Landslides modify the natural landscape and cause fatalities and property damage worldwide. Quantifying landslide dynamics is challenging due to the stochastic nature of the environment. With its large area of ~1 km2 and perennial motions at ~10–20 mm per day, the Slumgullion landslide in Colorado, USA, represents an ideal natural laboratory to better understand landslide behavior. Here, we use hybrid remote sensing data and methods to recover the four-dimensional surface motions during 2011–2018. We refine the boundaries of an area of ~0.35 km2 below the crest of the prehistoric landslide. We construct a mechanical framework to quantify the rheology, subsurface channel geometry, mass flow rate, and spatiotemporally dependent pore-water pressure feedback through a joint analysis of displacement and hydrometeorological measurements from ground, air and space. Our study demonstrates the importance of remotely characterizing often inaccessible, dangerous slopes to better understand landslides and other quasi-static mass fluxes in natural and industrial environments, which will ultimately help reduce associated hazards.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-020-16617-7","usgsCitation":"Hu, X., Bürgmann, R., Schulz, W.H., and Fielding, E.J., 2020, Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing: Nature Communications, v. 11, 2792, 9 p., https://doi.org/10.1038/s41467-020-16617-7.","productDescription":"2792, 9 p.","ipdsId":"IP-117085","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":456500,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-020-16617-7","text":"Publisher Index Page"},{"id":436941,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TCQDD5","text":"USGS data release","linkHelpText":"Data from in-situ displacement monitoring, Slumgullion landslide, Hinsdale County, Colorado"},{"id":376466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Slumgullion landslide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.30132102966309,\n 37.97600347500009\n ],\n [\n -107.22647666931152,\n 37.97600347500009\n ],\n [\n -107.22647666931152,\n 38.01212375706868\n ],\n [\n -107.30132102966309,\n 38.01212375706868\n ],\n [\n -107.30132102966309,\n 37.97600347500009\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2020-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Hu, Xie","contributorId":177306,"corporation":false,"usgs":false,"family":"Hu","given":"Xie","email":"","affiliations":[],"preferred":false,"id":793138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bürgmann, Roland","contributorId":195087,"corporation":false,"usgs":false,"family":"Bürgmann","given":"Roland","affiliations":[],"preferred":false,"id":793139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":793140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fielding, Eric J.","contributorId":218096,"corporation":false,"usgs":false,"family":"Fielding","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":39742,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.","active":true,"usgs":false}],"preferred":false,"id":793141,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211689,"text":"70211689 - 2020 - Analysis of movement recursions to detect reproductive events and estimate their fate in central place foragers","interactions":[],"lastModifiedDate":"2020-08-07T14:15:21.151798","indexId":"70211689","displayToPublicDate":"2020-06-03T09:13:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of movement recursions to detect reproductive events and estimate their fate in central place foragers","docAbstract":"Recursive movement patterns have been used to detect behavioral structure within individual movement trajectories in the context of foraging ecology, home-ranging behavior, and predator avoidance. Some animals exhibit movement recursions to locations that are tied to reproductive functions, including nests and dens; while existing literature recognizes that, no method is currently available to explicitly target different types of revisited locations. Moreover, the temporal persistence of recursive movements to a breeding location can carry information regarding the fate of breeding attempts, but it has never been used as a metric to quantify recursive movement patterns. Here, we introduce a method to locate breeding attempts and estimate their fate from GPStracking data of central place foragers. We tested the performance of our method in three bird species differing in breeding ecology (wood stork (Mycteria americana), lesser kestrel (Falco naumanni), Mediterranean gull (Ichthyaetus melanocephalus)) and implemented it in the R package ‘nestR’. Methods: We identified breeding sites based on the analysis of recursive movements within individual tracks. Using trajectories with known breeding attempts, we estimated a set of species-specific criteria for the identification of nest sites, which we further validated using non-reproductive individuals as controls. We then estimated individual nest survival as a binary measure of reproductive fate (success, corresponding to fledging of at least one chick, or failure) from nest-site revisitation histories during breeding attempts, using a Bayesian hierarchical modeling approach that accounted for temporally variable revisitation patterns, probability of visit detection, and missing data. Results: Across the three species, positive predictive value of the nest-site detection algorithm varied between 87 and 100% and sensitivity between 88 and 92%, and we correctly estimated the fate of 86–100% breeding attempts.
","language":"English","publisher":"Springer","doi":"10.1186/s40462-020-00201-1","usgsCitation":"Picardi, S., Smith, B., Boone, M.E., Frederick, P.C., Cecere, J.G., Rubolini, D., Serra, L., Pirrello, S., Borkhataria, R.R., and Basille, M., 2020, Analysis of movement recursions to detect reproductive events and estimate their fate in central place foragers: Movement Ecology, v. 8, 24, 14 p., https://doi.org/10.1186/s40462-020-00201-1.","productDescription":"24, 14 p.","ipdsId":"IP-105411","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":456508,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-020-00201-1","text":"Publisher Index Page"},{"id":377175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2020-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Picardi, Simona 0000-0002-2623-6623","orcid":"https://orcid.org/0000-0002-2623-6623","contributorId":237045,"corporation":false,"usgs":false,"family":"Picardi","given":"Simona","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":795078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Brian 0000-0002-0531-0492","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":218457,"corporation":false,"usgs":true,"family":"Smith","given":"Brian","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":795079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boone, Matthew E. 0000-0002-8070-4715","orcid":"https://orcid.org/0000-0002-8070-4715","contributorId":237046,"corporation":false,"usgs":false,"family":"Boone","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":795080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frederick, Peter C.","contributorId":215042,"corporation":false,"usgs":false,"family":"Frederick","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":39161,"text":"Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America","active":true,"usgs":false}],"preferred":false,"id":795081,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cecere, Jacopo G. 0000-0002-4925-2730","orcid":"https://orcid.org/0000-0002-4925-2730","contributorId":237048,"corporation":false,"usgs":false,"family":"Cecere","given":"Jacopo","email":"","middleInitial":"G.","affiliations":[{"id":47591,"text":"Istituto Superiore per la Protezione e la Ricerca Ambientale","active":true,"usgs":false}],"preferred":false,"id":795082,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rubolini, Diego 0000-0003-2703-5783","orcid":"https://orcid.org/0000-0003-2703-5783","contributorId":237050,"corporation":false,"usgs":false,"family":"Rubolini","given":"Diego","email":"","affiliations":[{"id":47592,"text":"Università degli Studi di Milano","active":true,"usgs":false}],"preferred":false,"id":795083,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Serra, Lorenzo 0000-0002-8911-8050","orcid":"https://orcid.org/0000-0002-8911-8050","contributorId":237052,"corporation":false,"usgs":false,"family":"Serra","given":"Lorenzo","email":"","affiliations":[{"id":47591,"text":"Istituto Superiore per la Protezione e la Ricerca Ambientale","active":true,"usgs":false}],"preferred":false,"id":795084,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pirrello, Simone 0000-0002-9471-106X","orcid":"https://orcid.org/0000-0002-9471-106X","contributorId":237054,"corporation":false,"usgs":false,"family":"Pirrello","given":"Simone","email":"","affiliations":[{"id":47591,"text":"Istituto Superiore per la Protezione e la Ricerca Ambientale","active":true,"usgs":false}],"preferred":false,"id":795085,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Borkhataria, Rena R.","contributorId":197425,"corporation":false,"usgs":false,"family":"Borkhataria","given":"Rena","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":795086,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Basille, Mathieu","contributorId":175274,"corporation":false,"usgs":false,"family":"Basille","given":"Mathieu","email":"","affiliations":[],"preferred":false,"id":795087,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70229337,"text":"70229337 - 2020 - Remarkable response of native fishes to invasive trout suppression varies with trout density, temperature, and annual hydrology","interactions":[],"lastModifiedDate":"2022-03-04T13:15:54.648199","indexId":"70229337","displayToPublicDate":"2020-06-03T07:12:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Remarkable response of native fishes to invasive trout suppression varies with trout density, temperature, and annual hydrology","docAbstract":"This report provides an overview of model-based climate science in a risk management context. In addition, it summarizes how the U.S. Geological Survey (USGS) will continue to follow best scientific practices and when and how the results of this research will be delivered to the U.S. Department of the Interior (DOI) and other stakeholders to inform policymaking. Climate change is a risk management challenge for society because of the uncertain consequences for natural and human systems across decades to centuries. Climate-related science activities within the USGS emphasize research on adaptation to climate change. This research helps inform adaptive management processes and planning activities within other DOI bureaus and by DOI stakeholders.
Global climate models are sophisticated numerical representations of the Earth’s climate system. Research groups from around the world regularly participate in a coordinated effort to produce a suite of climate models. This global effort provides a test bed to assess model performance and analyze projections of future change under various prescribed climate scenarios. These climate scenarios describe a plausible future outcome associated with a specific set of societal actions. Because scenarios are developed in a risk-based framework with a high degree of uncertainty about future societal developments, they are usually not assigned a formal likelihood of occurrence. Examining a range of projected climate outcomes based on multiple scenarios is a recommended best practice because it allows decision makers to better consider both short- and long-term risks and opportunities.
As part of its routine science practices, the USGS regularly reviews the state of knowledge of climate science, develops and maintains best practices in using global climate models to project climate change impacts, and provides data and interpretations of potential impacts to the DOI and other stakeholders. Management and policy decisions within the DOI will reflect different tolerances for risk, which has implications for what type of information should be considered and how that information should be used. It is suggested that a followup document be produced that would describe in more detail how these management decisions with differing risk tolerances can be made effectively and consistently in light of an uncertain future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201058","usgsCitation":"Terando, A., Reidmiller, D., Hostetler, S.W., Littell, J.S., Beard, T.D., Jr., Weiskopf, S.R., Belnap, J., and Plumlee, G.S., 2020, Using information from global climate models to inform policymaking—The role of the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2020–1058, 25 p., https://doi.org/10.3133/ofr20201058.","productDescription":"v, 25 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118715","costCenters":[{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true}],"links":[{"id":375144,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1058/coverthb.jpg"},{"id":375198,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1058/ofr20201058.pdf","text":"Report","size":"1.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1058"}],"contact":"National Climate Adaptation Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 516
Reston, VA 20192
https://www.usgs.gov/land-resources/
climate-adaptation-science-centers
Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake’s watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near-stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near-stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake-specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2187","usgsCitation":"Stachelek, J., Weng, W., Carey, C.C., Kemanian, A.R., Cobourn, K.M., Wagner, T., Weathers, K., and Soranno, P.A., 2020, Granular measures of agricultural land use influence lake nitrogen and phosphorus differently at macroscales: Ecological Applications, v. 30, no. 8, e02187, 13 p., https://doi.org/10.1002/eap.2187.","productDescription":"e02187, 13 p.","ipdsId":"IP-114603","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":456515,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.2187","text":"External Repository"},{"id":395692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, New Hampshire, New York, Ohio, Pennsylvania, 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C.","affiliations":[{"id":56760,"text":"Carey Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":833993,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Soranno, P. A.","contributorId":275324,"corporation":false,"usgs":false,"family":"Soranno","given":"P.","email":"","middleInitial":"A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833994,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210357,"text":"ofr20201060 - 2020 - Assessing the risks posed by SARS-CoV-2 in and via North American bats — Decision framing and rapid risk assessment","interactions":[],"lastModifiedDate":"2024-03-04T18:33:03.535283","indexId":"ofr20201060","displayToPublicDate":"2020-06-02T11:10:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1060","displayTitle":"Assessing the Risks Posed by SARS-CoV-2 in and via North American Bats—Decision Framing and Rapid Risk Assessment","title":"Assessing the risks posed by SARS-CoV-2 in and via North American bats — Decision framing and rapid risk assessment","docAbstract":"The novel β-coronavirus, SARS-CoV-2, may pose a threat to North American bat populations if bats are exposed to the virus through interaction with humans, if the virus can subsequently infect bats and be transmitted among them, and if the virus causes morbidity or mortality in bats. Further, if SARS-CoV-2 became established in bat populations, it could possibly serve as a source for new infection in humans, domesticated animals, or other wild animals. Wildlife management agencies in the United States are concerned about these potential risks and have begun to issue guidance regarding work that brings humans into contact with bats, but decision making is difficult because of the high degree of uncertainty about many of the relevant processes that could lead to virus transmission and establishment. The risk assessment described in this report was undertaken to provide management agencies with an understanding of the likelihood that the various steps in the causal pathways would lead to SARS-CoV-2 infection of North American bats from people. This assessment focused on the active season for bats in the temperate zone of North America (April 15 through November 15), and used Myotis lucifugus (little brown bats) as a surrogate species. At the time of this work (April 2020), no empirical data about the effects of SARS-CoV-2 on North American bats were available, so a formal process of expert judgment was used to elicit estimates of the underlying parameters. Twelve experts in bat ecology, epidemiology, virology, and wildlife disease from the United States, United Kingdom, and Australia participated in the elicitation. A Monte Carlo simulation model was used to integrate the parameter estimates elicited from the experts and to predict the likelihood of exposure and infection in bats through a series of transmission pathways, with particular attention to capturing uncertainty in the predictions.
Given the current state of knowledge as expressed by the expert panel, the results of this assessment indicate that there is a non-negligible risk of transmission of SARS-CoV-2 from humans to bats. For example, if a research scientist were shedding SARS-CoV-2 virus while handling bats under the field protocols used in North America prior to the COVID-19 pandemic, the risk model indicates that 50 percent (uncertainty, 15–84 percent) of those bats could be exposed to virus, and 17 percent (uncertainty, 3–51 percent) could become infected. Use of personal protective equipment, especially a respirator, is expected to reduce the exposure risk. The expert panel estimated that exposure risk from research scientists could be reduced 94–96 percent (uncertainty, 86–99 percent) through proper use of appropriate N95 respirators (a type of mechanical filter worn over the nose and mouth), dedicated clothing (such as Tyvek coveralls), and gloves. Should any North American bats become infected with SARS-CoV-2, the expert panel estimated that there is an approximately 33-percent chance the virus could spread within a bat population.
This study, conducted by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, identified several critical uncertainties that could affect the estimate of risks associated with SARS-CoV-2 entering bat populations—notably, the underlying probability that a human would be shedding virus while working with bats, the likelihood of the virus replicating in bat tissue, and the likelihood of transmission of the virus within bat populations. Ongoing empirical work during May–October 2020 may shed light on these issues. Follow-up work is needed to better understand the probability of transmission of SARS-CoV-2 to bats from the general public; the manner in which the probabilities of exposure, infection, and transmission would differ during hibernation compared to the breeding season; and the likelihood of important effects, like morbidity and mortality in bats, the possibility of zoonosis from a North American bat reservoir, and effects of and on other wildlife.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201060","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Runge, M.C., Grant, E.H.C., Coleman, J.T.H., Reichard, J.D., Gibbs, S.E.J., Cryan, P.M., Olival, K.J., Walsh, D.P., Blehert, D.S., Hopkins, M.C., and Sleeman, J.M., 2020, Assessing the risks posed by SARS-CoV-2 in and via North American bats—Decision framing and rapid risk assessment: U.S. Geological Survey Open-File Report 2020–1060, 43 p., https://doi.org/10.3133/ofr20201060.","productDescription":"vi, 43 p.","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118911","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":375248,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1060/ofr20201060.pdf","text":"Report","size":"4.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1060"},{"id":375199,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1060/coverthb.jpg"}],"country":"Canada, Mexico, United States","otherGeospatial":"North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.1640625,\n 13.581920900545844\n ],\n [\n -84.72656249999999,\n 19.973348786110602\n ],\n [\n -75.9375,\n 27.371767300523047\n ],\n [\n -55.8984375,\n 45.336701909968134\n ],\n [\n -48.8671875,\n 45.336701909968134\n ],\n [\n -65.0390625,\n 61.77312286453146\n ],\n [\n -58.71093750000001,\n 67.47492238478702\n ],\n [\n -84.375,\n 74.1160468394894\n ],\n [\n -125.5078125,\n 75.32002523220804\n ],\n [\n -135.703125,\n 70.95969716686398\n ],\n [\n -156.4453125,\n 71.52490903732816\n ],\n [\n -167.34375,\n 68.9110048456202\n ],\n [\n -168.046875,\n 61.60639637138628\n ],\n [\n -166.2890625,\n 53.12040528310657\n ],\n [\n -146.95312499999997,\n 57.326521225217064\n ],\n [\n -138.515625,\n 56.559482483762245\n ],\n [\n -131.484375,\n 48.922499263758255\n ],\n [\n -127.96875,\n 40.17887331434696\n ],\n [\n -116.01562499999999,\n 24.84656534821976\n ],\n [\n -98.7890625,\n 13.239945499286312\n ],\n [\n -93.1640625,\n 13.581920900545844\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
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The Kansas River provides drinking water for multiple cities in northeastern Kansas and is used for recreational purposes. Thus, improving the scientific knowledge of streamflow velocities and traveltimes will greatly aid in water-treatment plans and response to critical events and threats to water supplies. Dye-tracer studies are usually done to enhance knowledge of transport characteristics, which include streamflow velocities, traveltimes, and dispersion rates, within a river system. To achieve this in the Kansas River, rhodamine water-tracing dye is planned to be injected into the Kansas River during three different flow ranges at three locations: Manhattan, Topeka, and Eudora. The primary purpose of doing a dye-tracer study in the Kansas River is to calibrate a time-of-travel model used for estimating streamflow velocities and traveltimes, which can be used by the public as well as drinking water suppliers to protect water resources and public-water supplies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201039","collaboration":"Prepared in cooperation with the Kansas Water Office, Kansas Department of Health and Environment, The Nature Conservancy, City of Topeka, Johnson County WaterOne, City of Manhattan, and City of Olathe","usgsCitation":"Davis, C.A., Lukasz, B.S., and May, M.R., 2020, Dye-tracing plan for verifying the Kansas River time-of-travel model: U.S. Geological Survey Open-File Report 2020–1039, 10 p., https://doi.org/10.3133/ofr20201039.","productDescription":"iv, 10 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-107718","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":375197,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1039/ofr20201039.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1039"},{"id":375196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1039/coverthb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.1630859375,\n 38.53097889440024\n ],\n [\n -94.4384765625,\n 38.53097889440024\n ],\n [\n -94.4384765625,\n 39.926588421909436\n ],\n [\n -97.1630859375,\n 39.926588421909436\n ],\n [\n -97.1630859375,\n 38.53097889440024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
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Efforts to conserve biodiversity increasingly focus on identifying climate‐change refugia – areas relatively buffered from contemporary climate change over time that enable species persistence. Identification of refugia typically includes modeling the distribution of a species’ current habitat and then extrapolating that distribution given projected changes in temperature and precipitation, or by mapping topographic features that buffer species from regional climate extremes. However, the function of those hypothesized refugia must be validated (or challenged) with independent data not used in the initial identification of the refugia. Although doing so would facilitate the incorporation of climate‐change refugia into conservation and management decision making, a synthesis of validation methods is currently lacking. We reviewed the literature and defined four methods to test refugia predictions. We propose that such bottom‐up approaches can lead to improved protected‐area designations and on‐the‐ground management actions to reduce influences from non‐climate stressors within potential refugia.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2205","usgsCitation":"Barrows, C., Ramirez, A.R., Sweet, L.C., Morelli, T.L., Millar, C., Frakes, N., Rodgers, J., and Mahalovich, M.F., 2020, Validating climate‐change refugia: Empirical bottom‐up approaches to support management actions: Frontiers in Ecology and the Environment, v. 18, no. 5, p. 298-306, https://doi.org/10.1002/fee.2205.","productDescription":"9 p.","startPage":"298","endPage":"306","ipdsId":"IP-112734","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":456527,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fee.2205","text":"Publisher Index Page"},{"id":376823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.806640625,\n 42.01665183556825\n ],\n [\n -106.6552734375,\n 42.01665183556825\n ],\n [\n -106.6552734375,\n 48.922499263758255\n ],\n [\n -116.806640625,\n 48.922499263758255\n ],\n [\n -116.806640625,\n 42.01665183556825\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barrows, Cameron W.","contributorId":236818,"corporation":false,"usgs":false,"family":"Barrows","given":"Cameron W.","affiliations":[],"preferred":false,"id":794274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramirez, Aaron R.","contributorId":149780,"corporation":false,"usgs":false,"family":"Ramirez","given":"Aaron","email":"","middleInitial":"R.","affiliations":[{"id":17824,"text":"UC Berkeley, CA","active":true,"usgs":false}],"preferred":false,"id":794275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweet, Lynn C.","contributorId":149951,"corporation":false,"usgs":false,"family":"Sweet","given":"Lynn","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":794277,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":794278,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Millar, Constance I.","contributorId":99005,"corporation":false,"usgs":true,"family":"Millar","given":"Constance I.","affiliations":[],"preferred":false,"id":794279,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Frakes, Neil","contributorId":177303,"corporation":false,"usgs":false,"family":"Frakes","given":"Neil","email":"","affiliations":[],"preferred":false,"id":794280,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rodgers, Jane","contributorId":236752,"corporation":false,"usgs":false,"family":"Rodgers","given":"Jane","email":"","affiliations":[],"preferred":false,"id":794282,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mahalovich, Mary Frances","contributorId":200724,"corporation":false,"usgs":false,"family":"Mahalovich","given":"Mary","email":"","middleInitial":"Frances","affiliations":[{"id":27110,"text":"U.S. Dept of Agriculture, Forest Service","active":true,"usgs":false}],"preferred":false,"id":794276,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209203,"text":"70209203 - 2020 - Modeling geologic sequestration of carbon dioxide in a deep saline carbonate reservoir with TOUGH2–ChemPlugin, a new tool for reactive transport modeling","interactions":[],"lastModifiedDate":"2020-06-08T13:52:10.668552","indexId":"70209203","displayToPublicDate":"2020-06-01T19:04:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1541,"text":"Environmental Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Modeling geologic sequestration of carbon dioxide in a deep saline carbonate reservoir with TOUGH2–ChemPlugin, a new tool for reactive transport modeling","docAbstract":"This paper outlines the development and demonstration of a new tool, TOUGH2–ChemPlugin (T2CPI) for predicting rock–water–CO2 interaction following injection of supercritical CO2 into a heterogeneous carbonate system. Specifically, modeling capabilities of TOUGH2, which examines multiphase flow and supercritical CO2 behavior, were combined with the geochemical modeling capabilities of The Geochemist’s Workbench® (GWB), using ChemPluginTM. ChemPlugin is a self-linking re-entrant software object that, when coupled to a transport simulator, retains the flow and transport capabilities of the simulator but enables incorporation of reactive chemistry via GWB. To test and assess the capabilities of T2CPI, results from T2CPI simulations were compared to those of TOUGHREACT, using the same carbonate reservoir parameters (based on the Dollar Bay Formation of the South Florida Basin). Overall, results of simulations from TOUGHREACT and T2CPI were very similar for nearly all evaluated parameters. Dissimilarities between the two programs included qualitative differences in how TOUGHREACT and T2CPI predicted calcite dissolution and the subsequent spatial pattern of the porosity gain caused by how each handles evaporation of water near the injection point. The TOUGHREACT program is a proven, widely used tool for evaluating CO2–brine–rock interaction following supercritical CO2 injection. The T2CPI tool offers similar capabilities and strengths of TOUGHREACT, with the ability to read in and use databases for a wide range of activity coefficient types. This program also has abilities to use a wide range of kinetic constraints, define those kinetic constraints with scripts or compiled libraries, account for colloidal transport, and/or account for a wide range of surface sorption models.
","language":"English","publisher":"AAPG","doi":"10.1306/eg.08061919003","collaboration":"None","usgsCitation":"Roberts-Ashby, T., Berger, P.M., Cunningham, J.A., Kumar, R., and Blondes, M., 2020, Modeling geologic sequestration of carbon dioxide in a deep saline carbonate reservoir with TOUGH2–ChemPlugin, a new tool for reactive transport modeling: Environmental Geosciences, v. 27, no. 2, p. 103-116, https://doi.org/10.1306/eg.08061919003.","productDescription":"14 p.","startPage":"103","endPage":"116","ipdsId":"IP-098127","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":375280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Dollar Bay Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.18298339843749,\n 24.297040469311558\n ],\n [\n -79.73876953125,\n 24.297040469311558\n ],\n [\n -79.73876953125,\n 27.81478637667891\n ],\n [\n -83.18298339843749,\n 27.81478637667891\n ],\n [\n -83.18298339843749,\n 24.297040469311558\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts-Ashby, Tina L. 0000-0003-2940-1740","orcid":"https://orcid.org/0000-0003-2940-1740","contributorId":205925,"corporation":false,"usgs":true,"family":"Roberts-Ashby","given":"Tina L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":785375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berger, Peter M.","contributorId":223538,"corporation":false,"usgs":false,"family":"Berger","given":"Peter","email":"","middleInitial":"M.","affiliations":[{"id":40735,"text":"Illionois State Geological Survey","active":true,"usgs":false}],"preferred":false,"id":785376,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cunningham, Jeffrey A.","contributorId":223539,"corporation":false,"usgs":false,"family":"Cunningham","given":"Jeffrey","email":"","middleInitial":"A.","affiliations":[{"id":40736,"text":"Dept of Civil and Environmental Engineering, University of South Florida","active":true,"usgs":false}],"preferred":false,"id":785377,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kumar, Ram","contributorId":223540,"corporation":false,"usgs":false,"family":"Kumar","given":"Ram","email":"","affiliations":[{"id":40737,"text":"Dept. of Chemical and Biomedical Engineering, Univ. of South Florida","active":true,"usgs":false}],"preferred":false,"id":790254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":785378,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210492,"text":"70210492 - 2020 - Validation of laboratory tests for infectious diseases in wild mammals: Review and recommendations","interactions":[],"lastModifiedDate":"2020-11-13T15:42:29.145635","indexId":"70210492","displayToPublicDate":"2020-06-01T16:58:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2492,"text":"Journal of Veterinary Diagnostic Investigation","active":true,"publicationSubtype":{"id":10}},"title":"Validation of laboratory tests for infectious diseases in wild mammals: Review and recommendations","docAbstract":"Evaluation of the diagnostic sensitivity (DSe) and specificity (DSp) of tests for infectious diseases in wild animals is challenging, and some of the limitations may affect compliance with the OIE-recommended test validation pathway. We conducted a methodologic review of test validation studies for OIE-listed diseases in wild mammals published between 2008 and 2017 and focused on study design, statistical analysis, and reporting of results. Most published papers addressed Mycobacterium bovis infection in one or more wildlife species. Our review revealed limitations or missing information about sampled animals, identification criteria for positive and negative samples (case definition), representativeness of source and target populations, and species in the study, as well as information identifying animals sampled for calculations of DSe and DSp as naturally infected captive, free-ranging, or experimentally challenged animals. The deficiencies may have reflected omissions in reporting rather than design flaws, although lack of random sampling might have induced bias in estimates of DSe and DSp. We used case studies of validation of tests for hemorrhagic diseases in deer and white-nose syndrome in hibernating bats to demonstrate approaches for validation when new pathogen serotypes or genotypes are detected and diagnostic algorithms are changed, and how purposes of tests evolve together with the evolution of the pathogen after identification. We describe potential benefits of experimental challenge studies for obtaining DSe and DSp estimates, methods to maintain sample integrity, and Bayesian latent class models for statistical analysis. We make recommendations for improvements in future studies of detection test accuracy in wild mammals.
","language":"English","publisher":"Sage","doi":"10.1177/1040638720920346","usgsCitation":"Beibei, J., Colling, D., Stallknecht, D., Blehert, D.S., Bingham, J., Crossley, B., Eagles, D., and Gardner, I.A., 2020, Validation of laboratory tests for infectious diseases in wild mammals: Review and recommendations: Journal of Veterinary Diagnostic Investigation, v. 32, no. 6, p. 776-792, https://doi.org/10.1177/1040638720920346.","productDescription":"17 p.","startPage":"776","endPage":"792","ipdsId":"IP-110153","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":456534,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/1040638720920346","text":"Publisher Index Page"},{"id":375380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Beibei, Jia","contributorId":225105,"corporation":false,"usgs":false,"family":"Beibei","given":"Jia","email":"","affiliations":[{"id":41036,"text":"Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, Canada","active":true,"usgs":false}],"preferred":false,"id":790361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colling, David","contributorId":225106,"corporation":false,"usgs":false,"family":"Colling","given":"David","email":"","affiliations":[{"id":41037,"text":"CSIRO Australian Animal Health Laboratory, Geelong, Victoria, Australia","active":true,"usgs":false}],"preferred":false,"id":790362,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stallknecht, David E.","contributorId":225107,"corporation":false,"usgs":false,"family":"Stallknecht","given":"David E.","affiliations":[{"id":36701,"text":"Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":790363,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blehert, David S. 0000-0002-1065-9760 dblehert@usgs.gov","orcid":"https://orcid.org/0000-0002-1065-9760","contributorId":140397,"corporation":false,"usgs":true,"family":"Blehert","given":"David","email":"dblehert@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":790364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bingham, John","contributorId":225108,"corporation":false,"usgs":false,"family":"Bingham","given":"John","email":"","affiliations":[{"id":41037,"text":"CSIRO Australian Animal Health Laboratory, Geelong, Victoria, Australia","active":true,"usgs":false}],"preferred":false,"id":790365,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crossley, Beate","contributorId":225109,"corporation":false,"usgs":false,"family":"Crossley","given":"Beate","email":"","affiliations":[{"id":41039,"text":"and California Animal Health and Food Safety Laboratory, University of California Davis, USA","active":true,"usgs":false}],"preferred":false,"id":790366,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eagles, Debbie","contributorId":225110,"corporation":false,"usgs":false,"family":"Eagles","given":"Debbie","email":"","affiliations":[{"id":41037,"text":"CSIRO Australian Animal Health Laboratory, Geelong, Victoria, Australia","active":true,"usgs":false}],"preferred":false,"id":790367,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gardner, Ian A","contributorId":168476,"corporation":false,"usgs":false,"family":"Gardner","given":"Ian","email":"","middleInitial":"A","affiliations":[{"id":25301,"text":"Atlantic Veterinary College, University of Prince Edward Island, 550 University Avenue, 9 Charlottetown, Prince Edward Island C1A 4P3, Canada","active":true,"usgs":false}],"preferred":false,"id":790368,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211962,"text":"70211962 - 2020 - An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern","interactions":[],"lastModifiedDate":"2020-08-12T21:20:12.67786","indexId":"70211962","displayToPublicDate":"2020-06-01T16:14:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":956,"text":"BMC Genomics","active":true,"publicationSubtype":{"id":10}},"title":"An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern","docAbstract":"Use of genomic tools to characterize wildlife populations has increased in recent years. In the past, genetic characterization has been accomplished with more traditional genetic tools (e.g., microsatellites). The explosion of genomic methods and the subsequent creation of large SNP datasets has led to the promise of increased precision in population genetic parameter estimates and identification of demographically and evolutionarily independent groups, as well as questions about the future usefulness of the more traditional genetic tools. At present, few empirical comparisons of population genetic parameters and clustering analyses performed with microsatellites and SNPs have been conducted.
Here we used microsatellite and SNP data generated from Gunnison sage-grouse (Centrocercus minimus) samples to evaluate concordance of the results obtained from each dataset for common metrics of genetic diversity (HO, HE, FIS, AR) and differentiation (FST, GST, DJost). Additionally, we evaluated clustering of individuals using putatively neutral (SNPs and microsatellites), putatively adaptive, and a combined dataset of putatively neutral and adaptive loci. We took particular interest in the conservation implications of any differences. Generally, we found high concordance between microsatellites and SNPs for HE, FIS, AR, and all differentiation estimates. Although there was strong correlation between metrics from SNPs and microsatellites, the magnitude of the diversity and differentiation metrics were quite different in some cases. Clustering analyses also showed similar patterns, though SNP data was able to cluster individuals into more distinct groups. Importantly, clustering analyses with SNP data suggest strong demographic independence among the six distinct populations of Gunnison sage-grouse with some indication of evolutionary independence in two or three populations; a finding that was not revealed by microsatellite data.
We demonstrate that SNPs have three main advantages over microsatellites: more precise estimates of population-level diversity, higher power to identify groups in clustering methods, and the ability to consider local adaptation. This study adds to a growing body of work comparing the use of SNPs and microsatellites to evaluate genetic diversity and differentiation for a species of conservation concern with relatively high population structure and using the most common method of obtaining SNP genotypes for non-model organisms.
","language":"English","publisher":"BMC","doi":"10.1186/s12864-020-06783-9","usgsCitation":"Zimmerman, S.J., Aldridge, C., and Oyler-McCance, S.J., 2020, An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern: BMC Genomics, v. 21, 382, 16 p., https://doi.org/10.1186/s12864-020-06783-9.","productDescription":"382, 16 p.","ipdsId":"IP-114139","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":456536,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s12864-020-06783-9","text":"Publisher Index Page"},{"id":436945,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94ET592","text":"USGS data release","linkHelpText":"Sample collection information and SNP data for Gunnison Sage-grouse across the species range generated in the Molecular Ecology Lab during 2015-2018"},{"id":436944,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P920WO0Q","text":"USGS data release","linkHelpText":"Sample collection information and microsatellite data for Gunnison sage-grouse pre and post translocation"},{"id":377446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.59912109375,\n 35.764343479667176\n ],\n [\n -106.0400390625,\n 35.764343479667176\n ],\n [\n -106.0400390625,\n 39.740986355883564\n ],\n [\n -111.59912109375,\n 39.740986355883564\n ],\n [\n -111.59912109375,\n 35.764343479667176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationDate":"2020-06-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmerman, Shawna J 0000-0003-3394-6102 szimmerman@usgs.gov","orcid":"https://orcid.org/0000-0003-3394-6102","contributorId":238076,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Shawna","email":"szimmerman@usgs.gov","middleInitial":"J","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":795971,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":213471,"corporation":false,"usgs":false,"family":"Aldridge","given":"Cameron L.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":795972,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":795973,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209091,"text":"sir20205026 - 2020 - Application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin","interactions":[],"lastModifiedDate":"2020-09-01T12:26:51.639849","indexId":"sir20205026","displayToPublicDate":"2020-06-01T14:36:39","publicationYear":"2020","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":"2020-5026","displayTitle":"Application of the Precipitation-Runoff Modeling System (PRMS) To Simulate Near-Native Streamflow in the Upper Rio Grande Basin","title":"Application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin","docAbstract":"The U.S. Geological Survey’s Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic response and streamflow. This study developed, calibrated, and assessed a PRMS model that simulates near-native or naturalized streamflow conditions in the Upper Rio Grande Basin. A PRMS model framework of 1,021 hydrologic response units was constructed for the basin. Subbasins within the larger Upper Rio Grande Basin range from snow-dominated northern basins to monsoon driven southern basins. The 1,021 hydrologic response units were grouped into 133 subareas within the basin, and solar radiation and potential evapotranspiration data were used to calibrate corresponding PRMS parameters in each subarea independently. Nine subbasins with streamgages distributed across the basin were identified as “near-native” subbasins, or those basins with low anthropogenic disturbance. Model parameters that affect streamflow were calibrated for the near-native subbasins, and the calibrated parameters were distributed to the remaining hydrologic response units on the basis of terrain, soil, and vegetation conditions linked to a distribution and weighting algorithm developed for this study. The parameter distribution method was validated in three of the nine near-native subbasins. Calibration results demonstrated that the PRMS model developed in this study with distributed model parameters for the entire Upper Rio Grande Basin was successful in applying local information to improve model performance over the National Hydrologic Model, and that the new model is appropriate to use to simulate near-native conditions throughout the basin. The result is a model that can simulate naturalized flow and other variables that affect the water budget (including soil moisture, evapotranspiration, recharge) at the daily time step for current and future climate conditions, and that can also be used in conjunction with other models developed for the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205026","collaboration":"U.S. Geological Survey National Water Census and Water Availability and Use Science Program","usgsCitation":"Chavarria, S.B., Moeser, C.D., and Douglas-Mankin, K.R., 2020, Application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin: U.S. Geological Survey Scientific Investigations Report 2020–5026, 38 p., https://doi.org/10.3133/sir20205026.","productDescription":"Report: vi, 38 p.; Data Release","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-111974","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":436948,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ML93QB","text":"USGS data release","linkHelpText":"Hydrologic simulations using projected climate data as input to the Precipitation-Runoff Modeling System (PRMS) in the Upper Rio Grande Basin"},{"id":375137,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YOPYW7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input and output data for the application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin"},{"id":375136,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5026/sir20205026.pdf","text":"Report","size":"15.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5026"},{"id":375135,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5026/coverthb.jpg"}],"country":"United States","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.74316406249999,\n 31.466153715024294\n ],\n [\n -106.0400390625,\n 31.052933985705163\n ],\n [\n -105.380859375,\n 30.90222470517144\n ],\n [\n -105.0732421875,\n 31.12819929911196\n ],\n [\n -105.5126953125,\n 32.175612478499325\n ],\n [\n -105.2490234375,\n 32.80574473290688\n ],\n [\n -105.732421875,\n 33.211116472416855\n ],\n [\n -105.16113281249999,\n 33.797408767572485\n ],\n [\n -104.8974609375,\n 34.66935854524543\n ],\n [\n -105.380859375,\n 35.460669951495305\n ],\n [\n -104.5458984375,\n 36.80928470205937\n ],\n [\n -104.94140625,\n 38.03078569382294\n ],\n [\n -106.34765625,\n 38.54816542304656\n ],\n [\n -107.314453125,\n 37.92686760148135\n ],\n [\n -106.8310546875,\n 37.33522435930639\n ],\n [\n -108.06152343749999,\n 35.99578538642032\n ],\n [\n -107.75390625,\n 34.488447837809304\n ],\n [\n -108.19335937499999,\n 33.61461929233378\n ],\n [\n -108.984375,\n 32.65787573695528\n ],\n [\n -108.80859375,\n 31.541089879585808\n ],\n [\n -108.45703125,\n 31.27855085894653\n ],\n [\n -107.7978515625,\n 32.287132632616384\n ],\n [\n -107.05078125,\n 32.39851580247402\n ],\n [\n -106.74316406249999,\n 31.466153715024294\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
The U.S. Fish and Wildlife Service (USFWS) is responsible for reviewing the biological status of hundreds of species to determine federal status designations under the Endangered Species Act. The longleaf pine Pinus palustris ecological system supports many priority at-risk species designated for review, including five species of herpetofauna: gopher tortoise Gopherus polyphemus, southern hognose snake Heterodon simus, Florida pine snake Pituophis melanoleucus mugitus, gopher frog Lithobates (Rana) capito, and striped newt Notophthalmus perstriatus. To inform status decisions and conservation planning, we developed habitat suitability models to 1) identify habitat features that best predict species presence and 2) estimate the amount and distribution of suitable habitat across each species' range under current conditions. We incorporated expert judgment from federal, state, and other partners to capture variation in ecological settings across species' ranges, prioritize predictor variables to test in models, mitigate data limitations by informing the selection of pseudoabsence points, qualitatively evaluate model estimates, and improve the likelihood that experts will trust and use model predictions for conservation. Soil characteristics, land cover, and fire interval strongly influenced habitat suitability for all species. Suitable habitat was distributed on known species strongholds, as well as private lands without known species records. Between 4.7% (gopher frog) and 14.6% (gopher tortoise) of the area in a species' range was classified as suitable habitat, and between 28.1% (southern hognose snake) and 47.5% (gopher frog) of suitable habitat was located in patches larger than 1 km2 (100 ha) on publicly owned lands. By overlaying predictions for each species, we identified areas of suitable habitat for multiple species on protected and unprotected lands. These results have direct applications to management and conservation planning: partners can tailor site-level management based on attributes associated with high habitat suitability for species of concern; allocate survey effort in areas with suitable habitat but no known species records; and identify priority areas for management, land acquisitions, or other strategies based on the distribution of species records, suitable habitat, and land protection status. These results can aid regional partners in implementing effective conservation strategies and inform status designation decisions of the USFWS.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/092019-JFWM-075","usgsCitation":"Crawford, B.A., Maerz, J.C., and Moore, C.T., 2020, Expert-informed habitat suitability analysis for at-risk species assessment and conservation planning: Journal of Fish and Wildlife Management, v. 11, no. 1, p. 130-150, https://doi.org/10.3996/092019-JFWM-075.","productDescription":"21 p.","startPage":"130","endPage":"150","ipdsId":"IP-110784","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456539,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/092019-jfwm-075","text":"Publisher Index Page"},{"id":395703,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90,\n 29.477861195816843\n ],\n [\n -89.3408203125,\n 28.92163128242129\n ],\n [\n -89.033203125,\n 29.05616970274342\n ],\n [\n -88.92333984375,\n 30.12612436422458\n ],\n [\n -87.7587890625,\n 30.088107753367257\n ],\n [\n -86.17675781249999,\n 30.107117887092357\n ],\n [\n -85.4736328125,\n 29.592565403314087\n ],\n [\n -85.01220703125,\n 29.477861195816843\n ],\n [\n -84.13330078125,\n 29.897805610155874\n ],\n [\n -83.34228515625,\n 29.171348850951507\n ],\n [\n -82.85888671875,\n 28.86391842622456\n ],\n [\n -83.12255859375,\n 27.955591004642553\n ],\n [\n -82.68310546875,\n 26.96124577052697\n ],\n [\n -82.24365234375,\n 26.56887654795065\n ],\n [\n -81.9580078125,\n 25.898761936567023\n ],\n [\n -81.6064453125,\n 25.760319754713887\n ],\n [\n -81.2548828125,\n 24.906367237907997\n ],\n [\n -80.15625,\n 25.085598897064752\n ],\n [\n -79.8046875,\n 26.56887654795065\n ],\n [\n -80.068359375,\n 28.130127737874005\n ],\n [\n -80.70556640625,\n 28.844673680771795\n ],\n [\n -81.34277343749999,\n 30.732392734006083\n ],\n [\n -81.03515625,\n 31.55981453201843\n ],\n [\n -80.52978515625,\n 32.1570124860701\n ],\n [\n -79.1455078125,\n 33.04550781490999\n ],\n [\n -78.837890625,\n 33.55970664841198\n ],\n [\n -78.57421875,\n 33.779147331286474\n ],\n [\n -77.95898437499999,\n 33.76088200086917\n ],\n [\n -77.54150390625,\n 34.361576287484176\n ],\n [\n -76.57470703125,\n 34.65128519895413\n ],\n [\n -76.0693359375,\n 35.0120020431607\n ],\n [\n -80.74951171875,\n 35.51434313431818\n ],\n [\n -80.68359375,\n 34.867904962568716\n ],\n [\n -82.353515625,\n 33.88865750124075\n ],\n [\n -88.43994140625,\n 32.39851580247402\n ],\n [\n -90.72509765625,\n 31.034108344903512\n ],\n [\n -90,\n 29.477861195816843\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Crawford, Brian A.","contributorId":204802,"corporation":false,"usgs":false,"family":"Crawford","given":"Brian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":833910,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maerz, John C.","contributorId":171763,"corporation":false,"usgs":false,"family":"Maerz","given":"John","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":833911,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833912,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210861,"text":"70210861 - 2020 - Data release of reprocessed select National Uranium Resources Evaluation program samples in Wyoming","interactions":[],"lastModifiedDate":"2021-07-07T17:05:49.62074","indexId":"70210861","displayToPublicDate":"2020-06-01T11:58:25","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":128,"text":"Open-File Report","active":false,"publicationSubtype":{"id":2}},"seriesNumber":"2020-7","title":"Data release of reprocessed select National Uranium Resources Evaluation program samples in Wyoming","docAbstract":"The U.S. Atomic Energy Commission established the National Uranium Resources Evaluation (NURE) program in 1973 to identify uranium resources throughout the United States. Part of this program focused on the collection of stream-sediment samples and subsequent geochemical analyses of these samples for uranium, in addition to 47 other elements. As part of the original program, 18,424 stream-sediment samples were collected from Wyoming and analyzed. All original samples are stored at the U.S. Geological Survey’s (USGS) National Geochemical Sample Archive (NGSA). The Wyoming State Geological Survey (WSGS) recently selected 159 of the original Wyoming NURE stream samples to be reanalyzed using modern and standardized analytical equipment. The raw results of the reanalysis are provided with this report.
","language":"English","publisher":"Wyoming State Geological Survey","collaboration":"Wyoming State Geological Survey","usgsCitation":"Lucke, D.W., Smith, S.M., Azain, J., and Ingraham, A.D., 2020, Data release of reprocessed select National Uranium Resources Evaluation program samples in Wyoming: Open-File Report 2020-7, 9 p.","productDescription":"9 p.","ipdsId":"IP-118527","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":386992,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375997,"type":{"id":15,"text":"Index Page"},"url":"https://sales.wsgs.wyo.gov/data-release-of-reprocessed-select-national-uranium-resources-evaluation-program-samples-in-wyoming-2020/"}],"country":"United 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Here, we evaluate the potential impacts of projected climatic change on prescribed burning in the south-eastern United States by applying a set of burn window criteria that capture temperature, relative humidity and wind speed to projections from an ensemble of Global Climate Models under two greenhouse gas emission scenarios. Regionally, the percentage of suitable days for burning changes little during winter but decreases substantially in summer owing to rising temperatures by the end of the 21st century compared with historical conditions. Management implications of such changes for six representative land management units include seasonal shifts in burning opportunities from summer to cool-season months, but with considerable regional variation. 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Basin marshes","docAbstract":"This project was funded by the Louisiana Trustee Implementation Group (LA TIG) to support decisions related to investments in long-term monitoring. The LA TIG seeks to ensure long-term monitoring informs coastal restoration activities with the goal of sustaining and improving fisheries impacted by the Deepwater Horizon (DWH) Oil Spill. The project objective was to compare nekton catch across an estuarine gradient using different sampling gear with the goal of identifying trade-offs among nekton sampling approaches. To accomplish this objective, Louisiana Department of Wildlife and Fisheries (LDWF), The Water Institute of the Gulf (the Institute), Dynamic Solutions, LLC, Louisiana State University Agricultural Center (LSU AgCenter), and the U.S. Geological Survey (USGS) completed a field gear comparison study from 2018 to 2019. This work compared electrofisher and seine sampling at 12 fixed stations in Barataria Basin using data collected by LDWF. In addition, and in conjunction with LDWF monthly sampling, the same 12 fixed stations were sampled in May 2019 using a throw trap to compare nekton catch and assemblages collected with the throw trap, seine and electrofisher. LDWF has been conducting seine sampling since 1986, and seine data are used by the State of Louisiana to assess juvenile shrimp, crab and fish abundances, sizes and overall assemblages. In 2018, LDWF began conducting electrofisher sampling at 12 Barataria Basin seine stations in order to determine if the two gear types sample similar species and assemblages for potential future replacement of long-term seine sampling with electrofishing. Throw traps were included as they provide density estimates, which are ultimately the desired statistic used in modeling trophic webs, and are used in assessing habitat restoration outcomes.
The project compared the nekton catch and assemblages collected using seine, electrofisher, and throw trap data from marsh edge habitats located across the estuarine gradient in Barataria Basin. Specifically, catch per unit effort (CPUE), species richness, species-specific total length (mm) distribution and nekton assemblages were compared between gear types. The first dataset was collected in May 2019 with throw trap (Appendix A), seine (LDWF data), and electrofisher (LDWF data) gear, and the second dataset (collected by LDWF) spanned 14 months of seine and electrofisher monthly sampling occurring from May 2018 through June 2019 at 12 stations in Barataria Basin.
Key findings include that gear bias was not evident across the range of water quality conditions (salinity, temperature, o C, dissolved oxygen, mg L-1 , turbidity, NTU; Appendix B: scatter plots) captured during this pilot study, but differences in nekton catch per unit effort (CPUE) and assemblages were evident between gear types. However, those differences largely depended on the parameter examined. For example, the overall CPUE was highest for electrofishing, followed by seine, and then throw trap. When grass shrimp (the most abundant taxon collected) were removed from CPUE, the electrofisher and seine results were similar in CPUE. When CPUE was corrected for gear efficiency and total area sampled, the throw trap had the highest reported density of nekton sampled, followed by electrofisher and seine results. Electrofishing captured the highest number of species, which included more unique species compared to seine or throw trap catches, though all gear types captured at least one unique species. These highlight a need for caution in interpreting assemblage and density data when comparing datasets derived from different sampling methodologies.
These key findings can help inform implementation and interpretation of long-term monitoring data in Louisiana as management decisions are made about coastal restoration projects to sustain and improve fisheries. There are trade-offs in selecting gear types for estuarine nekton monitoring of density, abundance, species richness, and assemblages. The table below (Table 1) summarizes some considerations when selecting gear types for long-term monitoring of estuarine nekton. In addition to biological and ecological considerations, other important considerations include cost, the labor required to conduct sampling, logistics, and potential uncertainties related to how effective each gear type is for sampling the wide variety of conditions found across Louisiana’s coastal habitats. For example, although electrofishing may capture higher CPUE, the equipment is more expensive to obtain and maintain compared to the other gear types. Most importantly, this table highlights differences in the nekton assemblages sampled by each gear type; this consideration is critical when designing the goals of a long-term monitoring program as it will inform how the data can be used and interpreted in the future.
This report provides caveats, assumptions, and recommendations that can help support the Louisiana Coastal Protection and Restoration Authority (CPRA), LDWF and the LA TIG in comparing data from different gear types, and in making decisions for future monitoring. Findings from this study are limited to the range of water quality conditions occurring during these data collection events; these data and analyses could benefit from sampling across a wider range of water quality conditions, and collection of habitat structure and bottom type data which are not routinely collected but critically influence nekton. Further investigation examining how relative differences detected in key species abundances between gear types might impact ecosystem indicators and energetics in a modeled food web would provide valuable input to understand outputs of the Comprehensive Aquatic System Model for Barataria Basin, including the potential impacts of nekton monitoring decisions on food web models.
","language":"English","publisher":"NOAA","usgsCitation":"Taylor, C., La Peyre, M., Sable, S., Kiskaddon, E.P., and Baustian, M., 2020, Gear comparison study for sampling nekton in Barataria Basin marshes: Technical Report Administrative Summary, 67 p.","productDescription":"67 p.","ipdsId":"IP-118449","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395893,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395891,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.gulfspillrestoration.noaa.gov/sites/default/files/2020-08%20LA%20TO65_GearCompReport_final_June2020.pdf"}],"country":"United States","state":"Louisiana","otherGeospatial":"Barataria Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.20599365234375,\n 29.106576445680258\n ],\n [\n -89.2694091796875,\n 29.14496502116881\n ],\n [\n -89.40948486328125,\n 29.346269551093652\n ],\n [\n -89.48089599609375,\n 29.336692606945483\n ],\n [\n -89.637451171875,\n 29.432421529604852\n ],\n [\n -89.82696533203125,\n 29.58540020340835\n ],\n [\n -90.09063720703124,\n 29.738147333955528\n ],\n [\n -90.2252197265625,\n 29.654642479663647\n ],\n [\n -90.42022705078125,\n 29.649868677972304\n ],\n [\n -90.22796630859375,\n 29.27442054681336\n ],\n [\n -90.20599365234375,\n 29.106576445680258\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Caleb","contributorId":278588,"corporation":false,"usgs":false,"family":"Taylor","given":"Caleb","affiliations":[],"preferred":false,"id":834706,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834707,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sable, Shaye","contributorId":147275,"corporation":false,"usgs":false,"family":"Sable","given":"Shaye","affiliations":[{"id":16816,"text":"Dynamic Solutions, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":834708,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kiskaddon, Erin P.","contributorId":272886,"corporation":false,"usgs":false,"family":"Kiskaddon","given":"Erin","email":"","middleInitial":"P.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":834709,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baustian, Melissa M.","contributorId":189569,"corporation":false,"usgs":false,"family":"Baustian","given":"Melissa M.","affiliations":[],"preferred":false,"id":834818,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237093,"text":"70237093 - 2020 - Real-time performance of the PLUM earthquake early warning method during the 2019 M6.4 and M7.1 Ridgecrest, California, Earthquakes","interactions":[],"lastModifiedDate":"2022-09-29T15:11:20.640006","indexId":"70237093","displayToPublicDate":"2020-06-01T10:04:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Real-time performance of the PLUM earthquake early warning method during the 2019 M6.4 and M7.1 Ridgecrest, California, Earthquakes","docAbstract":"We evaluate the timeliness and accuracy of ground‐motion‐based earthquake early warning (EEW) during the July 2019 M6.4 and 7.1 Ridgecrest earthquakes. In 2018, we began retrospective and internal real‐time testing of the propagation of local undamped motion (PLUM) method for earthquake warning in California, Oregon, and Washington, with the potential that PLUM might one day be included in the ShakeAlert EEW system. A real‐time version of PLUM was running on one of the ShakeAlert EEW system’s development servers at the time of the 2019 Ridgecrest sequence, allowing us to evaluate the timeliness and accuracy of PLUM’s warnings for the M6.4 and 7.1 mainshocks in real time with the actual data availability and latencies of the operational ShakeAlert EEW system. The latter is especially important because high‐data latencies during the M7.1 earthquake degraded ShakeAlert’s performance. PLUM proved to be largely immune to these latencies. In this article, we present a retrospective analysis of PLUM performance and explore three potential regional alerting strategies ranging from spatially large regions (counties), to moderate‐size regions (National Weather Service public forecast zones), to high‐spatial specificity (50 km regular geographic grid). PLUM generated initial shaking forecasts for the two mainshocks 5 and 6 s after their respective origin times, and faster than the ShakeAlert system’s first alerts. PLUM was also able to accurately forecast shaking across southern California for all three alerting strategies studied. As would be expected, a cost‐benefit analysis of each approach illustrates trade‐offs between increasing warning time and minimizing the area receiving unneeded alerts. Choosing an optimal alerting strategy requires knowledge of users’ false alarm tolerance and minimum required warning time for taking protective action, as well as the time required to distribute alerts to users.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200021","usgsCitation":"Minson, S.E., Saunders, J.K., Bunn, J., Cochran, E.S., Baltay Sundstrom, A.S., Kilb, D.L., Hoshiba, M., and Kodera, Y., 2020, Real-time performance of the PLUM earthquake early warning method during the 2019 M6.4 and M7.1 Ridgecrest, California, Earthquakes: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1887-1903, https://doi.org/10.1785/0120200021.","productDescription":"7 p.","startPage":"1887","endPage":"1903","ipdsId":"IP-115052","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456548,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://resolver.caltech.edu/CaltechAUTHORS:20200827-142633476","text":"External Repository"},{"id":407602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.79335021972655,\n 35.58529318061384\n ],\n [\n -117.55233764648438,\n 35.58529318061384\n ],\n [\n -117.55233764648438,\n 35.70749253887843\n ],\n [\n -117.79335021972655,\n 35.70749253887843\n ],\n [\n -117.79335021972655,\n 35.58529318061384\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saunders, Jessie Kate 0000-0001-5340-6715","orcid":"https://orcid.org/0000-0001-5340-6715","contributorId":290634,"corporation":false,"usgs":true,"family":"Saunders","given":"Jessie","email":"","middleInitial":"Kate","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853318,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunn, Julian","contributorId":216379,"corporation":false,"usgs":false,"family":"Bunn","given":"Julian","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":853319,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":853321,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kilb, Deborah L.","contributorId":216380,"corporation":false,"usgs":false,"family":"Kilb","given":"Deborah","email":"","middleInitial":"L.","affiliations":[{"id":37799,"text":"SCRIPPS","active":true,"usgs":false}],"preferred":false,"id":853322,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hoshiba, Mitsuyuki","contributorId":216382,"corporation":false,"usgs":false,"family":"Hoshiba","given":"Mitsuyuki","email":"","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":853323,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kodera, Yuki","contributorId":290636,"corporation":false,"usgs":false,"family":"Kodera","given":"Yuki","email":"","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":853324,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70236144,"text":"70236144 - 2020 - Basinwide hydroclimatic drought in the Colorado River basin","interactions":[],"lastModifiedDate":"2022-08-30T13:55:23.296854","indexId":"70236144","displayToPublicDate":"2020-06-01T08:50:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Basinwide hydroclimatic drought in the Colorado River basin","docAbstract":"The Colorado River basin (CRB) supplies water to approximately 40 million people and is essential to hydropower generation, agriculture, and industry. In this study, a monthly water balance model is used to compute hydroclimatic water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff) for the period 1901–2014 across the entire CRB. The time series of monthly runoff is aggregated to compute water-year runoff and then used to identify drought periods in the basin. For the 1901–2014 period, eight basinwide drought periods were identified. The driest drought period spanned years 1901–04, whereas the longest drought period occurred during 1943–56. The eight droughts were primarily driven by winter precipitation deficits rather than warm temperature anomalies. In addition, an analysis of prehistoric drought for the CRB—computed using tree-ring-based reconstructions of the Palmer drought severity index—indicates that during some past centuries drought frequency was higher than during the twentieth century and that some centuries experienced droughts that were much longer than those during the twentieth century. More frequent or longer droughts than those that occurred during the twentieth century, combined with continued warming associated with climate change, may lead to substantial future water deficits in the CRB.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/EI-D-20-0001.1","usgsCitation":"McCabe, G.J., Wolock, D.M., Woodhouse, C., Pederson, G.T., McAfee, S.A., Gray, S., and Csank, A., 2020, Basinwide hydroclimatic drought in the Colorado River basin: Earth Interactions, v. 24, no. 2, p. 1-20, https://doi.org/10.1175/EI-D-20-0001.1.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-117843","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":456554,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/ei-d-20-0001.1","text":"Publisher Index Page"},{"id":405901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.75195312499999,\n 32.76880048488168\n ],\n [\n -113.8623046875,\n 32.39851580247402\n ],\n [\n -111.357421875,\n 31.42866311735861\n ],\n [\n -109.2041015625,\n 31.353636941500987\n ],\n [\n -108.369140625,\n 31.353636941500987\n ],\n [\n -108.10546875,\n 32.91648534731439\n ],\n [\n -107.9296875,\n 34.84987503195418\n ],\n [\n -107.5341796875,\n 36.35052700542763\n ],\n [\n -105.46875,\n 37.71859032558816\n ],\n [\n -105.380859375,\n 38.95940879245423\n ],\n [\n -105.29296874999999,\n 39.90973623453719\n ],\n [\n -105.5126953125,\n 40.97989806962013\n ],\n [\n -107.138671875,\n 42.4234565179383\n ],\n [\n -108.8525390625,\n 43.70759350405294\n ],\n [\n -110.0830078125,\n 43.929549935614595\n ],\n [\n -110.6982421875,\n 43.03677585761058\n ],\n [\n -111.005859375,\n 41.31082388091818\n ],\n [\n -112.19238281249999,\n 38.54816542304656\n ],\n [\n -112.9833984375,\n 37.96152331396614\n ],\n [\n -114.08203125,\n 38.34165619279595\n ],\n [\n -115.1806640625,\n 39.50404070558415\n ],\n [\n -116.103515625,\n 39.232253141714885\n ],\n [\n -115.97167968750001,\n 37.579412513438385\n ],\n [\n -115.79589843749999,\n 35.71083783530009\n ],\n [\n -115.3564453125,\n 34.30714385628804\n ],\n [\n -115.75195312499999,\n 32.76880048488168\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":438,"text":"National Research Program - 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2020 - Managing climate refugia for freshwater fishes under an expanding human footprint","interactions":[],"lastModifiedDate":"2020-08-06T18:47:55.209802","indexId":"70211651","displayToPublicDate":"2020-06-01T08:42:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5993,"text":"Frontiers in Ecology and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Managing climate refugia for freshwater fishes under an expanding human footprint","docAbstract":"Within the context of climate adaptation, the concept of climate refugia has emerged as a framework for addressing future threats to freshwater fish populations. We evaluated recent climate‐refugia management associated with water use and landscape modification by comparing efforts in the US states of Oregon and Massachusetts, for which there are contrasting resource use patterns. Using these examples, we discuss tools and principles that can be applied more broadly. Although many early efforts to identify climate refugia have focused on water temperature, substantial gains in evaluating other factors and processes regulating climate refugia (eg stream flow, groundwater availability) are facilitating refined mapping of refugia and assessment of their ecological value. Major challenges remain for incorporating climate refugia into water‐quality standards, evaluating trade‐offs among policy options, addressing multiple species’ needs, and planning for uncertainty. However, with a procedurally transparent and conceptually sound framework to build upon, recent efforts have revealed a promising path forward.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2206","usgsCitation":"Ebersole, J.L., Quinones, R.M., Clements, S., and Letcher, B., 2020, Managing climate refugia for freshwater fishes under an expanding human footprint: Frontiers in Ecology and Environment, v. 18, no. 5, p. 271-280, https://doi.org/10.1002/fee.2206.","productDescription":"10 p.","startPage":"271","endPage":"280","ipdsId":"IP-106631","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":456556,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fee.2206","text":"Publisher Index Page"},{"id":377080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts, 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\"}}]}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ebersole, Joseph L.","contributorId":146938,"corporation":false,"usgs":false,"family":"Ebersole","given":"Joseph","email":"","middleInitial":"L.","affiliations":[{"id":12657,"text":"EPA NEIC","active":true,"usgs":false}],"preferred":false,"id":794934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quinones, Rebecca M.","contributorId":172968,"corporation":false,"usgs":false,"family":"Quinones","given":"Rebecca","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":794935,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clements, Shaun","contributorId":171685,"corporation":false,"usgs":false,"family":"Clements","given":"Shaun","email":"","affiliations":[],"preferred":false,"id":794936,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Letcher, Benjamin 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":169305,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794937,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229340,"text":"70229340 - 2020 - Fish predation on a landscape scale","interactions":[],"lastModifiedDate":"2022-03-04T12:50:54.554435","indexId":"70229340","displayToPublicDate":"2020-06-01T06:43:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Fish predation on a landscape scale","docAbstract":"Predator–prey dynamics can have landscape-level impacts on ecosystems, and yet, spatial patterns and environmental predictors of predator–prey dynamics are often investigated at discrete locations, limiting our understanding of the broader impacts. At these broader scales, landscapes often contain multiple complex and heterogeneous habitats, requiring a spatially representative sampling design. This challenge is especially pronounced in California’s Sacramento–San Joaquin River Delta, where managers require information on the landscape-scale impacts of non-native fish predators on multiple imperiled native prey fish populations. We quantified relative predation risk in the southern half of the Delta (South Delta) in 2017 using floating baited tethers that record the exact time and location of predation events. We selected 20 study sites using a generalized random tessellation stratified survey design, which allowed us to infer relationships between key environmental covariates and predation across a broader spatial scale than previous studies. Covariates included distance-to-nearest predators, water temperature, turbidity, depth, bottom slope, bottom roughness, water velocity, and distance-to-nearest riverbank and nearest aquatic vegetation bed. Model selection determined the covariates that best predicted relative predation risk: water temperature, time of day, mean predator distance, and river bottom roughness. Using this model, we estimated predation risk for the South Delta landscape at a 1-day and 1-km resolution. This effort identified hot spots of predation risk and allowed us to generate predicted survival for migrating fish transiting the South Delta. This methodology can be applied to other systems to evaluate spatio-temporal dynamics in predation risk, and their biotic and abiotic predictors.