In 2007 the U.S. Geological Survey (USGS) completed an assessment of undiscovered, technically recoverable oil and gas resources in the East Greenland Rift Basins Province of Northeast Greenland. The province was selected as the prototype for the U.S. Geological Survey Circum-Arctic Resource Appraisal (CARA). In collaboration with the Geological Survey of Denmark and Greenland (GEUS), the province was subdivided into nine geologically distinctive areas. Seven of these were defined as Assessment Units (AUs), of which five were quantitatively assessed. These are: North Danmarkshavn Salt Basin, South Danmarkshavn Basin, Thetis Basin, Northeast Greenland Volcanic Province, and Liverpool Land Basin. Jameson Land Basin and the Jameson Land Basin Subvolcanic Extension were defined as AUs but were not quantitatively assessed.
Onshore studies by GEUS and other organizations suggest that at least four stratigraphic intervals may contain potential source rocks for petroleum. The geological history of related areas in western Norway and burial history modeling suggest that Upper Jurassic strata are most likely to contain petroleum source rocks. A wide variety of possible trapping mechanisms are expected within the province. Potential traps in the North Danmarkshavn Salt Basin AU are dominated by structures formed through salt tectonics; those in the South Danmarkshavn Basin and the Northeast Greenland Volcanic Province are characterized by extensional structures and by stratigraphic traps in submarine fan complexes. Prospective inversion structures of Tertiary age are present along the western margin of South Danmarkshavn Basin AU, and the large horst block structures that separate the Danmarkshavn and Thetis Basins may provide numerous opportunities for traps in fault blocks and along various facies-related permeability barriers. Possible reservoirs include shallow marine to nonmarine sandstones of Middle Jurassic age, sandstones in Upper Jurassic synrift deposits, Cretaceous sandstones in submarine fan complexes, sandstones in Paleogene progradational sequences, and in Upper Carboniferous to Lower Permian warm-water carbonate sequences, especially in northern Danmarkshavn Basin. Marine shales are expected to provide the main sealing lithologies in most AUs.
Most of the undiscovered oil, gas, and natural gas liquids are likely to be in the offshore areas of the province and are inferred to belong to an Upper Jurassic Composite Total Petroleum System. The USGS estimated that the East Greenland Rift Basins Province contains approximately (mean) 31,400 million barrels oil equivalent (MMBOE) of oil, natural gas, and natural gas liquids. Of the five assessed AUs, North Danmarkshavn Salt Basin and the South Danmarkshavn Basin are estimated to contain most of the undiscovered petroleum.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824K","usgsCitation":"Gautier, D.L., 2019, Geology and assessment of undiscovered oil and gas resources of the East Greenland Rift Basins Province, 2008, chap. K of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 20 p., https://doi.org/10.3133/pp1824K.","productDescription":"Report: vii, 20 p.; Appendices 1-7","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-051000","costCenters":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":368214,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K"},{"id":368213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/k/coverthb.jpg"},{"id":368223,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appendixes.zip","text":"Appendixes","size":"150 KB","linkFileType":{"id":6,"text":"zip"},"description":"Professional Paper 1824 K"},{"id":368222,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx7.pdf","text":"Appendix 7","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Jameson Land Basin Subvolcanic Extension Assessment Unit"},{"id":368221,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx6.pdf","text":"Appendix 6","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Jameson Land Basin Assessment Unit"},{"id":368220,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx5.pdf","text":"Appendix 5","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Liverpool Land Basin Assessment Unit"},{"id":368218,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx3.pdf","text":"Appendix 3","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Northeast Greenland Volcanic Province Assessment Unit"},{"id":368217,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx2.pdf","text":"Appendix 2","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the South Danmarkshavn Basin Assessment Unit"},{"id":368216,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx1.pdf","text":"Appendix 1","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the North Danmarkshavn Salt Basin Assessment Unit"},{"id":368219,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx4.pdf","text":"Appendix 4","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Thetis Basin Assessment Unit"}],"country":"Greenland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -21.181640624999996,\n 70.11048478105927\n ],\n [\n -31.9921875,\n 69.1312712296365\n ],\n [\n -43.505859375,\n 66.65297740055279\n ],\n [\n -46.7578125,\n 62.103882522897855\n ],\n [\n -43.33007812499999,\n 57.70414723434193\n ],\n [\n -36.03515625,\n 60.973107109199404\n ],\n [\n -21.357421875,\n 68.33437594128185\n ],\n [\n -21.181640624999996,\n 70.11048478105927\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
The construction of Glen Canyon Dam in northern Arizona has greatly reduced the supply of sand to the Colorado River corridor through Glen Canyon National Recreation Area and Grand Canyon National Park, hereafter referred to as Glen Canyon and Grand Canyon, respectively. This deficit has strongly affected the natural sediment cycle in this iconic landscape and has lowered the availability of windblown (aeolian) river sand that previously shielded hundreds of unique prehistoric and historic cultural sites. U.S. Geological Survey scientists and their cooperators have conducted a range of studies to assess whether, and under what circumstances, river-derived sand can still reach and protect these sites under current dam operations. Results indicate that most cultural sites hosted in river-derived sand have an elevated risk of erosion that threatens their long-term preservation. However, repeated high-water releases from the dam following downstream tributary inputs of sand to the Colorado River, combined with riparian vegetation removal, could offset some of the erosion caused by wind and precipitation-driven hillslope runoff at some locales. These findings are helping managers conserve limited sand resources to preserve river-corridor cultural sites while still meeting the growing demands for hydropower and water in the Southwestern United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193054","usgsCitation":"Cook, T., East, A., Fairley, H., and Sankey, J.B., 2019, Managing sand along the Colorado River to protect cultural sites downstream of Glen Canyon Dam: U.S. Geological Survey Fact Sheet 2019–3054, 6 p., https://doi.org/10.3133/fs20193054.","productDescription":"6 p. ","numberOfPages":"6","ipdsId":"IP-110267","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":368207,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3054/fs20193054.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2019-3054"},{"id":368206,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3054/coverthb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.0435791015625,\n 35.53222622770337\n ],\n [\n -111.3409423828125,\n 35.53222622770337\n ],\n [\n -111.3409423828125,\n 36.98939086733937\n ],\n [\n -114.0435791015625,\n 36.98939086733937\n ],\n [\n -114.0435791015625,\n 35.53222622770337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Southwest Biological Science Center
Grand Canyon Monitoring and Research Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
United States
Temporary wetlands are characterized by frequent drying resulting in a unique, highly specialized assemblage of often rare or specialized plant and animal species. They are found on all continents and in a variety of landscape settings. Although accurate estimates of the abundance of temporary wetlands are available in only a few countries, global estimations identify a decline in number and quality. The key environmental factors driving the structure of ecological communities in temporary wetlands are the duration, timing, frequency and predictability of the aquatic and dry phases, which varies greatly with region and hydrogeomorphic setting. Temporary wetlands have been historically neglected, but improved social awareness of the functions and values of, and increases in scientific interest, suggest that this is changing. They play an ecological role in both global cycles (i.e., CO2 emissions) and biodiversity (in proportion to their size, they contribute disproportionately to regional and global biodiversity). Moreover, they provide valuable ecosystem services including wildlife habitat, nutrient flux to adjacent ecosystems, flood control, water filtration, and cultural services. Effective conservation of temporary wetlands requires addressing threats (i.e., inconsistent and inadequate regulatory protections; climate change; changes in land use) and management challenges (i.e., management at both local and landscape scales; incomplete understanding of the ecosystem services provided by them; the need to enhance inventories). The most suitable approaches for conserving temporary wetlands include (1) regulations or other forms of protection; (2) sustainable management; (3) restoration and creation; and (4) collaborative conservation.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference Module in Earth Systems and Environmental Sciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-409548-9.12003-2","usgsCitation":"Boix, D., Calhoun, A.J., Mushet, D.M., Bell, K.P., Fitzsimons, J.A., and Isselin-Nondedeu, F., 2019, Conservation of temporary wetlands, chap. of Reference Module in Earth Systems and Environmental Sciences, https://doi.org/10.1016/B978-0-12-409548-9.12003-2.","ipdsId":"IP-109438","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":502628,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/chapter/Conservation_of_Temporary_Wetlands/20674152","text":"External Repository"},{"id":369820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boix, Dani","contributorId":177733,"corporation":false,"usgs":false,"family":"Boix","given":"Dani","affiliations":[],"preferred":false,"id":774855,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calhoun, Aram J.K.","contributorId":177732,"corporation":false,"usgs":false,"family":"Calhoun","given":"Aram","email":"","middleInitial":"J.K.","affiliations":[{"id":13065,"text":"Department of Wildlife, Fisheries, and Conservation Biology, University of Maine","active":true,"usgs":false}],"preferred":false,"id":774856,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":774854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bell, Kathleen P.","contributorId":171584,"corporation":false,"usgs":false,"family":"Bell","given":"Kathleen","email":"","middleInitial":"P.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":774857,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzsimons, James A.","contributorId":177734,"corporation":false,"usgs":false,"family":"Fitzsimons","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":774858,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isselin-Nondedeu, Francis","contributorId":177735,"corporation":false,"usgs":false,"family":"Isselin-Nondedeu","given":"Francis","email":"","affiliations":[],"preferred":false,"id":774859,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70207517,"text":"70207517 - 2019 - Modeling control of Common Carp (Cyprinus carpio) in a shallow lake–wetland system","interactions":[],"lastModifiedDate":"2019-12-21T10:37:06","indexId":"70207517","displayToPublicDate":"2019-10-09T10:35:06","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Modeling control of Common Carp (Cyprinus carpio) in a shallow lake–wetland system","docAbstract":"The introduction of Common Carp (Cyprinus carpio) into North American waterways has led to widespread alteration of aquatic ecosystems. Control of this invader has proven extremely difficult due to its capacity for rapid population growth. To help understand how Common Carp can potentially be controlled we developed a population dynamics model (CarpMOD) to explore the efficacy of active and passive control measures that impose mortality on multiple life stages (embryos, juveniles and adults). We applied CarpMOD to Common Carp in Malheur Lake, a large shallow lake in Southeast Oregon, USA. Simulated control measures included commercial harvest of adults, trapping of juveniles, embryo electroshocking, and passive removal imposed via avian predation. Results from CarpMOD suggest that no single active removal method would decrease Common Carp biomass below the targeted 50 kg/ha threshold. Combinations of two or all three active removal methods could, however, reduce biomass below the desired threshold due to cumulative mortality on multiple life stages. CarpMOD simulations suggest that the level of carp removal necessary to reach the desired biomass threshold is approximately 40% at each life-stage, which may be unrealistic to maintain over longer time scales. Passive removal via avian predation may also contribute to suppression of Common Carp, but was not sufficient in isolation to reduce biomass below the desired threshold. Collectively, our results indicate control of Common Carp as a sole means of ecosystem restoration is unlikely to be effective in the system we modeled. This suggests additional means of restoration may be warranted, perhaps in combination with control of Common Carp, or development of more effective control measures.","language":"English","publisher":"Springer","doi":"10.1007/s11273-019-09685-0","usgsCitation":"Pearson, J.B., Dunham, J.B., Bellmore, J., and Lyons, D.E., 2019, Modeling control of Common Carp (Cyprinus carpio) in a shallow lake–wetland system: Wetlands Ecology and Management, v. 27, no. 5-6, p. 663-682, https://doi.org/10.1007/s11273-019-09685-0.","productDescription":"20 p.","startPage":"663","endPage":"682","ipdsId":"IP-106782","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":370603,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Malheur Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.35272216796874,\n 43.135065496929165\n ],\n [\n -118.51776123046875,\n 43.135065496929165\n ],\n [\n -118.51776123046875,\n 43.50274467820439\n ],\n [\n -119.35272216796874,\n 43.50274467820439\n ],\n [\n -119.35272216796874,\n 43.135065496929165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"5-6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearson, James B","contributorId":221480,"corporation":false,"usgs":false,"family":"Pearson","given":"James","email":"","middleInitial":"B","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":778338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bellmore, J Ryan","contributorId":178561,"corporation":false,"usgs":false,"family":"Bellmore","given":"J Ryan","affiliations":[],"preferred":false,"id":778340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, Donald E.","contributorId":204663,"corporation":false,"usgs":false,"family":"Lyons","given":"Donald","email":"","middleInitial":"E.","affiliations":[{"id":13016,"text":"Department of Fisheries and Wildlife, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":778341,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215328,"text":"70215328 - 2019 - Foraging ecology mediates response to ecological mismatch during migratory stopover","interactions":[],"lastModifiedDate":"2020-10-16T14:09:17.600138","indexId":"70215328","displayToPublicDate":"2019-10-09T09:04:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Foraging ecology mediates response to ecological mismatch during migratory stopover","docAbstract":"Impacts of ecological mismatches should be most pronounced at points of the annual cycle when populations depend on a predictable, abundant, and aggregated food resource that changes in timing or distribution. The degree to which species specialize on a key prey item, therefore, should determine their sensitivity to mismatches. We evaluated the hypothesis that the effects of ecological mismatch during migratory stopover are mediated by a species’ foraging ecology by comparing two similar long‐distance migratory species that differ in their foraging strategies during stopover. We predicted that a specialist foraging strategy would make species more sensitive to effects of mismatch with a historically abundant prey, while an active, generalist foraging strategy should help buffer against changing local conditions. We estimated arrival times, start of mass gain, and rate of mass gain during spring stopover in Delaware Bay, USA. At this site, shorebirds feed on a temporally aggregated food resource (horseshoe crab Limulus polyphemus eggs), the timing of which is linked to water temperature; red knot (Calidris canutus rufa) specializes on these while the ruddy turnstone (Arenaria interpres) feeds more generally. We used a hierarchical nonlinear model to estimate the effect of mismatch between shorebird arrivals and timing of crab spawning on the timing and rate of mass gain over 22 yr. In years with cooler water temperature, crabs spawned later, which was associated with later and faster mass gain for the knots. Turnstones exhibited less inter‐annual variation in the timing and rate of mass gain than knots, and we found no relationship between mass gain dynamics and the availability of horseshoe crab eggs for this generalist species. Long‐distance migrants rely on predictable resources en route and even when these linkages are simple and predictable, populations can be vulnerable to change; these results suggest that generalist foraging strategies may buffer migratory species against phenological mismatch. We provide a framework to evaluate population responses to changes in prey phenology at sites vulnerable to climatic change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2898","usgsCitation":"Tucker, A., McGowan, C.P., Catalano, M., Derose-Wilson, A., Robinson, R., and Zimmerman, J., 2019, Foraging ecology mediates response to ecological mismatch during migratory stopover: Ecosphere, v. 10, no. 10, e02898, 17 p., https://doi.org/10.1002/ecs2.2898.","productDescription":"e02898, 17 p.","ipdsId":"IP-104145","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":459585,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2898","text":"Publisher Index Page"},{"id":379464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Despite its compromised flight ability, the chick fledged, reached independence, and lived until 13 months of age. Necropsy revealed a healed fracture near the left carpus likely resulting from trauma.","language":"English","publisher":"Wildlife Disease Association","doi":"10.7589/2018-09-234","usgsCitation":"Vasseur, P.L., Zimorski, S.E., Szyszkoski, E.K., LaCour, J.M., and Lankton, J.S., 2019, Wing abnormality in wild-hatched Whooping Crane (Grus americana) chick from the Louisiana nonmigratory population: Journal of Wildlife Diseases, v. 55, no. 4, p. 954-957, https://doi.org/10.7589/2018-09-234.","productDescription":"4 p.","startPage":"954","endPage":"957","ipdsId":"IP-098915","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":368505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"55","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vasseur, Phillip L.","contributorId":204493,"corporation":false,"usgs":false,"family":"Vasseur","given":"Phillip","email":"","middleInitial":"L.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":773584,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimorski, Sara E.","contributorId":204494,"corporation":false,"usgs":false,"family":"Zimorski","given":"Sara","email":"","middleInitial":"E.","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":773585,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Szyszkoski, Eva K","contributorId":219934,"corporation":false,"usgs":false,"family":"Szyszkoski","given":"Eva","email":"","middleInitial":"K","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":773586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"LaCour, James M","contributorId":219935,"corporation":false,"usgs":false,"family":"LaCour","given":"James","email":"","middleInitial":"M","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":773587,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lankton, Julia S. 0000-0002-6843-4388 jlankton@usgs.gov","orcid":"https://orcid.org/0000-0002-6843-4388","contributorId":5888,"corporation":false,"usgs":true,"family":"Lankton","given":"Julia","email":"jlankton@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":773583,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212825,"text":"70212825 - 2019 - Interactions of microhabitat and time control grassland bacterial and fungal composition","interactions":[],"lastModifiedDate":"2020-08-31T13:08:10.476551","indexId":"70212825","displayToPublicDate":"2019-10-09T08:04:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Interactions of microhabitat and time control grassland bacterial and fungal composition","docAbstract":"Dryland grasslands are vast and globally important and, as in all terrestrial ecosystems, soil microbial communities play fundamental roles in regulating dryland ecosystem function. A typical characteristic of drylands is the spatial mosaic of vascular plant cover surrounded by interspace soils, where biological soil crusts (biocrusts)—a complex community of organisms including bacteria, fungi, algae, mosses, and lichens—are common. The implications of this heterogeneity, where plants and biocrust cover co-occur, are often explored in the context of soil fertility and hydrology, but rarely has the impact of these multiple microhabitat types been simultaneously explored to determine the influence on bacterial and fungal communities, key biological players in these ecosystems. Further, our understanding of the temporal dynamics of bacterial and fungal communities in grasslands, and of how these dynamics depend on the microhabitat within the ecosystem, is notably poor. Here we used a temporally and spatially explicit approach to assess bacterial and fungal communities in a grassland on the Colorado Plateau, and to link variation in these communities to edaphic characteristics. We found that microhabitat (e.g., vascular plant rhizosphere, biocrust, and below biocrust) was the strongest driver of differences in bacterial and fungal community richness, diversity, and composition. Microhabitat type also significantly mediated the impact of temporal change in shaping community composition. Taken together, 29% of the variation in bacterial community composition could be explained by microhabitat, date, and microhabitat-by-date interactions, while only 11% of the variation in fungal community composition could be explained by the same factors, suggesting important differences in community assembly processes. Soil microbial communities dictate myriad critical ecosystem functions, thus understanding the factors that control their compostition is crucial to considering and forecasting how terrestrial ecosystems work. Overall, this case study provides insights for future studies on the spatial and temporal dynamics of bacterial and fungal communities in dryland grasslands.
Informed management of American black bears (Ursus americanus) requires knowledge of the distribution and pathology of diseases affecting the species. Little information is available on pathogen prevalence from black bear populations in the Southwest, US, and it is unknown how these infections may influence black bear populations or disease transmission. We captured New Mexico black bears (Ursus americanus amblyceps) during 2016–17 as part of a long-term monitoring project and opportunistically collected 36 blood samples from 12 female and 17 male black bears. We wanted to determine prior exposure to canine distemper virus, canine parvovirus, Yersinia pestis, Francisella tularensis, West Nile virus, Toxoplasma gondii, and the tick-borne pathogens, Anaplasma spp., Ehrlichia spp., Borrelia burgdorferi, Rickettsia spp., and Babesia spp. Approximately half (55%, 16/29) of the individuals sampled had antibodies to Y. pestis, and 37% (10/27) had antibodies to T. gondii. Prevalence of antibodies to West Nile virus, F. tularensis, and canine parvovirus were lower (i.e., 11, 10, and 3%, respectively). We detected no antibodies to canine distemper, B. burgdorferi, Rickettsia spp., or Babesia spp. We documented changes in antibody titer levels for both sexes of several recaptured black bears. Our data will inform managers of pathogen prevalence and distribution in black bears in north-central New Mexico and provide a vital baseline dataset for future pathogen monitoring. Additionally, these data support actions to minimize exposure through handling wild individuals or through hunter harvest activities.
U.S. Geological Survey
National Water Use Science Project Team
An assessment of groundwater/surface-water interactions along Ellerbe Creek, a major tributary to upper Falls Lake in Durham County, North Carolina, was conducted from July 2016 to March 2018 to determine if groundwater is a likely source of elevated nitrate input to the stream. Groundwater/surface-water interactions were characterized by synoptic streamflow measurements, groundwater-level monitoring, hydrograph-separation methods, and a continuous streambed temperature survey to aid in the collection and interpretation of water-quality data. A streamflow gain-loss survey identified gaining and losing reaches within the stream and found that surface-water inflow, including that from a treated wastewater outfall, provided much of the streamflow gain within the study reach. Through the use of two hydrograph-separation methods, base flow for the Ellerbe Creek study reach was estimated to be between 14.0 and 17.7 cubic feet per second during the study period, contributing up to 57 percent of mean streamflow, with the remaining contributions coming from surface runoff to the stream. The effluent discharge accounted for most of the estimated base-flow contribution to the stream below the North Durham Water Reclamation Facility outfall. Hydraulic gradients within the groundwater were determined to flow upward and toward the stream during base-flow conditions and reverse during storm events. Nitrate concentrations ranged from below the method detection level to 2.69 milligrams per liter, with the highest concentrations just downstream from the wastewater outfall. Bank seeps and groundwater samples had lower nitrate concentrations than surface-water samples, ranging from below the method detection level to 1.04 milligrams per liter, with the highest concentration at the piezometer within the stream. Results indicate that groundwater is not a large component of streamflow within Ellerbe Creek nor a major source of nitrate within the study reach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195097","collaboration":"Prepared in cooperation with the City of Durham Public Works Department, Stormwater and GIS Services Division","usgsCitation":"Antolino, D.J., 2019, Groundwater/surface-water interactions along Ellerbe Creek in Durham, North Carolina, 2016–18: U.S. Geological Survey Scientific Investigations Report 2019–5097, 32 p., https://doi.org/10.3133/sir20195097.","productDescription":"viii, 32 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097853","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":437312,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YFET78","text":"USGS data release","linkHelpText":"Groundwater-Surface Water Interactions in Ellerbe Creek in Durham, North Carolina, 2016-2018"},{"id":368078,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5b6630abe4b006a11f75221b","text":"USGS data release","linkHelpText":"Groundwater-Surface Water Interactions in Ellerbe Creek in Durham, North Carolina, 2016-2018"},{"id":368058,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5097/coverthb.jpg"},{"id":368059,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5097/sir20195097.pdf","text":"Report","size":"4.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5097"}],"country":"United States","state":"North Carolina","county":"Durham County, Wake County","city":"Durham","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-78.8019,36.2361],[-78.8059,36.0928],[-78.8059,36.0878],[-78.7986,36.085],[-78.7957,36.0858],[-78.7923,36.0854],[-78.7919,36.0772],[-78.7879,36.0758],[-78.7852,36.0703],[-78.7749,36.0707],[-78.7498,36.0718],[-78.7088,36.0768],[-78.6895,36.0752],[-78.5922,36.0378],[-78.5465,36.0218],[-78.4307,35.9795],[-78.3969,35.9387],[-78.3567,35.9318],[-78.351,35.909],[-78.3385,35.9052],[-78.3347,35.8997],[-78.3302,35.896],[-78.3245,35.896],[-78.3177,35.8963],[-78.3137,35.8976],[-78.3081,35.8935],[-78.2948,35.8797],[-78.292,35.8792],[-78.2893,35.8741],[-78.2859,35.8713],[-78.2831,35.8681],[-78.2782,35.8631],[-78.2749,35.8567],[-78.2756,35.8494],[-78.2707,35.843],[-78.2657,35.8361],[-78.2652,35.8325],[-78.2613,35.8315],[-78.2591,35.826],[-78.2599,35.8183],[-78.3731,35.7523],[-78.4635,35.7072],[-78.4686,35.7087],[-78.4709,35.7078],[-78.4732,35.7046],[-78.4778,35.7011],[-78.5716,35.6255],[-78.708,35.5191],[-78.9196,35.5857],[-78.9956,35.6104],[-78.9796,35.6656],[-78.9439,35.7515],[-78.9421,35.756],[-78.9403,35.7615],[-78.9337,35.7859],[-78.9191,35.8216],[-78.9096,35.8506],[-78.9076,35.8678],[-78.9144,35.8674],[-78.9332,35.8667],[-78.9587,35.866],[-78.986,35.8644],[-78.9985,35.8641],[-79.011,35.8633],[-79.0161,35.8633],[-79.0142,35.8755],[-79.0124,35.886],[-78.9507,36.2393],[-78.8019,36.2361]]]},\"properties\":{\"name\":\"Durham\",\"state\":\"NC\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Do you ever wonder where your water comes from? If you live in Nassau or Suffolk County, the answer is, groundwater. Groundwater is water that started out as precipitation (rain and snow melt) and seeped into the ground. This seepage recharges the freshwater stored underground, in the spaces between the grains of sand and gravel in what are referred to as aquifers. Long Island has three primary aquifers—the upper glacial, Magothy, and Lloyd—which are part of the Long Island aquifer system. Currently [2019], this aquifer system contains about 50 trillion gallons of freshwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193052","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Masterson, J.P., and Breault, R., 2019, Water for Long Island—Now and for the future: U.S. Geological Survey Fact Sheet 2019–3052, 2 p., https://doi.org/10.3133/fs20193052.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-111757","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":368088,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3052/fs20193052.pdf","text":"Report","size":"703 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3052"},{"id":367575,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3052/coverthb2.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0423583984375,\n 40.588928169693745\n ],\n [\n -73.9324951171875,\n 40.52423878069866\n ],\n [\n -73.39691162109375,\n 40.5930995321649\n ],\n [\n -73.2403564453125,\n 40.61186744303007\n ],\n [\n -72.872314453125,\n 40.70979201243495\n ],\n [\n -72.257080078125,\n 40.89482963261175\n ],\n [\n -71.8011474609375,\n 41.0565730874647\n ],\n [\n -72.2735595703125,\n 41.168316941075766\n ],\n [\n -72.66632080078125,\n 41.008920735004885\n ],\n [\n -73.2073974609375,\n 40.994410999439516\n ],\n [\n -73.212890625,\n 40.94048973170136\n ],\n [\n -73.41064453125,\n 40.97160353279909\n ],\n [\n -73.68255615234375,\n 40.91351257612758\n ],\n [\n -73.79241943359375,\n 40.79925662005228\n ],\n [\n -73.87138366699219,\n 40.79613778833378\n ],\n [\n -73.90159606933594,\n 40.79717741518766\n ],\n [\n -73.93867492675781,\n 40.7737818731648\n ],\n [\n -73.96614074707031,\n 40.73529128534676\n ],\n [\n -73.97420883178711,\n 40.709141393000124\n ],\n [\n -73.99944305419922,\n 40.70471701226026\n ],\n [\n -74.00733947753906,\n 40.692483371144434\n ],\n [\n -74.02399063110352,\n 40.67686269237614\n ],\n [\n -74.02090072631836,\n 40.662800951382906\n ],\n [\n -74.03841018676758,\n 40.643265836603\n ],\n [\n -74.04544830322264,\n 40.63089063912002\n ],\n [\n -74.04356002807617,\n 40.605742384748446\n ],\n [\n -74.0423583984375,\n 40.588928169693745\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180
The association of seismic‐wave arrivals with causative earthquakes becomes progressively more challenging as arrival detection methods become more sensitive, and particularly when earthquake rates are high. For instance, seismic waves arriving across a monitoring network from several sources may overlap in time, false arrivals may be detected, and some arrivals may be of unknown phase (e.g., P or S waves). We propose an automated method to associate arrivals with earthquake sources and obtain source locations applicable to such situations. To do so, we use a pattern detection metric based on the principle of backprojection to reveal candidate sources followed by graph‐theory‐based clustering and an integer linear optimization routine to associate arrivals with the minimum number of sources necessary to explain the data. This method solves for all sources and phase assignments simultaneously, rather than in a sequential greedy procedure as is common in other association routines. We demonstrate our method on both synthetic and real data from the Integrated Plate Boundary Observatory Chile seismic network of northern Chile. For the synthetic tests, we report results for cases with varying complexity, including rates of 500 earthquakes/day and 500 false arrivals/station/day, for which we measure true positive detection accuracy of >95%. For the real data, we develop a new catalog between 1 January 2010 and 31 December 2017 containing 817,548 earthquakes, with detection rates on average 279 earthquakes/day and a magnitude‐of‐completion of M∼1.8. A subset of detections are identified as sources related to quarry and industrial site activity, and we also detect thousands of foreshocks and aftershocks of the 1 April 2014 Mw 8.2 Iquique earthquake. During the highest rate of aftershock activity, >600 earthquakes/day are detected in the vicinity of the Iquique earthquake rupture zone.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120190081","usgsCitation":"McBrearty, I., Gomberg, J.S., Delorey, A., and Johnson, P., 2019, Earthquake arrival association with backprojection and graph theory: Bulletin of the Seismological Society of America, v. 109, no. 6, p. 2510-2531, https://doi.org/10.1785/0120190081.","productDescription":"22 p.","startPage":"2510","endPage":"2531","ipdsId":"IP-106212","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":459595,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/1904.00980","text":"External Repository"},{"id":379299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"109","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-10-08","publicationStatus":"PW","contributors":{"authors":[{"text":"McBrearty, Ian","contributorId":242933,"corporation":false,"usgs":false,"family":"McBrearty","given":"Ian","email":"","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":801143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":801144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Delorey, Andrew","contributorId":189149,"corporation":false,"usgs":false,"family":"Delorey","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":801145,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Paul","contributorId":189150,"corporation":false,"usgs":false,"family":"Johnson","given":"Paul","email":"","affiliations":[],"preferred":false,"id":801146,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215092,"text":"70215092 - 2019 - Spatial and temporal variation in river corridor exchange across a 5th order mountain stream network","interactions":[],"lastModifiedDate":"2020-10-07T20:21:30.905358","indexId":"70215092","displayToPublicDate":"2019-10-07T15:14:05","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal variation in river corridor exchange across a 5th order mountain stream network","docAbstract":"Although most field and modeling studies of river corridor exchange have been conducted a scales ranging from 10’s to 100’s of meters; results of these studies are used to predict their ecological and hydrological influences at the scale of river networks. Further complicating prediction, exchanges are expected to vary with hydrologic forcing and the local geomorphic setting. While we desire predictive power, we lack a complete spatiotemporal relationship relating discharge to the variation in geologic setting and hydrologic forcing that are expected across a river basin. Indeed, Wondzell’s (2011) conceptual model predicts systematic variation in river corridor exchange as a function of (1) variation in baseflow over time at a fixed location, (2) variation in discharge with location in the river network, and (3) local geomorphic setting. To test this conceptual model we conducted more than 60 solute tracer studies including a synoptic campaign in the 5th order river network of the H.J. Andrews Experimental Forest (Oregon, USA) and replicate-in-time experiments in four watersheds. We interpret the data using a series of metrics describing river corridor exchange and solute transport, testing for consistent direction and magnitude of relationships relating these metrics to discharge and local geomorphic setting. We confirmed systematic decrease in river corridor exchange space through the river networks, from headwaters to the larger mainstem. However, we did not find systematic variation with changes in discharge through time, nor with local geomorphic setting. While interpretation of our results is complicated by problems with the analytical methods, they are sufficiently robust for us to conclude that space-for-time and time-for-space substitutions are not appropriate in our study system. Finally, we suggest two strategies that will improve the interpretability of tracer test results and help the hyporheic community develop robust data sets that will enable comparisons across multiple sites and/or discharge conditions.","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-23-5199-2019","usgsCitation":"Ward, A.S., Wondzell, S., Schmadel, N., Herzog, S., Zarnetske, J.P., Baranov, V., Blaen, P.J., Brekenfeld, N., Chu, R., Derelle, R., Drummond, J.D., Fleckenstein, J., Garayburu-Caruso, V., Graham, E.B., Hannah, D., Harman, C., Hixson, J., Knapp, J., Krause, S., Kurz, M., Lewandowski, J., Li, A., Martí, E., Miller, M., Milner, A., Neil, K., Orsini, L., Packman, A.I., Plont, S., Renteria, L., Roche, K., Royer, T.V., Segura, C., Stegen, J., Toyoda, J., Wells, J., and Wisnoski, N., 2019, Spatial and temporal variation in river corridor exchange across a 5th order mountain stream network: Hydrology and Earth System Sciences, v. 23, no. 12, p. 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In water year 2018, this network included 219 real-time streamgages. A water year is the 12-month period from October 1 through September 30 and is designated by the calendar year in which it ends. Real-time data are calibrated and validated by U.S. Geological Survey personnel throughout the year with regular measurements of streamflow, streamgage height, and lake levels. These data and accompanying analyses provide an overview of hydrologic conditions in Kansas and help advance the understanding of water resources in the State. Annual assessments of hydrologic conditions are made by comparing statistical analyses of current and past water year data for the period of record. Long-term monitoring of hydrologic conditions in Kansas provides imperative information for protecting human life and property, managing water supplies, forecasting floods, operating reservoirs, designing bridges and culverts, processing interstate and intrastate water rights claims, forecasting ecological conditions, and many other uses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193042","usgsCitation":"Unrein, A.H., 2019, Hydrologic conditions in Kansas, water year 2018: U.S. Geological Survey Fact Sheet 2019–3042, 4 p., https://doi.org/10.3133/fs20193042.\n","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-106493","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":368023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3042/fs20193042.pdf","text":"Report","size":"4.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3042"},{"id":368022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3042/coverthb.jpg"}],"country":"United 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U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The Monte Blanco borate deposits are located along the southern margin of Death Valley’s Furnace Creek Wash, south of Twenty Mule Team Canyon road in California. Topographic and geologic mapping by S. Muessig and F.M. Byers, Jr., in 1954 documented these deposits’ geologic settings, geometries, mineralogies, and chemical characteristics. They estimated borate resources at the time to be in excess of 550,000 tons B2O3.
The borate bodies are composed of predominantly ulexite and colemanite. They lie beneath Monte Blanco itself and along a northwest-trending series of conspicuous, white hills and mounds formed by northeasterly dipping, fine-grained sedimentary beds and basaltic volcanic rocks of the Miocene and Pliocene Furnace Creek Formation.
Geologic data suggest that in Miocene and Pliocene time, fine-grained sediments, volcanic debris and flows, and volcanically associated, boron-rich fluids gradually filled a fairly flat playa-like environment. At times, thick beds of felty crystals of ulexite developed and were interlayered as lenses in a thick series of mudstones as is seen today at the Eagle Borax works. After burial, the exterior of the ulexite deposit was altered to massive colemanite by ground water, which produced the “shell” of colemanite that typically surrounds the presently outcropping ulexite bodies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191111","usgsCitation":"Muessig, S.J., Pennell, W.M, Knott, J.R., and Calzia, J.P., 2019, Geology of the Monte Blanco borate deposits, Furnace Creek Wash, Death Valley, California: U.S. Geological Survey Open-File Report 2019–1111, 35, p., 2 plates, scales 1:2,400 and 1: 2,000, https://doi.org/10.3133/ofr20191111.","productDescription":"Report: v, 30 p.; 2 Plates: 28.00 x 29.75 and 18.11 x 24.96 inches","numberOfPages":"37","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-088268","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":368047,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1111/coverthb.jpg"},{"id":368050,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1111/ofr20191111_plate2.pdf","text":"Plate 2","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1111"},{"id":368048,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1111/ofr20191111_pamphlet.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1111"},{"id":368049,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1111/ofr20191111_plate1.pdf","text":"Plate 1","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1111"}],"country":"United States","state":"California","otherGeospatial":"Death Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.76245117187499,\n 35.60818490437746\n ],\n [\n -116.06506347656251,\n 35.60818490437746\n ],\n [\n -116.06506347656251,\n 37.19095471582605\n ],\n [\n -117.76245117187499,\n 37.19095471582605\n ],\n [\n -117.76245117187499,\n 35.60818490437746\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
The key to Eastern Meadowlark (Sturnella magna) management is providing large areas of contiguous grassland of moderate height with significant grass cover and moderate forb density. Eastern Meadowlarks have been reported to use habitats with 10–187 centimeters (cm) average vegetation height, 6–88 cm visual obstruction reading, 53–86 percent grass cover, 4–50 percent forb cover, less than or equal to (≤) 4 percent shrub cover, less than 38 percent bare ground, 6–23 percent litter cover, and ≤13 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842MM","usgsCitation":"Hull, S.D., Shaffer, J.A., and Igl, L.D., 2019, The effects of management practices on grassland birds—Eastern Meadowlark (Sturnella magna), chap. MM 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, 26 p., https://doi.org/10.3133/pp1842MM.","productDescription":"iv, 26 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":367879,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/mm/coverthb.jpg"},{"id":367880,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/mm/pp1842mm.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–MM"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Keys to Ferruginous Hawk (Buteo regalis) management are providing suitable nest sites, protecting active nest areas from human disturbance, and providing suitable habitat for prey.
Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
The key to Swainson's Hawk (Buteo swainsoni) management is providing open grasslands that contain patches of trees for nesting and perching, sometimes near cultivated areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842M","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Dinkins, M.F., Goldade, C.M., Wooten, T.L., and Euliss, B.R., 2019, The effects of management practices on grassland birds—Swainson’s Hawk (Buteo swainsoni), chap. M 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, 9 p., https://doi.org/10.3133/pp1842M.","productDescription":"iv, 9 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-093865","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":367870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/m/coverthb.jpg"},{"id":367871,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/m/pp1842m.pdf","text":"Report","size":"2.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–M"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
The key to Northern Harrier (Circus hudsonius) management is providing tall, dense vegetation in extensive mesic or xeric grasslands or in wetlands. Northern Harriers have been reported to use habitats with 15–106 centimeter (cm) average vegetation height, 28–75 cm visual obstruction reading, 24–53 percent grass cover, 18–25 percent forb cover, less than or equal to 2 percent shrub cover, 3–5 percent bare ground, 23–30 percent litter cover, and 2–6 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842L","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Nenneman, M.P., Thiele, J.P., and Euliss, B.R., 2019, The effects of management practices on grassland birds—Northern Harrier (Circus hudsonius), chap. L 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, 11 p., https://doi.org/10.3133/pp1842L.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-093911","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":367868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/l/coverthb.jpg"},{"id":367869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/l/pp1842l.pdf","text":"Report","size":"1.70 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–L"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Keys to Greater Sage-Grouse (Centrocercus urophasianus) management are maintenance of expansive stands of sagebrush (Artemisia species [spp.]), especially varieties of big sagebrush (Artemisia tridentata) with abundant forbs in the understory, particularly during spring; undisturbed and somewhat open sites for leks; and healthy perennial grass and forb stands intermixed with sagebrush for brood rearing. Within suitable habitats, areas should have 15–25 percent canopy cover of sagebrush 30–80 centimeters (cm) tall for nesting and 10–25 percent canopy cover 40–80 cm tall for brood rearing. In winter habitats, shrubs should be exposed 25–35 cm above snow and have 10–30 percent canopy cover exposed above snow. In nesting and brood-rearing habitats, the understory should have at least 15 percent cover of grasses and at least 10 percent cover of forbs greater than or equal to 18 cm tall. Greater Sage-Grouse have been reported to use habitats with 5–110 cm average vegetation height, 5–160 cm visual obstruction reading, 3–51 percent grass cover, 3–20 percent forb cover, 3–69 percent shrub cover, 7–63 percent sagebrush cover, 14–51 percent bare ground, and 0–18 percent litter cover. Unless otherwise noted, this account refers to habitat requirements and environmental factors affecting Greater Sage-Grouse but not Gunnison Sage-Grouse (Centrocercus minimus). Habitats used by Gunnison Sage-Grouse are generally similar to habitats used by Greater Sage-Grouse, but some differences have been reported. The Greater Sage-Grouse is a game bird and is hunted throughout most of its current range. This account does not address harvest or its effects on populations; rather, this account focuses on the effects of habitat management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842B","usgsCitation":"Rowland, M.M., 2019, The effects of management practices on grassland birds—Greater Sage-Grouse (Centrocercus urophasianus), chap. B 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, 50 p., https://doi.org/10.3133/pp1842B.","productDescription":"v, 50 p.","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":367863,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/b/pp1842b.pdf","text":"Report","size":"2.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–B"},{"id":367862,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/b/coverthb.jpg"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
In 2015, the U.S. Geological Survey, in cooperation with the Iroquois River Conservancy District, deployed continuous water-quality monitors and began collecting representative discrete water-quality samples at the Iroquois River near Foresman, Indiana, streamflow-gaging station (U.S. Geological Survey station 05524500). By relating continuously monitored water-quality data and discrete water-quality samples collected from April 2015 through July 2018, regression models that estimate concentrations of suspended sediment, total nitrogen, and total phosphorus were developed. Developed regression models indicated a strong correlation between turbidity and streamflow with suspended-sediment concentration (adjusted coefficient of determination equals 0.84, predicted residual error sum of squares equals 0.493), nitrate plus nitrite and streamflow with total nitrogen (adjusted coefficient of determination equals 0.99, predicted residual error sum of squares equals 0.0202), and specific conductance and turbidity with total phosphorus (adjusted coefficient of determination equals 0.84, predicted residual error sum of squares equals 0.0935).
Daily loads of suspended sediment, total nitrogen, and total phosphorus were computed as the product of daily mean regression model concentrations and daily mean streamflow. During periods when regression model concentrations could not be computed, rloadest models, the R programming language version of the LOADEST FORTRAN program, were used to compute daily loads of each constituent. For 2016 and 2017, the estimated annual suspended-sediment loads were 25,000 and 32,100 tons; estimated total nitrogen loads were 4,260 and 5,780 tons; and estimated total phosphorus loads were 104 and 128 tons, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195087","collaboration":"Prepared in cooperation with the Iroquois River Conservancy District","usgsCitation":"Lathrop, T.R., Bunch, A.R., Downhour, M.S., and Perkins, D.M., 2019, Regression models for estimating sediment and nutrient concentrations and loads at the Iroquois River near Foresman, Indiana, March 2015 through July 2018: U.S. Geological Survey Scientific Investigation Report 2019–5087, 14 p., https://doi.org/10.3133/sir20195087.","productDescription":"Report: vi, 14 p.; Data Releases","numberOfPages":"24","ipdsId":"IP-107470","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368030,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YCAELC","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data and regression models for total nitrogen and total phosphorus for the Iroquois River near Foresman, Indiana, March 20, 2015, to July 19, 2018"},{"id":368029,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RFLONI","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data and regression model for suspended sediment for Iroquois River near Foresman, Indiana, March 20, 2015, to July 19, 2018"},{"id":368028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91FL2GY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Suspended sediment, total nitrogen, and total phosphorus loads for Iroquois River near Foresman, Indiana, April 2015 to July 2018"},{"id":368027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5087/sir20195087.pdf","text":"Report","size":"855 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5087"},{"id":368026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5087/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Newton County","city":"Foresman","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.5263,41.1661],[-87.4801,41.1701],[-87.4587,41.1702],[-87.4484,41.1744],[-87.4466,41.174],[-87.4411,41.1731],[-87.4147,41.1619],[-87.4055,41.1625],[-87.4,41.1625],[-87.394,41.1625],[-87.38,41.1726],[-87.3448,41.1824],[-87.3405,41.1824],[-87.3313,41.1829],[-87.3241,41.1862],[-87.2859,41.2154],[-87.2762,41.2187],[-87.2757,41.1733],[-87.2754,41.0866],[-87.275,40.9991],[-87.2768,40.9405],[-87.2759,40.9133],[-87.268,40.9134],[-87.2664,40.8249],[-87.2655,40.7383],[-87.3807,40.738],[-87.4905,40.7381],[-87.5263,40.7378],[-87.5263,40.741],[-87.5265,40.839],[-87.5262,40.981],[-87.5262,40.9832],[-87.5265,41.0142],[-87.5264,41.1231],[-87.5263,41.1661]]]},\"properties\":{\"name\":\"Newton\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Razorback sucker (Xyrauchen texanus) and flannelmouth sucker (Catostomus latipinnis) live in sympatry in the Colorado River basin. Although morphological intermediates have been described since 1889, hybrids were seemingly rare. Rarity of hybrids was likely attributed to razorback suckers' ability to find conspecific mates throughout the basin. Dams have segmented the Colorado River, altering habitat and isolating native fish populations. As a result, razorback suckers became endangered. Razorback suckers are uncommon in the Colorado River and hybridization could increase because of limited conspecific mates. To understand the impacts of hybridization on recovery of the razorback sucker, information on hybrid viability is needed. We compared hatch success and larval survival of artificially spawned razorback sucker, flannelmouth sucker, and their hybrids. We were able to successfully spawn and rear all combinations, implying that there are limited pre- and postzygotic isolation mechanisms, and hybrids are likely to survive in the wild.
","language":"English","publisher":"BioOne Complete","doi":"10.1894/0038-4909-63-4-280","usgsCitation":"Wolters, P.N., Rogowski, D.L., Ward, D., and Gibb, A.C., 2019, Viability of Razorback-Flannelmouth Sucker hybrids: The Southwestern Naturalist, v. 63, no. 4, p. 280-283, https://doi.org/10.1894/0038-4909-63-4-280.","productDescription":"4 p.","startPage":"280","endPage":"283","ipdsId":"IP-088815","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":377620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, Baja California, California, Colorado, Nevada, Sonora, Utah","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.79919815063477,\n 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