The Kankakee River in northern Indiana flows through the area once known as the Grand Marsh. Beginning in the 1860s, anthropogenic changes to the river within Indiana resulted in downstream flooding and additional transport of sediment and nutrients. In 2015, the U.S. Geological Survey, in cooperation with the Indiana Department of Environmental Management, upgraded the gaging station Kankakee River at Shelby, Indiana, to include the collection of water-quality data. By relating continuously monitored water-quality data to discrete data collected from December 2015 through May 2018, linear regression was used to develop models for estimating concentrations of suspended sediment, total nitrogen, and total phosphorus. Developed regression models indicated a strong correlation between turbidity and specific conductance with suspended-sediment concentration (adjusted coefficient of determination equals 0.92, predicted residual error sum of squares equals 0.151), nitrate plus nitrite and specific conductance with total nitrogen (adjusted coefficient of determination equals 0.95, predicted residual error sum of squares equals 0.0248), and turbidity with total phosphorus (adjusted coefficient of determination equals 0.89, predicted residual error sum of squares equals 0.0103).
Daily loads of suspended sediment, total nitrogen, and total phosphorus were computed as the product of daily mean regression model concentrations and daily mean streamflow. Rloadest models were used to compute daily loads of each constituent during gaps in regression model loads. For 2016 and 2017, the estimated annual suspended-sediment loads were 105,000 and 91,000 tons; estimated total nitrogen loads were 8,690 and 8,890 tons; and estimated total phosphorus loads were 265 and 236 tons, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195005","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management","usgsCitation":"Lathrop, T.R., Bunch, A.R., and Downhour, M.S., 2019, Regression models for estimating sediment and nutrient concentrations and loads at the Kankakee River, Shelby, Indiana, December 2015 through May 2018: U.S. Geological Survey Scientific Investigation Report 2019–5005, 13 p., https://doi.org/10.3133/sir20195005.","productDescription":"Report: v, 13 p.; 2 Data Releases","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101520","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":362192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5005/coverthb.jpg"},{"id":362193,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5005/sir20195005.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5005"},{"id":362194,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PE9PTD","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and rloadest models for suspended sediment, total nitrogen, and total phosphorus for Kankakee River at Shelby, Indiana, January 5, 2016 to May 31, 2018"},{"id":362195,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90EKU6X","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and Surrogate Models for Suspended Sediment, Total Nitrogen, and Total Phosphorus for the Kankakee River at Shelby, Indiana, January 5, 2016 to May 31, 2018"}],"country":"United States","state":"Indiana","city":"Shelby","otherGeospatial":"Kankakee River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.81396484375,\n 41.34897943069752\n ],\n [\n -86.08337402343749,\n 41.34588656996287\n ],\n [\n -86.08337402343749,\n 41.76721469421018\n ],\n [\n -86.81259155273438,\n 41.76823896512856\n ],\n [\n -86.81396484375,\n 41.34897943069752\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Threats to aquatic biodiversity are expressed at broad spatial scales, but identifying regional trends in abundance is challenging owing to variable sampling designs, and temporal and spatial variation in abundance. We compiled a regional dataset of brook trout Salvelinus fontinalis counts across their southern range representing 326 sites from eight states between 1982-2014, and conducted a statistical power analysis using Bayesian state-space models to evaluate the ability to detect temporal trends by characterizing posterior distributions with three approaches. A combination of monitoring periods, number of sites and electrofishing passes, decline magnitude and different revisit patterns were tested. Power increased with monitoring periods and decline magnitude. Trends in adults were better detected than young-of-the-year fish, which showed greater inter-annual variation in abundance. The addition of weather covariates to account for the temporal variation increased power only slightly. Single- and three-pass electrofishing methods were similar in power. Finally, power was higher for sampling designs with more frequent revisits over the duration of the monitoring program. Our results provide guidance for broad-scale monitoring designs for temporal trend detection.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2018-0241","usgsCitation":"Pregler, K.C., Hanks, R.D., Childress, E., Hitt, N.P., Hocking, D.J., Letcher, B., and Kanno, Y., 2019, State-space analysis of power to detect regional brook trout population trends over time: Canadian Journal of Fisheries and Aquatic Sciences, v. 76, no. 11, p. 2145-2155, https://doi.org/10.1139/cjfas-2018-0241.","productDescription":"11 p.","startPage":"2145","endPage":"2155","ipdsId":"IP-098679","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":362209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"11","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pregler, Kasey C.","contributorId":149616,"corporation":false,"usgs":false,"family":"Pregler","given":"Kasey","email":"","middleInitial":"C.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":759565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanks, R. Daniel","contributorId":214286,"corporation":false,"usgs":false,"family":"Hanks","given":"R.","email":"","middleInitial":"Daniel","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":759566,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Childress, Evan S.","contributorId":214287,"corporation":false,"usgs":false,"family":"Childress","given":"Evan S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":759567,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":759564,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hocking, Daniel J.","contributorId":214288,"corporation":false,"usgs":false,"family":"Hocking","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":39006,"text":"Frostburg State University","active":true,"usgs":false}],"preferred":false,"id":759568,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":167313,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":759569,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kanno, Yoichiro","contributorId":210653,"corporation":false,"usgs":false,"family":"Kanno","given":"Yoichiro","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":759570,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202756,"text":"70202756 - 2019 - Better approaches to managing drought in the American Southwest","interactions":[],"lastModifiedDate":"2019-04-01T15:57:28","indexId":"70202756","displayToPublicDate":"2019-03-20T12:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1578,"text":"Eos, Transactions, American Geophysical Union","onlineIssn":"2324-9250","printIssn":"0096-394","active":true,"publicationSubtype":{"id":10}},"title":"Better approaches to managing drought in the American Southwest","docAbstract":"The second in a series of USGS Southwest Region (SWR) “Science Exchange” annual workshops, focused on USGS drought science. The participants considered how extreme drought conditions are evolving in much of the American southwest, with an emphasis on integrated drought science planning at the USGS bureau and program levels. The increased need for interdisciplinary science to support resource-management decisions systems, was highlighted. \nThe workshop brought together scientists and program managers from USGS with Bureau of Land Management, National Oceanic and Atmospheric Administration, US Bureau of Reclamation and state water management departments. Key objectives of the workshop were to improve awareness of ongoing drought-science work within the region, highlight the capabilities of USGS-SWR science centers in drought science, and build new relationships to advance best approaches in drought science. Topics covered in presentations and demonstrations were broad-ranging and included monitoring for drought early warning; water use and water production associated with petroleum production; paleo perspectives on drought, and ecological consequences of drought to native fish.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019EO118533","usgsCitation":"Lambert, P., Titus, T.N., and Ostroff, A., 2019, Better approaches to managing drought in the American Southwest: Eos, Transactions, American Geophysical Union, v. 100, HTML Document, https://doi.org/10.1029/2019EO118533.","productDescription":"HTML Document","ipdsId":"IP-102913","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":467797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019eo118533","text":"Publisher Index Page"},{"id":362627,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"100","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lambert, Patrick 0000-0001-6808-2303 plambert@usgs.gov","orcid":"https://orcid.org/0000-0001-6808-2303","contributorId":214412,"corporation":false,"usgs":true,"family":"Lambert","given":"Patrick","email":"plambert@usgs.gov","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":759840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":759839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ostroff, Andrea","contributorId":214413,"corporation":false,"usgs":true,"family":"Ostroff","given":"Andrea","email":"","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":759841,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202439,"text":"tm7C22 - 2019 - User’s manual for the Draper climate-distribution software suite with data‑evaluation tools","interactions":[],"lastModifiedDate":"2019-07-26T12:05:14","indexId":"tm7C22","displayToPublicDate":"2019-03-20T11:25:22","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C22","displayTitle":"User’s Manual for the Draper Climate-Distribution Software Suite with Data-Evaluation Tools","title":"User’s manual for the Draper climate-distribution software suite with data‑evaluation tools","docAbstract":"Development of a time series of spatially distributed climate data is an important step in the process of developing physically based environmental models requiring distributed inputs of climate data beyond what is available from observations collected at climate stations. To prepare inputs required for model-mapping units across the study area, climate data (temperature and precipitation) are distributed by combining data from gridded surfaces of mean-monthly climate-data values with (often) widely spaced daily point observations. Examples of climate-data files used to develop PRMS-formatted input files for the Merced River Basin Precipitation-Runoff Modeling System (PRMS) are included in this manual.
The Draper Climate-Distribution Software Suite (Draper Suite) consists of the Draper climate-distribution program (Draper) and several supporting pre- and post-processing applications. Draper combines spatially distributed input in the form of monthly averaged values for precipitation, maximum temperature, and minimum temperature with daily observed data from climate stations to estimate distributed climate-data values at predefined locations across a study area (typically a drainage basin) on a daily time step. Alternative methods are used when station data are limited or missing for a particular day. Draper uses a set of required and optional input and output files with defined formats and naming conventions. A shell application also is available to manage multiple runs of the Draper application.
Other applications in the Draper Suite include (1) a tool to find and interactively remove outliers in the input data, (2) a tool to check and enforce a minimum daily temperature range, and (3) a tool to view output diagnostic information as time-series graphs. These tools can be used iteratively to evaluate and improve the results from Draper as part of a workflow involving physically based environmental models, such as the Precipitation-Runoff Modeling System (PRMS).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C22","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Donovan, J.M., and Koczot, K.M., 2019, User’s manual for the Draper climate-distribution software suite with data‑evaluation tools: U.S. Geological Survey Techniques and Methods 7-C22, 55 p., https://doi.org/10.3133/tm7C22. ","productDescription":"viii, 55 p","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-086388","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":362190,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c22/coverthb.jpg"},{"id":362191,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c22/tm7c22.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C22"},{"id":365983,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://code.usgs.gov/cawsc/draper","text":"Source code and executables","linkHelpText":"- Users are required to create an account to access the distribution"}],"contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Sex determination is the first step toward the establishment of phenotypic sex in most vertebrates. Aquatic poikilotherms such as teleost fishes exhibit a high diversity of sex-determination mechanisms and gonadal phenotypes that are remarkably plastic and responsive to a variety of environmental factors (e.g., water temperature, pH, salinity, photoperiod, population density). This chapter reviews current knowledge of genotypic and environmental sex determination systems in fishes with special reference to Atheriniformes—one of the best-characterized taxa in this field—and offers perspectives to guide and stimulate further research.
","language":"English","publisher":"Elsevier","doi":"10.1016/bs.ctdb.2019.02.003","usgsCitation":"Yamamoto, Y., Hattori, R.S., Patino, R., and Strüssmann, C., 2019, Environmental regulation of sex determination in fishes: Insights from Atheriniformes, p. 49-69, https://doi.org/10.1016/bs.ctdb.2019.02.003.","productDescription":"21 p.","startPage":"49","endPage":"69","ipdsId":"IP-102625","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388418,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yamamoto, Y.","contributorId":264653,"corporation":false,"usgs":false,"family":"Yamamoto","given":"Y.","affiliations":[{"id":47624,"text":"Tokyo University of Marine Science and Technology","active":true,"usgs":false}],"preferred":false,"id":821813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hattori, R. S.","contributorId":264656,"corporation":false,"usgs":false,"family":"Hattori","given":"R.","email":"","middleInitial":"S.","affiliations":[{"id":54527,"text":"Sao Paulo Fisheries Institute","active":true,"usgs":false}],"preferred":false,"id":821814,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":821815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strüssmann, C. A.","contributorId":264657,"corporation":false,"usgs":false,"family":"Strüssmann","given":"C. A.","affiliations":[{"id":47624,"text":"Tokyo University of Marine Science and Technology","active":true,"usgs":false}],"preferred":false,"id":821816,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215766,"text":"70215766 - 2019 - Growth disparity in sympatric kokanee breeding groups","interactions":[],"lastModifiedDate":"2020-10-30T11:55:18.644759","indexId":"70215766","displayToPublicDate":"2019-03-20T06:48:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2885,"text":"North American Journal of Aquaculture","active":true,"publicationSubtype":{"id":10}},"title":"Growth disparity in sympatric kokanee breeding groups","docAbstract":"Growth is arguably the most important dynamic rate function due to its interaction with survival and recruitment. As such, understanding the mechanisms underlying growth is a primary focus of fisheries research. Kokanee Oncorhynchus nerka in Lake Pend Oreille, Idaho, provide an interesting case study for investigating the factors that influence growth. Early‐run and late‐run kokanee occur in Lake Pend Oreille, but early‐run fish generally grow faster than late‐run fish. The observed growth disparity between early‐ and late‐run fish could be due to genetic differences between the two groups. Conversely, a common hatchery practice of slowing growth by reducing feed has been hypothesized to elicit a compensatory growth response in early‐run fish and to explain the size difference between breeding groups. Using two different experiments, we tested the hypotheses that (1) early‐run kokanee are genetically disposed to grow faster than late‐run kokanee at identical water temperatures; and (2) feed restriction elicits a compensatory growth response in early‐run kokanee that explains the observed size difference between breeding groups. Estimates of mean FL, weight, Fulton's condition factor (K), and specific growth rate (SGR) were not significantly different (P ≥ 0.05) between early‐run and late‐run fish in the first experiment. However, water temperature was positively related to mean FL, weight, K, and SGR for both breeding groups. Fish that were subjected to food deprivation exhibited an increased growth rate and obtained weights similar to those of control fish. Overall, our results suggest that early‐ and late‐run fish have similar growth potential, but certain hatchery practices likely provide early‐run fish with an initial advantage in growth, size, or both.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/naaq.10084","usgsCitation":"Klein, Z.B., Quist, M.C., Dux, A.M., and Corsi, M.P., 2019, Growth disparity in sympatric kokanee breeding groups: North American Journal of Aquaculture, v. 81, no. 2, p. 169-177, https://doi.org/10.1002/naaq.10084.","productDescription":"9 p.","startPage":"169","endPage":"177","ipdsId":"IP-103305","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486797,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2499838","text":"External Repository"},{"id":379957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Lake Pend Oreille","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.70501708984376,\n 47.91450120703987\n ],\n [\n -116.13372802734375,\n 47.91450120703987\n ],\n [\n -116.13372802734375,\n 48.323386716330916\n ],\n [\n -116.70501708984376,\n 48.323386716330916\n ],\n [\n -116.70501708984376,\n 47.91450120703987\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Klein, Zachary B.","contributorId":171709,"corporation":false,"usgs":false,"family":"Klein","given":"Zachary","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":803344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839 mquist@usgs.gov","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":171392,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","email":"mquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":803345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dux, Andrew M.","contributorId":212798,"corporation":false,"usgs":false,"family":"Dux","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":803346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corsi, Matthew P.","contributorId":212797,"corporation":false,"usgs":false,"family":"Corsi","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":803347,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216766,"text":"70216766 - 2019 - Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water","interactions":[],"lastModifiedDate":"2020-12-04T22:03:41.306725","indexId":"70216766","displayToPublicDate":"2019-03-19T15:59:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water","docAbstract":"Measures of energy expenditure can be used to inform animal conservation and management, but methods for measuring the energy expenditure of free‐ranging animals have a variety of limitations. Advancements in biologging technologies have enabled the use of dynamic body acceleration derived from accelerometers as a proxy for energy expenditure. Although dynamic body acceleration has been shown to strongly correlate with oxygen consumption in captive animals, it has been validated in only a few studies on free‐ranging animals. Here, we use relationships between oxygen consumption and overall dynamic body acceleration in resting and walking polar bears Ursus maritimus and published values for the costs of swimming in polar bears to estimate the total energy expenditure of 6 free‐ranging polar bears that were primarily using the sea ice of the Beaufort Sea. Energetic models based on accelerometry were compared to models of energy expenditure on the same individuals derived from doubly labeled water methods. Accelerometer‐based estimates of energy expenditure on average predicted total energy expenditure to be 30% less than estimates derived from doubly labeled water. Nevertheless, accelerometer‐based measures of energy expenditure strongly correlated (r2 = 0.70) with measures derived from doubly labeled water. Our findings highlight the strengths and limitations in dynamic body acceleration as a measure of total energy expenditure while also further supporting its use as a proxy for instantaneous, detailed energy expenditure in free‐ranging animals.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5053","usgsCitation":"Pagano, A.M., and Williams, T.M., 2019, Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water: Ecology and Evolution, v. 9, no. 7, p. 4210-4219, https://doi.org/10.1002/ece3.5053.","productDescription":"10 p.","startPage":"4210","endPage":"4219","ipdsId":"IP-101615","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":467799,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5053","text":"Publisher Index Page"},{"id":381005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, Northwest Territories, Yukon","otherGeospatial":"Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127.35351562499999,\n 70.19999407534661\n ],\n [\n -125.859375,\n 72.97118902284586\n ],\n [\n -157.58789062499997,\n 73.17589717422607\n ],\n [\n -156.62109374999997,\n 71.30079291637452\n ],\n [\n -148.798828125,\n 70.25945200030638\n ],\n [\n -145.1953125,\n 70.05059634999759\n ],\n [\n -139.5703125,\n 69.47296854140573\n ],\n [\n -135.703125,\n 68.65655498475735\n ],\n [\n -127.35351562499999,\n 70.19999407534661\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"7","noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Pagano, Anthony M. 0000-0003-2176-0909 apagano@usgs.gov","orcid":"https://orcid.org/0000-0003-2176-0909","contributorId":3884,"corporation":false,"usgs":true,"family":"Pagano","given":"Anthony","email":"apagano@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":806132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Terrie M.","contributorId":191735,"corporation":false,"usgs":false,"family":"Williams","given":"Terrie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":806133,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70204348,"text":"70204348 - 2019 - Discovery of an extensive deep-sea fossil serpulid reef associated with a cold seep, Santa Monica Basin, California","interactions":[],"lastModifiedDate":"2019-07-18T14:04:15","indexId":"70204348","displayToPublicDate":"2019-03-19T13:48:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Discovery of an extensive deep-sea fossil serpulid reef associated with a cold seep, Santa Monica Basin, California","docAbstract":"Multi-beam mapping of the Santa Monica Basin in the eastern Pacific has revealed the existence of a number of elevated bathymetric features, or mounds, harboring cold seep communities. During 2013-2014, mounds at ~600 m water depth were observed for the first time and sampled by Monterey Bay Aquarium Research Institute’s ROV Doc Ricketts. Active cold seeps were found, but surprisingly one of these mounds was characterized by massive deposits composed of fossil serpulid worm tubes (Annelida: Serpulidae) exhibiting various states of mineralization by authigenic carbonate. No living serpulids with equivalent tube morphologies were found at the site; hence the mound was termed ‘Fossil Hill’. In the present study, the identity of the fossil serpulids and associated fossil community, the ages of fossils and authigenic carbonates, the formation of the fossil serpulid aggregation, and the geological structure of the mound are explored. Results indicate that the tubes were most likely made by a deep-sea serpulid lineage, with radiocarbon dating suggesting that they have a very recent origin during the Late Pleistocene, specifically to the Last Glacial Maximum ~20,000 years ago. Additional U-Th analyses of authigenic carbonates mostly corroborate the radiocarbon dates, and also indicate that seepage was occurring while the tubes were being formed. We also document similar, older deposits along the approximate trajectory of the San Pedro Basin Fault. We suggest that the serpulid tube facies formed in situ, and that the vast aggregation of these tubes at Fossil Hill is likely due to a combination of optimal physical environmental conditions and chemosynthetic production, which may have been particularly intense as a result of sea-level lowstand during the Last Glacial Maximum.","language":"English","publisher":"Frontiers in Marine Science","doi":"10.3389/fmars.2019.00115","usgsCitation":"Georgieva, M.N., Paull, C.K., Little, C.T., McGann, M., Sahy, D., Condon, D., Lundsten, L., Pewsey, J., Caress, D., and Vrijenhoek, R.C., 2019, Discovery of an extensive deep-sea fossil serpulid reef associated with a cold seep, Santa Monica Basin, California: Frontiers in Marine Science, https://doi.org/10.3389/fmars.2019.00115.","ipdsId":"IP-105040","costCenters":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science 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]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Georgieva, Magdalena N","contributorId":217245,"corporation":false,"usgs":false,"family":"Georgieva","given":"Magdalena","email":"","middleInitial":"N","affiliations":[{"id":39584,"text":"Life Sciences Department, Natural History Museum, London, UK","active":true,"usgs":false}],"preferred":false,"id":766451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paull, Charles K. 0000-0001-5940-3443","orcid":"https://orcid.org/0000-0001-5940-3443","contributorId":55825,"corporation":false,"usgs":false,"family":"Paull","given":"Charles","email":"","middleInitial":"K.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":true,"id":766450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Little, Crispin TS","contributorId":217246,"corporation":false,"usgs":false,"family":"Little","given":"Crispin","email":"","middleInitial":"TS","affiliations":[{"id":39585,"text":"School of Earth and Environment, University of Leeds, Leeds, UK","active":true,"usgs":false}],"preferred":false,"id":766452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":766449,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sahy, Diana","contributorId":169649,"corporation":false,"usgs":false,"family":"Sahy","given":"Diana","email":"","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":766453,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Condon, Daniel","contributorId":217247,"corporation":false,"usgs":false,"family":"Condon","given":"Daniel","email":"","affiliations":[{"id":39586,"text":"NERC Isotope Geosciences Laboratory, British Geological Survey, Keyworth, UK","active":true,"usgs":false}],"preferred":false,"id":766454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lundsten, Lonny","contributorId":217248,"corporation":false,"usgs":false,"family":"Lundsten","given":"Lonny","email":"","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":766455,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pewsey, Jack","contributorId":217250,"corporation":false,"usgs":false,"family":"Pewsey","given":"Jack","email":"","affiliations":[{"id":39585,"text":"School of Earth and Environment, University of Leeds, Leeds, UK","active":true,"usgs":false}],"preferred":false,"id":766458,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Caress, David W","contributorId":147194,"corporation":false,"usgs":false,"family":"Caress","given":"David W","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":766456,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vrijenhoek, Robert C","contributorId":217249,"corporation":false,"usgs":false,"family":"Vrijenhoek","given":"Robert","email":"","middleInitial":"C","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":766457,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70228118,"text":"70228118 - 2019 - Spatial heterogeneity of prion gene polymorphisms in an area recently infected by chronic wasting disease","interactions":[],"lastModifiedDate":"2022-02-04T17:48:16.868842","indexId":"70228118","displayToPublicDate":"2019-03-19T11:43:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3121,"text":"Prion","onlineIssn":"1933-690X","printIssn":"1933-6896","active":true,"publicationSubtype":{"id":10}},"title":"Spatial heterogeneity of prion gene polymorphisms in an area recently infected by chronic wasting disease","docAbstract":"Genetic variability in the prion protein (Prnp) gene influences host susceptibility to many pathogenic prion diseases. Understanding the distribution of susceptible Prnp variants and determining factors influencing spatial genetic patterns are important components of many chronic wasting disease mitigation strategies. Here, we describe Prnp variability in white-tailed deer (Odocoileus virginianus) from the Mid-Atlantic region of the United States of America, an area with a recent history of infection and low disease incidence. This population is characterized by lower rates of polymorphism and significantly higher frequencies of the more susceptible 96GG genotype compared to previously surveyed populations. The prevalence of the most susceptible genotypes at disease-associated loci did vary among subregions, indicating that populations have innate differences in genotype-dictated susceptibility.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/19336896.2019.1583042","usgsCitation":"Miller, W., and Walter, W., 2019, Spatial heterogeneity of prion gene polymorphisms in an area recently infected by chronic wasting disease: Prion, v. 13, no. 1, p. 65-76, https://doi.org/10.1080/19336896.2019.1583042.","productDescription":"11 p.","startPage":"65","endPage":"76","ipdsId":"IP-102694","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467800,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/19336896.2019.1583042","text":"Publisher Index Page"},{"id":395454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.408935546875,\n 38.324420427006544\n ],\n [\n -75.706787109375,\n 38.324420427006544\n ],\n [\n -75.706787109375,\n 41.95131994679697\n ],\n [\n -80.408935546875,\n 41.95131994679697\n ],\n [\n -80.408935546875,\n 38.324420427006544\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, William L.","contributorId":274622,"corporation":false,"usgs":false,"family":"Miller","given":"William L.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":833167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833166,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70212534,"text":"70212534 - 2019 - Tectono-magmatic evolution of porphyry belts in the central Tethys region of Turkey, the Caucasus, Iran, western Pakistan, and southern Afghanistan","interactions":[],"lastModifiedDate":"2020-08-19T17:20:59.562078","indexId":"70212534","displayToPublicDate":"2019-03-19T11:38:30","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Tectono-magmatic evolution of porphyry belts in the central Tethys region of Turkey, the Caucasus, Iran, western Pakistan, and southern Afghanistan","docAbstract":"Exploration in the central Tethys region of Turkey, Armenia, Azerbaijan, Georgia, Iran, and western Pakistan has led to the identification of the giant Reko Diq (24 Mt Cu and 1300 t Au), Sar Cheshmeh (8.9 Mt Cu and 0.46 Mt Mo), Sungun (5.1 Mt Cu and 0.20 Mt Mo), and Kadjaran (4.6 Mt Cu, 0.94 Mt Mo, and 1100 t Au), and 10 other large (1–2 Mt Cu) porphyry deposits including Saindak, Cevizlidere, Teghout, Meiduk, and Halilağa. Continued exploration efforts have also resulted in the development of porphyry-related gold deposits such as Kişladağ (9.6 Moz Au), Çöpler (3.7 Moz Au), Aği Daği (1.7 Moz Au), and Sary Gunay (3.0 Moz Au), and in the generation of several other promising exploration projects.
The distribution in space and time of porphyry deposits in the central Tethys region was shaped by complex pre- to post-mineral tectonic, igneous, collisional, uplift and burial events. These events are represented by a partially-overlapping and variably exhumed and covered collage of twenty-six Early Jurassic to Holocene magmatic belts permissive for the occurrence of porphyry deposits (porphyry tracts and sub-tracts). Twelve tracts or sub-tracts are characterized by compressional continental arcs that formed on drifting terranes or continental margins, 10 developed in compressional to extensional intra-oceanic arc and backarc-rift settings, and 4 formed in extensional post-collisional environments over amalgamated terranes. Eight of these belts were variably affected by coeval and younger metamorphic, fold-and-thrust, and extensional faulting events.
Fifty-four porphyry Au-(Cu), Cu-Au, Cu-Mo, Mo-Cu deposits, 15 porphyry-related Au, Au-(Mo) and W-(Mo-Au) deposits, 239 porphyry prospects, and 68 other porphyry-related mineral sites were identified in the study region. Of the 376 porphyry and porphyry-related sites, about 11% formed in island arc, 42% in continental arc, 20% in backarc, and 27% in post-collisional settings. Of the 69 porphyry and porphyry-related deposits, 7% developed in intra-oceanic arc, 41% in continental arc, 27% in backarc, and 25% in post-collisional settings. The largest occur in either compressional continental arc (18 deposits including the Reko Diq and Sar Cheshmeh giants) or post-collisional (13 deposits including the Kadjaran and Sungun giants) environments. Ninety percent of the largest porphyry or porphyry-related deposits occur in only 9 of the 26 permissive porphyry tracts or sub-tracts. Moreover, 88, 90, and 77% of the identified Cu, Mo, and Au resources are contained in porphyry deposits that occur in only 4 of these 9 tracts. Of these 4 tracts, 3 outline arc settings, and one delimits a post-collisional environment.
The compositional diversity of porphyry intrusions in these tectono-magmatic environments generally varies from island arc settings with the most restricted range (partly alkaline but mainly calc-alkaline dioritic to granodioritic-tonalitic), to continental arc (calc-alkaline dioritic-quartz dioritic, granodioritic, quartz monzonitic-granitic, and less commonly mildly alkaline), to backarc (mildly alkaline and calc-alkaline dioritic to granitic), to post-collisional settings with the most expansive range (alkaline and calc-alkaline mafic to felsic, and weakly peraluminous). Metal associations also vary broadly as a function of porphyry intrusion composition from weakly peraluminous to metaluminous felsic (Mo[±W ± Cu]; <2% of porphyry-related systems [i.e., Tyrnyauz]), to metaluminous felsic and intermediate (Cu-Mo[±Au]; 85% [i.e., Cevizlidere, Haft Cheshmeh, Kahang, Sar Cheshmeh, Sungun, Teghout, Reko Diq, Saindak]), to mildly alkaline felsic and intermediate (Cu-Au[±Mo] [i.e., Agarak, Kadjaran, Kale Kafi]) and mafic (Au-Cu; 12% [i.e., Çöpler]), and to alkaline felsic (Au-Mo; 1% of porphyry-related systems [i.e., Kişladağ).
Tectonic changes were critical in triggering the formation of large porphyry deposits in the region. Large porphyry deposits were preferentially emplaced in continental arc settings shortly before major collisional events (Dar Alu, Kahang, Meiduk, Now Chun, and the giant Sar Cheshmeh and Reko Diq deposits), or in post-subduction environments shortly after collision (Bakirçay, Güzelyayla, Haft Cheshmeh, Masjed Daghi, and the giant Kadjaran and Sungun deposits) or during periods of prominent extension (Aği Daği, Halilağa, Kişladağ, Sari Gunay, and Zarshuran porphyry-related deposits). Collision-induced uplift, erosion, and removal of coeval volcanic rocks favorably exposed the hypabyssal level of subduction-related porphyry deposits. Extensional structures that developed parallel and orthogonal to the compressional principal stress component along transtensional or transpressional strike-slip faults or in pull-apart basins commonly controlled porphyry-related deposits in post-collisional settings. The latter deposits typically exhibit shallow epithermal levels of emplacement because of preservation by burial.
Seventeen porphyry deposits and one porphyry-related deposit in the study region are reported to contain significant supergene resources. Relatively mature levels of secondary copper enrichment in dominantly granodioritic to granitic porphyry deposits occur in areas where large pyrite-rich quartz-sericite alteration zones have been preserved and exposed to surface oxidation (Güzelyayla and Ulutaş in northeastern Turkey; Agarak, Ankavan, Dastakert, Kadjaran, and Teghout in Armenia; Ali Javad in northern Iran; Kale Kafi in central Iran; Darreh Zar, Meiduk, Now Chun, and Sar Cheshmeh in southeastern Iran; and Tanjeel in southwestern Pakistan). Chalcocite blankets also developed over porphyry deposits in regions where significant post-mineral faulting has occurred (Muratdere and Sarıçayıryayla in western Turkey). Normal faulting also enhanced secondary enrichment of gold in the Halilağa porphyry and Sary Gunay porphyry-related deposits located respectively in western Turkey and northern Iran.
Evaluation of provincial as well as local controls strongly suggests that continued exploration in the region will lead to the identification of additional porphyry and porphyry-related deposits. These deposits will likely be found under younger cover formations in porphyry belts that are already known, and in association with superjacent high- and intermediate-sulfidation epithermal deposits, or increasingly peripheral skarn, carbonate-replacement, and sediment-hosted deposits. Application of suitable exploration techniques to detect concealed and/or deformed deposits in porphyry belts that remain under-explored may also prove productive.
By coupling with the ground, wind causes ground motion that appears on seismic records as noise across a wide bandwidth. This wind-generated noise can drown out important features such as small earthquakes and prevent observation of normal modes from large earthquakes. Because the wind field is heterogeneous at local scales due to structures, diurnal heating, and topography, wind-induced seismic noise may be different on seismometers installed just meters apart. We have investigated the spatial variability of wind-induced noise using two weather sensors separated by approximately ~100 m and co-located with one deep borehole and four near-surface broadband seismometers. We found that at longer periods (>5 s), increasing wind speed causes increases in noise on the horizontal components of seismometers. While this has been previously observed, we also measured a γ2-coherence of less than 0.2 between the wind speed, wind direction, and the pressure recorded by our weather stations. We further observed a loss of coherence between the vertical components of our seismometers from 8 s to 20 s period. The amplitude of the drop-in coherence appears to depend on the substrate surrounding the seismometer. Based on two previously-developed theoretical models, we found that the local material surrounding the sensor could be amplifying the wind-generated noise. We also investigated the frequency dependence of wind-induced noise and found that the dominant source of high-frequency seismic noise at some sites could be anthropogenic rather than induced by wind. Additionally, we estimated the linear relationship between the root mean squares (RMS) of wind speed and RMS seismic velocity for all sensors, finding substantial variability between different installments. A more detailed understanding of the complex processes by which wind-induced noise is generated can inform the installation of sensors and the development of methods for mitigation of these effects, thus improving the overall quality of seismic data.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120180227","usgsCitation":"Dybing, S., Ringler, A.T., Wilson, D.C., and Anthony, R.E., 2019, Characteristics and spatial variability of wind noise on near-surface broadband seismometers: Bulletin of the Seismological Society of America, v. 109, no. 3, p. 1082-1098, https://doi.org/10.1785/0120180227.","productDescription":"17 p.","startPage":"1082","endPage":"1098","ipdsId":"IP-103523","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":381855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Alburquerque","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.84478759765624,\n 34.95799531086792\n ],\n [\n -106.46575927734375,\n 34.95799531086792\n ],\n [\n -106.46575927734375,\n 35.240011164750456\n ],\n [\n -106.84478759765624,\n 35.240011164750456\n ],\n [\n -106.84478759765624,\n 34.95799531086792\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"109","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Dybing, S. 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N.","affiliations":[{"id":35028,"text":"Washington University in St. Louis","active":true,"usgs":false}],"preferred":false,"id":807510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807513,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202458,"text":"ds1108 - 2019 - Quality of surface water in Missouri, water year 2017","interactions":[],"lastModifiedDate":"2019-03-19T16:29:55","indexId":"ds1108","displayToPublicDate":"2019-03-19T11:02:36","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1108","displayTitle":"Quality of Surface Water in Missouri, Water Year 2017","title":"Quality of surface water in Missouri, water year 2017","docAbstract":"The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a network of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2017 (October 1, 2016, through September 30, 2017), data presented in this report were collected at 72 stations: 70 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Among the 72 stations in this report, 4 stations have data presented from additional sampling performed in cooperation with the U.S. Army Corps of Engineers. Summaries of the concentrations of dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticide compounds are presented. Most of the stations have been classified based on the physiographic province or primary land use in the watershed represented by the station. Some stations have been classified based on the unique hydrology of the waterbodies they monitor. A summary of hydrologic conditions in the State including peak streamflows, monthly mean streamflows, and 7-day low flows also are presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1108","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Barr, M.N., and Bartels, K.A., 2019, Quality of surface water in Missouri, water year 2017: U.S. Geological Survey Data Series 1108, 25 p., https://doi.org/10.3133/ds1108.","productDescription":"v, 24 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-101659","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":362075,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1108/coverthb.jpg"},{"id":362076,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1108/ds1108.pdf","text":"Report","size":"2.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1108"}],"country":"United 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\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
An important predictive variable for site amplification is the site dominant frequency (ƒd). At seismic monitoring stations, ƒd can be calculated from the peak of the horizontal‐to‐vertical spectral ratios (HVSRs) obtained from earthquake recordings (eHVSR). For other sites, ƒd can be estimated from microseismic (mHVSR) observations. We compare the ƒd values derived from eHVSR (5% damped response spectra from the Next Generation Attenuation‐West2 [NGA‐West2] database; Ancheta et al., 2014) with those derived from mHVSR (Fourier spectra from Yong et al., 2013) for seismic stations in California. We show that the logarithm of eHVSR ƒd scales linearly with the logarithm of mHVSR ƒd, with a standard deviation of 0.14log10 units for mHVSR ƒd larger than 0.2 Hz. The relationship holds for microseismic surveys at distances up to 300 m away from the seismic stations. The results of this study have beneficial implications for the characterization of site response in modern ground‐motion models as well as in building codes.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120180267","usgsCitation":"Behzad Hassani, Yong, A., Gail M. Atkinson, Feng, T., and Meng, L., 2019, Comparison of site dominant frequency from earthquake and microseismic data in California: Bulletin of the Seismological Society of America, v. 109, no. 3, p. 1034-1040, https://doi.org/10.1785/0120180267.","productDescription":"7 p.","startPage":"1034","endPage":"1040","ipdsId":"IP-100202","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482282,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Atkinson","affiliations":[{"id":83925,"text":"Western University, Department of Earth Sciences 1151 Richmond Street London, Ontario Canada N6A 5B7 of Earth Sciences 1151 Richmond Street London, Ontario Canada N6A 5B7","active":true,"usgs":false}],"preferred":false,"id":927971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feng, Tian","contributorId":351139,"corporation":false,"usgs":false,"family":"Feng","given":"Tian","affiliations":[{"id":83926,"text":"University of California, Los Angeles, Los Angeles, California, USA","active":true,"usgs":false}],"preferred":false,"id":928000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meng, Lingseng","contributorId":351143,"corporation":false,"usgs":false,"family":"Meng","given":"Lingseng","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":928001,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205205,"text":"70205205 - 2019 - Relationships between diatom metrics based on species nutrient traits and agricultural land use","interactions":[],"lastModifiedDate":"2019-09-06T10:11:09","indexId":"70205205","displayToPublicDate":"2019-03-19T09:55:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Relationships between diatom metrics based on species nutrient traits and agricultural land use","docAbstract":"We assessed how diatom metrics were related to different ranges of agricultural land use. Diatom assemblage composition, nutrients, and landscape characteristics were determined at 232 sites in eight agriculturally dominated study areas of the continental United States. Two regional groups based on differences in diatom relations to human disturbance were determined. Changes in diatom species composition were related to nutrients,pH,and conductivity in the eastern study areas (due to more wetlands) and more exclusively to nutrients in the west-central study areas. Homogenization of diatom flora among streams was related to high agricultural disturbance at this transcontinental scale. Species traits were developed separately for the east and west central study groups and calculated two ways: indicator species analysis for taxa in low and high TN or TP conditions and weighted average partial least squares models of TN and TP concentration. These diatom metrics were significantly related to many indicators of agricultural land use in watersheds, especially percent row crops. Further analysis was conducted on only the west-central region due to its larger sample size.Overall, diatom metrics using species responses to N gradients were better related to agricultural land use than were species responses to P gradients. Most nutrient-based diatom metrics changed greatly in response to low ranges of percent row crops, but only a few high N diatom metrics responded to high row crop conditions. The greater response of diatoms to changes in low agriculture conditions may be due to past diatom evolution occurring when most waters had low nutrient conditions.","language":"English","publisher":"Springer","doi":"10.1007/s10661-019-7357-8","usgsCitation":"Pillsbury, R., Stevenson, R.J., Munn, M., and Waite, I.R., 2019, Relationships between diatom metrics based on species nutrient traits and agricultural land use: Environmental Monitoring and Assessment, v. 191, 228, 28 p., https://doi.org/10.1007/s10661-019-7357-8.","productDescription":"228, 28 p.","ipdsId":"IP-098519","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":367251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Delaware, Florida, Georgia, Idaho, Indiana, Maryland, Minnesota, Mississippi, 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PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Pillsbury, Robert","contributorId":218819,"corporation":false,"usgs":false,"family":"Pillsbury","given":"Robert","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":770350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevenson, R. Jan","contributorId":139110,"corporation":false,"usgs":false,"family":"Stevenson","given":"R.","email":"","middleInitial":"Jan","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":770352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munn, Mark D. 0000-0002-7154-7252","orcid":"https://orcid.org/0000-0002-7154-7252","contributorId":205360,"corporation":false,"usgs":true,"family":"Munn","given":"Mark D.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770349,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770351,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206912,"text":"70206912 - 2019 - Integrated assessment of wastewater reuse, exposure risk, and fish endocrine disruption in the Shenandoah River watershed","interactions":[],"lastModifiedDate":"2019-11-27T08:18:04","indexId":"70206912","displayToPublicDate":"2019-03-19T07:55:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Integrated assessment of wastewater reuse, exposure risk, and fish endocrine disruption in the Shenandoah River watershed","docAbstract":"Reuse of municipal and industrial wastewater treatment plant (WWTP) effluent is an important component in augmenting global freshwater supplies. The Shenandoah River Watershed was selected to conduct on-site exposure experiments to assess endocrine disrupting characteristics of different source waters. This investigation of the Shenandoah River Watershed integrates WWTP wastewater reuse modeling, hydrological and chemical characterization, and in vivo endocrine disruption bioassessment to assess contaminant sources, exposure pathways, and biological effects. The percentage of accumulated WWTP effluent in each river reach (ACCWW) was used to predict environmental concentrations for consumer product chemicals (boron), pharmaceutical compounds (carbamazepine), and steroidal estrogens (estrone, 17-beta-estradiol, estriol, and 17-alpha-ethinylestradiol). Fish endocrine disruption was evaluated using vitellogenin induction in male or juvenile fathead minnows. Water samples were analyzed for >500 inorganic and organic constituents to characterize the complex contaminant mixtures. Municipal ACCWW at drinking water treatment plant surface-water intakes ranged from <0.01 to 2.1 % under mean-annual streamflow and up to 4.7 % under August streamflow. Measured and predicted environmental concentrations resulted in 17-beta-estradiol equivalency quotients ranging from <0.05 to 5.1 ng L-1 indicating low-to-moderate risk of fish endocrine disruption. Results from the fish exposure experiments also showed limited estrogenic effects as indicated by the low (0.5- to 3.2-fold) vitellogenin induction.","language":"English","publisher":"ACS","doi":"10.1021/acs.est.8b05655","usgsCitation":"Barber, L., Krstolic, J.L., Kandel, C., Keefe, S.H., Rice, J., Westerhoff, P., Bertolatus, D., and Vajda, A.M., 2019, Integrated assessment of wastewater reuse, exposure risk, and fish endocrine disruption in the Shenandoah River watershed: Environmental Science & Technology, v. 53, no. 7, p. 3429-3440, https://doi.org/10.1021/acs.est.8b05655.","productDescription":"12 p.","startPage":"3429","endPage":"3440","ipdsId":"IP-099041","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":437536,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QF8S22","text":"USGS data release","linkHelpText":"Assessment of Endocrine Disruption in the Shenandoah River Watershed - Chemical and Biological Data from Mobile Laboratory Fish Exposures and Other Experiments Conducted during 2014, 2015, and 2016"},{"id":369690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Shenandoah River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.8828125,\n 41.64007838467894\n ],\n [\n -77.255859375,\n 42.00032514831621\n ],\n [\n -78.75,\n 40.27952566881291\n ],\n [\n -81.650390625,\n 36.491973470593685\n ],\n [\n -80.2880859375,\n 36.59788913307022\n ],\n [\n -76.5087890625,\n 36.491973470593685\n ],\n [\n -75.234375,\n 39.232253141714885\n ],\n [\n -74.8828125,\n 41.64007838467894\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"53","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Barber, Larry B. 0000-0002-0561-0831","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":218953,"corporation":false,"usgs":true,"family":"Barber","given":"Larry B.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":776234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krstolic, Jennifer L. 0000-0003-2253-9886 jkrstoli@usgs.gov","orcid":"https://orcid.org/0000-0003-2253-9886","contributorId":3677,"corporation":false,"usgs":true,"family":"Krstolic","given":"Jennifer","email":"jkrstoli@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kandel, Chintamani 0000-0002-3932-9247 ckandel@usgs.gov","orcid":"https://orcid.org/0000-0002-3932-9247","contributorId":197343,"corporation":false,"usgs":true,"family":"Kandel","given":"Chintamani","email":"ckandel@usgs.gov","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keefe, Steffanie H. 0000-0002-3805-6101 shkeefe@usgs.gov","orcid":"https://orcid.org/0000-0002-3805-6101","contributorId":2843,"corporation":false,"usgs":true,"family":"Keefe","given":"Steffanie","email":"shkeefe@usgs.gov","middleInitial":"H.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":776237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rice, Jacelyn","contributorId":204155,"corporation":false,"usgs":false,"family":"Rice","given":"Jacelyn","email":"","affiliations":[{"id":36866,"text":"University of North Carolina Charlotte","active":true,"usgs":false}],"preferred":false,"id":776238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Westerhoff, Paul","contributorId":204153,"corporation":false,"usgs":false,"family":"Westerhoff","given":"Paul","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":776239,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bertolatus, David 0000-0002-6829-9454","orcid":"https://orcid.org/0000-0002-6829-9454","contributorId":220848,"corporation":false,"usgs":false,"family":"Bertolatus","given":"David","email":"","affiliations":[{"id":16824,"text":"University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":776240,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vajda, Alan M.","contributorId":179189,"corporation":false,"usgs":false,"family":"Vajda","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":776241,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70203264,"text":"70203264 - 2019 - Delayed dynamic triggering of disposal-induced earthquakes observed by a dense array in Northern Oklahoma","interactions":[],"lastModifiedDate":"2019-05-02T08:21:17","indexId":"70203264","displayToPublicDate":"2019-03-19T07:23:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Delayed dynamic triggering of disposal-induced earthquakes observed by a dense array in Northern Oklahoma","docAbstract":"Recent increases in earthquake occurrence rates in Oklahoma have been linked to the injection of large volumes of saltwater, a byproduct of oil and gas extraction. Here we present a detailed study of remote earthquake triggering in an area of active injection‐induced seismicity in northern Oklahoma using data from the LArge‐n Seismic Survey in Oklahoma (LASSO) temporary array and nearby permanent broadband seismic stations. We estimate changes in earthquake rates and calculate the Coulomb failure stress changes on potential receiver faults due to passing teleseismic surface waves. A statistically significant increase in seismicity is observed ∼8 hr after the 16 April 2016 Mw 7.8 Ecuador earthquake. The Coulomb stress changes associated with the Ecuador earthquake are on the order of ∼1 kPa. Physical mechanisms consistent with the observed dynamic stress threshold include failure driven by activation of aseismic slip or hydrological response of the fault system.
Conventional markets can underprovide ecosystem services. Deliberate creation of a market for ecosystem services [e.g., a payments for ecosystem services (PES) scheme] can close the gap. The new ecosystem service market alters behaviors and quantities of ecosystem service provided and reveals prices for the ecosystems service: a market-clearing equilibrium. Assessing the potential for PES programs, which often act as ecological infrastructure investment mechanisms, requires forecasting the market-clearing equilibrium. Forecasting the equilibrium is complicated, especially at relevant social and ecological scales. It requires greater disciplinary integration than valuing ecosystem services or computing the marginal cost of making a land-use change to produce a service. We conduct an ex ante benefit–cost assessment and forecast market-clearing prices and quantities for ecological infrastructure investment contracts in the Panama Canal Watershed. The Panama Canal Authority could offer contracts to private farmers to change land use to increase dry-season water flow and reduce sedimentation. A feasible voluntary contracting system yields a small program of about 1,840 ha of land conversion in a 279,000-ha watershed and generates a 4.9 benefit–cost ratio. Physical and social constraints limit market supply and scalability. Service delays, caused by lags between the time payments must be made and the time services stemming from ecosystem change are realized, hinder program feasibility. Targeting opportunities raise the benefit–cost ratio but reduce the hectares likely to be converted. We compare and contrast our results with prior state-of-the-art assessments on this system.
Coastal inundation due to sea level rise (SLR) is projected to displace hundreds of millions of people worldwide over the next century, creating significant economic, humanitarian, and national-security challenges. However, the majority of previous efforts to characterize potential coastal impacts of climate change have focused primarily on long-term SLR with a static tide level, and have not comprehensively accounted for dynamic physical drivers such as tidal non-linearity, storms, short-term climate variability, erosion response and consequent flooding responses. Here we present a dynamic modeling approach that estimates climate-driven changes in flood-hazard exposure by integrating the effects of SLR, tides, waves, storms, and coastal change (i.e. beach erosion and cliff retreat). We show that for California, USA, the world’s 5th largest economy, over $150 billion of property equating to more than 6% of the state’s GDP and 600,000 people could be impacted by dynamic flooding by 2100; a three-fold increase in exposed population than if only SLR and a static coastline are considered. The potential for underestimating societal exposure to coastal flooding is greater for smaller SLR scenarios, up to a seven-fold increase in exposed population and economic interests when considering storm conditions in addition to SLR. These results highlight the importance of including climate-change driven dynamic coastal processes and impacts in both short-term hazard mitigation and long-term adaptation planning.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-019-40742-z","usgsCitation":"Barnard, P., Erikson, L.H., Foxgrover, A.C., Finzi Hart, J., Limber, P.W., O'Neill, A., van Ormondt, M., Vitousek, S., Wood, N.J., Hayden, M.K., and Jones, J.M., 2019, Dynamic flood modeling essential to assess the coastal impacts of climate change: Scientific Reports, v. 9, p. 1-13, https://doi.org/10.1038/s41598-019-40742-z.","productDescription":"Article number: 4309; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-092817","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467802,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-40742-z","text":"Publisher Index Page"},{"id":362162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foxgrover, Amy C. 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herbivory","docAbstract":"The seasonal assembly of arthropod communities is shaped by biotic and abiotic aspects of the habitat that limit the appearance or activity phenology of potential community members. In addition, previous interactions within the community, such as herbivore-induced plant defensive responses, aggregation, and predator avoidance likely affect the assembly of arthropod communities on individual plants. We observed the phenology of arthropod communities and defensive plant traits on 100 milkweed (Asclepias eriocarpa) individuals at monthly intervals over a growing season. We experimentally wounded a subset of plants each month (April–August) to observe the effect of simulated added herbivore damage on the seasonal assembly of these arthropod communities. All plant traits and measures of arthropod communities changed over the season. The observed response to experimental leaf damage suggested a trend of induced susceptibility in early months, but not late months. Plants receiving early-season simulated herbivory experienced more subsequent leaf damage than unmanipulated plants. We observed several lagged correlations in our study indicating that blue milkweed beetle (Chrysochus cobaltinus) abundance was lower in months following high natural leaf damage, and that the abundance of a secondary omnivore (Lygaeus kalmii) and total predator abundance tended to follow months with high C. cobaltinus abundance. Ahistorical habitat factors determined much of the observed seasonality of arthropod communities, but induced responses to simulated herbivory also contributed historical effects that influenced arthropod community assembly.
","language":"English","publisher":"Springer","doi":"10.1007/s11829-018-9660-7","usgsCitation":"Pearse, I.S., McMunn, M., and Yang, L.H., 2019, Seasonal assembly of arthropod communities on milkweeds experiencing simulated herbivory: Arthropod-Plant Interactions, v. 13, no. 1, p. 99-108, https://doi.org/10.1007/s11829-018-9660-7.","productDescription":"10 p.","startPage":"99","endPage":"108","ipdsId":"IP-090327","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467803,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/6s64d530","text":"External Repository"},{"id":437537,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99MQF8J","text":"USGS data release","linkHelpText":"Measurements of milkweeds and associated arthropods at Hastings Preserve, California in 2013"},{"id":362161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearse, Ian S. 0000-0001-7098-0495 ipearse@usgs.gov","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":196309,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","email":"ipearse@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":759496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McMunn, Marshall","contributorId":214268,"corporation":false,"usgs":false,"family":"McMunn","given":"Marshall","email":"","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":759497,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yang, Louie H.","contributorId":214269,"corporation":false,"usgs":false,"family":"Yang","given":"Louie","email":"","middleInitial":"H.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":759498,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202689,"text":"70202689 - 2019 - Stability of temperate coral Astrangia poculata microbiome is reflected across different sequencing methodologies","interactions":[],"lastModifiedDate":"2019-03-18T16:35:28","indexId":"70202689","displayToPublicDate":"2019-03-18T16:35:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5818,"text":"AIMS Microbiology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Stability of temperate coral Astrangia poculata microbiome is reflected across different sequencing methodologies","title":"Stability of temperate coral Astrangia poculata microbiome is reflected across different sequencing methodologies","docAbstract":"The microbiome of the temperate coral Astrangia poculata was first described in 2017 using next-generation Illumina sequencing to examine the coral’s bacterial and archaeal associates across seasons and among hosts of differing symbiotic status. To assess the impact of methodology on the detectable diversity of the coral’s microbiome, we obtained near full-length Sanger sequences from clone libraries constructed from a subset of the same A. poculata samples. Eight samples were analyzed: two sets of paired symbiotic (brown) and aposymbiotic (white) colonies collected in the fall (September) and two sets collected in the spring (April). Analysis of the Sanger sequences revealed that the microbiome of A. poculataexhibited a high level of richness; 806 OTUs were identified among 1390 bacterial sequences. While the Illumina study revealed that A. poculata’s microbial communities did not significantly vary according to symbiotic state, but did vary by season, Sanger sequencing did not expose seasonal or symbiotic differences in the microbiomes. Proteobacteria dominated the microbiome, forming the majority (55% to 80%) of classifiable bacteria in every sample, and the five bacterial classes with the highest mean relative portion (5% to 35%) were the same as those determined by prior Illumina sequencing. Sanger sequencing also captured the same core taxa previously identified by next-generation sequencing. Alignment of all sequences and construction of a phylogenetic tree revealed that both sequencing methods provided similar portrayals of the phylogenetic diversity within A. poculata’s bacterial associates. Consistent with previous findings, the results demonstrated that the Astrangia microbiome is stable notwithstanding the choice of sequencing method and the far fewer sequences generated by clone libraries (46 to 326 sequences per sample) compared to next-generation sequencing (3634 to 48481 sequences per sample). Moreover, the near-full length 16S rRNA sequences produced by this study are presented as a resource for the community studying this model system since they provide necessary information for designing primers and probes to further our understanding of this coral’s microbiome.
","language":"English","publisher":"AIMS Press","doi":"10.3934/microbiol.2019.1.62","usgsCitation":"Goldsmith, D.B., Pratte, Z.A., Kellogg, C.A., Snader, S.E., and Sharp, K.H., 2019, Stability of temperate coral Astrangia poculata microbiome is reflected across different sequencing methodologies: AIMS Microbiology, v. 5, no. 1, p. 62-76, https://doi.org/10.3934/microbiol.2019.1.62.","productDescription":"15 p.","startPage":"62","endPage":"76","ipdsId":"IP-102024","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467804,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3934/microbiol.2019.1.62","text":"Publisher Index Page"},{"id":437538,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C2XCQQ","text":"USGS data release","linkHelpText":"Cold-water Coral Microbiomes (Astrangia poculata) from Narragansett Bay: Sequence Data"},{"id":362160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goldsmith, Dawn B. 0000-0003-0080-5346 dgoldsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-0080-5346","contributorId":191764,"corporation":false,"usgs":true,"family":"Goldsmith","given":"Dawn","email":"dgoldsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pratte, Zoe A.","contributorId":214260,"corporation":false,"usgs":false,"family":"Pratte","given":"Zoe","email":"","middleInitial":"A.","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":759487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kellogg, Christina A. 0000-0002-6492-9455 ckellogg@usgs.gov","orcid":"https://orcid.org/0000-0002-6492-9455","contributorId":391,"corporation":false,"usgs":true,"family":"Kellogg","given":"Christina","email":"ckellogg@usgs.gov","middleInitial":"A.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759488,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snader, Sara E.","contributorId":214261,"corporation":false,"usgs":false,"family":"Snader","given":"Sara","email":"","middleInitial":"E.","affiliations":[{"id":25340,"text":"Cherokee Nation Technologies","active":true,"usgs":false}],"preferred":false,"id":759489,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sharp, Koty H.","contributorId":214262,"corporation":false,"usgs":false,"family":"Sharp","given":"Koty","email":"","middleInitial":"H.","affiliations":[{"id":39003,"text":"Roger Williams University","active":true,"usgs":false}],"preferred":false,"id":759490,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202446,"text":"fs20193009 - 2019 - The Missouri groundwater-level observation network","interactions":[],"lastModifiedDate":"2025-05-15T13:22:59.054456","indexId":"fs20193009","displayToPublicDate":"2019-03-18T14:30:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3009","displayTitle":"The Missouri Groundwater-level Observation Network","title":"The Missouri groundwater-level observation network","docAbstract":"The Missouri groundwater-level observation well network is a series of wells across the State of Missouri in which groundwater levels are monitored in real time and periodically. The wells monitor the water levels in multiple key aquifers, such as the Ozark aquifer in the Salem and Springfield Plateaus and the Mississippi Alluvial Plain aquifer in the South-eastern Lowlands. As of 2018, 150 real-time sites are operated as a cooperative effort between the Missouri Department of Natural Resources (MoDNR) and the U.S. Geological Survey. This fact sheet describes the network and well data from the network.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193009","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Smith, D.C., 2019, The Missouri groundwater-level observation well network (ver. 1.1, March 22, 2019): U.S. Geological Survey Fact Sheet 2019–3009, 2 p., https://doi.org/10.3133/fs20193009.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-098850","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":362261,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2019/3009/versionHist.txt","size":"1 MB","linkFileType":{"id":2,"text":"txt"}},{"id":361919,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3009/fs20193009.pdf","text":"Report","size":"5.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 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U.S. Geological Survey
1400 Independence Road, MS-100
Rolla, MO 65401
Mercury monitoring results from about 300 Morone saxatilis (striped bass) muscle tissue samples collected by the State of Utah from Lake Powell resulted in a Utah/Arizona fish consumption advisory issued in 2012 for approximately the lower 100 kilometers of the reservoir. Chemical, physical, and biological data were collected during two synoptic sampling cruises on Lake Powell during May/June 2014 and August 2015 to test three hypotheses associated with a conceptual model developed to explain the observed geographic concentration gradient of Hg in fish tissue samples. This model proposes that in the transition from a primarily riverine system to a reservoir, there is a change in the concentration and composition of water-column particulate material, increasing in the proportion of organic content moving downstream, as the larger size fractions of the inorganic particulate load are deposited in the upper reservoir. This change alleviates light limitation of phytoplankton production and leads to a higher proportion of autochthonous primary production in the downstream direction. This, in turn, drives increased microbial methylmercury (MeHg) production in the benthos and potentially the water column, in the downstream direction, and results in the observed elevated fish Hg levels in the lower part of the reservoir. The model also proposes that there are differences between the main stem of Lake Powell and side canyons, embayments, or secondary rivers entering the reservoir, in terms of Hg cycling dynamics and bioaccumulations, driven mainly by differences in hydrology. Finally, seasonal differences in Hg dynamics within the reservoir are proposed, based on seasonal dynamics associated with primary production and the physical process of seasonal stratification.
A total of three statistically testable hypotheses were proposed and postulated that measurable differences in key Hg and non-Hg metrics exist between: (1) the upper and lower reservoir; (2) main stem and river arm/side canyon/embayment sites; and (3) early-season (May/June 2014, less stratified) and late-season (August 2015, stratified) conditions. Statistically modeled least square means in combination with the graphical analysis of Hg and non-Hg parameters were used to examine the data collected during the study and test these hypotheses. Data collected during the study are included in a U.S. Geological Survey data release and are available online at https://doi.org/10.5066/F74X560J.
In general, water-column, plankton, and surface sediment samples collected during the synoptic sampling cruises are supportive of the three hypotheses associated with the conceptual model. In support of hypothesis 1 (comparing upper and lower reservoir sites), the least square mean for turbidity was higher in the upper reservoir. In contrast, surface water particulate organic carbon (as a percentage of total particulate mass), particulate MeHg (by mass [in nanograms per gram] and as a percentage of total mercury [THg]), and particulate-dissolved partitioning coefficients for THg and MeHg were higher in the lower reservoir. Plankton THg concentrations also were significantly (probability [p] less than (<) 0.05) higher in the lower reservoir. Surface sediment metrics in support of hypothesis 1 include higher MeHg production potential rates in the lower reservoir. In contrast, there were no statistically significant differences between the upper and lower reservoir for surface sediment percent of MeHg and MeHg concentration, percent MeHg, or methylation rate constants. These spatial trends associated with hypothesis 1 indicate a pathway for enhanced Hg bioavailability in the lower reservoir.
Hypothesis 2, which tested for differences between main stem and river arm/side canyon/embayment sites, was supported by a number of water-column parameters, including particulate THg and MeHg concentrations by mass (in nanograms per gram) and percent particulate MeHg being significantly (p<0.05) higher in the river arms, side canyons, and embayments relative to the main stem channel. Plankton MeHg concentrations (by mass [in nanograms per gram] and volume [in nanograms per liter] and as a percentage of THg) were elevated in river arm/side canyon/embayment sites compared to main stem sites, indicating an enhanced potential for MeHg bioaccumulation at the base of the pelagic food web in river arms, side canyons, and embayments. In contrast, few of the sediment metrics differed between main stem and river arm/side canyon/embayment sampling sites; however, the potential for MeHg degradation in surface sediment was significantly higher in the main stem. The data indicate that river arm/side canyon/embayment sites may experience enhanced Hg bioaccumulation, compared to the main stem, because of higher MeHg levels at the base of the pelagic food web. This conclusion is supported by the elevated Hg detected in striped bass muscle tissue samples collected in the San Juan Arm during this study (2014). Fish collected from the lower reservoir exhibited a distinct Hg isotopic signature that was enriched in delta (δ)202Hg and capital delta (Δ)199Hg relative to fish samples collected from either Good Hope Bay or the San Juan Arm.
Hypothesis 3 tested for differences between early (May/June) high-flow and late (August) low-flow seasons. This test was supported by a range of non-Hg metrics (nitrate, phosphate, chlorophyll a, dissolved oxygen, fluorescent dissolved organic matter, temperature, and pH) that reflect the increase in chlorophyll a, decrease in nutrients, and buildup of stratified conditions in the transition from early- to late-season sampling periods. Significant seasonal differences also were noted for multiple Hg metrics, including (a) water-column filtered and particulate (by mass) MeHg and THg concentrations; (b) plankton MeHg and THg concentration (by mass); and (c) sediment percent MeHg, Hg(II)-methylation rate constant, and microbial ribosomal ribonucleic acid, small subunit 16 (16S rRNA) abundance, all of which were higher during the late-season synoptic sampling. Overall, the surface sediment metrics are consistent with a seasonal shift from the early-season synoptic results, when the availability of Hg(II) exerts a primary control on MeHg production, to the late-season synoptic sampling, when microbial activity is a dominant driver of MeHg production.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181159","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Naftz, D.L., Marvin-DiPasquale, M., Krabbenhoft, D.P., Aiken, G., Boyd, E.S., Conaway, C.H., Ogorek, J., and Anderson, G.M., 2019, Biogeochemical and physical processes controlling mercury methylation and bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015: U.S. Geological Survey Open-File Report 2018–1159, 81 p., https://doi.org/10.3133/ofr20181159.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-095917","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359576,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1159/coverthb.jpg"},{"id":359577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1159/ofr20181159.pdf","text":"Report","size":"9.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1159"},{"id":359578,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74X560J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for Biogeochemical and Physical Processes Controlling Mercury Methylation and Bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014–2015"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Glen Canyon, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.63551330566406,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 36.75594019674357\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle West
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