The motivation for earthquake early warning (EEW) is the fact that in many applications a few extra seconds of notice ahead of the about-imminent strong shaking can provide significant benefit. Reducing data latencies, accelerating processing times, and tuning seismic station distributions increase time available for warning. We assess the feasibility of EEW for Hawai‘i and examine how additional stations or upgrades to existing stations can improve warning times. We designed an objective method to identify the most efficient sites for improving an existing seismic network’s coverage, taking both seismic station distribution and seismic hazard into account. The choice of locations for new seismic station sites is informed by improvements in warning time, considering the distribution of seismic hazard and exposure. New sites that improve warning time from earthquakes that are most likely to generate significant ground motions are given preference. This technique may be applied to any seismically active region and target infrastructure in which seismic hazard is spatially defined. We demonstrate this method’s use on the Island of Hawai‘i, with focus on warnings to astronomical observatories on Mauna Kea and island population centers Hilo and Kailua-Kona. We identified 13 candidate sites for new sensors, telemetry upgrades, or new station installations that should provide an additional 1–4 s of warning for the most probable damaging earthquakes in southern Ka‘ū and northern offshore regions in which 2–14 s and <4 s of warning are currently estimated, respectively.
The Water, Energy, and Biogeochemical Model (WEBMOD) uses the framework of the U.S. Geological Survey (USGS) Modular Modeling System to simulate fluxes of water and solutes through watersheds. WEBMOD divides watersheds into model response units (MRU) where fluxes and reactions are simulated for the following eight hillslope reservoir types: canopy; snowpack; ponding on impervious surfaces; O-horizon; two reservoirs in the unsaturated zone, which represent preferential flow and matrix flow; and two reservoirs in the saturated zone, which also represent preferential flow and matrix flow. The reservoir representing ponding on impervious surfaces, currently not functional (2016), will be implemented once the model is applied to urban areas. MRUs discharge to one or more stream reservoirs that flow to the outlet of the watershed. Hydrologic fluxes in the watershed are simulated by modules derived from the USGS Precipitation Runoff Modeling System; the National Weather Service Hydro-17 snow model; and a topography-driven hydrologic model (TOPMODEL). Modifications to the standard TOPMODEL include the addition of heterogeneous vertical infiltration rates; irrigation; lateral and vertical preferential flows through the unsaturated zone; pipe flow draining the saturated zone; gains and losses to regional aquifer systems; and the option to simulate baseflow discharge by using an exponential, parabolic, or linear decrease in transmissivity. PHREEQC, an aqueous geochemical model, is incorporated to simulate chemical reactions as waters evaporate, mix, and react within the various reservoirs of the model. The reactions that can be specified for a reservoir include equilibrium reactions among water; minerals; surfaces; exchangers; and kinetic reactions such as kinetic mineral dissolution or precipitation, biologically mediated reactions, and radioactive decay. WEBMOD also simulates variations in the concentrations of the stable isotopes deuterium and oxygen-18 as a result of varying inputs, mixing, and evaporation. This manual describes the WEBMOD input and output files, along with the algorithms and procedures used to simulate the hydrology and water quality in a watershed. Examples are presented that demonstrate hydrologic processes, weathering reactions, and isotopic evolution in an alpine watershed and the effect of irrigation on water flows and salinity in an intensively farmed agricultural area.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Surface Water in Book 6: Modeling Techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6B35","issn":"2328-7055","usgsCitation":"Webb, R.M.T., and Parkhurst, D.L., 2017, Water, Energy, and Biogeochemical Model (WEBMOD), user’s manual, version 1: U.S. Geological Survey Techniques and Methods, book 6, chap. B35, 171 p., https://doi.org/10.3133/tm6B35.","productDescription":"xiv, 171 p.","numberOfPages":"190","onlineOnly":"Y","costCenters":[{"id":144,"text":"Branch of Regional Research","active":false,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":438440,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26W9K","text":"USGS data release","linkHelpText":"Water, Energy, and Biogeochemical Model"},{"id":334918,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/b35/tm6b35.pdf","text":"Report","size":"8.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 6-B35"},{"id":334917,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/b35/coverthb.jpg"},{"id":334980,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F7P26W9K","text":"Water, Energy, and Biogeochemical Model (WEBMOD)"}],"publicComments":"This report is Chapter 35 of Section B: Surface Water in Book 6 Modeling Techniques","contact":"Chief, National Research Program, Central Branch
U.S. Geological Survey
Box 25585, Mail Stop 418
Denver, CO 80225-0585
Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
https://ca.water.usgs.gov
Demographic characteristics of bats are often insufficiently described for modeling populations. In data poor situations, experts are often relied upon for characterizing ecological systems. In concert with the development of a matrix model describing Indiana bat (Myotis sodalis) demography, we elicited estimates for parameterizing this model from 12 experts. We conducted this elicitation in two stages, requesting expert values for 12 demographic rates. These rates were adult and juvenile seasonal (winter, summer, fall) survival rates, pup survival in fall, and propensity and success at breeding. Experts were most in agreement about adult fall survival (3% Coefficient of Variation) and least in agreement about propensity of juveniles to breed (37% CV). The experts showed greater concordance for adult ( mean CV, adult = 6.2%) than for juvenile parameters ( mean CV, juvenile = 16.4%), and slightly more agreement for survival (mean CV, survival = 9.8%) compared to reproductive rates ( mean CV, reproduction = 15.1%). However, survival and reproduction were negatively and positively biased, respectively, relative to a stationary dynamic. Despite the species exhibiting near stationary dynamics for two decades prior to the onset of a potential extinction-causing agent, white-nose syndrome, expert estimates indicated a population decline of -11% per year (95% CI = -2%, -20%); quasi-extinction was predicted within a century ( mean = 61 years to QE, range = 32, 97) by 10 of the 12 experts. Were we to use these expert estimates in our modeling efforts, we would have errantly trained our models to a rapidly declining demography asymptomatic of recent demographic behavior. While experts are sometimes the only source of information, a clear understanding of the temporal and spatial context of the information being elicited is necessary to guard against wayward predictions.
","language":"English","publisher":"PeerJ","doi":"10.7287/peerj.preprints.2790v1","usgsCitation":"Thogmartin, W.E., Sanders-Reed, C., Szymanski, J., Pruitt, L., and Runge, M.C., 2017, Experts correctly describe demography associated with historical decline of the endangered Indiana bat, but not recent period of stationarity: PeerJ, v. 5, e2790v1, https://doi.org/10.7287/peerj.preprints.2790v1.","productDescription":"e2790v1","ipdsId":"IP-029961","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":461755,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.7287/peerj.preprints.2790v1","text":"External Repository"},{"id":338812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58de194de4b02ff32c699c93","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":687426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanders-Reed, Carol A.","contributorId":86441,"corporation":false,"usgs":true,"family":"Sanders-Reed","given":"Carol A.","affiliations":[],"preferred":false,"id":687708,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Szymanski, Jennifer","contributorId":15123,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":687709,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pruitt, Lori","contributorId":17468,"corporation":false,"usgs":true,"family":"Pruitt","given":"Lori","email":"","affiliations":[],"preferred":false,"id":687710,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":687711,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179809,"text":"ofr20171002 - 2017 - Forested floristic quality index: An assessment tool for forested wetland habitats using the quality and quantity of woody vegetation at Coastwide Reference Monitoring System (CRMS) vegetation monitoring stations","interactions":[],"lastModifiedDate":"2017-02-08T11:49:54","indexId":"ofr20171002","displayToPublicDate":"2017-02-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1002","title":"Forested floristic quality index: An assessment tool for forested wetland habitats using the quality and quantity of woody vegetation at Coastwide Reference Monitoring System (CRMS) vegetation monitoring stations","docAbstract":"The U.S. Geological Survey, in cooperation with the Coastal Protection and Restoration Authority of Louisiana and the Coastal Wetlands Planning, Protection and Restoration Act, developed the Forested Floristic Quality Index (FFQI) for the Coastwide Reference Monitoring System (CRMS). The FFQI will help evaluate forested wetland sites on a continuum from severely degraded to healthy and will assist in defining areas where forested wetland restoration can be successful by projecting the trajectories of change. At each CRMS forested wetland site there are stations for quantifying the overstory, understory, and herbaceous vegetation layers. Rapidly responding overstory canopy cover and herbaceous layer composition are measured annually, while gradually changing overstory basal area and species composition are collected on a 3-year cycle.
A CRMS analytical team has tailored these data into an index much like the Floristic Quality Index (FQI) currently used for herbaceous marsh and for the herbaceous layer of the swamp vegetation. The core of the FFQI uses basal area by species to assess the quality and quantity of the overstory at each of three stations within each CRMS forested wetland site. Trees that are considered by experts to be higher quality swamp species like Taxodium distichum (bald cypress) and Nyssa aquatica (water tupelo) are scored higher than tree species like Triadica sebifera (Chinese tallow) and Salix nigra (black willow) that are indicators of recent disturbance. This base FFQI is further enhanced by the percent canopy cover in the overstory and the presence of indicator species at the forest floor. This systemic approach attempts to differentiate between locations with similar basal areas that are on different ecosystem trajectories. Because of these varying states of habitat degradation, paired use of the FQI and the FFQI is useful to interpret the vegetative data in transitional locations. There is often an inverse relation between the health of the overstory and health of the herbaceous community beneath it because of resource competition (for example, light) and differing environmental preferences between the two communities. The herbaceous layer vegetation responds rapidly to basic environmental factors such as flooding, salinity, and nutrients and can offer insight into the sustainability of swamps on a temporal scale shorter than tha of the slowly growing woody vegetation.
The FFQI will be available via the CRMS spatial viewer (http://lacoast.gov/crms2/home.aspx), and a new score will be calculated annually for each CRMS forested wetland site as data are collected to establish trends, to compare among sites, and to evaluate specific restoration projects when applicable. The FFQI will identify forested wetland areas in need of restoration and conservation and will help define targets and trajectories for restoration planning.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171002","collaboration":"Prepared in cooperation with the Coastal Protection and Restoration Authority of Louisiana and the Coastal Wetlands Planning, Protection and Restoration Act","usgsCitation":"Wood, W.B., Shaffer, G.P., Visser, J.M., Krauss, K.W., Piazza, S.C., Sharp, L.A., and Cretini, K.F., 2017, Forested Floristic Quality Index—An assessment tool for forested wetland habitats using the quality and quantity of woody vegetation at Coastwide Reference Monitoring System (CRMS) vegetation monitoring stations: U.S. Geological Survey Open-File Report 2017–1002, 15 p., https://doi.org/10.3133/ofr20171002.","productDescription":"iv, 15 p.","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-059586","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":334901,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1002/ofr20171002.pdf","text":"Report","size":"2.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1002"},{"id":334900,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1002/coverthb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.691650390625,\n 28.603814407841327\n ],\n [\n -92.691650390625,\n 30.779598396611537\n ],\n [\n -88.52783203125,\n 30.779598396611537\n ],\n [\n -88.52783203125,\n 28.603814407841327\n ],\n [\n -92.691650390625,\n 28.603814407841327\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc
","tableOfContents":"This protocol describes the processing steps for 45 g and 9 g soil samples potentially contaminated with Bacillus anthracis spores. The protocol is designed to separate and concentrate the spores from bulk soil down to a pellet that can be used for further analysis. Soil extraction solution and mechanical shaking are used to disrupt soil particle aggregates and to aid in the separation of spores from soil particles. Soil samples are washed twice with soil extraction solution to maximize recovery. Differential centrifugation is used to separate spores from the majority of the soil material. The 45 g protocol has been demonstrated by two laboratories using both loamy and sandy soil types. There were no significant differences overall between the two laboratories for either soil type, suggesting that the processing protocol would be robust enough to use at multiple laboratories while achieving comparable recoveries. The 45 g protocol has demonstrated a matrix limit of detection at 14 spores/gram of soil for loamy and sandy soils.
","language":"English","publisher":"United States Environmental Protection Agency","publisherLocation":"Cincinnati, OH","usgsCitation":"Silvestri, E.E., and Griffin, D.W., 2017, Processing protocol for soil samples potentially contaminated with Bacillus anthracis spores [HS7.52.02 - 514], vi, 18 p.","productDescription":"vi, 18 p.","ipdsId":"IP-071772","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":342673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":342662,"type":{"id":15,"text":"Index Page"},"url":"https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=335822"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"594a3429e4b062508e36af53","contributors":{"authors":[{"text":"Silvestri, Erin E.","contributorId":127343,"corporation":false,"usgs":false,"family":"Silvestri","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":698739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":698738,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70177806,"text":"sim3370 - 2017 - Stratigraphic cross sections of the Niobrara interval of the Cody Shale and associated rocks in the Wind River Basin, central Wyoming","interactions":[],"lastModifiedDate":"2017-02-08T09:14:11","indexId":"sim3370","displayToPublicDate":"2017-02-07T18:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3370","title":"Stratigraphic cross sections of the Niobrara interval of the Cody Shale and associated rocks in the Wind River Basin, central Wyoming","docAbstract":"The Wind River Basin in Wyoming is one of many structural and sedimentary basins that formed in the Rocky Mountain foreland during the Laramide orogeny. The basin is nearly 200 miles long, 70 miles wide, and encompasses about 7,400 square miles in central Wyoming. The basin is bounded by the Washakie Range, Owl Creek uplift, and southern Bighorn Mountains on the north, the Casper arch on the east, the Granite Mountains on the south, and Wind River Range on the west.
Many important conventional oil and gas fields producing from reservoirs ranging in age from Mississippian through Tertiary have been discovered in this basin. In addition, an extensive unconventional overpressured basin-centered gas accumulation has been identified in Cretaceous and Tertiary strata in the deeper parts of the basin. It has long been suggested that various Upper Cretaceous marine shales, including the Cody Shale, are the principal hydrocarbon source rocks for many of these accumulations. With recent advances and success in horizontal drilling and multistage fracture stimulation, there has been an increase in exploration and completion of wells in these marine shales in other Rocky Mountain Laramide basins that were traditionally thought of only as hydrocarbon source rocks.
The two stratigraphic cross sections presented in this report were constructed as part of a project carried out by the U.S. Geological Survey to characterize and evaluate the undiscovered continuous (unconventional) oil and gas resources of the Niobrara interval of the Upper Cretaceous Cody Shale in the Wind River Basin in central Wyoming. The primary purpose of the cross sections is to show the stratigraphic relationship of the Niobrara equivalent strata and associated rocks in the lower part of the Cody Shale in the Wind River Basin. These two cross sections were constructed using borehole geophysical logs from 37 wells drilled for oil and gas exploration and production, and one surface section along East Sheep Creek near Shotgun Butte in the northwestern part of the basin. Both lines originate at the East Sheep Creek surface section and end near Clarkson Hill in the extreme southeastern part of the basin. The stratigraphic interval extends from the upper part of the Frontier Formation to the middle part of the Cody Shale. The datum is the base of the “chalk kick” marker bed, a distinctive resistivity peak or zone in the lower part of the Cody Shale. A gamma ray and (or) spontaneous potential (SP) log was used in combination with a resistivity log to identify and correlate units. Marine molluscan index fossils collected from nearby outcrop sections were projected into the subsurface to help determine the relative ages of the strata and aid in correlation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3370","usgsCitation":"Finn, T.M., 2017, Stratigraphic cross sections of the Niobrara interval of the Cody Shale and associated rocks in the Wind River Basin, central Wyoming: U.S. Geological Survey Scientific Investigations Map 3370, 19 p., 1 sheet, https://doi.org/10.3133/sim3370.","productDescription":"Report: iv, 19 p.; Sheet: 56.00 x 29.00 inches","onlineOnly":"Y","ipdsId":"IP-073462","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":334322,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3370/coverthb.jpg"},{"id":334325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3370/sim3370.pdf","text":"Report","size":"5.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3370 Pamphlet"},{"id":334327,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3370/sim3370_sheet.pdf","text":"Map","size":"1.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3370 Map"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110,\n 42.5\n ],\n [\n -110,\n 43.75\n ],\n [\n -106.5,\n 43.75\n ],\n [\n -106.5,\n 42.5\n ],\n [\n -110,\n 42.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, Mail Stop 939
Denver Federal Center
Denver, CO 80225
In 1916, Swedish geologist Ernst Jakob Lennart von Post delivered a provocative lecture in Oslo, Norway, advocating the use of pollen grains in bog sediments as indicators of past vegetation and climate. The lecture spawned many applications and represents a landmark in multidisciplinary science.
","largerWorkTitle":"Nature Plants","language":"English","doi":"10.1038/nplants.2017.1","usgsCitation":"Edwards, K.J., Fyfe, R., and Jackson, S., 2017, The first 100 years of pollen analysis: Nature Plants, v. 3, 17001, https://doi.org/10.1038/nplants.2017.1.","productDescription":"17001","ipdsId":"IP-082947","costCenters":[{"id":41166,"text":"Southwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":470080,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/2164/9078","text":"External Repository"},{"id":381137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationDate":"2017-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Edwards, Kevin J.","contributorId":245536,"corporation":false,"usgs":false,"family":"Edwards","given":"Kevin","email":"","middleInitial":"J.","affiliations":[{"id":39960,"text":"University of Aberdeen, UK","active":true,"usgs":false}],"preferred":false,"id":806384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fyfe, Ralph","contributorId":245537,"corporation":false,"usgs":false,"family":"Fyfe","given":"Ralph","email":"","affiliations":[{"id":39461,"text":"University of Plymouth, UK","active":true,"usgs":false}],"preferred":false,"id":806385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Stephen T. 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":245538,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen T.","affiliations":[{"id":41166,"text":"Southwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":806386,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189688,"text":"70189688 - 2017 - Comparing stream-specific to generalized temperature models to guide salmonid management in a changing climate","interactions":[],"lastModifiedDate":"2018-04-24T13:34:59","indexId":"70189688","displayToPublicDate":"2017-02-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Comparing stream-specific to generalized temperature models to guide salmonid management in a changing climate","docAbstract":"Global climate change is predicted to increase air and stream temperatures and alter thermal habitat suitability for growth and survival of coldwater fishes, including brook charr (Salvelinus fontinalis), brown trout (Salmo trutta), and rainbow trout (Oncorhynchus mykiss). In a changing climate, accurate stream temperature modeling is increasingly important for sustainable salmonid management throughout the world. However, finite resource availability (e.g. funding, personnel) drives a tradeoff between thermal model accuracy and efficiency (i.e. cost-effective applicability at management-relevant spatial extents). Using different projected climate change scenarios, we compared the accuracy and efficiency of stream-specific and generalized (i.e. region-specific) temperature models for coldwater salmonids within and outside the State of Michigan, USA, a region with long-term stream temperature data and productive coldwater fisheries. Projected stream temperature warming between 2016 and 2056 ranged from 0.1 to 3.8 °C in groundwater-dominated streams and 0.2–6.8 °C in surface-runoff dominated systems in the State of Michigan. Despite their generally lower accuracy in predicting exact stream temperatures, generalized models accurately projected salmonid thermal habitat suitability in 82% of groundwater-dominated streams, including those with brook charr (80% accuracy), brown trout (89% accuracy), and rainbow trout (75% accuracy). In contrast, generalized models predicted thermal habitat suitability in runoff-dominated streams with much lower accuracy (54%). These results suggest that, amidst climate change and constraints in resource availability, generalized models are appropriate to forecast thermal conditions in groundwater-dominated streams within and outside Michigan and inform regional-level salmonid management strategies that are practical for coldwater fisheries managers, policy makers, and the public. We recommend fisheries professionals reserve resource-intensive stream-specific models for runoff-dominated systems containing high-priority fisheries resources (e.g. trophy individuals, endangered species) that will be directly impacted by projected stream warming.","language":"English","publisher":"SpringerLink","doi":"10.1007/s11160-017-9467-0","usgsCitation":"Andrew K. Carlson, Taylor, W.W., Hartikainen, K.M., Dana M. Infante, Beard, and Lynch, A., 2017, Comparing stream-specific to generalized temperature models to guide salmonid management in a changing climate: Reviews in Fish Biology and Fisheries, v. 2, no. 27, p. 443-462, https://doi.org/10.1007/s11160-017-9467-0.","productDescription":"19 p. 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Box 25046, Mail Stop 980
Denver, CO 80225
The U.S. Geological Survey Branch of Quality Systems operates the Precipitation Chemistry Quality Assurance Project (PCQA) for the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) and National Atmospheric Deposition Program/Mercury Deposition Network (NADP/MDN). Since 1978, various programs have been implemented by the PCQA to estimate data variability and bias contributed by changing protocols, equipment, and sample submission schemes within NADP networks. These programs independently measure the field and laboratory components which contribute to the overall variability of NADP wet-deposition chemistry and precipitation depth measurements. The PCQA evaluates the quality of analyte-specific chemical analyses from the two, currently (2016) contracted NADP laboratories, Central Analytical Laboratory and Mercury Analytical Laboratory, by comparing laboratory performance among participating national and international laboratories. Sample contamination and stability are evaluated for NTN and MDN by using externally field-processed blank samples provided by the Branch of Quality Systems. A colocated sampler program evaluates the overall variability of NTN measurements and bias between dissimilar precipitation gages and sample collectors.
This report documents historical PCQA operations and general procedures for each of the external quality-assurance programs from 2007 to 2016.
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USGS Branch of Quality Systems
Box 25046, Mail Stop 401
Denver, CO 80225
Heavy rainfall occurred across Louisiana and southwestern Mississippi in August 2016 as a result of a slow-moving area of low pressure and a high amount of atmospheric moisture. The storm caused major flooding in the southern portions of Louisiana including areas surrounding Baton Rouge and Lafayette. Flooding occurred along the rivers such as the Amite, Comite, Tangipahoa, Tickfaw, Vermilion, and Mermentau Rivers. Over 31 inches of rain was reported in the city of Watson, 20 miles northeast of Baton Rouge, La., over the duration of the event. Streamflow-gaging stations operated by the U.S. Geological Survey (USGS) recorded peak streamflows of record at 10 locations, and 7 other locations experienced peak streamflows ranking in the top five for the duration of the period of record. In August 2016, USGS hydrographers made 50 discharge measurements at 21 locations on streams in Louisiana. Many of those discharge measurements were made for the purpose of verifying the accuracy of stage-streamflow relations at gaging stations operated by the USGS. Following the storm event, USGS hydrographers recovered and documented 590 high-water marks, noting location and height of the water above land surface. Many of these high-water marks were used to create 12 flood-inundation maps for selected communities of Louisiana that experienced flooding in August 2016. Digital datasets of the inundation area, modeling boundary, water depth rasters, and final map products are available online.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175005","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Watson, K.M., Storm, J.B., Breaker, B.K., and Rose, C.E., 2017, Characterization of peak streamflows and flood inundation of selected areas in Louisiana from the August 2016 flood: U.S. Geological Survey Scientific Investigations Report 2017–5005, 26 p., https://doi.org/10.3133/sir20175005.","productDescription":"Report: v, 26 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081535","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":334119,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5005/sir20175005.pdf","text":"Report","size":"7.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5005"},{"id":334118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5005/coverthb2.jpg"},{"id":334120,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79K48C1","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Flood Inundation Extent and Depth in Selected Areas of Louisiana in August 2016"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.3123779296875,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 28.859107573773\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
For this study, a methodology was developed for assessing impacts of wind energy generation on populations of birds and bats at regional to national scales. The approach combines existing methods in applied ecology for prioritizing species in terms of their potential risk from wind energy facilities and estimating impacts of fatalities on population status and trend caused by collisions with wind energy infrastructure. Methods include a qualitative prioritization approach, demographic models, and potential biological removal. The approach can be used to prioritize species in need of more thorough study as well as to identify species with minimal risk. However, the components of this methodology require simplifying assumptions and the data required may be unavailable or of poor quality for some species. These issues should be carefully considered before using the methodology. The approach will increase in value as more data become available and will broaden the understanding of anthropogenic sources of mortality on bird and bat populations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Wind energy and wildlife interactions","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer International Publishing","doi":"10.1007/978-3-319-51272-3_4","usgsCitation":"Diffendorfer, J., Beston, J.A., Merrill, M., Stanton, J.C., Corum, M., Loss, S., Thogmartin, W.E., Johnson, D.H., Erickson, R.A., and Heist, K.W., 2017, A method to assess the population-level consequences of wind energy facilities on bird and bat species, chap. of Wind energy and wildlife interactions, p. 65-76, https://doi.org/10.1007/978-3-319-51272-3_4.","productDescription":"12 p.","startPage":"65","endPage":"76","ipdsId":"IP-073352","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":339790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-03","publicationStatus":"PW","scienceBaseUri":"58f5d43de4b0f2e20545e405","contributors":{"authors":[{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":649358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beston, Julie A. jbeston@usgs.gov","contributorId":5673,"corporation":false,"usgs":true,"family":"Beston","given":"Julie","email":"jbeston@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":649359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merrill, Matthew D. 0000-0003-3766-847X mmerrill@usgs.gov","orcid":"https://orcid.org/0000-0003-3766-847X","contributorId":145534,"corporation":false,"usgs":true,"family":"Merrill","given":"Matthew D.","email":"mmerrill@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":649360,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":649361,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corum, M.D. 0000-0002-9038-3935 mcorum@usgs.gov","orcid":"https://orcid.org/0000-0002-9038-3935","contributorId":2249,"corporation":false,"usgs":true,"family":"Corum","given":"M.D.","email":"mcorum@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":649362,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loss, Scott R.","contributorId":140471,"corporation":false,"usgs":false,"family":"Loss","given":"Scott R.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":649363,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":649364,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Douglas H. 0000-0002-7778-6641 douglas_h_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":1387,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas","email":"douglas_h_johnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":649365,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":649366,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heist, Kevin W.","contributorId":83040,"corporation":false,"usgs":false,"family":"Heist","given":"Kevin","email":"","middleInitial":"W.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":649367,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70176334,"text":"ds1019 - 2017 - Groundwater-quality data for the Madera/Chowchilla–Kings shallow aquifer study unit, 2013–14: Results from the California GAMA Program","interactions":[],"lastModifiedDate":"2017-02-06T09:46:13","indexId":"ds1019","displayToPublicDate":"2017-02-03T00:00:00","publicationYear":"2017","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":"1019","title":"Groundwater-quality data for the Madera/Chowchilla–Kings shallow aquifer study unit, 2013–14: Results from the California GAMA Program","docAbstract":"Groundwater quality in the 2,390-square-mile Madera/Chowchilla–Kings Shallow Aquifer study unit was investigated by the U.S. Geological Survey from August 2013 to April 2014 as part of the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment Program’s Priority Basin Project. The study was designed to provide a statistically unbiased, spatially distributed assessment of untreated groundwater quality in the shallow aquifer systems of the Madera, Chowchilla, and Kings subbasins of the San Joaquin Valley groundwater basin. The shallow aquifer system corresponds to the part of the aquifer system generally used by domestic wells and is shallower than the part of the aquifer system generally used by public-supply wells. This report presents the data collected for the study and a brief preliminary description of the results.
Groundwater samples were collected from 77 wells and were analyzed for organic constituents, inorganic constituents, selected isotopic and age-dating tracers, and microbial indicators. Most of the wells sampled for this study were private domestic wells. Unlike groundwater from public-supply wells, the groundwater from private domestic wells is not regulated for quality in California and is rarely analyzed for water-quality constituents. To provide context for the sampling results, however, concentrations of constituents measured in the untreated groundwater were compared with regulatory and non-regulatory benchmarks established for drinking-water quality by the U.S. Environmental Protection Agency, the State of California, and the U.S. Geological Survey.
Of the 319 organic constituents assessed in this study (90 volatile organic compounds and 229 pesticides and pesticide degradates), 17 volatile organic compounds and 23 pesticides and pesticide degradates were detected in groundwater samples; concentrations of all but 2 were less than the respective benchmarks. The fumigants 1,2-dibromo-3-chloropropane (DBCP) and 1,2-dibromoethane (EDB) were detected at concentrations above their respective regulatory benchmarks in samples from a total of four wells.
Most detections of inorganic constituents were at concentrations or activities less than the respective benchmark levels. Five inorganic constituents were detected in groundwater samples from one or more wells at concentrations or activities greater than their respective regulatory, health-based benchmarks: arsenic, uranium, nitrate, adjusted gross alpha particle activity, and gross beta particle activity. Four inorganic constituents were detected in samples from one or more wells at concentrations or activities greater than their respective non-regulatory, health-based benchmarks: manganese, molybdenum, vanadium, and radon-222. Three inorganic constituents were detected in groundwater samples from one or more wells at concentrations greater than their respective non-regulatory, aesthetic-based benchmarks: iron, sulfate, and total dissolved solids.
Microbial indicators (Escherichia coli, total coliform, and enterococci) were analyzed for presence or absence. The presence of Escherichia coli (E. coli) was not detected; the presence of total coliform was detected in samples from 10 of the 72 grid wells for which it was analyzed, and the presence of enterococci was detected in samples from 5 of the 73 grid wells analyzed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1019","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Shelton, J.L., and Fram, M.S., 2017, Groundwater-quality data for the Madera/Chowchilla–Kings shallow aquifer study unit, 2013–14: Results from the California GAMA Program: U.S. Geological Survey Data Series 1019, 115 p., https://doi.org/10.3133/ds1019.","productDescription":"Report: viii, 115 p.","numberOfPages":"128","onlineOnly":"N","ipdsId":"IP-056132","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":334554,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1019/ds1019.pdf","text":"Report","size":"3.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1019"},{"id":334553,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1019/coverthb2.jpg"}],"country":"United States","state":"California","otherGeospatial":"Madera/Chowchilla-Kings Shallow Aquifer study unit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.666667,\n 37.416667\n ],\n [\n -120.666667,\n 36\n ],\n [\n -119.166667,\n 36\n ],\n [\n -119.166667,\n 37.416667\n ],\n [\n -120.666667,\n 37.416667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey (USGS) Wetland and Aquatic Research Center (WARC) has two primary locations (Gainesville, Florida, and Lafayette, Louisiana) and field stations throughout the southeastern United States and Caribbean. WARC’s roots are in U.S. Fish and Wildlife Service (USFWS) and National Park Service research units that were brought into the USGS as the Biological Research Division in 1996. Founded in 2015, WARC was created from the merger of two long-standing USGS biology science Centers—the Southeast Ecological Science Center and the National Wetlands Research Center—to bring together expertise in biology, ecology, landscape science, geospatial applications, and decision support in order to address issues nationally and internationally. WARC scientists apply their expertise to a variety of wetland and aquatic research and monitoring issues that require coordinated, integrated efforts to better understand natural environments. By increasing basic understanding of the biology of important species and broader ecological and physiological processes, this research provides information to policymakers and aids managers in their stewardship of natural resources and in regulatory functions.
This strategic science plan (SSP) was developed to guide WARC research during the next 5–10 years in support of Department of the Interior (DOI) partnering bureaus such as the USFWS, the National Park Service, and the Bureau of Ocean Energy Management, as well as other Federal, State, and local natural resource management agencies. The SSP demonstrates the alignment of the WARC goals with the USGS mission areas, associated programs, and other DOI initiatives. The SSP is necessary for workforce planning and, as such, will be used as a guide for future needs for personnel. The SSP also will be instrumental in developing internal funding priorities and in promoting WARC’s capabilities to both external cooperators and other groups within the USGS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161193","usgsCitation":"U.S. Geological Survey, 2017, The Wetland and Aquatic Research Center strategic science plan: U.S. Geological Survey Open-File Report 2016–1193, 17 p., https://doi.org/10.3133/ofr20161193.","productDescription":"iii, 17 p.","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-072367","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":334602,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1193/ofr20161193.pdf","text":"Report","size":"2.60 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1193"},{"id":331220,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1193/coverthb.jpg"}],"contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
7920 NW 71st Street
Gainesville, FL 32653
https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc
","tableOfContents":"Renibacterium salmoninarum, the causative agent of salmonid bacterial kidney disease (BKD), is endemic in many wild trout species in northerly regions. The aim of the present study was to determine the optimal R. salmoninarum sampling/testing strategy for wild brown trout (Salmo trutta L.) populations in Iceland. Fish were netted in a lake and multiple organs—kidney, spleen, gills, oesophagus and mid-gut—were sampled and subjected to five detection tests i.e. culture, polyclonal enzyme-linked immunosorbent assay (pELISA) and three different PCR tests. The results showed that each fish had encountered R. salmoninarum but there were marked differences between results obtained depending on organ and test. The bacterium was not cultured from any kidney sample while all kidney samples were positive by pELISA. At least one organ from 92.9% of the fish tested positive by PCR. The results demonstrated that the choice of tissue and diagnostic method can dramatically influence the outcome of R. salmoninarum surveys.
","language":"English","publisher":"European Association of Fish Pathologists","usgsCitation":"Guomundsdottir, S., Applegate, L.M., Arnason, I., Kristmundsson, A., Purcell, M., and Elliott, D.G., 2017, Detecting Renibacterium salmoninarum in wild brown trout by use of multiple organ samples and diagnostic methods: Bulletin of the European Association of Fish Pathologists, v. 37, no. 1, p. 31-40.","productDescription":"12 p.","startPage":"31","endPage":"40","numberOfPages":"12","ipdsId":"IP-080542","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":347174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347206,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://eafp.org/bulletin-contents-37-1/"}],"volume":"37","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59eeffa9e4b0220bbd988fb5","contributors":{"authors":[{"text":"Guomundsdottir, S.","contributorId":198069,"corporation":false,"usgs":false,"family":"Guomundsdottir","given":"S.","email":"","affiliations":[],"preferred":false,"id":714997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Applegate, Lynn M. capplegate@usgs.gov","contributorId":5647,"corporation":false,"usgs":true,"family":"Applegate","given":"Lynn","email":"capplegate@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":714998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arnason, I.O.","contributorId":198070,"corporation":false,"usgs":false,"family":"Arnason","given":"I.O.","email":"","affiliations":[],"preferred":false,"id":714999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kristmundsson, A.","contributorId":198071,"corporation":false,"usgs":false,"family":"Kristmundsson","given":"A.","email":"","affiliations":[],"preferred":false,"id":715000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Purcell, Maureen K. mpurcell@usgs.gov","contributorId":3061,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen K.","email":"mpurcell@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":715001,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Elliott, Diane G. 0000-0002-4809-6692 dgelliott@usgs.gov","orcid":"https://orcid.org/0000-0002-4809-6692","contributorId":2947,"corporation":false,"usgs":true,"family":"Elliott","given":"Diane","email":"dgelliott@usgs.gov","middleInitial":"G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":715002,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199536,"text":"70199536 - 2017 - Resampling of spatially correlated data with preferential sampling for the estimation of frequency distributions and semivariograms","interactions":[],"lastModifiedDate":"2018-09-20T15:32:04","indexId":"70199536","displayToPublicDate":"2017-02-01T15:31:58","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3478,"text":"Stochastic Environmental Research and Risk Assessment","active":true,"publicationSubtype":{"id":10}},"title":"Resampling of spatially correlated data with preferential sampling for the estimation of frequency distributions and semivariograms","docAbstract":"Spatial data are commonly minimal and may have been collected in the process of confirming the profitability of a mining venture or investigating a contaminated site. In such situations, it is common to have measurements preferentially taken in the most critical areas (sweet spots, allegedly contaminated areas), thus conditionally biasing the sample. While preferential sampling makes good practical sense, its direct use leads to distorted sample moments and percentiles. Spatial clusters are a problem that has been identified in the past and solved with approaches ranging from ad hoc solutions to highly elaborate mathematical formulations, covering mostly the effect of clustering on the cumulative frequency distribution. The method proposed here is a form of resample, free of special assumptions, does not use weights to ponder the measurements, does not find solutions by successive approximation and provides variability in the results. The new method is illustrated with a synthetic dataset with an exponential semivariogram and purposely generated to follow a lognormal distribution. The lognormal distribution is both difficult to work with and typical of many attributes of practical interest. Testing of the new solution shows that sample subsets derived from resampled datasets can closely approximate the true probability distribution and the semivariogram, clearly outperforming the original preferentially sampled data.
","language":"English","publisher":"Springer","doi":"10.1007/s00477-016-1289-4","usgsCitation":"Olea, R., 2017, Resampling of spatially correlated data with preferential sampling for the estimation of frequency distributions and semivariograms: Stochastic Environmental Research and Risk Assessment, v. 31, no. 2, p. 481-491, https://doi.org/10.1007/s00477-016-1289-4.","productDescription":"11 p.","startPage":"481","endPage":"491","ipdsId":"IP-075731","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":357567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-09","publicationStatus":"PW","scienceBaseUri":"5bc031e1e4b0fc368eb53a4e","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":26436,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":745812,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70200023,"text":"70200023 - 2017 - In situ assessment of lampricide toxicity to age-0 lake sturgeon","interactions":[],"lastModifiedDate":"2018-10-11T11:01:41","indexId":"70200023","displayToPublicDate":"2017-02-01T11:01:35","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"In situ assessment of lampricide toxicity to age-0 lake sturgeon","title":"In situ assessment of lampricide toxicity to age-0 lake sturgeon","docAbstract":"The lampricides 3-trifluoromethyl-4-nitrophenol (TFM) and 2′, 5-dichloro-4′-nitrosalicylanilide (niclosamide) are used to control sea lamprey (Petromyzon marinus), an invasive species in the Great Lakes. Age-0 lake sturgeon (Acipenser fulvescens), a species of conservationconcern, share similar stream habitats with larval sea lampreys and these streams can be targeted for lampricide applications on a 3- to 5-year cycle. Previous laboratory researchfound that lake sturgeon smaller than 100 mm could be susceptible to lampricide treatments. We conducted stream-side toxicity (bioassay) and in situ studies in conjunction with 10 lampricide applications in nine Great Lakes tributaries to determine whether sea lamprey treatments could result in in situ age-0 lake sturgeon mortality, and developed a logistic model to help predict lake sturgeon survival during future treatments. In the bioassays the observed concentrations where no lake sturgeon mortality occurred (no observable effect concentration, NOEC) were at or greater than the observed sea lamprey minimum lethal concentration (MLC or LC99) in 7 of 10 tests. We found that the mean in situ survival of age-0 lake sturgeon during 10 lampricide applications was 80%, with a range of 45–100% survival within streams. Modeling indicated that in age-0 lake sturgeon survival was negatively correlated with absolute TFM concentration and stream alkalinity, and positively correlated with stream pH and temperature. Overall survival was higher than expected based on previous research, and we expect that these data will help managers with decisions on the trade-offs between sea lamprey control and the effect on stream-specific populations of age-0 lake sturgeon.
When and whence does wood enter large mountain alluvial rivers? How stable through time are characteristics and quantities of wood deposited in a reach? These simple questions related to the complex practice of wood budgeting are explored on the Isère River in France. We hypothesise that (i) the wood originates from the riparian zone all along the alluvial reach and that (ii) the characters and quantity of wood in the reach can vary through time according to flood occurrence and provenance. In order to validate these hypotheses, two complementary approaches were performed: (i) wood pieces were surveyed along 190 km river length and taxonomy, in-channel wood macromorphology, and dendrochemistry were used to infer wood origin (local vs. upstream, respective subbasin contributions) and transport conditions; (ii) wood movement was monitored using both tracking techniques in specific sampling plots and with an experiment orchestrated using wood placement coupled with a significant artificial flood. Surveys were done over a period of 3 years so as to include two distinct sampling events to explore wood deposition and mobilisation within a channel network under different flood conditions. One of the subbasins, the Arly River, underwent a 1-in-30-year flood in 2004, allowing us to assess its effect on in-channel wood quantity and characteristics.
Results confirm that wood is primarily introduced by erosion from river banks but they are not always as close as expected from the sites of deposition. Temporal variability of wood introduced, deposited, and transferred downstream is also significant in terms of abundance and origin as shown by dendrochemical and macromorphological signatures. The types of wood observed along the channel length changes through time. Large flood signature can be detected from wood characteristics and uplands make a slight contribution. But in average, wood characteristics do not change much (no significant difference between years and tributaries in wood characteristics based on discriminant analysis). Data suggest that the interannual variability is fairly low, so that the diversity of wood characteristics is maintained by the complex and multiple sources of wood in the network. Further research is needed to better understand such patterns and to study physical breakage in space and time to better infer distance between sources and depositional zones.
We present a new methodology to combine spatially dense high-resolution airborne electromagnetic (AEM) data and sparse borehole information to construct multiple plausible geological structures using a stochastic approach. The method developed allows for quantification of the performance of groundwater models built from different geological realizations of structure. Multiple structural realizations are generated using geostatistical Monte Carlo simulations that treat sparse borehole lithological observations as hard data and dense geophysically derived structural probabilities as soft data. Each structural model is used to define 3-D hydrostratigraphical zones of a groundwater model, and the hydraulic parameter values of the zones are estimated by using nonlinear regression to fit hydrological data (hydraulic head and river discharge measurements). Use of the methodology is demonstrated for a synthetic domain having structures of categorical deposits consisting of sand, silt, or clay. It is shown that using dense AEM data with the methodology can significantly improve the estimated accuracy of the sediment distribution as compared to when borehole data are used alone. It is also shown that this use of AEM data can improve the predictive capability of a calibrated groundwater model that uses the geological structures as zones. However, such structural models will always contain errors because even with dense AEM data it is not possible to perfectly resolve the structures of a groundwater system. It is shown that when using such erroneous structures in a groundwater model, they can lead to biased parameter estimates and biased model predictions, therefore impairing the model's predictive capability.
","language":"English","publisher":"AGU","doi":"10.1002/2016WR019141","usgsCitation":"Christensen, N.K., Minsley, B.J., and Christensen, S., 2017, Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error: Water Resources Research, v. 53, no. 2, p. 1019-1038, https://doi.org/10.1002/2016WR019141.","productDescription":"20 p.","startPage":"1019","endPage":"1038","ipdsId":"IP-081403","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":488731,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/dcdb9b5e-bf3c-4826-83aa-0fb5cd606845","text":"External Repository"},{"id":348146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2ea5e4b0531197b27f85","contributors":{"authors":[{"text":"Christensen, Nikolaj K","contributorId":199736,"corporation":false,"usgs":false,"family":"Christensen","given":"Nikolaj","email":"","middleInitial":"K","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":719889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":719888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christensen, Steen","contributorId":199737,"corporation":false,"usgs":false,"family":"Christensen","given":"Steen","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":719890,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70182740,"text":"70182740 - 2017 - Comparison of in vitro estrogenic activity and estrogen concentrations insource and treated waters from 25 U.S. drinking water treatment plants","interactions":[],"lastModifiedDate":"2017-02-28T11:28:47","indexId":"70182740","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of in vitro estrogenic activity and estrogen concentrations insource and treated waters from 25 U.S. drinking water treatment plants","docAbstract":"In vitro bioassays have been successfully used to screen for estrogenic activity in wastewater and surface water,\nhowever, few have been applied to treated drinking water. Here, extracts of source and treated water samples\nwere assayed for estrogenic activity using T47D-KBluc cells and analyzed by liquid chromatography-Fourier transform\nmass spectrometry (LC-FTMS) for natural and synthetic estrogens (including estrone, 17β-estradiol, estriol,\nand ethinyl estradiol). None of the estrogenswere detected above the LC-FTMS quantification limits in treated samples\nand only 5 source waters had quantifiable concentrations of estrone, whereas 3 treated samples and 16 source\nsamples displayed in vitro estrogenicity. Estrone accounted for themajority of estrogenic activity in respective samples,\nhowever the remaining samples that displayed estrogenic activity had no quantitative detections of known estrogenic\ncompounds by chemical analyses. Source water estrogenicity (max, 0.47 ng 17β-estradiol equivalents\n(E2Eq) L−1) was below levels that have been linked to adverse effects in fish and other aquatic organisms. Treated\nwater estrogenicity (max, 0.078 ng E2Eq L−1) was considerably below levels that are expected to be biologically\nrelevant to human consumers. Overall, the advantage of using in vitro techniques in addition to analytical chemical\ndeterminations was displayed by the sensitivity of the T47D-KBluc bioassay, coupled with the ability tomeasure cumulative\neffects of mixtures, specifically when unknown chemicals may be present.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.02.093","collaboration":"U.S. Environmental Protection Agency","usgsCitation":"Conley, J.M., Evans, N., Mash, H., Rosenblum, L., Schenck, K., Glassmeyer, S., Furlong, E.T., Kolpin, D.W., and Wilson, V.S., 2017, Comparison of in vitro estrogenic activity and estrogen concentrations insource and treated waters from 25 U.S. drinking water treatment plants: Science of the Total Environment, v. 579, p. 1610-1617, https://doi.org/10.1016/j.scitotenv.2016.02.093.","productDescription":"8 p. ","startPage":"1610","endPage":"1617","ipdsId":"IP-072842","costCenters":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"links":[{"id":336329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":336298,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0048969716303035"}],"volume":"579","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b69a3fe4b01ccd54ff3f80","contributors":{"authors":[{"text":"Conley, Justin M.","contributorId":184086,"corporation":false,"usgs":false,"family":"Conley","given":"Justin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":673522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Nicola","contributorId":184087,"corporation":false,"usgs":false,"family":"Evans","given":"Nicola","email":"","affiliations":[],"preferred":false,"id":673523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mash, Heath","contributorId":184088,"corporation":false,"usgs":false,"family":"Mash","given":"Heath","affiliations":[],"preferred":false,"id":673524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosenblum, Laura","contributorId":184089,"corporation":false,"usgs":false,"family":"Rosenblum","given":"Laura","email":"","affiliations":[],"preferred":false,"id":673525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schenck, Kathleen","contributorId":184090,"corporation":false,"usgs":false,"family":"Schenck","given":"Kathleen","affiliations":[],"preferred":false,"id":673526,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Glassmeyer, Susan","contributorId":184091,"corporation":false,"usgs":false,"family":"Glassmeyer","given":"Susan","affiliations":[],"preferred":false,"id":673527,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Furlong, Edward T. 0000-0002-7305-4603 efurlong@usgs.gov","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":740,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","email":"efurlong@usgs.gov","middleInitial":"T.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":673521,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673528,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, Vickie S. 0000-0003-1661-8481","orcid":"https://orcid.org/0000-0003-1661-8481","contributorId":184092,"corporation":false,"usgs":false,"family":"Wilson","given":"Vickie","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":673529,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70186151,"text":"70186151 - 2017 - Managing American Oystercatcher (Haematopus palliatus) population qrowth by targeting nesting season vital rates","interactions":[],"lastModifiedDate":"2017-03-30T11:16:32","indexId":"70186151","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"Managing American Oystercatcher (Haematopus palliatus) population qrowth by targeting nesting season vital rates","docAbstract":"In populations of long-lived species, adult survival typically has a relatively high influence on population growth. From a management perspective, however, adult survival can be difficult to increase in some instances, so other component rates must be considered to reverse population declines. In North Carolina, USA, management to conserve the American Oystercatcher (Haematopus palliatus) targets component vital rates related to fecundity, specifically nest and chick survival. The effectiveness of such a management approach in North Carolina was assessed by creating a three-stage female-based deterministic matrix model. Isoclines were produced from the matrix model to evaluate minimum nest and chick survival rates necessary to reverse population decline, assuming all other vital rates remained stable at mean values. Assuming accurate vital rates, breeding populations within North Carolina appear to be declining. To reverse this decline, combined nest and chick survival would need to increase from 0.14 to ≤ 0.27, a rate that appears to be attainable based on historical estimates. Results are heavily dependent on assumptions of other vital rates, most notably adult survival, revealing the need for accurate estimates of all vital rates to inform management actions. This approach provides valuable insights for evaluating conservation goals for species of concern.
","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.040.sp106","usgsCitation":"Felton, S.K., Hostetter, N.J., Pollock, K.H., and Simons, T.R., 2017, Managing American Oystercatcher (Haematopus palliatus) population qrowth by targeting nesting season vital rates: Waterbirds, v. 40, no. sp1, p. 44-54, https://doi.org/10.1675/063.040.sp106.","productDescription":"11 p.","startPage":"44","endPage":"54","ipdsId":"IP-071195","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":461763,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1675/063.040.sp106","text":"Publisher Index Page"},{"id":338800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"sp1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58de194ee4b02ff32c699c95","contributors":{"authors":[{"text":"Felton, Shilo K.","contributorId":190179,"corporation":false,"usgs":false,"family":"Felton","given":"Shilo","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":687694,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetter, Nathan J.","contributorId":171690,"corporation":false,"usgs":false,"family":"Hostetter","given":"Nathan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":687695,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pollock, Kenneth H.","contributorId":8590,"corporation":false,"usgs":false,"family":"Pollock","given":"Kenneth","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":687696,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simons, Theodore R. 0000-0002-1884-6229 tsimons@usgs.gov","orcid":"https://orcid.org/0000-0002-1884-6229","contributorId":2623,"corporation":false,"usgs":true,"family":"Simons","given":"Theodore","email":"tsimons@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":687677,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70186149,"text":"70186149 - 2017 - The American Oystercatcher (Haematopus palliatus) Working Group: 15 years of collaborative focal species research and management","interactions":[],"lastModifiedDate":"2017-03-30T11:19:25","indexId":"70186149","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"The American Oystercatcher (Haematopus palliatus) Working Group: 15 years of collaborative focal species research and management","docAbstract":"The American Oystercatcher (Haematopus palliatus) Working Group formed spontaneously in 2001 as coastal waterbird biologists recognized the potential for American Oystercatchers to serve as focal species for collaborative research and management. Accomplishments over the past 15 years include the establishment of rangewide surveys, color-banding protocols, mark-resight studies, a revision of the Birds of North America species account, and new mechanisms for sharing ideas and data. Collaborations among State, Federal, and private sector scientists, natural resource managers, and dedicated volunteers have provided insights into the biology and conservation of American Oystercatchers in the United States and abroad that would not have been possible without the relationships formed through the Working Group. These accomplishments illustrate how broad collaborative approaches and the engagement of the public are key elements of effective shorebird conservation programs.
","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.040.sp102","usgsCitation":"Simons, T.R., 2017, The American Oystercatcher (Haematopus palliatus) Working Group: 15 years of collaborative focal species research and management: Waterbirds, v. 40, no. sp1, p. 1-9, https://doi.org/10.1675/063.040.sp102.","productDescription":"9 p.","startPage":"1","endPage":"9","ipdsId":"IP-071196","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":461767,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1675/063.040.sp102","text":"Publisher Index Page"},{"id":338802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"sp1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58de194ee4b02ff32c699c97","contributors":{"authors":[{"text":"Simons, Theodore R. 0000-0002-1884-6229 tsimons@usgs.gov","orcid":"https://orcid.org/0000-0002-1884-6229","contributorId":2623,"corporation":false,"usgs":true,"family":"Simons","given":"Theodore","email":"tsimons@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":687674,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}