacts has taken on increased urgency in seagrass systems due to declines in seagrasses globally, caused in part by widespread eutrophication favoring seagrass overgrowth by faster-growing algae. Using cage-free field experiments in two seasons (fall and summer), we present experimental confirmation that mesograzer reduction and nutrients can promote blooms of epiphytic algae growing on eelgrass (Zostera marina). In this study, nutrient additions increased epiphytes only in the fall following natural decline of mesograzers. In the summer, experimental mesograzer reduction stimulated a 447% increase in epiphytes, appearing to exacerbate seasonal dieback of eelgrass. Using structural equation modeling, we illuminate the temporal dynamics of complex interactions between macrophytes, mesograzers, and epiphytes in the summer experiment. An unexpected result emerged from investigating the interaction network: drift macroalgae indirectly reduced epiphytes by providing structure for mesograzers, suggesting that the net effect of macroalgae on seagrass depends on macroalgal density. Our results show that mesograzers can control proliferation of epiphytic algae, that top-down and bottom-up forcing are temporally variable, and that the presence of macroalgae can strengthen top-down control of epiphytic algae, potentially contributing to eelgrass persistence.

","language":"English","publisher":"Ecological Society of America","publisherLocation":"Ithaca, NY","doi":"10.1890/12-0156.1","usgsCitation":"Whalen, M.A., Duffy, J.E., and Grace, J.B., 2013, Temporal shifts in top-down vs. bottom-up control of epiphytic algae in a seagrass ecosystem: Ecology, v. 94, no. 2, p. 510-520, https://doi.org/10.1890/12-0156.1.","productDescription":"11 p.","startPage":"510","endPage":"520","ipdsId":"IP-035382","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":487244,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarworks.wm.edu/vimsarticles/1732","text":"External Repository"},{"id":364455,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515d4161e4b0803bd2eec4fb","contributors":{"authors":[{"text":"Whalen, Matthew A.","contributorId":94180,"corporation":false,"usgs":true,"family":"Whalen","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":517643,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duffy, J. Emmett","contributorId":78186,"corporation":false,"usgs":true,"family":"Duffy","given":"J.","email":"","middleInitial":"Emmett","affiliations":[],"preferred":false,"id":517642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":517641,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70047359,"text":"70047359 - 2013 - Representing the acquisition and use of energy by individuals in agent-based models of animal populations","interactions":[],"lastModifiedDate":"2013-08-01T15:57:24","indexId":"70047359","displayToPublicDate":"2013-02-01T15:53:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Representing the acquisition and use of energy by individuals in agent-based models of animal populations","docAbstract":"1. Agent-based models (ABMs) are widely used to predict how populations respond to changing environments. As the availability of food varies in space and time, individuals should have their own energy budgets, but there is no consensus as to how these should be modelled. Here, we use knowledge of physiological ecology to identify major issues confronting the modeller and to make recommendations about how energy budgets for use in ABMs should be constructed.\n
\n
\n2. Our proposal is that modelled animals forage as necessary to supply their energy needs for maintenance, growth and reproduction. If there is sufficient energy intake, an animal allocates the energy obtained in the order: maintenance, growth, reproduction, energy storage, until its energy stores reach an optimal level. If there is a shortfall, the priorities for maintenance and growth/reproduction remain the same until reserves fall to a critical threshold below which all are allocated to maintenance. Rates of ingestion and allocation depend on body mass and temperature. We make suggestions for how each of these processes should be modelled mathematically.\n
\n
\n3. Mortality rates vary with body mass and temperature according to known relationships, and these can be used to obtain estimates of background mortality rate.\n
\n
\n4. If parameter values cannot be obtained directly, then values may provisionally be obtained by parameter borrowing, pattern-oriented modelling, artificial evolution or from allometric equations.\n
\n
\n5. The development of ABMs incorporating individual energy budgets is essential for realistic modelling of populations affected by food availability. Such ABMs are already being used to guide conservation planning of nature reserves and shell fisheries, to assess environmental impacts of building proposals including wind farms and highways and to assess the effects on nontarget organisms of chemicals for the control of agricultural pests.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Methods in Ecology and Evolution","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210x.12002","usgsCitation":"Sibly, R.M., Grimm, V., Martin, B.T., Johnston, A., Kulakowska, K., Topping, C.J., Calow, P., Nabe-Nielsen, J., Thorbek, P., and DeAngelis, D., 2013, Representing the acquisition and use of energy by individuals in agent-based models of animal populations: Methods in Ecology and Evolution, v. 4, no. 2, p. 151-161, https://doi.org/10.1111/2041-210x.12002.","productDescription":"11 p.","startPage":"151","endPage":"161","numberOfPages":"11","ipdsId":"IP-037071","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":473955,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/2041-210x.12002","text":"External Repository"},{"id":275897,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275896,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/2041-210x.12002"}],"volume":"4","issue":"2","noUsgsAuthors":false,"publicationDate":"2012-11-16","publicationStatus":"PW","scienceBaseUri":"51fbca7fe4b04b00e3d890d1","contributors":{"authors":[{"text":"Sibly, Richard M.","contributorId":104383,"corporation":false,"usgs":true,"family":"Sibly","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":481827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grimm, Volker","contributorId":89656,"corporation":false,"usgs":true,"family":"Grimm","given":"Volker","email":"","affiliations":[],"preferred":false,"id":481826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Benjamin T.","contributorId":13122,"corporation":false,"usgs":true,"family":"Martin","given":"Benjamin","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":481819,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnston, Alice","contributorId":32438,"corporation":false,"usgs":true,"family":"Johnston","given":"Alice","email":"","affiliations":[],"preferred":false,"id":481821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kulakowska, Katarzyna","contributorId":37237,"corporation":false,"usgs":true,"family":"Kulakowska","given":"Katarzyna","email":"","affiliations":[],"preferred":false,"id":481823,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Topping, Christopher J.","contributorId":25851,"corporation":false,"usgs":true,"family":"Topping","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":481820,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Calow, Peter","contributorId":76215,"corporation":false,"usgs":true,"family":"Calow","given":"Peter","email":"","affiliations":[],"preferred":false,"id":481824,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nabe-Nielsen, Jacob","contributorId":12767,"corporation":false,"usgs":true,"family":"Nabe-Nielsen","given":"Jacob","email":"","affiliations":[],"preferred":false,"id":481818,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thorbek, Pernille","contributorId":35058,"corporation":false,"usgs":true,"family":"Thorbek","given":"Pernille","email":"","affiliations":[],"preferred":false,"id":481822,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":88015,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald L.","affiliations":[],"preferred":false,"id":481825,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70047360,"text":"70047360 - 2013 - Trait contributions to fish community assembly emerge from trophicinteractions in an individual-based model","interactions":[],"lastModifiedDate":"2013-08-01T15:50:16","indexId":"70047360","displayToPublicDate":"2013-02-01T15:43:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Trait contributions to fish community assembly emerge from trophicinteractions in an individual-based model","docAbstract":"Community ecology seeks to understand and predict the characteristics of communities that can develop under different environmental conditions, but most theory has been built on analytical models that are limited in the diversity of species traits that can be considered simultaneously. We address that limitation with an individual-based model to simulate assembly of fish communities characterized by life history and trophic interactions with multiple physiological tradeoffs as constraints on species performance. Simulation experiments were carried out to evaluate the distribution of 6 life history and 4 feeding traits along gradients of resource productivity and prey accessibility. These experiments revealed that traits differ greatly in importance for species sorting along the gradients. Body growth rate emerged as a key factor distinguishing community types and defining patterns of community stability and coexistence, followed by egg size and maximum body size. Dominance by fast-growing, relatively large, and fecund species occurred more frequently in cases where functional responses were saturated (i.e. high productivity and/or prey accessibility). Such dominance was associated with large biomass fluctuations and priority effects, which prevented richness from increasing with productivity and may have limited selection on secondary traits, such as spawning strategies and relative size at maturation. Our results illustrate that the distribution of species traits and the consequences for community dynamics are intimately linked and strictly dependent on how the benefits and costs of these traits are balanced across different conditions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecological Modelling","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2012.12.003","usgsCitation":"Giacomini, H.C., DeAngelis, D., Trexler, J.C., and Petrere, M., 2013, Trait contributions to fish community assembly emerge from trophicinteractions in an individual-based model: Ecological Modelling, v. 251, p. 32-43, https://doi.org/10.1016/j.ecolmodel.2012.12.003.","productDescription":"12 p.","startPage":"32","endPage":"43","numberOfPages":"12","ipdsId":"IP-042265","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":275895,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275894,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecolmodel.2012.12.003"}],"volume":"251","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51fbca84e4b04b00e3d8912d","contributors":{"authors":[{"text":"Giacomini, Henrique C.","contributorId":62913,"corporation":false,"usgs":true,"family":"Giacomini","given":"Henrique","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":481830,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald","contributorId":30126,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","affiliations":[],"preferred":false,"id":481828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trexler, Joel C.","contributorId":36267,"corporation":false,"usgs":false,"family":"Trexler","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":481829,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Petrere, Miguel Jr.","contributorId":84655,"corporation":false,"usgs":true,"family":"Petrere","given":"Miguel","suffix":"Jr.","email":"","affiliations":[],"preferred":false,"id":481831,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70047358,"text":"70047358 - 2013 - Deep subsurface drip irrigation using coal-bed sodic water: part II. geochemistry","interactions":[],"lastModifiedDate":"2013-08-01T15:41:09","indexId":"70047358","displayToPublicDate":"2013-02-01T15:35:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Deep subsurface drip irrigation using coal-bed sodic water: part II. geochemistry","docAbstract":"Waters with low salinity and high sodium adsorption ratios (SARs) present a challenge to irrigation because they degrade soil structure and infiltration capacity. In the Powder River Basin of Wyoming, such low salinity (electrical conductivity, EC 2.1 mS cm-1) and high-SAR (54) waters are co-produced with coal-bed methane and some are used for subsurface drip irrigation(SDI). The SDI system studied mixes sulfuric acid with irrigation water and applies water year-round via drip tubing buried 92 cm deep. After six years of irrigation, SAR values between 0 and 30 cm depth (0.5-1.2) are only slightly increased over non-irrigated soils (0.1-0.5). Only 8-15% of added Na has accumulated above the drip tubing. Sodicity has increased in soil surrounding the drip tubing, and geochemical simulations show that two pathways can generate sodic conditions. In soil between 45-cm depth and the drip tubing, Na from the irrigation water accumulates as evapotranspiration concentrates solutes. SAR values >12, measured by 1:1 water-soil extracts, are caused by concentration of solutes by factors up to 13. Low-EC (<0.7 mS cm-1) is caused by rain and snowmelt flushing the soil and displacing ions in soil solution. Soil below the drip tubing experiences lower solute concentration factors (1-1.65) due to excess irrigation water and also contains relatively abundant native gypsum (2.4 ± 1.7 wt.%). Geochemical simulations show gypsum dissolution decreases soil-water SAR to <7 and increases the EC to around 4.1 mS cm-1, thus limiting negative impacts from sodicity. With sustained irrigation, however, downward flow of excess irrigation water depletes gypsum, increasing soil-water SAR to >14 and decreasing EC in soil water to 3.2 mS cm-1. Increased sodicity in the subsurface, rather than the surface, indicates that deep SDI can be a viable means of irrigating with sodic waters.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Agricultural Water Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2012.11.013","usgsCitation":"Bern, C., Breit, G.N., Healy, R.W., and Zupancic, J.W., 2013, Deep subsurface drip irrigation using coal-bed sodic water: part II. geochemistry: Agricultural Water Management, v. 118, p. 135-149, https://doi.org/10.1016/j.agwat.2012.11.013.","productDescription":"15 p.","startPage":"135","endPage":"149","numberOfPages":"15","ipdsId":"IP-036925","costCenters":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"links":[{"id":275893,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275802,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.agwat.2012.11.013"},{"id":275803,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S037837741200306X"}],"country":"United States","state":"Wyoming","otherGeospatial":"Powder River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.269986,44.690745 ], [ -106.269986,44.955734 ], [ -106.858878,44.955734 ], [ -106.858878,44.690745 ], [ -106.269986,44.690745 ] ] ] } } ] }","volume":"118","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51fbca70e4b04b00e3d88fa4","chorus":{"doi":"10.1016/j.agwat.2012.11.013","url":"http://dx.doi.org/10.1016/j.agwat.2012.11.013","publisher":"Elsevier BV","authors":"Bern Carleton R., Breit George N., Healy Richard W., Zupancic John W.","journalName":"Agricultural Water Management","publicationDate":"2/2013","auditedOn":"11/1/2014"},"contributors":{"authors":[{"text":"Bern, Carleton R.","contributorId":59325,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton R.","affiliations":[],"preferred":false,"id":481816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breit, George N. 0000-0003-2188-6798 gbreit@usgs.gov","orcid":"https://orcid.org/0000-0003-2188-6798","contributorId":1480,"corporation":false,"usgs":true,"family":"Breit","given":"George","email":"gbreit@usgs.gov","middleInitial":"N.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":481815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Healy, Richard W. 0000-0002-0224-1858 rwhealy@usgs.gov","orcid":"https://orcid.org/0000-0002-0224-1858","contributorId":658,"corporation":false,"usgs":true,"family":"Healy","given":"Richard","email":"rwhealy@usgs.gov","middleInitial":"W.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":481814,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zupancic, John W.","contributorId":73885,"corporation":false,"usgs":true,"family":"Zupancic","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":481817,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70047357,"text":"70047357 - 2013 - Deep subsurface drip irrigation using coal-bed sodic water: part I. water and solute movement","interactions":[],"lastModifiedDate":"2013-08-01T15:34:53","indexId":"70047357","displayToPublicDate":"2013-02-01T15:26:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Deep subsurface drip irrigation using coal-bed sodic water: part I. water and solute movement","docAbstract":"Water co-produced with coal-bed methane (CBM) in the semi-arid Powder River Basin of Wyoming and Montana commonly has relatively low salinity and high sodium adsorption ratios that can degrade soil permeability where used for irrigation. Nevertheless, a desire to derive beneficial use from the water and a need to dispose of large volumes of it have motivated the design of a deep subsurface drip irrigation (SDI) system capable of utilizing that water. Drip tubing is buried 92 cm deep and irrigates at a relatively constant rate year-round, while evapotranspiration by the alfalfa and grass crops grown is seasonal. We use field data from two sites and computer simulations of unsaturated flow to understand water and solute movements in the SDI fields. Combined irrigation and precipitation exceed potential evapotranspiration by 300-480 mm annually. Initially, excess water contributes to increased storage in the unsaturated zone, and then drainage causes cyclical rises in the water table beneath the fields. Native chloride and nitrate below 200 cm depth are leached by the drainage. Some CBM water moves upward from the drip tubing, drawn by drier conditions above. Chloride from CBM water accumulates there as root uptake removes the water. Year over year accumulations indicated by computer simulations illustrate that infiltration of precipitation water from the surface only partially leaches such accumulations away. Field data show that 7% and 27% of added chloride has accumulated above the drip tubing in an alfalfa and grass field, respectively, following 6 years of irrigation. Maximum chloride concentrations in the alfalfa field are around 45 cm depth but reach the surface in parts of the grass field, illustrating differences driven by crop physiology. Deep SDI offers a means of utilizing marginal quality irrigation waters and managing the accumulation of their associated solutes in the crop rooting zone.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Agricultural Water Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2012.11.014","usgsCitation":"Bern, C., Breit, G.N., Healy, R.W., Zupancic, J.W., and Hammack, R., 2013, Deep subsurface drip irrigation using coal-bed sodic water: part I. water and solute movement: Agricultural Water Management, v. 118, p. 122-134, https://doi.org/10.1016/j.agwat.2012.11.014.","productDescription":"13 p.","startPage":"122","endPage":"134","numberOfPages":"13","ipdsId":"IP-036926","costCenters":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"links":[{"id":275891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275800,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.agwat.2012.11.014"},{"id":275801,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0378377412003071"}],"country":"United States","state":"Wyoming","otherGeospatial":"Powder River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.858878,44.690745 ], [ -106.858878,44.955734 ], [ -106.269986,44.955734 ], [ -106.269986,44.690745 ], [ -106.858878,44.690745 ] ] ] } } ] }","volume":"118","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51fbca70e4b04b00e3d88fa0","chorus":{"doi":"10.1016/j.agwat.2012.11.014","url":"http://dx.doi.org/10.1016/j.agwat.2012.11.014","publisher":"Elsevier BV","authors":"Bern Carleton R., Breit George N., Healy Richard W., Zupancic John W., Hammack Richard","journalName":"Agricultural Water Management","publicationDate":"2/2013","auditedOn":"11/1/2014"},"contributors":{"authors":[{"text":"Bern, Carleton R.","contributorId":59325,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton R.","affiliations":[],"preferred":false,"id":481812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breit, George N. 0000-0003-2188-6798 gbreit@usgs.gov","orcid":"https://orcid.org/0000-0003-2188-6798","contributorId":1480,"corporation":false,"usgs":true,"family":"Breit","given":"George","email":"gbreit@usgs.gov","middleInitial":"N.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":481810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Healy, Richard W. 0000-0002-0224-1858 rwhealy@usgs.gov","orcid":"https://orcid.org/0000-0002-0224-1858","contributorId":658,"corporation":false,"usgs":true,"family":"Healy","given":"Richard","email":"rwhealy@usgs.gov","middleInitial":"W.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":481809,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zupancic, John W.","contributorId":73885,"corporation":false,"usgs":true,"family":"Zupancic","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":481813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammack, Richard","contributorId":44449,"corporation":false,"usgs":true,"family":"Hammack","given":"Richard","affiliations":[],"preferred":false,"id":481811,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70048251,"text":"70048251 - 2013 - Summary of 2012 reconnaissance field studies related to the petroleum geology of the Nenana Basin, interior Alaska","interactions":[],"lastModifiedDate":"2023-06-05T15:31:45.308232","indexId":"70048251","displayToPublicDate":"2013-02-01T14:48:37","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":239,"text":"Alaska Division of Geological & Geophysical Surveys Preliminary Interpretive Report","active":false,"publicationSubtype":{"id":4}},"seriesNumber":"2013-2","title":"Summary of 2012 reconnaissance field studies related to the petroleum geology of the Nenana Basin, interior Alaska","docAbstract":"

The Alaska Division of Geological & Geophysical Surveys (DGGS) recently initiated a multi-year review of the hydrocarbon potential of frontier sedimentary basins in Alaska (Swenson and others, 2012). In collaboration with the Alaska \nDivision of Oil & Gas and the U.S. Geological Survey we conducted reconnaissance field studies in two basins with recognized \nnatural gas potential—the Susitna basin and the Nenana basin (LePain and others, 2012). This paper summarizes our initial \nwork on the Nenana basin; a brief summary of our work in the Susitna basin can be found in Gillis and others (in press).

\n
\n

During early May 2012, we conducted ten days of helicopter-supported fieldwork and reconnaissance sampling along \nthe northern Alaska Range foothills and Yukon–Tanana upland near Fairbanks (fig. 1). The goal of this work was to improve \nour understanding of the geologic development of the Nenana basin and to collect a suite of samples to better evaluate \nhydrocarbon potential. Most laboratory analyses have not yet been completed, so this preliminary report serves as a summary of field data and sets the framework for future, more comprehensive analysis to be presented in later publications.

","language":"English","publisher":"Alaska Division of Geological and Geophysical Surveys","publisherLocation":"Fairbanks, AK","usgsCitation":"Wartes, M.A., Gillis, R., Herriott, T., Stanley, R.G., Helmold, K.P., Peterson, C.S., and Benowitz, J.A., 2013, Summary of 2012 reconnaissance field studies related to the petroleum geology of the Nenana Basin, interior Alaska: Alaska Division of Geological & Geophysical Surveys Preliminary Interpretive Report 2013-2, 13 p.","productDescription":"13 p.","numberOfPages":"17","ipdsId":"IP-043902","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":279288,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":277832,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.dggs.dnr.state.ak.us/pubs/id/24880"}],"country":"United States","state":"Alaska","otherGeospatial":"Nenana Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -151.96,62.49 ], [ -151.96,66.0 ], [ -143.25,66.0 ], [ -143.25,62.49 ], [ -151.96,62.49 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7845e4b0abf75cf2d009","contributors":{"authors":[{"text":"Wartes, Marwan A.","contributorId":47476,"corporation":false,"usgs":true,"family":"Wartes","given":"Marwan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":484158,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gillis, Robert J.","contributorId":69438,"corporation":false,"usgs":true,"family":"Gillis","given":"Robert J.","affiliations":[],"preferred":false,"id":484161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herriott, Trystan M.","contributorId":68845,"corporation":false,"usgs":true,"family":"Herriott","given":"Trystan M.","affiliations":[],"preferred":false,"id":484160,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanley, Richard G. 0000-0001-6192-8783 rstanley@usgs.gov","orcid":"https://orcid.org/0000-0001-6192-8783","contributorId":1832,"corporation":false,"usgs":true,"family":"Stanley","given":"Richard","email":"rstanley@usgs.gov","middleInitial":"G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":484156,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Helmold, Kenneth P.","contributorId":69456,"corporation":false,"usgs":true,"family":"Helmold","given":"Kenneth","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":484162,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peterson, C. Shaun","contributorId":54100,"corporation":false,"usgs":true,"family":"Peterson","given":"C.","email":"","middleInitial":"Shaun","affiliations":[],"preferred":false,"id":484159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Benowitz, Jeffrey A.","contributorId":11928,"corporation":false,"usgs":true,"family":"Benowitz","given":"Jeffrey","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":484157,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70199859,"text":"70199859 - 2013 - Modeling plant species distributions under future climates: how fine scale do climate projections need to be?","interactions":[],"lastModifiedDate":"2018-10-01T14:47:22","indexId":"70199859","displayToPublicDate":"2013-02-01T14:46:36","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling plant species distributions under future climates: how fine scale do climate projections need to be?","docAbstract":"

Recent studies suggest that species distribution models (SDMs) based on fine‐scale climate data may provide markedly different estimates of climate‐change impacts than coarse‐scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse‐scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000‐fold range of spatial scales (0.008–16 km2). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate‐data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine‐ and coarse‐scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.

","language":"English","publisher":"Wiley","doi":"10.1111/gcb.12051","usgsCitation":"Franklin, J., Davis, F.W., Ikegami, M., Syphard, A.D., Flint, L.E., Flint, A.L., and Hannah, L., 2013, Modeling plant species distributions under future climates: how fine scale do climate projections need to be?: Global Change Biology, v. 19, no. 2, p. 473-483, https://doi.org/10.1111/gcb.12051.","productDescription":"11 p.","startPage":"473","endPage":"483","ipdsId":"IP-041557","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":473956,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/75k42636","text":"External Repository"},{"id":357976,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746936,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hannah, Lee","contributorId":208392,"corporation":false,"usgs":false,"family":"Hannah","given":"Lee","email":"","affiliations":[{"id":16938,"text":"Conservation International","active":true,"usgs":false}],"preferred":false,"id":746941,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70048253,"text":"70048253 - 2013 - Hydrocarbon-bearing sandstone in the Upper Jurassic Naknek Formation on the south shore of Kamishak Bay","interactions":[],"lastModifiedDate":"2023-06-05T15:33:47.539409","indexId":"70048253","displayToPublicDate":"2013-02-01T14:40:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":102,"text":"Alaska Division of Geological & Geophysical Surveys Preliminary Interpretive Report","active":false,"publicationSubtype":{"id":2}},"seriesNumber":"2013-1E","title":"Hydrocarbon-bearing sandstone in the Upper Jurassic Naknek Formation on the south shore of Kamishak Bay","docAbstract":"The presence of an active petroleum system in Kamishak Bay is demonstrated by an outcrop of hydrocarbon-bearing \nsandstone in the Upper Jurassic Naknek Formation near the south shore of the bay (fig. 1). The outcrop is about 140 km \nsouthwest of Homer on a small, unnamed island near the mouth of the Douglas River (fig. 17). The existence of this outcrop was kindly reported to us by Les Magoon (U.S. Geological Survey, emeritus), who also provided a topographic map \nshowing its exact position. The outcrop was mentioned very briefly in publications by Magoon and others (1975, p. 19) \nand by Lyle and Morehouse (1977, p. E-1), but to our knowledge there are no detailed descriptions of this outcrop or its \nhydrocarbons in the published scientific literature.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Overview of 2012 field studies: Upper Alaska Peninsula and west side of lower Cook Inlet, Alaska","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"Alaska Division of Geological and Geophysical Surveys","usgsCitation":"Stanley, R.G., Herriott, T., Helmold, K.P., Gillis, R., and Lillis, P.G., 2013, Hydrocarbon-bearing sandstone in the Upper Jurassic Naknek Formation on the south shore of Kamishak Bay: Alaska Division of Geological & Geophysical Surveys Preliminary Interpretive Report 2013-1E, 5 p.","productDescription":"5 p.","startPage":"19","endPage":"23","numberOfPages":"5","ipdsId":"IP-042892","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":287702,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280711,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.dggs.alaska.gov/pubs/id/24848"}],"country":"United States","state":"Alaska","otherGeospatial":"Douglas River, Kamishak Bay","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -156.0,58.0 ], [ -156.0,61.0 ], [ -151.5,61.0 ], [ -151.5,58.0 ], [ -156.0,58.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53870569e4b0aa26cd7b53ac","contributors":{"authors":[{"text":"Stanley, Richard G. 0000-0001-6192-8783 rstanley@usgs.gov","orcid":"https://orcid.org/0000-0001-6192-8783","contributorId":1832,"corporation":false,"usgs":true,"family":"Stanley","given":"Richard","email":"rstanley@usgs.gov","middleInitial":"G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":484169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herriott, Trystan M.","contributorId":68845,"corporation":false,"usgs":true,"family":"Herriott","given":"Trystan M.","affiliations":[],"preferred":false,"id":484170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Helmold, Kenneth P.","contributorId":69456,"corporation":false,"usgs":true,"family":"Helmold","given":"Kenneth","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":484172,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gillis, Robert J.","contributorId":69438,"corporation":false,"usgs":true,"family":"Gillis","given":"Robert J.","affiliations":[],"preferred":false,"id":484171,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lillis, Paul G. 0000-0002-7508-1699 plillis@usgs.gov","orcid":"https://orcid.org/0000-0002-7508-1699","contributorId":1817,"corporation":false,"usgs":true,"family":"Lillis","given":"Paul","email":"plillis@usgs.gov","middleInitial":"G.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":484168,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70048252,"text":"70048252 - 2013 - Reconnaissance studies of potential petroleum source rocks in the Middle Jurassic Tuxedni Group near Red Glacier, eastern slope of Iliamna Volcano","interactions":[],"lastModifiedDate":"2023-06-05T15:36:48.77785","indexId":"70048252","displayToPublicDate":"2013-02-01T14:38:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":239,"text":"Alaska Division of Geological & Geophysical Surveys Preliminary Interpretive Report","active":false,"publicationSubtype":{"id":4}},"seriesNumber":"2013-1B","title":"Reconnaissance studies of potential petroleum source rocks in the Middle Jurassic Tuxedni Group near Red Glacier, eastern slope of Iliamna Volcano","docAbstract":"Previous geological and organic geochemical studies have concluded that organic-rich marine shale in the Middle Jurassic Tuxedni Group is the principal source rock of oil and associated gas in Cook Inlet (Magoon and Anders, 1992; Magoon, 1994; Lillis and Stanley, 2011; LePain and others, 2012; LePain and others, submitted). During May 2009 helicopter-assisted field studies, 19 samples of dark-colored, fine-grained rocks were collected from exposures of the Red Glacier Formation of the Tuxedni Group near Red Glacier, about 70 km west of Ninilchik on the eastern flank of Iliamna Volcano (figs. 1 and 3). The rock samples were submitted to a commercial laboratory for analysis by Rock-Eval pyrolysis and to the U.S. Geological Survey organic geochemical laboratory in Denver, Colorado, for analysis of vitrinite reflectance. The results show that values of vitrinite reflectance (percent Ro) in our samples average about 2 percent, much higher than the oil window range of 0.6–1.3 percent (Johnsson and others, 1993). The high vitrinite reflectance values indicate that the rock samples experienced significant heating and furthermore suggest that these rocks may have generated oil and gas in the past but no longer have any hydrocarbon source potential. The high thermal maturity of the rock samples may have resulted from (1) the thermaleffects of igneous activity (including intrusion by igneous rocks), (2) deep burial beneath Jurassic, Cretaceous, and Tertiary strata that were subsequently removed by uplift and erosion, or (3) the combined effects of igneous activity and burial.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Overview of 2012 field studies: Upper Alaska Peninsula and west side of lower Cook Inlet, Alaska (Alaska Division of Geological & Geophysical Surveys Preliminary Interpretive Report 2013-1)","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Alaska Division of Geological and Geophysical Surveys","usgsCitation":"Stanley, R.G., Herriott, T., LePain, D., Helmold, K.P., and Peterson, C.S., 2013, Reconnaissance studies of potential petroleum source rocks in the Middle Jurassic Tuxedni Group near Red Glacier, eastern slope of Iliamna Volcano: Alaska Division of Geological & Geophysical Surveys Preliminary Interpretive Report 2013-1B, 5 p.","productDescription":"5 p.","startPage":"5","endPage":"9","ipdsId":"IP-042894","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":279275,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":277833,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.dggs.dnr.state.ak.us/pubs/id/24824"}],"country":"United States","state":"Alaska","otherGeospatial":"Iliamna Volcano, Red Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -153.37585474005056,\n 60.326871087572016\n ],\n [\n -153.37585474005056,\n 59.76667686431813\n ],\n [\n -152.56184991356824,\n 59.76667686431813\n ],\n [\n -152.56184991356824,\n 60.326871087572016\n ],\n [\n -153.37585474005056,\n 60.326871087572016\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7804e4b0abf75cf2c7c0","contributors":{"authors":[{"text":"Stanley, Richard G. 0000-0001-6192-8783 rstanley@usgs.gov","orcid":"https://orcid.org/0000-0001-6192-8783","contributorId":1832,"corporation":false,"usgs":true,"family":"Stanley","given":"Richard","email":"rstanley@usgs.gov","middleInitial":"G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":484163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herriott, Trystan M.","contributorId":68845,"corporation":false,"usgs":true,"family":"Herriott","given":"Trystan M.","affiliations":[],"preferred":false,"id":484165,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LePain, David L.","contributorId":105209,"corporation":false,"usgs":true,"family":"LePain","given":"David L.","affiliations":[],"preferred":false,"id":484167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Helmold, Kenneth P.","contributorId":69456,"corporation":false,"usgs":true,"family":"Helmold","given":"Kenneth","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":484166,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peterson, C. 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