This document describes a standard operating procedure (SOP) for the preparation of purposely killed juvenile salmon, implanted with telemetry transmitters, to be released into rivers, lakes, or streams to test one of the survival model assumptions. Procedures for releases of purposely killed fish (hereinafter dead fish releases) were developed by staff from the U.S. Geological Survey’s Columbia River Research Laboratory, on the basis of laboratory experiments and practical experience with telemetry studies in the Columbia River Basin. Initially, we used extended exposure to high dose anesthetic baths to euthanize fish for dead fish releases. This approach was selected on the basis of euthanization procedures described in the literature for studies that required an effective and rapid procedure, such as stress physiology assessments. Ultimately, this technique was deemed insufficient because detection records suggested that some fish seemed to revive and continue their migration with limited effect. That is, the detection histories of dead fish were very similar to those of live fish. To overcome this challenge, we adapted our procedures to require a combination of euthanization procedures on individual fish to ensure that there was no opportunity for revival. A combination of euthanization procedures for dead fish releases was used in one study in Germany. This SOP has been used by the U.S. Geological Survey to test survival model assumptions in several field studies and has consistently performed well. In addition, limited laboratory tests were completed to ensure that no live juvenile salmon were found in holding tanks for 24 hours following the procedures described in this SOP.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201083","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Tomka, R.G., Liedtke, T.L., Frost, C., and Smith, C.D., 2020, A standard operating procedure for the preparation of purposely killed juvenile salmon used to test survival model assumptions: U.S. Geological Survey Open-File Report 2020–1083, 11 p., https://doi.org/10.3133/ofr20201083.","productDescription":"iv, 11 p.","onlineOnly":"Y","ipdsId":"IP-116988","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":376754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1083/coverthb.jpg"},{"id":376755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1083/ofr20201083.pdf","text":"Report","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1083"}],"contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The effect of nutrients on phytoplankton biomass in lakes continues to be a subject of debate by aquatic scientists. However, determining whether or not chlorophyll a (CHL) is limited by phosphorus (P) and/or nitrogen (N) is rarely considered using a probabilistic method in studies of hundreds of lakes across broad spatial extents. Several studies have applied a unified CHL-nutrient relationship to determine nutrient limitation, but pose a risk of ecological fallacy because they neglect spatial heterogeneity in ecological contexts. To examine whether or not CHL is limited by P, N, or both nutrients in hundreds of lakes and across diverse ecological settings, a probabilistic machine learning method, Bayesian Network, was applied. Spatial heterogeneity in ecological context was accommodated by the probabilistic nature of the results. We analyzed data from 1382 lakes in 17 US states to evaluate the cause-effect relationships between CHL and nutrients. Observations of CHL, total phosphorus (TP), and total nitrogen (TN) were discretized into three trophic states (oligo-mesotrophic, eutrophic, and hypereutrophic) to train the model. We found that although both nutrients were related to CHL trophic state, TP was more related to CHL than TN, especially under oligo-mesotrophic and eutrophic CHL conditions. However, when the CHL trophic state was hypereutrophic, both TP and TN were important. These results provide additional evidence that P-limitation is more likely under oligo-mesotrophic or eutrophic CHL conditions and that co-limitation of P and N occurs under hypereutrophic CHL conditions. We also found a decreasing pattern of the TN/TP ratio with increasing CHL concentrations, which might be a key driver for the role change of nutrients. Previous work performed at smaller scales support our findings, indicating potential for extension of our findings to other regions. Our findings enhance the understanding of nutrient limitation at macroscales and revealed that the current debate on the limiting nutrient might be caused by failure to consider CHL trophic state. Our findings also provide prior information for the site-specific eutrophication management of unsampled or data-limited lakes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2020.116236","usgsCitation":"Liang, Z., Soranno, P., and Wagner, T., 2020, The role of phosphorus and nitrogen on chlorophyll a: Evidence from hundreds of lakes: Water Research, v. 185, 116236, 9 p., https://doi.org/10.1016/j.watres.2020.116236.","productDescription":"116236, 9 p.","ipdsId":"IP-113421","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":455860,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2020.116236","text":"Publisher Index Page"},{"id":396014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liang, Zhongyao","contributorId":279427,"corporation":false,"usgs":false,"family":"Liang","given":"Zhongyao","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":834941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soranno, Patricia A.","contributorId":279428,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":834942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":834940,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215736,"text":"70215736 - 2020 - Integrating perspectives to understand lake ice dynamics in a changing world","interactions":[],"lastModifiedDate":"2020-10-28T13:02:25.289795","indexId":"70215736","displayToPublicDate":"2020-07-27T07:58:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Integrating perspectives to understand lake ice dynamics in a changing world","docAbstract":"Ice cover plays a critical role in physical, biogeochemical, and ecological processes in lakes. Despite its importance, winter limnology remains relatively understudied. Here, we provide a primer on the predominant drivers of freshwater lake ice cover and the current methodologies used to study lake ice, including in situ and remote sensing observations, physical based models, and experiments. We highlight opportunities for future research by integrating these four disciplines to address key knowledge gaps in our understanding of lake ice dynamics in changing winters. Advances in technology, data integration, and interdisciplinary collaboration will allow the field to move toward developing global forecasts of lake ice cover for small to large lakes across broad spatial and temporal scales, quantifying ice quality and ice thickness, moving from binary to continuous ice records, and determining how winter ice conditions and quality impact ecosystem processes in lakes over winter. Ultimately, integrating disciplines will improve our ability to understand the impacts of changing winters on lake ice.
The Winnebago Pool is a chain of 4 shallow lakes in Wisconsin. Because of high external phosphorus (P) inputs to the lakes, the lakes became highly eutrophic, with much P contained in their sediments. In developing a total maximum daily load (TMDL) for these lakes, it is important to determine how their phosphorus concentrations should respond to changes in external P loading. In many TMDLs, internal P loading is assumed to be negligible or it is estimated based on sediment release rates and dissolved oxygen conditions in the lake, and each lake is considered independently. To evaluate these assumptions, internal P loading and external P loading were quantified by developing detailed P budgets for the Winnebago Pool chain of lakes. This information was then inputted into 2 eutrophication models (BATHTUB and Jensen models), which were used to simulate the steady-state and transient effects of various P reduction strategies. The importance of internal P loading varied among lakes, from being a minor source to representing almost 60% of the summer P input. Model results indicate that each lake responds to external P reductions, but internal loading can delay the lake responses, especially in the most downstream lake, Lake Winnebago, where internal P loading was most important to its summer P budget. Accurately quantifying net internal P loading and using this information in lake models are important in evaluating how large shallow lakes should respond to P reduction strategies, setting realistic expectations from watershed P reductions, and guiding TMDL efforts.
In order to comply with a current U.S. Environmental Protection Agency watershed-based National Pollutant Discharge Elimination System permit, the City of Albuquerque required a better understanding of the rainfall-runoff processes in its small urban watersheds. That requirement prompted the initiation of the assessment of three existing watershed models that were developed to simulate those processes. Three existing rainfall-runoff modeling software packages—Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) (using two sets of methods), Program for Predicting Polluting Particle Passage Through Pits, Puddles, and Ponds (P8), and Arid-Lands Hydrologic Model (AHYMO)—were compared to determine which provided the best balance of accuracy and usability for simulating storm runoff in small watersheds in the urbanized area of Albuquerque, New Mexico. Additionally, results of this study could help inform model users who have interest in simulating storm runoff in similar urban areas throughout the United States. Each model was used to simulate storm runoff in the Hahn Arroyo watershed, an urbanized watershed with concrete-lined arroyo channels in the northeastern quadrant of Albuquerque that exhibits flashy, monsoonal-driven storm runoff. Model results were compared to observed discharge data, according to literature-recommended performance measures and performance evaluation criteria. The HEC-HMS model using the Soil Conservation Service (SCS) curve number (CN) and SCS unit hydrograph methods ranked the highest when averaging the individual performance measures (Nash-Sutcliffe Efficiency, percent bias, and coefficient of determination) rankings together across the hourly calibration and validation periods, followed by P8, which was tied with the HEC-HMS initial and constant approach. For daily rankings using the same rank-averaging approach, the HEC-HMS CN-based model and P8 were tied for the highest ranking, followed by the HEC-HMS initial and constant approach. Alternatively, rating performance using validation period results as an indication of the expected confidence in forecasted results for future conditions, the P8 model performed best for both hourly and daily time-steps, followed by the HEC-HMS CN-based model and the HEC-HMS initial and constant-based model. However, based on the literature performance evaluation criteria, the HEC-HMS and P8 models overall had marginally satisfactory performance only for operation at the daily time-step. Direct comparison of the HEC-HMS and P8 models to the AHYMO is difficult, given the different performance assessment criteria used to assess these models separately in this study, as recommended by the literature. The AHYMO results generally lacked precision, given the wide range in the performance assessment values across events in percent error in peak discharge, difference in timing of peak discharge, percent error in total runoff volume, and difference in duration of event relative to observed data. For some events, however, the AHYMO results were fairly accurate, and AHYMO was likely a good predictor of the timing of storm runoff and the shape of the hydrograph. This study did not assess the results for all potential applications of the models in the Albuquerque urbanized area. Further study may be required to assess the model performance capabilities in other modeling applications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205058","collaboration":"Prepared in cooperation with the City of Albuquerque","usgsCitation":"Shephard, Z.M., and Douglas-Mankin, K.R., 2020, Comparison of storm runoff models for a small watershed in an urban metropolitan area, Albuquerque, New Mexico: U.S. Geological Survey Scientific Investigations Report 2020–5058, 30 p., https://doi.org/10.3133/sir20205058.","productDescription":"Report: viii, 30 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-113457","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":376561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5058/coverthb.jpg"},{"id":376562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5058/sir20205058.pdf","text":"Report","size":"1.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5058"},{"id":376563,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P930WKCH","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input and output data used to compare storm runoff models for a small watershed in an urban metropolitan area, Albuquerque, New Mexico"}],"country":"United States","state":"New Mexico","city":"Albuquerque","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.71844482421874,\n 35.08395557927643\n ],\n [\n -106.44996643066406,\n 35.08395557927643\n ],\n [\n -106.45545959472653,\n 35.19345038573419\n ],\n [\n -106.66694641113278,\n 35.19232810975203\n ],\n [\n -106.71844482421874,\n 35.08395557927643\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113
The Chattahoochee River National Recreation Area (CRNRA) is a National Park Service unit/park with 48 miles of urban waterway in the Atlanta metropolitan area. The Chattahoochee River within the CRNRA is a popular place for water-based recreation but is known to periodically experience elevated levels of fecal-coliform bacteria associated with warm-blooded animals that can result in a variety of pathogen-related human illnesses. In 2000, the National Park Service entered into a public-private partnership with the U.S. Geological Survey (USGS) and the Chattahoochee Riverkeeper, called the Chattahoochee River BacteriALERT program, to monitor Escherichia coli (E. coli), which is a fecal indicator bacteria and a proxy for human health risk from waterborne pathogens. The BacteriALERT network monitors E. coli densities at three stations on the Chattahoochee River within the CRNRA, at Norcross (USGS station 02335000), Powers Ferry (USGS station 02335880), and Atlanta (USGS station 02336000). E. coli densities determined from water samples were compared to the U.S. Environmental Protection Agency’s Beach Action Value (BAV) of 235 colony forming units per 100 milliliters to assess whether conditions were considered safe for freshwater, primary contact recreational use. Sample E. coli densities exceeded the BAV for 15.5 percent of the samples collected at Norcross (n = 1,969) and 30.3 percent of the samples at Atlanta (n = 1,938) for the study period October 23, 2000, to May 23, 2019, and 33.6 percent of the samples from Powers Ferry (n = 134) for the study period May 5, 2016, to May 23, 2019.
Models to predict E. coli densities in near real-time were developed for the three BacteriALERT stations. Models were developed using forward-stepwise multiple linear regression with the Bayesian Information Criteria and were calibrated with samples collected between October 4, 2007, and May 23, 2019. Explanatory variables included season, turbidity, water temperature, streamflow, upstream tributary streamflows, and temporal trend. The most statistically significant explanatory variables in the models were turbidity, upstream tributary streamflows, and season. The Norcross model had an increasing trend in E. coli densities of 2.3 percent per year. A significant trend was not detected for the Atlanta station, while trends were not assessed for Powers Ferry models due to the short (3-year) calibration period. Model adjusted R2s ranged from 0.686 (Atlanta) to 0.795 (Norcross with time trend) indicating that the models explained a substantial portion of the variations in E. coli densities. Evaluation of model predictions and residuals indicated that models were well posed and exhibited little bias. The models performed well in accurately determining compliance and exceedance of the BAV with low misidentification rates ranging from 3.5 percent (Norcross) to 11.3 percent (Powers Ferry). Misidentification was most common for densities near the BAV, and misidentification rates in the study were low despite fairly low model precisions because E. coli densities were infrequently near the BAV. The precisions of the models developed herein were comparable to the more complex models developed by Lawrence (2012) that were never implemented in the BacteriALERT program due to their computational complexity. The predictive E. coli models developed herein will improve the ability to assess the health risks of water-based recreational activities in the CRNRA in near real-time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201048","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Aulenbach, B.T., and McKee, A.M., 2020, Monitoring and real-time modeling of Escherichia coli bacteria for the Chattahoochee River, Chattahoochee River National Recreation Area, Georgia, 2000–2019: U.S. Geological Survey Open-File Report 2020–1048, 43 p., https://doi.org/10.3133/ofr20201048.","productDescription":"x, 43 p.","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-113124","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":376643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1048/coverthb.jpg"},{"id":376663,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1048/ofr20201048.pdf","text":"Report","size":"5.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1048"}],"country":"United States","state":"Georgia","city":"Atlanta","otherGeospatial":"Chattahoochee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.1168212890625,\n 34.17772537282446\n ],\n [\n -84.48211669921875,\n 34.01396527491264\n ],\n [\n -84.44915771484375,\n 33.747180448149855\n ],\n [\n -84.29809570312499,\n 33.76544869849223\n ],\n [\n -84.1387939453125,\n 33.902336404480685\n ],\n [\n -84.0509033203125,\n 34.075412438417395\n ],\n [\n -84.04541015625,\n 34.14363482031264\n ],\n [\n -84.1168212890625,\n 34.17772537282446\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Plant associations with soil microbiota can modulate tree seedling growth and survival via mutualistic or antagonistic interactions. It is uncertain, however, whether soil microbiota influence seedling growth of coastal trees when exposed to extreme flooding regimes. We evaluated the role of soil microbes in promoting baldcypress (Taxodium distichum) seedling performance under different inundation scenarios and determined the influence of flooding on the colonization of in planta beneficial microbes. Seedlings reared in sterile and non-sterile soil were exposed to three different flooding regimes historically experienced in Louisiana swamps. Seedling growth was assessed, and the colonization by beneficial symbionts such as arbuscular mycorrizhal fungi (AMF), and dark septate endophytes (DSE) was evaluated in harvested roots. Seedlings grown in sterile soil had six times higher growth than seedlings reared in non-sterile soil. As a result, we evaluated pathogen load in the roots by assessing oomycete colonization. Flooding influenced the in planta colonization of DSE and oomycetes, but did not affect the colonization of mutualist AMF fungi. DSE and oomycetes were rarer in flooded conditions, while AMF remained abundant. Seedling biomass production was not correlated with in planta fungal colonization or pathogen load. Soil microbiota can negatively influence baldcypress seedling growth, and no growth benefit was evidenced from the root colonization of mutualist fungi. Flooding can modify baldcypress-fungal interactions by diminishing colonization of DSE. Overall, baldycpress seedlings were more sensitive to the presence of microbiota than flooding, and thus restoration efforts should focus on having a better understanding of plant–microbe interactions in swamps.
","language":"English","publisher":"Springer","doi":"10.1007/s11258-020-01059-4","usgsCitation":"Torres-Martinez, L., Sanchez-Julia, M., Kimbrough, E., Hendrix, T., Hendrix, M., Day, R.H., Krauss, K.W., and Van Bael, S.A., 2020, Influence of soil microbiota on Taxodium distichum seedling performance during extreme flooding events: Plant Ecology, v. 221, p. 773-793, https://doi.org/10.1007/s11258-020-01059-4.","productDescription":"21 p.","startPage":"773","endPage":"793","ipdsId":"IP-101293","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":376749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Bayou Chevreuil","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.75153350830078,\n 29.881136828132842\n ],\n [\n -90.5990982055664,\n 29.881136828132842\n ],\n [\n -90.5990982055664,\n 29.912090918781505\n ],\n [\n -90.75153350830078,\n 29.912090918781505\n ],\n [\n -90.75153350830078,\n 29.881136828132842\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"221","noUsgsAuthors":false,"publicationDate":"2020-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Torres-Martinez, Lorena 0000-0002-0903-8633","orcid":"https://orcid.org/0000-0002-0903-8633","contributorId":229687,"corporation":false,"usgs":false,"family":"Torres-Martinez","given":"Lorena","email":"","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":793955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanchez-Julia, Mareli","contributorId":229688,"corporation":false,"usgs":false,"family":"Sanchez-Julia","given":"Mareli","email":"","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":793956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kimbrough, Elizabeth 0000-0002-4007-6304","orcid":"https://orcid.org/0000-0002-4007-6304","contributorId":228831,"corporation":false,"usgs":false,"family":"Kimbrough","given":"Elizabeth","email":"","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":793957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hendrix, Trey","contributorId":229689,"corporation":false,"usgs":false,"family":"Hendrix","given":"Trey","email":"","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":793958,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hendrix, Miranda","contributorId":229690,"corporation":false,"usgs":false,"family":"Hendrix","given":"Miranda","email":"","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":793959,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Day, Richard H. 0000-0002-5959-7054 dayr@usgs.gov","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":2427,"corporation":false,"usgs":true,"family":"Day","given":"Richard","email":"dayr@usgs.gov","middleInitial":"H.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":793960,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":793961,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Van Bael, Sunshine A 0000-0001-7317-3533","orcid":"https://orcid.org/0000-0001-7317-3533","contributorId":228832,"corporation":false,"usgs":false,"family":"Van Bael","given":"Sunshine","email":"","middleInitial":"A","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":793962,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211309,"text":"70211309 - 2020 - The utility of zooarchaeological data to guide listing efforts for an imperiled mussel species (Bivalvia: Unionidae: Pleurobema riddellii)","interactions":[],"lastModifiedDate":"2023-03-27T17:18:32.279224","indexId":"70211309","displayToPublicDate":"2020-07-22T09:19:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The utility of zooarchaeological data to guide listing efforts for an imperiled mussel species (Bivalvia: Unionidae: Pleurobema riddellii)","title":"The utility of zooarchaeological data to guide listing efforts for an imperiled mussel species (Bivalvia: Unionidae: Pleurobema riddellii)","docAbstract":"The status of species in freshwater systems shift over time due to natural and anthropogenic causes. Determining the magnitude and cause of these shifts requires a long-term perspective. This process is complicated when there are also questions about the taxonomic validity of a species. Addressing these issues is important because both can undermine conservation and management efforts if incorrect. Pleurobema riddellii, Louisiana Pigtoe, is under review for protection under the U.S. Endangered Species Act, but its status in the Trinity River basin, where the taxon was described, remains in doubt due to questions about its taxonomy and occurrence within this basin. To address these questions, we compared shell morphometrics of P. riddellii dating to the late Holocene with modern P. riddellii, late Holocene Fusconaia sp., and modern Fusconaia sp. using multivariate analyses to test associations between the putative morphotypes. Based on these analyses, we demonstrate that P. riddellii was likely present in the Trinity during the late Holocene, which indicates questions about its taxonomic validity or presence in this basin are unfounded. Our study further highlights the role zooarchaeological studies can play in status assessments and their utility in better understanding biogeographic patterns for rare species.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.253","usgsCitation":"Randklev, C.R., Wolverton, S., Johnson, N., Smith, C.H., DuBose, T., Robertson, C., and Conley, J., 2020, The utility of zooarchaeological data to guide listing efforts for an imperiled mussel species (Bivalvia: Unionidae: Pleurobema riddellii): Conservation Science and Practice, v. 2, no. 9, e253, 12 p., https://doi.org/10.1111/csp2.253.","productDescription":"e253, 12 p.","ipdsId":"IP-113751","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455913,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.253","text":"Publisher Index Page"},{"id":376664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Trinity River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.119140625,\n 31.090574094954192\n ],\n [\n -94.39453125,\n 31.090574094954192\n ],\n [\n -94.39453125,\n 33.7243396617476\n ],\n [\n -97.119140625,\n 33.7243396617476\n ],\n [\n -97.119140625,\n 31.090574094954192\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Randklev, Charles R.","contributorId":202530,"corporation":false,"usgs":false,"family":"Randklev","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":793687,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolverton, Steve","contributorId":229617,"corporation":false,"usgs":false,"family":"Wolverton","given":"Steve","email":"","affiliations":[{"id":34637,"text":"University of North Texas","active":true,"usgs":false}],"preferred":false,"id":793688,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nathan A. 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":218986,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":793689,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Chase H. 0000-0002-1499-0311","orcid":"https://orcid.org/0000-0002-1499-0311","contributorId":225140,"corporation":false,"usgs":false,"family":"Smith","given":"Chase","email":"","middleInitial":"H.","affiliations":[{"id":13716,"text":"Baylor University","active":true,"usgs":false}],"preferred":false,"id":793690,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DuBose, Traci","contributorId":229618,"corporation":false,"usgs":false,"family":"DuBose","given":"Traci","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":793691,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Robertson, Clint","contributorId":206217,"corporation":false,"usgs":false,"family":"Robertson","given":"Clint","affiliations":[{"id":37288,"text":"Texas Parks and Wildife","active":true,"usgs":false}],"preferred":false,"id":793692,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Conley, Julian","contributorId":229619,"corporation":false,"usgs":false,"family":"Conley","given":"Julian","email":"","affiliations":[{"id":41695,"text":"Eastern Tennessee State University","active":true,"usgs":false}],"preferred":false,"id":793693,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211243,"text":"70211243 - 2020 - The chemostratigraphy of the Murray formation and role of diagenesis at Vera Rubin ridge in Gale crater, Mars, as observed by the ChemCam instrument","interactions":[],"lastModifiedDate":"2020-09-23T15:45:28.995398","indexId":"70211243","displayToPublicDate":"2020-07-21T13:08:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5718,"text":"Journal of Geophysical Research: Planets","onlineIssn":"2169-9100","active":true,"publicationSubtype":{"id":10}},"title":"The chemostratigraphy of the Murray formation and role of diagenesis at Vera Rubin ridge in Gale crater, Mars, as observed by the ChemCam instrument","docAbstract":"Geochemical results are presented from Curiosity’s exploration of the Vera Rubin ridge (VRR), in addition to the full chemostratigraphy of the predominantly lacustrine mudstone Murray formation up to and including VRR. VRR is a prominent ridge flanking Aeolis Mons (informally Mt. Sharp), the central mound in Gale crater, Mars, and was a key area of interest for the Mars Science Laboratory mission. ChemCam data show that VRR is overall geochemically similar to lower-lying members of the Murray formation, even though the top of VRR is characterized by strong hematite spectral signatures as observed from orbit. While overall geochemically similar, VRR is characterized by a prominent decrease in Li abundance and Chemical Index of Alteration across the ridge. This decrease follows the morphology of the ridge rather than elevation and is inferred to reflect a non-depositionally controlled decrease in clay mineral abundance in VRR rocks. Additionally, a notable enrichment in Mn above baseline levels is observed on VRR. While not supporting a single model, the results suggest that VRR rocks were likely affected by multiple episodes of post-depositional groundwater interactions that made them more erosionally resistant than surrounding Murray rocks – thus resulting in the modern-day ridge after subsequent erosion.","language":"English","publisher":"Wiley","doi":"10.1029/2019JE006320","usgsCitation":"Frydenvang, J., Mangold, N., Wiens, R.C., Fraeman, A.A., Edgar, L.A., Fedo, C.M., L’Haridon, J., Bedford, C.C., Gupta, S., Grotzinger, J.P., Bridges, J.C., Clark, B.C., Rampe, E.B., Gasnaut, O., Maurice, S., Gasda, P.J., Lanza, N.L., Olilla, A.M., Meslin, P., Payre, V., Calef, F.J., Salvatore, M.R., and House, C.H., 2020, The chemostratigraphy of the Murray formation and role of diagenesis at Vera Rubin ridge in Gale crater, Mars, as observed by the ChemCam instrument: Journal of Geophysical Research: Planets, v. 125, no. 9, e2019JE006320, 27 p., https://doi.org/10.1029/2019JE006320.","productDescription":"e2019JE006320, 27 p.","ipdsId":"IP-114161","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":455924,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://researchprofiles.ku.dk/da/publications/886dac4a-2433-4e47-b184-f1e458d94940","text":"External Repository"},{"id":376567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"125","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Frydenvang, Jens","contributorId":173225,"corporation":false,"usgs":false,"family":"Frydenvang","given":"Jens","email":"","affiliations":[{"id":27196,"text":"LANL","active":true,"usgs":false}],"preferred":false,"id":793367,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mangold, Nicholas","contributorId":195301,"corporation":false,"usgs":false,"family":"Mangold","given":"Nicholas","email":"","affiliations":[],"preferred":false,"id":793368,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiens, Roger C.","contributorId":140330,"corporation":false,"usgs":false,"family":"Wiens","given":"Roger","email":"","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":793369,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fraeman, Abigail A.","contributorId":200404,"corporation":false,"usgs":false,"family":"Fraeman","given":"Abigail","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":793370,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edgar, Lauren A. 0000-0001-7512-7813 ledgar@usgs.gov","orcid":"https://orcid.org/0000-0001-7512-7813","contributorId":167501,"corporation":false,"usgs":true,"family":"Edgar","given":"Lauren","email":"ledgar@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":793371,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fedo, Christopher M.","contributorId":229497,"corporation":false,"usgs":false,"family":"Fedo","given":"Christopher","email":"","middleInitial":"M.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":793372,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"L’Haridon, Jonas","contributorId":229498,"corporation":false,"usgs":false,"family":"L’Haridon","given":"Jonas","email":"","affiliations":[{"id":41660,"text":"Université de Nantes","active":true,"usgs":false}],"preferred":false,"id":793373,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bedford, Candice C.","contributorId":229499,"corporation":false,"usgs":false,"family":"Bedford","given":"Candice","email":"","middleInitial":"C.","affiliations":[{"id":12445,"text":"Lunar and Planetary Institute","active":true,"usgs":false}],"preferred":false,"id":793374,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gupta, Sanjeev","contributorId":172302,"corporation":false,"usgs":false,"family":"Gupta","given":"Sanjeev","email":"","affiliations":[{"id":24608,"text":"Imperial College London","active":true,"usgs":false}],"preferred":false,"id":793375,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Grotzinger, John P.","contributorId":58011,"corporation":false,"usgs":false,"family":"Grotzinger","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":7218,"text":"California Institute of 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H","contributorId":229504,"corporation":false,"usgs":false,"family":"House","given":"Christopher","email":"","middleInitial":"H","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":793389,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}} ,{"id":70211089,"text":"sir20205062 - 2020 - Discharge and dissolved-solids characteristics and trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018","interactions":[],"lastModifiedDate":"2020-07-22T13:53:50.908568","indexId":"sir20205062","displayToPublicDate":"2020-07-21T12:57:13","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":"2020-5062","displayTitle":"Discharge and Dissolved-Solids Characteristics and Trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018","title":"Discharge and dissolved-solids characteristics and trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018","docAbstract":"The headwaters of the Snake River are in the mountains of northwestern Wyoming. Maintaining the recognized high quality of water in Grand Teton National Park is a National Park Service (NPS) priority. To characterize and understand the water resources of Grand Teton National Park, the NPS established a monitoring program to monitor the quality of area surface waters. Beginning in 2006, water was sampled by the NPS and analyzed for a range of chemical species at the Snake River above Jackson Lake at Flagg Ranch streamgage 13010065 (hereafter referred to as “Snake River at Flagg Ranch”), a site where the U.S. Geological Survey (USGS) previously sampled and analyzed water from 1986 through 2004. The USGS, in cooperation with the NPS, evaluated water-quality data collected by both entities to determine if discharge and total dissolved solids (referred to as dissolved solids) have changed in the Snake River at the Flagg Ranch.
To understand potential changes with time in dissolved solids, discharge was analyzed between January 1986 and December 2018, which corresponds with the time period when water-quality data were collected. Mean annual discharge varied during this time, with high, low, mean, and median flows generally increasing from 1986 through 1998, decreasing through 2005, and then generally increasing through 2018.
Combining water-quality data collected by the USGS and NPS provides a longer, more complete dataset for analyses. During the period of time when NPS was the sampling agency, specific conductance data were collected, but dissolved-solids data were not. The specific conductance data from both agencies were evaluated to determine if combining the data was justified. The interquartile ranges of data collected by both agencies are similar, and rapid, large changes in values during the period of transition between USGS and NPS sampling do not occur. The USGS and NPS datasets are not statistically different in the spring, summer, or fall, but are statistically different in the winter. The winter differences could be a function of the lack of wintertime NPS sampling, which excludes higher-concentration, lower-discharge data or a function of changes in the actual concentration in the stream. Although there is some difference in the winter datasets, the similarity in sampling methods and general overall data characteristics justifies combining the data for trend analyses.
Because the dissolved-solids parameter is useful for managers, it is often calculated from specific conductance using a linear regression model when dissolved-solids data are absent. For this study, creating a modeled dataset of dissolved solids for the NPS data collection period of time provided a longer, more complete dataset of dissolved-solids concentrations.
The concentrations of dissolved solids over time are identified by season and indicate that samples collected in the fall and winter have higher concentrations than samples collected in spring and summer. Specifically, the mean dissolved-solids concentrations in fall and winter are around 188 milligrams per liter (mg/L), whereas the mean concentrations are around 130 mg/L in spring and summer. This difference is generally attributed to the dilution of spring and summer samples by snowmelt generated runoff during the high-flow period of the year.
Trend analyses of dissolved-solids concentrations and loads indicate that an upward trend in concentration from 1986 to 2018 is likely, and a downward trend in load is highly likely. Comparing 1986 to 2018, dissolved-solids concentration is estimated to have increased by 2.25 mg/L (1.4 percent). During that same period, the dissolved-solids load is estimated to have decreased 11.8 million kilograms per year (12-percent decrease). This decrease is consistent with the estimated decrease in annual mean of daily mean discharge. Because 10 percent of the total change in dissolved-solids load is related to a change in the concentration-discharge relationship and 2 percent is related to changes in discharge, the decreased load is related less to changes in discharge and more to landscape scale processes that are affecting the concentration-discharge relationship.
As noted above, the data collected by the USGS and NPS are generally comparable with regards to sampling and analytical methods, and data collected by both agencies were used as one dataset for trend analyses. The current NPS sampling schedule, however, is creating a dataset biased towards lower concentration dissolved-solids data, which occurs during higher summer flows, by only sampling during April through November. From 1986 to 2018, the percentage of NPS samples is small enough that the effect on trends is expected to be minimal. Because of the importance of low flow (winter season) data, it is likely that an April through November sampling regime may affect the ability to detect trends or determine seasonality in the future. Collection of winter data in particular is important based on the findings that the changes in the modeled concentration-discharge relationship over time have been most pronounced during the winter (represented by February) months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205062","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Miller, O.L., and Eddy-Miller, C.A., 2020, Discharge and dissolved-solids characteristics and trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018: U.S. Geological Survey Scientific Investigations Report 2020–5062, 19 p., https://doi.org/10.3133/sir20205062.","productDescription":"vi, 19 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-116863","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":376357,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5062/coverthb.jpg"},{"id":376358,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5062/sir20205062.pdf","text":"Report","size":"2.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5062"}],"country":"United States","state":"Wyoming","otherGeospatial":"Flagg Ranch watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.05804443359375,\n 44.081666311450526\n ],\n [\n -110.23681640625,\n 44.081666311450526\n ],\n [\n -110.23681640625,\n 44.457309801319305\n ],\n [\n -111.05804443359375,\n 44.457309801319305\n ],\n [\n -111.05804443359375,\n 44.081666311450526\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Boseman Avenue
Helena, MT 59601
Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
West Valley City, UT 84119–2047
The 2019 Ridgecrest sequence provides the first opportunity to evaluate Uniform California Earthquake Rupture Forecast v.3 with epidemic‐type aftershock sequences (UCERF3‐ETAS) in a pseudoprospective sense. For comparison, we include a version of the model without explicit faults more closely mimicking traditional ETAS models (UCERF3‐NoFaults). We evaluate the forecasts with new metrics developed within the Collaboratory for the Study of Earthquake Predictability (CSEP). The metrics consider synthetic catalogs simulated by the models rather than synoptic probability maps, thereby relaxing the Poisson assumption of previous CSEP tests. Our approach compares statistics from the synthetic catalogs directly against observations, providing a flexible approach that can account for dependencies and uncertainties encoded in the models. We find that, to the first order, both UCERF3‐ETAS and UCERF3‐NoFaults approximately capture the spatiotemporal evolution of the Ridgecrest sequence, adding to the growing body of evidence that ETAS models can be informative forecasting tools. However, we also find that both models mildly overpredict the seismicity rate, on average, aggregated over the evaluation period. More severe testing indicates the overpredictions occur too often for observations to be statistically indistinguishable from the model. Magnitude tests indicate that the models do not include enough variability in forecasted magnitude‐number distributions to match the data. Spatial tests highlight discrepancies between the forecasts and observations, but the greatest differences between the two models appear when aftershocks occur on modeled UCERF3‐ETAS faults. Therefore, any predictability associated with embedding earthquake triggering on the (modeled) fault network may only crystalize during the presumably rare sequences with aftershocks on these faults. Accounting for uncertainty in the model parameters could improve test results during future experiments.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200026","usgsCitation":"Savran, W.J., Werner, M.J., Marzocchi, W., Rhoades, D.A., Jackson, D., Milner, K.R., Field, E., and Michael, A.J., 2020, Pseudo-prospective evaluation of UCERF3-ETAS forecasts during the 2019 Ridgecrest sequence: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1799-1817, https://doi.org/10.1785/0120200026.","productDescription":"19 p.","startPage":"1799","endPage":"1817","ipdsId":"IP-119947","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":455927,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research-information.bris.ac.uk/en/publications/df68ca9c-ddc4-4173-90ae-2483322e4b51","text":"External Repository"},{"id":377692,"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 -118.32275390624999,\n 35.24561909420681\n ],\n [\n -117.1636962890625,\n 35.24561909420681\n ],\n [\n -117.1636962890625,\n 36.02244668175846\n ],\n [\n -118.32275390624999,\n 36.02244668175846\n ],\n [\n -118.32275390624999,\n 35.24561909420681\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Savran, William J.","contributorId":238831,"corporation":false,"usgs":false,"family":"Savran","given":"William","email":"","middleInitial":"J.","affiliations":[{"id":47795,"text":"USC","active":true,"usgs":false}],"preferred":false,"id":796655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Werner, Maximillian J.","contributorId":211807,"corporation":false,"usgs":false,"family":"Werner","given":"Maximillian","email":"","middleInitial":"J.","affiliations":[{"id":38325,"text":"University of Bristol, UK","active":true,"usgs":false}],"preferred":false,"id":796656,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marzocchi, W.","contributorId":238499,"corporation":false,"usgs":false,"family":"Marzocchi","given":"W.","affiliations":[{"id":47714,"text":"University of Naples","active":true,"usgs":false}],"preferred":false,"id":796657,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rhoades, David A.","contributorId":238832,"corporation":false,"usgs":false,"family":"Rhoades","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":47796,"text":"GNS, New Zealand","active":true,"usgs":false}],"preferred":false,"id":796658,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackson, David D.","contributorId":238833,"corporation":false,"usgs":false,"family":"Jackson","given":"David D.","affiliations":[{"id":47797,"text":"University of California at Los Angeles","active":true,"usgs":false}],"preferred":false,"id":796659,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Milner, Kevin R.","contributorId":194141,"corporation":false,"usgs":false,"family":"Milner","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":796660,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":1165,"corporation":false,"usgs":true,"family":"Field","given":"Edward H.","email":"field@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":796661,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Michael, Andrew J. 0000-0002-2403-5019 michael@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-5019","contributorId":1280,"corporation":false,"usgs":true,"family":"Michael","given":"Andrew","email":"michael@usgs.gov","middleInitial":"J.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":796662,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211348,"text":"70211348 - 2020 - Evidence of previous faulting along the 2019 Ridgecrest, California earthquake ruptures","interactions":[],"lastModifiedDate":"2020-08-26T19:26:41.428295","indexId":"70211348","displayToPublicDate":"2020-07-21T11:43:24","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":"Evidence of previous faulting along the 2019 Ridgecrest, California earthquake ruptures","docAbstract":"The July 2019 Ridgecrest earthquake sequence in southeastern California was characterized as surprising because only ~35% of the rupture occurred on previously mapped faults. Employing more detailed inspection of pre-event high-resolution topography and imagery in combination with field observations, we document evidence of active faulting in the landscape along the entire fault system. Scarps, deflected drainages, and lineaments and contrasts in topography, vegetation, and ground color demonstrate previous slip on a dense network of orthogonal faults, consistent with patterns of surface rupture observed in 2019. Not all of these newly mapped fault strands ruptured in 2019. Outcrop-scale field observations additionally reveal tufa lineaments and sheared Quaternary deposits. Neotectonic features are commonly short (<2 km), discontinuous, and display en echelon patterns along both the M 6.4 and M 7.1 ruptures. These features are generally more prominent and better preserved outside the late Pleistocene lake basins. Fault expression may also be related to deformation style: scarps and topographic lineaments are more prevalent in areas where substantial vertical motion occurred in 2019. Where strike-slip displacement dominated in 2019, the faults are mainly expressed by less prominent tonal and vegetation features. Both the NE- and NW-trending active fault systems are subparallel to regional bedrock fabrics that were established as early as ~150 Ma, and may be reactivating these older structures. Overall, we estimate that 50-70% (i.e., an additional 15-35%) of the 2019 surface ruptures could have been recognized as active faults with detailed inspection of pre-event data. Similar detailed mapping of potential neotectonic features could help improve seismic hazard analyses in other regions of eastern California and elsewhere that have distributed faulting or incompletely mapped faults. In areas where faults cannot be resolved as single thoroughgoing structures, a zone of potential faulting should be used as a hazard model input.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200041","usgsCitation":"Thompson Jobe, J., Philibosian, B.E., Chupik, C., Dawson, T.E., Bennett, S.E., Gold, R.D., DuRoss, C., Ladinsky, T.C., Kendrick, K.J., Haddon, E., Pierce, I., Swanson, B.J., and Seitz, G., 2020, Evidence of previous faulting along the 2019 Ridgecrest, California earthquake ruptures: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1427-1456, https://doi.org/10.1785/0120200041.","productDescription":"30 p.","startPage":"1427","endPage":"1456","ipdsId":"IP-115636","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":436866,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ENA24Y","text":"USGS data release","linkHelpText":"Pre-existing features associated with active faulting in the vicinity of the 2019 Ridgecrest, California earthquake sequence"},{"id":376748,"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 -119.30603027343749,\n 34.46127728843705\n ],\n [\n -116.49902343749999,\n 34.46127728843705\n ],\n [\n -116.49902343749999,\n 36.59788913307022\n ],\n [\n -119.30603027343749,\n 36.59788913307022\n ],\n [\n -119.30603027343749,\n 34.46127728843705\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson Jobe, Jessica 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":225113,"corporation":false,"usgs":false,"family":"Thompson Jobe","given":"Jessica","email":"","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":793963,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":793964,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chupik, Colin","contributorId":217357,"corporation":false,"usgs":false,"family":"Chupik","given":"Colin","email":"","affiliations":[{"id":39606,"text":"Univ. of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":793965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dawson, Timothy E.","contributorId":24429,"corporation":false,"usgs":false,"family":"Dawson","given":"Timothy","email":"","middleInitial":"E.","affiliations":[{"id":7099,"text":"Calif. 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Since ∼1906, the surface ruptures have been largely covered by water, but with water levels at near‐historic low levels in 2008–2011, we observed that the 1906 surface ruptures were no longer visible. As a fault imaging test, we acquired refraction tomography and guided‐wave data across the 1906 surface ruptures in 2011. We found that individual fault traces, as mapped by Schussler (1906), can be identified on the basis of discrete low‐velocity zones (VS and VP, reduced ∼40% and ∼34%, respectively) and high‐amplitude guided waves. Guided waves have traditionally been observed as large‐amplitude waveforms over wide (hundreds of meters to kilometers) zones of faulting, but we demonstrate that by evaluating guided waves (including Rayleigh/Love‐ and P/SV‐types) in terms of peak ground velocity (PGV), individual near‐surface fault traces within a fault zone can be precisely located, even more than 100 yr after the surface ruptures. Such precise exploration can be used to focus paleoseismic trenching efforts and to identify or exclude faulting at specific sites. We evaluated PGV of both S‐wave‐type and Fϕ‐mode‐type guided waves and found that both wave types can be used to identify subsurface fault traces. At San Andreas Lake (main fault), S‐wave‐type guided waves travel up to 18% slower than S body waves, and Fϕ‐mode guided waves travel ∼60% slower than P body waves but ∼15% faster than S body waves. We found that guided‐wave amplitudes vary with frequency but are up to five times higher than those of body waves, including the S wave. Our data are consistent with the concept that guided waves can be a strong‐shaking hazard during large‐magnitude earthquakes.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200136","usgsCitation":"Catchings, R.D., Rymer, M., and Goldman, M., 2020, San Andreas fault exploration using refraction tomography and S-wave-type and Fϕ-mode guided waves: Bulletin of the Seismological Society of America, v. 110, no. 6, p. 3088-3102, https://doi.org/10.1785/0120200136.","productDescription":"15 p.","startPage":"3088","endPage":"3102","ipdsId":"IP-102153","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Calfornia","otherGeospatial":"San Andreas fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.44607249804032,\n 37.61113945668713\n ],\n [\n -122.44607249804032,\n 37.57485979697452\n ],\n [\n -122.39941099606784,\n 37.57485979697452\n ],\n [\n -122.39941099606784,\n 37.61113945668713\n ],\n [\n -122.44607249804032,\n 37.61113945668713\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rymer, Michael 0000-0002-5429-5073 mrymer@usgs.gov","orcid":"https://orcid.org/0000-0002-5429-5073","contributorId":220757,"corporation":false,"usgs":true,"family":"Rymer","given":"Michael","email":"mrymer@usgs.gov","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldman, Mark 0000-0002-0802-829X","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":205863,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927566,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211329,"text":"70211329 - 2020 - Conservative plumage masks extraordinary phylogenetic diversity in the Grallaria rufula (Rufous Antpitta) complex of the humid Andes","interactions":[],"lastModifiedDate":"2020-07-27T14:29:15.75271","indexId":"70211329","displayToPublicDate":"2020-07-21T09:19:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5981,"text":"Auk","active":true,"publicationSubtype":{"id":10}},"title":"Conservative plumage masks extraordinary phylogenetic diversity in the Grallaria rufula (Rufous Antpitta) complex of the humid Andes","docAbstract":"The Grallaria rufula complex is currently considered to consist of 2 species, G. rufula (Rufous Antpitta) and G. blakei (Chestnut Antpitta). However, it has been suggested that the complex, populations of which occur in humid montane forests from Venezuela to Bolivia, comprises a suite of vocally distinct yet morphologically cryptic species. We sequenced nuclear and mitochondrial DNA for 80 individuals from across the distribution of the complex to determine the extent of genetic variation between and within described taxa. Our results revealed 18 geographically coherent clades separated by substantial genetic divergence: 14 within rufula, 3 within blakei, and 1 corresponding to G. rufocinerea (Bicolored Antpitta), a species with distinctive plumage found to be nested within the complex. Neither G. rufula nor G. blakei as presently defined was monophyletic. Although 6 of the 7 recognized subspecies of G. rufula were monophyletic, several subspecies contained substantial genetic differentiation. Genetic variation was largely partitioned across recognized geographic barriers, especially across deep river valleys in Peru and Colombia. Coalescent modeling identified 17 of the 18 clades as significantly differentiated lineages, whereas analyses of vocalizations delineated 16 biological species within the complex. The G. rufula complex seems unusually diverse even among birds of the humid Andes, a prime location for cryptic speciation; however, the extent to which other dispersal-limited Andean species groups exhibit similar degrees of cryptic differentiation awaits further study.","language":"English","publisher":"Oxford Academic","doi":"10.1093/auk/ukaa009","usgsCitation":"Chesser, T., Isler, M.L., Cuervo, A.M., Cadena, C., Galen, S.C., Bergner, L.M., Fleischer, R.C., Bravo, G., Lane, D.F., and Hosner, P., 2020, Conservative plumage masks extraordinary phylogenetic diversity in the Grallaria rufula (Rufous Antpitta) complex of the humid Andes: Auk, v. 137, no. 3, ukaa009, 25 p., https://doi.org/10.1093/auk/ukaa009.","productDescription":"ukaa009, 25 p.","ipdsId":"IP-112868","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455933,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External 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C","contributorId":229671,"corporation":false,"usgs":false,"family":"Galen","given":"Spencer","email":"","middleInitial":"C","affiliations":[],"preferred":false,"id":793816,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bergner, Laura M.","contributorId":207385,"corporation":false,"usgs":false,"family":"Bergner","given":"Laura","email":"","middleInitial":"M.","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":793817,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fleischer, Robert C.","contributorId":127479,"corporation":false,"usgs":false,"family":"Fleischer","given":"Robert","email":"","middleInitial":"C.","affiliations":[{"id":7035,"text":"Smithsonian Conservation Biology Institute, National Zoological Park","active":true,"usgs":false}],"preferred":false,"id":793818,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bravo, Gustavo A","contributorId":215473,"corporation":false,"usgs":false,"family":"Bravo","given":"Gustavo A","affiliations":[{"id":16810,"text":"Harvard Univ.","active":true,"usgs":false}],"preferred":false,"id":793819,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lane, Daniel F","contributorId":229672,"corporation":false,"usgs":false,"family":"Lane","given":"Daniel","email":"","middleInitial":"F","affiliations":[{"id":39571,"text":"Louisiana State Univ.","active":true,"usgs":false}],"preferred":false,"id":793820,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hosner, Peter A.","contributorId":207389,"corporation":false,"usgs":false,"family":"Hosner","given":"Peter A.","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":793821,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70211091,"text":"sir20205050 - 2020 - Groundwater age and susceptibility of south Atlantic and Gulf Coast principal aquifers of the contiguous United States","interactions":[],"lastModifiedDate":"2020-07-22T13:25:01.096535","indexId":"sir20205050","displayToPublicDate":"2020-07-21T07:42:52","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":"2020-5050","displayTitle":"Groundwater Age and Susceptibility of South Atlantic and Gulf Coast Principal Aquifers of the Contiguous United States","title":"Groundwater age and susceptibility of south Atlantic and Gulf Coast principal aquifers of the contiguous United States","docAbstract":"Groundwater susceptibility to contamination was investigated by using environmental tracer-based groundwater age metrics in the south Atlantic and Gulf Coast principal aquifer systems of the Southeastern Coastal Plain, Mississippi embayment–Texas coastal uplands, and the Coastal Lowlands. Samples of dissolved gas, tritium, sulfur hexafluoride, tritiogenic helium, and carbon-14 were collected from 231 public supply wells in the 3 principal aquifer systems. Dissolved gas models were used to characterize recharge conditions and they identified recharge mechanisms that ranged from rapid, but short-lived, water table rises (possibly associated with large scale flooding), to slower diffuse recharge not associated with large water table fluctuations. Dissolved gas and geochemical correction models were used to calculate and (or) correct tracer concentrations before input to lumped parameter models of groundwater age. Lumped parameter models that were fit to tracer concentrations indicated groundwater was relatively old across the aquifer systems, with an estimated mean age of about 30,000 years. Estimates of groundwater age were related to hydrogeology, with increasing groundwater ages associated with greater depth, confinement, and distance from the recharge zone. Young groundwater with mean ages less than 2,000 years generally was in unconfined parts of the aquifer system, except for local areas of heavy groundwater extraction from unconfined aquifer units where estimated mean ages were up to 15,000 years. Lumped parameter model optimized age distributions describe the relative contribution of differing flow paths to the mean age, and a composite distribution of all samples from the three aquifer systems indicated that about 15 percent of the total sampled water had an age of less than 100 years. Various metrics of susceptibility, to land surface and geogenic contamination sources, derived from the age distributions, indicated geogenic sources as the primary threat to groundwater quality in the aquifer systems. Values of the susceptibility index (unitless) and fraction of recharge since 2,000 and 15,000 years before present are provided for assessment of individual well susceptibility. The data and interpretation methods presented here provide an additional means of investigating the susceptibility and sustainability of groundwater resources of the Southeastern Coastal Plain, Mississippi embayment–Texas coastal uplands, and the Coastal Lowlands aquifer systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205050","collaboration":"National Water-Quality ProgramNAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Infectious disease epidemics present a difficult task for policymakers, requiring the implementation of control strategies under significant time constraints and uncertainty. Mathematical models can be used to predict the outcome of control interventions, providing useful information to policymakers in the event of such an epidemic. However, these models suffer in the early stages of an outbreak from a lack of accurate, relevant information regarding the dynamics and spread of the disease and the efficacy of control. As such, recommendations provided by these models are often incorporated in an ad hoc fashion, as and when more reliable information becomes available. In this work, we show that such trial-and-error-type approaches to management, which do not formally take into account the resolution of uncertainty and how control actions affect this, can lead to sub-optimal management outcomes. We compare three approaches to managing a theoretical epidemic: a non-adaptive management (AM) approach that does not use real-time outbreak information to adapt control, a passive AM approach that incorporates real-time information if and when it becomes available, and an active AM approach that explicitly incorporates the future resolution of uncertainty through gathering real-time information into its initial recommendations. The structured framework of active AM encourages the specification of quantifiable objectives, models of system behaviour and possible control and monitoring actions, followed by an iterative learning and control phase that is able to employ complex control optimisations and resolve system uncertainty. The result is a management framework that is able to provide dynamic, long-term projections to help policymakers meet the objectives of management. We investigate in detail the effect of different methods of incorporating up-to-date outbreak information. We find that, even in a highly simplified system, the method of incorporating new data can lead to different results that may influence initial policy decisions, with an active AM approach to management providing better information that can lead to more desirable outcomes from an epidemic.
Biological nitrogen fixation (BNF) supports terrestrial primary productivity and plays key roles in mediating human-induced changes in global nitrogen (N) and carbon cycling. However, there are still critical uncertainties in our understanding of the amount of BNF occurring across terrestrial ecosystems, and of how terrestrial BNF will respond to global change. We synthesized BNF data from Latin America, a region reported to sustain some of the highest BNF rates on Earth, but that is underrepresented in previous data syntheses. We used meta-analysis and modeling approaches to estimate BNF rates across Latin America's major biomes and to evaluate the potential effects of increased N deposition and land-use change on these rates. Unmanaged tropical and subtropical moist forests sustained observed and predicted total BNF rates of 10 ± 1 and 14 ± 1 kg N ha−1 y−1, respectively, supporting the hypothesis that these forests sustain lower BNF rates than previously thought. Free-living BNF accounted for two-thirds of the total BNF in these forests. Despite an average 30% reduction of free-living BNF in response to experimental N-addition, our results suggest free-living BNF rate responses to current and projected N deposition across tropical and subtropical moist forests are small. In contrast, the conversion of unmanaged ecosystems to crop and pasture lands increased BNF rates across all terrestrial biomes, mostly in savannas, grasslands, and dry forests, increasing BNF rates 2-fold. The information obtained here provides a more comprehensive understanding of BNF patterns for Latin America.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.140998","usgsCitation":"Reis, C., Pacheco, F.S., Reed, S., Tejada, G., Nardoto, G.B., Forti, M.C., and Ometto, J., 2020, Biological nitrogen fixation across major biomes in Latin America: Patterns and global change effects: Science of the Total Environment, v. 746, 140998, 15 p., https://doi.org/10.1016/j.scitotenv.2020.140998.","productDescription":"140998, 15 p.","ipdsId":"IP-120603","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.140998","text":"Publisher Index Page"},{"id":378396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Latin America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -62.22656249999999,\n -55.77657301866769\n ],\n [\n -65.7421875,\n -52.26815737376816\n ],\n [\n -32.34375,\n -5.615985819155327\n ],\n [\n -66.09375,\n 13.923403897723347\n ],\n [\n -78.3984375,\n 12.211180191503997\n ],\n [\n -87.1875,\n 22.917922936146045\n ],\n [\n -98.0859375,\n 24.5271348225978\n ],\n [\n -97.03125,\n 26.03704188651584\n ],\n [\n -98.87695312499999,\n 26.588527147308614\n ],\n [\n -102.216796875,\n 29.916852233070173\n ],\n [\n -103.271484375,\n 28.998531814051795\n ],\n [\n -106.25976562499999,\n 31.50362930577303\n ],\n [\n -109.072265625,\n 31.203404950917395\n ],\n [\n -114.43359375,\n 32.76880048488168\n ],\n [\n -117.333984375,\n 32.47269502206151\n ],\n [\n -115.31249999999999,\n 27.137368359795584\n ],\n [\n -110.21484375,\n 22.43134015636061\n ],\n [\n -101.77734374999999,\n 16.97274101999902\n ],\n [\n -95.2734375,\n 14.604847155053898\n ],\n [\n -94.306640625,\n 15.453680224345835\n ],\n [\n -91.7578125,\n 13.325484885597936\n ],\n [\n -87.5390625,\n 12.46876014482322\n ],\n [\n -85.078125,\n 8.059229627200192\n ],\n [\n -80.947265625,\n 6.140554782450308\n ],\n [\n -78.134765625,\n 3.2502085616531686\n ],\n [\n -82.96875,\n -0.4394488164139641\n ],\n [\n -81.298828125,\n -8.49410453755187\n ],\n [\n -75.05859375,\n -18.646245142670598\n ],\n [\n -72.24609375,\n -19.808054128088575\n ],\n [\n -75.05859375,\n -39.368279149160124\n ],\n [\n -76.2890625,\n -47.39834920035925\n ],\n [\n -73.125,\n -52.908902047770255\n ],\n [\n -70.48828125,\n -55.87531083569677\n ],\n [\n -62.22656249999999,\n -55.77657301866769\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"746","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reis, Carla R. G.","contributorId":240660,"corporation":false,"usgs":false,"family":"Reis","given":"Carla R. 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Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area defined as continental to global cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 Countriescovering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3,351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) cloud-platform. We used the pixel-based supervised Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 94 percent with roughly 14 percent omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of croplands occupying 18 percent of the land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. For the entire Landsat-derived 30-m product the overall accuracy was 93.8% with cropland class providing producers accuracy of 86.5% (errors of omissions = 13.5%) and users accuracy of 85.7% (errors of commissions = 14.3%). This Landsat-derived 30-m cropland product (GFSAD30) provided 10-30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30m global land cover (GLC30) product providing an overall accuracy of 88.8 percent (Kappa 0.7) with producers cropland similarity of 89.2 percent (errors of omissions = 10.8%) and users cropland similarity of 81.8 percent (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strength of GFSAD30 product, compared to other products, were in: 1. Identifying precise location of croplands, and 2. Capturing fragmented croplands. The cropland extent map dataset is available through NASAs Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global CroplandsVegetation buffers local diurnal land surface temperatures, however, this effect has found limited applications for remote vegetation characterization. In this work, we parameterize diurnal temperature variations as the thermal decay rate derived by using satellite daytime and nighttime land surface temperatures and modeled using Newton’s law of cooling. The relationship between the thermal decay rate and vegetation depends on many factors including vegetation type, size, water content, location, and local conditions. The theoretical relationships are elucidated, and empirical relationships are presented. Results show that the decay rate summarizes both vegetation structure and function and exhibits a high correlation with other established vegetation-related observations. As proof of concept, we interpret 15-year spatially explicit trends in the annual thermal decay rates over Africa and discuss results. Given recent increases in availability of finer spatial resolution satellite thermal measurements, the thermal decay rate may be a useful index for monitoring vegetation.
","language":"English","publisher":"Springer","doi":"10.1038/s41598-020-66193-5","usgsCitation":"Kumar, S.S., Prihodko, L., Lind, B.M., Anchang, J., Ji, W., Ross, C.W., Kahiu, M.N., Velpuri, N., and Hanan, P.N., 2020, Remotely sensed thermal decay rate: An index for vegetation monitoring: Nature, v. 10, 9812, 11 p., https://doi.org/10.1038/s41598-020-66193-5.","productDescription":"9812, 11 p.","ipdsId":"IP-111239","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":455976,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-66193-5","text":"Publisher Index Page"},{"id":421601,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n 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Range Science","active":true,"usgs":false}],"preferred":false,"id":885287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lind, Brianna M. 0000-0002-5306-9963","orcid":"https://orcid.org/0000-0002-5306-9963","contributorId":330553,"corporation":false,"usgs":false,"family":"Lind","given":"Brianna","email":"","middleInitial":"M.","affiliations":[{"id":78932,"text":"NMSU, Plant and Environmental Science","active":true,"usgs":false}],"preferred":false,"id":885288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anchang, Julius","contributorId":265510,"corporation":false,"usgs":false,"family":"Anchang","given":"Julius","email":"","affiliations":[{"id":54703,"text":"Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM 88003","active":true,"usgs":false}],"preferred":false,"id":885289,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ji, Wenjie 0000-0003-2554-4373","orcid":"https://orcid.org/0000-0003-2554-4373","contributorId":330554,"corporation":false,"usgs":false,"family":"Ji","given":"Wenjie","email":"","affiliations":[{"id":78932,"text":"NMSU, Plant and Environmental Science","active":true,"usgs":false}],"preferred":false,"id":885290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ross, Christopher Wade 0000-0002-2989-7945","orcid":"https://orcid.org/0000-0002-2989-7945","contributorId":330555,"corporation":false,"usgs":false,"family":"Ross","given":"Christopher","email":"","middleInitial":"Wade","affiliations":[{"id":78932,"text":"NMSU, Plant and Environmental Science","active":true,"usgs":false}],"preferred":false,"id":885291,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kahiu, Milkah Njoki 0000-0002-7416-947X","orcid":"https://orcid.org/0000-0002-7416-947X","contributorId":330556,"corporation":false,"usgs":false,"family":"Kahiu","given":"Milkah","email":"","middleInitial":"Njoki","affiliations":[{"id":78933,"text":"International Livestock Research Institute (ILRI), PO Box 30709, Nairobi 00100, Kenya.","active":true,"usgs":false}],"preferred":false,"id":885292,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Velpuri, Naga Manohar 0000-0002-6370-1926","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":222983,"corporation":false,"usgs":false,"family":"Velpuri","given":"Naga Manohar","affiliations":[{"id":40633,"text":"CIGAR","active":true,"usgs":false}],"preferred":false,"id":885293,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hanan, P. Niall 0000-0002-9130-5306","orcid":"https://orcid.org/0000-0002-9130-5306","contributorId":330557,"corporation":false,"usgs":false,"family":"Hanan","given":"P.","email":"","middleInitial":"Niall","affiliations":[{"id":78932,"text":"NMSU, Plant and Environmental Science","active":true,"usgs":false}],"preferred":false,"id":885294,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70213339,"text":"70213339 - 2020 - GIS-Modeling of island hopping through the Philippines demonstrates trade-offs migrant grey-faced buzzards during oceanic crossings","interactions":[],"lastModifiedDate":"2020-09-17T14:47:04.508154","indexId":"70213339","displayToPublicDate":"2020-07-17T09:40:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6516,"text":"Journal of Engineering, Environment and Agriculture Research","active":true,"publicationSubtype":{"id":10}},"title":"GIS-Modeling of island hopping through the Philippines demonstrates trade-offs migrant grey-faced buzzards during oceanic crossings","docAbstract":"Migration can be costly with consequences that can influence population trajectories. These costs and consequences are especially heightened during over-water travels, which can be high-risk events for birds. We created spatial models to evaluate potential migratory responses of “oceanic”, island-hopping grey-faced buzzards that encounter variation in landscape parameters and weather as they move through and out of the Philippine archipelago. We constrained the modeled routes to enter the island chain at Basco and to use one of four potential exit points in the south of the country, either Balabac, Bongao, Balut Island, or Cape San Agustin. We used all possible combinations of our three external parameters (stopover sites, water crossings and wind direction) to model alternative migratory routes for each of the four exit points (n = 20 migratory routes). Modeled grey-faced buzzard routes were between 1,582 and 2,970 km. Routes overlapped over eastern and central Luzon, along a leading line created by the Sierra Madre Mountains. Routes also overlapped and suggested unavoidable over-water crossings between Mindoro and Palawan, Negros and Zamboanga del Norte, and Leyte and Surigao. Our models suggest that the optimal migratory strategy for these birds is to find the shortest route to an exit point with the greatest possible access to stopover habitats and fewest open-water crossings under wind resistance. Understanding how each of these external factors affected the geography and characteristics of the migratory routes helps us to understand the context for different migratory strategies of birds that face dangerous open-water crossings on migration.
","language":"English","publisher":"Mindanao State University","doi":"10.34002/jeear.v2i0.40","usgsCitation":"Concepcion, C.B., Bildstein, K.L., and Katzner, T., 2020, GIS-Modeling of island hopping through the Philippines demonstrates trade-offs migrant grey-faced buzzards during oceanic crossings: Journal of Engineering, Environment and Agriculture Research, v. 2, p. 11-28, https://doi.org/10.34002/jeear.v2i0.40.","productDescription":"18 p.","startPage":"11","endPage":"28","ipdsId":"IP-082427","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":455979,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.34002/jeear.v2i0.40","text":"Publisher Index Page"},{"id":378503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Philippines","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 125.52978515625001,\n 5.353521355337334\n ],\n [\n 126.7822265625,\n 6.7737162387535\n ],\n [\n 126.65039062499999,\n 9.102096738726456\n ],\n [\n 125.5078125,\n 12.661777510388525\n ],\n [\n 124.25537109375,\n 14.370833973406821\n ],\n [\n 122.3876953125,\n 18.8335153964335\n ],\n [\n 122.36572265625,\n 19.663280219987662\n ],\n [\n 120.58593749999999,\n 19.642587534013032\n ],\n [\n 118.564453125,\n 16.235772090429855\n ],\n [\n 120.234375,\n 12.768946439455956\n ],\n [\n 121.5087890625,\n 9.925565912405506\n ],\n [\n 122.25585937500001,\n 8.841651120809145\n ],\n [\n 121.59667968749999,\n 6.795535025719518\n ],\n [\n 122.08007812499999,\n 5.922044619883305\n ],\n [\n 125.52978515625001,\n 5.353521355337334\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationDate":"2020-07-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Concepcion, Camille B.","contributorId":190164,"corporation":false,"usgs":false,"family":"Concepcion","given":"Camille","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":799059,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bildstein, Keith L.","contributorId":150854,"corporation":false,"usgs":false,"family":"Bildstein","given":"Keith","email":"","middleInitial":"L.","affiliations":[{"id":18119,"text":"Hawk Mountain Sanctuary, Acopian Center for Conservation Learning","active":true,"usgs":false}],"preferred":false,"id":799060,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":799061,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211624,"text":"70211624 - 2020 - Observations of an extreme atmospheric river storm with a diverse sensor network","interactions":[],"lastModifiedDate":"2021-10-26T16:02:25.708294","indexId":"70211624","displayToPublicDate":"2020-07-17T09:38:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Observations of an extreme atmospheric river storm with a diverse sensor network","docAbstract":"Observational networks enhance real‐time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multitiered observational network in California provided insights into hydrometeorological processes and impacts during a 3‐day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows: (1) the validation of record cool season precipitable water observations over southern California; (2) the identification of phenomena that produce natural hazards and present difficulties for short‐term weather forecast models, such as extreme precipitation amounts and snow level variability; (3) the use of soil moisture data to improve hydrologic model forecast skill in northern California's Russian River basin; and (4) the combination of meteorological data with seismic observations to identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020EA001129","usgsCitation":"Hatchett, B.J., Cao, Q., Dawson, P.B., Ellis, C.J., Hecht, C.W., Kawzenuk, B., Lancaster, J.T., Osborne, T.C., Wilson, A.M., Anderson, M.L., Dettinger, M., Kalansky, J.F., Kaplan, M.L., Lettenmaier, D.P., Oakley, N.S., Ralph, R., Reynolds, D.W., White, A.B., Sierks, M., and Sumargo, E., 2020, Observations of an extreme atmospheric river storm with a diverse sensor network: Earth and Space Science, v. 7, no. 8, e2020EA001129, 21 p., https://doi.org/10.1029/2020EA001129.","productDescription":"e2020EA001129, 21 p.","ipdsId":"IP-115218","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":455982,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020ea001129","text":"Publisher Index 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