Evaporation-rate estimates at Lake Mead and Lake Mohave, Nevada and Arizona, were based on eddy covariance and available energy measurements from March 2010 through April 2019 at Lake Mead and May 2013 through April 2019 at Lake Mohave. The continuous data needed to compute monthly evaporation were collected from floating-platform and land-based measurement stations located at each reservoir. Collected data include latent- and sensible-heat fluxes, net radiation, air temperature, wind speed, humidity, and water-temperature profiles. Data collection, analysis methods, and monthly evaporation results for Lake Mead through February 2012 were documented in a U.S. Geological Survey (USGS) Scientific-Investigations Report, 2013–5229. Monthly evaporation and associated datasets for both reservoirs through April 2015 were published in a USGS Data Release (https://doi.org/10.5066/F79C6VG3). Average annual evaporation at Lake Mead was 1,896 millimeters (mm), which is a 10 percent difference from the 1,718 mm average annual evaporation at Lake Mohave; this was primarily due to differences in available energy. Average annual available energy at Lake Mead was 139 watts per square meter (W/m2), which is an 18 percent difference from the 116 W/m2 average annual available energy at Lake Mohave. Differences in available energy are driven by differences in advected heat between Lake Mead and Lake Mohave; advected heat at Lake Mohave is lower due to colder inflows and warmer outflows. Lake Mead monthly evaporation estimates for this study compare reasonably well to the Bureau of Reclamation’s 24-Month Study (24MS) evaporation coefficients, which are based on pioneering studies from the 1950s. Temporal trends in this study indicate that the effects of heat storage at Lake Mead were underestimated in the 24MS, particularly during the fall months when energy was released from the lake. Mean monthly evaporation rates at Lake Mead were greater than Lake Mohave from June through November during the study period. The seasonal pattern of evaporation at Lake Mohave in this study indicates that the effects of available energy were underestimated in the 24MS coefficients for this reservoir, and that evaporation was substantially overestimated from spring through summer
during the study period of 2013 through 2019.
Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Little information exists on the habitat use and feeding ecology of insectivorous bats in arid ecosystems, especially at and near uranium mines in northern Arizona, within the Grand Canyon watershed. In 2015–2016, we conducted mist-netting, nightly acoustic monitoring (>1 year), and diet analyses of bats, as well as insect sampling, at 2 uranium mines (Pinenut and Arizona 1) with water containment ponds. Because of physical barriers and limited general access to areas within the mine yard, mist-netting was limited to outside of the perimeter fence and away from the containment ponds. Mist-netting also occurred at 2 nearby sites that served as proxies to the mines. Bats captured directly at the mines included one pregnant Antrozous pallidus and 3 adult male Parastrellus hesperus. At the proxy sites, we captured 45 individuals identified as A. pallidus, Corynorhinus townsendii, Eptesicus fuscus, Euderma maculatum, Lasionycteris noctivagans, Myotis californicus, Myotis ciliolabrum, P. hesperus, and Tadarida brasiliensis. The nightly and seasonal presence of bats, as shown through acoustic recordings at each mine, coincided with the seasonal migratory and hibernation behaviors of the bat species. Statistical comparisons of acoustic recordings with precipitation data collected over one year show that seasonal monsoon rains generally had a negative effect on the nightly activity and presence of bats. Diets of P. hesperus from both mines were comprised mostly of coleopterans but also included smaller volumes of Hymenoptera, Hemiptera, Lepidoptera, Diptera, and Neuroptera. The diet of A. pallidus was comprised solely of Coleoptera. Diets of bat species from the proxy sites were characteristic of their known feeding ecology, which ranged from the consumption of soft-bodied insects (e.g., moths) by C. townsendii to the consumption of hard-bodied insects (e.g., beetles) by E. fuscus. Ultimately, the increased knowledge of the natural history of bats through multiple methods of data collection allows for a better understanding of complex arid ecosystems. It also provides resources needed for the management of habitat associated with alternative energy, such as uranium mining.
Accurate population estimates provide the foundation for managing feral horses (Equus caballus ferus) across the western United States. Certain feral horse populations are protected by the Wild and Free-Roaming Horses and Burros Act of 1971 and managed by the Bureau of Land Management (BLM) or the United States Forest Service on designated herd management areas (HMAs) or wild horse territories, respectively. Horses are managed to achieve an appropriate management level (AML), which represents the number of horses determined by BLM to contribute to a thriving natural ecological balance and avoid deterioration of the range. To achieve AML for each HMA, BLM resource managers need accurate and precise population estimates. We tested the use of non-invasive fecal samples in a genetic capture-recapture framework to estimate population size in a closed horse population at the Little Book Cliffs HMA, Colorado, USA, with a known size of 153 individuals. We collected 1,957 samples over 3 independent sampling periods in 2014 and amplified them at 8 microsatellite loci. We applied mark-recapture models to determine population size using 954 samples that amplified at all 8 loci. We subsampled and reanalyzed our dataset to simulate different data collection protocols and evaluated effects on accuracy and precision of estimates using N-mixture modeling, full likelihood closed-capture modeling, and capwire single-occasion modeling that used data from all 3 sampling periods. Our model results were accurate and precise for analyses that used data from all 3 occasions; however, capwire single-occasion modeling was not accurate when we analyzed each sampling period separately. For all subsampling analysis scenarios, reducing sample size decreased precision, whether by reducing number of field staff, field days, or geographic areas surveyed on each period. Reducing spatial coverage of the survey area did not result in accurate population estimates and only marginally lowered the number of samples that would need to be collected to maintain accuracy. Because laboratory analysis contributes the greatest expense for this method ($80 U.S./sample), reducing fecal sample size is advantageous. Our results demonstrate that non-invasive sampling combined with good survey design and careful genetic and capture-recapture analyses can provide an alternative method to estimate the number of feral horses in a closed population. This method may be especially appropriate in situations where aerial inventories are not practical or accurate because of low sighting conditions. But the higher costs associated with laboratory sample analyses may reduce the method's feasibility compared to helicopter surveys.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22056","usgsCitation":"Schoenecker, K., King, S.R., Ekernas, L.S., and Oyler-McCance, S.J., 2021, Using fecal DNA and closed-capture models to estimate feral horse population size: Journal of Wildlife Management, v. 85, no. 6, p. 1150-1161, https://doi.org/10.1002/jwmg.22056.","productDescription":"12 p.","startPage":"1150","endPage":"1161","ipdsId":"IP-103976","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":396432,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Little Book Cliffs Horse management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.49136352539062,\n 39.13432124527173\n ],\n [\n -108.34304809570312,\n 39.13432124527173\n ],\n [\n -108.34304809570312,\n 39.27691581029594\n ],\n [\n -108.49136352539062,\n 39.27691581029594\n ],\n [\n -108.49136352539062,\n 39.13432124527173\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":835961,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Sarah R. 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Stefan 0000-0002-9205-1985","orcid":"https://orcid.org/0000-0002-9205-1985","contributorId":223034,"corporation":false,"usgs":true,"family":"Ekernas","given":"L.","email":"","middleInitial":"Stefan","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":835963,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":835964,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220306,"text":"70220306 - 2021 - Surface Rupture Map of the 2020 M 6.5 Monte Cristo Range earthquake, Esmeralda and Mineral counties, Nevada","interactions":[],"lastModifiedDate":"2021-06-03T11:53:49.707121","indexId":"70220306","displayToPublicDate":"2021-05-10T10:02:04","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5655,"text":"Nevada Bureau of Mines and Geology Map","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"190","title":"Surface Rupture Map of the 2020 M 6.5 Monte Cristo Range earthquake, Esmeralda and Mineral counties, Nevada","docAbstract":"The 15 May 2020, M6.5 Monte Cristo Range earthquake was the largest earthquake in Nevada in over 66 years and occurred in a sparsely populated area of western Nevada about 74 km southeast of the town of Hawthorne. The earthquake produced surface rupture distributed across a 28-km-long zone along the eastward projection of the Candelaria fault in the Mina deflection of the central Walker Lane. Post-event field surveys mapped surface ruptures and measured displacements, which reached up to ~20 cm of oblique slip. Additional detailed mapping was completed using centimeter-resolution orthomosaics generated from Uncrewed Aerial Vehicle surveys. The rupture observations and displacement data are compiled into this 1:14,000-scale map, data tables, and accompanying digital dataset. The rupture consists of two distinct deformational domains roughly separated by U.S. Highway 95: ENE-trending ruptures with normal and left-oblique displacements in the western domain, and N- to NNE-trending ruptures with normal and right-oblique displacement in the eastern domain. The complex pattern of surface rupture is consistent with the projections of mapped bedrock and Quaternary faults in the area and illustrates the kinematics of slip partitioning at the junction of variably oriented structures in the shallow subsurface.
","language":"English","publisher":"University of Nevada, Reno","usgsCitation":"Dee, S., Koehler, R.D., Elliott, A.J., Hatem, A.E., Pickering, A., Pierce, I., Seitz, G.G., Collett, C.M., Dawson, T.E., De Masi, C., dePolo, C.M., Hartsorn, E., Madugo, C., Trexler, C.C., Verdugo, D.M., Wesnousky, S.G., and Zachariasen, J., 2021, Surface Rupture Map of the 2020 M 6.5 Monte Cristo Range earthquake, Esmeralda and Mineral counties, Nevada: Nevada Bureau of Mines and Geology Map 190, Report: 26 p.; 2 Sheets: 42.00 x 42.00 inches; GIS Files.","productDescription":"Report: 26 p.; 2 Sheets: 42.00 x 42.00 inches; GIS Files","ipdsId":"IP-127048","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":386127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":386126,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.nbmg.unr.edu/Monte-Cristo-Range-EQ-p/m190.htm"}],"country":"United 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Reno","active":true,"usgs":false}],"preferred":false,"id":815091,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seitz, Gordon G.","contributorId":139062,"corporation":false,"usgs":false,"family":"Seitz","given":"Gordon","email":"","middleInitial":"G.","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":815092,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Collett, Camille Marie 0000-0003-4836-0243","orcid":"https://orcid.org/0000-0003-4836-0243","contributorId":257819,"corporation":false,"usgs":true,"family":"Collett","given":"Camille","email":"","middleInitial":"Marie","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815093,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dawson, Timothy E.","contributorId":24429,"corporation":false,"usgs":false,"family":"Dawson","given":"Timothy","email":"","middleInitial":"E.","affiliations":[{"id":7099,"text":"Calif. 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Survey","active":true,"usgs":false}],"preferred":false,"id":815094,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"De Masi, Conni","contributorId":257820,"corporation":false,"usgs":false,"family":"De Masi","given":"Conni","email":"","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":815095,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"dePolo, Craig M","contributorId":257821,"corporation":false,"usgs":false,"family":"dePolo","given":"Craig","email":"","middleInitial":"M","affiliations":[{"id":6689,"text":"Nevada Bureau of Mines and Geology","active":true,"usgs":false}],"preferred":false,"id":815096,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hartsorn, Evan","contributorId":257822,"corporation":false,"usgs":false,"family":"Hartsorn","given":"Evan","email":"","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":815097,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Madugo, Christopher","contributorId":225600,"corporation":false,"usgs":false,"family":"Madugo","given":"Christopher","email":"","affiliations":[{"id":41169,"text":"Pacific Gas and Electric Company","active":true,"usgs":false}],"preferred":false,"id":815098,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Trexler, Charles Cashman 0000-0001-5046-9729","orcid":"https://orcid.org/0000-0001-5046-9729","contributorId":257823,"corporation":false,"usgs":true,"family":"Trexler","given":"Charles","email":"","middleInitial":"Cashman","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":815099,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Verdugo, Danielle M","contributorId":257824,"corporation":false,"usgs":false,"family":"Verdugo","given":"Danielle","email":"","middleInitial":"M","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":815100,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wesnousky, Steven G.","contributorId":193416,"corporation":false,"usgs":false,"family":"Wesnousky","given":"Steven","email":"","middleInitial":"G.","affiliations":[{"id":33746,"text":"Center for Neotectonic Studies, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":815101,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zachariasen, Judith","contributorId":195131,"corporation":false,"usgs":false,"family":"Zachariasen","given":"Judith","email":"","affiliations":[],"preferred":false,"id":815102,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70220421,"text":"70220421 - 2021 - Virus shedding kinetics and unconventional virulence tradeoffs","interactions":[],"lastModifiedDate":"2021-05-13T12:02:23.891402","indexId":"70220421","displayToPublicDate":"2021-05-10T07:01:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2981,"text":"PLoS Pathogens","active":true,"publicationSubtype":{"id":10}},"title":"Virus shedding kinetics and unconventional virulence tradeoffs","docAbstract":"Tradeoff theory, which postulates that virulence provides both transmission costs and benefits for pathogens, has become widely adopted by the scientific community. Although theoretical literature exploring virulence-tradeoffs is vast, empirical studies validating various assumptions still remain sparse. In particular, truncation of transmission duration as a cost of virulence has been difficult to quantify with robust controlled in vivo studies. We sought to fill this knowledge gap by investigating how transmission rate and duration were associated with virulence for infectious hematopoietic necrosis virus (IHNV) in rainbow trout (Oncorhynchus mykiss). Using host mortality to quantify virulence and viral shedding to quantify transmission, we found that IHNV did not conform to classical tradeoff theory. More virulent genotypes of the virus were found to have longer transmission durations due to lower recovery rates of infected hosts, but the relationship was not saturating as assumed by tradeoff theory. Furthermore, the impact of host mortality on limiting transmission duration was minimal and greatly outweighed by recovery. Transmission rate differences between high and low virulence genotypes were also small and inconsistent. Ultimately, more virulent genotypes were found to have the overall fitness advantage, and there was no apparent constraint on the evolution of increased virulence for IHNV. However, using a mathematical model parameterized with experimental data, it was found that host culling resurrected the virulence tradeoff and provided low virulence genotypes with the advantage. Human-induced or natural culling, as well as host population fragmentation, may be some of the mechanisms by which virulence diversity is maintained in nature. This work highlights the importance of considering non-classical virulence tradeoffs.
The need of citizens in any nation to access geospatial data in readily usable form is critical to societal well-being, and in the United States (US), demands for information by scientists, students, professionals and citizens continue to grow. Areas such as public health, urbanization, resource management, economic development and environmental management require a variety of data collected from many sources to identify problems, monitor trends and propose solutions. Such information needs and demands have driven the coordination of federal and regional government agencies with respective private sector participation to develop national geospatial data infrastructures in many countries.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Handbook of big geospatial data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-55462-0_23","usgsCitation":"Buttenfield, B.P., Stanislawski, L., Kronenfeld, B.J., and Shavers, E.J., 2021, The 4th paradigm in multiscale data representation: Modernizing the National Geospatial Data Infrastructure, chap. of Handbook of big geospatial data, p. 589-618, https://doi.org/10.1007/978-3-030-55462-0_23.","productDescription":"30 p.","startPage":"589","endPage":"618","ipdsId":"IP-125863","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":423017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-05-08","publicationStatus":"PW","contributors":{"editors":[{"text":"Werner, Martin","contributorId":331851,"corporation":false,"usgs":false,"family":"Werner","given":"Martin","email":"","affiliations":[],"preferred":false,"id":888929,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Chiang, Yao-Yi","contributorId":288084,"corporation":false,"usgs":false,"family":"Chiang","given":"Yao-Yi","email":"","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":888930,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Buttenfield, Barbara P. 0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":888724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":888725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kronenfeld, Barry J. 0000-0002-9518-2462","orcid":"https://orcid.org/0000-0002-9518-2462","contributorId":207104,"corporation":false,"usgs":false,"family":"Kronenfeld","given":"Barry","email":"","middleInitial":"J.","affiliations":[{"id":5043,"text":"Eastern Illinois University","active":true,"usgs":false}],"preferred":false,"id":888726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":888727,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220508,"text":"70220508 - 2021 - Spatial data reduction through element -of-interest (EOI) extraction","interactions":[],"lastModifiedDate":"2021-05-18T13:06:11.149552","indexId":"70220508","displayToPublicDate":"2021-05-08T08:04:24","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Spatial data reduction through element -of-interest (EOI) extraction","docAbstract":"Any large, multifaceted data collection that is challenging to handle with traditional management practices can be branded ‘Big Data.’ Any big data containing geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven enterprise. Challenges in the big spatial data domain can be summarized as the ‘Big Vs’ – variety, volume, velocity, veracity and value. Big spatial data sources can be considered in two broad classes, active and passive, as each is impacted to varying degrees. Some of these challenges may be alleviated by reducing unprocessed, or minimally processed, (raw) data to features, which we refer to as the extraction of Elements of Interest (EOI). In fact, many applications require EOI extraction from raw data to enable their basic employment. This chapter presents current state-of-the-art methods to create EOI from some types of georeferenced big data. We classify the data types into two realms: active and passive. Active data are those collected specifically for the purpose to which they are applied. Passive data are those collected for purposes other than those for which they are utilized, included those ‘collected’ for no particular purpose at all. The chapter then presents use cases from both the active and passive spatial realms, including the active applications of terrain feature extraction from digital elevation models and vegetation mapping from remotely-sensed imagery and passive applications like building identification from VGI and point-of-interest data mining from social networks for land use classification. Finally, the chapter concludes with future research needs.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Handbook of big geospatial data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-55462-0_5","usgsCitation":"Arundel, S., and Usery, E., 2021, Spatial data reduction through element -of-interest (EOI) extraction, chap. of Handbook of big geospatial data, p. 119-134, https://doi.org/10.1007/978-3-030-55462-0_5.","productDescription":"16 p.","startPage":"119","endPage":"134","ipdsId":"IP-113380","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":385704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":815856,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Usery, E. Lynn 0000-0002-2766-2173","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":204684,"corporation":false,"usgs":true,"family":"Usery","given":"E. Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":815857,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70220388,"text":"70220388 - 2021 - Using the Landsat Burned Area products to derive fire history relevant for fire management and conservation in the state of Florida, southeastern USA","interactions":[],"lastModifiedDate":"2024-05-16T15:27:57.216807","indexId":"70220388","displayToPublicDate":"2021-05-08T06:59:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Using the Landsat Burned Area products to derive fire history relevant for fire management and conservation in the state of Florida, southeastern USA","docAbstract":"Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.
","language":"English","publisher":"MDPI","doi":"10.3390/fire4020026","usgsCitation":"Teske, C., Vanderhoof, M.K., Hawbaker, T., Noble, J., and Hires, J.K., 2021, Using the Landsat Burned Area products to derive fire history relevant for fire management and conservation in the state of Florida, southeastern USA: Fire, v. 4, no. 2, 26, 21 p., https://doi.org/10.3390/fire4020026.","productDescription":"26, 21 p.","ipdsId":"IP-126697","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":452347,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire4020026","text":"Publisher Index Page"},{"id":385562,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Panhandle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.64892578125,\n 29.554345125748267\n ],\n [\n -83.43017578125,\n 29.554345125748267\n ],\n [\n -83.43017578125,\n 30.939924331023445\n ],\n [\n -87.64892578125,\n 30.939924331023445\n ],\n [\n -87.64892578125,\n 29.554345125748267\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Teske, Casey","contributorId":224732,"corporation":false,"usgs":false,"family":"Teske","given":"Casey","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":815369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":815372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":815371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noble, Joe","contributorId":257938,"corporation":false,"usgs":false,"family":"Noble","given":"Joe","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":815370,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hires, J. Kevin","contributorId":257941,"corporation":false,"usgs":false,"family":"Hires","given":"J.","email":"","middleInitial":"Kevin","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":815373,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221481,"text":"70221481 - 2021 - Stochastic inversion of gravity, magnetic, tracer, lithology, and fault data for geologically realistic structural models: Patua Geothermal Field case study","interactions":[],"lastModifiedDate":"2021-06-17T11:49:03.530012","indexId":"70221481","displayToPublicDate":"2021-05-08T06:44:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"title":"Stochastic inversion of gravity, magnetic, tracer, lithology, and fault data for geologically realistic structural models: Patua Geothermal Field case study","docAbstract":"Financial risk due to geological uncertainty is a major barrier for geothermal development. Production from a geothermal well depends on the unknown location of subsurface geological structures, such as faults that contain hydrothermal fluids. Traditionally, geoscientists collect many different datasets, interpret the datasets manually, and create a single model estimating faults' locations. This method, however, does not provide information about the uncertainty regarding the location of faults and often does not fully respect all observed datasets. Previous researchers investigated the use of stochastic inversion schemes for addressing geological uncertainty, but often at the expense of geologic realism. In this paper, we present algorithms and open-source code to stochastically invert five typical datasets for creating geologically realistic structural models. Using a case study with real data from the Patua Geothermal Field, we show that these inversion algorithms are successful in finding an ensemble of structural models that are geologically realistic and match the observed data sufficiently. Geoscientists can use this ensemble of models to optimize reservoir management decisions given structural uncertainty.
The unknown onshore extent of megathrust earthquake rupture in the Cascadia subduction zone represents a key uncertainty in earthquake hazard for the Pacific Northwest that is governed by the physical state and mechanical properties of the plate interface. The Cascadia plate interface is segmented into an interseismically locked zone located primarily offshore that is expected to rupture in large earthquakes, a region of aseismic slow slip at greater depth, and an intervening transition zone of uncertain rupture potential. Here we image the evolution of the ratio of seismic compressional to shear wave velocities from the locked zone to the transition zone, which is related to changes in fluid content of the plate boundary zone, using a dense onshore–offshore seismic dataset from southernmost Cascadia. The locked zone shows evidence of high fluid content implying a high porosity, yet the downdip transition zone shows an order of magnitude lower porosity. This strong variation is consistent with models that contain a ductile region between the earthquake rupture and slow slip zones that would inhibit onshore propagation of future large earthquake ruptures and hence reduce seismic hazard.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s41561-021-00740-1","usgsCitation":"Guo, H., McGuire, J., and Zhang, H., 2021, Correlation of porosity variations and rheological transitions on the southern Cascadia megathrust: Nature Geoscience, v. 14, no. 5, p. 341-348, https://doi.org/10.1038/s41561-021-00740-1.","productDescription":"8 p.","startPage":"341","endPage":"348","ipdsId":"IP-106445","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":387387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Cascadia subduction zone, Mendocino triple junction","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.925048828125,\n 39.85915479295669\n ],\n [\n -123.28857421875,\n 39.85915479295669\n ],\n [\n -123.28857421875,\n 40.96330795307353\n ],\n [\n -126.925048828125,\n 40.96330795307353\n ],\n [\n -126.925048828125,\n 39.85915479295669\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Guo, Hao","contributorId":261277,"corporation":false,"usgs":false,"family":"Guo","given":"Hao","email":"","affiliations":[{"id":52789,"text":"Univ. of Science and Technology of China","active":true,"usgs":false}],"preferred":false,"id":819659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":819661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Haijiang","contributorId":174443,"corporation":false,"usgs":false,"family":"Zhang","given":"Haijiang","email":"","affiliations":[{"id":36359,"text":"University of Science and Technology of China, Anhui, China","active":true,"usgs":false}],"preferred":false,"id":819663,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220322,"text":"sir20215021 - 2021 - Hydraulic characterization of carbonate-rock and basin-fill aquifers near Long Canyon, Goshute Valley, northeastern Nevada","interactions":[],"lastModifiedDate":"2025-05-14T18:34:47.405035","indexId":"sir20215021","displayToPublicDate":"2021-05-07T07:51:36","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5021","displayTitle":"Hydraulic Characterization of Carbonate-Rock and Basin-Fill Aquifers near Long Canyon, Goshute Valley, Northeastern Nevada","title":"Hydraulic characterization of carbonate-rock and basin-fill aquifers near Long Canyon, Goshute Valley, northeastern Nevada","docAbstract":"Understanding groundwater flow and pumping effects near pending mining operations requires accurate subsurface hydraulic characterization. To improve conceptual models of groundwater flow and development in the complex hydrogeologic system near Long Canyon Mine, in northwestern Goshute Valley, northeastern Nevada, the U.S. Geological Survey characterized the hydraulic properties of carbonate rocks and basin-fill aquifers using an integrated analysis of steady-state and stressed aquifer conditions informed by water chemistry and aquifer-test data. Hydraulic gradients and groundwater-age data in northern Goshute Valley indicate carbonate rocks in the Pequop Mountains just west and south of the Long Canyon Mine project area constitute a more permeable and active flow system than saturated rocks in the northern Pequop Mountains, western Toano Range, and basin fill. Permeable carbonate rocks in the northern Pequop Mountains, in part, discharge to the Johnson Springs wetland complex (JSWC), where mean groundwater ages range from 500 to 2,400 years and samples all contain a small fraction of modern waters, relative to mean ages of 8,600 to more than 22,000 years for most groundwater sampled to the north and east. Recharge to the JSWC occurs from a roughly 27-square-mile area in the upgradient Pequop Mountains to the west, composed mostly of permeable carbonate rock and fractured quartzite, and bounded by low-permeability shales and marbleized and siliclastic rocks.
Single-well aquifer-test analyses provided transmissivity estimates at pumped wells. Transmissivity estimates ranged from 7,000 to 400,000 feet squared per day (ft2/d) in carbonate rocks and from 2,000 to 80,000 ft2/d in basin fill near the Long Canyon Mine. Water-level drawdown from multiple-well aquifer testing and rise from unintentional leakage into the overlying basin-fill aquifer were estimated and distinguished from natural fluctuations in 93 pumping and monitoring sites using analytical water-level models. Leakage of disposed aquifer-test pumpage occurred south of the aquifer test area through an unlined irrigation ditch. Drawdown was detected at distances of as much as 3 miles (mi) from pumping wells at all but one carbonate-rock site, at basin-fill sites on the alluvial fan immediately downgradient from pumping wells, and in Big Spring and spring NS-05. Similar drawdowns in carbonate rocks within the drawdown detection area suggest all wells penetrate a highly transmissive zone (HTZ) that is bounded by low-permeability rocks. Drawdown was not detected in carbonate rocks to the west of Canyon fault, in any basin-fill sites on the valley floor east of the Hardy fault, or at volcanic sites to the north, indicating that these major fault structures and (or) permeability contrasts between hydrogeologic units impeded groundwater flow or obscured pumping signals. Alternatively, unintentional leakage might have obscured drawdown at basin-fill sites on the valley floor, where water-level rise was detected at nine sites over 3 mi.
Consistent hydraulic properties were estimated by simultaneously interpreting steady-state flow during predevelopment conditions and changes in groundwater levels and springflows from the 2016 carbonate-rock aquifer test with an integrated groundwater-flow model. Hydraulic properties were distributed across carbonate rocks, basin fill, volcanic rocks, and siliciclastic rocks with a hydrogeologic framework developed from geologic mapping and hydraulic testing. Estimated transmissivity distributions spanned at least three orders of magnitude in each rock unit. In the HTZ, simulated transmissivities ranged from 10,000 to 23,000,000 ft2/d, with the most transmissive areas occurring around Big Spring. Comparatively low carbonate-rock transmissivities of less than 10,000 ft2/d were estimated in the northern Pequop Mountains and poorly defined values of less than 1,000 ft2/d were estimated in the western Toano Range. Transmissivities in basin fill ranged from less than 10 to 80,000 ft2/d and were minimally constrained by the 2016 carbonate-rock aquifer test because poorly quantified leakage affected water levels more so than pumping. The most transmissive areas were informed by single-well aquifer tests along the eastern edge of the Pequop Mountains near Long Canyon Mine and could be indicative of a hydraulic connection between basin fill and more transmissive underlying carbonate rocks. Simulated transmissivities of volcanic and low-permeability rocks mostly are less than 1,000 ft2/d. The estimated hydraulic-property distributions and informed interpretation of hydraulic connections among hydrogeologic units improved the characterization and representation of groundwater flow near the Long Canyon Mine.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215021","collaboration":"Prepared in cooperation with the Nevada Division of Water Resources","usgsCitation":"Garcia, C.A., Halford, K.J., Gardner, P.M., and Smith, D.W., 2021, Hydraulic characterization of carbonate-rock and basin-fill aquifers near Long Canyon, Goshute Valley, northeastern Nevada: U.S. Geological Survey Scientific Investigations Report 2021–5021, 99 p., https://doi.org/10.3133/sir20215021.","productDescription":"Report: xii, 99 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-094004","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":397361,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5021/sir20215021.XML"},{"id":397360,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5021/images"},{"id":385454,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P1P7QV","text":"USGS data release","description":"USGS data release","linkHelpText":"Appendixes and supplemental data—Hydraulic characterization of carbonate-rock and basin-fill aquifers near Long Canyon, Goshute Valley, northeastern Nevada, 2011–16."},{"id":385453,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JI8NQF","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-2005 and PEST models used to simulate the 2016 carbonate-rock aquifer test and characterize hydraulic properties of carbonate-rock and basin-fill aquifers near Long Canyon, Goshute Valley, northeastern Nevada."},{"id":385451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5021/coverthb.jpg"},{"id":385452,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5021/sir20215021.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5021"}],"country":"United States","state":"Nevada","otherGeospatial":"Goshute Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.98840332031249,\n 40.55554790286311\n ],\n [\n -114.2633056640625,\n 40.55554790286311\n ],\n [\n -114.2633056640625,\n 41.693424216151314\n ],\n [\n -114.98840332031249,\n 41.693424216151314\n ],\n [\n -114.98840332031249,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Dall’s sheep (Ovis dalli dalli) are important herbivores in the mountainous ecosystems of northwestern North America, and recent declines in some populations have sparked concern. Our aim was to improve capabilities for fecal metabarcoding diet analysis of Dall’s sheep and other herbivores by contributing new sequence data for arctic and alpine plants. This expanded reference library will provide critical reference sequence data that will facilitate metabarcoding diet analysis of Dall’s sheep and thus improve understanding of plant-animal interactions in a region undergoing rapid climate change.
We provide sequences for the chloroplast rbcL gene of 16 arctic-alpine vascular plant species that are known to comprise the diet of Dall’s sheep. These sequences contribute to a growing reference library that can be used in diet studies of arctic herbivores.
","language":"English","publisher":"Springer","doi":"10.1186/s13104-021-05590-z","usgsCitation":"Williams, K.E., Menning, D.M., Wald, E.J., Talbot, S.L., Rattenbury, K.L., and Prugh, L., 2021, Using next generation sequencing of alpine plants to improve fecal metabarcoding diet analysis for Dall’s sheep: BMC Research Notes, v. 14, 173, 4 p., https://doi.org/10.1186/s13104-021-05590-z.","productDescription":"173, 4 p.","ipdsId":"IP-124845","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":452366,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13104-021-05590-z","text":"Publisher Index Page"},{"id":385559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2021-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Kelly E. 0000-0003-1275-5761","orcid":"https://orcid.org/0000-0003-1275-5761","contributorId":257954,"corporation":false,"usgs":false,"family":"Williams","given":"Kelly","email":"","middleInitial":"E.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":815394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Menning, Damian M. 0000-0003-3547-3062 dmenning@usgs.gov","orcid":"https://orcid.org/0000-0003-3547-3062","contributorId":205131,"corporation":false,"usgs":true,"family":"Menning","given":"Damian","email":"dmenning@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":815395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, Eric J.","contributorId":257955,"corporation":false,"usgs":false,"family":"Wald","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":815396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":815397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rattenbury, Kumi L.","contributorId":257956,"corporation":false,"usgs":false,"family":"Rattenbury","given":"Kumi","email":"","middleInitial":"L.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":815398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Prugh, Laura R.","contributorId":257957,"corporation":false,"usgs":false,"family":"Prugh","given":"Laura R.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":815399,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262200,"text":"70262200 - 2021 - Environmental DNA metabarcoding as a tool for biodiversity assessment and monitoring: Reconstructing established fish communities of north-temperate lakes and rivers","interactions":[],"lastModifiedDate":"2025-01-15T16:30:27.357865","indexId":"70262200","displayToPublicDate":"2021-05-06T10:24:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Environmental DNA metabarcoding as a tool for biodiversity assessment and monitoring: Reconstructing established fish communities of north-temperate lakes and rivers","docAbstract":"To evaluate the ability of precipitation-based environmental DNA (eDNA) sample collection and mitochondrial 12S metabarcoding sequencing to reconstruct well-studied fish communities in lakes and rivers. Specific objectives were to 1) determine correlations between eDNA species detections and known community composition based on conventional field sampling, 2) compare efficiency of eDNA to detect fish biodiversity among systems with variable morphologies and trophic states, and 3) determine if species habitat preferences predict eDNA detection.
Upper Great Lakes Region, North America.
Fish community composition was estimated for seven lakes and two Mississippi River navigation pools using sequence data from the mitochondrial 12S gene amplified from 10 to 50 water samples per waterbody collected in 50-mL centrifuge tubes at a single time point. Environmental DNA (eDNA) was concentrated without filtration by centrifuging samples to reduce per-sample handling time. Taxonomic detections from eDNA were compared to established community monitoring databases containing up to 40 years of sampling and a detailed habitat/substrate preference matrix to identify patterns of bias.
Mitochondrial 12S gene metabarcoding detected 15%–47% of the known species at each waterbody and 30%–76% of known genera. Non-metric multidimensional scaling (NMDS) assessment of the community structure indicated that eDNA-detected communities grouped in a similar pattern as known communities. Discriminant analysis of principal components indicated that there was a high degree of overlap in habitat/substrate preference of eDNA-detected and eDNA-undetected species suggesting limited habitat bias for eDNA sampling.
Large numbers of small volume samples sequenced at the mitochondrial 12S gene can describe the coarse community structure of freshwater systems. However, additional conventional sampling and environmental DNA sampling may be necessary for a complete diversity census.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13253","usgsCitation":"Euclide, P., Lor, Y., Spear, M., Tajjioui, T., Vander Zanden, M., Larson, W., and Amberg, J., 2021, Environmental DNA metabarcoding as a tool for biodiversity assessment and monitoring: Reconstructing established fish communities of north-temperate lakes and rivers: Diversity and Distributions, v. 27, no. 10, p. 1966-1980, https://doi.org/10.1111/ddi.13253.","productDescription":"15 p.","startPage":"1966","endPage":"1980","ipdsId":"IP-124028","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":487537,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13253","text":"Publisher Index Page"},{"id":466425,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.69650645627553,\n 42.67261502369823\n ],\n [\n -87.50037188847926,\n 44.96099256889755\n ],\n [\n -88.12421244787475,\n 45.82427169426791\n ],\n [\n -90.90876519937515,\n 46.80434775293887\n ],\n [\n -92.74682473245406,\n 46.577434240390176\n ],\n [\n -92.71276788305403,\n 44.70480675177035\n ],\n [\n -91.11604087595344,\n 43.46353667736918\n ],\n [\n -91.4600402858816,\n 42.70232293796812\n ],\n [\n -90.81653570303575,\n 41.91372416048236\n ],\n [\n -91.73687842169363,\n 40.83446553068484\n ],\n [\n -91.1461518055107,\n 40.30547432621668\n ],\n [\n -89.98143751284452,\n 42.22475699654589\n ],\n [\n -87.69650645627553,\n 42.67261502369823\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Euclide, Peter T.","contributorId":348493,"corporation":false,"usgs":false,"family":"Euclide","given":"Peter T.","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":923476,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lor, Yer 0000-0002-5738-2412","orcid":"https://orcid.org/0000-0002-5738-2412","contributorId":210011,"corporation":false,"usgs":true,"family":"Lor","given":"Yer","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":923477,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spear, Michael J.","contributorId":348494,"corporation":false,"usgs":false,"family":"Spear","given":"Michael J.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":923478,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tajjioui, Tariq 0000-0002-0113-0451","orcid":"https://orcid.org/0000-0002-0113-0451","contributorId":215091,"corporation":false,"usgs":true,"family":"Tajjioui","given":"Tariq","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":923479,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vander Zanden, M. Jake","contributorId":348495,"corporation":false,"usgs":false,"family":"Vander Zanden","given":"M. Jake","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":923480,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923475,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Amberg, Jon 0000-0002-8351-4861 jamberg@usgs.gov","orcid":"https://orcid.org/0000-0002-8351-4861","contributorId":149785,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":923481,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70220449,"text":"70220449 - 2021 - Stopover ecology of red knots in southwestern James Bay during southbound migration","interactions":[],"lastModifiedDate":"2021-06-30T18:53:10.924923","indexId":"70220449","displayToPublicDate":"2021-05-06T07:57:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Stopover ecology of red knots in southwestern James Bay during southbound migration","docAbstract":"Many shorebirds rely on small numbers of staging sites during long annual migrations. Numerous shorebird species are declining and understanding the importance of these staging sites is important for successful conservation. We surveyed endangered rufa red knots (Calidris canutus rufa) staging in James Bay, Ontario, Canada, during southbound migration in 2017 and 2018. We used mark‐resight data and count data in an integrated Bayesian analysis to quantify migration phenology, estimate passage population size, and model the age structure of the stopover population. Many adult red knots arrived in James Bay in a single wave in early August in 2017, whereas adult red knots arrived in multiple smaller waves in July and mid‐August in 2018. These waves may correspond with breeding phenology where more red knots bred successfully and arrived in one large event in 2017 and the higher number of earlier arrivals in July 2018 may have been failed breeders. We included a binomial generalized linear model in the integrated analysis to estimate that 20% and 10% of staging red knots were juveniles in 2017 and 2018, respectively. In future applications, this method could provide a metric to assess breeding performance and develop our understanding of its role in population declines. Overall, we estimated that up to 23% of the estimated rufa red knot population staged in southwestern James Bay for an average of 10–12 days. The region is a key staging site for endangered red knots and could be included in conservation planning.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22059","usgsCitation":"MacDonald, A., Smith, P., Friis, C., Lyons, J., Aubry, Y., and Nol, E., 2021, Stopover ecology of red knots in southwestern James Bay during southbound migration: Journal of Wildlife Management, v. 85, no. 5, p. 932-944, https://doi.org/10.1002/jwmg.22059.","productDescription":"13 p.","startPage":"932","endPage":"944","ipdsId":"IP-123446","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":385641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","otherGeospatial":"James Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.8369140625,\n 51.17934297928927\n ],\n [\n -77.2998046875,\n 51.17934297928927\n ],\n [\n -77.2998046875,\n 55.229023057406344\n ],\n [\n -82.8369140625,\n 55.229023057406344\n ],\n [\n -82.8369140625,\n 51.17934297928927\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"MacDonald, Amie 0000-0002-6424-7761","orcid":"https://orcid.org/0000-0002-6424-7761","contributorId":258022,"corporation":false,"usgs":false,"family":"MacDonald","given":"Amie","email":"","affiliations":[{"id":36679,"text":"Trent University","active":true,"usgs":false}],"preferred":false,"id":815564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Paul","contributorId":147639,"corporation":false,"usgs":false,"family":"Smith","given":"Paul","affiliations":[],"preferred":false,"id":815565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friis, Christian","contributorId":194605,"corporation":false,"usgs":false,"family":"Friis","given":"Christian","email":"","affiliations":[],"preferred":false,"id":815566,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":210574,"corporation":false,"usgs":true,"family":"Lyons","given":"James E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":815567,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aubry, Yves","contributorId":202279,"corporation":false,"usgs":false,"family":"Aubry","given":"Yves","email":"","affiliations":[],"preferred":false,"id":815568,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nol, Erica","contributorId":216259,"corporation":false,"usgs":false,"family":"Nol","given":"Erica","email":"","affiliations":[{"id":36679,"text":"Trent University","active":true,"usgs":false}],"preferred":false,"id":815569,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221165,"text":"70221165 - 2021 - When a typical jumper skips: Itineraries and staging habitats used by Red Knots (Calidris canutus piersmai) migrating between northwest Australia and the New Siberian Islands","interactions":[],"lastModifiedDate":"2021-10-06T14:56:44.934507","indexId":"70221165","displayToPublicDate":"2021-05-06T07:07:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"title":"When a typical jumper skips: Itineraries and staging habitats used by Red Knots (Calidris canutus piersmai) migrating between northwest Australia and the New Siberian Islands","docAbstract":"The ecological reasons for variation in avian migration, with some populations migrating across thousands of kilometres between breeding and non-breeding areas with one or few refuelling stops, in contrast to others that stop more often, remain to be pinned down. Red Knots Calidris canutus are a textbook example of a shorebird species that makes long migrations with only a few stops. Recognizing that such behaviours are not necessarily species-specific but determined by ecological context, we here provide a description of the migrations of a relatively recently described subspecies (piersmai). Based on data from tagging of Red Knots on the terminal non-breeding grounds in northwest Australia with 4.5- and 2.5-g solar-powered Platform Terminal Transmitters (PTTs) and 1.0-g geolocators, we obtained information on 19 route-records of 17 individuals, resulting in seven complete return migrations. We confirm published evidence that Red Knots of the piersmai subspecies migrate from NW Australia and breed on the New Siberian Islands in the Russian Arctic and that they stage along the coasts of southeastern Asia, especially in the northern Yellow Sea in China. Red Knots arrived on the tundra breeding grounds from 8 June onwards. Southward departures mainly occurred in the last week of July and the first week of August. We documented six non-stop flights of over c. 5000 km (with a maximum of 6500 km, lasting 6.6 days). Nevertheless, rather than staging at a single location for multiple weeks halfway during migration, piersmai-knots made several stops of up to a week. This was especially evident during northward migration, when birds often stopped along the way in southeast Asia and ‘hugged’ the coast of China, thus flying an additional 1000–1500 km compared with the shortest possible (great circle route) flights between NW Australia and the Yellow Sea. The birds staged longest in areas in northern China, along the shores of Bohai Bay and upper Liaodong Bay, where the bivalve Potamocorbula laevis, known as a particularly suitable food for Red Knots, was present. The use of multiple food-rich stopping sites during northward migration by piersmai is atypical among subspecies of Red Knots. Although piersmai apparently has the benefit of multiple suitable stopping areas along the flyway, it is a subspecies in decline and their mortality away from the NW Australian non-breeding grounds has been elevated.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.12964","usgsCitation":"Piersma, T., Kok, E., Hassell, C.J., Verkuil, Y.I., Lei, G., Peng, H., Rakhimberdiev, E., Howey, P., Tibbitts, T., Chan, Y., and Karagicheva, J., 2021, When a typical jumper skips: Itineraries and staging habitats used by Red Knots (Calidris canutus piersmai) migrating between northwest Australia and the New Siberian Islands: Ibis, v. 163, no. 4, p. 1235-1251, https://doi.org/10.1111/ibi.12964.","productDescription":"17 p.","startPage":"1235","endPage":"1251","ipdsId":"IP-122078","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":452387,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ibi.12964","text":"Publisher Index Page"},{"id":386193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia, China, Russia, Vietnam","otherGeospatial":"New Siberian Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 120.234375,\n -19.973348786110602\n ],\n [\n 127.79296875,\n -13.581920900545844\n ],\n [\n 151.69921875,\n 74.86788912917916\n ],\n [\n 137.98828125,\n 76.14295846479848\n ],\n [\n 125.33203125,\n 73.32785809840696\n ],\n [\n 106.171875,\n 11.695272733029402\n ],\n [\n 120.234375,\n -19.973348786110602\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"163","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Piersma, Theunis 0000-0001-9668-466X","orcid":"https://orcid.org/0000-0001-9668-466X","contributorId":203123,"corporation":false,"usgs":false,"family":"Piersma","given":"Theunis","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":816922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kok, Eva","contributorId":225537,"corporation":false,"usgs":false,"family":"Kok","given":"Eva","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":816944,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hassell, Chris J.","contributorId":127818,"corporation":false,"usgs":false,"family":"Hassell","given":"Chris","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":816942,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Verkuil, Yvonne I.","contributorId":194622,"corporation":false,"usgs":false,"family":"Verkuil","given":"Yvonne","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":816945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lei, Guangchun","contributorId":259278,"corporation":false,"usgs":false,"family":"Lei","given":"Guangchun","email":"","affiliations":[],"preferred":false,"id":816946,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peng, He-Bo","contributorId":218155,"corporation":false,"usgs":false,"family":"Peng","given":"He-Bo","email":"","affiliations":[{"id":39765,"text":"University of Groningen, the Netherlands; Royal Netherlands Institute for Sea Research; Fudan University, Shanghai, China","active":true,"usgs":false}],"preferred":false,"id":816943,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rakhimberdiev, Eldar","contributorId":209701,"corporation":false,"usgs":false,"family":"Rakhimberdiev","given":"Eldar","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":816948,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Howey, Paul","contributorId":225538,"corporation":false,"usgs":false,"family":"Howey","given":"Paul","email":"","affiliations":[{"id":41157,"text":"Microwave Telemetry Ltd","active":true,"usgs":false}],"preferred":false,"id":816949,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Karagicheva, Julia","contributorId":209703,"corporation":false,"usgs":false,"family":"Karagicheva","given":"Julia","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":816947,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tibbitts, T. Lee 0000-0002-0290-7592","orcid":"https://orcid.org/0000-0002-0290-7592","contributorId":224104,"corporation":false,"usgs":true,"family":"Tibbitts","given":"T. Lee","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":816923,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Chan, Ying-Chi","contributorId":167762,"corporation":false,"usgs":false,"family":"Chan","given":"Ying-Chi","email":"","affiliations":[{"id":24822,"text":"Department of Marine Ecology, NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":816924,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70220670,"text":"70220670 - 2021 - Weighing the unknowns: Value of information for biological and operational uncertainty in invasion management","interactions":[],"lastModifiedDate":"2021-08-17T15:40:15.787164","indexId":"70220670","displayToPublicDate":"2021-05-05T08:01:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Weighing the unknowns: Value of information for biological and operational uncertainty in invasion management","docAbstract":"On sea turtle nesting beaches, artificial lighting associated with human development interferes with hatchling orientation from nest to sea. Although hatchling disorientation has been documented for many beaches, data that managers can use in understanding, predicting, and managing the issue are of limited detail. The present study provides baseline hatchling orientation data that can be compared to those from beaches with artificial lighting to prioritize light-management efforts there. In 2014, the precision of hatchling orientation was quantified for 87 nests on a naturally lighted beach that had little to no artificial lighting. Precision of hatchling orientation was regressed against seven environmental variables: beach slope, distance from nest to dune, dune height, apparent dune silhouette height relative to nest site, moon illumination percentage, cloud cover percentage, and relative humidity. Results favored a regression model that included distance from nest to dune, with shorter distances from the dune predicting a narrower angular range (i.e., greater precision) of hatchling orientation. The study confirmed findings of an earlier laboratory experiment that highlighted the importance to accurate hatchling orientation of a dark silhouette (dune) on the side of the nest site opposite the ocean side. Reducing artificial light and promoting the planting of pioneer plants that assist dune formation can increase hatchling survival.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jembe.2021.151568","usgsCitation":"Hirama, S., Witherington, B., Kneifl, K., Sylvai, A., Wideroff, M., and Carthy, R., 2021, Environmental factors predicting the orientation of sea turtle hatchlings on a naturally lighted beach: A baseline for light-management goals: Journal of Experimental Marine Biology and Ecology, v. 541, 151568, 7 p., https://doi.org/10.1016/j.jembe.2021.151568.","productDescription":"151568, 7 p.","ipdsId":"IP-128875","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":452420,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jembe.2021.151568","text":"Publisher Index Page"},{"id":397164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","county":"Brevard County","otherGeospatial":"Canaveral National Seashore, Playalinda","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.71956,\n 28.77088\n ],\n [\n -80.62646,\n 28.77088\n ],\n [\n -80.62646,\n 28.64748\n ],\n [\n -80.71956,\n 28.64748\n ],\n [\n -80.71956,\n 28.77088\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"541","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hirama, S.","contributorId":288634,"corporation":false,"usgs":false,"family":"Hirama","given":"S.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":838152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Witherington, B.","contributorId":288637,"corporation":false,"usgs":false,"family":"Witherington","given":"B.","affiliations":[{"id":61821,"text":"Inwater Research Group, Inc","active":true,"usgs":false}],"preferred":false,"id":838153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kneifl, K.","contributorId":288638,"corporation":false,"usgs":false,"family":"Kneifl","given":"K.","email":"","affiliations":[{"id":61824,"text":"Canaveral National Seashore","active":true,"usgs":false}],"preferred":false,"id":838154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sylvai, A.","contributorId":288639,"corporation":false,"usgs":false,"family":"Sylvai","given":"A.","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":838155,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wideroff, M.","contributorId":288640,"corporation":false,"usgs":false,"family":"Wideroff","given":"M.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":838156,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838157,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220332,"text":"70220332 - 2021 - Postwildfire soil‐hydraulic recovery and the persistence of debris flow hazards","interactions":[],"lastModifiedDate":"2021-06-30T18:48:49.487954","indexId":"70220332","displayToPublicDate":"2021-05-03T09:12:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Postwildfire soil‐hydraulic recovery and the persistence of debris flow hazards","docAbstract":"Deadly and destructive debris flows often follow wildfire, but understanding of changes in the hazard potential with time since fire is poor. We develop a simulation‐based framework to quantify changes in the hydrologic triggering conditions for debris flows as postwildfire infiltration properties evolve through time. Our approach produces time‐varying rainfall intensity‐duration thresholds for runoff‐ and infiltration‐generated debris flows with physics‐based hydrologic simulations that are parameterized with widely available hydroclimatic, vegetation reflectance, and soil texture data. When we apply our thresholding protocol to a test case in the San Gabriel Mountains (California, USA), the results are consistent with existing regional empirical thresholds and rainstorms that caused runoff‐ and infiltration‐generated debris flows soon after and three years following a wildfire, respectively. We find that the hydrologic triggering mechanisms for the two observed debris flow types are coupled with the effects of fire on the soil saturated hydraulic conductivity. Specifically, the rainfall intensity needed to generate debris flows via runoff increases with time following wildfire while the rainfall duration needed to produce debris flows via subsurface pore‐water pressures decreases. We also find that variations in soil moisture, rainfall climatology, median grain size, and root reinforcement could impact the median annual probability of postwildfire debris flows. We conclude that a simulation‐based method for calculating rainfall thresholds is a tractable approach to improve situational awareness of debris flow hazard in the years following wildfire. Further development of our framework will be important to quantify postwildfire hazard levels in variable climates, vegetation types, and fire regimes.
","language":"English","publisher":"Wiley","doi":"10.1029/2021JF006091","usgsCitation":"Thomas, M.A., Rengers, F.K., Kean, J.W., McGuire, L.A., Staley, D.M., Barnhart, K.R., and Ebel, B., 2021, Postwildfire soil‐hydraulic recovery and the persistence of debris flow hazards: Journal of Geophysical Research: Earth Surface, v. 126, no. 6, e2021JF006091, 25 p., https://doi.org/10.1029/2021JF006091.","productDescription":"e2021JF006091, 25 p.","ipdsId":"IP-126218","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":452437,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021jf006091","text":"External Repository"},{"id":436384,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QLP6XG","text":"USGS data release","linkHelpText":"Soil moisture monitoring following the 2009 Station Fire, California, USA, 2016-2019"},{"id":385458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGuire, Luke A. 0000-0001-8178-7922 lmcguire@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-7922","contributorId":203420,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":815192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815194,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":815195,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223682,"text":"70223682 - 2021 - Horizontal-to-vertical spectral ratios from California sites: Open-source database and data interpretation to establish site parameters","interactions":[],"lastModifiedDate":"2021-09-01T12:51:44.044038","indexId":"70223682","displayToPublicDate":"2021-05-03T07:46:33","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Horizontal-to-vertical spectral ratios from California sites: Open-source database and data interpretation to establish site parameters","docAbstract":"Frequency-dependent horizontal-to-vertical spectral ratios (HVSR) of Fourier amplitudes from three-component recordings can provide information on one or more site resonant frequencies and relative levels of amplification at those frequencies. Such information is potentially useful for predicting site amplification but is not present in site databases that have been developed over the last 15–20 years for the Next-Generation Attenuation (NGA) projects, which instead use the time-averaged shear-wave velocity (VS) in the upper 30 m of the site (VS30) as the primary site parameter and are supplemented with basin depth terms where available. As a consequence, HVSR parameters are also not used in NGA ground motion models.
In order for HVSR-based parameters to be used in future versions of site databases, a publicly accessible repository of this information is needed. We adapt a relational database developed to archive and disseminate VS data to also include HVSR. The database provides relevant microtremor-based HVSR data (mHVSR) and supporting metadata. We consider the most relevant data to be the frequency-dependent mHVSR, where the horizontal is taken as the median component and also as a function of horizontal azimuth (referred to as polar plots). Relevant metadata includes site location information, details about the equipment used to make the measurements, and processing details related to windowing, anti-trigger routines, and filtering. We describe the database schema developed to organize and present this information.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"GIRS 2021-06","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"California Geological Survey","doi":"10.34948/N3KW20","usgsCitation":"Wang, P., Zimmaro, P., Gospe, T., Ahdi, S.K., Yong, A., and Stewart, J.P., 2021, Horizontal-to-vertical spectral ratios from California sites: Open-source database and data interpretation to establish site parameters, xi, 64 p., https://doi.org/10.34948/N3KW20.","productDescription":"xi, 64 p.","ipdsId":"IP-128357","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":388720,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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0000-0003-0274-5180","orcid":"https://orcid.org/0000-0003-0274-5180","contributorId":265143,"corporation":false,"usgs":true,"family":"Ahdi","given":"Sean","email":"","middleInitial":"Kamran","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":822311,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yong, Alan 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":204730,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":822312,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stewart, Jonathan P.","contributorId":100110,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":822313,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221327,"text":"70221327 - 2021 - Relating Tmax and hydrogen index to vitrinite and solid bitumen reflectance in hydrous pyrolysis residues: Comparisons to natural thermal indices","interactions":[],"lastModifiedDate":"2021-06-10T12:34:40.076535","indexId":"70221327","displayToPublicDate":"2021-05-03T07:33:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Relating Tmax and hydrogen index to vitrinite and solid bitumen reflectance in hydrous pyrolysis residues: Comparisons to natural thermal indices","docAbstract":"Vitrinite reflectance (VRo; %) generally is considered the most reliable technique to determine the thermal maturity of sedimentary rocks. However, it is a time-consuming process to collect reflectance (Ro; %) measurements and is subjective to the interpretation of each trained technician, who must be able to discern between vitrinite and solid bitumen and other organic matter types. Inadvertent misidentification of solid bitumen for vitrinite can lead to reports of ‘suppressed’ VRo, especially at lower thermal maturities (< 1.0% Ro). Programmed pyrolysis data, such as Tmax and hydrogen index (HI), are comparatively inexpensive and more time-efficient to obtain than Ro data and are determined by instrument settings, rather than by operator decision, and are therefore independent of operator-based training or experience bias. This study uses hydrous pyrolysis (HP) residues from various coals and shales to relate measured VRo and solid bitumen reflectance (BRo; %) values to their respective Tmax and HI values and determines whether these relationships can be used as a proxy to calculate Ro in naturally matured samples. Although the estimation of Ro is not always accurate, the results demonstrate that relational equations for shales and coals derived from the Tmax and HI data of HP residues can effectively calculate Ro in natural series. Approximately 60% of calculated Ro from Tmax and 83% of calculated Ro from HI relational equations are within interlaboratory reproducibility limits (± 0.2% shale BRo; ± 0.06% coal VRo) when compared to their respective measured Ro values from natural series. Variables that may affect accuracy of the applied relational equations include variable sedimentary organic matter composition of samples, differences of maturation reaction kinetics of the sedimentary organic matter in experimental versus natural settings, and decreasing reliability of all thermal proxy measurements at higher maturities.
Control of invasive sea lamprey in the Great Lakes with a selective pesticide (lampricide) that targeted larval sea lamprey began in the late 1950's and continues to be one of the main methods for control. Although the Great Lakes Fishery Commission, which was formed with the mandate of controlling sea lamprey, often expresses the success of the sea lamprey control program in terms of percent reduction from lake-wide pre-lampricide control adult sea lamprey abundances, there remains a large amount of uncertainty surrounding these estimates. In this study, we gathered historical data on adult sea lamprey captures from trapping efforts from the mid-1950's through the late 1970's to better understand pre-control abundance. We used this information to estimate lake-wide population abundances of adult sea lamprey using a weighted linear regression that includes environmental and lampricide treatment predictor variables. We varied trapping efficiency for early trapping data to evaluate the uncertainty in abundance estimates. Pre-control adult sea lamprey abundances in all lakes were much greater than current population sizes, but estimates were quite sensitive to trapping efficiency. In Lake Superior, declines in abundance aligned with increases in control efforts, but in other lakes, declines were occurring prior to the onset of lampricide application, perhaps because of a loss of prey. We suggest that previous estimates of pre-control adult sea lamprey abundance may have been underestimated unless trapping efficiency was greater than what is currently achieved in the basin.
The National Park Service (NPS) manages the Nation’s most iconic destinations that attract millions of visitors from across the Nation and around the world. Trip-related spending by NPS visitors generates and supports economic activity within park gateway communities. This report summarizes the annual economic contribution analysis that measures how NPS visitor spending cycles through local economies, generating business sales and supporting jobs and income. In 2020, the National Park System received over 237 million recreation visits (down 28% from 2019). Visitors to national parks spent an estimated $14.5 billion in local gateway regions (down 31% from 2019). The estimated contribution of this spending to the national economy was 234,000 jobs, $9.7 billion in labor income, $16.7 billion in value added, and $28.6 billion in economic output. The lodging sector saw the highest direct effects, with $5 billion in economic output directly contributed to this sector nationally. The restaurants sector saw the next greatest effects, with $3 billion in economic output directly contributed to this sector nationally. Results from the Visitor Spending Effects report series are available online via an interactive tool. Users can view year-by-year trend data and explore current year visitor spending, jobs, labor income, value added, and economic output effects by sector for national, state, and local economies. The interactive tool is available at https://www.nps.gov/subjects/socialscience/vse.htm.
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This dataset compiles measured, estimated, and calculated values for volcanic eruption mass eruption rates, column heights, and durations. Data comes from 213 eruptions, with about a third from New Zealand volcanoes. 65% of eruptions have a value for all three parameters. Eruptions are further classified according to magma and eruption type to prepare for further analysis. This dataset will be used to develop prior probability density functions (PDFs) for mass eruption rate (MER), column height, and duration to quantify uncertainty in volcanological inputs with the aim of production of real-time probabilistic ashfall forecasts.","language":"English","publisher":"GNS Science","doi":"10.21420/P18W-7674","usgsCitation":"Deligne, N.I., 2021, Mass eruption rate, column height, and duration dataset for volcanic eruptions: GNS Science Report, 22 p., https://doi.org/10.21420/P18W-7674.","productDescription":"22 p.","ipdsId":"IP-126198","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":386138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[173.02037,-40.91905],[173.24723,-41.332],[173.95841,-40.9267],[174.24759,-41.34916],[174.24852,-41.77001],[173.87645,-42.23318],[173.22274,-42.97004],[172.71125,-43.37229],[173.08011,-43.85334],[172.30858,-43.86569],[171.45293,-44.24252],[171.18514,-44.8971],[170.6167,-45.90893],[169.83142,-46.35577],[169.33233,-46.64124],[168.41135,-46.61994],[167.76374,-46.2902],[166.67689,-46.21992],[166.50914,-45.8527],[167.04642,-45.11094],[168.30376,-44.12397],[168.94941,-43.93582],[169.66781,-43.55533],[170.52492,-43.03169],[171.12509,-42.51275],[171.56971,-41.76742],[171.94871,-41.51442],[172.09723,-40.9561],[172.79858,-40.49396],[173.02037,-40.91905]]],[[[174.61201,-36.1564],[175.33662,-37.2091],[175.3576,-36.52619],[175.80889,-36.79894],[175.95849,-37.55538],[176.7632,-37.88125],[177.43881,-37.96125],[178.01035,-37.57982],[178.51709,-37.69537],[178.27473,-38.58281],[177.97046,-39.16634],[177.20699,-39.14578],[176.93998,-39.44974],[177.03295,-39.87994],[176.88582,-40.06598],[176.50802,-40.60481],[176.01244,-41.28962],[175.23957,-41.68831],[175.0679,-41.42589],[174.65097,-41.28182],[175.22763,-40.45924],[174.90016,-39.90893],[173.82405,-39.50885],[173.85226,-39.1466],[174.5748,-38.79768],[174.74347,-38.02781],[174.69702,-37.38113],[174.29203,-36.71109],[174.319,-36.53482],[173.841,-36.12198],[173.05417,-35.23713],[172.63601,-34.52911],[173.00704,-34.45066],[173.5513,-35.00618],[174.32939,-35.2655],[174.61201,-36.1564]]]]},\"properties\":{\"name\":\"New Zealand\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Deligne, Natalia I. 0000-0001-9221-8581","orcid":"https://orcid.org/0000-0001-9221-8581","contributorId":257389,"corporation":false,"usgs":true,"family":"Deligne","given":"Natalia","email":"","middleInitial":"I.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":814155,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}