The U.S. Geological Survey is studying regional-scale assessments of resources and reserves of primary coal beds in the major coal bed basins in the United States to help formulate policy for Federal, State, and local energy and land use. This report summarizes the geology and coal resources and reserves in the Little Snake River coal field and Red Desert assessment area in the Greater Green River Basin, southwestern Wyoming. These areas are contiguous and referred to as the “assessment area” in this report. The assessment area covers about 2,300 square miles of the eastern section of the 15,400-square-mile Greater Green River Basin. This area was prioritized for assessment because no comprehensive resource assessment had previously been completed in the area; abundant, previously unavailable drill hole data from cooperators and stakeholders became available; and there are active coal mines in the Greater Green River Basin area producing from some of the same formations as those found in the assessment area.
Coal-bearing Eocene, Paleocene, and Upper Cretaceous formations have a composite thickness of more than 11,000 feet in the assessment area. Stratigraphic sequences that contain multiple coal beds within a formation or member are referred to as coal zones in this report. Paleogene coal beds are found within coal zones in the Eocene Wasatch Formation and the Paleocene Fort Union Formation. Cretaceous coal beds are within coal zones in the Lance, Almond, and Allen Ridge Formations.
A total of 4,214 drill holes and measured sections were used to construct a geologic database for this assessment. From these data, 7 coal zones containing 55 individual coal beds were identified. Not all 55 coal beds were assessed; only those beds that were at least 3 feet thick and had at least a 2-square-mile areal extent were considered. Using a geology-based assessment methodology, the U.S. Geological Survey estimated original, available, and recoverable coal resources for 33 coal beds that met those criteria.
An original resource of 73.2 billion short tons of coal was calculated for the 33 coal beds that met the criteria in the assessment area. To be considered extractable by surface mining methods, coal beds had to be equal to or greater than 3 feet thick and less than 300 feet deep. Of the 73.2 billion short tons (BST), 19.3 BST were determined to be recoverable resources, of which approximately 2.1 BST were considered as recoverable resources by surface mining methods at a stripping ratio of 10:1 or less. (defined as recoverable resources for this assessment, based on economic modeling and regional mining analogs). Recoverable resources for underground mining methods (coal 8 to 15 feet thick and between 300 and 3,000 feet deep) totaled 17.2 BST. Out of the 19.3 BST assessed as recoverable coal resources, approximately 167 million short tons (MST) were considered to be reserves.
Within the 7 coal zones, the Wasatch coal zone contains an original resource of about 6.8 BST billion short tons of coal, of which 2.6 BST are considered a recoverable resource and approximately 26.7 MST are considered reserves. The Overland coal zone, in the Fort Union Formation, contains an original resource of approximately 23 BST of coal, of which 8.4 BST are considered a recoverable resource and approximately 74 MST are considered reserves. The China Butte coal zone, in the Fort Union Formation, contains an original resource of 36.2 BST of coal, of which 6.3 BST are considered a recoverable resource and approximately 5.5 MST are considered reserves. The Almond coal zone, in the Almond Formation, contains an original resource of 7.0 BST, of which approximately 2.0 BST are considered a recoverable resource and 61 MST are considered reserves. Resources were not calculated for the coal zones within the Niland Tongue of the Wasatch Formation or the Cretaceous Lance and Allen Ridge Formations because the coal beds in those zones are relatively thin, discontinuous, and have a limited areal extent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1836","usgsCitation":"Scott, D.C., Shaffer, B.N., Haacke, J.E., Pierce, P.E., and Kinney, S.A., 2019, Coal geology and assessment of resources and reserves in the Little Snake River coal field and Red Desert assessment area, Greater Green River Basin, Wyoming: U.S. Geological Survey Professional Paper 1836, 169 p., https://doi.org/10.3133/pp1836.","productDescription":"xiii, 169 p.","onlineOnly":"Y","ipdsId":"IP-077210","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":370383,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20193053","text":"Assessment of Coal Resources and Reserves in the Little Snake River Coal Field and Red Desert Assessment Area, Greater Green River Basin, Wyoming"},{"id":356626,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1836/pp1836.pdf","text":"Report","size":"32.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1836"},{"id":356625,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1836/coverthb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.02783203125,\n 43.14909399920127\n ],\n [\n -111.07177734375,\n 41.02964338716638\n ],\n [\n -108.3251953125,\n 40.9964840143779\n ],\n [\n -106.8310546875,\n 41.02964338716638\n ],\n [\n -105.380859375,\n 41.04621681452063\n ],\n [\n -104.9853515625,\n 41.47566020027821\n ],\n [\n -107.24853515625,\n 42.90816007196054\n ],\n [\n -109.09423828125,\n 43.8028187190472\n ],\n [\n -110.9619140625,\n 43.8503744993026\n ],\n [\n -111.02783203125,\n 43.14909399920127\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
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The glacial kettle ponds in the Hyannis Ponds Wildlife Management Area in Barnstable, Massachusetts, support a community of rare and endangered plants. The ponds are hydraulically connected to the unconfined aquifer that underlies Cape Cod. The plants are adapted to the rise and fall of water levels in the ponds as the water table fluctuates in response to seasonal and year-to-year natural changes in recharge. Pumping from wells for public water supply and recharge of wastewater at water pollution control facilities and septic systems also affect groundwater levels. The Hyannis Water System has proposed to install two additional wells in the Hyannis Ponds Wildlife Management Area and adjust rates of withdrawals and recharge of wastewater return flows for the municipal system that serves the village of Hyannis in the town of Barnstable. The proposal has raised concerns that pumping from the proposed wells could cause long-term average changes in pond levels that could adversely affect the critical pond-shore plant habitat.
An available three-dimensional steady-state groundwater-flow model was used to simulate the hydrologic effects of nine pumping and wastewater return-flow scenarios prepared by the Hyannis Water System. These effects were quantified by comparison of water levels simulated for the scenarios to water levels simulated for a reference condition based on 2015 withdrawal and wastewater return-flow rates. Maps of water-level responses were prepared to show the effects of pumping from a single well at different locations in the Hyannis Ponds Wildlife Management Area on the water levels of six ponds. Steady-state simulations of the nine scenarios indicated that the shapes of the simulated water-table contours near the wildlife management area changed only slightly at the regional scale, with the largest shifts near the wildlife management area and the Barnstable Water Pollution Control Facility. The simulated changes in pond levels at 10 ponds of interest for the nine scenarios relative to the simulated pond levels for the 2015 reference condition ranged from small increases (less than 0.1 foot) in one pond each in two scenarios to declines (drawdowns) of 1.03–1.11 feet at three ponds in one scenario. Water levels at the Barnstable Water Pollution Control Facility increased because part of the increase in total withdrawals from the Hyannis Water System wells was recharged as wastewater at the water pollution control facility.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195121","collaboration":"Prepared in cooperation with the Town of Barnstable","usgsCitation":"LeBlanc, D.R., McCobb, T.D., and Barbaro, J.R., 2019, Simulated water-table and pond-level responses to proposed public water-supply withdrawals in the Hyannis Ponds Wildlife Management Area, Barnstable, Massachusetts: U.S. Geological Survey Scientific Investigations Report 2019–5121, 32 p., https://doi.org/10.3133/sir20195121.","productDescription":"Report: vii, 32 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109032","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":370347,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5121/sir20195121.pdf","text":"Report","size":"3.89 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5121"},{"id":370346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5121/coverthb.jpg"},{"id":370348,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U5AKLC","text":"USGS data release","linkHelpText":"MODFLOW2005 groundwater-flow model used to simulate water-supply pumping scenarios near the Hyannis Ponds Wildlife Management Area, Barnstable, Massachusetts"}],"country":"United States","state":"Massachusetts","city":"Barnstable","otherGeospatial":"Hyannis Ponds Wildlife Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.28074264526367,\n 41.67297593102651\n ],\n [\n -70.26091575622559,\n 41.67297593102651\n ],\n [\n -70.26091575622559,\n 41.68771986229327\n ],\n [\n -70.28074264526367,\n 41.68771986229327\n ],\n [\n -70.28074264526367,\n 41.67297593102651\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
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The National Hydrography Dataset Plus High Resolution (NHDPlus HR) is a scalable geospatial hydrography framework built from the High Resolution (1:24,000-scale or better) National Hydrography Dataset (NHD), nationally complete Watershed Boundary Dataset (WBD), and ⅓-arc-second (10-meter ground spacing) 3D Elevation Program (3DEP) digital elevation model (DEM) data. The NHDPlus HR brings modeling and assessment to a local neighborhood level while nesting seamlessly into the national context.
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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191096","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Moore, R.B., McKay, L.D., Rea, A.H., Bondelid, T.R., Price, C.V., Dewald, T.G., and Johnston, C.M., 2019, User's guide for the national hydrography dataset plus (NHDPlus) high resolution: U.S. Geological Survey Open-File Report 2019–1096, 66 p., https://doi.org/10.3133/ofr20191096.","productDescription":"Report: x, 66 p.; Dataset","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091493","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":399414,"rank":4,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The key to Thick-billed Longspur (Rhynchophanes mccownii) management is providing short, sparsely vegetated native grasslands of adequate size. Mixed-grass prairies can be made suitable for breeding Thick-billed Longspurs by implementing moderate-to-heavy or season-long grazing. Thick-billed Longspurs have been reported to use habitats with 5–42 centimeters (cm) average vegetation height, 3–7 cm visual obstruction reading, 15–67 percent grass cover, less than (<) 8 percent forb cover, <7 percent shrub cover, 2–60 percent bare ground, 10–63 percent litter cover, and <5 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842Y","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Rabie, P.A., Wooten, T.L., and Euliss, B.R., 2019, The effects of management practices on grassland birds—Thick-billed Longspur (Rhynchophanes mccownii) (ver. 1.1, March 2022), chap. Y of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 10 p., https://doi.org/10.3133/pp1842Y.","productDescription":"iv, 10 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095144","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":370325,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/y/coverthb2.jpg"},{"id":370326,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/y/pp1842y.pdf","text":"Report","size":"1.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–Y"},{"id":397826,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/1842/y/versionhist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: December 17, 2019; Version 1.1: March 31, 2022","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Newly emerging plants provide the best forage for herbivores. To exploit this fleeting resource, migrating herbivores align their movements to surf the wave of spring green-up. With new technology to track migrating animals, the Green Wave Hypothesis has steadily gained empirical support across a diversity of migratory taxa. This hypothesis assumes the green wave is controlled by variation in climate, weather, and topography, and its progression dictates the timing, pace, and extent of migrations. However, aggregate grazers that are also capable of engineering grassland ecosystems make some of the world’s most impressive migrations, and it is unclear how the green wave determines their movements. Here we show that Yellowstone’s bison (Bison bison) do not choreograph their migratory movements to the wave of spring green-up. Instead, bison modify the green wave as they migrate and graze. While most bison surfed during early spring, they eventually slowed and let the green wave pass them by. However, small-scale experiments indicated that feedback from grazing sustained forage quality. Most importantly, a 6-fold decadal shift in bison density revealed that intense grazing caused grasslands to green up faster, more intensely, and for a longer duration. Our finding broadens our understanding of the ways in which animal movements underpin the foraging benefit of migration. The widely accepted Green Wave Hypothesis needs to be revised to include large aggregate grazers that not only move to find forage, but also engineer plant phenology through grazing, thereby shaping their own migratory movements.
Constraining the magnitude of past hydrological change may improve understanding and predictions of future shifts in water availability. Here we demonstrate that water-table depth, a sensitive indicator of hydroclimate, can be quantitatively reconstructed using Kr and Xe isotopes in groundwater. We present the first-ever measurements of these dissolved noble gas isotopes in groundwater at high precision (≤0.005‰ amu−1; 1σ), which reveal depth-proportional signals set by gravitational settling in soil air at the time of recharge. Analyses of California groundwater successfully reproduce modern groundwater levels and indicate a 17.9 ± 1.3 m (±1 SE) decline in water-table depth in Southern California during the last deglaciation. This hydroclimatic transition from the wetter glacial period to more arid Holocene accompanies a surface warming of 6.2 ± 0.6 °C (±1 SE). This new hydroclimate proxy builds upon an existing paleo-temperature application of noble gases and may identify regions prone to future hydrological change.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-019-13693-2","usgsCitation":"Seltzer, A.M., Ng, J., Danskin, W.R., Kulongoski, J.T., Gannon, R., Stute, M., and Severinghaus, J.P., 2019, Deglacial water-table decline in Southern California recorded by noble gas isotopes: Nature Communications, v. 10, 5739, 6 p., https://doi.org/10.1038/s41467-019-13693-2.","productDescription":"5739, 6 p.","ipdsId":"IP-108743","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":458949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-019-13693-2","text":"Publisher Index Page"},{"id":391384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Diego","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.44384765625,\n 32.56996256044998\n ],\n [\n -116.663818359375,\n 32.56996256044998\n ],\n [\n -116.663818359375,\n 32.99484290420988\n ],\n [\n -117.44384765625,\n 32.99484290420988\n ],\n [\n -117.44384765625,\n 32.56996256044998\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2019-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Seltzer, Alan M.","contributorId":192321,"corporation":false,"usgs":false,"family":"Seltzer","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":826385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ng, Jessica","contributorId":268304,"corporation":false,"usgs":false,"family":"Ng","given":"Jessica","email":"","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":826386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danskin, Wesley R. 0000-0001-8672-5501 wdanskin@usgs.gov","orcid":"https://orcid.org/0000-0001-8672-5501","contributorId":1034,"corporation":false,"usgs":true,"family":"Danskin","given":"Wesley","email":"wdanskin@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gannon, Riley 0000-0002-1239-1083","orcid":"https://orcid.org/0000-0002-1239-1083","contributorId":205967,"corporation":false,"usgs":true,"family":"Gannon","given":"Riley","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826389,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stute, Martin","contributorId":131127,"corporation":false,"usgs":false,"family":"Stute","given":"Martin","email":"","affiliations":[{"id":7254,"text":"Columbia University - Lamont Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":826390,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Severinghaus, Jeffery P. 0000-0001-8883-3119","orcid":"https://orcid.org/0000-0001-8883-3119","contributorId":268306,"corporation":false,"usgs":false,"family":"Severinghaus","given":"Jeffery","email":"","middleInitial":"P.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":826391,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228351,"text":"70228351 - 2019 - Evaluation of Potential Translocation Sites for an Imperiled Cyprinid, theHornyhead Chub","interactions":[],"lastModifiedDate":"2022-02-09T23:58:16.272557","indexId":"70228351","displayToPublicDate":"2019-12-15T17:52:09","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of Potential Translocation Sites for an Imperiled Cyprinid, theHornyhead Chub","docAbstract":"Translocation of isolated species into suitable habitats may help secure vulnerable, geographically limited species. Due to the decline of Wyoming Hornyhead Chub Nocomis biguttatus, conservation actions such as translocation of populations within the plausible historical range are being considered to improve population redundancy and resiliency to disturbance events. Translocation of Wyoming Hornyhead Chub must be rigorously evaluated because a hatchery stock does not exist, so all fish used in translocations will come from the wild population. We present an approach to identify best available translocation sites prior to translocation efforts taking place. We evaluated fish community composition and habitat conditions at 54 potential translocation sites for Hornyhead Chub within 12 streams of the North Platte River Basin of Wyoming. We used two analyses to identify translocation sites most similar to currently occupied Hornyhead Chub sites on the Laramie River: hurdle models to predict hypothetical abundance of Hornyhead Chub at translocation sites and non-metric multidimensional scaling (NMDS) with fish community and habitat conditions. Presence and abundance of Hornyhead Chub was related to lack of nonnative predators and habitat features characteristic of backwater and velocity refuge habitats. We used a rank scoring system to weight the outcomes of each analysis and the highest ranking translocation sites occurred at a historical locality, the Sweetwater River. Our approach may be appropriate for other at-risk species with isolated distributions and little historical data.
","language":"English","doi":"10.1002/nafm.10261","usgsCitation":"Hickerson, B.T., and Walters, A.W., 2019, Evaluation of Potential Translocation Sites for an Imperiled Cyprinid, theHornyhead Chub: Transactions of the American Fisheries Society, v. 39, p. 205-218, https://doi.org/10.1002/nafm.10261.","productDescription":"14 p.","startPage":"205","endPage":"218","ipdsId":"IP-098524","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Laramie River, North Platte River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.38635253906249,\n 41.970722347928096\n ],\n [\n -104.54315185546875,\n 41.970722347928096\n ],\n [\n -104.54315185546875,\n 42.20817645934742\n ],\n [\n -105.38635253906249,\n 42.20817645934742\n ],\n [\n -105.38635253906249,\n 41.970722347928096\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2019-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hickerson, Brian T.","contributorId":275272,"corporation":false,"usgs":false,"family":"Hickerson","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":833909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833908,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215096,"text":"70215096 - 2019 - Time to branch out? Application of hierarchical survival models in plant phenology","interactions":[],"lastModifiedDate":"2020-10-08T11:54:37.37837","indexId":"70215096","displayToPublicDate":"2019-12-15T14:38:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Time to branch out? Application of hierarchical survival models in plant phenology","docAbstract":"The sensitivity of phenology to environmental drivers can vary across geography and species. As such, models developed to predict phenology are typically site- or taxon-specific. Generation of site- and taxon-specific models is limited by the intensive in-situ phenological monitoring effort required to generate sufficient data to parameterize each model. Where in-situ phenological observations exist, the data are often subject to analytical issues due to the limited duration of any individual monitoring program, spotty site- and species- level coverage, lack of standardized methodology, and infrequent or variable census intervals. Together, these characteristics constrain our ability to make phenological inferences outside of select sites and taxa where long-duration, intensive monitoring has occurred. In this study, we leveraged two national, standardized phenology datasets to develop a multi-species and multi-site state-space survival model of the onset of deciduous tree and shrub spring (leaf out) and fall (leaf-color) events across temperate ecoregions of the United States. We used data from two national-scale phenological databases, a 9-year, broadly distributed dataset from the USA National Phenology Network and a 4-year dataset from the National Ecological Observatory Network, to quantify regional and interspecific variation in sensitivity to environmental drivers for both spring and fall leaf phenophases. Spring leaf out was generally promoted by longer days, spring growing degree day accumulation, overwinter chilling, and was suppressed by frost events, whereas fall leaf color was promoted by shorter days and cold accumulation. The sensitivity to most environmental drivers tended to be more variable among species than among the regions as defined here (EPA ecoregions of North America, excluding desert and tropical areas). The results of this study lay the groundwork for incorporating the growing collection of phenological observations into a generalized framework for predicting the transition states for any species, in any location.","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2019.107694","usgsCitation":"Elmendorf, S., Crimmins, T., Gerst, K.L., and Weltzin, J., 2019, Time to branch out? Application of hierarchical survival models in plant phenology: Agricultural and Forest Meteorology, v. 279, 107694, 8 p., https://doi.org/10.1016/j.agrformet.2019.107694.","productDescription":"107694, 8 p.","ipdsId":"IP-107695","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":458954,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2019.107694","text":"Publisher Index Page"},{"id":379194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"279","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Elmendorf, Sarah","contributorId":147651,"corporation":false,"usgs":false,"family":"Elmendorf","given":"Sarah","affiliations":[{"id":16880,"text":"National Ecological Observatory Network (NEON), 1685 38th St., Boulder, CO 80301, USA","active":true,"usgs":false}],"preferred":false,"id":800827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crimmins, Theresa 0000-0001-9592-625X","orcid":"https://orcid.org/0000-0001-9592-625X","contributorId":222414,"corporation":false,"usgs":false,"family":"Crimmins","given":"Theresa","email":"","affiliations":[{"id":40537,"text":"USA National Phenology Network, National Coordinating Office; University of Arizona, School of Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":800828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gerst, Katharine L.","contributorId":175227,"corporation":false,"usgs":false,"family":"Gerst","given":"Katharine","email":"","middleInitial":"L.","affiliations":[{"id":27543,"text":"National Phenology Network, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":800829,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weltzin, Jake 0000-0001-8641-6645 jweltzin@usgs.gov","orcid":"https://orcid.org/0000-0001-8641-6645","contributorId":196323,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake","email":"jweltzin@usgs.gov","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":800830,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216009,"text":"70216009 - 2019 - High rates of inflation during a noneruptive episode of seismic unrest at Semisopochnoi Volcano, Alaska in 2014–2015","interactions":[],"lastModifiedDate":"2020-11-11T14:35:23.35128","indexId":"70216009","displayToPublicDate":"2019-12-15T07:30:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"High rates of inflation during a noneruptive episode of seismic unrest at Semisopochnoi Volcano, Alaska in 2014–2015","docAbstract":"Magma intrusion rate is a key parameter in eruption triggering but is poorly quantified in existing geodetic studies. Here we examine two episodes of rapid inflation in this context. Two noneruptive microseismic swarms were recorded at Semisopochnoi Volcano, Alaska in 2014–2015. We use differential SAR techniques and TerraSAR‐X images to document surface deformation from 2011 to 2015, which comprises island‐wide radial inflation totaling ~25 cm (+/−1 cm) line of sight displacement in 2014–2015. Multiple source geometries are tested in an inversion of the deformation data, and InSAR data are best fit by a spheroid trending to the northeast and plunging to the southeast, with a major axis of ~4 km and minor axes of ~1 km, directly under the central caldera of Semisopochnoi. In 2014, a modeled influx of 0.043 km3 of magma caused line of sight displacement of ~17 cm. This magma was stored at a depth of ~8 km, until 2015 when 0.029 km3 was added. Along with the definition of inflation source parameters, the recorded seismic events are relocated using differential travel times. These relocated events outline a linear aseismic area within a larger zone of shallow (<10 km) seismicity. This aseismic region aligns with the centroid of the deformation model. Based on these geodetic and seismic models, the plumbing system at Semisopochnoi is interpreted as a spheroidal magma storage zone at a depth of ˜8 km below a linear feature of partial melt. The observed deformation and seismicity appear to result from rapid injection into this main storage region.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GC008720","usgsCitation":"Degrandpre, K., Pesicek, J.D., Lu, Z., DeShon, H.R., and Roman, D., 2019, High rates of inflation during a noneruptive episode of seismic unrest at Semisopochnoi Volcano, Alaska in 2014–2015: Journal of Geophysical Research, v. 20, no. 12, p. 6163-6186, https://doi.org/10.1029/2019GC008720.","productDescription":"24 p.","startPage":"6163","endPage":"6186","ipdsId":"IP-098799","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":380068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Semisopochnoi Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 179.47265625,\n 51.839171715043946\n ],\n [\n 179.77203369140625,\n 51.839171715043946\n ],\n [\n 179.77203369140625,\n 52.04742324502936\n ],\n [\n 179.47265625,\n 52.04742324502936\n ],\n [\n 179.47265625,\n 51.839171715043946\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Degrandpre, Kimberly","contributorId":244311,"corporation":false,"usgs":false,"family":"Degrandpre","given":"Kimberly","email":"","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":803746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pesicek, Jeremy D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":202042,"corporation":false,"usgs":true,"family":"Pesicek","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":803747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lu, Zhong","contributorId":199794,"corporation":false,"usgs":false,"family":"Lu","given":"Zhong","affiliations":[],"preferred":false,"id":803748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeShon, Heather R.","contributorId":244313,"corporation":false,"usgs":false,"family":"DeShon","given":"Heather","email":"","middleInitial":"R.","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":803749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roman, Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":803777,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70207382,"text":"70207382 - 2019 - Validating a landsat time-series of fractional component cover across western U.S. Rangelands","interactions":[],"lastModifiedDate":"2022-02-16T21:32:14.39285","indexId":"70207382","displayToPublicDate":"2019-12-13T19:22:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Validating a landsat time-series of fractional component cover across western U.S. Rangelands","docAbstract":"Western U.S. rangelands have been quantified as six fractional cover (0%–100%) components\nover the Landsat archive (1985–2018) at a 30 m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. Here, we used field data collected concurrently with high-resolution satellite (HRS) images over multiple locations (n = 42) and years. Field observations were used to train regression tree models, predicting the component cover across each HRS image. Our objectives were to evaluate the spatial and temporal relationships between HRS and BIT component cover and compare spatio-temporal climate responses. First, for each HRS site-year (n = 77) we averaged both the HRS and BIT predictions within each site separately and regressed the averages to quantify the temporal accuracy. Next, we regressed individual pixel values of corresponding HRS and BIT predictions to quantify the spatio-temporal accuracy. Results showed strong temporal correlations with an average R2 of 0.63 and Root Mean Square Error (RMSE) of 5.47% as well as strong spatio-temporal correlations with an average R2 of 0.52 and RMSE of 7.89% across components. Our approach increased the validation sample size relative to direct comparison of field observations. Validation results showed robust spatio-temporal relationships between HRS and BIT data, providing increased user confidence in the data.","language":"English","publisher":"MPDI","doi":"10.3390/rs11243009","usgsCitation":"Rigge, M.B., Homer, C.G., Shi, H., and Meyer, D.K., 2019, Validating a landsat time-series of fractional component cover across western U.S. Rangelands: Remote Sensing, v. 11, no. 24, 3009, 16 p.; Data release, https://doi.org/10.3390/rs11243009.","productDescription":"3009, 16 p.; Data release","ipdsId":"IP-113763","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458961,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11243009","text":"Publisher Index Page"},{"id":370436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396049,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90Q8BCP","text":"USGS data release","description":"USGS data release","linkHelpText":"Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands"}],"country":"Unites States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.05859375,\n 38.89103282648846\n ],\n [\n -115.31249999999999,\n 38.89103282648846\n ],\n [\n -115.31249999999999,\n 42.09822241118974\n ],\n [\n -120.05859375,\n 42.09822241118974\n ],\n [\n -120.05859375,\n 38.89103282648846\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.533203125,\n 41.04621681452063\n ],\n [\n -103.974609375,\n 41.04621681452063\n ],\n [\n -103.974609375,\n 45.213003555993964\n ],\n [\n -111.533203125,\n 45.213003555993964\n ],\n [\n -111.533203125,\n 41.04621681452063\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"24","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":777869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":777870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":777871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyer, Debra K. 0000-0002-8841-697X dkmeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":3145,"corporation":false,"usgs":true,"family":"Meyer","given":"Debra","email":"dkmeyer@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":777872,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207249,"text":"ofr20191128 - 2019 - Depth to bedrock based on modeling of gravity data of the eastern part of Edwards Air Force Base, California","interactions":[],"lastModifiedDate":"2019-12-14T06:09:21","indexId":"ofr20191128","displayToPublicDate":"2019-12-13T11:19:45","publicationYear":"2019","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":"2019-1128","displayTitle":"Depth to Bedrock Based on Modeling of Gravity Data of the Eastern Part of Edwards Air Force Base, California","title":"Depth to bedrock based on modeling of gravity data of the eastern part of Edwards Air Force Base, California","docAbstract":"We describe a gravity survey acquired to determine the thickness of basin-fill deposits (depth to bedrock) and to delineate geologic structures that might influence groundwater flow beneath the eastern part of Edwards Air Force Base, California. Inversion of these gravity data combined with geologic map and well information provides an estimate of the thickness of basin-fill deposits (defined here as Cenozoic sedimentary and volcanic rocks). After removing the gravitational effect of the basin-fill deposits, the inversion also results in a gravity map that reflects variations in the bedrock density. The depth to bedrock is generally less than 1 kilometer in the map area, except for localized depressions north and south of Kramer Hills, northwest-trending pockets about 4 kilometers northeast of Rogers Lake, and a large depression southwest of Rogers Lake. In the area near Leuhman Ridge, depth to bedrock is shallow. The Spring and Leuhman faults do not coincide with large variations in basin-fill thickness or with prominent gravity gradients, suggestive of minor vertical displacement and minor horizontal displacement at their southeastern mapped extents where they project across a large gravity low.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191128","collaboration":"Prepared in cooperation with the Air Force Civil Engineer Center","usgsCitation":"Langenheim, V.E., Morita, A., Christensen, A.H., Cromwell, G., and Ely, C., 2019, Depth to bedrock based on modeling of gravity data of the eastern part of Edwards Air Force Base, California: U.S. Geological Survey Open-File Report 2019–1128, 12 p., https://doi.org/10.3133/ofr20191128.\n","productDescription":"Report: iv, 12 p.; Dataset; Metadata","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109233","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":370252,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1128"},{"id":370253,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_basementwells.csv","text":"Basement Wells","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2019-1128"},{"id":370254,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_basinwells.csv","text":"Basin Wells","size":"6.5 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2019-1128"},{"id":370251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1128/coverthb.jpg"},{"id":370255,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_depthtobedrock.csv","text":"Depth to Bedrock","size":"1 MB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2019-1128"},{"id":370256,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_gravitydata.csv","text":"Gravity Data","size":"225 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2019-1128"},{"id":370257,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_metadata.xml","size":"22 KB xml","description":"OFR 2019-1128"},{"id":370258,"rank":8,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_readmedata.rtf","size":"15 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2019-1128"}],"country":"United States","state":"California","otherGeospatial":"Edwards Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.10302734374999,\n 34.7506398050501\n ],\n [\n -117.65258789062499,\n 34.7506398050501\n ],\n [\n -117.65258789062499,\n 35.0254981588326\n ],\n [\n -118.10302734374999,\n 35.0254981588326\n ],\n [\n -118.10302734374999,\n 34.7506398050501\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Worldwide, human activities have modified hydrology and nutrient loading regimes in coastal wetlands. Understanding the interplay between these drivers and subsequent response of wetland plant communities is essential to informing wetland management and restoration efforts. Recent restoration strategies in Louisiana proposes to use sediment diversions from the Mississippi River to build land in adjacent wetlands and reduce the rate of land to open water conversion. In conjunction with sediment delivery, diversions can increase nutrient loads and water levels in the receiving basins. We conducted a greenhouse mesocosm experiment in which we exposed three common tidal freshwater and brackish marsh plants (Panicum hemitomon, Sagittaria lancifolia, and Spartina patens) to two nitrate loading rates [high (35 g N m2 year−1) and low (0.25 g N m2 year−1)], and two flooding treatments (with and without diversion pulsing). Experimental units were set at two different elevations within the treatment tanks to simulate both a healthy and degraded marsh. Plant growth metrics and soil physicochemical properties were measured monthly. Final total biomass was determined at the study’s conclusion. Growth responses differed between species but were not significantly influenced by the treatments. Soil redox potential decreased significantly following the increase in flooding associated with the diversion pulse, but recovered to pre-diversion levels after a 3-month recovery period. Our study suggests short flooding pulses with a recovery period may be key for maintaining healthy marshes, however there remains a need for longer-term empirical studies to understand marsh response to pressures associated with river sediment diversions over time.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-019-09699-8","usgsCitation":"McCoy, M.M., Sloey, T.M., Howard, R.J., and Hester, M.W., 2019, Response of tidal marsh vegetation to pulsed increases in flooding and nitrogen: Wetlands Ecology and Management, v. 28, p. 119-135, https://doi.org/10.1007/s11273-019-09699-8.","productDescription":"17 p.","startPage":"119","endPage":"135","ipdsId":"IP-106945","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":370302,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Jean Lafitte National Historical Park and Preserve, Lake Salvador Wildlife Management Area ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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M","contributorId":149516,"corporation":false,"usgs":false,"family":"Sloey","given":"Taylor","email":"","middleInitial":"M","affiliations":[{"id":17763,"text":"University of Louisiana, Lafayette","active":true,"usgs":false}],"preferred":false,"id":777557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Rebecca J. 0000-0001-7264-4364 howardr@usgs.gov","orcid":"https://orcid.org/0000-0001-7264-4364","contributorId":2429,"corporation":false,"usgs":true,"family":"Howard","given":"Rebecca","email":"howardr@usgs.gov","middleInitial":"J.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":777555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hester, Mark W.","contributorId":195572,"corporation":false,"usgs":false,"family":"Hester","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":34316,"text":"University of Louisiana at Lafayette, Lafayette, LA, USA","active":true,"usgs":false}],"preferred":false,"id":777558,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207510,"text":"70207510 - 2019 - Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model","interactions":[],"lastModifiedDate":"2019-12-22T14:03:15","indexId":"70207510","displayToPublicDate":"2019-12-12T14:00:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model","docAbstract":"Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe in western North America, for example, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modelling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different Plant Function Types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrush-steppe. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP), using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach: 1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. 2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. 3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two Eddy Covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent.","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-12-4585-2019","usgsCitation":"Pandit, K., Dasthi, H., Glenn, N., Flores, A., Maguire, K.C., Shinneman, D.J., Flerchinger, G., and Fellow, A., 2019, Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model: Geoscientific Model Development, v. 12, p. 4585-4601, https://doi.org/10.5194/gmd-12-4585-2019.","productDescription":"17 p.","startPage":"4585","endPage":"4601","ipdsId":"IP-102648","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":458969,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-12-4585-2019","text":"Publisher Index Page"},{"id":370607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.56347656249999,\n 42.032974332441405\n ],\n [\n -118.16894531249999,\n 35.35321610123823\n ],\n [\n -112.2802734375,\n 34.59704151614417\n ],\n [\n -109.248046875,\n 38.37611542403604\n ],\n [\n -110.0830078125,\n 43.13306116240612\n ],\n [\n -112.8955078125,\n 44.02442151965934\n ],\n [\n -115.6201171875,\n 43.58039085560784\n ],\n [\n -119.35546875000001,\n 44.15068115978094\n ],\n [\n -121.025390625,\n 44.08758502824516\n ],\n [\n -122.56347656249999,\n 42.032974332441405\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pandit, Karun","contributorId":221464,"corporation":false,"usgs":false,"family":"Pandit","given":"Karun","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":778308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dasthi, Hamid","contributorId":221465,"corporation":false,"usgs":false,"family":"Dasthi","given":"Hamid","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":778309,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, Nancy","contributorId":181558,"corporation":false,"usgs":false,"family":"Glenn","given":"Nancy","affiliations":[],"preferred":false,"id":778310,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flores, Alejandro","contributorId":221466,"corporation":false,"usgs":false,"family":"Flores","given":"Alejandro","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":778311,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maguire, Kaitlin C. 0000-0001-8193-2384","orcid":"https://orcid.org/0000-0001-8193-2384","contributorId":203419,"corporation":false,"usgs":true,"family":"Maguire","given":"Kaitlin","email":"","middleInitial":"C.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778312,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778307,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flerchinger, Gerald","contributorId":221467,"corporation":false,"usgs":false,"family":"Flerchinger","given":"Gerald","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":778313,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fellow, Aaron","contributorId":221468,"corporation":false,"usgs":false,"family":"Fellow","given":"Aaron","email":"","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":778314,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210612,"text":"70210612 - 2019 - Generation of lamprey monoclonal antibodies (Lampribodies) using the phage display system","interactions":[],"lastModifiedDate":"2020-06-12T17:22:56.373722","indexId":"70210612","displayToPublicDate":"2019-12-12T12:19:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5966,"text":"Biomolecules","active":true,"publicationSubtype":{"id":10}},"title":"Generation of lamprey monoclonal antibodies (Lampribodies) using the phage display system","docAbstract":"The variable lymphocyte receptors (VLRs) consist of leucine rich repeats (LRRs) and comprise the humoral antibodies produced by lampreys and hagfishes. The diversity of the molecules is generated by stepwise genomic rearrangements of LRR cassettes dispersed throughout the VLRB locus. Previously, target-specific monovalent VLRB antibodies were isolated from sea lamprey larvae after immunization with model antigens. Further, the cloned VLR cDNAs from activated lamprey leukocytes were transfected into human cell lines or yeast to select best binders. Here, we expand on the overall utility of the VLRB technology by introducing it into a filamentous phage display system. We first tested the efficacy of isolating phage into which known VLRB molecules were cloned after a series of dilutions. These experiments showed that targeted VLRB clones could easily be recovered even after extensive dilutions (1 to 109). We further utilized the system to isolate target-specific “lampribodies” from phage display libraries from immunized animals and observed an amplification of binders with relative high affinities by competitive binding. The lampribodies can be individually purified and ostensibly utilized for applications for which conventional monoclonal antibodies are employed.
","language":"English","publisher":"MDPI","doi":"10.3390/biom9120868","usgsCitation":"Hassan, K.M., Hansen, J.D., Herrin, B.R., and Amemiya, C.T., 2019, Generation of lamprey monoclonal antibodies (Lampribodies) using the phage display system: Biomolecules, v. 9, no. 12, 868, 18 p., https://doi.org/10.3390/biom9120868.","productDescription":"868, 18 p.","ipdsId":"IP-107248","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":458972,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/biom9120868","text":"Publisher Index Page"},{"id":375562,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Hassan, Khan M A","contributorId":225255,"corporation":false,"usgs":false,"family":"Hassan","given":"Khan","email":"","middleInitial":"M A","affiliations":[{"id":41083,"text":"University of California-Merced, Molecular Cell Biology, Merced CA 95343","active":true,"usgs":false}],"preferred":false,"id":790844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, John D. 0000-0002-3006-2734","orcid":"https://orcid.org/0000-0002-3006-2734","contributorId":220725,"corporation":false,"usgs":true,"family":"Hansen","given":"John","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":790845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrin, Brantley R","contributorId":225256,"corporation":false,"usgs":false,"family":"Herrin","given":"Brantley","email":"","middleInitial":"R","affiliations":[{"id":41084,"text":"Emory University, Department of Pathology and Laboratory Medicine, Atlanta GA 30322 USA","active":true,"usgs":false}],"preferred":false,"id":790846,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amemiya, Chris T","contributorId":225257,"corporation":false,"usgs":false,"family":"Amemiya","given":"Chris","email":"","middleInitial":"T","affiliations":[{"id":41083,"text":"University of California-Merced, Molecular Cell Biology, Merced CA 95343","active":true,"usgs":false}],"preferred":false,"id":790847,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208062,"text":"70208062 - 2019 - High-resolution and accurate topography reconstruction of Mount Etna from Pleiades satellite data","interactions":[],"lastModifiedDate":"2020-01-29T16:34:42","indexId":"70208062","displayToPublicDate":"2019-12-12T07:32:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"High-resolution and accurate topography reconstruction of Mount Etna from Pleiades satellite data","docAbstract":"The areas characterized by dynamic and rapid morphological changes need accurate topography information with frequent updates, especially if these are populated and involve infrastructures. This is particularly true in active volcanic areas such as Mount (Mt.) Etna, located in the northeastern portion of Sicily, Italy. The Mt. Etna volcano is periodically characterized by explosive and effusive eruptions and represents a potential hazard for several thousands of local people and hundreds of tourists present on the volcano itself. In this work, a high-resolution, high vertical accuracy digital surface model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set. We believe that this is the first time that the ASP using Pleiades imagery has been applied to Mt. Etna with sub-meter vertical root mean square error (RMSE) results. The model covers an area of about 400 km2 with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical RMSE of 0.78 m. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short amount of processing time, making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11242983","usgsCitation":"Palaseanu-Lovejoy, M., Bisson, M., Spinetti, C., Buongiorno, M.F., Alexandrov, O., and Cecere, T., 2019, High-resolution and accurate topography reconstruction of Mount Etna from Pleiades satellite data: Remote Sensing, v. 11, no. 24, 2983, 17 p., https://doi.org/10.3390/rs11242983.","productDescription":"2983, 17 p.","ipdsId":"IP-112349","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":458977,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11242983","text":"Publisher Index Page"},{"id":437261,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IGLDYE","text":"USGS data release","linkHelpText":"Digital Surface Model of Mt. Etna, Italy, derived from 2015 Pleiades Satellite Imagery"},{"id":371637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mount Etna","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 14.84733581542969,\n 37.62238973852369\n ],\n [\n 15.128173828125,\n 37.62238973852369\n ],\n [\n 15.128173828125,\n 37.84015683604136\n ],\n [\n 14.84733581542969,\n 37.84015683604136\n ],\n [\n 14.84733581542969,\n 37.62238973852369\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"24","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":780322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bisson, Marina 0000-0002-7104-9210","orcid":"https://orcid.org/0000-0002-7104-9210","contributorId":221724,"corporation":false,"usgs":false,"family":"Bisson","given":"Marina","email":"","affiliations":[{"id":40408,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Della Faggiola, Pisa, 56126, Italy","active":true,"usgs":false}],"preferred":false,"id":780323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spinetti, Claudia 0000-0002-1861-5666","orcid":"https://orcid.org/0000-0002-1861-5666","contributorId":221725,"corporation":false,"usgs":false,"family":"Spinetti","given":"Claudia","email":"","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":780324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buongiorno, Maria Fabrizia 0000-0002-6095-6974","orcid":"https://orcid.org/0000-0002-6095-6974","contributorId":221726,"corporation":false,"usgs":false,"family":"Buongiorno","given":"Maria","email":"","middleInitial":"Fabrizia","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":780325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alexandrov, Oleg","contributorId":167662,"corporation":false,"usgs":false,"family":"Alexandrov","given":"Oleg","email":"","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":780326,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cecere, Thomas 0000-0001-5254-8404 tcecere@usgs.gov","orcid":"https://orcid.org/0000-0001-5254-8404","contributorId":221727,"corporation":false,"usgs":true,"family":"Cecere","given":"Thomas","email":"tcecere@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":780327,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208671,"text":"70208671 - 2019 - A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat","interactions":[],"lastModifiedDate":"2020-02-24T19:21:44","indexId":"70208671","displayToPublicDate":"2019-12-11T19:18:17","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat","docAbstract":"Conservation management often requires decision-making without perfect knowledge of the at-risk species or ecosystem. Species distribution models (SDMs) are useful but largely under-utilized due to model uncertainty. We provide a case study that utilizes an ensemble modeling approach of two independently derived SDMs to explicitly address common modeling impediments and to directly inform conservation decision-making for piping plovers in a heavily populated mid-Atlantic (USA) coastal zone. We summarized previously published Bayesian network and maximum entropy modeling approaches to highlight similarities and differences in model structure, and we compared the relative importance of predictors used. Despite marked differences in analytical approach, the relative importance of factors driving nest-site selection was consistent. Comparison of raw suitability scores revealed high dissimilarity between modeling approaches, but models demonstrated considerable agreement when comparing a binary (suitable/unsuitable) measure of suitability. Instances of model consensus (i.e., overlapping areas of predicted piping plover nesting habitat between models) provide a stronger ‘signal’ in model results, reducing uncertainty related to biases or errors associated with either model. We tested model accuracy using a common dataset of plover nests initiated within the focal areas between 2013 and 2015, and we examined congruency in model outputs. Nearly 90% of all nests occurred in areas predicted suitable by at least one model, and at least 33% of the total nests were predicted in areas suitable by both. Because models predominantly agreed on what drives piping plover nest-site selection, areas predicted suitable by a single model should not be discounted. This case study demonstrates how models can effectively inform conservation planning by explicitly identifying the management objective, presenting robust evidence to allow managers to evaluate outcomes of alternative management decisions, and clearly communicating results that address real-world conservation problems. The results presented here can greatly increase the piping plover management community’s ability to prioritize candidate sites for future protection, manage existing nesting habitat appropriately, and make a compelling case for conservation actions against competing land use objectives. ","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.150","usgsCitation":"Maslo, B., Zeigler, S., Drake, E., Pover, T., and Plant, N.G., 2019, A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat: Conservation Science and Practice, v. 2, no. 2, e150, 18 p., https://doi.org/10.1111/csp2.150.","productDescription":"e150, 18 p.","ipdsId":"IP-111943","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":458978,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.150","text":"Publisher Index Page"},{"id":372594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.24310302734375,\n 40.697299008636755\n ],\n [\n -73.6578369140625,\n 40.64938745451835\n ],\n [\n -73.9324951171875,\n 40.66188943992171\n ],\n [\n -74.102783203125,\n 40.65355504328839\n ],\n [\n -74.24560546875,\n 40.57015381856105\n ],\n [\n -74.28131103515625,\n 40.46575594018434\n ],\n [\n -74.08355712890625,\n 40.35700974577561\n ],\n [\n -74.06158447265624,\n 40.21873275657034\n ],\n [\n -74.20440673828125,\n 39.96238554917605\n ],\n [\n -74.36920166015625,\n 39.66702799810167\n ],\n [\n -74.56146240234375,\n 39.48496522541802\n ],\n [\n -74.67681884765624,\n 39.34491849236129\n ],\n [\n -74.5751953125,\n 39.27266344858914\n ],\n [\n -74.34722900390625,\n 39.35341418045878\n ],\n [\n -73.98468017578125,\n 39.72620102617506\n ],\n [\n -73.95721435546875,\n 40.113789191575236\n ],\n [\n -73.82537841796875,\n 40.46157664398329\n ],\n [\n -73.49578857421875,\n 40.55763465737646\n ],\n [\n -73.23211669921875,\n 40.697299008636755\n ],\n [\n -73.24310302734375,\n 40.697299008636755\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Maslo, Brooke","contributorId":192146,"corporation":false,"usgs":false,"family":"Maslo","given":"Brooke","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":782951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zeigler, Sara L. 0000-0002-5472-769X","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":222703,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":782950,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drake, Evan","contributorId":222704,"corporation":false,"usgs":false,"family":"Drake","given":"Evan","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":782952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pover, Todd","contributorId":222705,"corporation":false,"usgs":false,"family":"Pover","given":"Todd","email":"","affiliations":[{"id":40592,"text":"Conserve Wildlife Foundation of New Jersey","active":true,"usgs":false}],"preferred":false,"id":782954,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":782953,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198586,"text":"sir20185111 - 2019 - Recent sandy deposits at five northern California coastal wetlands — Stratigraphy, diatoms, and implications for storm and tsunami hazards","interactions":[],"lastModifiedDate":"2022-04-22T21:09:08.356356","indexId":"sir20185111","displayToPublicDate":"2019-12-11T15:33:18","publicationYear":"2019","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":"2018-5111","displayTitle":"Recent Sandy Deposits at Five Northern California Coastal Wetlands — Stratigraphy, Diatoms, and Implications for Storm and Tsunami Hazards","title":"Recent sandy deposits at five northern California coastal wetlands — Stratigraphy, diatoms, and implications for storm and tsunami hazards","docAbstract":"A recent geological record of inundation by tsunamis or storm surges is evidenced by deposits found within the first few meters of the modern surface at five wetlands on the northern California coast. The study sites include three locations in the Crescent City area (Marhoffer Creek marsh, Elk Creek wetland, and Sand Mine marsh), O’rekw marsh in the lower Redwood Creek alluvial valley, and Pillar Point marsh at the northern end of Half Moon Bay.
Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
The location of a point on the landscape within a stream network (hydrologic position) can be an important predictive measure in hydrology. Hydrologic position is defined here by two metrics: lateral position and distance from stream to divide, both measured horizontally. Lateral position (dimensionless) is the relative position of a point between the stream and its watershed divide. Distance from stream to divide (units of length) is an indicator of position within a watershed: generally small near a confluence and generally large in headwater areas. Watersheds and watershed divides are defined here by Thiessen polygons rather than topographic divides. Lateral position and distance from stream to divide are also defined in the context of hydrologic order. Hydrologic order “n” is defined as the network of streams, and associated divides, of order n and higher. And given that a point can have different positions in different hydrologic orders the term multiorder hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. MOHP was mapped across the conterminous United States for nine hydrologic orders at a spatial resolution of 30 m (about 8.7 billion pixels). There are 18 metrics for each pixel. Four case studies are presented that use MOHP metrics as explanatory factors in random forest machine learning models. The case studies show that lower order MOHP metrics can serve as indicators of hydrologic process while higher‐order metrics serve as indicators of location. MOHP is shown to have utility as a predictor variable across a large range of scales (50,000 to 8,000,000 km2).
Glass composition-based correlations of volcanic ash (tephra) traditionally rely on extensive manual plotting. Many previous statistical methods for testing correlations are limited by using geochemical means, masking diagnostic variability. We suggest that machine learning classifiers can expedite correlation, quickly narrowing the list of likely candidates using well-trained models. Eruptives from Alaska's Aleutian Arc-Alaska Peninsula and Wrangell volcanic field were used as a test environment for 11 supervised classification algorithms, trained on nearly 2000 electron probe microanalysis measurements of glass major oxides, representing 10 volcanic sources. Artificial neural networks and random forests were consistently among the top-performing learners (accuracy and kappa > 0.96). Their combination as an average ensemble effectively improves their performance. Using this combined model on tephras from Eklutna Lake, south-central Alaska, showed that predictions match traditional methods and can speed correlation. Although classifiers are useful tools, they should aid expert analysis, not replace it. The Eklutna Lake tephras are mostly from Redoubt Volcano. Besides tephras from known Holocene-active sources, Holocene tephra geochemically consistent with Pleistocene Emmons Lake Volcanic Center (Dawson tephra), but from a yet unknown source, is evident. These tephras are mostly anchored by a highly resolved varved chronology and represent new important regional stratigraphic markers.