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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M.","contributorId":275322,"corporation":false,"usgs":false,"family":"Cobourn","given":"K.","email":"","middleInitial":"M.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":833992,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833987,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weathers, K. C.","contributorId":275323,"corporation":false,"usgs":false,"family":"Weathers","given":"K. 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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
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
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":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife 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":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.
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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lucke, David W.","contributorId":225587,"corporation":false,"usgs":false,"family":"Lucke","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":41168,"text":"Wyoming State Geological Survey (WSGS)","active":true,"usgs":false}],"preferred":false,"id":791760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Steven M. 0000-0003-3591-5377 smsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-3591-5377","contributorId":1460,"corporation":false,"usgs":true,"family":"Smith","given":"Steven","email":"smsmith@usgs.gov","middleInitial":"M.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":791761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Azain, Jaime S. 0000-0002-8256-7494","orcid":"https://orcid.org/0000-0002-8256-7494","contributorId":201966,"corporation":false,"usgs":true,"family":"Azain","given":"Jaime S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":791762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ingraham, Andrew David 0000-0001-7347-6171","orcid":"https://orcid.org/0000-0001-7347-6171","contributorId":225588,"corporation":false,"usgs":true,"family":"Ingraham","given":"Andrew","email":"","middleInitial":"David","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":791763,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228591,"text":"70228591 - 2020 - Gear comparison study for sampling nekton in Barataria Basin marshes","interactions":[],"lastModifiedDate":"2022-02-14T16:43:43.772396","indexId":"70228591","displayToPublicDate":"2020-06-01T10:38:17","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":10110,"text":"Technical Report Administrative Summary","active":true,"publicationSubtype":{"id":1}},"title":"Gear comparison study for sampling nekton in Barataria 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":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","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":70211207,"text":"70211207 - 2020 - Detrital record of the late Oligocene – Early Miocene mafic volcanic arc in the southern Patagonian Andes (~51 °S) from single-clast geochronology and trace element geochemistry","interactions":[],"lastModifiedDate":"2020-07-20T12:45:37.322765","indexId":"70211207","displayToPublicDate":"2020-05-30T13:15:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2304,"text":"Journal of Geodynamics","active":true,"publicationSubtype":{"id":10}},"title":"Detrital record of the late Oligocene – Early Miocene mafic volcanic arc in the southern Patagonian Andes (~51 °S) from single-clast geochronology and trace element geochemistry","docAbstract":"Retroarc foreland basins are important archives of continental arc magmatism and upper plate deformational processes that control the evolution of continental lithosphere. However, resolving source areas in foreland basin infill dominated from mixed mafic and recycled sediment using conventional methods such as detrital zircon geochronology poses a challenge to thorough analysis due to lower zircon fertility and the higher susceptibility to weathering of mafic lithologies. Here, we integrate whole rock 40Ar/39Ar geochronology and major and trace element geochemistry data from volcanic clasts from the lower Miocene infill of the Magallanes-Austral Basin and local Sierra Baguales intrusive rocks to understand the distribution of mafic sources and Neogene changes in arc magmatism in between multiple ridge subduction events in the southern Patagonian Andes. Potential source areas for the coarse-grained mafic detritus include the Eocene plateau lavas, the Late Jurassic-Miocene Southern Patagonian batholith, and the Late Jurassic Sarmiento Ophiolitic Complex. Published detrital zircon U-Pb age spectra suggest that all three sources are viable contributors to the basin, though the paucity of Jurassic and Eocene zircons preclude these as major sources. Here, new 40Ar/39Ar dating of the volcanic clasts from the early Miocene Río Guillermo Formation reveals latest Oligocene to early Miocene eruptive ages (∼25-22 Ma), indicating syndepositional eruption with the ancestral Río Guillermo fluvial sedimentation. A single dated clast yields a Late Cretaceous age (∼102 Ma). The clasts are dominantly basaltic andesite with Ba/Ta ∼500-1000, La/Ta >20, and Ba/La >15, indicating an arc-derived melt source. We propose that the clasts record a Miocene mafic continental arc source area in the Patagonian Andes, which has since been removed by erosion and is thus sparsely represented in the batholith. Furthermore, we suggest that this early Miocene phase of arc volcanism, which postdates Eocene and Oligocene backarc magmatism and pre-dates middle Miocene to recent Chile Ridge backarc magmatism, reflects a return to normal arc volcanism along the Patagonian margin following a cessation due to ridge subduction and subsequent slab window migration. New geochemistry and 40Ar/39Ar data from a basaltic dike in the Sierra Baguales, which crosscuts the Cenozoic stratigraphic section, records plateau magmatism ∼16 Ma associated with incipient Chile Ridge slab window volcanism.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jog.2020.101751","usgsCitation":"VanderLeest, R.A., Fosdick, J., Leonard, J.S., and Morgan, L.E., 2020, Detrital record of the late Oligocene – Early Miocene mafic volcanic arc in the southern Patagonian Andes (~51 °S) from single-clast geochronology and trace element geochemistry: Journal of Geodynamics, v. 138, 1001751, 15 p., https://doi.org/10.1016/j.jog.2020.101751.","productDescription":"1001751, 15 p.","ipdsId":"IP-113443","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":456573,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jog.2020.101751","text":"Publisher Index Page"},{"id":436951,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FN6J0L","text":"USGS data release","linkHelpText":"Argon data for Southern Patagonian Andes"},{"id":376475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina, Chile","otherGeospatial":"Patagonian Andes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.3984375,\n -56.75272287205735\n ],\n [\n -65.21484375,\n -56.75272287205735\n ],\n [\n -65.21484375,\n -39.368279149160124\n ],\n [\n -78.3984375,\n -39.368279149160124\n ],\n [\n -78.3984375,\n -56.75272287205735\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"138","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"VanderLeest, Rebecca A.","contributorId":229447,"corporation":false,"usgs":false,"family":"VanderLeest","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":793199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fosdick, Julie C","contributorId":229448,"corporation":false,"usgs":false,"family":"Fosdick","given":"Julie C","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":793200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leonard, Joel S","contributorId":229449,"corporation":false,"usgs":false,"family":"Leonard","given":"Joel","email":"","middleInitial":"S","affiliations":[{"id":41647,"text":"Arizona State University,","active":true,"usgs":false}],"preferred":false,"id":793201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morgan, Leah E. 0000-0001-9930-524X lemorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-9930-524X","contributorId":176174,"corporation":false,"usgs":true,"family":"Morgan","given":"Leah","email":"lemorgan@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":793202,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211483,"text":"70211483 - 2020 - Examining the mechanisms of species responses to climate change: Are there biological thresholds?","interactions":[],"lastModifiedDate":"2020-07-30T16:23:57.002172","indexId":"70211483","displayToPublicDate":"2020-05-30T11:18:26","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Examining the mechanisms of species responses to climate change: Are there biological thresholds?","docAbstract":"Climate-change-driven shifts in distribution and abundance have been documented in many species. However, in order to better predict species responses, managers are seeking to understand the mechanisms that are driving these changes, including any thresholds that might soon be crossed. Leveraging the research that has already been supported by the Northeast Climate Adaptation Science Center and its partners, this project used the latest modeling techniques combined with robust field data to examine the impact of specific climate variables, land use change, and species interactions on the future distribution and abundance of species of conservation concern. Moreover, this project documented biological thresholds related to climate variability and change for critical species in the Northeastern and Midwestern U.S. Specifically, our objectives were to identify the primary drivers (climate change vs. urban growth) of species distribution changes in the Northeast; examine the nature of species landscape capability change over time to identify potential thresholds; determine how changing temperatures and snowpack characteristics will drive species interactions; analyze the sensitivity of tree and bird responses to the magnitude, variability, periodicity, and seasonality of temperature and precipitation under climate change in the eastern U.S.; and identify how discrete climate triggers such as extreme events will correlate with known biological thresholds. Major outcomes included 1) refining the understanding of the mechanisms that drive projected changes in the distribution of vulnerable populations; and 2) improving how these results are conveyed to stakeholders by identifying understandable responses in the form of thresholds.","language":"English","publisher":"Northeast Climate Adaptation Science Center","usgsCitation":"DeLuca, W., Bonnot, T.W., Siren, A., Horton, R.M., Griffin, C.R., and Morelli, T.L., 2020, Examining the mechanisms of species responses to climate change: Are there biological thresholds?, 34 p.","productDescription":"34 p.","ipdsId":"IP-117230","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":376907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376805,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f8c648de4b0546c0c397b43/57b35c6de4b03bcb01039665"}],"country":"United States","state":"Connecticut, Delaware, Illinois, Indiana, Iowa, Kentucky, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, New Hampshire New Jersey, New York, Ohio, Pennsylvania, Rhode Island. 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We assessed trends in intertidal oyster populations, river discharge, and commercial fishing activity in the Suwannee River estuary within the Big Bend region using fisheries‐independent data from irregular monitoring efforts and publicly available environmental data. We used generalized linear models to evaluate counts of oysters from line‐transect surveys over time and space. We assessed model performance using simulation to understand potential bias and then evaluated whether these counts were related to freshwater inputs from the Suwannee River and commercial oyster fishing effort and landings at different time lags. We found that intertidal oyster counts have declined over time and that most of these declines are found in inshore intertidal oyster bars, which are becoming degraded. We also found a significant relationship between oyster counts and a 1‐year lag on mean daily Suwannee River discharge, but including commercial fishery trips or landings did not improve model fit. It is unclear whether declines in intertidal oyster bars are offset by formation of new oyster reefs elsewhere. These results quantify rapid declines in intertidal oyster reefs in a region of coastline with high conservation value that can be used to inform ongoing and proposed restoration projects in the region.","language":"English","publisher":"Wiley","doi":"10.1002/mcf2.10117","usgsCitation":"Moore, J.F., Pine, W.E., Frederick, P., Becker, S., Moreno, M., Dodrill, M., Boone, M., Sturmer, L., and Yurek, S., 2020, Trends in oyster populations in the northeastern Gulf of Mexico: An assessment of river discharge and fishing effects over time and space: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 12, no. 3, p. 191-204, https://doi.org/10.1002/mcf2.10117.","productDescription":"14 p.","startPage":"191","endPage":"204","ipdsId":"IP-114295","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":456589,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10117","text":"Publisher Index Page"},{"id":377005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.51943969726561,\n 28.9913248161703\n ],\n [\n -82.67898559570311,\n 28.9913248161703\n ],\n [\n -82.67898559570311,\n 29.684473609006847\n ],\n [\n -83.51943969726561,\n 29.684473609006847\n ],\n [\n -83.51943969726561,\n 28.9913248161703\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, J. 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In the United States (U.S.), inventories of existing karst depressions are piecemeal, and are often obtained through inconsistent methodologies applied at the state or county level and at various scales. Here, we present a first attempt at defining a karst closed depression inventory across the conterminous U.S. using a common methodology. Automated algorithms for extraction of closed depressions from 1/3 arc-second (approximately 10 m resolution) National Elevation Dataset (NED) were run on the U.S. Geological Survey (USGS) “Yeti” high-performance computing cluster. The full NED was first conditioned to reduce the creation of artificial closed depressions by breaching digital dams at road and stream crossings, using the flowlines and transportation route vectors from the USGS National Map. The resulting depressions were selected according to location within geologic units having the potential for karst, and screened for occurrence in areas of developed land, open water and wetlands, and areas of glacial and alluvial sediment cover. The results were used as the input to create a nationwide depression density map. Our results were compared with karst depression density maps for diverse karst regions within states that have existing closed depression inventories. The individual state-scale maps compared favorably to the results obtained from the method applied universally across the nation and illustrated regional sinkhole hotspots in known areas of well-developed karst. Limitations of the automated method includes false positive depressions resulting from artifacts generated during the computer processing of the elevation models, and inclusion of depressions resulting from non-karst geomorphic processes. More thorough examination of the screening criteria for depressions is required.
Predicting the success of future investments in coastal and estuarine ecosystem restorations is limited by scarce data quantifying sediment budgets and transport processes of prior restorations. This study provides detailed analyses of the hydrodynamics and sediment fluxes of a recently restored U.S. Pacific Northwest estuary, a 61 ha former agricultural area near the mouth of the Stillaguamish River in Washington, USA. Water level, flow velocity, and suspended-sediment concentration (SSC) were measured between 21 March 2014 and 1 June 2015 at breaches excavated in the former flood-protection levee to determine transport patterns and the net sediment budget of the restoration area. SSC within the restoration area was primarily controlled by SSC variability of the nearby main stem Stillaguamish, but coastal processes also played a major role in sediment delivery. Fluvial sediment loading was dominated by runoff events associated with rainfall that lasted hours to a few days. Additionally, the 22 March 2014 SR 530 (Oso) landslide elevated sediment supply to the restoration area and coastal region for several weeks, indicating the importance of distal geomorphic events to coastal sediment budgets in small mountainous river systems. Sediment fluxes were controlled by river SSC and tidal dynamics, which set the quantity of water transported into the restoration area. Peak water discharge at the restoration area was about 12% of the river discharge, and peak sediment flux at the restoration area was about 5% of the river sediment discharge, although net sediment import was <1% of the total river load. Although sediment was imported to the restoration area, and inferred rates of accretion appear sufficient to keep pace with present rates of local sea-level rise, full recovery is challenged by significant lost grade from historical subsidence and will likely take decades to centuries. These results have implications for estuary restoration planning globally and indicate the importance of understanding coupled fluvial–coastal processes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2020.106869","usgsCitation":"Nowacki, D.J., and Grossman, E.E., 2020, Sediment transport in a restored, river-influenced Pacific Northwest estuary: Estuarine, Coastal and Shelf Science, v. 242, 106869, 10 p., https://doi.org/10.1016/j.ecss.2020.106869.","productDescription":"106869, 10 p.","ipdsId":"IP-117166","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456594,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2020.106869","text":"Publisher Index Page"},{"id":436953,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RK8H7X","text":"USGS data release","linkHelpText":"Oceanographic measurements collected in the Stillaguamish River Delta, Port Susan, Washington, USA from March 2014 to July 2015"},{"id":375454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.86148071289061,\n 47.814076743593624\n ],\n [\n -122.16110229492186,\n 47.814076743593624\n ],\n [\n -122.16110229492186,\n 48.438312142641244\n ],\n [\n -122.86148071289061,\n 48.438312142641244\n ],\n [\n -122.86148071289061,\n 47.814076743593624\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"242","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nowacki, Daniel J. 0000-0002-7015-3710 dnowacki@usgs.gov","orcid":"https://orcid.org/0000-0002-7015-3710","contributorId":174586,"corporation":false,"usgs":true,"family":"Nowacki","given":"Daniel","email":"dnowacki@usgs.gov","middleInitial":"J.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":790572,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grossman, Eric E. 0000-0003-0269-6307 egrossman@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-6307","contributorId":196610,"corporation":false,"usgs":true,"family":"Grossman","given":"Eric","email":"egrossman@usgs.gov","middleInitial":"E.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":790573,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210102,"text":"70210102 - 2020 - Landslides across the United States: Occurrence, susceptibility, and data limitations","interactions":[],"lastModifiedDate":"2020-10-12T16:54:29.257116","indexId":"70210102","displayToPublicDate":"2020-05-29T10:10:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"Landslides across the United States: Occurrence, susceptibility, and data limitations","docAbstract":"Detailed information about landslide occurrence is the foundation for advancing process understanding, susceptibility mapping, and risk reduction. Despite the recent revolution in digital elevation data and remote sensing technologies, landslide mapping remains resource intensive. Consequently, a modern, comprehensive map of landslide occurrence across the United States (USA) has not been compiled. As a first step toward this goal, we present a national-scale compilation of existing, publicly available landslide inventories. This geodatabase can be downloaded in its entirety or viewed through an online, searchable map, with parsimonious attributes and direct links to the contributing sources with additional details. The mapped spatial pattern and concentration of landslides are consistent with prior characterization of susceptibility within the conterminous USA, with some notable exceptions on the West Coast. Although the database is evolving and known to be incomplete in many regions, it confirms that landslides do occur across the country, thus highlighting the importance of our national-scale assessment. The map illustrates regions where high-quality mapping has occurred and, in contrast, where additional resources could improve confidence in landslide characterization. For example, borders between states and other jurisdictions are quite apparent, indicating the variation in approaches to data collection by different agencies and disparity between the resources dedicated to landslide characterization. Further investigations are needed to better assess susceptibility and to determine whether regions with high relief and steep topography, but without mapped landslides, require further landslide inventory mapping. Overall, this map provides a new resource for accessing information about known landslides across the USA.
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However, forest disturbances and subsequent recovery are shifting with global changes in climate and land use, altering these dynamics. Changes in environmental drivers, land use, and disturbance regimes are forcing forests toward younger, shorter stands. Rising carbon dioxide, acclimation, adaptation, and migration can influence these impacts. Recent developments in Earth system models support increasingly realistic simulations of vegetation dynamics. In parallel, emerging remote sensing datasets promise qualitatively new and more abundant data on the underlying processes and consequences for vegetation structure. When combined, these advances hold promise for improving the scientific understanding of changes in vegetation demographics and disturbances.
The U.S. Geological Survey, in cooperation with the U.S. Centers for Disease Control and Prevention and the Maine Center for Disease Control and Prevention, assessed the chemical characteristics and the occurrence, distribution, and oxidation state of inorganic arsenic in drinking water from selected domestic well-water supplies in Maine in 2001–2 and 2006–7.
The data collected provide support for evaluating arsenic-removal efficiencies of household water-purification systems and provide information to State and local officials that can be used in determining a water-treatment approach for the removal of arsenic from drinking water.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1125","collaboration":"Prepared in cooperation with the U.S. Centers for Disease Control and Prevention and the Maine Center for Disease Control and Prevention","usgsCitation":"Culbertson, C.W., Caldwell, J.M., Schalk, L.F., Manassaram, D., Backer, L.C., and Smith, A.E., 2020, Methods of collection and quality assessment of arsenic data in well-water supplies in Maine, 2001–2 and 2006–7: U.S. Geological Survey Data Series 1125, 11 p., https://doi.org/10.3133/ds1125.","productDescription":"Report: v, 11 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-025715","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":375105,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1125/ds1125.pdf","text":"Report","size":"1.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1125"},{"id":375104,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X5HVDF","text":"USGS data release","linkHelpText":"Arsenic datasets and other physical and chemical measurements for selected domestic well-water supplies in Maine—2001–2 and 2006–7"},{"id":375103,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1125/coverthb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.16943359374999,\n 43.02071359427862\n ],\n [\n -66.15966796874999,\n 43.02071359427862\n ],\n [\n -66.15966796874999,\n 44.87144275016589\n ],\n [\n -71.16943359374999,\n 44.87144275016589\n ],\n [\n -71.16943359374999,\n 43.02071359427862\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Mesoproterozoic and Neoproterozoic basins in western North America record the evolving position of the Laurentian craton within two supercontinents during their growth and dismemberment: Columbia (Nuna) and Rodinia. The western-most exposures of the Columbia rift-related Belt–Purcell Supergroup are preserved in northeastern Washington, structurally overlain by the Deer Trail Group and depositionally overlying the Neoproterozoic Windermere Supergroup. It has been disputed whether the Deer Trail Group is correlative with the Belt–Purcell Supergroup, or younger. To help resolve the uncertain correlation of these units and their bearing on supercontinent evolution, we characterized the detrital zircon age populations of units from the Deer Trail Group, the Windermere Supergroup, and the Belt–Purcell Supergroup in northeastern Washington. These data show that the western part of the Columbia supercontinent (now located in Australia and eastern Antarctica) remained attached to western Laurentia and continued to supply 1600–1500 Ma detrital zircon grains to the Belt–Purcell Supergroup until after ca. 1391 Ma. The Deer Trail Group is younger than the Belt–Purcell strata, with the basal unit younger than ca. 1362 Ma and a middle unit younger than ca. 1300 Ma. The Deer Trail Group has a pre-Grenville-age provenance from the southwestern USA and possibly east Antarctica. The Buffalo Hump Formation is younger than the Deer Trail Group, with Grenville-age (ca. 1112 Ma) detrital zircon grains and a detrital zircon signature like that of the overlying Neoproterozoic Windermere Supergroup. We interpret the Deer Trail Group to have been deposited during the rift-demise of supercontinent Columbia and before the Grenville-age assembly of the supercontinent Rodinia.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjes-2019-0188","usgsCitation":"Box, S.E., Pritchard, C.J., Stephens, T.S., and O’Sullivan, P.B., 2020, Between the supercontinents: Mesoproterozoic Deer Trail Group, an intermediate age unit between the Mesoproterozoic Belt–Purcell Supergroup and the Neoproterozoic Windermere Supergroup in northeastern Washington, USA: Canadian Journal of Earth Sciences, v. 57, no. 12, p. 1411-1427, https://doi.org/10.1139/cjes-2019-0188.","productDescription":"17 p.","startPage":"1411","endPage":"1427","ipdsId":"IP-112626","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":500791,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/101706","text":"External Repository"},{"id":377140,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","city":"Chewelah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.81188964843751,\n 47.89056441663247\n ],\n [\n -117.04833984375001,\n 47.89056441663247\n ],\n [\n -117.04833984375001,\n 48.669198799260045\n ],\n [\n -117.81188964843751,\n 48.669198799260045\n ],\n [\n -117.81188964843751,\n 47.89056441663247\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Box, Stephen E. 0000-0002-5268-8375 sbox@usgs.gov","orcid":"https://orcid.org/0000-0002-5268-8375","contributorId":1843,"corporation":false,"usgs":true,"family":"Box","given":"Stephen","email":"sbox@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pritchard, Chad J. 0000-0002-4608-7776","orcid":"https://orcid.org/0000-0002-4608-7776","contributorId":237042,"corporation":false,"usgs":false,"family":"Pritchard","given":"Chad","email":"","middleInitial":"J.","affiliations":[{"id":47590,"text":"Eastern Washington University, Dept. of Geology","active":true,"usgs":false}],"preferred":false,"id":795049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stephens, Travis S.","contributorId":237044,"corporation":false,"usgs":false,"family":"Stephens","given":"Travis","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":795050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Sullivan, Paul B.","contributorId":193544,"corporation":false,"usgs":false,"family":"O’Sullivan","given":"Paul","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":795051,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211973,"text":"70211973 - 2020 - Deglacierization of a marginal basin and implications for outburst floods, Mendenhall Glacier, Alaska","interactions":[],"lastModifiedDate":"2020-08-12T21:56:14.477434","indexId":"70211973","displayToPublicDate":"2020-05-27T16:44:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Deglacierization of a marginal basin and implications for outburst floods, Mendenhall Glacier, Alaska","docAbstract":"Suicide Basin is a partly glacierized marginal basin of Mendenhall Glacier, Alaska, that has released glacier lake outburst floods (GLOFs) annually since 2011. The floods cause inundation and erosion in the Mendenhall Valley, impacting homes and other infrastructure. Here, we utilize in-situ and remote sensing data to assess the recent evolution and current state of Suicide Basin. We focus on the 2018 and 2019 melt seasons, during which we collected most of our data, partly using unmanned aerial vehicles (UAVs). To provide longer-term context, we analyze DEMs collected since 2006 and model glacier surface mass balance over the 2006–2019 period. During the 2018 and 2019 outburst flood events, Suicide Basin released ~30 × 106 m3 of water within approximately 4–5 days. Since lake drainage was partial in both years, these ~30 × 106 m3 represent only a fraction (~60%) of the basin's total storage capacity. In contrast to previous years, subglacial drainage was preceded by supraglacial outflow over the ice dam, which lasted ~1 day in 2018 and 6 days in 2019. Two large calving events occurred in 2018 and 2019, with submerged ice breaking off the main glacier during lake filling, thereby increasing the basin's storage capacity. In 2018, the floating ice in the basin was 36 m thick on average. In 2019, ice thickness was 29 m, suggesting rapid decay of the ice tongue despite increasing ice inflow from Mendenhall Glacier. The ice dam at the basin entrance thinned by more than 5 m a–1 from 2018 to 2019, which is approximately double the rate of the reference period 2006–2018. While ice-dam thinning reduces water storage capacity in the basin, that capacity is increased by declining ice volume in the basin and longitudinal lake expansion, with the latter process challenging to predict. The potential for premature drainage onset (i.e., drainage before the lake's storage capacity is reached), intermittent drainage decelerations, and early drainage termination further complicates prediction of future GLOF events.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2020.00137","usgsCitation":"Kienholz, C., Pierce, J., Hood, E., Amundson, J.M., Wolken, G., Jacobs, A., Hart, S., Wikstrom-Jones, K., Abdel-Fattah, D., Johnson, C., and Conaway, J.S., 2020, Deglacierization of a marginal basin and implications for outburst floods, Mendenhall Glacier, Alaska: Frontiers in Earth Science, v. 8, 137, 21 p., https://doi.org/10.3389/feart.2020.00137.","productDescription":"137, 21 p.","ipdsId":"IP-114163","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":456622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00137","text":"Publisher Index Page"},{"id":377453,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Mendenhall Glacier, Suicide Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -134.78164672851562,\n 58.32679897129091\n ],\n [\n -134.06890869140625,\n 58.32679897129091\n ],\n [\n -134.06890869140625,\n 58.73186643857013\n ],\n [\n -134.78164672851562,\n 58.73186643857013\n ],\n [\n -134.78164672851562,\n 58.32679897129091\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Kienholz, Christian","contributorId":220416,"corporation":false,"usgs":false,"family":"Kienholz","given":"Christian","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":796031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pierce, Jamie","contributorId":218174,"corporation":false,"usgs":true,"family":"Pierce","given":"Jamie","email":"","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":796032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hood, Eran","contributorId":106802,"corporation":false,"usgs":false,"family":"Hood","given":"Eran","affiliations":[],"preferred":false,"id":796033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amundson, Jason M.","contributorId":26944,"corporation":false,"usgs":true,"family":"Amundson","given":"Jason","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":796034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wolken, Gabriel","contributorId":204863,"corporation":false,"usgs":false,"family":"Wolken","given":"Gabriel","affiliations":[{"id":37000,"text":"DGGS","active":true,"usgs":false}],"preferred":false,"id":796035,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jacobs, Aaron","contributorId":204855,"corporation":false,"usgs":false,"family":"Jacobs","given":"Aaron","email":"","affiliations":[{"id":36995,"text":"NWS","active":true,"usgs":false}],"preferred":false,"id":796036,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Skye","contributorId":238101,"corporation":false,"usgs":false,"family":"Hart","given":"Skye","email":"","affiliations":[],"preferred":false,"id":796037,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wikstrom-Jones, Katreen","contributorId":238102,"corporation":false,"usgs":false,"family":"Wikstrom-Jones","given":"Katreen","email":"","affiliations":[],"preferred":false,"id":796038,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Abdel-Fattah, Dina","contributorId":238103,"corporation":false,"usgs":false,"family":"Abdel-Fattah","given":"Dina","email":"","affiliations":[],"preferred":false,"id":796039,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Johnson, Crane","contributorId":238104,"corporation":false,"usgs":false,"family":"Johnson","given":"Crane","email":"","affiliations":[],"preferred":false,"id":796040,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Conaway, Jeffrey S. 0000-0002-3036-592X jconaway@usgs.gov","orcid":"https://orcid.org/0000-0002-3036-592X","contributorId":2026,"corporation":false,"usgs":true,"family":"Conaway","given":"Jeffrey","email":"jconaway@usgs.gov","middleInitial":"S.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":796041,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70260155,"text":"70260155 - 2020 - Capturing, preserving and digitizing legacy seismic data from the Montserrat Volcano Observatory analog seismic network, July 1995 – December 2004","interactions":[],"lastModifiedDate":"2024-10-30T22:19:06.067897","indexId":"70260155","displayToPublicDate":"2020-05-27T11:18:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Capturing, preserving and digitizing legacy seismic data from the Montserrat Volcano Observatory analog seismic network, July 1995 – December 2004","docAbstract":"An eruption of the Soufrière Hills Volcano (SHV) on the eastern Caribbean island of Montserrat began on 18 July 1995 and continued until February 2010. Within nine days of the eruption onset, an existing four‐station analog seismic network (ASN) was expanded to 10 sites. Telemetered data from this network were recorded, processed, and archived locally using a system developed by scientists from the U.S. Geological Survey (USGS) Volcano Disaster Assistance Program (VDAP). In October 1996, a digital seismic network (DSN) was deployed with the ability to capture larger amplitude signals across a broader frequency range. These two networks operated in parallel until December 2004, with separate telemetry and acquisition systems (analysis systems were merged in March 2001). Although the DSN provided better quality data for research, the ASN featured superior real‐time monitoring tools and captured valuable data including the only seismic data from the first 15 months of the eruption. These successes of the ASN have been rather overlooked. This article documents the evolution of the ASN, the VDAP system, the original data captured, and the recovery and conversion of more than 230,000 seismic events from legacy SUDS, Hypo71, and Seislog formats into Seisan database with waveform data in miniSEED format. No digital catalog existed for these events, but students at the University of South Florida have classified two-thirds of the 40,000 events that were captured between July 1995 and October 1996. Locations and magnitudes were recovered for ~10,000 of these events. Real-time seismic amplitude measurement, seismic spectral amplitude measurement, and tiltmeter data were also captured. The result is that the ASN seismic dataset is now more discoverable, accessible, and reusable, in accordance with FAIR data principles. These efforts could catalyze new research on the 1995–2010 SHV eruption. Furthermore, many observatories have data in these same legacy data formats and might benefit from procedures and codes documented here.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220200012","usgsCitation":"Thompson, G., Power, J., Braunmiller, J., Lockhart, A., Lynch, L., McCausland, W., Rowe, C., Shea, T., White, R., and Breithaupt, C., 2020, Capturing, preserving and digitizing legacy seismic data from the Montserrat Volcano Observatory analog seismic network, July 1995 – December 2004: Seismological Research Letters, v. 91, no. 4, p. 2127-2140, https://doi.org/10.1785/0220200012.","productDescription":"14 p.","startPage":"2127","endPage":"2140","ipdsId":"IP-115441","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":463354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Monserrat","otherGeospatial":"Soufrière Hills Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -62.20648979760041,\n 16.723228121283853\n ],\n [\n -62.20648979760041,\n 16.684378490612588\n ],\n [\n -62.15099565340141,\n 16.684378490612588\n ],\n [\n -62.15099565340141,\n 16.723228121283853\n ],\n [\n -62.20648979760041,\n 16.723228121283853\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"91","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Glenn","contributorId":345675,"corporation":false,"usgs":false,"family":"Thompson","given":"Glenn","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":917233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Power, John 0000-0002-7233-4398","orcid":"https://orcid.org/0000-0002-7233-4398","contributorId":215240,"corporation":false,"usgs":true,"family":"Power","given":"John","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Braunmiller, Jochen","contributorId":345676,"corporation":false,"usgs":false,"family":"Braunmiller","given":"Jochen","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":917235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lockhart, Andrew 0000-0002-1591-3254 ablock@usgs.gov","orcid":"https://orcid.org/0000-0002-1591-3254","contributorId":204748,"corporation":false,"usgs":true,"family":"Lockhart","given":"Andrew","email":"ablock@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917236,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lynch, Lloyd","contributorId":345677,"corporation":false,"usgs":false,"family":"Lynch","given":"Lloyd","affiliations":[{"id":82692,"text":"Seismic Research Unit","active":true,"usgs":false}],"preferred":false,"id":917237,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCausland, Wendy 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":345678,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917238,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rowe, Charlotte","contributorId":345679,"corporation":false,"usgs":false,"family":"Rowe","given":"Charlotte","email":"","affiliations":[{"id":82693,"text":"Los Alamos National Labs","active":true,"usgs":false}],"preferred":false,"id":917239,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shea, Thomas","contributorId":345680,"corporation":false,"usgs":false,"family":"Shea","given":"Thomas","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":917240,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"White, Randall A. 0000-0003-4074-8577","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":344964,"corporation":false,"usgs":false,"family":"White","given":"Randall A.","affiliations":[{"id":82444,"text":"none, retired USGS","active":true,"usgs":false}],"preferred":false,"id":917241,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Breithaupt, Charles","contributorId":345681,"corporation":false,"usgs":false,"family":"Breithaupt","given":"Charles","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":917242,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70222338,"text":"70222338 - 2020 - Challenges in quantifying air-water carbon dioxide flux using estuarine water quality data: Case study for Chesapeake Bay","interactions":[],"lastModifiedDate":"2021-07-22T15:12:18.885038","indexId":"70222338","displayToPublicDate":"2020-05-27T10:10:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7159,"text":"JGR Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Challenges in quantifying air-water carbon dioxide flux using estuarine water quality data: Case study for Chesapeake Bay","docAbstract":"Estuaries play an uncertain but potentially important role in the global carbon cycle via CO2 outgassing. The uncertainty mainly stems from the paucity of studies that document the full spatial and temporal variability of estuarine surface water partial pressure of carbon dioxide ( pCO2). Here, we explore the potential of utilizing the abundance of pH data from historical water quality monitoring programs to fill the data void via a case study of the mainstem Chesapeake Bay (eastern United States). We calculate pCO2 and the air-water CO2 flux at monthly resolution from 1998 to 2018 from tidal fresh to polyhaline waters, paying special attention to the error estimation. The biggest error is due to the pH measurement error, and errors due to the gas transfer velocity, temporal sampling, the alkalinity mixing model, and the organic alkalinity estimation are 72%, 27%, 15%, and 5%, respectively, of the error due to pH. Seasonal, interannual, and spatial variability in the air-water flux and surface pCO2 is high, and a correlation analysis with oxygen reveals that this variability is driven largely by biological processes. Averaged over 1998–2018, the mainstem bay is a weak net source of CO2 to the atmosphere of 1.2 (1.1, 1.4) mol m−2 yr−1 (best estimate and 95% confidence interval). Our findings suggest that the abundance of historical pH measurements in estuaries around the globe should be mined in order to constrain the large spatial and temporal variability of the CO2 exchange between estuaries and the atmosphere.
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Our response focused primarily on the United States’ Navy’s China Lake Naval Air Weapons Station base (NAWSCL). This focus was because much of the surface rupture occurred on the NAWSCL and because of NAWSCL access restrictions only permitting Federal and State of California personnel. In total, we measured or are still measuring at 24 sites, 14 of which were on the NAWSCL and, as of this writing, operational. The majority of sites were set up as continuous stations logging at either 1 sample per second or 1 sample per 15 s. Two stations were recording a 200 m cross‐rupture aperture starting ∼10 hr after the M 6.4 event, and they recorded the coseismic displacements of the M 7.1. Approximately, 1 hr after the M 7.1 event, two new stations were recording a ∼200 m cross‐rupture aperture of the surface rupture. In the days following, we established the rest of the stations ranging to a distance of ∼15 km from the M 7.1 principal rupture trace. The lack of differential displacement across the M 6.4 rupture during the M 7.1 event suggests that it did not reactivate the M 6.4 plane. The lack of differential cross‐fault displacement for both events suggests that rapid shallow afterslip did not occur at those two locations. The postseismic time series from these stations shows centimeters of horizontal displacement over periods of a few months. They record a mixture of fault‐parallel and fault‐normal displacements that, in conjunction with analysis of more spatially complete Interferometric Synthetic Aperture Radar displacement fields, suggest that both poroelastic and afterslip phenomena occur along the M 6.4 and 7.1 rupture planes. Using preliminary data from these and other regional stations, we also explore the Ridgecrest sequence’s effect on regional GNSS time series and the differentiation of long‐term postseismic motions and secular deformation rates. We find that redefining a common‐mode noise filter using different GNSS stations that are assumed to be unaffected by the earthquakes results in small but systematic differences in the regional velocity field estimate.
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