In the deserts of the Southwestern United States, increased off-highway vehicle use can lead to widespread vehicular damage to desert ecosystems. As the popularity and intensity of vehicle use on public lands continues, the Bureau of Land Management (BLM) is challenged to manage the routes used by recreationists while minimizing activity beyond designated routes and mitigating environmental impacts. Ecosystem function and habitat quality can be degraded by vehicle activities, especially when the activities are occurring outside authorized routes or authorized open areas. Restoration mitigates damage to soils and vegetation; however, methods vary across the desert, results appear to be inconsistent, and standardized monitoring plans do not exist. The Desert Renewable Energy Conservation Plan Land Use Plan Amendment to the California Desert Conservation Area Land Use Plan identified the need for, and directed implementation of, standardized monitoring of restoration, which includes minimizing surface disturbance to agency prescribed levels in areas of critical environmental concern and on California Desert National Conservation Lands. To assist the BLM in implementing the Desert Renewable Energy Conservation Plan Land Use Plan Amendment, we define ecological restoration as the process of halting or minimizing future degradation while simultaneously assisting the recovery of ecosystem function and community composition in relation to intact reference sites. The monitoring strategies provided in this protocol are used to restore degraded ecosystems after use of non-routes has ceased (non-designated routes or vehicle-caused linear disturbances) by applying techniques to improve edaphic properties, hydrologic function, and biotic community composition. This protocol also provides criteria that can be used to distinguish the status of non-routes and land parcels as “restored” or “disturbed.” This protocol was developed by the U.S. Geological Survey, in collaboration with BLM restoration practitioners, to identify standard restoration methods and establish criteria to determine when restoration is achieved. This protocol also develops new methods to increase restoration rates and successes on public lands in the southern California deserts. BLM’s long-term implementation plan for the evaluation of road restoration described in this report is to transition toward managing the work, including developing the workforce and long-term storage and management of the data during the next several years. This report is intended to be regularly updated as the program develops.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2A17","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Esque, T.C., Jackson, K.R., Rice, A.M., Childers, J.K., Woods, C.S., Fesnock-Parker, A., Johnson, A.C., Price, L.J., Forgrave, K.E., Scoles-Sciulla, S.J., and DeFalco, L.A., 2021, Protocol for route restoration in California’s desert renewable energy conservation plan area: U.S. Geological Survey Techniques and Methods 2-A17, 60 p., https://doi.org/10.3133/tm2A17.","productDescription":"viii, 60 p.","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126835","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":390844,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a17/covrthb.jpg"},{"id":390845,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a17/tm2a17.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390846,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/a17/tm2a17.xml"},{"id":390847,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/a17/images"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.09204101562501,\n 35.137879119634185\n ],\n [\n -116.03759765625,\n 35.137879119634185\n ],\n [\n -116.03759765625,\n 36.4566360115962\n ],\n [\n -118.09204101562501,\n 36.4566360115962\n ],\n [\n -118.09204101562501,\n 35.137879119634185\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Translocations of North American prairie-grouse (genus Tympanuchus) present a conservation paradox wherein they are performed to augment, restore, or reintroduce populations, but translocated individuals exhibit a diminished ability to contribute to population restoration. For reintroduced populations without immigration, persistence can only be achieved through reproductive contributions by translocated individuals and their progeny. Due to the disruptive nature of translocation (e.g., physiological chronic stress), progeny produced at restoration sites may outperform founder populations in terms of demographics, but this hypothesis has yet to be tested. We reintroduced Columbian Sharp-tailed Grouse (T. phasianellus columbianus; CSTG) to north central Nevada from 2013 to 2017 and used integrated population models (IPMs) to evaluate the process of population establishment and estimate latent contributions of progeny hatched at the restoration site to population rate of change (
The whale shark (Rhincodon typus) is an endangered and highly migratory species, of which solitary individuals or aggregations are observed in oceans worldwide and for which conservation efforts are hindered by a lack of comprehensive data on genetic population connectivity. Tissue samples were collected from wandering whale sharks in Pacific Panama to determine genetic diversity, phylogeographic origin, and possible global and local connectivity patterns using a 700–800 bp fragment of the mitochondrial control region gene. Genetic diversity among samples was high, with five new haplotypes and nine polymorphic sites identified among the 15 sequences. Haplotype diversity (Hd = 0.83) and nucleotide diversity (π = 0.00516) were similar to those reported in other studies. Our sequences, in particular haplotypes PTY1 and PTY2, were similar to those previously reported in the Arabian Gulf and the Western Indian Ocean populations (a novel occurrence in the latter case). Haplotypes PTY3, PTY4, and PTY5 were similar to populations in Mexico and the Gulf of California. In contrast, the only populations to which our Panamanian sequences were genetically dissimilar were those from the Atlantic Ocean. The absence of reference sequences in GenBank from southern sites in the Eastern Tropical Pacific, such as Galapagos (Ecuador), Gorgona and Malpelo Islands (Colombia), and Coco Island (Costa Rica), reduced our capacity to genetically define regional patterns. Genetic differentiation and connectivity were also assessed using an analysis of molecular variance (AMOVA), which showed a similar population structure (five groups) to the neighbor-joining tree. Other population features based on neutrality tests, such as Tajima’s D and Fu’s Fs statistics, showed positive values for Panama of 0.79 and 1.61, respectively. Positive values of these statistics indicate a lack of evidence for population expansion among the sampled individuals. Our results agree with previous reports suggesting that whale sharks can travel over long distances and that transboundary conservation measures may be effective for species protection.
The USGS ShakeAlert® earthquake early warning (EEW) system is operational and providing public alerting in three West Coast states: California, Washington, and Oregon. Since 2006 the USGS has pursued a strategy of incrementally developing and rolling out EEW for increasingly larger areas and uses. As funding from federal and state budgets grew the system became more capable, detection methods were developed and improved, core network sensor stations were built or upgraded, and partners were enlisted to deliver alerts and implement protective actions. In the fall of 2018, the system became sufficiently functional to publicly declare it “open for business” in all three states for use by licensed partners to alert personnel in limited settings and take automated machine-to-machine actions. State-wide public alerting began in California in October of 2019, expanded to Oregon in March of 2021, and to Washington in May of 2021. Today millions of people can receive ShakeAlert-powered EEW through a variety of delivery methods and dozens of machine-to-machine protective systems are in place in transportation systems, utilities, fire stations, schools, hospitals, and public and private buildings. The ShakeAlert System implementation plan calls for a supporting network of 1,675 seismic stations. 1,129 (73%) have been completed and the rest should be done by 2025.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SMIP 2021 seminar on utilization of strong-motion data","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"California Department of Conservation","usgsCitation":"Given, D.D., and West Coast ShakeAlert Project Team, 2021, ShakeAlert® earthquake warning: The challenge of transforming ground motion into protective actions, in Proceedings of SMIP 2021 seminar on utilization of strong-motion data, p. 70-76.","productDescription":"7 p.","startPage":"70","endPage":"76","ipdsId":"IP-133383","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":480811,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.conservation.ca.gov/cgs/pages/program-smi/seminar/smip21_toc.aspx"},{"id":481135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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sensors"},"lastModifiedDate":"2024-12-02T22:51:03.795019","indexId":"ofr20211030H","displayToPublicDate":"2021-10-21T06:01:24","publicationYear":"2021","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":"2021-1030","chapter":"H","displayTitle":"System Characterization Report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) Sensor","title":"System characterization report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor","docAbstract":"This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16 launch vehicle. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Advanced Wide Field Sensor and LISS–3, as well as the Linear Imaging Self Scanning-4 for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that LISS–3 has an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 meters (m; 0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.096 to 0.036 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 1.87–1.95 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.045–0.070.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030H","usgsCitation":"Ramaseri Chandra, S.N., Christopherson, J., Anderson, C., Stensaas, G.L., and Kim, M., 2021, System characterization report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor (ver. 1.2, December 2024), chap. H of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 20 p., https://doi.org/10.3133/ofr20211030H.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126659","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":433262,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/h/versionHist.txt","text":"Version History","size":"2.07 KB","linkFileType":{"id":2,"text":"txt"}},{"id":390427,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/h/images"},{"id":390426,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/h/ofr20211030h.xml","size":"75.7 kB","linkFileType":{"id":8,"text":"xml"}},{"id":390425,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/h/ofr20211030h.pdf","text":"Report","size":"3.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–H"},{"id":390424,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/h/coverthb4.jpg"},{"id":464526,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030H/full"}],"edition":"Version 1.0: October 21, 2021; Version 1.1: August 29, 2024; Version 1.2: December 2, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
(1) Many forage fishes, such as Leucisids (minnows) have depauperate studies on diet composition or stable isotope signatures, as these fishes are often only viewed as food for higher trophic levels. The need exists to understand and document the diet and stable isotope signatures of Leucisids (redside shiner, longnose dace, lake chub) in relation to the community ecology and food-web dynamics in Yellowstone Lake, especially given an invasive piscivore introduction and potential future effects of climate change on the Yellowstone Lake ecosystem. (2) Diet data collected during summer of 2020 were analyzed by species using proportion by number, frequency of occurrence, and mean proportion by weight, and diet overlap was compared using Schoener’s index (D). Stable isotope (δ15N and δ13C) values were estimated by collecting tissue during the summer of 2020 by species, and isotopic overlap was compared using 40% Bayesian ellipses. (3) Nonnative redside shiners and lake chub had similar diets, and native longnose dace diet differed from the nonnative Leucisids. Diet overlap was also higher between the nonnative Leucisids, and insignificant when comparing native and nonnative Leucisids. No evidence existed to suggest a difference in δ15N signatures among the species. Longnose dace had a mean δ13C signature of −15.65, indicating an decreased reliance on pelagic prey compared to nonnative Leucisids. Nonnative redside shiners and lake chub shared 95% of isotopic niche space, but stable isotope overlap was <25% for comparisons between native longnose dace and the nonnative Leucisids. (4) This study established the diet composition and stable isotope signatures of Leusicids residing in Yellowstone Lake, thus expanding our knowledge of Leucisid feeding patterns and ecology in relation to the native and nonnative species in the ecosystem. We also expand upon our knowledge of Leucisids in North America. Additionally, quantifying minnow diets can provide a baseline for understanding food web response to invasive suppression management actions.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes6040051","usgsCitation":"Glassic, H., Guy, C.S., and Koel, T., 2021, Diets and stable isotope signatures of native and nonnative Leucisid fishes advances our understanding of the Yellowstone Lake food web: Fishes, v. 6, no. 4, 51, 10 p., https://doi.org/10.3390/fishes6040051.","productDescription":"51, 10 p.","ipdsId":"IP-130868","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450387,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes6040051","text":"Publisher Index Page"},{"id":397183,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Lake Yellowstone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58837890625,\n 44.23929609118664\n ],\n [\n -110.15,\n 44.23929609118664\n ],\n [\n -110.15,\n 44.60806814444478\n ],\n [\n -110.58837890625,\n 44.60806814444478\n ],\n [\n -110.58837890625,\n 44.23929609118664\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Glassic, Hayley C.","contributorId":288563,"corporation":false,"usgs":false,"family":"Glassic","given":"Hayley C.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":838086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":838085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koel, Todd M.","contributorId":288564,"corporation":false,"usgs":false,"family":"Koel","given":"Todd M.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":838087,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225534,"text":"pp1867G - 2021 - A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption","interactions":[{"subject":{"id":70225534,"text":"pp1867G - 2021 - A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption","indexId":"pp1867G","publicationYear":"2021","noYear":false,"chapter":"G","displayTitle":"A Decade of Geodetic Change at Kīlauea’s Summit— Observations, Interpretations, and Unanswered Questions from Studies of the 2008–2018 Halema‘uma‘u Eruption","title":"A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption"},"predicate":"IS_PART_OF","object":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"id":1}],"isPartOf":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"lastModifiedDate":"2024-06-26T15:54:23.569219","indexId":"pp1867G","displayToPublicDate":"2021-10-20T10:42:24","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1867","chapter":"G","displayTitle":"A Decade of Geodetic Change at Kīlauea’s Summit— Observations, Interpretations, and Unanswered Questions from Studies of the 2008–2018 Halema‘uma‘u Eruption","title":"A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption","docAbstract":"On March 19, 2008, a small explosion heralded the onset of an extraordinary eruption at the summit of Kīlauea Volcano. The following 10 years provided unprecedented access to an actively circulating lava lake located within a region monitored by numerous geodetic tools, including Global Navigation Satellite System (GNSS), interferometric synthetic aperture radar (InSAR), tilt, and gravity. These datasets revealed a range of processes occurring on widely different timescales. Over years, pressure change within the summit magmatic system, determined from ground deformation and lava-lake surface height, seems to have been governed by broad variations in the rate of magma supply from the mantle to the volcano’s shallow magmatic system, as well as changes in the efficiency of East Rift Zone (ERZ) magma transport and eruption. Over weeks to months, intrusions at the summit and along the ERZ, where new eruptive vents commonly formed and intrusions were primed by extension from south-flank motion, were a result of short-term increases in magma supply or waning lava effusion from the ERZ. Waning lava effusion caused magma to back up behind the ERZ eruptive vent all the way to the summit. ERZ intrusions and eruptions caused rapid depressurization of the summit magmatic system, whereas summit intrusions resulted in complex deformation patterns as magma moved to and from two main sub-caldera storage areas. Over hours to days, pressure changes were caused by episodic deflation-inflation (DI) events and possibly small summit intrusions, and deformation of the rim of the summit eruptive vent revealed instabilities that indicated an increased potential for collapse and minor explosive activity. Finally, over timescales of minutes to hours, gas pistoning, summit explosions, very-long-period seismic events, and even the airborne eruptive plume had clear manifestations in geodetic datasets, providing insights into the causes and consequences of those processes. The diversity and quantity of geodetic observations shed important light on this exceptional and well-documented decade-long summit eruption and its accompanying phenomena, yet numerous questions remain about the causal mechanisms, physical processes, and magmatic conditions associated with eruptive and intrusive activity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1867G","usgsCitation":"Poland, M.P., Miklius, A., Johanson, I.A., and Anderson, K.R., 2021, A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption, chap. G of Patrick, M., Orr, T., Swanson, D., and Houghton, B., eds., The 2008–2018 summit lava lake at Kīlauea Volcano, Hawaiʻi: U.S. Geological Survey Professional Paper 1867, 29 p., https://doi.org/10.3133/pp1867G.","productDescription":"vi, 29 p.","numberOfPages":"29","onlineOnly":"N","ipdsId":"IP-123914","costCenters":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":390677,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1867/g/pp1867g.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1867/g/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.32539367675778,\n 19.37334071336406\n ],\n [\n -155.20797729492188,\n 19.37334071336406\n ],\n [\n -155.20797729492188,\n 19.454938719968595\n ],\n [\n -155.32539367675778,\n 19.454938719968595\n ],\n [\n -155.32539367675778,\n 19.37334071336406\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact HVO
Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue
Suite A-8
Hilo, HI 96720
Increases in nitrogen applications to the land surface since the 1950s have led to a cascade of negative environmental impacts, including degradation of drinking water supplies, nutrient enrichment of aquatic ecosystems and contributions to global climate change. In this study, groundwater, streambed porewater, and stream sampling were used to establish trends in nitrate concentrations and how redox gradients influence nitrate transport across diverse glacial terranes. Decadal sampling has found that elevated nitrate concentrations in shallow groundwater beneath cropland have been sustained for decades. Redox gradients established in the saturated zone using dissolved O2, iron, nitrate and excess N2 from denitrification suggest that nitrate-bearing zones are thin in glacial terranes dominated by fine materials. These thin nitrate-bearing zones lead to suboxic, low nitrate streambed porewater and limit the contributions of nitrate to streams from slow-flow groundwater. In contrast, thick oxic zones in more coarse-grained glacial terranes allow nitrate to reach deeper groundwater, resulting in streambed porewater with elevated nitrate concentrations and causing a large portion of stream nitrate to be derived from slow-flow groundwater. Groundwater age tracer data indicate that denitrification occurs more quickly in the terrane dominated by fine material than in the more coarse-grained terrane. The quicker depletion of nitrate in the more fine-grained terrane suggests that the thinner oxic zone in this terrane is due, in part, to the greater availability and reactivity of electron donors in this terrane than in the more coarse-grained terrane. Groundwater age tracer data and hydrograph separation analysis suggest that saturated zone lag times between when changes in land use practices occur and when changes in stream water are fully observed may vary widely across hydrogeologic settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.150200","usgsCitation":"Tesoriero, A.J., Stratton, L., and Miller, M., 2021, Influence of redox gradients on nitrate transport from the landscape to groundwater and streams: Science of the Total Environment, v. 800, p. 1-12, https://doi.org/10.1016/j.scitotenv.2021.150200.","productDescription":"150200, 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-123707","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":436140,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WVKF1K","text":"USGS data release","linkHelpText":"Dissolved Gas Modeling Results for Groundwater Samples Collected in the Western Lake Michigan Drainages and Eastern Iowa Basins Study Areas of the United States: 2007, 2017"},{"id":390680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, Wisconsin","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.658203125,\n 40.59727063442024\n ],\n [\n -86.220703125,\n 40.59727063442024\n ],\n [\n -86.220703125,\n 46.649436163350245\n ],\n [\n -94.658203125,\n 46.649436163350245\n ],\n [\n -94.658203125,\n 40.59727063442024\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"800","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stratton, Laurel E. 0000-0001-8567-8619","orcid":"https://orcid.org/0000-0001-8567-8619","contributorId":215056,"corporation":false,"usgs":true,"family":"Stratton","given":"Laurel E.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":825389,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225518,"text":"70225518 - 2021 - Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA","interactions":[],"lastModifiedDate":"2021-10-20T15:36:49.819571","indexId":"70225518","displayToPublicDate":"2021-10-20T10:26:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA","docAbstract":"Study Region: Rocky Mountains, United States
Study Focus: Groundwater-flow modeling requires estimates of hydraulic properties, namely hydraulic conductivity. Hydraulic conductivity values commonly vary over orders of magnitudes however and estimation may require extensive field campaigns applying slug or pumping tests. As an alternative, specific-capacity tests can be used to estimate hydraulic properties for large areas when benchmarked with slug or pumping tests. This study combined aquifer testing with specific capacity data to estimate hydraulic properties in a large alluvial aquifer.
New hydrological insights for region: In the Wet Mountain Valley, Colorado, both slug tests and pumping tests were conducted, resulting in a likely range of hydraulic-conductivity values. Aquifer-testing results were related to specific-capacity data, a more spatially distributed dataset, to expand the area of aquifer characterization beyond the distribution of wells included in aquifer testing. Specific-capacity data were used in two ways: (1) a regression was built between specific-capacity values and transmissivity derived from aquifer testing; and (2) an iterative method was utilized to estimate transmissivity from specific capacity at all sites (including sites lacking aquifer tests). Study results indicate that there is a statistically significant difference between hydraulic-conductivity values estimated using the two approaches and that the regression method yields systematically greater values. These results indicate that careful consideration of methods that use specific capacity for extrapolating aquifer properties is warranted as bias could be introduced depending on the applied methodology.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100949","usgsCitation":"Newman, C.P., Kisfalusi, Z.D., and Holmberg, M.J., 2021, Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA: Journal of Hydrology: Regional Studies, v. 38, p. 1-20, https://doi.org/10.1016/j.ejrh.2021.100949.","productDescription":"100949, 20 p.","startPage":"1","endPage":"20","ipdsId":"IP-109533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":450390,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100949","text":"Publisher Index Page"},{"id":436141,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W7DHLY","text":"USGS data release","linkHelpText":"Water-level and well-discharge data related to aquifer testing in Wet Mountain Valley, Colorado, 2019"},{"id":390678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains, Wet Mountain Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.85052490234375,\n 38.31149091244452\n ],\n [\n -105.50033569335938,\n 37.79676317682161\n ],\n [\n -105.08010864257812,\n 37.95394377350263\n ],\n [\n -105.47012329101562,\n 38.449286817153556\n ],\n [\n -105.85052490234375,\n 38.31149091244452\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kisfalusi, Zachary D. 0000-0001-6016-3213","orcid":"https://orcid.org/0000-0001-6016-3213","contributorId":222422,"corporation":false,"usgs":true,"family":"Kisfalusi","given":"Zachary","email":"","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmberg, Michael J. 0000-0002-1316-0412 mholmber@usgs.gov","orcid":"https://orcid.org/0000-0002-1316-0412","contributorId":190084,"corporation":false,"usgs":true,"family":"Holmberg","given":"Michael","email":"mholmber@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825482,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224031,"text":"ofr20211089 - 2021 - Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon","interactions":[],"lastModifiedDate":"2021-10-20T14:18:57.158711","indexId":"ofr20211089","displayToPublicDate":"2021-10-20T10:20:00","publicationYear":"2021","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":"2021-1089","displayTitle":"Managed Aquifer Recharge Suitability—Regional Screening and Case Studies in Jordan and Lebanon","title":"Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon","docAbstract":"The U.S. Geological Survey, at the request of the U.S. Agency for International Development, led a 5-year regional project to develop and apply methods for water availability and suitability mapping for managed aquifer recharge (MAR) in the Middle East and North Africa region. A regional model of surface runoff for the period from 1984 to 2015 was developed to characterize water availability using remote sensing data on climate, vegetation, and topography in Jordan, Lebanon, and surrounding areas. Surface runoff was accumulated to characterize potential streamflow available for MAR and these data were combined with land surface slope to prepare a regional screening map of MAR suitability, illustrating suitability mapping concepts and methods. The application of the methods is demonstrated by the evaluation of water availability and suitability for potential MAR in study areas in Jordan and Lebanon. Locations suitable for MAR are present in both Jordan and Lebanon, but limitations exist in both countries, related primarily to water availability in Jordan and land areas of suitable terrain in Lebanon. An additional feasibility study including field investigations would likely provide decision makers with essential information for further development of the use of MAR in Jordan, Lebanon, and the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211089","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Goode, D.J., ed., 2021, Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon: U.S. Geological Survey Open-File Report 2021–1089, 87 p., https://doi.org/10.3133/ofr20211089.","productDescription":"Report: xi, 87 p.; 2 Data Releases","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124064","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":436143,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDQ4VF","text":"USGS data release","linkHelpText":"Regional screening for managed aquifer recharge suitability in Jordan, Lebanon, and surrounding areas"},{"id":390660,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9WDQ4VF","text":"USGS data release","linkHelpText":"- Regional screening for managed aquifer recharge suitability in Jordan, Lebanon, and surrounding areas"},{"id":389216,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971ZVHF","text":"USGS data release","linkHelpText":"Assembly of satellite-based rainfall datasets in situ data and rainfall climatology contours for the MENA region"},{"id":389217,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TXLT1X","text":"USGS data release","linkHelpText":"Modeling accumulated surface runoff and water availability for aquifer storage and recovery in the MENA region from 1984–2015"},{"id":389215,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1089/ofr20211089.pdf","text":"Report","size":"22.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1089"},{"id":389214,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1089/coverthb.jpg"}],"country":"Jordan, Lebanon","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[35.54567,32.39399],[35.71992,32.70919],[36.83406,32.31294],[38.79234,33.37869],[39.19547,32.16101],[39.00489,32.01022],[37.00217,31.50841],[37.99885,30.5085],[37.66812,30.33867],[37.50358,30.00378],[36.74053,29.86528],[36.50121,29.50525],[36.06894,29.19749],[34.95604,29.35655],[34.9226,29.50133],[35.42092,31.10007],[35.39756,31.48909],[35.54525,31.7825],[35.54567,32.39399]]],[[[35.8211,33.27743],[35.5528,33.26427],[35.46071,33.08904],[35.12605,33.0909],[35.48221,33.90545],[35.97959,34.61006],[35.9984,34.64491],[36.44819,34.59394],[36.61175,34.20179],[36.06646,33.82491],[35.8211,33.27743]]]]},\"properties\":{\"name\":\"Jordan\"}}]}","contact":"U.S. Geological Survey
Office of International Programs
917 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
directoroip@usgs.gov
Habitat loss from land-use change is one of the top causes of declines in wildlife species of concern. As such, it is critical to assess and reassess habitat suitability as land cover and anthropogenic features change for both monitoring and developing current information to inform management decisions. However, there are obstacles that must be overcome to develop consistent assessments through time. A range-wide lek habitat suitability model for the lesser prairie-chicken (Tympanuchus pallidicinctus), currently under review by the U. S. Fish and Wildlife Service for potential listing under the Endangered Species Act) was published in 2016. This model was based on lek data from 2002 to 2012, land cover data ranging from 2001 to 2013, and anthropogenic features from circa 2011, and has been used to help guide lesser prairie-chicken management and anthropogenic development actions. We created a second iteration model based on new lek surveys (2015 to 2019) and updated predictor layers (2016 land cover and cleaned/ updated anthropogenic data) to evaluate changes in lek suitability and to quantify current range-wide habitat suitability. Only three of 11 predictor variables were directly comparable between the iterations, making it difficult to directly assess what predicted changes resulted from changes in model inputs versus actual landscape change. The second iteration model showed a similar positive relationship with land cover and negative with anthropogenic features to the first iteration, but exhibited more variation among candidate models. Range-wide, more suitable habitat was predicted in the second iteration. The Shinnery Oak Ecoregion, however, exhibited a loss in predicted suitable habitat which could be due to predictor source changes. Iterated models such as this are important to ensure current information is being used in conservation and development decisions.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0256633","usgsCitation":"Jarnevich, C.S., Belamaric, P.N., Fricke, K., Houts, M., Rossi, L., Beauprez, G.M., Cooper, B., and Martin, R., 2021, Challenges in updating habitat suitability models: An example with the lesser prairie-chicken: PLoSOne, v. 16, no. 9, e0256633, 19 p., https://doi.org/10.1371/journal.pone.0256633.","productDescription":"e0256633, 19 p.","ipdsId":"IP-120427","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450394,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0256633","text":"Publisher Index Page"},{"id":436145,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MS0QR0","text":"USGS data release","linkHelpText":"Second Iteration of Range Wide Lesser Prairie Chicken Lek Habitat Suitability in 2019, Predicted in Southern Great Plains"},{"id":390673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.64453124999999,\n 31.98944183792288\n ],\n [\n -101.2060546875,\n 31.98944183792288\n ],\n [\n -101.22802734375,\n 34.34343606848294\n ],\n [\n -99.97558593749999,\n 34.361576287484176\n ],\n [\n -96.328125,\n 36.98500309285596\n ],\n [\n -96.328125,\n 40.01078714046552\n ],\n [\n -102.10693359375,\n 40.027614437486655\n ],\n [\n -102.12890625,\n 39.62261494094297\n ],\n [\n -104.0625,\n 39.67337039176558\n ],\n [\n -103.11767578124999,\n 37.03763967977139\n ],\n [\n -103.07373046875,\n 36.4566360115962\n ],\n [\n -105.62255859375,\n 34.65128519895413\n ],\n [\n -105.64453124999999,\n 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Geological Survey Fort Collins Science Center","active":true,"usgs":false}],"preferred":true,"id":825401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fricke, Kent","contributorId":267847,"corporation":false,"usgs":false,"family":"Fricke","given":"Kent","affiliations":[{"id":40289,"text":"Kansas Department of Wildlife, Parks, and Tourism","active":true,"usgs":false}],"preferred":false,"id":825402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Houts, Mike","contributorId":267848,"corporation":false,"usgs":false,"family":"Houts","given":"Mike","email":"","affiliations":[{"id":33109,"text":"Kansas Biological Survey, Lawrence, KS","active":true,"usgs":false},{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":825403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rossi, Liza","contributorId":267849,"corporation":false,"usgs":false,"family":"Rossi","given":"Liza","email":"","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":825404,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beauprez, Grant M.","contributorId":172889,"corporation":false,"usgs":false,"family":"Beauprez","given":"Grant","email":"","middleInitial":"M.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":825405,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cooper, Brett","contributorId":267850,"corporation":false,"usgs":false,"family":"Cooper","given":"Brett","email":"","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":825406,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, Russell","contributorId":267876,"corporation":false,"usgs":false,"family":"Martin","given":"Russell","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":825471,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225524,"text":"70225524 - 2021 - Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","interactions":[],"lastModifiedDate":"2023-11-08T16:34:39.150126","indexId":"70225524","displayToPublicDate":"2021-10-20T08:25:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","docAbstract":"Study region: The study was conducted in the Northern Atlantic Coastal Plain aquifer system, eastern USA, an important water supply in a densely populated region.
Study focus: Manganese (Mn), an emerging health concern and common nuisance contaminant in drinking water, is mapped and modeled using the XGBoost machine learning method, predictions of pH and redox conditions from previous models, and other explanatory variables that describe the groundwater flow system and surface characteristics. Methods to address the imbalanced occurrence of elevated and low Mn concentrations are compared and used to more accurately predict concentrations of interest for human health and drinking water quality.
New hydrological insights for the region: Elevated Mn concentrations were more likely in shallow groundwater, close to recharge areas and in topographically low areas where soil or unsaturated processes influence groundwater quality. Predicted concentrations greater than the health threshold of 300 micrograms per liter extended across 17 % of the surficial aquifer area, but across <1% of the areas of underlying aquifers. pH and variables related to flow-system position and near-surface processes were more important predictors than the probability of low dissolved oxygen (DO). Mapped variable influence (SHAP values) showed that both pH and DO variables were related to hydrogeologic conditions. Class weights, which improved the predictive ability for elevated Mn without altering the data, was the preferred method to address class imbalance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100925","usgsCitation":"DeSimone, L.A., and Ransom, K.M., 2021, Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning: Journal of Hydrology: Regional Studies, v. 37, 100925, 20 p., https://doi.org/10.1016/j.ejrh.2021.100925.","productDescription":"100925, 20 p.","ipdsId":"IP-126500","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":450397,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100925","text":"Publisher Index Page"},{"id":436146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M64CD1","text":"USGS data release","linkHelpText":"Data used to model and map manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA"},{"id":390662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New Jersey, New York, North Carolina, Pennsylvania, Virginia","city":"Baltimore, New York, Philadelphia, Richmond, Washington D.C.","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.1142578125,\n 41.22824901518529\n ],\n [\n -73.63037109375,\n 40.94671366508002\n ],\n [\n -75.816650390625,\n 39.78321267821705\n ],\n [\n -78.321533203125,\n 38.30718056188316\n ],\n [\n -78.079833984375,\n 35.200744801724014\n ],\n [\n -77.069091796875,\n 35.06597313798418\n ],\n [\n -76.80541992187499,\n 34.94899072578227\n ],\n [\n -76.475830078125,\n 35.092945313732635\n ],\n [\n -76.387939453125,\n 35.34425514918409\n ],\n [\n -76.036376953125,\n 35.29943548054545\n ],\n [\n -75.509033203125,\n 35.84453450421662\n ],\n [\n -75.882568359375,\n 36.43012234551576\n ],\n [\n -75.904541015625,\n 37.055177106660814\n ],\n [\n -75.860595703125,\n 37.28279464911045\n ],\n [\n -75.201416015625,\n 38.07404145941957\n ],\n [\n -74.893798828125,\n 38.496593518947584\n ],\n [\n -75.289306640625,\n 39.16414104768742\n ],\n [\n -74.94873046875,\n 39.138581990583525\n ],\n [\n -75.069580078125,\n 38.976492485539396\n ],\n [\n -74.87182617187499,\n 38.865374851611634\n ],\n [\n -74.124755859375,\n 39.715638134796336\n ],\n [\n -73.883056640625,\n 40.38839687388361\n ],\n [\n -74.058837890625,\n 40.50544628405211\n ],\n [\n -71.74072265625,\n 40.98819156349393\n ],\n [\n -72.1142578125,\n 41.22824901518529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"DeSimone, Leslie A. 0000-0003-0774-9607 ldesimon@usgs.gov","orcid":"https://orcid.org/0000-0003-0774-9607","contributorId":195635,"corporation":false,"usgs":true,"family":"DeSimone","given":"Leslie","email":"ldesimon@usgs.gov","middleInitial":"A.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825413,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225698,"text":"70225698 - 2021 - Hierarchical clustering for paired watershed experiments: Case study in southeastern Arizona, U.S.A.","interactions":[],"lastModifiedDate":"2021-11-03T12:50:00.117476","indexId":"70225698","displayToPublicDate":"2021-10-20T07:46:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Hierarchical clustering for paired watershed experiments: Case study in southeastern Arizona, U.S.A.","docAbstract":"Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.
Satellite-derived bathymetry (SDB) based upon an empirical band ratio method is a cost-effective means for mapping nearshore bathymetry in coastal areas vulnerable to natural hazards. This is particularly important for the low-lying coastal community of Unalakleet, Alaska, that has been negatively affected not only by flooding, storm surge, and historically strong storms but also by high erosion rates stemming from the Unalakleet River and Norton Sound. The purpose of this study was to assess the viability of different satellite imagery, including Landsat 8 (L8) Operational Land Imager, Sentinel–2B, WorldView–3, and L8 Provisional Aquatic Reflectance science product, for deriving SDB for Unalakleet, Alaska. Correlations were performed between satellite imagery band ratios and topobathymetric (topobathy) light detection and ranging (lidar) and in situ single-beam sound navigation and ranging (sonar). The satellite imagery correlations with topobathy lidar did not yield as high of a linear relation with water depths as the satellite imagery correlations with the single-beam sonar. An extinction depth, where light no longer attenuates through the water column, was not identified because of the shallow depths within the topobathy lidar and single-beam sonar datasets. Although some single-beam soundings measured at 7 meters deep, the correlations with the SDB band ratios did not yield a strong linear relation. Satellite imagery band ratio correlations with Electronic Navigational Chart soundings did not yield a strong linear relation because of older source data. Less than optimal linear regressions were most likely due to the geography of Unalakleet, Alaska, a low-lying coastal community subject to high erosion rates from surrounding waters. This study is one of the first attempts to compare different satellite imagery band ratio correlations with topobathy lidar and in situ sonar to assess the viability for nearshore SDB for coastal Unalakleet, Alaska.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215097","usgsCitation":"Poppenga, S.K., and Danielson, J.J., 2021, A comparison of Landsat 8 Operational Land Imager and Provisional Aquatic Reflectance science product, Sentinel–2B, and WorldView–3 imagery for empirical satellite-derived bathymetry, Unalakleet, Alaska: U.S. Geological Survey Scientific Investigations Report 2021–5097, 15 p., https://doi.org/10.3133/sir20215097.","productDescription":"Report: vii, 15 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-132009","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":390537,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5097/coverthb.jpg"},{"id":390538,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5097/sir20215097.pdf","text":"Report","size":"1.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5097"},{"id":390539,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9238F8K","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Nearshore bathymetry data from the Unalakleet River mouth, Alaska, 2019"}],"country":"United States","state":"Alaska","city":"Unalakleet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -164.4873046875,\n 63.16675579239305\n ],\n [\n -159.6038818359375,\n 63.16675579239305\n ],\n [\n -159.6038818359375,\n 64.58146958015028\n ],\n [\n -164.4873046875,\n 64.58146958015028\n ],\n [\n -164.4873046875,\n 63.16675579239305\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Groundwater is an important source of drinking water supplies in the conterminous United State (CONUS), and presence of high nitrate concentrations may limit usability of groundwater in some areas because of the potential negative health effects. Prediction of locations of high nitrate groundwater is needed to focus mitigation and relief efforts. A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate. Nitrate was predicted at a 1 km resolution for two drinking water zones, each of variable depth, one for domestic supply and one for public supply. The model used measured nitrate concentrations from 12,082 wells and included predictor variables representing well characteristics, hydrologic conditions, soil type, geology, land use, climate, and nitrogen inputs. Predictor variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and conditions. The model provided accurate estimates at national and regional scales: the training (R2 of 0.83) and hold-out (R2 of 0.49) data fits compared favorably to previous studies. Predicted nitrate concentrations were less than 1 mg/L across most of the CONUS. Nationally, well depth, soil and climate characteristics, and the absence of developed land use were among the most influential explanatory factors. Only 1% of the area in either water supply zone had predicted nitrate concentrations greater than 10 mg/L; however, about 1.4 M people depend on groundwater for their drinking supplies in those areas. Predicted high concentrations of nitrate were most prevalent in the central CONUS. In areas of predicted high nitrate concentration, applied manure, farm fertilizer, and agricultural land use were influential predictor variables. This work represents the first application of XGB to a three-dimensional national-scale groundwater quality model and provides a significant milestone in the efforts to document nitrate in groundwater across the CONUS.
Winter carbon loss in northern ecosystems is estimated to be greater than the average growing season carbon uptake and is primarily driven by microbial decomposers. Viruses modulate microbial carbon cycling via induced mortality and metabolic controls, but it is unknown whether viruses are active under winter conditions (anoxic and sub-freezing temperatures).
We used stable isotope probing (SIP) targeted metagenomics to reveal the genomic potential of active soil microbial populations under simulated winter conditions, with an emphasis on viruses and virus-host dynamics. Arctic peat soils from the Bonanza Creek Long-Term Ecological Research site in Alaska were incubated under sub-freezing anoxic conditions with H218O or natural abundance water for 184 and 370 days. We sequenced 23 SIP-metagenomes and measured carbon dioxide (CO2) efflux throughout the experiment. We identified 46 bacterial populations (spanning 9 phyla) and 243 viral populations that actively took up 18O in soil and respired CO2 throughout the incubation. Active bacterial populations represented only a small portion of the detected microbial community and were capable of fermentation and organic matter degradation. In contrast, active viral populations represented a large portion of the detected viral community and one third were linked to active bacterial populations. We identified 86 auxiliary metabolic genes and other environmentally relevant genes. The majority of these genes were carried by active viral populations and had diverse functions such as carbon utilization and scavenging that could provide their host with a fitness advantage for utilizing much-needed carbon sources or acquiring essential nutrients.
Overall, there was a stark difference in the identity and function of the active bacterial and viral community compared to the unlabeled community that would have been overlooked with a non-targeted standard metagenomic analysis. Our results illustrate that substantial active virus-host interactions occur in sub-freezing anoxic conditions and highlight viruses as a major community-structuring agent that likely modulates carbon loss in peat soils during winter, which may be pivotal for understanding the future fate of arctic soils' vast carbon stocks.
","language":"English","publisher":"Springer","doi":"10.1186/s40168-021-01154-2","usgsCitation":"Trubl, G., Kimbrel, J.A., Liquet-Gonzalez, J., Nuccio, E.E., Weber, P.K., Pett-Ridge, J., Jansson, J.K., Waldrop, M., and Blazewicz, S., 2021, Active virus-host interactions at sub-freezing temperatures in Arctic peat soil: Microbiome, v. 9, 208, 15 p., https://doi.org/10.1186/s40168-021-01154-2.","productDescription":"208, 15 p.","ipdsId":"IP-128011","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467223,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40168-021-01154-2","text":"Publisher Index Page"},{"id":462901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Trubl, Gareth","contributorId":345156,"corporation":false,"usgs":false,"family":"Trubl","given":"Gareth","email":"","affiliations":[{"id":82502,"text":"Lawrence Livermore National Labs","active":true,"usgs":false}],"preferred":false,"id":915862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kimbrel, Jeffrey A","contributorId":345157,"corporation":false,"usgs":false,"family":"Kimbrel","given":"Jeffrey","email":"","middleInitial":"A","affiliations":[{"id":82502,"text":"Lawrence Livermore National Labs","active":true,"usgs":false}],"preferred":false,"id":915863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liquet-Gonzalez, Jose","contributorId":345158,"corporation":false,"usgs":false,"family":"Liquet-Gonzalez","given":"Jose","email":"","affiliations":[{"id":82502,"text":"Lawrence Livermore National Labs","active":true,"usgs":false}],"preferred":false,"id":915864,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nuccio, Erin E.","contributorId":345159,"corporation":false,"usgs":false,"family":"Nuccio","given":"Erin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":915865,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weber, Peter K.","contributorId":345160,"corporation":false,"usgs":false,"family":"Weber","given":"Peter","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":915866,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pett-Ridge, Jennifer","contributorId":254974,"corporation":false,"usgs":false,"family":"Pett-Ridge","given":"Jennifer","affiliations":[{"id":51376,"text":"Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore CA 94551","active":true,"usgs":false}],"preferred":false,"id":915867,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jansson, Janet K.","contributorId":345161,"corporation":false,"usgs":false,"family":"Jansson","given":"Janet","email":"","middleInitial":"K.","affiliations":[{"id":82503,"text":"Pacific Northwest National Labs","active":true,"usgs":false}],"preferred":false,"id":915868,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":915869,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blazewicz, Steve 0000-0001-7517-1750","orcid":"https://orcid.org/0000-0001-7517-1750","contributorId":272100,"corporation":false,"usgs":false,"family":"Blazewicz","given":"Steve","email":"","affiliations":[{"id":13621,"text":"Lawrence Livermore National Laboratory","active":true,"usgs":false}],"preferred":false,"id":915870,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70226823,"text":"70226823 - 2021 - Effects of hydrologic variability and remedial actions on first flush and metal loading from streams draining the Silverton caldera, 1992–2014","interactions":[],"lastModifiedDate":"2021-12-14T12:52:04.069102","indexId":"70226823","displayToPublicDate":"2021-10-18T06:45:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effects of hydrologic variability and remedial actions on first flush and metal loading from streams draining the Silverton caldera, 1992–2014","docAbstract":"This study examined water quality in the upper Animas River watershed, a mined watershed that gained notoriety following the 2015 Gold King mine release of acid mine drainage to downstream communities. Water-quality data were used to evaluate trends in metal concentrations and loads over a two-decade period. Selected sites included three sites on tributary streams and one main-stem site on the Animas River downstream from the tributary confluences. During the study period, metal concentrations and loads varied seasonally and annually because of hydrologic variability and remedial actions designed to ameliorate the effects of acid mine drainage. Water-quality data were divided into two periods based on the timing of remedial activities in the watershed. The first period includes active water treatment, surface reclamation and installation of bulkheads in adits; the second period includes the decade following these activities. Water-quality data were used to estimate annual and monthly zinc loads using the Adjusted Maximum Likelihood Method (using LOADEST software) and U.S. Geological Survey streamflow data. This study presents one of the first applications of LOADEST focused on metal loads. Monthly flow-weighted concentrations were analysed using a Mann-Kendall trend test to determine the direction, magnitude, and significance of temporal trends in zinc loading in any given month and using t-test comparisons between the two periods. Zinc loads estimated for the Animas River below the tributaries indicate decreased zinc loading during the rising limb of the hydrograph in the second period, perhaps reflecting a reduction of snowmelt-derived zinc load following surface reclamation activities. In contrast, base-flow zinc loading increased at the main-stem site, perhaps because of the cessation of water treatment in tributary streams. Flow weighting of monthly load estimates yielded increased statistical significance and enabled more nuanced differentiation between the effects of hydrologic variability and remedial activities on zinc loading.
We present a new SIngle-scintillator Neutron and Gamma Ray spectrometer (SINGR) instrument for use with both passive and active measurement techniques. Here we discuss the application of SINGR for planetary exploration missions, however, hydrology, nuclear non-proliferation, and resource prospecting are all potential areas where the instrument could be applied. SINGR uses an elpasolite scintillator, Cs2YLiCl6:Ce (CLYC), that has been shown to have high neutron efficiency even at small volumes, with a gamma-ray energy resolution of approximately 4% full-width-at-half-maximum at 662 keV. Active gamma-ray and neutron (GRNS) measurements were performed with SINGR at the NASA Goddard Space Flight Center (GSFC) Goddard Geophysical and Astronomical Observatory (GGAO) outdoor test site using a pulsed neutron generator (PNG) to interrogate geologically relevant materials (basalt and granite monuments). These experimental results, combined with simulations, demonstrate that SINGR is capable of generating neutron die-away curves that can be used to reconstruct the bulk hydrogen abundance and the depth distribution of hydrogen within the monuments. We compare our experimental results with Monte Carlo N-Particle (MCNP) 6.1 transport simulations to constrain the uncertainties in depth and hydrogen abundance from the neutron die-away data generated by SINGR. For future planetary exploration missions, SINGR provides a single detector system for interrogating the shallow subsurface to characterize the presence and abundance of hydrated phases and to provide bulk elemental analysis.
Soil organic carbon (SOC) stocks of Prairie Pothole Region (PPR) wetlands in the central plains of Canada and the United States are highly variable due to natural variation in biota, soils, climate, hydrology, and topography. Land-use history (cropland, grassland) and land-management practices (drainage, restoration) also affect SOC stocks. We conducted a region-wide assessment of wetland SOC stocks using data from the Canadian and US portions of the PPR under various management types. Natural wetlands with no disturbance history in the wetland basin or surrounding catchment had considerably greater average SOC stocks in the upper (0–15 cm) soil profile than wetlands surrounded by cropland. Hydrologically restored wetlands did not show significantly greater SOC stocks than drained wetlands, but wetlands surrounded by restored grasslands did have greater SOC stocks in the upper soil profile than those surrounded by croplands. Similarities among cropped and restored wetlands likely were due to insufficient time since restoration, as well as high variability attributable to several environmental factors within the region. We conclude that avoided loss of natural wetlands from drainage and avoided loss of native grasslands from cropping have the most benefit for preserving wetland SOC stocks. Robust PPR SOC models that incorporate multiple abiotic, biotic, and land-use factors are required to determine where and when restoration is most effective for SOC sequestration.
Infrasound (low-frequency acoustic waves) has proven useful to detect and characterize subaerial volcanic activity, but understanding the infrasonic source during sustained eruptions is still an area of active research. Preliminary comparison between acoustic eruption spectra and the jet noise similarity spectra suggests that volcanoes can produce an infrasonic form of jet noise from turbulence. The jet noise similarity spectra, empirically derived from audible laboratory jets, consist of two noise sources: large-scale turbulence (LST) and fine-scale turbulence (FST). We fit the similarity spectra quantitatively to eruptions of Mount St. Helens in 2005, Tungurahua in 2006, and Kīlauea in 2018 using nonlinear least squares fitting. By fitting over a wide infrasonic frequency band (0.05–10 Hz) and restricting the peak frequency above 0.15 Hz, we observe a better fit during times of eruption versus non-eruptive background noise. Fitting smaller overlapping frequency bands highlights changes in the fit of LST and FST spectra, which aligns with observed changes in eruption dynamics. Our results indicate that future quantitative spectral fitting of eruption data will help identify changes in eruption source parameters such as velocity, jet diameter, and ash content which are critical for effective hazard monitoring and response.
Scientists collect fleas (Siphonaptera) to survey for Yersinia pestis, the bacterial agent of plague. When studying fleas parasitizing prairie dogs (Cynomys spp.), two primary methods are used: (1) combing fleas from live-trapped prairie dogs and (2) swabbing fleas from burrows with cloth swabs attached to metal cables. Ideally, burrow swabbing, the cheaper and easier method, would explain flea burdens on prairie dogs and provide reliable information on plague prevalence. In a linear regression analysis of data from 1-month intervals (June–August 2010–2011) on 13 colonies of black-tailed prairie dogs (Cynomys ludovicianus, BTPDs) in New Mexico, flea abundance on swabs explained 0–26% of variation in BTPD flea burdens. In an analysis of data (May–August 2016) from six colonies of BTPDs in Montana, flea abundance on swabs explained 2% of variation in BTPD flea burdens. In an analysis of data from a short-term interval (July 23–27, 2019) on four colonies of BTPDs in Montana, flea abundance on swabs explained 0.1% of variation in BTPD flea burdens. In an analysis of data from 1-week intervals (August–October 2000) on four colonies of white-tailed prairie dogs (Cynomys leucurus, WTPD) in Utah, swabbing data explained 0.1% of variation in WTPD flea burdens. Pools of fleas from two WTPD colonies were tested for Y. pestis by mouse inoculation and isolation; 65% from WTPDs tested positive, whereas 4% from burrows tested positive. Data herein also show that results from burrow swabbing can misrepresent flea species composition and phenology on prairie dogs. Burrow swabbing is useful for some purposes, but limitations should be acknowledged, and accumulated data should be interpreted with caution.
","language":"English","publisher":"Mary Ann Liebert Inc.","doi":"10.1089/vbz.2021.0025","usgsCitation":"Eads, D.A., Matchett, M.R., Poje, J., and Biggins, D.E., 2021, Comparison of flea sampling methods and Yersinia pestis detection on prairie dog colonies: Vector-Borne and Zoonotic Diseases, v. 21, no. 10, p. 753-761, https://doi.org/10.1089/vbz.2021.0025.","productDescription":"9 p.","startPage":"753","endPage":"761","ipdsId":"IP-125380","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":436164,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AFVPEJ","text":"USGS data release","linkHelpText":"Mean flea counts from prairie dogs and their burrows in Utah (2000), New Mexico (2010-2012), and Montana (2016, 2019)"},{"id":396357,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, New Mexico, 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