A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 59th annual report of the River Master of the Delaware River. It covers the 2012 River Master report year, the period from December 1, 2011 to November 30, 2012.
During the report year, precipitation in the upper Delaware River Basin was 43.35 inches or 97 percent of the long-term average. Combined storage in the Pepacton, Cannonsville, and Neversink Reservoirs remained high through late May, declined from then until mid-September, decreasing below 80 percent of combined capacity in late August, increased in late October, and decreased slightly in November 2012. Delaware River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 52 days during the report year. Interim Excess Release Quantity and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year. An agreement was signed on October 25, 2012, to increase discharge mitigation releases from the Neversink Reservoir due to potential impacts from Hurricane Sandy.
The quality of water in the Delaware River estuary between Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at various locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211095","usgsCitation":"DiFrenna, V.J., Andrews, W.J., Russell, K.L., Norris, J.M., and Mason, R.R., Jr., 2022, Report of the River Master of the Delaware River for the period December 1, 2011–November 30, 2012: U.S. Geological Survey Open-File Report 2021–1095, 101 p., https://doi.org/10.3133/ofr20211095.","productDescription":"x, 101 p.","numberOfPages":"101","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123829","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":395538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1095/coverthb.jpg"},{"id":395539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1095/ofr20211095.pdf","text":"Report","size":"4.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1095"},{"id":501528,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112444.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Jersey, New York, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.66259765625,\n 39.67337039176558\n ],\n [\n -73.65234375,\n 39.67337039176558\n ],\n [\n -73.65234375,\n 42.52069952914966\n ],\n [\n -76.66259765625,\n 42.52069952914966\n ],\n [\n -76.66259765625,\n 39.67337039176558\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Delaware River Master
Office of the Delaware River Master
U.S. Geological Survey
120 Route 209 South
Milford, PA 18337
The hydrologic system in the Yucaipa Valley watershed (YVW) was simulated using the coupled Groundwater and Surface-water FLOW model (GSFLOW; Markstrom and others, 2008). This study uses version 2.0 of GSFLOW, which is a combination of the Precipitation-Runoff Modeling System (PRMS; Markstrom and others, 2015), and the Newton-Raphson formulation of the Modular Groundwater-Flow Model (MODFLOW-NWT; hereafter referred to as MODFLOW; Harbaugh, 2005; Niswonger and others, 2011).
GSFLOW partitions the hydrologic system into three regions (fig. B1) that are linked by the exchange of unsaturated and saturated groundwater and surface water. The properties and processes within each region influence the flow of both groundwater and surface water into, out of, and within each region. The PRMS component of GSFLOW simulates Region 1, and the MODFLOW component simulates Regions 2 and 3. In the YVW, GSFLOW was applied as the simulation code and is referred to herein as the Yucaipa Integrated Hydrologic Model (YIHM; Alzraiee and others, 2022). In the YIHM, Region 1 includes the plant canopy, snowpack, and the soil zone; Region 2 includes the stream network; and Region 3 includes the subsurface beneath Regions 1 and 2 and consists of both the saturated and unsaturated zones. Soil-moisture conditions and head relations control the flow of both groundwater and surface water between regions. The maximum lateral extents of Regions 1 and 3 were defined using the surface-water drainage divides described in the “Description of Study Area” section of chapter A of this report. The boundaries for Region 2 are the lowest elevation of the streambeds, the stream channel widths, and the horizontal extent of the stream channels in the YVW. Flow across the unsaturated part of Region 3 is assumed to be vertical and does not cross the lateral boundary.
To simulate hydrologic processes occurring within the YVW using GSFLOW, a model domain was defined to match the surface watershed such that the domain includes each surficial hydrologic unit coinciding (at least partially) with the Yucaipa groundwater subbasin (hereafter referred to as “Yucaipa subbasin”) as defined in California Bulletin 118 (California Department of Water Resources, 2016). The resulting simulated domain (fig. B2) includes the Yucaipa subbasin and intersects partially with parts of the San Bernardino and San Timoteo groundwater subbasins (fig. B2). The area of the active model domain in YIHM is about 121 square miles (mi2). The developed YIHM can be used to improve understanding of the hydrologic processes in YVW and to simulate future management scenarios with different climatic and anthropogenic changes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215118B","collaboration":"Prepared in cooperation with San Bernardino Valley Municipal Water District","usgsCitation":"Alzraiee, A.H., Engott, J.A., Cromwell, G., and Woolfenden, L., 2022, Yucaipa valley integrated hydrological model, chap. B in Cromwell, G., and Alzraiee, A.H., eds., Hydrology of the Yucaipa groundwater subbasin—Characterization and integrated numerical model, San Bernardino and Riverside Counties, California: U.S. Geological Survey Scientific Investigations Report 2021–5118-B, 76 p., https://doi.org/10.3133/sir20215118B.","productDescription":"Report: ix, 76 p., and data release","numberOfPages":"76","onlineOnly":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":395797,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118b.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":395800,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215118A","text":"SIR 2021-5118 Chapter A","linkHelpText":"- Hydrogeologic Characterization of the Yucaipa Groundwater Subbasin"},{"id":395798,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118b.xml"},{"id":395799,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5118/images"},{"id":395795,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5118/covrthbb.jpg"},{"id":395809,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K540DV","description":"Alzraiee, A.H., Engott, J.A., Cromwell, G., and Woolfenden, L., 2022, Yucaipa valley integrated hydrological model, chap. B in Cromwell, G., and Alzraiee, A.H., eds., Hydrology of the Yucaipa groundwater subbasin—Characterization and integrated numerical model, San Bernardino and Riverside Counties, California: U.S. Geological Survey Scientific Investigations Report 2021–5118-B, 76 p., https://doi.org/10.3133/sir20215118B."}],"contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Natural resource agencies need effective strategies to control the spread of aquatic invasive species (AIS) such as invasive fish, which can expand their range using rivers as hydrological pathways to access new areas. Lock and dam structures within major rivers are prospective locations to deploy techniques, such as carbon dioxide (CO2) infusion into lock water, that could impede upstream AIS migration without disrupting vessel passage and lock operation. The current pesticide label for CO2 in the United States allows injections of 100–150 mg/LCO2 as a behavioral deterrent treatment for invasive carps. This research describes the first operationalizing and testing of a CO2 injection and manifold distribution system at a 1,548,000-L navigation lock chamber on the Fox River near Kaukauna, Wisconsin, USA. Two chemical distribution manifolds located on the floor and wall of the chamber were independently tested to quantify mixing time, mixing homogeneity, injection efficiency, and operational power requirements under a range of operating parameters. Both manifold configurations were able to meet most performance benchmarks established during previous fish behavior studies. Certain limitations were exhibited and quantified for both manifold configurations in terms of mixing homogeneity and operational power. This research details the design and performance of CO2-to-water infusion systems that could be used to deter the spread of AIS at navigation pinch-points. These results may inform future CO2 system designs and operating conditions to support natural resource management plans to limit the spread of AIS.
Water management in the Santa Ana River watershed in San Bernardino and Riverside Counties in southern California (fig. A1) is complex with various water purveyors navigating geographic, geologic, hydrologic, and political challenges to provide a reliable water supply to stakeholders. As the population has increased throughout southern California, so has the demand for water. The Yucaipa groundwater subbasin (hereafter referred to as “Yucaipa subbasin”), one of nine groundwater subbasins in what the California Department of Water Resources (DWR) refers to as the Upper Santa Ana Valley groundwater basin (California Department of Water Resources, 2016; fig. A1; the DWR naming convention is used within this report), is no exception; steady population growth since the 1940s and changes in water use have forced local water purveyors to regularly adapt their water infrastructure. Water demands within the Yucaipa subbasin have historically been supplied by groundwater, but water imported via the California State Water Project has augmented the total water supply through direct use and through anthropogenic recharge at the Wilson Creek and Oak Glen Creek spreading basins since 2002. Overall demand for groundwater continues to rise, and local water managers are concerned that despite the influx of imported water, groundwater levels may decline to a point where producing water will be uneconomical, severely limiting the ability of local agencies to meet water-supply demand.
To better understand the hydrogeology and water resources in the Yucaipa subbasin, the U.S. Geological Survey (USGS) initiated a study in cooperation with the San Bernardino Valley Municipal Water District (SBVMWD) to characterize and model the hydrologic system of the Yucaipa subbasin and the surrounding Yucaipa Valley watershed (YVW; fig. A2). To gain this comprehensive understanding, a three-dimensional (3D) hydrogeologic framework model (HFM; Cromwell and Matti, 2022) was constructed to quantify the structure and extent of hydrogeologic units in the YVW; the hydrologic system was conceptualized and quantified (described in chapter A); and the Yucaipa Integrated Hydrological Model (YIHM; described in chapter B) was developed to simulate the integrated surface-water and aquifer systems, including natural and anthropogenic recharge and discharge throughout the study area during 1947–2014.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215118A","collaboration":"Prepared in cooperation with San Bernardino Valley Municipal Water District","usgsCitation":"Cromwell, G., Engott, J.A., Alzraiee, A.H., Stamos, C.L., Mendez, G.O., Dick, M.C., and Bond, S., 2022, Hydrogeologic characterization of the Yucaipa groundwater subbasin, chap. A in Cromwell, G., and Alzraiee, A.H., eds., Hydrology of the Yucaipa groundwater subbasin—Characterization and integrated numerical model, San Bernardino and Riverside Counties, California: U.S. Geological Survey Scientific Investigations Report 2021–5118–A, 81 p., https://doi.org/10.3133/sir20215118A.","productDescription":"Report: vii, 81 p., and data release","numberOfPages":"81","onlineOnly":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":395793,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F7OYQR","description":"Cromwell, G., Matti, J.C., and Roberts, S.A., 2022, Data release of hydrogeologic data of the Yucaipa groundwater subbasin, San Bernardino and Riverside Counties, California: U.S. Geological Survey Sciencebase data release, https://doi.org/ 10.5066/ P9F7OYQR.","linkHelpText":"Data release of hydrogeologic data of the Yucaipa groundwater subbasin, San Bernardino and Riverside Counties, California"},{"id":395789,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5118/covrthba.jpg"},{"id":395790,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118a.pdf","text":"Report","size":"60 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":395791,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118a.xml"},{"id":395792,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5118/images"},{"id":395794,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215118B","text":"SIR 2021-5118 Chapter B","linkHelpText":"- Yucaipa valley integrated hydrological model"}],"contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Subsurface microbial (biogenic) methane production is an important part of the global carbon cycle that has resulted in natural gas accumulations in many coal beds worldwide. Laboratory studies suggest that complex carbon-containing nutrients (e.g., yeast or algae extract) can stimulate methane production, yet the effectiveness of these nutrients within coal beds is unknown. Here, we use downhole monitoring methods in combination with deuterated water (D2O) and a 200-liter injection of 0.1% yeast extract (YE) to stimulate and isotopically label newly generated methane. A total dissolved gas pressure sensor enabled real-time gas measurements (641 days preinjection and for 478 days postinjection). Downhole samples, collected with subsurface environmental samplers, indicate that methane increased 132% above preinjection levels based on isotopic labeling from D2O, 108% based on pressure readings, and 183% based on methane measurements 266 days postinjection. Demonstrating that YE enhances biogenic coalbed methane production in situ using multiple novel measurement methods has immediate implications for other field-scale biogenic methane investigations, including in situ methods to detect and track microbial activities related to the methanogenic turnover of recalcitrant carbon in the subsurface.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.1c05979","usgsCitation":"Barnhart, E.P., Ruppert, L., Heibert, R., Smith, H.J., Schweitzer, H., Clark, A., Weeks, E., Orem, W.H., Varonka, M., Platt, G.A., Shelton, J., Davis, K., Hyatt, R., McIntosh, J.C., Ashley, K., Ono, S., Martini, A.M., Hackley, K., Gerlach, R., Spangler, L., Phillips, A., Barry, M., Cunningham, A.B., and Fields, M.W., 2022, In situ enhancement and isotopic labeling of biogenic coalbed methane: Environmental Science and Technology, v. 56, no. 5, p. 3225-3233, https://doi.org/10.1021/acs.est.1c05979.","productDescription":"9 p.","startPage":"3225","endPage":"3233","ipdsId":"IP-135929","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":448833,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.1c05979","text":"Publisher Index Page"},{"id":396017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","county":"Rosebud County","otherGeospatial":"Flowers-Goodale coal bed at the Birney Test Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.72393798828125,\n 45.10066901851988\n ],\n [\n -106.37237548828125,\n 45.10066901851988\n ],\n [\n -106.37237548828125,\n 45.48516915340121\n ],\n [\n -106.72393798828125,\n 45.48516915340121\n ],\n [\n -106.72393798828125,\n 45.10066901851988\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, Elliott P. 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University","active":true,"usgs":false}],"preferred":false,"id":834999,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Spangler, Lee","contributorId":279495,"corporation":false,"usgs":false,"family":"Spangler","given":"Lee","email":"","affiliations":[{"id":41008,"text":"Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":835000,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Phillips, Adrienne","contributorId":279496,"corporation":false,"usgs":false,"family":"Phillips","given":"Adrienne","email":"","affiliations":[{"id":41008,"text":"Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":835001,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Barry, Mark","contributorId":279497,"corporation":false,"usgs":false,"family":"Barry","given":"Mark","email":"","affiliations":[{"id":57260,"text":"Pro-Oceanus, Bridgewater, Nova Scotia","active":true,"usgs":false}],"preferred":false,"id":835002,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Cunningham, Alfred B.","contributorId":172389,"corporation":false,"usgs":false,"family":"Cunningham","given":"Alfred","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":835003,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":835004,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70228682,"text":"70228682 - 2022 - Optimizing trilateration estimates for tracking fine-scale movement of wildlife using automated radio telemetry networks","interactions":[],"lastModifiedDate":"2022-02-16T15:18:50.20089","indexId":"70228682","displayToPublicDate":"2022-02-10T09:10:25","publicationYear":"2022","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":"Optimizing trilateration estimates for tracking fine-scale movement of wildlife using automated radio telemetry networks","docAbstract":"A major advancement in the use of radio telemetry has been the development of automated radio tracking systems (ARTS), which allow animal movements to be tracked continuously. A new ARTS approach is the use of a network of simple radio receivers (nodes) that collect radio signal strength (RSS) values from animal-borne radio transmitters. However, the use of RSS-based localization methods in wildlife tracking research is new, and analytical approaches critical for determining high-quality location data have lagged behind technological developments. We present an analytical approach to optimize RSS-based localization estimates for a node network designed to track fine-scale animal movements in a localized area. Specifically, we test the application of analytical filters (signal strength, distance among nodes) to data from real and simulated node networks that differ in the density and configuration of nodes. We evaluate how different filters and network configurations (density and regularity of node spacing) may influence the accuracy of RSS-based localization estimates. Overall, the use of signal strength and distance-based filters resulted in a 3- to 9-fold increase in median accuracy of location estimates over unfiltered estimates, with the most stringent filters providing location estimates with a median accuracy ranging from 28 to 73 m depending on the configuration and spacing of the node network. We found that distance filters performed significantly better than RSS filters for networks with evenly spaced nodes, but the advantage diminished when nodes were less uniformly spaced within a network. Our results not only provide analytical approaches to greatly increase the accuracy of RSS-based localization estimates, as well as the computer code to do so, but also provide guidance on how to best configure node networks to maximize the accuracy and capabilities of such systems for wildlife tracking studies.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8561","usgsCitation":"Paxton, K.L., Baker, K.M., Crytser, Z., Guinto, R.M., Brinck, K., Rogers, H., and Paxton, E.H., 2022, Optimizing trilateration estimates for tracking fine-scale movement of wildlife using automated radio telemetry networks: Ecology and Evolution, v. 12, no. 2, e8561, 12 p., https://doi.org/10.1002/ece3.8561.","productDescription":"e8561, 12 p.","ipdsId":"IP-133962","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":448836,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8561","text":"External Repository"},{"id":435973,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94LQWIE","text":"USGS data release","linkHelpText":"Guam automated radio telemetry network test data 2021"},{"id":396012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.83379364013672,\n 13.54721129739022\n ],\n [\n 144.95773315429685,\n 13.54721129739022\n ],\n [\n 144.95773315429685,\n 13.660666140853907\n ],\n [\n 144.83379364013672,\n 13.660666140853907\n ],\n [\n 144.83379364013672,\n 13.54721129739022\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Paxton, Kristina L. 0000-0003-2321-5090","orcid":"https://orcid.org/0000-0003-2321-5090","contributorId":41917,"corporation":false,"usgs":false,"family":"Paxton","given":"Kristina","email":"","middleInitial":"L.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false},{"id":12981,"text":"Department of Biological Sciences, University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":835023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Kayla M","contributorId":279515,"corporation":false,"usgs":false,"family":"Baker","given":"Kayla","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":835030,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crytser, Zia","contributorId":279506,"corporation":false,"usgs":false,"family":"Crytser","given":"Zia","email":"","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":835025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guinto, Ray Mark Provido 0000-0001-5481-9781","orcid":"https://orcid.org/0000-0001-5481-9781","contributorId":279507,"corporation":false,"usgs":true,"family":"Guinto","given":"Ray","email":"","middleInitial":"Mark Provido","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":835026,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":3847,"corporation":false,"usgs":true,"family":"Brinck","given":"Kevin W.","email":"kbrinck@usgs.gov","affiliations":[],"preferred":false,"id":835027,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rogers, Haldre","contributorId":279510,"corporation":false,"usgs":false,"family":"Rogers","given":"Haldre","email":"","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":835028,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":835029,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256733,"text":"70256733 - 2022 - Seed treatments containing neonicotinoids and fungicides reduce aquatic insect richness and abundance in midwestern USA–managed floodplain wetlands","interactions":[],"lastModifiedDate":"2024-09-04T13:50:54.981462","indexId":"70256733","displayToPublicDate":"2022-02-10T08:32:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1564,"text":"Environmental Science and Pollution Research","active":true,"publicationSubtype":{"id":10}},"title":"Seed treatments containing neonicotinoids and fungicides reduce aquatic insect richness and abundance in midwestern USA–managed floodplain wetlands","docAbstract":"Agrochemicals including neonicotinoid insecticides and fungicides are frequently applied as seed treatments on corn, soybeans, and other common row crops. Crops grown from pesticide-treated seed are often directly planted in managed floodplain wetlands and used as a soil disturbance or food resource for wildlife. We quantified invertebrate communities within mid-latitude floodplain wetlands and assessed their response to use of pesticide-treated seeds within the floodplain. We collected and tested aqueous and sediment samples for pesticides in addition to sampling aquatic invertebrates from 22 paired wetlands. Samples were collected twice in 2016 (spring [pre-water level drawdown] and autumn [post-water level flood-up]) followed by a third sampling period (spring 2017). Meanwhile, during the summer of 2016, a portion of study wetlands were planted with either pesticide-treated or untreated corn seed. Neonicotinoid toxic equivalencies (NI-EQs) for sediment (X̅ = 0.58 μg/kg), water (X̅ = 0.02 μg/L), and sediment fungicide concentrations (X̅ = 0.10 μg/kg) were used to assess potential effects on wetland invertebrates. An overall decrease in aquatic insect richness and abundance was associated with greater NI-EQs in wetland water and sediments, as well as with sediment fungicide concentration. Post-treatment, treated wetlands displayed a decrease in insect taxa-richness and abundance before recovering by the spring of 2017. Information on timing and magnitude of aquatic insect declines will be useful when considering the use of seed treatments for wildlife management. More broadly, this study brings attention to how agriculture is used in wetland management and conservation planning.
","language":"English","publisher":"Springer","doi":"10.1007/s11356-022-18991-9","usgsCitation":"Kuechle, K., Webb, E.B., Mengel, D., and Main, A., 2022, Seed treatments containing neonicotinoids and fungicides reduce aquatic insect richness and abundance in midwestern USA–managed floodplain wetlands: Environmental Science and Pollution Research, v. 29, p. 45261-45275, https://doi.org/10.1007/s11356-022-18991-9.","productDescription":"15 p.","startPage":"45261","endPage":"45275","ipdsId":"IP-117132","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"29","noUsgsAuthors":false,"publicationDate":"2022-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Kuechle, K.J.","contributorId":270317,"corporation":false,"usgs":false,"family":"Kuechle","given":"K.J.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908817,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908818,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mengel, D.","contributorId":244519,"corporation":false,"usgs":false,"family":"Mengel","given":"D.","email":"","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":908819,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Main, A.R.","contributorId":244517,"corporation":false,"usgs":false,"family":"Main","given":"A.R.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908820,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228746,"text":"70228746 - 2022 - A novel approach for directly incorporating disease into fish stock assessment: A case study with seroprevalence data","interactions":[],"lastModifiedDate":"2022-03-28T16:49:12.186131","indexId":"70228746","displayToPublicDate":"2022-02-10T06:49:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A novel approach for directly incorporating disease into fish stock assessment: A case study with seroprevalence data","docAbstract":"Disastrous floods in the twin cities of Nogales, Arizona, USA, and Nogales, Sonora, Mexico (collectively referred to as Ambos Nogales) occur annually in response to monsoonal summer rains. Flood-related hazards include property damage, impairment to sewage systems, sewage discharge, water contamination, erosion, and loss of life. Flood risk, particularly in Nogales, Sonora, is amplified by informal, “squatter” settlements in the watershed floodplain and associated development and infrastructure. The expected increase in precipitation intensity, resulting from climate change, poses further risk to flooding therein. We explore binational community perceptions of flooding, preferences for watershed management, and potential actions to address flooding and increase socio-ecological resilience in Ambos Nogales using standardized questionnaires and interviews to collect data about people and their preferences. We conducted 25 semi-structured interviews with local subject matter experts and gathered survey responses from community members in Ambos Nogales. Though survey response was limited, expected frequencies were high enough to conduct Chi-squared tests of independence to test for statistically significant relationships between survey variables. Results showed that respondents with previous experience with flooding corresponded with their level of concern about future floods. Additionally, respondents perceived greater flood-related risks from traveling across town and damage to vehicles than from inundation or damages to their homes or neighborhoods. Binationally, women respondents felt less prepared for future floods than men. On both sides of the border, community members and local experts agreed that Ambos Nogales lacks adequate preparation for future floods. To increase preparedness, they recommended flood risk education and awareness campaigns, implementation of green infrastructure, additional stormwater infrastructure (such as drainage systems), enhanced flood early warning systems, and reduction of flood flows through regulations to reduce the expansion of hard surfaces. This study contributes systematic collection of information about flood risk perceptions across an international border, including novel data regarding risks related to climate change and gender-based assessments of flood risk. Our finding of commonalities across both border communities, in perceptions of flood risk and in the types of risk reduction solutions recommended by community members, provides clear directions for flood risk education, outreach, and preparedness, as well as measures to enhance cross-border cooperation.
","language":"English","publisher":"Springer Link","doi":"10.1007/s11069-022-05225-x","usgsCitation":"Freimund, C.A., Garfin, G.M., Norman, L., Fisher, L., and Buizer, J., 2022, Flood resilience in paired US–Mexico border cities: A study of binational risk perceptions: Natural Hazards, v. 112, p. 1247-1271, https://doi.org/10.1007/s11069-022-05225-x.","productDescription":"25 p.","startPage":"1247","endPage":"1271","ipdsId":"IP-126099","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":448843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11069-022-05225-x","text":"Publisher Index Page"},{"id":396437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, Sonora","city":"Nogales","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5606689453125,\n 30.840931139029916\n ],\n [\n -110.3466796875,\n 30.840931139029916\n ],\n [\n -110.3466796875,\n 32.194208672875384\n ],\n [\n -111.5606689453125,\n 32.194208672875384\n ],\n [\n -111.5606689453125,\n 30.840931139029916\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"112","noUsgsAuthors":false,"publicationDate":"2022-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Freimund, Christopher A.","contributorId":280033,"corporation":false,"usgs":false,"family":"Freimund","given":"Christopher","email":"","middleInitial":"A.","affiliations":[{"id":50057,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":835919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garfin, Gregg M.","contributorId":205905,"corporation":false,"usgs":false,"family":"Garfin","given":"Gregg","email":"","middleInitial":"M.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":835921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":835920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Larry A.","contributorId":280034,"corporation":false,"usgs":false,"family":"Fisher","given":"Larry A.","affiliations":[{"id":50057,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":835922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buizer, James","contributorId":280035,"corporation":false,"usgs":false,"family":"Buizer","given":"James","email":"","affiliations":[{"id":50057,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":835923,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261013,"text":"70261013 - 2022 - Plant effects on and response to soil microbes in native and non-native Phragmites australis","interactions":[],"lastModifiedDate":"2024-11-20T16:52:50.129682","indexId":"70261013","displayToPublicDate":"2022-02-09T10:49:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Plant effects on and response to soil microbes in native and non-native Phragmites australis","title":"Plant effects on and response to soil microbes in native and non-native Phragmites australis","docAbstract":"Plant–soil feedbacks (PSFs) mediate plant community dynamics and may plausibly facilitate plant invasions. Microbially mediated PSFs are defined by plant effects on soil microbes and subsequent changes in plant performance (responses), both positive and negative. For microbial interactions to benefit invasive plants disproportionately, native and invasive plants must either (1) have different effects on and responses to soil microbial communities or (2) only respond differently to similar microbial communities. In other words, invasive plants do not need to cultivate different microbial communities than natives if they respond differently to them. However, effects and responses are not often explored separately, making it difficult to determine the underlying causes of performance differences. We performed a reciprocal-transplant PSF experiment with multiple microbial inhibition treatments to determine how native and non-native lineages of Phragmites australis affect and respond to soil bacteria, fungi, and oomycetes. Non-native Phragmites is a large, fast-growing, cosmopolitan invasive plant, whereas the North American native variety is comparatively smaller, slower growing, and typically considered a desirable wetland plant. We identified the effects of each plant lineage on soil microbes using DNA meta-barcoding and linked plant responses to microbial communities. Both Phragmites lineages displayed equally weak, insignificant PSFs. We found evidence of slight differential effects on microbial community composition, but no significant differential plant responses. Soils conditioned by each lineage differed only slightly in bacterial community composition, but not in fungal composition. Additionally, native and non-native Phragmites lineages did not significantly differ in their response to similar soil microbial communities. Neither lineage appreciably differed when plant biomass was compared between those grown in sterile and live soils. Targeted microbial inhibitor treatments revealed both lineages were negatively impacted by soil bacteria, but the negative response was stronger in non-native Phragmites. These observations were opposite of expectations from invasion theory and imply that the success of non-native Phragmites, relative to the native lineage, does not result from its interaction with soil microorganisms. More broadly, quantifying plant effects on, and responses to soil microbes separately provides detailed and nuanced insight into plant-microbial interactions and their role in invasions, which could inform management outcomes for invasive plants.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2565","usgsCitation":"Bickford, W.A., Goldberg, D., Zak, D., Snow, D.S., and Kowalski, K., 2022, Plant effects on and response to soil microbes in native and non-native Phragmites australis: Ecological Applications, v. 32, no. 4, e2565, 18 p., https://doi.org/10.1002/eap.2565.","productDescription":"e2565, 18 p.","ipdsId":"IP-122406","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467201,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.2565","text":"External Repository"},{"id":464361,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Bickford, Wesley A. 0000-0001-7612-1325 wbickford@usgs.gov","orcid":"https://orcid.org/0000-0001-7612-1325","contributorId":5687,"corporation":false,"usgs":true,"family":"Bickford","given":"Wesley","email":"wbickford@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":918919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldberg, Deborah E.","contributorId":346406,"corporation":false,"usgs":false,"family":"Goldberg","given":"Deborah E.","affiliations":[],"preferred":false,"id":918920,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zak, Donald R.","contributorId":346407,"corporation":false,"usgs":false,"family":"Zak","given":"Donald R.","affiliations":[],"preferred":false,"id":918921,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snow, Danielle S.","contributorId":346423,"corporation":false,"usgs":false,"family":"Snow","given":"Danielle","email":"","middleInitial":"S.","affiliations":[{"id":65481,"text":"Akima Systems Engineering","active":true,"usgs":false}],"preferred":false,"id":918922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":918923,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236283,"text":"70236283 - 2022 - Foreword to the special Issue on ‘The rapidly expanding role of drones as a tool for wildlife research’","interactions":[],"lastModifiedDate":"2022-08-31T13:53:09.571447","indexId":"70236283","displayToPublicDate":"2022-02-09T08:51:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3777,"text":"Wildlife Research","active":true,"publicationSubtype":{"id":10}},"title":"Foreword to the special Issue on ‘The rapidly expanding role of drones as a tool for wildlife research’","docAbstract":"Drones have emerged as a popular wildlife research tool, but their use for many species and environments remains untested and research is needed on validation of sampling approaches that are optimised for unpiloted aircraft. Here, we present a foreword to a special issue that features studies pushing the taxonomic and innovation boundaries of drone research and thus helps address these knowledge and application gaps. We then conclude by highlighting future drone research ideas that are likely to push biology and conservation in exciting new directions.
","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WR22006","usgsCitation":"Wirsing, A.J., Johnston, A.N., and Kiszka, J.J., 2022, Foreword to the special Issue on ‘The rapidly expanding role of drones as a tool for wildlife research’: Wildlife Research, v. 49, no. 1, p. i-v, https://doi.org/10.1071/WR22006.","productDescription":"5 p.","startPage":"i","endPage":"v","ipdsId":"IP-136864","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":448845,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr22006","text":"Publisher Index Page"},{"id":405994,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"49","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Wirsing, Aaron J","contributorId":296041,"corporation":false,"usgs":false,"family":"Wirsing","given":"Aaron","email":"","middleInitial":"J","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":850433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnston, Aaron N. 0000-0003-4659-0504","orcid":"https://orcid.org/0000-0003-4659-0504","contributorId":201768,"corporation":false,"usgs":true,"family":"Johnston","given":"Aaron","email":"","middleInitial":"N.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":850434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiszka, Jeremy J.","contributorId":292061,"corporation":false,"usgs":false,"family":"Kiszka","given":"Jeremy","email":"","middleInitial":"J.","affiliations":[{"id":62816,"text":"Institute of Environment, Department of Biological Sciences, Florida International University","active":true,"usgs":false}],"preferred":false,"id":850435,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229086,"text":"70229086 - 2022 - Prospective and retrospective evaluation of the U.S. Geological Survey public aftershock forecast for the 2019-2021 Southwest Puerto Rico Earthquake and aftershocks","interactions":[],"lastModifiedDate":"2022-02-28T14:52:31.521726","indexId":"70229086","displayToPublicDate":"2022-02-09T08:47:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Prospective and retrospective evaluation of the U.S. Geological Survey public aftershock forecast for the 2019-2021 Southwest Puerto Rico Earthquake and aftershocks","docAbstract":"The
The invasion of exotic annual grass (EAG), e.g., cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), into rangeland ecosystems of the western United States is a broad-scale problem that affects wildlife habitats, increases wildfire frequency, and adds to land management costs. However, identifying individual species of EAG abundance from remote sensing, particularly at early stages of invasion or growth, can be problematic because of overlapping controls and similar phenological characteristics among native and other exotic vegetation. Subsequently, refining and developing tools capable of quantifying the abundance and phenology of annual and perennial grass species would be beneficial to help inform conservation and management efforts at local to regional scales. Here, we deploy an enhanced version of the U.S. Geological Survey Rangeland Exotic Plant Monitoring System to develop timely and accurate maps of annual (2016–2020) and intra-annual (May 2021 and July 2021) abundances of exotic annual and perennial grass species throughout the rangelands of the western United States. This monitoring system leverages field observations and remote-sensing data with artificial intelligence/machine learning to rapidly produce annual and early season estimates of species abundances at a 30-m spatial resolution. We introduce a fully automated and multi-task deep-learning framework to simultaneously predict and generate weekly, near-seamless composites of Harmonized Landsat Sentinel-2 spectral data. These data, along with auxiliary datasets and time series metrics, are incorporated into an ensemble of independent XGBoost models. This study demonstrates that inclusion of the Normalized Difference Vegetation Index and Normalized Difference Wetness Index time-series data generated from our deep-learning framework enables near real-time and accurate mapping of EAG (Median Absolute Error (MdAE): 3.22, 2.72, and 0.02; and correlation coefficient (r): 0.82, 0.81, and 0.73; respectively for EAG, cheatgrass, and medusahead) and native perennial grass abundance (MdAE: 2.51, r:0.72 for Sandberg bluegrass (Poa secunda)). Our approach and the resulting data provide insights into rangeland grass dynamics, which will be useful for applications, such as fire and drought monitoring, habitat suitability mapping, as well as land-cover and land-change modelling. Spatially explicit, timely, and accurate species-specific abundance datasets provide invaluable information to land managers.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14040807","usgsCitation":"Dahal, D., Pastick, N.J., Boyte, S., Parajuli, S., Oimoen, M.J., and Megard, L.J., 2022, Multi-species inference of exotic annual and native perennial grasses in rangelands of the western United States using Harmonized Landsat and Sentinel-2 data: Remote Sensing, v. 14, no. 4, Article: 807, 21 p. ; 3 Data Releases, https://doi.org/10.3390/rs14040807.","productDescription":"Article: 807, 21 p. ; 3 Data Releases","ipdsId":"IP-135991","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":448849,"rank":6,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14040807","text":"Publisher Index Page"},{"id":486325,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14VQEGO","text":"USGS data release","linkHelpText":"Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2025"},{"id":435974,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1Y5TZBM","text":"USGS data release","linkHelpText":"Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2024"},{"id":397952,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GC5JVG","text":"USGS data release","description":"USGS data release","linkHelpText":"Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species and Sandberg bluegrass in the Sagebrush Biome, USA, 2016 - 2020"},{"id":397951,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AVGRH8","text":"USGS data release","description":"USGS data release","linkHelpText":"Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, May 2021, v1"},{"id":395764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397950,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FG6X9Q","text":"USGS data release","description":"USGS data release","linkHelpText":"Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, July 2021, (ver 2.0, January 2022)"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.865234375,\n 30.031055426540206\n ],\n [\n -103.3154296875,\n 32.65787573695528\n ],\n [\n -103.6669921875,\n 34.74161249883172\n ],\n [\n -100.6787109375,\n 36.63316209558658\n ],\n [\n -100.72265625,\n 36.98500309285596\n ],\n [\n -101.29394531249999,\n 37.64903402157866\n ],\n [\n -103.4033203125,\n 39.198205348894795\n ],\n [\n -104.2822265625,\n 40.713955826286046\n ],\n [\n -102.7880859375,\n 43.004647127794435\n ],\n [\n -102.3046875,\n 47.84265762816538\n ],\n [\n -103.53515625,\n 48.980216985374994\n ],\n [\n -121.201171875,\n 49.009050809382046\n ],\n [\n -121.5087890625,\n 46.92025531537451\n ],\n [\n -122.25585937500001,\n 43.96119063892024\n ],\n [\n -122.25585937500001,\n 41.27780646738183\n ],\n [\n -123.662109375,\n 39.605688178320804\n ],\n [\n -124.01367187499999,\n 38.95940879245423\n ],\n [\n -121.86035156249999,\n 36.38591277287651\n ],\n [\n -120.58593749999999,\n 34.70549341022544\n ],\n [\n -120.673828125,\n 33.97980872872457\n ],\n [\n -117.99316406249999,\n 32.91648534731439\n ],\n [\n -117.2900390625,\n 32.69486597787505\n ],\n [\n -114.9609375,\n 32.54681317351514\n ],\n [\n -110.74218749999999,\n 31.27855085894653\n ],\n [\n -108.06152343749999,\n 31.466153715024294\n ],\n [\n -108.06152343749999,\n 31.952162238024975\n ],\n [\n -105.29296874999999,\n 30.977609093348686\n ],\n [\n -104.19433593749999,\n 29.611670115197377\n ],\n [\n -102.919921875,\n 28.998531814051795\n ],\n [\n -101.865234375,\n 30.031055426540206\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Dahal, Devendra 0000-0001-9594-1249","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":192023,"corporation":false,"usgs":false,"family":"Dahal","given":"Devendra","affiliations":[],"preferred":false,"id":834192,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":834193,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":834194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parajuli, Sujan 0000-0002-1652-3063","orcid":"https://orcid.org/0000-0002-1652-3063","contributorId":275653,"corporation":false,"usgs":false,"family":"Parajuli","given":"Sujan","affiliations":[{"id":56871,"text":"KBR Inc. Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":834195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oimoen, Michael J. 0000-0003-3611-6227","orcid":"https://orcid.org/0000-0003-3611-6227","contributorId":275654,"corporation":false,"usgs":false,"family":"Oimoen","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":56871,"text":"KBR Inc. Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":834196,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Megard, Logan J. 0000-0002-0150-4521","orcid":"https://orcid.org/0000-0002-0150-4521","contributorId":275655,"corporation":false,"usgs":false,"family":"Megard","given":"Logan","email":"","middleInitial":"J.","affiliations":[{"id":56872,"text":"C2G Inc. Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":834197,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230388,"text":"70230388 - 2022 - Ground failure triggered by the 7 January 2020 M6.4 Puerto Rico earthquake","interactions":[],"lastModifiedDate":"2022-04-11T12:09:16.143542","indexId":"70230388","displayToPublicDate":"2022-02-09T07:07:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Ground failure triggered by the 7 January 2020 M6.4 Puerto Rico earthquake","docAbstract":"The 7 January 2020 M 6.4 Puerto Rico earthquake, the mainshock of an extended earthquake sequence, triggered significant ground failure. In this study, we detail the ground failure that occurred based largely on a postearthquake field reconnaissance campaign that we conducted. We documented more than 300 landslides, mainly rock falls that were concentrated in areas where peak ground acceleration (PGA) exceeded 30%g; sparse smaller landslides occurred in highly susceptible areas more than 50 km from the epicenter and at PGA values <10%g. Though some of the largest mass movements were in natural slopes, rock falls in road cuts had more impact because they caused widespread transportation disruption. Some structures also were damaged by landslides, but no landslide‐related fatalities were reported. Liquefaction and related lateral spreading were severe in some areas, causing damage to residential and commercial structures and a power plant. Most of the liquefaction occurred in coastal areas where shaking exceeded 50%g, though some of the most damaging instances were in Ponce, where shaking estimates were as low as 20%g. In this article, we summarize the most notable ground failures and detail the overall patterns that we observed. The observations and compiled inventory datasets presented here are valuable because no island‐wide hazard maps for earthquake‐triggered landslides or liquefaction have been developed for Puerto Rico, and postevent inventories are vital for such an effort. In general, the datasets presented here contribute to a global understanding of coseismic ground‐failure hazard by presenting detailed observations for a relatively moderate ground‐failure event with well‐constrained shaking estimates; current models and expectations are biased by the tendency to collect detailed datasets mainly for exceptional events.
Viti Levu, Fiji, provides one of the best exposed Phanerozoic analogues for the formation of juvenile continental crust in an intra-oceanic setting. Tonalites and trondhjemites are present in several large (75–150 km2) adjacent, mid-Cenozoic plutons. We report major and trace element data including rare earth element (REE) and high-precision high field strength element (HFSE) compositions, new Hf-Nd-Sr-Pb isotope data, and zircon U/Pb-ages, O-Hf isotopes, and trace elements, from five different plutons. The Eocene Yavuna pluton and the Miocene Colo plutons are mainly composed of tonalites and trondhjemites and represent the exposed middle crust of the former Vitiaz island arc. The plutons can be divided into three suites. One suite is light REE (LREE) depleted with some trace element ratios lower than average normal mid-ocean ridge basalts (N-MORB). A second suite has flat REE patterns similar to local island arc basalts. Both suites occur near the coast of Viti Levu, include a wide compositional spectrum from gabbro to tonalite, and can be produced mostly by fractional crystallization of mafic precursor melts. The third suite is characterized by LREE enrichments with higher LaN/YbN (2.3–4.9), higher Zr/Y (4.3–7.1), and lower Nb/Ta (9.6–12.4). They occur closer to the center of the island and are bimodal trondhjemite-gabbro intrusions. These characteristics are consistent with formation mostly by partial melting of mafic crust. Trace element modeling shows that the trace element ratios of the third suite can be produced by 10–20 % melting of the mafic crust in the presence of residual amphibole, resulting in the retention of the medium REE (MREE) and diagnostic trace element ratios including low Nb/Ta and high Zr/Y. Geochemical similarities of the LREE enriched suite to typical “low”-pressure Archean tonalites-trondhjemites-granodiorites (TTGs) imply a common petrogenetic origin and similar mechanisms for the generation of juvenile Archean and modern differentiated crust by partial melting of mafic crust with residual amphibole. In modern oceanic arcs, genetically unrelated felsic plutonic as well as volcanic rocks co-exist, and in this regard, the Fijian plutons accompany major tectonic disruptions to arc processes.
The causative agents of most coral diseases today remain unknown, complicating disease response and restoration efforts. Pathogen identifications can be hampered by complex microbial communities naturally associated with corals and seawater, which create complicating “background noise” that can potentially obscure a pathogen’s signal. Here, we outline an approach to investigate waterborne coral diseases that use a combination of coral mesocosms, tangential flow filtration, and size fractionation to reduce the impact of this background microbial diversity, compensate for unknown infectious dose, and further narrow the suspect pool of potential pathogens. As proof of concept, we use this method to compare the bacterial communities shed into six Montastraea cavernosa coral mesocosms and demonstrate this method effectively detects differences between diseased and healthy coral colonies. We found several amplicon sequence variants (ASVs) in the diseased mesocosms that represented 100% matches with ASVs identified in prior studies of diseased coral tissue, further illustrating the effectiveness of our approach. Our described method is an effective alternative to using coral tissue or mucus to investigate waterborne coral diseases of unknown etiology and can help more quickly narrow the pool of possible pathogens to better aid in disease response efforts. Additionally, this versatile method can be easily adapted to characterize either the entire microbial community associated with a coral or target-specific microbial groups, making it a beneficial approach regardless of whether a causative agent is suspected or is completely unknown.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/biomethods/bpac007","usgsCitation":"Evans, J.S., Paul, V.J., Ushijima, B., and Kellogg, C.A., 2022, Combining tangential flow filtration and size fractionation of mesocosm water as a method for the investigation of waterborne coral diseases: Biology Methods and Protocols, v. 7, no. 1, bpac007, 8 p., https://doi.org/10.1093/biomethods/bpac007.","productDescription":"bpac007, 8 p.","ipdsId":"IP-133508","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448856,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biomethods/bpac007","text":"Publisher Index Page"},{"id":396118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","city":"Marathon","otherGeospatial":"Key West Nursery","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.14340209960938,\n 24.442148824865637\n ],\n [\n -81.6668701171875,\n 24.442148824865637\n ],\n [\n -81.6668701171875,\n 24.669482313373848\n ],\n [\n -82.14340209960938,\n 24.669482313373848\n ],\n [\n -82.14340209960938,\n 24.442148824865637\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.19583129882812,\n 24.61830581010988\n ],\n [\n -80.90469360351562,\n 24.61830581010988\n ],\n [\n -80.90469360351562,\n 24.79047481357294\n ],\n [\n -81.19583129882812,\n 24.79047481357294\n ],\n [\n -81.19583129882812,\n 24.61830581010988\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-02-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, James S. 0000-0002-9977-1627 jsevans@usgs.gov","orcid":"https://orcid.org/0000-0002-9977-1627","contributorId":279528,"corporation":false,"usgs":true,"family":"Evans","given":"James","email":"jsevans@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":835095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paul, Valerie J. 0000-0002-4691-1569","orcid":"https://orcid.org/0000-0002-4691-1569","contributorId":279530,"corporation":false,"usgs":false,"family":"Paul","given":"Valerie","email":"","middleInitial":"J.","affiliations":[{"id":57268,"text":"Smithsonian Marine Station","active":true,"usgs":false}],"preferred":false,"id":835096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ushijima, Blake","contributorId":91782,"corporation":false,"usgs":false,"family":"Ushijima","given":"Blake","email":"","affiliations":[{"id":13394,"text":"Hawai‘i Institute of Marine Biology","active":true,"usgs":false}],"preferred":false,"id":835097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":835098,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70222925,"text":"70222925 - 2022 - Mature diffuse tectonic block boundary revealed by the 2020 southwestern Puerto Rico seismic sequence","interactions":[],"lastModifiedDate":"2022-03-23T15:27:18.978702","indexId":"70222925","displayToPublicDate":"2022-02-08T10:14:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Mature diffuse tectonic block boundary revealed by the 2020 southwestern Puerto Rico seismic sequence","docAbstract":"Distributed faulting typically tends to coalesce into one or a few faults with repeated deformation. The progression of clustered medium-sized (≥Mw4.5) earthquakes during the 2020 seismic sequence in southwestern Puerto Rico (SWPR), modeling shoreline subsidence from InSAR, and sub-seafloor mapping by high-resolution seismic reflection profiles, suggest that the 2020 SWPR seismic sequence was distributed across several short intersecting strike-slip and normal faults beneath the insular shelf and upper slope of Guayanilla submarine canyon. Multibeam bathymetry map of the seafloor shows significant erosion and retreat of the shelf edge in the area of seismic activity as well as slope-parallel lineaments and submarine canyon meanders that typically develop over geological time. The T-axis of the moderate earthquakes further matches the extension direction previously measured on post early Pliocene (∼>3 Ma) faults. We conclude that although similar deformation has likely taken place in this area during recent geologic time, it does not appear to have coalesced during this time. The deformation may represent the southernmost part of a diffuse boundary, the Western Puerto Rico Deformation Boundary, which accommodates differential movement between the Puerto Rico and Hispaniola arc blocks. This differential movement is possibly driven by the differential seismic coupling along the Puerto Rico—Hispaniola subduction zone. We propose that the compositional heterogeneity across the island arc retards the process of focusing the deformation into a single fault. Given the evidence presented here, we should not expect a single large event in this area but similar diffuse sequences in the future.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021TC006896","usgsCitation":"ten Brink, U., Vanacore, L., Fielding, E.J., Chaytor, J., Lopez-Venegas, A., Baldwin, W.E., Foster, D.S., and Andrews, B.D., 2022, Mature diffuse tectonic block boundary revealed by the 2020 southwestern Puerto Rico seismic sequence: Tectonics, v. 41, no. 3, e2021TC006896, 18 p., https://doi.org/10.1029/2021TC006896.","productDescription":"e2021TC006896, 18 p.","ipdsId":"IP-129343","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448860,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021tc006896","text":"External Repository"},{"id":435976,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96GY6TQ","text":"USGS data release","linkHelpText":"Multichannel seismic-reflection and navigation data collected using SIG ELC1200 and Applied Acoustics Delta Sparkers and Geometrics GeoEel digital streamers during USGS field activity 2020-014-FA."},{"id":397463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"Caribbean Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.6630859375,\n 13.752724664396988\n ],\n [\n -64.4677734375,\n 13.752724664396988\n ],\n [\n -64.4677734375,\n 21.12549763660628\n ],\n [\n -74.6630859375,\n 21.12549763660628\n ],\n [\n -74.6630859375,\n 13.752724664396988\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"ten Brink, Uri S. 0000-0001-6858-3001 utenbrink@usgs.gov","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":127560,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri S.","email":"utenbrink@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":820819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vanacore, L","contributorId":263421,"corporation":false,"usgs":false,"family":"Vanacore","given":"L","email":"","affiliations":[{"id":53976,"text":"Dept. of Geology, U. of Puerto Rico, Mayaguez, PR","active":true,"usgs":false}],"preferred":false,"id":820820,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fielding, E. 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In this work, we used two methods to estimate the asymmetric wave shape from data at three sites. The first method converted wave measurements made at the surface to idealized near-bottom wave-orbital velocities using a set of empirical equations: the “parameterized” waveforms. The second method involved direct measurements of velocities and pressure made near the seabed: the “direct” waveforms. Estimates from the two methods were well correlated at all three sites (Pearson’s correlation coefficient greater than 0.85). Both methods were used to drive bedload-transport calculations that accounted for asymmetric waves, and the results were compared with a traditional excess-stress formulation and field estimates of bedload transport derived from ripple migration rates based on sonar imagery. The cumulative bedload transport from the parameterized waveform was 25% greater than the direct waveform, mainly because the parameterized waveform did not account for negative skewness. Calculated transport rates were comparable to rates estimated from ripple migration except during the largest event, when calculated rates were as much as 100 times greater, which occurred during high period waves.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse10020223","usgsCitation":"Kalra, T., Suttles, S.E., Sherwood, C.R., Warner, J.C., Aretxabaleta, A., and Leavitt, G.R., 2022, Shoaling wave shape estimates from field observations and derived bedload sediment rates: Journal of Marine Science and Engineering, v. 10, no. 2, 223, 27 p., https://doi.org/10.3390/jmse10020223.","productDescription":"223, 27 p.","ipdsId":"IP-130494","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448866,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse10020223","text":"Publisher Index Page"},{"id":395674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Massachusetts, New York","otherGeospatial":"Fire Island, Martha Vineyard, Matanzas Inlet","geographicExtents":"{\n \"type\": 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ssuttles@usgs.gov","orcid":"https://orcid.org/0000-0002-4119-8370","contributorId":192272,"corporation":false,"usgs":true,"family":"Suttles","given":"Steven","email":"ssuttles@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834052,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leavitt, Gibson Robert Scott 0000-0001-5362-9150","orcid":"https://orcid.org/0000-0001-5362-9150","contributorId":275364,"corporation":false,"usgs":true,"family":"Leavitt","given":"Gibson","email":"","middleInitial":"Robert Scott","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834053,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70232989,"text":"70232989 - 2022 - Empirical map-based nonergodic models of site response in the greater Los Angeles area","interactions":[],"lastModifiedDate":"2022-07-15T13:43:13.704946","indexId":"70232989","displayToPublicDate":"2022-02-08T08:31:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Empirical map-based nonergodic models of site response in the greater Los Angeles area","docAbstract":"We develop empirical estimates of site response at seismic stations in the Los Angeles area using recorded ground motions from 414 M 3–7.3 earthquakes in southern California. The data are from a combination of the Next Generation Attenuation‐West2 project, the 2019 Ridgecrest earthquakes, and about 10,000 newly processed records. We estimate site response using an iterative mixed‐effects residuals partitioning approach, accounting for azimuthal variations in anelastic attenuation and potential bias due to spatial clusters of colocated earthquakes. This process yields site response for peak ground acceleration, peak ground velocity, and pseudospectral acceleration relative to a 760 m/s shear‐wave velocity (
The 2018 Kīlauea caldera collapse produced extraordinary sequences of seismicity and deformation, with 62 episodic collapse events which significantly altered the landscape of the summit region. Despite decades of focused scientific studies at Kīlauea, detailed information about the internal structure of the volcano is limited. Recently developed techniques in seismic interferometry can be used to monitor the internal structure of an active volcano more directly by detecting subtle spatiotemporal changes in seismic wave velocity, but their utility relies on accurate interpretations of the underlying phenomena causing those velocity changes. Here, we retrospectively apply repeating-earthquake-based seismic interferometry to the 2018 Kīlauea eruption sequence. We find that seismic velocities changed over two distinct time scales: a sudden increase followed by a slower decrease in velocity in the hours following each collapse event, and a gradual, long-term decrease in velocity over several weeks that ceased approximately 1 month prior to the end of the eruption. Modeling suggests that short-term changes can be explained by magma reservoir pressurization which specifically closed vertical ring fractures. Long-term changes are related to subsidence of the caldera and likely include the influence of inelastic strain from the formation of new fractures. These observations provide new insights into the evolution of Kīlauea during its progressive collapse and will inform future interpretations for near-real-time monitoring at hazardous volcanoes around the world using similar techniques, especially where a dominant fracture orientation is present.
As coastal communities grow more vulnerable to sea-level rise and increased storminess, communities have turned to nature-based solutions to bolster coastal resilience and protection. Marshes have significant wave attenuation properties and can play an important role in coastal protection for many communities. Many restoration projects seek to maximize this ecosystem service but how much marsh restoration is enough to deliver measurable coastal protection benefits is still unknown. This question is critical to guiding assessments of cost effectiveness and for funding, implementation, and optimizing of marsh restoration for risk reduction projects. This study uses SWAN model simulations to determine empirical relationships between wave attenuation and marsh vegetation. The model runs consider several different common marsh morphologies (including systems with channels, ponds, and fringing mudflats), vegetation placement, and simulated storm intensity. Up to a 95% reduction in wave energy is seen at as low as 50% vegetation cover. Although these empirical relationships between vegetative cover and wave attenuation provide essential insight for marsh restoration, it is also important to factor in lifespan estimates of restored marshes when making overall restoration decisions. The results of this study are important for coastal practitioners and managers seeking performance goals and metrics for marsh restoration, enhancement, and creation.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.756670","usgsCitation":"Castagno, K.A., Ganju, N., Beck, M.W., Bowden, A., and Scyphers, S.B., 2022, How much marsh restoration is enough to deliver wave attenuation coastal protection benefits?: Frontiers in Marine Science, v. 8, 756670, 10 p., https://doi.org/10.3389/fmars.2021.756670.","productDescription":"756670, 10 p.","ipdsId":"IP-132439","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448878,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2021.756670","text":"Publisher Index Page"},{"id":395623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2022-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Castagno, Katherine A. 0000-0003-4060-926X","orcid":"https://orcid.org/0000-0003-4060-926X","contributorId":267188,"corporation":false,"usgs":false,"family":"Castagno","given":"Katherine","email":"","middleInitial":"A.","affiliations":[{"id":55434,"text":"Center for Coastal Studies, Provincetown, MA, USA","active":true,"usgs":false}],"preferred":false,"id":833497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":833499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bowden, Alison","contributorId":274903,"corporation":false,"usgs":false,"family":"Bowden","given":"Alison","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":833500,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scyphers, Steven B.","contributorId":274810,"corporation":false,"usgs":false,"family":"Scyphers","given":"Steven","middleInitial":"B.","affiliations":[{"id":56654,"text":"Northeastern University Marine Science Center, 430 Nahant Rd, Nahant, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":833501,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228204,"text":"70228204 - 2022 - Behavioral state-dependent habitat selection and implications for animal translocations","interactions":[],"lastModifiedDate":"2022-02-07T15:22:24.279529","indexId":"70228204","displayToPublicDate":"2022-02-07T09:10:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Behavioral state-dependent habitat selection and implications for animal translocations","docAbstract":"