Global sea level is rising and may accelerate with continued fossil fuel consumption from industrial and population growth. In 2012, the U.S. Geological Survey conducted more than 30 training and feedback sessions with Federal, State, and nongovernmental organization (NGO) coastal managers and planners across the northern Gulf of Mexico coast to evaluate user needs, potential benefits, current scientific understanding, and utilization of resource aids and modeling tools focused on sea-level rise. In response to the findings from the sessions, this sea-level rise modeling handbook has been designed as a guide to the science and simulation models for understanding the dynamics and impacts of sea-level rise on coastal ecosystems. The review herein of decision-support tools and predictive models was compiled from the training sessions, from online research, and from publications. The purpose of this guide is to describe and categorize the suite of data, methods, and models and their design, structure, and application for hindcasting and forecasting the potential impacts of sea-level rise in coastal ecosystems. The data and models cover a broad spectrum of disciplines involving different designs and scales of spatial and temporal complexity for predicting environmental change and ecosystem response. These data and models have not heretofore been synthesized, nor have appraisals been made of their utility or limitations. Some models are demonstration tools for non-experts, whereas others require more expert capacity to apply for any given park, refuge, or regional application. A simplified tabular context has been developed to list and contrast a host of decision-support tools and models from the ecological, geological, and hydrological perspectives. Criteria were established to distinguish the source, scale, and quality of information input and geographic datasets; physical and biological constraints and relations; datum characteristics of water and land components; utility options for setting sea-level rise and climate change scenarios; and ease or difficulty of storing, displaying, or interpreting model output. Coastal land managers, engineers, and scientists can benefit from this synthesis of tools and models that have been developed for projecting causes and consequences of sea-level change on the landscape and seascape.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1815","collaboration":"Prepared in cooperation with the Department of the Interior Southeast Climate Science Center","usgsCitation":"Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p.,\nhttps://dx.doi.org/10.3133/pp1815.","productDescription":"ix, 76 p.","numberOfPages":"89","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-045332","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":307080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1815/pp1815.pdf","text":"Report","size":"7.47","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1815"},{"id":307079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1815/coverthb.jpg"}],"contact":"Director, National Wetlands Research Center
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700 Cajundome Blvd.
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http://www.nwrc.usgs.gov/
MC1 was designed for application to large regions that include a wide range in elevation and topography, thereby encompassing a broad range in climates and vegetation types. The authors applied the dynamic global vegetation model MC1 to Wind Cave National Park (WCNP) in the southern Black Hills of South Dakota, USA, on the ecotone between ponderosa pine forest to the northwest and mixed-grass prairie to the southeast. They calibrated MC1 to simulate adequate fire effects in the warmer southeastern parts of the park to ensure grasslands there, while allowing forests to grow to the northwest, and then simulated future vegetation with climate projections from three GCMs. The results suggest that fire frequency, as affected by climate and/or human intervention, may be more important than the direct effects of climate in determining the distribution of ponderosa pine in the Black Hills region, both historically and in the future.
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To analyze these data, we developed a multispecies N-mixture model that allowed estimation of both positive and negative correlations between abundances of different species while also estimating the effects of habitat and the effects of errors in detection of each species. Using a combination of single- and multispecies N-mixture modeling, we examined for each species whether our measures of habitat were sufficient to account for the variation in encounter histories of individual birds or whether other habitat variables or interactions with other species needed to be considered. In the community that we studied, habitat appeared to be more influential than biological interactions in determining the distribution and abundance of most avian species. Our results lend support to the hypothesis that abundances of forest specialists are negatively affected by forest fragmentation. Our results also suggest that many species were associated with particular types of vegetation as measured by structural attributes of the forests. The abundances of 6 of the 73 species observed in our study were strongly correlated. These species included large birds (American Crow and Red-winged Blackbird) that forage on the ground in open habitats and small birds (Red-eyed Vireo, House Wren, Hooded Warbler, and Prairie Warbler) that are associated with dense shrub cover. Species abundances were positively correlated within each size group and negatively correlated between groups. Except for the American Crow, which preys on eggs and nestlings of small song birds, none of the other 5 species is known to display direct interactions, so we suspect that the correlations may have been associated with species-specific responses to habitat components not adequately measured by our covariates.
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A.","contributorId":146730,"corporation":false,"usgs":false,"family":"Askins","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":16738,"text":"Biology Department, Connecticut College, New London, CT","active":true,"usgs":false}],"preferred":false,"id":568807,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155232,"text":"sir20155107 - 2015 - Flood-inundation maps for White River at Petersburg, Indiana","interactions":[],"lastModifiedDate":"2015-08-24T12:33:36","indexId":"sir20155107","displayToPublicDate":"2015-08-20T09:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5107","title":"Flood-inundation maps for White River at Petersburg, Indiana","docAbstract":"Digital flood-inundation maps for a 7.7-mile reach of the White River at Petersburg, Indiana, were created by the U.S. Geological Survey (USGS), in cooperation with the Indiana Office of Community and Rural Affairs. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at White River at Petersburg, Ind. (03374000). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (PTRI3).
\nFlood profiles were computed for the White River at Petersburg reach by means of a one-dimensional step-backwater model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the most current stage-discharge relations at the White River at Petersburg, Ind., and the White River above Petersburg, Ind. (03373890), gages. The calibrated hydraulic model was then used to compute 18 water-surface profiles for flood stages at approximately 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a geographic information system digital elevation model to delineate the area flooded at each water level.
\nThe availability of these maps along with Internet information regarding current stage from the USGS streamgage at White River at Petersburg, Ind., and forecasted stream stages from the NWS provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155107","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Fowler, K.K., 2015, Flood-inundation maps for the White River at Petersburg, Indiana: U.S. Geological Survey Scientific Investigations Report 2015–5107, 11 p., https://dx.doi.org/10.3133/sir20155107.","productDescription":"Report: iv, 11 p.: Metadata: Readme: Spatial Data","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-063334","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":306743,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5107/coverthb.jpg"},{"id":306746,"rank":4,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2015/5107/downloads/shapefile/shapefile.zip","text":"Shapefiles","size":"4.51 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015-5107"},{"id":306744,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5107/sir20155107.pdf","text":"Report","size":"6.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5107"},{"id":306747,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5107/downloads/metadata_depth-grids.txt","text":"Metadata Depth Grids","size":"15.4 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2015-5107"},{"id":306745,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2015/5107/downloads/depth_grids/depth_grids.zip","text":"Depth Grids","size":"88.8 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015-5107"},{"id":306748,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5107/downloads/metadata_shapefiles.txt","text":"Metadata Shapefiles","size":"15.9 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2015-5107"},{"id":306809,"rank":7,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2015/5107/downloads/readme.pdf","text":"Information about the report - readme file","size":"26.3 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5107"}],"country":"United States","state":"Indiana","city":"Petersburg","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.34010696411133,\n 38.50666906026307\n ],\n [\n -87.34010696411133,\n 38.54198948702892\n ],\n [\n -87.22217559814453,\n 38.54198948702892\n ],\n [\n -87.22217559814453,\n 38.50666906026307\n ],\n [\n -87.34010696411133,\n 38.50666906026307\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
5957 Lakeside Blvd
Indianapolis, IN 46278
http://in.water.usgs.gov/
Digital flood-inundation maps for a 12.4-mile reach of Big Creek that extends from 260 feet above the McGinnis Ferry Road bridge to the U.S. Geological Survey (USGS) streamgage at Big Creek below Hog Wallow Creek at Roswell, Georgia (02335757), were developed by the USGS in cooperation with the cities of Alpharetta and Roswell, Georgia. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Big Creek near Alpharetta, Georgia (02335700). Real-time stage information from this USGS streamgage may be obtained at http://waterdata.usgs.gov/ and can be used in conjunction with these maps to estimate near real-time areas of inundation. The National Weather Service (NWS) is incorporating results from this study into the Advanced Hydrologic Prediction Service (AHPS) flood-warning system http://water.weather.gov/ahps/). The NWS forecasts flood hydrographs for many streams where the USGS operates streamgages and provides flow data. The forecasted peak-stage information for the USGS streamgage at Big Creek near Alpharetta (02335700), available through the AHPS Web site, may be used in conjunction with the maps developed for this study to show predicted areas of flood inundation.
\nA one-dimensional step-backwater model was developed using the U.S. Army Corps of Engineers HEC–RAS software for Big Creek and was used to compute flood profiles for a 12.4-mile reach of Big Creek. The model was calibrated using the most current (2015) stage-discharge relations at two USGS streamgages on Big Creek: Big Creek near Alpharetta (02335700) and Big Creek below Hog Wallow Creek at Roswell (02335757). The hydraulic model was then used to simulate 19 water-surface profiles at 0.5-foot intervals at the Big Creek near Alpharetta streamgage. The profiles ranged from just above bankfull stage (6.0 feet) to approximately 1.95 feet above the highest recorded water level at the Alpharetta streamgage site (15.0 feet). The simulated water-surface profiles were then combined with a geographic information system digital elevation model—derived from light detection and ranging data having a 3.0-foot horizontal resolution—to delineate the area flooded at each 0.5-foot interval of stream stage.
\nThe availability of these maps, when combined with real-time stage information from USGS streamgages and forecasted stream stage from the NWS, provides emergency management personnel and residents with critical information during flood-response activities such as evacuations and road closures, in addition to post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3338","collaboration":"Prepared in cooperation with the cities of Alpharetta, and Roswell, Georgia","usgsCitation":"Musser, J.W., 2015, Flood-inundation maps for Big Creek from the McGinnis Ferry Road bridge to the confluence of Hog Wallow, Alpharetta and Roswell, Georgia: U.S. Geological Survey Scientific Investigations Map 3338, 19 sheets, 10-p. pamphlet, https://dx.doi.org/10.3133/sim3338.","productDescription":"Report: vi, 10 p.; 19 Sheets: 29.0 x 30.0 inches; Metadata; Raw Data","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065512","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":306705,"rank":21,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3338/pdf/sim3338sheet19.pdf","text":"Sheet19 - Gage height of 15.0 feet and an elevation of 975.6 feet at streamgage 02335700","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3338"},{"id":306704,"rank":20,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3338/pdf//sim3338sheet18.pdf","text":"Sheet18 - 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U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
Many efforts have been made to quickly estimate the maximum run-up height of tsunamis associated with large earthquakes. This is a difficult task, because of the time it takes to construct a tsunami model using real time data from the source. It is possible to construct a database of potential seismic sources and their corresponding tsunami a priori.However, such models are generally based on uniform slip distributions and thus oversimplify the knowledge of the earthquake source. Here, we show how to predict tsunami run-up from any seismic source model using an analytic solution, that was specifically designed for subduction zones with a well defined geometry, i.e., Chile, Japan, Nicaragua, Alaska. The main idea of this work is to provide a tool for emergency response, trading off accuracy for speed. The solutions we present for large earthquakes appear promising. Here, run-up models are computed for: The 1992 Mw 7.7 Nicaragua Earthquake, the 2001 Mw 8.4 Perú Earthquake, the 2003Mw 8.3 Hokkaido Earthquake, the 2007 Mw 8.1 Perú Earthquake, the 2010 Mw 8.8 Maule Earthquake, the 2011 Mw 9.0 Tohoku Earthquake and the recent 2014 Mw 8.2 Iquique Earthquake. The maximum run-up estimations are consistent with measurements made inland after each event, with a peak of 9 m for Nicaragua, 8 m for Perú (2001), 32 m for Maule, 41 m for Tohoku, and 4.1 m for Iquique. Considering recent advances made in the analysis of real time GPS data and the ability to rapidly resolve the finiteness of a large earthquake close to existing GPS networks, it will be possible in the near future to perform these calculations within the first minutes after the occurrence of similar events. Thus, such calculations will provide faster run-up information than is available from existing uniform-slip seismic source databases or past events of pre-modeled seismic sources.
","language":"English","publisher":"Wiley","doi":"10.1002/2015JB012218","usgsCitation":"Riqueime, S., Fuentes, M., Hayes, G.P., and Campos, J., 2015, A rapid estimation of near field tsunami run-up: Journal of Geophysical Research, v. 120, no. 9, p. 6487-6500, https://doi.org/10.1002/2015JB012218.","productDescription":"14 p.","startPage":"6487","endPage":"6500","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068627","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471867,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012218","text":"Publisher Index Page"},{"id":308323,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-26","publicationStatus":"PW","scienceBaseUri":"56012a39e4b03bc34f5443ee","contributors":{"authors":[{"text":"Riqueime, Sebastian","contributorId":147554,"corporation":false,"usgs":false,"family":"Riqueime","given":"Sebastian","email":"","affiliations":[{"id":16869,"text":"National Seismological Center (CSN), University of Chile, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":571999,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuentes, Mauricio","contributorId":147555,"corporation":false,"usgs":false,"family":"Fuentes","given":"Mauricio","email":"","affiliations":[{"id":16870,"text":"Department of Geophysics, University of Chile, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":572000,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":572001,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campos, Jamie","contributorId":147557,"corporation":false,"usgs":false,"family":"Campos","given":"Jamie","email":"","affiliations":[{"id":16870,"text":"Department of Geophysics, University of Chile, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":572002,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70156263,"text":"70156263 - 2015 - Key seabird areas in southern New England identified using a community occupancy model","interactions":[],"lastModifiedDate":"2022-11-10T16:25:15.899818","indexId":"70156263","displayToPublicDate":"2015-08-18T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Key seabird areas in southern New England identified using a community occupancy model","docAbstract":"Seabirds are of conservation concern, and as new potential risks to seabirds are arising, the need to provide unbiased estimates of species’ distributions is growing. We applied community occupancy models to detection/non-detection data collected from repeated aerial strip-transect surveys conducted in 2 large study plots off southern New England, USA; one off the coast of Rhode Island and the other in Nantucket Sound. A total of 17 seabird species were observed at least once in each study plot. We found that detection varied by survey date and effort for most species and the average detection probability across species was less than 0.4. We estimated the influence of water depth, sea surface temperature, and sea surface chl a concentration on species-specific occupancy. Diving species showed large differences between the 2 study plots in their predicted winter distributions, which were largely explained by water depth acting as a stronger predictor of occupancy in Rhode Island than in Nantucket Sound. Conversely, similarities between the 2 study plots in predicted winter distributions of surface-feeding species were explained by sea surface temperature or chlorophyll a concentration acting as predictors of these species’ occupancy in both study plots. We predicted the number of species at each site using the observed data in order to detect ‘hot-spots’ of seabird diversity and use in the 2 study plots. These results provide new information on detection of species, areas of use, and relationships with environmental variables that will be valuable for biologists and planners interested in seabird conservation in the region.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps11316","usgsCitation":"O’Connell, A.F., Flanders, N.P., Gardner, B., Winiarski, K.J., Paton, P.W., and Allison, T., 2015, Key seabird areas in southern New England identified using a community occupancy model: Marine Ecology Progress Series, v. 533, p. 277-290, https://doi.org/10.3354/meps11316.","productDescription":"13 p.","startPage":"277","endPage":"290","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065754","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":489199,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/nrs_facpubs/684","text":"External Repository"},{"id":306869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts, Rhode Island","otherGeospatial":"Nantucket Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.08616827991628,\n 41.27118954497578\n ],\n [\n -70.0395161560485,\n 41.279537474410574\n ],\n [\n -69.99508556188886,\n 41.317924198169436\n ],\n [\n -70.01507932926107,\n 41.35795582003885\n ],\n [\n -69.99508556188886,\n 41.549432491630995\n ],\n [\n -69.95954108656126,\n 41.67898183236355\n ],\n [\n -70.14614958203185,\n 41.664047111649865\n ],\n [\n -70.18391558706759,\n 41.66736623791493\n ],\n [\n -70.22612465151963,\n 41.65740834581047\n ],\n [\n -70.25278300801521,\n 41.65906810143275\n ],\n [\n -70.32387195867095,\n 41.64412876095588\n ],\n [\n -70.43494844407006,\n 41.64744891381079\n ],\n [\n -70.50381586501766,\n 41.564393770796954\n ],\n [\n -70.56601869684138,\n 41.464586499997324\n ],\n [\n -70.57490481567342,\n 41.417957113748685\n ],\n [\n -70.50381586501766,\n 41.39129670826401\n ],\n [\n -70.30609972100689,\n 41.336275079067576\n ],\n [\n -70.26166912684724,\n 41.2978991610687\n ],\n [\n -70.2105739435637,\n 41.282876347251204\n ],\n [\n -70.08616827991628,\n 41.27118954497578\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.95882044294079,\n 41.367829886783426\n ],\n [\n -71.88995302199318,\n 41.23097435648074\n ],\n [\n -71.38566577828065,\n 41.33781303063924\n ],\n [\n -71.38344424857253,\n 41.42115900781667\n ],\n [\n -71.43898249127234,\n 41.45613246374154\n ],\n [\n -71.50118532309607,\n 41.44447673914905\n ],\n [\n -71.55894509550349,\n 41.39449991636312\n ],\n [\n -71.6366986352831,\n 41.40116571472586\n ],\n [\n -71.67668617002647,\n 41.37783242960529\n ],\n [\n -71.82108560104558,\n 41.34448463923664\n ],\n [\n -71.95882044294079,\n 41.367829886783426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"533","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55d44922e4b0518e35469477","contributors":{"authors":[{"text":"O’Connell, Allan F. 0000-0001-7032-7023 aoconnell@usgs.gov","orcid":"https://orcid.org/0000-0001-7032-7023","contributorId":471,"corporation":false,"usgs":true,"family":"O’Connell","given":"Allan","email":"aoconnell@usgs.gov","middleInitial":"F.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":568441,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flanders, Nicholas P.","contributorId":146614,"corporation":false,"usgs":false,"family":"Flanders","given":"Nicholas","email":"","middleInitial":"P.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":568442,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gardner, Beth","contributorId":91612,"corporation":false,"usgs":false,"family":"Gardner","given":"Beth","affiliations":[{"id":13553,"text":"University of Washington-Seattle","active":true,"usgs":false}],"preferred":false,"id":568443,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winiarski, Kristopher J.","contributorId":146615,"corporation":false,"usgs":false,"family":"Winiarski","given":"Kristopher","email":"","middleInitial":"J.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":568444,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paton, Peter W. 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We construct a 3-D dynamic rupture model of an earthquake on the Pitas Point and Lower Red Mountain faults to model low-frequency ground motion and the resulting tsunami, with a goal of elucidating the seismic and tsunami hazard in this area. Our model results in an average stress drop of 6 MPa, an average fault slip of 7.4 m, and a moment magnitude of 7.7, consistent with regional paleoseismic data. Our corresponding tsunami model uses final seafloor displacement from the rupture model as initial conditions to compute local propagation and inundation, resulting in large peak tsunami amplitudes northward and eastward due to site and path effects. Modeled inundation in the Ventura area is significantly greater than that indicated by state of California's current reference inundation line.
","language":"English","publisher":"Wiley","doi":"10.1002/2015GL064507","usgsCitation":"Kenny J. Ryan, Geist, E.L., Barall, M., and David D. 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We estimated consumption of Bythotrephes by planktivorous and benthivorous fishes, using bioenergetics and daily ration models at nearshore (18 m), intermediate (46 m), and offshore (110 m) depths along one western Lake Superior transect (April, and September-November) and two northern Lake Michigan transects (April, July, September). In Lake Superior, consumption (primarily by cisco Coregonus artedi) exceeded Bythotrephes production at all offshore sites in September-November (up to 396% of production consumed) and at the intermediate site in November (842%) with no evidence of consumption nearshore. By comparing Bythotrephes biomass following months of excessive consumption, we conservatively concluded that top-down control was evident only at the offshore site during September-October. In Lake Michigan, consumption by fishes (primarily alewife Alosa pseudoharengus) exceeded production at nearshore sites (up to 178%), but not in deeper sites (< 15%). Evidence for top-down control in the nearshore was not supported, however, as Bythotrephes never subsequently declined. Using generalized additive models, temperature, and not fish consumption, not zooplankton prey density, best explained variability in Bythotrephes biomass. The non-linear pattern revealed Bythotrephes to increase with temperature up to 16 °C, and then decline between 16 and 23 °C. We discuss how temperature likely has direct negative impacts on Bythotrephes when temperatures near 23 °C, but speculate that predation also contributes to declining biomass when temperatures exceed 16 °C.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2015.07.010","usgsCitation":"Keeler, K.M., Bunnell, D., Diana, J., Adams, J.V., Mychek-Londer, J., Warner, D.M., Yule, D., and Vinson, M., 2015, Evaluating the importance of abiotic and biotic drivers on Bythotrephes biomass in Lakes Superior and Michigan: Journal of Great Lakes Research, v. 41, no. 3, p. 150-160, https://doi.org/10.1016/j.jglr.2015.07.010.","productDescription":"11 p.","startPage":"150","endPage":"160","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067442","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":312183,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin and Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n 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2000–06","interactions":[],"lastModifiedDate":"2015-08-27T13:38:16","indexId":"sir20155073","displayToPublicDate":"2015-08-17T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5073","title":"Water-budgets and recharge-area simulations for the Spring Creek and Nittany Creek Basins and parts of the Spruce Creek Basin, Centre and Huntingdon Counties, Pennsylvania, Water Years 2000–06","docAbstract":"This report describes the results of a study by the U.S. Geological Survey in cooperation with ClearWater Conservancy and the Pennsylvania Department of Environmental Protection to develop a hydrologic model to simulate a water budget and identify areas of greater than average recharge for the Spring Creek Basin in central Pennsylvania. The model was developed to help policy makers, natural resource managers, and the public better understand and manage the water resources in the region. The Groundwater and Surface-water FLOW model (GSFLOW), which is an integration of the Precipitation-Runoff Modeling System (PRMS) and the Modular Groundwater Flow Model (MODFLOW-NWT), was used to simulate surface water and groundwater in the Spring Creek Basin for water years 2000–06. Because the groundwater and surface-water divides for the Spring Creek Basin do not coincide, the study area includes the Nittany Creek Basin and headwaters of the Spruce Creek Basin. The hydrologic model was developed by the use of a stepwise process: (1) develop and calibrate a PRMS model and steady-state MODFLOW-NWT model; (2) re-calibrate the steady-state MODFLOW-NWT model using potential recharge estimates simulated from the PRMS model, and (3) integrate the PRMS and MODFLOW-NWT models into GSFLOW. The individually calibrated PRMS and MODFLOW-NWT models were used as a starting point for the calibration of the fully coupled GSFLOW model. The GSFLOW model calibration was done by comparing observations and corresponding simulated values of streamflow from 11 streamgages and groundwater levels from 16 wells. The cumulative water budget and individual water budgets for water years 2000–06 were simulated by using GSFLOW. The largest source and sink terms are represented by precipitation and evapotranspiration, respectively. For the period simulated, a net surplus in the water budget was computed where inflows exceeded outflows by about 1.7 billion cubic feet (0.47 inches per year over the basin area); storage increased by about the same amount to balance the budget. The rate and distribution of recharge throughout the Spring Creek, Nittany Creek, and Spruce Creek Basins is variable as a result of the high degree of hydrogeologic heterogeneity and karst features. The greatest amount of recharge was simulated in the carbonate-bedrock valley, near the toe slopes of Nittany and Tussey Mountains, in the Scotia Barrens, and along the area coinciding with the Gatesburg Formation. Runoff extremes were observed for water years 2001 (dry year) and 2004 (wet year). Simulated average recharge rates (water reaching the saturated zone as defined in GSFLOW) for 2001 and 2004 were 5.4 in/yr and 22.0 in/yr, respectively. Areas where simulations show large variations in annual recharge between wet and dry years are the same areas where simulated recharge was large. Those areas where rates of groundwater recharge are much higher than average, and are capable of accepting substantially greater quantities of recharge during wet years, might be considered critical for maintaining the flow of springs, stream base flow, or the source of water to supply wells. The slopes of the Bald Eagle, Tussey, and Nittany Mountains are relatively insensitive to variations in recharge, primarily because of reduced infiltration rates and steep slopes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155073","collaboration":"Prepared in cooperation with the Clearwater Conservancy and Pennsylvania Department of Environmental Protection","usgsCitation":"Fulton, J.W., Risser, D.W., Regan, R.S., Walker, J.F., Hunt, R.J., Niswonger, R.G., Hoffman, S.A., and Markstrom, S.L., 2015, Water-budgets and recharge-area simulations for the Spring Creek and Nittany Creek Basins and parts of the Spruce Creek Basin, Centre and Huntingdon Counties, Pennsylvania, Water Years 2000–06: U.S. Geological Survey Scientific Investigations Report 2015–5073, 86 p, https://dx.doi.org/10.3133/sir20155073.","productDescription":"x, 86 p.","numberOfPages":"100","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-006529","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":306786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5073/coverthb.jpg"},{"id":306791,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5073/sir20155073.pdf","text":"Report","size":"27.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5-83"}],"country":"United States","state":"Pennsylvania","county":"Centre County, Huntingdon County","otherGeospatial":"Nittany Creek Basin, Spring Creek Basin, Spruce Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.7179718017578,\n 40.979898069620155\n ],\n [\n -77.55661010742188,\n 40.86004454780482\n ],\n [\n -77.76466369628905,\n 40.7743018636372\n ],\n [\n -77.87384033203124,\n 40.74205475883487\n ],\n [\n -77.93975830078125,\n 40.706148461723764\n ],\n [\n -78.05648803710938,\n 40.66188943992171\n ],\n [\n -78.11897277832031,\n 40.62385529380968\n ],\n [\n -78.16154479980469,\n 40.59283882963389\n ],\n [\n -78.277587890625,\n 40.643135583312805\n ],\n [\n -78.16497802734375,\n 40.730608477796636\n ],\n [\n -78.01666259765625,\n 40.82212357516945\n ],\n [\n -77.82440185546875,\n 40.9280401053324\n ],\n [\n -77.7179718017578,\n 40.979898069620155\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
http://pa.water.usgs.gov/
The U.S. Geological Survey Woods Hole Coastal and Marine Science Center has created a Data Library to organize, preserve, and make available the field, laboratory, and modeling data collected and processed by Woods Hole Coastal and Marine Science Center staff. This Data Library supports current research efforts by providing unique, historic datasets with accompanying metadata. The Woods Hole Coastal and Marine Science Center’s Data Library has custody of historic data and records that are still useful for research, and assists with preservation and distribution of marine science records and data in the course of scientific investigation and experimentation by researchers and staff at the science center.
\nThe data accession and retention policies employed by the Woods Hole Coastal and Marine Science Center Data Library are based on scientific need and the National Archives and Records Administration standards for Federal records retention. Criteria for inclusion of data and records into the Data Library, the scope of the Data Library holdings, and operating procedures for the management and running of the library are designed to support the research operations of the U.S. Geological Survey.
\nThis report explains the roles and detailed responsibilities of library and scientific staff, and provides step-by-step instructions for managing the collections of the Woods Hole Coastal and Marine Science Center Data Library.
\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151141","usgsCitation":"List, K.M., Buczkowski, B.J., McCarthy, L.P., and Orton, A.M., 2015, Collections management plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library: U.S. Geological Survey Open-File Report 2015–1141, 16 p., https://dx.doi.org/10.3133/ofr20151141.","productDescription":"vi, 16 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-060877","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":306752,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1141/ofr20151141.pdf","text":"Report","size":"1.87 MB","description":"OFR 2015-1141"},{"id":306751,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1141/coverthb.jpg"}],"contact":"
Director, Woods Hole Coastal and Marine
Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543-1598
WHSC_science_director@usgs.gov
508-548-8700 or 508-457-2200
Or visit our Web site at:
http://woodshole.er.usgs.gov/
Acknowledgments
\nAbstract
\nIntroduction to the Woods Hole Coastal and Marine Science Center Data Library
\nScope of the Collections in the Data Library
\nGeology Discipline Research Records Schedule
\nRoles and Responsibilities
\nData Library Operations
\nSummary
\nSelected References
\nDocumentation of the interacting effects of river regulation and climate on riparian vegetation has typically been limited to small segments of rivers or focused on individual plant species. We examine spatiotemporal variability in riparian vegetation for the Colorado River in Grand Canyon relative to river regulation and climate, over the five decades since completion of the upstream Glen Canyon Dam in 1963. Long-term changes along this highly modified, large segment of the river provide insights for management of similar riparian ecosystems around the world. We analyze vegetation extent based on maps and imagery from eight dates between 1965 and 2009, coupled with the instantaneous hydrograph for the entire period. Analysis confirms a net increase in vegetated area since completion of the dam. Magnitude and timing of such vegetation changes are river stage-dependent. Vegetation expansion is coincident with inundation frequency changes and is unlikely to occur for time periods when inundation frequency exceeds approximately 5%. Vegetation expansion at lower zones of the riparian area is greater during the periods with lower peak and higher base flows, while vegetation at higher zones couples with precipitation patterns and decreases during drought. Short pulses of high flow, such as the controlled floods of the Colorado River in 1996, 2004, and 2008, do not keep vegetation from expanding onto bare sand habitat. Management intended to promote resilience of riparian vegetation must contend with communities that are sensitive to the interacting effects of altered flood regimes and water availability from river and precipitation.
","language":"English","publisher":"Wiley","doi":"10.1002/2015JG002991","usgsCitation":"Sankey, J.B., Ralston, B.E., Grams, P.E., Schmidt, J.C., and Cagney, L.E., 2015, Riparian vegetation, Colorado River, and climate: five decades of spatiotemporal dynamics in the Grand Canyon with river regulation: Journal of Geophysical Research, v. 120, no. 8, p. 1532-1547, https://doi.org/10.1002/2015JG002991.","productDescription":"16 p.","startPage":"1532","endPage":"1547","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057695","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":471873,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jg002991","text":"Publisher Index Page"},{"id":438687,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J67F0P","text":"USGS data release","linkHelpText":"Riparian vegetation, Colorado River, and climate: five 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36.48306849708776\n ],\n [\n -112.0817929173868,\n 36.26967611412161\n ],\n [\n -112.32134689544301,\n 36.437976634436225\n ],\n [\n -112.43347854474568,\n 36.55270432815594\n ],\n [\n -112.36721893379399,\n 36.75306845094532\n ],\n [\n -112.60677291184987,\n 36.797976456919486\n ],\n [\n -112.81574553100502,\n 36.5690801206902\n ],\n [\n -112.73929213375327,\n 36.45437670922044\n ],\n [\n -113.14194669261299,\n 36.478970320448795\n ],\n [\n -113.50382610627192,\n 36.2285720354117\n ],\n [\n -113.53950435832286,\n 36.11748307883802\n ],\n [\n -113.75357387062816,\n 36.24912677737895\n ],\n [\n -113.97274027608363,\n 36.27789433508222\n ],\n [\n -114.29894143769162,\n 36.646817719900454\n ],\n [\n -114.87489036365587,\n 36.183332582730785\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"120","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-13","publicationStatus":"PW","scienceBaseUri":"55d2f7a4e4b0518e35468cc1","contributors":{"authors":[{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":568135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ralston, Barbara E. 0000-0001-9991-8994 bralston@usgs.gov","orcid":"https://orcid.org/0000-0001-9991-8994","contributorId":606,"corporation":false,"usgs":true,"family":"Ralston","given":"Barbara","email":"bralston@usgs.gov","middleInitial":"E.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":false,"id":568326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":568327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, John C. 0000-0002-2988-3869 jcschmidt@usgs.gov","orcid":"https://orcid.org/0000-0002-2988-3869","contributorId":1983,"corporation":false,"usgs":true,"family":"Schmidt","given":"John","email":"jcschmidt@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":568328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":568329,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70156155,"text":"70156155 - 2015 - Trends in Rocky Mountain amphibians and the role of beaver as a keystone species","interactions":[],"lastModifiedDate":"2019-12-11T10:16:41","indexId":"70156155","displayToPublicDate":"2015-08-17T04:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Trends in Rocky Mountain amphibians and the role of beaver as a keystone species","docAbstract":"Despite prevalent awareness of global amphibian declines, there is still little information on trends for many widespread species. To inform land managers of trends on protected landscapes and identify potential conservation strategies, we collected occurrence data for five wetland-breeding amphibian species in four national parks in the U.S. Rocky Mountains during 2002–2011. We used explicit dynamics models to estimate variation in annual occupancy, extinction, and colonization of wetlands according to summer drought and several biophysical characteristics (e.g., wetland size, elevation), including the influence of North American beaver (Castor canadensis). We found more declines in occupancy than increases, especially in Yellowstone and Grand Teton national parks (NP), where three of four species declined since 2002. However, most species in Rocky Mountain NP were too rare to include in our analysis, which likely reflects significant historical declines. Although beaver were uncommon, their creation or modification of wetlands was associated with higher colonization rates for 4 of 5 amphibian species, producing a 34% increase in occupancy in beaver-influenced wetlands compared to wetlands without beaver influence. Also, colonization rates and occupancy of boreal toads (Anaxyrus boreas) and Columbia spotted frogs (Rana luteiventris) were ⩾2 times higher in beaver-influenced wetlands. These strong relationships suggest management for beaver that fosters amphibian recovery could counter declines in some areas. Our data reinforce reports of widespread declines of formerly and currently common species, even in areas assumed to be protected from most forms of human disturbance, and demonstrate the close ecological association between beaver and wetland-dependent species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2015.05.005","usgsCitation":"Hossack, B.R., Gould, W., Patla, D.A., Muths, E.L., Daley, R., Legg, K., and Corn, P.S., 2015, Trends in Rocky Mountain amphibians and the role of beaver as a keystone species: Biological Conservation, v. 187, p. 260-269, https://doi.org/10.1016/j.biocon.2015.05.005.","productDescription":"9 p.","startPage":"260","endPage":"269","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061859","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":471874,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2015.05.005","text":"Publisher Index Page"},{"id":306813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.939453125,\n 48.86471476180277\n ],\n [\n -119.267578125,\n 48.980216985374994\n ],\n [\n -117.158203125,\n 46.86019101567027\n ],\n [\n -117.158203125,\n 43.32517767999296\n ],\n [\n -113.203125,\n 40.713955826286046\n ],\n [\n -112.587890625,\n 37.579412513438385\n ],\n [\n -105.029296875,\n 34.813803317113155\n ],\n [\n -104.23828125,\n 38.47939467327645\n ],\n [\n -107.22656249999999,\n 45.398449976304086\n ],\n [\n -112.939453125,\n 48.86471476180277\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"187","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55d2f7a0e4b0518e35468cb9","chorus":{"doi":"10.1016/j.biocon.2015.05.005","url":"http://dx.doi.org/10.1016/j.biocon.2015.05.005","publisher":"Elsevier BV","authors":"Hossack Blake R., Gould William R., Patla Debra A., Muths Erin, Daley Rob, Legg Kristin, Corn Paul Stephen","journalName":"Biological Conservation","publicationDate":"7/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":567924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gould, William R.","contributorId":63780,"corporation":false,"usgs":true,"family":"Gould","given":"William R.","affiliations":[],"preferred":false,"id":567925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patla, Debra A.","contributorId":40059,"corporation":false,"usgs":true,"family":"Patla","given":"Debra","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":567926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":567927,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Daley, Rob","contributorId":146450,"corporation":false,"usgs":false,"family":"Daley","given":"Rob","affiliations":[{"id":16696,"text":"5National Park Service, Greater Yellowstone Network, 2327 University Way, Suite 2, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":567928,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Legg, Kristin","contributorId":146451,"corporation":false,"usgs":false,"family":"Legg","given":"Kristin","affiliations":[{"id":16697,"text":"National Park Service, Greater Yellowstone Network, 2327 University Way, Suite 2, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":567929,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Corn, P. Stephen 0000-0002-4106-6335 steve_corn@usgs.gov","orcid":"https://orcid.org/0000-0002-4106-6335","contributorId":3227,"corporation":false,"usgs":true,"family":"Corn","given":"P.","email":"steve_corn@usgs.gov","middleInitial":"Stephen","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":567930,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70160533,"text":"70160533 - 2015 - The effects of body size and climate on post-weaning survival of elephant seals at Heard Island","interactions":[],"lastModifiedDate":"2019-12-12T09:51:14","indexId":"70160533","displayToPublicDate":"2015-08-15T01:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2515,"text":"Journal of Zoology","active":true,"publicationSubtype":{"id":10}},"title":"The effects of body size and climate on post-weaning survival of elephant seals at Heard Island","docAbstract":"The population size of southern elephant seals in the southern Indian and Pacific Oceans decreased precipitously between the 1950s and 1990s. To investigate the reasons behind this, we studied the population of southern elephant seals at Heard Island between 1949 and 1954, using data collected by the early Australian National Antarctic Research Expeditions. Seals were marked and measured (lengths) as weaned pups, and resighted at Heard and Marion islands and in the Vestfold Hills, Antarctica in subsequent years. Bayesian state-space mark-recapture models were used to determine post-weaning survival. Yearling survival was consistently lower (ϕy: 0.28–0.40) than sub-adult survival (ϕs: 0.79–0.83). We found evidence for constant sub-adult survival and time-dependent resight probabilities. Weaning length was an important determinate of yearling survival, with the probability of survival increasing with individual length. There was some suggestion that the Southern Annular Mode influenced yearling survival but this evidence was not strong. Nonetheless, our results provide further support showing that size at independence affects yearling survival. Given the known sensitivity of southern elephant seal populations to survival early in life, it is possible that the decline in population size at Heard Island between the 1950s and 1990s like that at Macquarie Island was due to low yearling survival mediated through maternal ability to produce large pups and the dominant environmental conditions mothers experience during pregnancy.
","language":"English","publisher":"Cambridge University Press","publisherLocation":"Cambridge, England","doi":"10.1111/jzo.12279","usgsCitation":"McMahon, C.R., New, L., Fairley, E., Hindell, M., and Burton, H., 2015, The effects of body size and climate on post-weaning survival of elephant seals at Heard Island: Journal of Zoology, v. 297, no. 4, p. 301-308, https://doi.org/10.1111/jzo.12279.","productDescription":"8 p.","startPage":"301","endPage":"308","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065166","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":312736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","otherGeospatial":"Heard Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 73.21563720703125,\n -53.21672395086342\n ],\n [\n 73.89129638671875,\n -53.21672395086342\n ],\n [\n 73.89129638671875,\n -52.95360230002848\n ],\n [\n 73.21563720703125,\n -52.95360230002848\n ],\n [\n 73.21563720703125,\n -53.21672395086342\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"297","issue":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-19","publicationStatus":"PW","scienceBaseUri":"567a8246e4b0a04ef490fd1d","contributors":{"authors":[{"text":"McMahon, Clive R","contributorId":150800,"corporation":false,"usgs":false,"family":"McMahon","given":"Clive","email":"","middleInitial":"R","affiliations":[{"id":18107,"text":"Sydney Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":583072,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"New, Leslie lnew@usgs.gov","contributorId":145484,"corporation":false,"usgs":true,"family":"New","given":"Leslie","email":"lnew@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":583071,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fairley, E.J.","contributorId":150809,"corporation":false,"usgs":false,"family":"Fairley","given":"E.J.","email":"","affiliations":[],"preferred":false,"id":583093,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hindell, M.A.","contributorId":150810,"corporation":false,"usgs":false,"family":"Hindell","given":"M.A.","email":"","affiliations":[],"preferred":false,"id":583094,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burton, H.R.","contributorId":150811,"corporation":false,"usgs":false,"family":"Burton","given":"H.R.","email":"","affiliations":[],"preferred":false,"id":583095,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70155952,"text":"ofr20151152 - 2015 - A conceptual model for site-level ecology of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California","interactions":[],"lastModifiedDate":"2015-08-17T09:41:12","indexId":"ofr20151152","displayToPublicDate":"2015-08-14T18:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1152","title":"A conceptual model for site-level ecology of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California","docAbstract":"Giant gartersnakes (Thamnophis gigas) comprise a species of semi-aquatic snakes precinctive to marshes in the Central Valley of California (Hansen and Brode, 1980; Rossman and others, 1996). Because more than 90 percent of their historical wetland habitat has been converted to other uses (Frayer and others, 1989; Garone, 2007), giant gartersnakes have been listed as threatened by the State of California (California Department of Fish and Game Commission , 1971) and the United States (U.S. Fish and Wildlife Service, 1993). Giant gartersnakes currently occur in a highly modified landscape, with most extant populations occurring in the rice - growing regions of the Sacramento Valley, especially near areas that historically were tule marsh habitat (Halstead and others, 2010, 2014).
\nIn ricelands and managed marshes, many operational decisions likely affect the health and viability of giant gartersnake populations. Land-use decisions, including the management of water, aquatic vegetation, terrestrial vegetation, and co-occurring species, have the potential to affect giant gartersnakes. Little is known, however, about the effects of these types of decisions on the viability of giant gartersnake populations. Bayesian network models are a useful tool to help guide decisions with uncertain outcomes. These models require the articulation of what experts think they know about a system, and facilitate learning about the hypothesized relations (Marcot and others, 2001; Uusitalo , 2007).
\nBayesian networks further provide a clear visual display of the model that facilitates understanding among various stakeholders (Marcot and others, 2001; Uusitalo , 2007). Empirical data and expert judgment can be combined, as continuous or categorical variables, to update knowledge about the system (Marcot and others, 2001; Uusitalo , 2007). Importantly, Bayesian network models allow inference from causes to consequences, but also from consequences to causes, so that data can inform the states of nodes (values of different random variables) in either direction (Marcot and others, 2001; Uusitalo , 2007). Because they can incorporate both decision nodes that represent management actions and utility nodes that quantify the costs and benefits of outcomes, Bayesian networks are ideally suited to risk analysis and adaptive management (Nyberg and others, 2006; Howes and others, 2010). Thus, Bayesian network models are useful in situations where empirical data are not available, such as questions concerning the responses of giant gartersnakes to management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151152","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Halstead, B.J., Wylie, G.D., Casazza, M.L., Hansen, E.C., Scherer, R.D., and Patterson, L.C., 2015, A conceptual model for site-level ecology of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California: U.S. Geological Survey Open-File Report 2015-1152, 152 p., https://dx.doi.org/10.3133/ofr20151152.","productDescription":"iv, 152 p.","numberOfPages":"160","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061941","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":306765,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1152/coverthb.jpg"},{"id":306766,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1152/ofr20151152.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1152"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.26684570312499,\n 38.47079371120379\n ],\n [\n -122.26684570312499,\n 39.51675478434244\n ],\n [\n -121.42639160156249,\n 39.51675478434244\n ],\n [\n -121.42639160156249,\n 38.47079371120379\n ],\n [\n -122.26684570312499,\n 38.47079371120379\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://werc.usgs.gov/
Characterizing the large-scale sedimentary make-up of heterogeneous riverbeds (Nelson et al., 2014), which consist of a patchwork of sediment types over small scales (less than one to several tens of meters) (Dietrich and Smith, 1984) requires high resolution measurements of sediment grain size. Capturing such variability with conventional physical (e.g. grabs, cores, and dredges) or underwater photographic sampling (Rubin et al., 2007; Buscombe et al., 2014a) would be prohibitively costly and time-consuming. However, characterizing bed sediments using high-frequency (several hundred kilohertz) acoustic backscatter from swath-mapping systems has the potential to provide near complete coverage of the bed (Brown and Blondel, 2009; Brown et al., 2011; Snellen et al., 2013), at resolutions down to a few centimeters, which photographic sampling could not practically achieve within the same time and with the same positional accuracy.
In shallow water, the physics of high frequency scattering of sound are relatively poorly understood, therefore acoustic sediment classification are almost always statistical (Snellen et al., 2013). Many such methods proposed to date are designed for characterizing large areas of seabed (Brown and Blondel, 2009; Brown et al., 2011) at relatively poor resolution (tens of meters to several hundred meters) and therefore rely on aggregation of data over scales much larger than the typical scales of sediment patchiness on heterogeneous riverbeds. In response to this need, Buscombe et al. (2014b, 2014c) developed a new statistical method for acoustic sediment classification based on spectral analysis of backscatter. This method is both continuous in coverage and of sufficient resolution (order meter or less) to characterize sediment variability on patchy riverbeds. Here, we apply these methods to multibeam echosounder (MBES) data collected from the bed of the Colorado River in Marble and Grand Canyons.
Sediment dynamics on the Colorado River in Grand Canyon National Park have been studied for several decades (e.g. Howard and Dolan, 1981; Rubin et al., 2002). Particular focus has been given to sandbars in large eddies downstream of tributary debris fans (Schmidt, 1990) because they are considered valuable resources by stakeholders and managers. Due to the severe limitations in sand supply imposed by Glen Canyon Dam (Howard and Dolan, 1981; Topping et al., 2000; Hazel et al., 2006), understanding the effectiveness of sandbar management practices, such as controlled floods (Rubin et al. 2002; Topping et al., 2006; Hazel et al., 2010), and the long-term fate of sand in Grand Canyon over decadal timescales, requires construction of accurate sand budgets, which involves detailed monitoring of influx, efflux and changes in sand storage (Topping et al., 2000; Topping et al., 2010; Grams et al., 2013) and assessments of uncertainties in sand-budget calculations (Grams et al., 2013).
In order to estimate the sand budget, it is necessary to estimate what component of observed morphological changes is sand and what component is coarser. Grams et al. (2013) classified sand and coarse substrates using topographic roughness derived from digital elevation models, but the classification skill was estimated to be only 60-70%. In addition, sand bedforms had to be delineated manually, and validation was based on grain-size observations with positional uncertainties up to tens of meters. Because the morphology of the Colorado riverbed in Grand Canyon is mapped - to a large extent - using MBES (Kaplinski et al., 2009), the primary motivation for the present study is to examine how uncertainties in sand budgets can be constrained by producing maps of surface sediment types using the completely automated methods of Buscombe et al (2014b, 2014c) based on statistical analysis of MBES acoustic backscatter.
","conferenceTitle":"3rd Joint Federal Interagency Conference","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","publisher":"Joint Federal Interagency Conference","usgsCitation":"Buscombe, D.D., Grams, P.E., Kaplinski, M., Tusso, R.B., and Rubin, D.M., 2015, Hydroacoustic signatures of Colorado Riverbed sediments in Marble and Grand Canyons using multibeam sonar, 3rd Joint Federal Interagency Conference, Reno, NV, April 19-23, 2015, 12 p.","productDescription":"12 p.","ipdsId":"IP-060883","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":342198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":353659,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2015/openconf/modules/request.php?module=oc_program&action=summary.php&id=76"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon, Marble Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.5,\n 36\n ],\n [\n -111.25,\n 36\n ],\n [\n -111.25,\n 37\n ],\n [\n -112.5,\n 37\n ],\n [\n -112.5,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593910b0e4b0764e6c5e888c","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584 dbuscombe@usgs.gov","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":5020,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","email":"dbuscombe@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547842,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kaplinski, Matthew","contributorId":14917,"corporation":false,"usgs":true,"family":"Kaplinski","given":"Matthew","affiliations":[],"preferred":false,"id":547843,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tusso, Robert B. 0000-0001-7541-3713 rtusso@usgs.gov","orcid":"https://orcid.org/0000-0001-7541-3713","contributorId":4079,"corporation":false,"usgs":true,"family":"Tusso","given":"Robert","email":"rtusso@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547844,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rubin, David M. 0000-0003-1169-1452 drubin@usgs.gov","orcid":"https://orcid.org/0000-0003-1169-1452","contributorId":3159,"corporation":false,"usgs":true,"family":"Rubin","given":"David","email":"drubin@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":547845,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182732,"text":"70182732 - 2015 - A rapid approach for automated comparison of independently derived stream networks","interactions":[],"lastModifiedDate":"2017-02-27T15:24:10","indexId":"70182732","displayToPublicDate":"2015-08-14T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1191,"text":"Cartography and Geographic Information Science","active":true,"publicationSubtype":{"id":10}},"title":"A rapid approach for automated comparison of independently derived stream networks","docAbstract":"This paper presents an improved coefficient of line correspondence (CLC) metric for automatically assessing the similarity of two different sets of linear features. Elevation-derived channels at 1:24,000 scale (24K) are generated from a weighted flow-accumulation model and compared to 24K National Hydrography Dataset (NHD) flowlines. The CLC process conflates two vector datasets through a raster line-density differencing approach that is faster and more reliable than earlier methods. Methods are tested on 30 subbasins distributed across different terrain and climate conditions of the conterminous United States. CLC values for the 30 subbasins indicate 44–83% of the features match between the two datasets, with the majority of the mismatching features comprised of first-order features. Relatively lower CLC values result from subbasins with less than about 1.5 degrees of slope. The primary difference between the two datasets may be explained by different data capture criteria. First-order, headwater tributaries derived from the flow-accumulation model are captured more comprehensively through drainage area and terrain conditions, whereas capture of headwater features in the NHD is cartographically constrained by tributary length. The addition of missing headwaters to the NHD, as guided by the elevation-derived channels, can substantially improve the scientific value of the NHD.
","language":"English","publisher":"Taylor & Francis ","doi":"10.1080/15230406.2015.1060869","usgsCitation":"Stanislawski, L.V., Buttenfield, B.P., and Doumbouya, A.T., 2015, A rapid approach for automated comparison of independently derived stream networks: Cartography and Geographic Information Science, p. 435-448, https://doi.org/10.1080/15230406.2015.1060869.","productDescription":"14 p. ","startPage":"435","endPage":"448","ipdsId":"IP-066429","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":336303,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-14","publicationStatus":"PW","scienceBaseUri":"58b548c2e4b01ccd54fddfc8","contributors":{"authors":[{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":673483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buttenfield, Barbara P.","contributorId":184069,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":673485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doumbouya, Ariel T. atdoumbouya@usgs.gov","contributorId":5764,"corporation":false,"usgs":true,"family":"Doumbouya","given":"Ariel","email":"atdoumbouya@usgs.gov","middleInitial":"T.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":673484,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70156015,"text":"70156015 - 2015 - The influence of grain size, grain color, and suspended-sediment concentration on light attenuation: why fine-grained terrestrial sediment is bad for coral reef ecosystems","interactions":[],"lastModifiedDate":"2015-08-13T14:35:48","indexId":"70156015","displayToPublicDate":"2015-08-13T15:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1338,"text":"Coral Reefs","active":true,"publicationSubtype":{"id":10}},"title":"The influence of grain size, grain color, and suspended-sediment concentration on light attenuation: why fine-grained terrestrial sediment is bad for coral reef ecosystems","docAbstract":"Sediment has been shown to be a major stressor to coral reefs globally. Although many researchers have tested the impact of sedimentation on coral reef ecosystems in both the laboratory and the field and some have measured the impact of suspended sediment on the photosynthetic response of corals, there has yet to be a detailed investigation on how properties of the sediment itself can affect light availability for photosynthesis. We show that finer-grained and darker-colored sediment at higher suspended-sediment concentrations attenuates photosynthetically active radiation (PAR) significantly more than coarser, lighter-colored sediment at lower concentrations and provide PAR attenuation coefficients for various grain sizes, colors, and suspended-sediment concentrations that are needed for biophysical modeling. Because finer-grained sediment particles settle more slowly and are more susceptible to resuspension, they remain in the water column longer, thus causing greater net impact by reducing light essential for photosynthesis over a greater duration. This indicates that coral reef monitoring studies investigating sediment impacts should concentrate on measuring fine-grained lateritic and volcanic soils, as opposed to coarser-grained siliceous and carbonate sediment. Similarly, coastal restoration efforts and engineering solutions addressing long-term coral reef ecosystem health should focus on preferentially retaining those fine-grained soils rather than coarse silt and sand particles.
","language":"English","publisher":"Springer","publisherLocation":"Heidelberg, Germany","doi":"10.1007/s00338-015-1268-0","usgsCitation":"Storlazzi, C.D., Norris, B., and Rosenberger, K.J., 2015, The influence of grain size, grain color, and suspended-sediment concentration on light attenuation: why fine-grained terrestrial sediment is bad for coral reef ecosystems: Coral Reefs, v. 34, no. 3, p. 967-975, https://doi.org/10.1007/s00338-015-1268-0.","productDescription":"9 p.","startPage":"967","endPage":"975","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060458","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":306678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-20","publicationStatus":"PW","scienceBaseUri":"55cdb1b1e4b08400b1fe13c7","contributors":{"authors":[{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":567667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norris, Benjamin","contributorId":65001,"corporation":false,"usgs":true,"family":"Norris","given":"Benjamin","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":567668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberger, Kurt J. 0000-0002-5185-5776 krosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5185-5776","contributorId":140453,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Kurt","email":"krosenberger@usgs.gov","middleInitial":"J.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":567669,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155976,"text":"70155976 - 2015 - Peclet number as affected by molecular diffusion controls transient anomalous transport in alluvial aquifer-aquitard complexes","interactions":[],"lastModifiedDate":"2018-09-04T16:29:55","indexId":"70155976","displayToPublicDate":"2015-08-13T14:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2233,"text":"Journal of Contaminant Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Peclet number as affected by molecular diffusion controls transient anomalous transport in alluvial aquifer-aquitard complexes","docAbstract":"This study evaluates the role of the Peclet number as affected by molecular diffusion in transient anomalous transport, which is one of the major knowledge gaps in anomalous transport, by combining Monte Carlo simulations and stochastic model analysis. Two alluvial settings containing either short- or long-connected hydrofacies are generated and used as media for flow and transport modeling. Numerical experiments show that 1) the Peclet number affects both the duration of the power-law segment of tracer breakthrough curves (BTCs) and the transition rate from anomalous to Fickian transport by determining the solute residence time for a given low-permeability layer, 2) mechanical dispersion has a limited contribution to the anomalous characteristics of late-time transport as compared to molecular diffusion due to an almost negligible velocity in floodplain deposits, and 3) the initial source dimensions only enhance the power-law tail of the BTCs at short travel distances. A tempered stable stochastic (TSS) model is then applied to analyze the modeled transport. Applications show that the time-nonlocal parameters in the TSS model relate to the Peclet number, Pe. In particular, the truncation parameter in the TSS model increases nonlinearly with a decrease in Pe due to the decrease of the mean residence time, and the capacity coefficient increases with an increase in molecular diffusion which is probably due to the increase in the number of immobile particles. The above numerical experiments and stochastic analysis therefore reveal that the Peclet number as affected by molecular diffusion controls transient anomalous transport in alluvial aquifer–aquitard complexes.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.jconhyd.2015.04.001","usgsCitation":"Zhang, Y., Green, C., and Tick, G.R., 2015, Peclet number as affected by molecular diffusion controls transient anomalous transport in alluvial aquifer-aquitard complexes: Journal of Contaminant Hydrology, v. 177-178, p. 220-238, https://doi.org/10.1016/j.jconhyd.2015.04.001.","productDescription":"19 p.","startPage":"220","endPage":"238","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061229","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":471875,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jconhyd.2015.04.001","text":"Publisher Index Page"},{"id":306666,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"177-178","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cdb1b0e4b08400b1fe13be","contributors":{"authors":[{"text":"Zhang, Yong","contributorId":19029,"corporation":false,"usgs":true,"family":"Zhang","given":"Yong","affiliations":[],"preferred":false,"id":567493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Green, Christopher T. ctgreen@usgs.gov","contributorId":146339,"corporation":false,"usgs":true,"family":"Green","given":"Christopher T.","email":"ctgreen@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":567492,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tick, Geoffrey R.","contributorId":146340,"corporation":false,"usgs":false,"family":"Tick","given":"Geoffrey","email":"","middleInitial":"R.","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":567494,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155956,"text":"70155956 - 2015 - Long-term shifts in the phenology of rare and endemic Rocky Mountain plants","interactions":[],"lastModifiedDate":"2015-08-25T13:22:47","indexId":"70155956","displayToPublicDate":"2015-08-13T14:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":724,"text":"American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Long-term shifts in the phenology of rare and endemic Rocky Mountain plants","docAbstract":"PREMISE OF THE STUDY: Mountainous regions support high plant productivity, diversity, and endemism, yet are highly vulnerable to climate change. Historical records and model predictions show increasing temperatures across high elevation regions including the Southern Rocky Mountains, which can have a strong influence on the performance and distribution of montane plant species. Rare plant species can be particularly vulnerable to climate change because of their limited abundance and distribution.
\nMETHODS: We tracked the phenology of rare and endemic species, which are identified as imperiled, across three different habitat types with herbarium records to determine if flowering time has changed over the last century, and if phenological change was related to shifts in climate.
\nKEY RESULTS: We found that the flowering date of rare species has accelerated 3.1 d every decade (42 d total) since the late 1800s, with plants in sagebrush interbasins showing the strongest accelerations in phenology. High winter temperatures were associated with the acceleration of phenology in low elevation sagebrush and barren river habitats, whereas high spring temperatures explained accelerated phenology in the high elevation alpine habitat. In contrast, high spring temperatures delayed the phenology of plant species in the two low-elevation habitats and precipitation had mixed effects depending on the season.
\nCONCLUSIONS: These results provide evidence for large shifts in the phenology of rare Rocky Mountain plants related to climate, which can have strong effects on plant fitness, the abundance of associated wildlife, and the future of plant conservation in mountainous regions.
","language":"English","publisher":"Botanical Society of America","publisherLocation":"Lawrence, KS","doi":"10.3732/ajb.1500156","usgsCitation":"Munson, S.M., and Sher, A.A., 2015, Long-term shifts in the phenology of rare and endemic Rocky Mountain plants: American Journal of Botany, v. 102, no. 8, p. 1268-1276, https://doi.org/10.3732/ajb.1500156.","productDescription":"9 p.","startPage":"1268","endPage":"1276","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065084","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":471876,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3732/ajb.1500156","text":"Publisher Index Page"},{"id":306657,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"102","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cdb1ace4b08400b1fe13a9","contributors":{"authors":[{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":567395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sher, Anna A","contributorId":146314,"corporation":false,"usgs":false,"family":"Sher","given":"Anna","email":"","middleInitial":"A","affiliations":[{"id":12651,"text":"University of Denver","active":true,"usgs":false}],"preferred":false,"id":567396,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70155954,"text":"70155954 - 2015 - Months between rejuvenation and volcanic eruption at Yellowstone caldera, Wyoming","interactions":[],"lastModifiedDate":"2015-08-13T13:15:14","indexId":"70155954","displayToPublicDate":"2015-08-13T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Months between rejuvenation and volcanic eruption at Yellowstone caldera, Wyoming","docAbstract":"Rejuvenation of previously intruded silicic magma is an important process leading to effusive rhyolite, which is the most common product of volcanism at calderas with protracted histories of eruption and unrest such as Yellowstone, Long Valley, and Valles, USA. Although orders of magnitude smaller in volume than rare caldera-forming super-eruptions, these relatively frequent effusions of rhyolite are comparable to the largest eruptions of the 20th century and pose a considerable volcanic hazard. However, the physical pathway from rejuvenation to eruption of silicic magma is unclear particularly because the time between reheating of a subvolcanic intrusion and eruption is poorly quantified. This study uses geospeedometry of trace element profiles with nanometer resolution in sanidine crystals to reveal that Yellowstone’s most recent volcanic cycle began when remobilization of a near- or sub-solidus silicic magma occurred less than 10 months prior to eruption, following a 220,000 year period of volcanic repose. Our results reveal a geologically rapid timescale for rejuvenation and effusion of ~3 km3 of high-silica rhyolite lava even after protracted cooling of the subvolcanic system, which is consistent with recent physical modeling that predict a timescale of several years or less. Future renewal of rhyolitic volcanism at Yellowstone is likely to require an energetic intrusion of mafic or silicic magma into the shallow subvolcanic reservoir and could rapidly generate an eruptible rhyolite on timescales similar to those documented here.
","language":"English","publisher":"Geological Society of America","publisherLocation":"Boulder, CO","doi":"10.1130/G36862.1","usgsCitation":"Till, C.B., Vazquez, J.A., and Boyce, J., 2015, Months between rejuvenation and volcanic eruption at Yellowstone caldera, Wyoming: Geology, v. 43, no. 8, p. 695-698, https://doi.org/10.1130/G36862.1.","productDescription":"4 p.","startPage":"695","endPage":"698","numberOfPages":"5","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063317","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":306656,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone caldera","volume":"43","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-01","publicationStatus":"PW","scienceBaseUri":"55cdb1ace4b08400b1fe13ab","contributors":{"authors":[{"text":"Till, Christy B. cbtill@usgs.gov","contributorId":4394,"corporation":false,"usgs":true,"family":"Till","given":"Christy","email":"cbtill@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":true,"id":567389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":567388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyce, Jeremy W","contributorId":146313,"corporation":false,"usgs":false,"family":"Boyce","given":"Jeremy W","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":567390,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155863,"text":"70155863 - 2015 - Lilac and honeysuckle phenology data 1956–2014","interactions":[],"lastModifiedDate":"2015-09-16T10:25:32","indexId":"70155863","displayToPublicDate":"2015-08-13T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Lilac and honeysuckle phenology data 1956–2014","docAbstract":"The dataset is comprised of leafing and flowering data collected across the continental United States from 1956 to 2014 for purple common lilac (Syringa vulgaris), a cloned lilac cultivar (S. x chinensis ‘Red Rothomagensis’) and two cloned honeysuckle cultivars (Lonicera tatarica ‘Arnold Red’ and L. korolkowii ‘Zabeli’). Applications of this observational dataset range from detecting regional weather patterns to understanding the impacts of global climate change on the onset of spring at the national scale. While minor changes in methods have occurred over time, and some documentation is lacking, outlier analyses identified fewer than 3% of records as unusually early or late. Lilac and honeysuckle phenology data have proven robust in both model development and climatic research.
","language":"English","publisher":"Nature Publishing Group","publisherLocation":"London, UK","doi":"10.1038/sdata.2015.38","usgsCitation":"Rosemartin, A.H., Denny, E.G., Weltzin, J., Marsh, R.L., Wilson, B.E., Mehdipoor, H., Zurita-Milla, R., and Schwartz, M., 2015, Lilac and honeysuckle phenology data 1956–2014: Scientific Data, v. 2, 150038; 8 p., https://doi.org/10.1038/sdata.2015.38.","productDescription":"150038; 8 p.","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"1956-01-01","temporalEnd":"2014-12-31","ipdsId":"IP-061549","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":471877,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2015.38","text":"Publisher Index Page"},{"id":308175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-21","publicationStatus":"PW","scienceBaseUri":"55fa92c1e4b05d6c4e501aa1","contributors":{"authors":[{"text":"Rosemartin, Alyssa H.","contributorId":30910,"corporation":false,"usgs":true,"family":"Rosemartin","given":"Alyssa","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":566620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Denny, Ellen G.","contributorId":79803,"corporation":false,"usgs":true,"family":"Denny","given":"Ellen","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":566621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weltzin, Jake F. jweltzin@usgs.gov","contributorId":296,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake F.","email":"jweltzin@usgs.gov","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":false,"id":566618,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marsh, R. Lee","contributorId":146211,"corporation":false,"usgs":false,"family":"Marsh","given":"R.","email":"","middleInitial":"Lee","affiliations":[{"id":16629,"text":"USA National Phenology Network, SNRE University of Arizona","active":true,"usgs":false}],"preferred":false,"id":566622,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Bruce E.","contributorId":94944,"corporation":false,"usgs":true,"family":"Wilson","given":"Bruce","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":566623,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mehdipoor, Hamed","contributorId":146212,"corporation":false,"usgs":false,"family":"Mehdipoor","given":"Hamed","email":"","affiliations":[{"id":16630,"text":"Department of Geo-Information Processing, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":566624,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zurita-Milla, Raul","contributorId":146213,"corporation":false,"usgs":false,"family":"Zurita-Milla","given":"Raul","email":"","affiliations":[{"id":16630,"text":"Department of Geo-Information Processing, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":566625,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schwartz, Mark D.","contributorId":11092,"corporation":false,"usgs":true,"family":"Schwartz","given":"Mark D.","affiliations":[],"preferred":false,"id":566619,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70154775,"text":"70154775 - 2015 - Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales","interactions":[],"lastModifiedDate":"2019-06-03T13:24:23","indexId":"70154775","displayToPublicDate":"2015-08-13T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales","docAbstract":"Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publicly available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0134781","usgsCitation":"Tack, J., and Fedy, B., 2015, Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales: PLoS ONE, v. 10, no. 8, e0134781: 18 p., https://doi.org/10.1371/journal.pone.0134781.","productDescription":"e0134781: 18 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066450","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471879,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0134781","text":"Publisher Index Page"},{"id":306635,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"10","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-11","publicationStatus":"PW","scienceBaseUri":"55cdb1ace4b08400b1fe13a7","contributors":{"authors":[{"text":"Tack, Jason D. jtack@usgs.gov","contributorId":145460,"corporation":false,"usgs":true,"family":"Tack","given":"Jason D.","email":"jtack@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":564104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fedy, Bradley C.","contributorId":40536,"corporation":false,"usgs":true,"family":"Fedy","given":"Bradley C.","affiliations":[],"preferred":false,"id":564105,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70155814,"text":"70155814 - 2015 - Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)","interactions":[],"lastModifiedDate":"2015-08-13T10:33:01","indexId":"70155814","displayToPublicDate":"2015-08-13T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)","docAbstract":"Groundwater has provided 50–90 % of the total water supply in Antelope Valley, California (USA). The associated groundwater-level declines have led the Los Angeles County Superior Court of California to recently rule that the Antelope Valley groundwater basin is in overdraft, i.e., annual pumpage exceeds annual recharge. Natural recharge consists primarily of mountain-front recharge and is an important component of the total groundwater budget in Antelope Valley. Therefore, natural recharge plays a major role in the Court’s decision. The exact quantity and distribution of natural recharge is uncertain, with total estimates from previous studies ranging from 37 to 200 gigaliters per year (GL/year). In order to better understand the uncertainty associated with natural recharge and to provide a tool for groundwater management, a numerical model of groundwater flow and land subsidence was developed. The transient model was calibrated using PEST with water-level and subsidence data; prior information was incorporated through the use of Tikhonov regularization. The calibrated estimate of natural recharge was 36 GL/year, which is appreciably less than the value used by the court (74 GL/year). The effect of parameter uncertainty on the estimation of natural recharge was addressed using the Null-Space Monte Carlo method. A Pareto trade-off method was also used to portray the reasonableness of larger natural recharge rates. The reasonableness of the 74 GL/year value and the effect of uncertain pumpage rates were also evaluated. The uncertainty analyses indicate that the total natural recharge likely ranges between 34.5 and 54.3 GL/year.
","language":"English","publisher":"Springer","publisherLocation":"Heidelberg, Germany","doi":"10.1007/s10040-015-1281-y","usgsCitation":"Siade, A.J., Nishikawa, T., and Martin, P., 2015, Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA): Hydrogeology Journal, v. 23, no. 6, p. 1267-1291, https://doi.org/10.1007/s10040-015-1281-y.","productDescription":"25 p.","startPage":"1267","endPage":"1291","numberOfPages":"25","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037195","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":471880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-015-1281-y","text":"Publisher Index Page"},{"id":306633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Antelope Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.19503784179688,\n 35.16819542676796\n ],\n [\n -118.90228271484374,\n 34.84536693184099\n ],\n [\n -118.91189575195312,\n 34.78222760653013\n ],\n [\n -117.45620727539062,\n 34.30260622622907\n ],\n [\n -117.54959106445312,\n 35.163704834815874\n ],\n [\n -118.19503784179688,\n 35.16819542676796\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-24","publicationStatus":"PW","scienceBaseUri":"55cdb1ade4b08400b1fe13b1","contributors":{"authors":[{"text":"Siade, Adam J. asiade@usgs.gov","contributorId":1533,"corporation":false,"usgs":true,"family":"Siade","given":"Adam","email":"asiade@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nishikawa, Tracy 0000-0002-7348-3838 tnish@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-3838","contributorId":1515,"corporation":false,"usgs":true,"family":"Nishikawa","given":"Tracy","email":"tnish@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Peter pmmartin@usgs.gov","contributorId":799,"corporation":false,"usgs":true,"family":"Martin","given":"Peter","email":"pmmartin@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566454,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70148339,"text":"70148339 - 2015 - Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model","interactions":[],"lastModifiedDate":"2020-10-29T20:20:51.220065","indexId":"70148339","displayToPublicDate":"2015-08-13T10:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1264,"text":"Cold Regions Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model","docAbstract":"Glide snow avalanches are dangerous and difficult to predict. Despite substantial recent research there is still inadequate understanding regarding the controls of glide snow avalanche release. Glide snow avalanches often occur in similar terrain or the same locations annually, and repeat observations and prior work suggest that specific topography may be critical. Thus, to gain a better understanding of the terrain component of these types of avalanches we examined terrain parameters associated with the specific area of glide snow avalanche release in comparison to avalanche starting zones where no glide snow avalanches were observed (i.e. non-glide snow avalanche terrain).
Glide snow avalanche occurrences visible from the Going-to-the-Sun Road corridor in Glacier National Park, Montana from 2003 to 2013 are investigated using a database of all avalanche occurrences derived of daily observations each year from 1 April to 1 June. This yielded 192 glide snow avalanches in 53 distinct avalanche paths. Each avalanche was digitized in a GIS using satellite, oblique, and aerial imagery as reference. A set of 117 non-glide snow avalanche starting zones were also selected in this manner. These were start zones with avalanche activity potential, but without glide avalanches observed. Topographical parameters such as area, slope, aspect, curvature, potential incoming solar radiation, distance from ridge, and elevation were then derived for the entire dataset utilizing tools with a GIS and a 10 m DEM. Ground class and a glide factor were calculated using a four level classification index with in-situ observations and a land surface type layer in a GIS.
A total of 21 terrain variables were examined using a univariate analysis between areas where glide snow avalanches occurred and areas where glide snow avalanches were never observed, despite crack formation. Only two variables were not significantly different. The significantly different variables were then used to train a classification tree to distinguish between glide and non-glide snow avalanche terrain. A 10-fold cross validated tree resulted in four decision nodes to classify the data. The nodes split on glide factor, maximum slope angle, seasonal sum of incoming solar radiation, and maximum curvature to distinguish between glide snow avalanche and non-glide snow avalanche terrain with an unweighted average accuracy (RPC) of 0.95 and probability of detection of events (POD) of 0.99.
Finally, the results of the cross-validated tree were used in a GIS to examine other areas, not used in the training dataset of the classification tree, of potential glide snow avalanche release within Glacier National Park. Using this understanding of the role of topographic parameters on glide snow avalanche activity, a spatial terrain based model was developed to identify other areas with high glide snow avalanche potential outside of the immediate observation area. This simple spatial model correctly classified 78 percent of actual glide snow avalanche terrain (pixel count) of a small test area of four independent observed glide snow avalanches.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coldregions.2015.08.002","usgsCitation":"Peitzsch, E.H., Hendrikx, J., and Fagre, D.B., 2015, Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model: Cold Regions Science and Technology, v. 120, p. 237-250, https://doi.org/10.1016/j.coldregions.2015.08.002.","productDescription":"14 p.","startPage":"237","endPage":"250","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061113","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":301089,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.345703125,\n 48.23016176791893\n ],\n [\n -113.15093994140625,\n 48.23016176791893\n ],\n [\n -113.15093994140625,\n 48.980216985374994\n ],\n [\n -114.345703125,\n 48.980216985374994\n ],\n [\n -114.345703125,\n 48.23016176791893\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55780e29e4b032353cbeb6f1","contributors":{"authors":[{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":547717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hendrikx, Jordy 0000-0001-6194-3596","orcid":"https://orcid.org/0000-0001-6194-3596","contributorId":140954,"corporation":false,"usgs":false,"family":"Hendrikx","given":"Jordy","email":"","affiliations":[{"id":13628,"text":"Department of Earth Sciences, P.O. Box 173480, Montana State University, Bozeman, MT, USA. 59717.","active":true,"usgs":false}],"preferred":false,"id":547718,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagre, Daniel B. 0000-0001-8552-9461 dan_fagre@usgs.gov","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":2036,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel","email":"dan_fagre@usgs.gov","middleInitial":"B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":547719,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}