The study of animal movement has always been a key element in ecological science, because it is inherently linked to critical processes that scale from individuals to populations and communities to ecosystems. Rapid improvements in biotelemetry data collection and processing technology have given rise to a variety of statistical methods for characterizing animal movement. The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement.
","language":"English","publisher":"CRC Press","isbn":"9781466582149","usgsCitation":"Hooten, M., Johnson, D., McClintock, B.T., and Morales, J.M., 2017, Animal movement: Statistical models for telemetry data, 306 p.","productDescription":"306 p.","ipdsId":"IP-075857","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":350690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350689,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.crcpress.com/Animal-Movement-Statistical-Models-for-Telemetry-Data/Hooten-Johnson-McClintock-Morales/p/book/9781466582149"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6c4c94e4b06e28e9cabafa","contributors":{"authors":[{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":716564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Devin S.","contributorId":47524,"corporation":false,"usgs":true,"family":"Johnson","given":"Devin S.","affiliations":[],"preferred":false,"id":725947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McClintock, Brett T. 0000-0001-6154-4376","orcid":"https://orcid.org/0000-0001-6154-4376","contributorId":83785,"corporation":false,"usgs":true,"family":"McClintock","given":"Brett","email":"","middleInitial":"T.","affiliations":[{"id":12448,"text":"U.S. National Oceanic and Atmospheric Administration","active":true,"usgs":false},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":725948,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morales, Juan M.","contributorId":171521,"corporation":false,"usgs":false,"family":"Morales","given":"Juan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":725949,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250956,"text":"70250956 - 2017 - Thermodynamic properties in the Fe(II)-Fe(III)-As(V)-HClO4–H2O and Fe(II)-Fe(III)-As(V)-HCl–H2O systems from 5 to 90 °C","interactions":[],"lastModifiedDate":"2024-01-16T12:17:08.51847","indexId":"70250956","displayToPublicDate":"2017-01-16T06:13:54","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Thermodynamic properties in the Fe(II)-Fe(III)-As(V)-HClO4–H2O and Fe(II)-Fe(III)-As(V)-HCl–H2O systems from 5 to 90 °C","docAbstract":"Fe-As mineral solubility and associated aqueous species have been intensively studied because of the environmental need to immobilize arsenic. The thermodynamic data for aqueous iron-arsenic species are inadequately characterized, however. The Gibbs free energy, enthalpy, entropy, and heat capacity and activity coefficients were refined in the Fe(II)-Fe(III)-As(V)-HClO4-H2O and Fe(II)-Fe(III)-As(V)-HCl-H2O systems using redox potential measurements from 5 to 90 °C. The association constants for FeHAsO4+ and FeH2AsO42 + at 25 °C were 1010.28 and 104.02 and the corresponding association reaction enthalpies and heat capacities were 25.74 and 8.73 kJ mol− 1 and 843.1 and − 529.6 J K −1mol− 1, respectively. Activity coefficients for H+, ClO4−, Fe2 +, Fe3 +, HAsO42 −, and H2AsO4− at 25 °C in the form of the Hückel equation were derived for ionic strengths up to 1 mol− 1 kg− 1. Newly derived activity coefficients and thermodynamic data were incorporated into PHREEQCI to calculate the Eh of laboratory solutions. The differences between calculated and measured Eh were all within 10 mV and relative differences were all lower than 1.5%.
The introduction of aquatic invasive species to non-native habitats can cause negative ecological effects and also billions of dollars in economic damage to governments and private industries. Once aquatic invasive species are introduced, eradication may be difficult without adversely affecting native species and habitats, urging resource managers to find preventative methods to protect non-invaded areas. The use of ozone (O3) as a non-physical barrier has shown promise as it is lethal to a wide range of aquatic taxa, requires a short contact time, and is relatively environmentally safe in aquatic systems when compared to other chemicals. However, before O3 can be considered as an approach to prevent the spread of aquatic invasive species, its effects on non-target organisms and already established aquatic invasive species must be fully evaluated. A review of the current literature was conducted to summarize data regarding the effects of O3 on aquatic taxa including fish, macroinvertebrates, zooplankton, phytoplankton, microbes, and pathogens. In addition, we assessed the practicality of ozone applications to control the movement of aquatic invasive species, and identified data gaps concerning the use of O3 as a non-physical barrier in field applications.
","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre (REABIC)","doi":"10.3391/mbi.2017.8.1.02","usgsCitation":"Buley, R., Hasler, C.T., Tix, J., Suski, C., and Hubert, T.D., 2017, Can ozone be used to control the spread of freshwater Aquatic Invasive Species?: Management of Biological Invasions, v. 8, no. 1, p. 13-24, https://doi.org/10.3391/mbi.2017.8.1.02.","productDescription":"12 p.","startPage":"13","endPage":"24","ipdsId":"IP-076249","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":470138,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2017.8.1.02","text":"Publisher Index Page"},{"id":338813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58de194fe4b02ff32c699c9f","contributors":{"authors":[{"text":"Buley, Riley P.","contributorId":190149,"corporation":false,"usgs":false,"family":"Buley","given":"Riley P.","affiliations":[],"preferred":false,"id":687408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hasler, Caleb T.","contributorId":190150,"corporation":false,"usgs":false,"family":"Hasler","given":"Caleb","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":687409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tix, John A.","contributorId":126766,"corporation":false,"usgs":false,"family":"Tix","given":"John A.","affiliations":[{"id":6602,"text":"Great Lakes Science Center, Hammond Bay Biological Station","active":true,"usgs":false}],"preferred":false,"id":687410,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Suski, C. D.","contributorId":190151,"corporation":false,"usgs":false,"family":"Suski","given":"C.","middleInitial":"D.","affiliations":[],"preferred":false,"id":687411,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hubert, Terrance D. 0000-0001-9712-1738 thubert@usgs.gov","orcid":"https://orcid.org/0000-0001-9712-1738","contributorId":3036,"corporation":false,"usgs":true,"family":"Hubert","given":"Terrance","email":"thubert@usgs.gov","middleInitial":"D.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":687412,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179654,"text":"sir20175001 - 2017 - Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in North Georgia","interactions":[],"lastModifiedDate":"2017-01-13T14:05:48","indexId":"sir20175001","displayToPublicDate":"2017-01-13T13:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5001","title":"Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in North Georgia","docAbstract":"The U.S. Geological Survey, in cooperation with the Georgia Department of Natural Resources, Environmental Protection Division, developed regional regression equations for estimating selected low-flow frequency and mean annual flow statistics for ungaged streams in north Georgia that are not substantially affected by regulation, diversions, or urbanization. Selected low-flow frequency statistics and basin characteristics for 56 streamgage locations within north Georgia and 75 miles beyond the State’s borders in Alabama, Tennessee, North Carolina, and South Carolina were combined to form the final dataset used in the regional regression analysis. Because some of the streamgages in the study recorded zero flow, the final regression equations were developed using weighted left-censored regression analysis to analyze the flow data in an unbiased manner, with weights based on the number of years of record. The set of equations includes the annual minimum 1- and 7-day average streamflow with the 10-year recurrence interval (referred to as 1Q10 and 7Q10), monthly 7Q10, and mean annual flow. The final regional regression equations are functions of drainage area, mean annual precipitation, and relief ratio for the selected low-flow frequency statistics and drainage area and mean annual precipitation for mean annual flow. The average standard error of estimate was 13.7 percent for the mean annual flow regression equation and ranged from 26.1 to 91.6 percent for the selected low-flow frequency equations.
The equations, which are based on data from streams with little to no flow alterations, can be used to provide estimates of the natural flows for selected ungaged stream locations in the area of Georgia north of the Fall Line. The regression equations are not to be used to estimate flows for streams that have been altered by the effects of major dams, surface-water withdrawals, groundwater withdrawals (pumping wells), diversions, or wastewater discharges. The regression equations should be used only for ungaged sites with drainage areas between 1.67 and 576 square miles, mean annual precipitation between 47.6 and 81.6 inches, and relief ratios between 0.146 and 0.607; these are the ranges of the explanatory variables used to develop the equations. An attempt was made to develop regional regression equations for the area of Georgia south of the Fall Line by using the same approach used during this study for north Georgia; however, the equations resulted with high average standard errors of estimates and poorly predicted flows below 0.5 cubic foot per second, which may be attributed to the karst topography common in that area.
The final regression equations developed from this study are planned to be incorporated into the U.S. Geological Survey StreamStats program. StreamStats is a Web-based geographic information system that provides users with access to an assortment of analytical tools useful for water-resources planning and management, and for engineering design applications, such as the design of bridges. The StreamStats program provides streamflow statistics and basin characteristics for U.S. Geological Survey streamgage locations and ungaged sites of interest. StreamStats also can compute basin characteristics and provide estimates of streamflow statistics for ungaged sites when users select the location of a site along any stream in Georgia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175001","collaboration":"Prepared in cooperation with the Georgia Department of Natural Resources, Environmental Protection Division","usgsCitation":"Gotvald, A.J., 2017, Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in North Georgia: U.S. Geological Survey Scientific Investigations Report 2017–5001, 25 p., https://doi.org/10.3133/sir20175001. ","productDescription":"Report: vi, 25 p.; 3 Tables","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077003","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":333138,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5001/sir20175001.pdf","text":"Report","size":"1.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5001"},{"id":333139,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5001/sir20175001_tables1-2-5.xlsx","text":"Tables 1, 2, and 5 - ","size":"74.8 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"Table 1. Description of streamgages evaluated for use in the regional regression analysis for north GeorgiaDirector, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
As part of the Barrier Island Evolution Research Project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted nearshore geophysical surveys around the northern Chandeleur Islands, Louisiana, in July and August of 2013. The objective of the study is to better understand barrier-island geomorphic evolution, particularly storm-related depositional and erosional processes that shape the islands over annual to interannual timescales (1‒5 years). Collecting geophysical data will allow us to identify relationships between the geologic history of the island and its present day morphology and sediment distribution. This mapping effort was the third in a series of three planned surveys in this area. High resolution geophysical data collected in each of three consecutive years along this rapidly changing barrier island system will provide a unique time-series dataset that will significantly further the analyses and geomorphological interpretations of this and other coastal systems, improving our understanding of coastal response and evolution over short time scales (1‒5 years).
This data series includes the geophysical data that were collected during two cruises (USGS Field Activity Numbers (FAN) 13BIM02, 13BIM03, and 13BIM04, in July 2013; and FANs 13BIM07 and 13BIM08 in August 2013) aboard the R/V Sallenger, the R/V Jabba Jaw, and the R/V Shark along the northern portion of the Chandeleur Islands, Breton National Wildlife Refuge, Louisiana. Primary data were acquired with the following equipment: (1) a Systems Engineering and Assessment, Ltd., SWATHplus interferometric sonar (468 kilohertz [kHz]), (2) an EdgeTech 424 (4‒24 kHz) chirp sub-bottom profiling system, and (3) two Odom Hydrographic Systems, Incorporated, Echotrach CV100 single beam echosounders.
This data series report serves as an archive of processed interferometric swath and single-beam bathymetry data. Geographic information system data products include an interpolated digital elevation model, trackline maps, and point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1032","usgsCitation":"DeWitt, N.T., Miselis, J.L., Fredericks, J.J., Bernier, J.C., Reynolds, B.J., Kelso, K.W., Thompson, D.M., Flocks, J.G., and Wiese, D.S., 2017, Coastal bathymetry data collected in 2013 from the Chandeleur Islands, Louisiana: U.S. Geological Survey Data Series Report 1032, https://doi.org/10.3133/ds1032.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078418","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":332772,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1032/coverthb.jpg"},{"id":332773,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1032/index.html","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1032"}],"country":"United States","state":"Louisiana","otherGeospatial":"Chandeleur Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.933333,\n 30.116667\n ],\n [\n -88.933333,\n 29.85\n ],\n [\n -88.733333,\n 29.85\n ],\n [\n -88.733333,\n 30.116667\n ],\n [\n -88.933333,\n 30.116667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
(727) 502-8000
http://coastal.er.usgs.gov/
Newly synthesized data indicate that the geochemistry of igneous rocks associated with epithermal mineral deposits varies extensively and continuously from subalkaline basaltic to rhyolitic compositions. Trace element and isotopic data for these rocks are consistent with subduction-related magmatism and suggest that the primary source magmas were generated by partial melting of the mantle-wedge above subducting oceanic slabs. Broad geochemical and petrographic diversity of individual igneous rock units associated with epithermal deposits indicate that the associated magmas evolved by open-system processes. Following migration to shallow crustal reservoirs, these magmas evolved by assimilation, recharge, and partial homogenization; these processes contribute to arc magmatism worldwide.
Although epithermal deposits with the largest Au and Ag production are associated with felsic to intermediate composition igneous rocks, demonstrable relationships between magmas having any particular composition and epithermal deposit genesis are completely absent because the composition of igneous rock units associated with epithermal deposits ranges from basalt to rhyolite. Consequently, igneous rock compositions do not constitute effective exploration criteria with respect to identification of terranes prospective for epithermal deposit formation. However, the close spatial and temporal association of igneous rocks and epithermal deposits does suggest a mutual genetic relationship. Igneous systems likely contribute heat and some of the fluids and metals involved in epithermal deposit formation. Accordingly, deposit formation requires optimization of source metal contents, appropriate fluid compositions and characteristics, structural features conducive to hydrothermal fluid flow and confinement, and receptive host rocks, but not magmas with special compositional characteristics.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2016.08.023","usgsCitation":"du Bray, E.A., 2017, Geochemical characteristics of igneous rocks associated with epithermal mineral deposits—A review: Ore Geology Reviews, v. 80, p. 767-783, https://doi.org/10.1016/j.oregeorev.2016.08.023.","productDescription":"17 p.","startPage":"767","endPage":"783","ipdsId":"IP-066206","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":333082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5878a489e4b04df303d957f8","contributors":{"authors":[{"text":"du Bray, Edward A. 0000-0002-4383-8394 edubray@usgs.gov","orcid":"https://orcid.org/0000-0002-4383-8394","contributorId":755,"corporation":false,"usgs":true,"family":"du Bray","given":"Edward","email":"edubray@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":658321,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70176238,"text":"fs20163069 - 2017 - Water resources of East Feliciana Parish, Louisiana","interactions":[],"lastModifiedDate":"2017-01-13T10:30:33","indexId":"fs20163069","displayToPublicDate":"2017-01-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3069","title":"Water resources of East Feliciana Parish, Louisiana","docAbstract":"Information concerning the availability, use, and quality of water in East Feliciana Parish, Louisiana, is critical for proper water-resource management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. Information is presented on the availability, past and current use, use trends, and water quality from groundwater and surface-water sources in the parish. Previously published reports and data stored in the U.S. Geological Survey’s National Water Information System (http://waterdata.usgs.gov/nwis) are the primary sources of the information presented here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163069","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., and Prakken, L.B., 2017, Water resources of East Feliciana Parish, Louisiana: U.S. Geological Survey Fact Sheet 2016–3069, 6 p., https://doi.org/10.3133/fs20163069.","productDescription":"6 p.","onlineOnly":"N","ipdsId":"IP-065609","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":333087,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3069/coverthb.jpg"},{"id":333088,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3069/fs20163069.pdf","text":"Fact Sheet","size":"1.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3069"}],"country":"United States","state":"Louisiana","otherGeospatial":"East Feliciana Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.0608,30.9995],[-90.8268,30.9992],[-90.829,30.9947],[-90.8386,30.9916],[-90.8435,30.9839],[-90.843,30.9747],[-90.8458,30.9619],[-90.8474,30.9597],[-90.8506,30.9565],[-90.8565,30.9515],[-90.8619,30.9451],[-90.8588,30.9355],[-90.8572,30.9309],[-90.8541,30.9186],[-90.852,30.9108],[-90.8553,30.9053],[-90.8532,30.9012],[-90.8505,30.8985],[-90.8484,30.8962],[-90.8491,30.8825],[-90.8528,30.8788],[-90.856,30.8761],[-90.8598,30.8743],[-90.8598,30.8679],[-90.8577,30.8629],[-90.8551,30.856],[-90.853,30.8528],[-90.8493,30.8505],[-90.8424,30.8486],[-90.8414,30.8449],[-90.8425,30.8427],[-90.8468,30.8372],[-90.8457,30.8335],[-90.8447,30.8308],[-90.8421,30.8271],[-90.8405,30.8248],[-90.8378,30.8207],[-90.8352,30.8193],[-90.8347,30.8166],[-90.8379,30.8102],[-90.839,30.8066],[-90.8385,30.8024],[-90.8396,30.797],[-90.8434,30.7933],[-90.8423,30.7906],[-90.8419,30.7869],[-90.8419,30.7782],[-90.8404,30.7732],[-90.8436,30.7641],[-90.8447,30.7623],[-90.8479,30.7563],[-90.8512,30.75],[-90.8523,30.744],[-90.8502,30.7417],[-90.846,30.7362],[-90.8418,30.7325],[-90.8407,30.7266],[-90.8413,30.7234],[-90.8434,30.7229],[-90.8461,30.7211],[-90.8477,30.7198],[-91.2492,30.7072],[-91.2577,30.7027],[-91.2636,30.7],[-91.2678,30.6949],[-91.2673,30.6917],[-91.2689,30.6895],[-91.2679,30.6863],[-91.2711,30.6808],[-91.2891,30.6781],[-91.2897,30.668],[-91.2929,30.6621],[-91.2934,30.6552],[-91.2977,30.6493],[-91.2993,30.6516],[-91.3051,30.6529],[-91.3051,30.6735],[-91.3088,30.6804],[-91.3125,30.6822],[-91.3146,30.6927],[-91.3194,30.6959],[-91.321,30.6991],[-91.3194,30.7005],[-91.3135,30.6987],[-91.3119,30.6991],[-91.3109,30.7023],[-91.3114,30.7083],[-91.3039,30.7078],[-91.3007,30.7156],[-91.2928,30.716],[-91.2922,30.7188],[-91.2959,30.7252],[-91.2964,30.727],[-91.2863,30.7347],[-91.2874,30.7439],[-91.2863,30.7475],[-91.282,30.7507],[-91.2799,30.7535],[-91.2761,30.7585],[-91.2602,30.7621],[-91.2591,30.7658],[-91.2628,30.7745],[-91.2617,30.7758],[-91.2575,30.7767],[-91.2532,30.7776],[-91.2473,30.7868],[-91.2372,30.7945],[-91.2383,30.7977],[-91.2425,30.8009],[-91.243,30.8046],[-91.2361,30.8068],[-91.2329,30.81],[-91.2302,30.8164],[-91.2345,30.8219],[-91.2345,30.8246],[-91.2339,30.8256],[-91.2307,30.8333],[-91.227,30.8383],[-91.2269,30.8406],[-91.2253,30.8484],[-91.2296,30.8544],[-91.2232,30.8566],[-91.2194,30.8644],[-91.2258,30.8703],[-91.2247,30.8726],[-91.222,30.8735],[-91.221,30.8772],[-91.2225,30.8813],[-91.2188,30.8868],[-91.2156,30.8922],[-91.2065,30.8927],[-91.2065,30.9004],[-91.2091,30.9055],[-91.2059,30.9109],[-91.208,30.9137],[-91.208,30.9183],[-91.2,30.9191],[-91.1947,30.931],[-91.1931,30.9328],[-91.1898,30.9333],[-91.1872,30.9369],[-91.1914,30.9438],[-91.193,30.9507],[-91.1956,30.9598],[-91.1919,30.9671],[-91.1908,30.9721],[-91.1892,30.973],[-91.186,30.9721],[-91.1822,30.9739],[-91.1801,30.9789],[-91.1811,30.9853],[-91.18,30.9908],[-91.1757,31],[-91.1115,31],[-91.0608,30.9995]]]},\"properties\":{\"name\":\"East Feliciana\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Calcasieu Parish, Louisiana, is critical for proper water-resource management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. Information on the availability, past and current use, use trends, and water quality from groundwater and surface-water sources in the parish is presented. Previously published reports and data stored in the U.S. Geological Survey’s National Water Information System (http://dx.doi.org/10.5066/F7P55KJN) are the primary sources of the information presented here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163066","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., and Prakken, L.B., 2017, Water resources of Calcasieu Parish, Louisiana: U.S. Geological Survey Fact Sheet 2016–3066, 6 p., https://dx.doi.org/10.3133/fs20163066.","productDescription":"6 p.","onlineOnly":"N","ipdsId":"IP-073096","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":333095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3066/fs20163066.pdf","text":"Fact Sheet","size":"1.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"2016–3066"},{"id":333094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3066/coverthb.jpg"}],"country":"United 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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
The efficacy of various sampling gears for age-0 Mountain Whitefish Prosopium williamsoni is largely unknown, which makes it difficult to investigate recruitment and early life history dynamics for the species. We compared four gears: seine, backpack electrofisher, minnow trap, and lighted minnow trap. Gears were tested in backwaters, large channels, and small channels in the Madison River, Montana. No age-0 Mountain Whitefish were captured in minnow traps or lighted minnow traps. Mean CPUE of age-0 Mountain Whitefish was higher for seining (0.18 fish/m2; SD, 0.39) than for electrofishing (0.01 fish/m2; SD, 0.03), and the CV was lower for seining. A broader length distribution was sampled by seining (17–41 mm) than with electrofishing (21–36 mm). Age-0 Mountain Whitefish CPUE in seines was highest in backwaters. In channel sites, Mountain Whitefish presence was associated with areas of still or slow water ≥2 m2. Relative to the other sampling gears we evaluated, seining was the most efficient gear for sampling age-0 Mountain Whitefish in a lotic ecosystem.
","language":"English","publisher":"Taylor and Francis Online","doi":"10.1080/02755947.2016.1254128","usgsCitation":"Boyer, J.K., Guy, C.S., Webb, M., Horton, T.B., and McMahon, T., 2017, Gear comparison for sampling age-0 Mountain Whitefish in the Madison River, Montana: North American Journal of Fisheries Management, v. 37, no. 1, p. 189-195, https://doi.org/10.1080/02755947.2016.1254128.","productDescription":"8 p. 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H.","contributorId":193590,"corporation":false,"usgs":false,"family":"Webb","given":"Molly A. H.","affiliations":[],"preferred":false,"id":700902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Horton, Travis B.","contributorId":193589,"corporation":false,"usgs":false,"family":"Horton","given":"Travis","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":700901,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McMahon, Thomas E.","contributorId":189425,"corporation":false,"usgs":false,"family":"McMahon","given":"Thomas E.","affiliations":[],"preferred":false,"id":700903,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188376,"text":"70188376 - 2017 - Multinomial N-mixture models improve the applicability of electrofishing for developing population estimates of stream-dwelling Smallmouth Bass","interactions":[],"lastModifiedDate":"2017-06-07T14:16:23","indexId":"70188376","displayToPublicDate":"2017-01-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Multinomial N-mixture models improve the applicability of electrofishing for developing population estimates of stream-dwelling Smallmouth Bass","title":"Multinomial N-mixture models improve the applicability of electrofishing for developing population estimates of stream-dwelling Smallmouth Bass","docAbstract":"Failure to account for variable detection across survey conditions constrains progressive stream ecology and can lead to erroneous stream fish management and conservation decisions. In addition to variable detection’s confounding long-term stream fish population trends, reliable abundance estimates across a wide range of survey conditions are fundamental to establishing species–environment relationships. Despite major advancements in accounting for variable detection when surveying animal populations, these approaches remain largely ignored by stream fish scientists, and CPUE remains the most common metric used by researchers and managers. One notable advancement for addressing the challenges of variable detection is the multinomial N-mixture model. Multinomial N-mixture models use a flexible hierarchical framework to model the detection process across sites as a function of covariates; they also accommodate common fisheries survey methods, such as removal and capture–recapture. Effective monitoring of stream-dwelling Smallmouth Bass Micropterus dolomieu populations has long been challenging; therefore, our objective was to examine the use of multinomial N-mixture models to improve the applicability of electrofishing for estimating absolute abundance. We sampled Smallmouth Bass populations by using tow-barge electrofishing across a range of environmental conditions in streams of the Ozark Highlands ecoregion. Using an information-theoretic approach, we identified effort, water clarity, wetted channel width, and water depth as covariates that were related to variable Smallmouth Bass electrofishing detection. Smallmouth Bass abundance estimates derived from our top model consistently agreed with baseline estimates obtained via snorkel surveys. Additionally, confidence intervals from the multinomial N-mixture models were consistently more precise than those of unbiased Petersen capture–recapture estimates due to the dependency among data sets in the hierarchical framework. We demonstrate the application of this contemporary population estimation method to address a longstanding stream fish management issue. We also detail the advantages and trade-offs of hierarchical population estimation methods relative to CPUE and estimation methods that model each site separately.
","language":"English","publisher":"American Fisheries Society","doi":"10.1080/02755947.2016.1254127","usgsCitation":"Mollenhauer, R., and Brewer, S.K., 2017, Multinomial N-mixture models improve the applicability of electrofishing for developing population estimates of stream-dwelling Smallmouth Bass: North American Journal of Fisheries Management, v. 37, no. 1, p. 211-224, https://doi.org/10.1080/02755947.2016.1254127.","productDescription":"14 p.","startPage":"211","endPage":"224","ipdsId":"IP-073138","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":342254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Oklahoma","otherGeospatial":"Ozark Highlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.28167724609375,\n 35.380092992092145\n ],\n [\n -94.46868896484375,\n 35.36217605914681\n ],\n [\n -93.64471435546875,\n 35.3509759564216\n ],\n [\n -93.6749267578125,\n 37.13623498442895\n ],\n [\n -95.2789306640625,\n 37.13623498442895\n ],\n [\n -95.28167724609375,\n 35.380092992092145\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-12","publicationStatus":"PW","scienceBaseUri":"593910ade4b0764e6c5e885c","contributors":{"authors":[{"text":"Mollenhauer, Robert","contributorId":176540,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[],"preferred":false,"id":697505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":697455,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70181013,"text":"70181013 - 2017 - The contribution of rice agriculture to methylmercury in surface waters: A review of data from the Sacramento Valley, California","interactions":[],"lastModifiedDate":"2018-09-26T15:43:11","indexId":"70181013","displayToPublicDate":"2017-01-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"The contribution of rice agriculture to methylmercury in surface waters: A review of data from the Sacramento Valley, California","docAbstract":"Methylmercury (MeHg) is a bioaccumulative pollutant produced in and exported from flooded soils, including those used for rice (Oriza sativa L.) production. Using unfiltered aqueous MeHg data from MeHg monitoring programs in the Sacramento River watershed from 1996 to 2007, we assessed the MeHg contribution from rice systems to the Sacramento River. Using a mixed-effects regression analysis, we compared MeHg concentrations in agricultural drainage water from rice-dominated regions (AgDrain) to MeHg concentrations in the Sacramento and Feather Rivers, both upstream and downstream of AgDrain inputs. We also calculated MeHg loads from AgDrains and the Sacramento and Feather Rivers. Seasonally, MeHg concentrations were higher during November through May than during June through October, but the differences varied by location. Relative to upstream, November through May AgDrain least-squares mean MeHg concentration (0.18 ng L−1, range 0.15–0.23 ng L−1) was 2.3-fold higher, while June through October AgDrain mean concentration (0.097 ng L−1, range 0.6–1.6 ng L−1) was not significantly different from upstream. June through October AgDrain MeHg loads contributed 10.7 to 14.8% of the total Sacramento River MeHg load. Missing flow data prevented calculation of the percent contribution of AgDrains in November through May. At sites where calculation was possible, November through May loads made up 70 to 90% of the total annual load. Elevated flow and MeHg concentration in November through May both contribute to the majority of the AgDrain MeHg load occurring during this period. Methylmercury reduction efforts should target elevated November through May MeHg concentrations in AgDrains. However, our findings suggest that the contribution and environmental impact of rice is an order of magnitude lower than previous studies in the California Yolo Bypass.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.","doi":"10.2134/jeq2016.07.0262","usgsCitation":"Tanner, K.C., Windham-Myers, L., Fleck, J., Tate, K.W., McCord, S.A., and Linquist, B.A., 2017, The contribution of rice agriculture to methylmercury in surface waters: A review of data from the Sacramento Valley, California: Journal of Environmental Quality, v. 46, no. 1, p. 133-142, https://doi.org/10.2134/jeq2016.07.0262.","productDescription":"9 p.","startPage":"133","endPage":"142","ipdsId":"IP-083271","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2134/jeq2016.07.0262","text":"Publisher Index Page"},{"id":335172,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.0745849609375,\n 38.53957267203905\n ],\n [\n -122.0745849609375,\n 39.41497702499074\n ],\n [\n -121.4044189453125,\n 39.41497702499074\n ],\n [\n -121.4044189453125,\n 38.53957267203905\n ],\n [\n -122.0745849609375,\n 38.53957267203905\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"589ffedee4b099f50d3e0430","contributors":{"authors":[{"text":"Tanner, K. Christy","contributorId":179307,"corporation":false,"usgs":false,"family":"Tanner","given":"K.","email":"","middleInitial":"Christy","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":663277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":663276,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleck, Jacob 0000-0002-3217-3972 jafleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3217-3972","contributorId":168694,"corporation":false,"usgs":true,"family":"Fleck","given":"Jacob","email":"jafleck@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":663278,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tate, Kenneth W.","contributorId":179308,"corporation":false,"usgs":false,"family":"Tate","given":"Kenneth","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":663279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCord, Stephen A.","contributorId":179309,"corporation":false,"usgs":false,"family":"McCord","given":"Stephen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":663280,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Linquist, Bruce A.","contributorId":179310,"corporation":false,"usgs":false,"family":"Linquist","given":"Bruce","email":"","middleInitial":"A.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":663281,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70185257,"text":"70185257 - 2017 - Local geology controlled the feasibility of vitrifying Iron Age buildings","interactions":[],"lastModifiedDate":"2017-03-17T08:58:28","indexId":"70185257","displayToPublicDate":"2017-01-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Local geology controlled the feasibility of vitrifying Iron Age buildings","docAbstract":"During European prehistory, hilltop enclosures made from polydisperse particle-and-block stone walling were exposed to temperatures sufficient to partially melt the constituent stonework, leading to the preservation of glassy walls called ‘vitrified forts’. During vitrification, the granular wall rocks partially melt, sinter viscously and densify, reducing inter-particle porosity. This process is strongly dependent on the solidus temperature, the particle sizes, the temperature-dependence of the viscosity of the evolving liquid phase, as well as the distribution and longevity of heat. Examination of the sintering behaviour of 45 European examples reveals that it is the raw building material that governs the vitrification efficiency. As Iron Age forts were commonly constructed from local stone, we conclude that local geology directly influenced the degree to which buildings were vitrified in the Iron Age. Additionally, we find that vitrification is accompanied by a bulk material strengthening of the aggregates of small sizes, and a partial weakening of larger blocks. We discuss these findings in the context of the debate surrounding the motive of the wall-builders. We conclude that if wall stability by bulk strengthening was the desired effect, then vitrification represents an Iron Age technology that failed to be effective in regions of refractory local geology.","language":"English","publisher":"Springer Nature: Nature Publishing Group","doi":"10.1038/srep40028","usgsCitation":"Fabian B Wadsworth, Michael J Heap, Damby, D., Hess, K., Jens Najorka, Jérémie Vasseur, Fahrner, D., and Dingwell, D.B., 2017, Local geology controlled the feasibility of vitrifying Iron Age buildings: Scientific Reports, no. 7, p. 1-7, https://doi.org/10.1038/srep40028.","productDescription":"7 p. ","startPage":"1","endPage":"7","ipdsId":"IP-081792","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470139,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/srep40028","text":"Publisher Index Page"},{"id":337791,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-12","publicationStatus":"PW","scienceBaseUri":"58ccf59ce4b0849ce97f0cde","contributors":{"authors":[{"text":"Fabian B Wadsworth","contributorId":189460,"corporation":false,"usgs":false,"family":"Fabian B Wadsworth","affiliations":[],"preferred":false,"id":684901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Michael J Heap","contributorId":189461,"corporation":false,"usgs":false,"family":"Michael J Heap","affiliations":[],"preferred":false,"id":684903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Damby, David 0000-0002-3238-3961 ddamby@usgs.gov","orcid":"https://orcid.org/0000-0002-3238-3961","contributorId":177453,"corporation":false,"usgs":true,"family":"Damby","given":"David","email":"ddamby@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":684902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hess, Kai-Uwe","contributorId":189462,"corporation":false,"usgs":false,"family":"Hess","given":"Kai-Uwe","email":"","affiliations":[],"preferred":false,"id":684904,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jens Najorka","contributorId":189463,"corporation":false,"usgs":false,"family":"Jens Najorka","affiliations":[],"preferred":false,"id":684905,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jérémie Vasseur","contributorId":189464,"corporation":false,"usgs":false,"family":"Jérémie Vasseur","affiliations":[],"preferred":false,"id":684906,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fahrner, Dominik","contributorId":189465,"corporation":false,"usgs":false,"family":"Fahrner","given":"Dominik","email":"","affiliations":[],"preferred":false,"id":684907,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dingwell, Donald B","contributorId":189458,"corporation":false,"usgs":false,"family":"Dingwell","given":"Donald","email":"","middleInitial":"B","affiliations":[],"preferred":false,"id":684908,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70179605,"text":"fs20163102 - 2017 - The USGS National Wildlife Health Center: Advancing wildlife and ecosystem health","interactions":[],"lastModifiedDate":"2019-03-26T15:01:32","indexId":"fs20163102","displayToPublicDate":"2017-01-11T16:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3102","title":"The USGS National Wildlife Health Center: Advancing wildlife and ecosystem health","docAbstract":"In 1975, the Federal government responded to the need for establishing national expertise in wildlife health by creating the National Wildlife Health Center (NWHC), a facility within the Department of the Interior; the NWHC is the only national center dedicated to wildlife disease detection, control, and prevention. Its mission is to provide national leadership to safeguard wildlife and ecosystem health through active partnerships and exceptional science. Comparisons are often made between the NWHC, which strives to protect the health of our Nation’s wildlife, and the Centers for Disease Control and Prevention (CDC), which strive to protect public health. The NWHC, a science center of the U.S. Geological Survey (USGS) with specialized laboratories, works to safeguard the Nation’s wildlife from diseases by studying the causes and drivers of these threats, and by developing strategies to prevent and manage them. In addition to the main campus, located in Madison, Wisconsin, the NWHC also operates the Honolulu Field Station that addresses wildlife health issues in Hawaii and the Pacific Region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163102","usgsCitation":"Moede Rogall, Gail, and Sleeman, J.M., 2017, The USGS National Wildlife Health Center: Advancing wildlife and ecosystem health: U.S. Geological Survey Fact Sheet 2016-3102, 6 p., https://doi.org/10.3133/fs20163102. ","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072962","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":333041,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3102/fs20163102.pdf","text":"Report","size":"6.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3102"},{"id":333040,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3102/coverthb.jpg"}],"contact":"Director, National Wildlife Health Center
U.S. Geological Survey
6006 Schroeder Road
Madison, WI 53711-6223
https://www.usgs.gov/nwhc
This paper describes the tectonic summaries for all magnitude 7 and larger earthquakes in the period 2000–2015, as produced by the U.S. Geological Survey National Earthquake Information Center during their routine response operations to global earthquakes. The goal of such summaries is to provide important event-specific information to the public rapidly and concisely, such that recent earthquakes can be understood within a global and regional seismotectonic framework. We compile these summaries here to provide a long-term archive for this information, and so that the variability in tectonic setting and earthquake history from region to region, and sometimes within a given region, can be more clearly understood.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161192","usgsCitation":"Hayes, G.P., Myers, E.K., Dewey, J.W., Briggs, R.W., Earle, P.S., Benz, H.M., Smoczyk, G.M., Flamme, H.E., Barnhart, W.D., Gold, R.D., and Furlong, K.P., 2017, Tectonic summaries of magnitude 7 and greater earthquakes from 2000 to 2015: U.S. Geological Survey Open-File Report 2016–1192, 148 p., https://doi.org/10.3133/ofr20161192.","productDescription":"Report: v, 148 p.; KMZ file","onlineOnly":"Y","ipdsId":"IP-077362","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":333007,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2016/1192/ofr20161192.kmz","text":"KMZ file","size":"112 kB","description":"OFR 2016-1192 KMZ file"},{"id":333005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1192/coverthb.jpg"},{"id":333006,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1192/ofr20161192.pdf","text":"Report","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1192"}],"otherGeospatial":"Earth","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225-0046
Heavy rainfall during December 2015 resulted in flooding across parts of Arkansas; rainfall amounts were as high as 12 inches over a period from December 27, 2015, to December 29, 2015. Although precipitation accumulations were highest in northwestern Arkansas, significant flooding occurred in other parts of the State. Flood damage occurred in several counties as water levels rose in streams, and disaster declarations were declared in 32 of the 75 counties in Arkansas.
Given the severity of the December 2015 flooding, the U.S. Geological Survey (USGS), in cooperation with the Federal Emergency Management Agency (FEMA), conducted a study to document the meteorological and hydrological conditions prior to and during the flood; compiled flood-peak gage heights, streamflows, and flood probabilities at USGS streamflow-gaging stations; and estimated streamflows and flood probabilities at selected ungaged locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161198","usgsCitation":"Breaker, B.K., 2017, Peak streamflow on selected streams in Arkansas, December 2015: U.S. Geological Survey Open-File Report 2016–1198, 7 p., https://doi.org/10.3133/ofr20161198.","productDescription":"7 p.","onlineOnly":"Y","ipdsId":"IP-079418","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":332997,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1198/coverthb2.jpg"},{"id":332995,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1198/ofr20161198.pdf","text":"Report","size":"622 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1198"}],"country":"United 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The Iġnik Sikumi Gas Hydrate Exchange Field Experiment was conducted by ConocoPhillips in partnership with the U.S. Department of Energy, the Japan Oil, Gas and Metals National Corporation, and the U.S. Geological Survey within the Prudhoe Bay Unit on the Alaska North Slope during 2011 and 2012. The primary goals of the program were to (1) determine the feasibility of gas injection into hydrate-bearing sand reservoirs and (2) observe reservoir response upon subsequent flowback in order to assess the potential for CO2 exchange for CH4 in naturally occurring gas hydrate reservoirs. Initial modeling determined that no feasible means of injection of pure CO2 was likely, given the presence of free water in the reservoir. Laboratory and numerical modeling studies indicated that the injection of a mixture of CO2 and N2 offered the best potential for gas injection and exchange. The test featured the following primary operational phases: (1) injection of a gaseous phase mixture of CO2, N2, and chemical tracers; (2) flowback conducted at downhole pressures above the stability threshold for native CH4 hydrate; and (3) an extended (30-days) flowback at pressures near, and then below, the stability threshold of native CH4 hydrate. The test findings indicate that the formation of a range of mixed-gas hydrates resulted in a net exchange of CO2 for CH4 in the reservoir, although the complexity of the subsurface environment renders the nature, extent, and efficiency of the exchange reaction uncertain. The next steps in the evaluation of exchange technology should feature multiple well applications; however, such field test programs will require extensive preparatory experimental and numerical modeling studies and will likely be a secondary priority to further field testing of production through depressurization. Additional insights gained from the field program include the following: (1) gas hydrate destabilization is self-limiting, dispelling any notion of the potential for uncontrolled destabilization; (2) gas hydrate test wells must be carefully designed to enable rapid remediation of wellbore blockages that will occur during any cessation in operations; (3) sand production during hydrate production likely can be managed through standard engineering controls; and (4) reservoir heat exchange during depressurization was more favorable than expected—mitigating concerns for near-wellbore freezing and enabling consideration of more aggressive pressure reduction.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.energyfuels.6b01909","usgsCitation":"Boswell, R., Schoderbek, D., Collett, T.S., Ohtsuki, S., White, M., and Anderson, B.J., 2017, The Iġnik Sikumi Field Experiment, Alaska North Slope: Design, operations, and implications for CO2−CH4 exchange in gas hydrate reservoirs: Energy & Fuels, v. 31, no. 1, p. 140-153, https://doi.org/10.1021/acs.energyfuels.6b01909.","productDescription":"14 p.","startPage":"140","endPage":"153","ipdsId":"IP-074604","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":333052,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-14","publicationStatus":"PW","scienceBaseUri":"58772077e4b0315b4c11fe24","contributors":{"authors":[{"text":"Boswell, Ray","contributorId":12307,"corporation":false,"usgs":true,"family":"Boswell","given":"Ray","affiliations":[],"preferred":false,"id":658205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoderbek, David","contributorId":178207,"corporation":false,"usgs":false,"family":"Schoderbek","given":"David","email":"","affiliations":[],"preferred":false,"id":658206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":658204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ohtsuki, Satoshi","contributorId":150141,"corporation":false,"usgs":false,"family":"Ohtsuki","given":"Satoshi","email":"","affiliations":[{"id":17917,"text":"Japan Oil, Gas and Metals National Corporation","active":true,"usgs":false}],"preferred":false,"id":658208,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"White, Mark","contributorId":150142,"corporation":false,"usgs":false,"family":"White","given":"Mark","email":"","affiliations":[{"id":6727,"text":"Pacific Northwest National Laboratory, Richland, WA","active":true,"usgs":false}],"preferred":false,"id":658209,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Brian J.","contributorId":147120,"corporation":false,"usgs":false,"family":"Anderson","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":658207,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70179655,"text":"70179655 - 2017 - Long-term flow-through column experiments and their relevance to natural granitoid weathering rates","interactions":[],"lastModifiedDate":"2017-02-24T10:45:06","indexId":"70179655","displayToPublicDate":"2017-01-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Long-term flow-through column experiments and their relevance to natural granitoid weathering rates","docAbstract":"Four pairs of fresh and partly-weathered granitoids, obtained from well-characterized watersheds—Merced River, CA, USA; Panola, GA, USA; Loch Vale, CO, USA, and Rio Icacos, Puerto Rico—were reacted in columns under ambient laboratory conditions for 13.8 yrs, the longest running experimental weathering study to date. Low total column mass losses (<1 wt. %), correlated with the absence of pitting or surface roughening of primary silicate grains. BET surface area (SBET) increased, primarily due to Fe-oxyhydroxide precipitation. Surface areas returned to within factors of 2 to 3 of their original values after dithionite extraction. Miscible displacement experiments indicated homogeneous plug flow with negligible immobile water, commonly cited for column experiments. Fresh granitoid effluent solute concentrations initially declined rapidly, followed by much slower decreases over the next decade. Weathered granitoid effluent concentrations increased modestly over the same time period, indicating losses of natural Fe-oxide and/or clay coatings and the increased exposure of primary mineral surfaces. Corresponding (fresh and weathered) elemental effluent concentrations trended toward convergence during the last decade of reaction. NETPATH/PHREEQC code simulations indicated non-stoichiometric dissolution involving Ca release from disseminated calcite and excess K release from interlayer biotite. Effluent 87Sr/85Sr ratios reflected a progressive weathering sequence beginning and ending with 87Sr/85Sr values of plagioclase with an additional calcite input and a radiogenic biotite excursion proportional to the granitoid ages.
Effluents became thermodynamically saturated with goethite and gibbsite, slightly under-saturated with kaolinite and strongly under-saturated with plagioclase, consistent with kinetically-limited weathering in which solutes such as Na varied with column flow rates. Effluent Na concentrations showed no clear trend with time during the last decade of reaction (fresh granitoids) or increased slowly with time (weathered granitoids). Analysis of cumulative Na release indicated that plagioclase dissolution achieved steady state in 3 of the 4 fresh granitoids during the last decade of reaction. Surface-area normalized plagioclase dissolution rates exhibited a narrow range (0.95 to 1.26 10-13 moles m-2 s-1), in spite of significant stoichiometric differences (An0.21 to An0.50). Rates were an order of magnitude slower than previously reported in shorter duration experiments but generally 2 to 3 orders of magnitude faster than corresponding natural analogs. CrunchFlow simulations indicated that more than a hundredfold decrease in column flow rates would be required to produce near-saturation reaction affinities that would start to slow plagioclase weathering to real-world levels. Extending simulations to approximate long term weathering in naturally weathered profiles required additional decreases in the intrinsic plagioclase dissolution and kaolinite precipitation rates and relatively large decreases in the fluid flow rate, implying that exposure to reactive mineral surfaces is significantly limited in the natural environment compared to column experiments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2016.11.042","usgsCitation":"White, A.F., Schulz, M., Lawrence, C.R., Vivit, D.V., and Stonestrom, D.A., 2017, Long-term flow-through column experiments and their relevance to natural granitoid weathering rates: Geochimica et Cosmochimica Acta, v. 202, p. 190-214, https://doi.org/10.1016/j.gca.2016.11.042.","productDescription":"25 p.","startPage":"190","endPage":"214","ipdsId":"IP-073779","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470143,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2016.11.042","text":"Publisher Index Page"},{"id":333021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"202","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58760113e4b04eac8e0746d1","chorus":{"doi":"10.1016/j.gca.2016.11.042","url":"http://dx.doi.org/10.1016/j.gca.2016.11.042","publisher":"Elsevier BV","authors":"White Art F., Schulz Marjorie S., Lawrence Corey R., Vivit Davison V., Stonestrom David A.","journalName":"Geochimica et Cosmochimica Acta","publicationDate":"4/2017"},"contributors":{"authors":[{"text":"White, Arthur F. afwhite@usgs.gov","contributorId":3718,"corporation":false,"usgs":true,"family":"White","given":"Arthur","email":"afwhite@usgs.gov","middleInitial":"F.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":658091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":658090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, Corey R. clawrence@usgs.gov","contributorId":167122,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","email":"clawrence@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":658092,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vivit, Davison V.","contributorId":178166,"corporation":false,"usgs":false,"family":"Vivit","given":"Davison","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":658094,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stonestrom, David A. 0000-0001-7883-3385 dastones@usgs.gov","orcid":"https://orcid.org/0000-0001-7883-3385","contributorId":2280,"corporation":false,"usgs":true,"family":"Stonestrom","given":"David","email":"dastones@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":658093,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179658,"text":"70179658 - 2017 - Timescales of carbon turnover in soils with mixed crystalline mineralogies","interactions":[],"lastModifiedDate":"2017-01-10T15:30:06","indexId":"70179658","displayToPublicDate":"2017-01-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5259,"text":"SOIL","active":true,"publicationSubtype":{"id":10}},"title":"Timescales of carbon turnover in soils with mixed crystalline mineralogies","docAbstract":"Organic matter–mineral associations stabilize much of the carbon (C) stored globally in soils. Metastable short-range-order (SRO) minerals such as allophane and ferrihydrite provide one mechanism for long-term stabilization of organic matter in young soil. However, in soils with few SRO minerals and a predominance of crystalline aluminosilicate or Fe (and Al) oxyhydroxide, C turnover should be governed by chemisorption with those minerals. Here, we correlate mineral composition from soils containing small amounts of SRO minerals with mean turnover time (TT) of C estimated from radiocarbon (14C) in bulk soil, free light fraction and mineral-associated organic matter. We varied the mineral amount and composition by sampling ancient soils formed on different lithologies in arid to subhumid climates in Kruger National Park (KNP), South Africa. Mineral contents in bulk soils were assessed using chemical extractions to quantify Fe oxyhydroxides and SRO minerals. Because of our interest in the role of silicate clay mineralogy, particularly smectite (2 : 1) and kaolinite (1 : 1), we separately quantified the mineralogy of the clay-sized fraction using X-ray diffraction (XRD) and measured 14C on the same fraction.
Density separation demonstrated that mineral associated C accounted for 40–70 % of bulk soil organic C in A and B1 horizons for granite, nephelinite and arid-zone gabbro soils, and > 80 % in other soils. Organic matter strongly associated with the isolated clay-sized fraction represented only 9–47 % of the bulk soil C. The mean TT of C strongly associated with the clay-sized fraction increased with the amount of smectite (2 : 1 clays); in samples with > 40 % smectite it averaged 1020 ± 460 years. The C not strongly associated with clay-sized minerals, including a combination of low-density C, the C associated with minerals of sizes between 2 µm and 2 cm (including Fe oxyhydroxides as coatings), and C removed from clay-sized material by 2 % hydrogen peroxide had TTs averaging 190 ± 190 years in surface horizons. Summed over the bulk soil profile, we found that smectite content correlated with the mean TT of bulk soil C across varied lithologies. The SRO mineral content in KNP soils was generally very low, except for the soils developed on gabbros under more humid climate that also had very high Fe and C contents with a surprisingly short, mean C TTs. In younger landscapes, SRO minerals are metastable and sequester C for long timescales. We hypothesize that in the KNP, SRO minerals represent a transient stage of mineral evolution and therefore lock up C for a shorter time.
Overall, we found crystalline Fe-oxyhydroxides (determined as the difference between Fe in dithionate citrate and oxalate extractions) to be the strongest predictor for soil C content, while the mean TT of soil C was best predicted from the amount of smectite, which was also related to more easily measured bulk properties such as cation exchange capacity or pH. Combined with previous research on C turnover times in 2 : 1 vs. 1 : 1 clays, our results hold promise for predicting C inventory and persistence based on intrinsic timescales of specific carbon–mineral interactions.
Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.
","language":"English","publisher":"Springer","doi":"10.1007/s13253-016-0274-1","usgsCitation":"Hefley, T.J., Hooten, M., Hanks, E.M., Russell, R.E., and Walsh, D.P., 2017, The Bayesian group lasso for confounded spatial data: Journal of Agricultural, Biological, and Environmental Statistics, v. 22, no. 1, p. 42-59, https://doi.org/10.1007/s13253-016-0274-1.","productDescription":"18 p.","startPage":"42","endPage":"59","ipdsId":"IP-071980","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":333019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"22","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-05","publicationStatus":"PW","scienceBaseUri":"58760114e4b04eac8e0746d5","contributors":{"authors":[{"text":"Hefley, Trevor J.","contributorId":147146,"corporation":false,"usgs":false,"family":"Hefley","given":"Trevor","email":"","middleInitial":"J.","affiliations":[{"id":16796,"text":"Dept Fish, Wildlife & Cons Biol, Colorado St Univ, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":657904,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":657903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanks, Ephraim M.","contributorId":178093,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":657905,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Russell, Robin E. 0000-0001-8726-7303 rerussell@usgs.gov","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":3998,"corporation":false,"usgs":true,"family":"Russell","given":"Robin","email":"rerussell@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445 dwalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":4758,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"dwalsh@usgs.gov","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657907,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179652,"text":"70179652 - 2017 - Identifying western yellow-billed cuckoo breeding habitat with a dual modelling approach","interactions":[],"lastModifiedDate":"2017-01-10T10:34:49","indexId":"70179652","displayToPublicDate":"2017-01-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Identifying western yellow-billed cuckoo breeding habitat with a dual modelling approach","docAbstract":"The western population of the yellow-billed cuckoo (Coccyzus americanus) was recently listed as threatened under the federal Endangered Species Act. Yellow-billed cuckoo conservation efforts require the identification of features and area requirements associated with high quality, riparian forest habitat at spatial scales that range from nest microhabitat to landscape, as well as lower-suitability areas that can be enhanced or restored. Spatially explicit models inform conservation efforts by increasing ecological understanding of a target species, especially at landscape scales. Previous yellow-billed cuckoo modelling efforts derived plant-community maps from aerial photography, an expensive and oftentimes inconsistent approach. Satellite models can remotely map vegetation features (e.g., vegetation density, heterogeneity in vegetation density or structure) across large areas with near perfect repeatability, but they usually cannot identify plant communities. We used aerial photos and satellite imagery, and a hierarchical spatial scale approach, to identify yellow-billed cuckoo breeding habitat along the Lower Colorado River and its tributaries. Aerial-photo and satellite models identified several key features associated with yellow-billed cuckoo breeding locations: (1) a 4.5 ha core area of dense cottonwood-willow vegetation, (2) a large native, heterogeneously dense forest (72 ha) around the core area, and (3) moderately rough topography. The odds of yellow-billed cuckoo occurrence decreased rapidly as the amount of tamarisk cover increased or when cottonwood-willow vegetation was limited. We achieved model accuracies of 75–80% in the project area the following year after updating the imagery and location data. The two model types had very similar probability maps, largely predicting the same areas as high quality habitat. While each model provided unique information, a dual-modelling approach provided a more complete picture of yellow-billed cuckoo habitat requirements and will be useful for management and conservation activities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2016.12.010","usgsCitation":"Johnson, M.J., Hatten, J.R., Holmes, J.A., and Shafroth, P.B., 2017, Identifying western yellow-billed cuckoo breeding habitat with a dual modelling approach: Ecological Modelling, v. 347, p. 50-62, https://doi.org/10.1016/j.ecolmodel.2016.12.010.","productDescription":"13 p.","startPage":"50","endPage":"62","ipdsId":"IP-075673","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":470144,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2016.12.010","text":"Publisher Index Page"},{"id":333009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"347","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58760114e4b04eac8e0746d3","contributors":{"authors":[{"text":"Johnson, Matthew J. mjjohnson@usgs.gov","contributorId":167197,"corporation":false,"usgs":false,"family":"Johnson","given":"Matthew","email":"mjjohnson@usgs.gov","middleInitial":"J.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":658079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, James R. 0000-0003-4676-8093 jhatten@usgs.gov","orcid":"https://orcid.org/0000-0003-4676-8093","contributorId":3431,"corporation":false,"usgs":true,"family":"Hatten","given":"James","email":"jhatten@usgs.gov","middleInitial":"R.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":658078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmes, Jennifer A.","contributorId":178159,"corporation":false,"usgs":false,"family":"Holmes","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":658080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":658081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193795,"text":"70193795 - 2017 - Quantifying site-specific physical heterogeneity within an estuarine seascape","interactions":[],"lastModifiedDate":"2017-11-08T13:30:35","indexId":"70193795","displayToPublicDate":"2017-01-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying site-specific physical heterogeneity within an estuarine seascape","docAbstract":"Quantifying physical heterogeneity is essential for meaningful ecological research and effective resource management. Spatial patterns of multiple, co-occurring physical features are rarely quantified across a seascape because of methodological challenges. Here, we identified approaches that measured total site-specific heterogeneity, an often overlooked aspect of estuarine ecosystems. Specifically, we examined 23 metrics that quantified four types of common physical features: (1) river and creek confluences, (2) bathymetric variation including underwater drop-offs, (3) land features such as islands/sandbars, and (4) major underwater channel networks. Our research at 40 sites throughout Plum Island Estuary (PIE) provided solutions to two problems. The first problem was that individual metrics that measured heterogeneity of a single physical feature showed different regional patterns. We solved this first problem by combining multiple metrics for a single feature using a within-physical feature cluster analysis. With this approach, we identified sites with four different types of confluences and three different types of underwater drop-offs. The second problem was that when multiple physical features co-occurred, new patterns of total site-specific heterogeneity were created across the seascape. This pattern of total heterogeneity has potential ecological relevance to structure-oriented predators. To address this second problem, we identified sites with similar types of total physical heterogeneity using an across-physical feature cluster analysis. Then, we calculated an additive heterogeneity index, which integrated all physical features at a site. Finally, we tested if site-specific additive heterogeneity index values differed for across-physical feature clusters. In PIE, the sites with the highest additive heterogeneity index values were clustered together and corresponded to sites where a fish predator, adult striped bass (Morone saxatilis), aggregated in a related acoustic tracking study. In summary, we have shown general approaches to quantifying site-specific heterogeneity.
","language":"English","publisher":"Springer International","doi":"10.1007/s12237-016-0207-9","usgsCitation":"Kennedy, C.G., Mather, M.E., and Smith, J.M., 2017, Quantifying site-specific physical heterogeneity within an estuarine seascape: Estuaries and Coasts, v. 40, no. 5, p. 1385-1397, https://doi.org/10.1007/s12237-016-0207-9.","productDescription":"13 p.","startPage":"1385","endPage":"1397","ipdsId":"IP-070125","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Plum Island Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.85855484008789,\n 42.69088969601617\n ],\n [\n -70.76482772827148,\n 42.69088969601617\n ],\n [\n -70.76482772827148,\n 42.76780873017273\n ],\n [\n -70.85855484008789,\n 42.76780873017273\n ],\n [\n -70.85855484008789,\n 42.69088969601617\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"5","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"5a0425bce4b0dc0b45b453b6","contributors":{"authors":[{"text":"Kennedy, Cristina G.","contributorId":200162,"corporation":false,"usgs":false,"family":"Kennedy","given":"Cristina","email":"","middleInitial":"G.","affiliations":[{"id":18918,"text":"Department of Environmental Conservation, University of Massachusetts, Amherst, MA, 01003, USA","active":true,"usgs":false}],"preferred":false,"id":721224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":720524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Joseph M.","contributorId":106712,"corporation":false,"usgs":false,"family":"Smith","given":"Joseph","email":"","middleInitial":"M.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false},{"id":17855,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":721225,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70179076,"text":"sim3371 - 2017 - Geologic map of the Fittstown 7.5΄ quadrangle, Pontotoc and Johnston Counties, Oklahoma","interactions":[],"lastModifiedDate":"2017-02-10T11:33:48","indexId":"sim3371","displayToPublicDate":"2017-01-09T13:30:00","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":"3371","title":"Geologic map of the Fittstown 7.5΄ quadrangle, Pontotoc and Johnston Counties, Oklahoma","docAbstract":"This 1:24,000-scale geologic map includes new geologic mapping as well as compilation and revision of previous geologic maps in the area. Field investigations were carried out during 2009–2011 that included mapping and investigations of the geology and hydrology of the Chickasaw National Recreation Area, Oklahoma, west of the map area.
The Fittstown quadrangle is in Pontotoc and Johnston Counties in south-central Oklahoma, which is in the northeastern part of the Arbuckle Mountains. The Arbuckle Mountains are composed of a thick sequence of Paleozoic sedimentary rocks that overlie Lower Cambrian and Precambrian igneous rocks; these latter rocks are not exposed in the quadrangle. From Middle to Late Pennsylvanian time, the Arbuckle Mountains region was folded, faulted, and uplifted. Periods of erosion followed these Pennsylvanian mountain-building events, beveling this region and ultimately developing the current subtle topography that includes hills and incised uplands. The southern and northwestern parts of the Fittstown quadrangle are directly underlain by Lower Ordovician dolomite of the Arbuckle Group that has eroded to form an extensive, stream-incised upland containing the broad, gently southeast-plunging, Pennsylvanian-age Hunton anticline. The northeastern part of the map area is underlain by Middle Ordovician to Pennsylvanian limestone, shale, and sandstone units that predominantly dip northeast and form the northeastern limb of the Hunton anticline; this limb is cut by steeply dipping, northwest-southeast striking faults of the Franks fault zone. This limb and the Franks fault zone define the southwestern margin of the Franks graben, which is underlain by Pennsylvanian rocks in the northeast part of the map area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3371","usgsCitation":"Lidke, D.J., and Blome, C.D., 2017, Geologic map of the Fittstown 7.5′ quadrangle, Pontotoc and Johnston Counties, Oklahoma: U.S. Geological Survey Scientific Investigations Map 3371, 14 p., 1 sheet, scale 1:24,000, https://doi.org/10.3133/sim3371.","productDescription":"Pamphlet: iv, 14 p.; 1 Sheet: 34.09 x 34.35 inches; Read Me; Spatial Data","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073124","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":438453,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77P8WJN","text":"USGS data release","linkHelpText":"Geologic map of the Fittstown 7 1/2' quadrangle, Pontotoc and Johnston Counties, Oklahoma"},{"id":332521,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3371/sim3371__map.pdf","text":"Map","size":"31.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3371 Map"},{"id":332522,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3371/sim3371__map_geo.pdf","text":"Georeferenced Map","size":"187 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3371 Georeferenced Map"},{"id":332520,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3371/sim3371_ReadMe_v2.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3371 Read Me"},{"id":332523,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://doi.org/10.5066/F77P8WJN","text":"Data Release","description":"SIM 3371 Data Release"},{"id":332519,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3371/sim3371_pamphlet.pdf","text":"Pamphlet","size":"3.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3371 Pamphlet"},{"id":332518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3371/coverthb_map2.jpg"}],"country":"United States","state":"Oklahoma","county":"Johnson County. Pontotoc County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.75,\n 34.625\n ],\n [\n -96.75,\n 34.5\n ],\n [\n -96.625,\n 34.5\n ],\n [\n -96.625,\n 34.625\n ],\n [\n -96.75,\n 34.625\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Geosciences and Environmental Change Science Center
Box 25046, Mail Stop 980
Denver, CO 80225
Cyanobacterial harmful algal blooms (CyanoHABs) tend to be spatially variable vertically in the water column and horizontally across the lake surface because of in-lake and weather-driven processes and can vary by orders of magnitude in concentration across relatively short distances (meters or less). Extreme spatial variability in cyanobacteria and associated compounds poses unique challenges to collecting representative samples for scientific study and public-health protection. The objective of this study was to assess the spatial variability of cyanobacteria and microcystin in Milford Lake, Kansas, using data collected on July 27 and August 31, 2015. Spatially dense near-surface data were collected by the U.S. Geological Survey, nearshore data were collected by the Kansas Department of Health and Environment, and open-water data were collected by U.S. Army Corps of Engineers. CyanoHABs are known to be spatially variable, but that variability is rarely quantified. A better understanding of the spatial variability of cyanobacteria and microcystin will inform sampling and management strategies for Milford Lake and for other lakes with CyanoHAB issues throughout the Nation.
The CyanoHABs in Milford Lake during July and August 2015 displayed the extreme spatial variability characteristic of cyanobacterial blooms. The phytoplankton community was almost exclusively cyanobacteria (greater than 90 percent) during July and August. Cyanobacteria (measured directly by cell counts and indirectly by regression-estimated chlorophyll) and microcystin (measured directly by enzyme-linked immunosorbent assay [ELISA] and indirectly by regression estimates) concentrations varied by orders of magnitude throughout the lake. During July and August 2015, cyanobacteria and microcystin concentrations decreased in the downlake (towards the outlet) direction.
Nearshore and open-water surface grabs were collected and analyzed for microcystin as part of this study. Samples were collected in the uplake (Zone C), midlake (Zone B), and downlake (Zone A) parts of the lake. Overall, no consistent pattern was indicated as to which sample location (nearshore or open water) had the highest microcystin concentrations. In July, the maximum microcystin concentration observed in each zone was detected at a nearshore site, and in August, maximum microcystin concentrations in each zone were detected at an open-water site.
The Kansas Department of Health and Environment uses two guidance levels (a watch and a warning level) to issue recreational public-health advisories for CyanoHABs in Kansas lakes. The levels are based on concentrations of microcystin and numbers of cyanobacteria. In July and August, discrete water-quality samples were predominantly indicative of warning status in Zone C, watch status in Zone B, and no advisories in Zone A. Regression-estimated microcystin concentrations, which provided more thorough coverage of Milford Lake (n=683–720) than discrete samples (n=21–24), generally indicated the same overall pattern. Regardless of the individual agencies sampling approach, the overall public-health advisory status of each zone in Milford Lake was similar according to the Kansas Department of Health and Environment guidance levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165168","collaboration":"Prepared in cooperation with the Kansas Department of Health and Environment and the U.S. Army Corps of Engineers, Kansas City District","usgsCitation":"Foster, G.M., Graham, J.L., Stiles, T.C., Boyer, M.G., King, L.R., and Loftin, K.A., 2017, Spatial variability of harmful algal blooms in Milford Lake, Kansas, July and August 2015: U.S. Geological Survey Scientific Investigations Report 2016–5168, 45 p., https://doi.org/10.3133/sir20165168.","productDescription":"Report: v, 45 p.; Data Releases","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078303","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":333877,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7V69GRH","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-quality data from two sites on Milford Lake, Kansas, July 26-27 and August 30-31, 2015"},{"id":332912,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5168/coverthb.jpg"},{"id":333876,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5168/sir20165168.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5168 Report PDF"},{"id":333878,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WQ01ZW","text":"USGS data release","description":"USGS data release","linkHelpText":" Milford Lake, Kansas, spatial water-quality data, July 27 and August 31, 2015"},{"id":333879,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RX9971","text":"USGS data release","description":"USGS data release","linkHelpText":"Phytoplankton data for Milford Lake, Kansas, July 27 and August 31, 2015"}],"country":"United States","state":"Kansas","otherGeospatial":"Milford Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.1630859375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 38.982897808179985\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
Taxonomic identification of pollen has historically been accomplished via light microscopy but requires specialized knowledge and reference collections, particularly when identification to lower taxonomic levels is necessary. Recently, next-generation sequencing technology has been used as a cost-effective alternative for identifying bee-collected pollen; however, this novel approach has not been tested on a spatially or temporally robust number of pollen samples. Here, we compare pollen identification results derived from light microscopy and DNA sequencing techniques with samples collected from honey bee colonies embedded within a gradient of intensive agricultural landscapes in the Northern Great Plains throughout the 2010–2011 growing seasons. We demonstrate that at all taxonomic levels, DNA sequencing was able to discern a greater number of taxa, and was particularly useful for the identification of infrequently detected species. Importantly, substantial phenological overlap did occur for commonly detected taxa using either technique, suggesting that DNA sequencing is an appropriate, and enhancing, substitutive technique for accurately capturing the breadth of bee-collected species of pollen present across agricultural landscapes. We also show that honey bees located in high and low intensity agricultural settings forage on dissimilar plants, though with overlap of the most abundantly collected pollen taxa. We highlight practical applications of utilizing sequencing technology, including addressing ecological issues surrounding land use, climate change, importance of taxa relative to abundance, and evaluating the impact of conservation program habitat enhancement efforts.
","language":"English","publisher":"Oxford University Journals","doi":"10.1093/ee/nvw159","usgsCitation":"Smart, M., Cornman, R.S., Iwanowicz, D.D., McDermott-Kubeczko, M., Pettis, J.S., Spivak, M., and Otto, C., 2017, A comparison of honey bee-collected pollen from working agricultural lands using light microscopy and ITS metabarcoding: Environmental Entomology, v. 46, no. 1, p. 38-49, https://doi.org/10.1093/ee/nvw159.","productDescription":"12 p.","startPage":"38","endPage":"49","ipdsId":"IP-078924","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470145,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ee/nvw159","text":"Publisher Index Page"},{"id":332984,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-06","publicationStatus":"PW","scienceBaseUri":"5874b0a9e4b0a829a320bb59","contributors":{"authors":[{"text":"Smart, Matthew 0000-0003-0711-3035 msmart@usgs.gov","orcid":"https://orcid.org/0000-0003-0711-3035","contributorId":174424,"corporation":false,"usgs":true,"family":"Smart","given":"Matthew","email":"msmart@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":658031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":658032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iwanowicz, Deborah D. 0000-0002-9613-8594 diwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-9613-8594","contributorId":2253,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Deborah","email":"diwanowicz@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":658033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDermott-Kubeczko, Margaret","contributorId":178147,"corporation":false,"usgs":false,"family":"McDermott-Kubeczko","given":"Margaret","email":"","affiliations":[],"preferred":false,"id":658034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pettis, Jeff S","contributorId":178149,"corporation":false,"usgs":false,"family":"Pettis","given":"Jeff","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":658036,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spivak, Marla S","contributorId":178148,"corporation":false,"usgs":false,"family":"Spivak","given":"Marla S","affiliations":[],"preferred":false,"id":658035,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":658037,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}