The National Park Service (NPS) manages the Nation’s most iconic destinations that attract millions of visitors from across the Nation and around the world. Trip-related spending by NPS visitors generates and supports a considerable amount of economic activity within park gateway communities. This economic effects analysis measures how NPS visitor spending cycles through local economies, generating business sales and supporting jobs and income. In 2017, the National Park System received an estimated 331 million recreation visits. Visitors to National Parks spent an estimated \\$18.2 billion in local gateway regions (defined as communities within 60 miles of a park). The contribution of this spending to the national economy was 306 thousand jobs, \\$11.9 billion in labor income, \\$20.3 billion in value added, and \\$35.8 billion in economic output. The lodging sector saw the highest direct contributions with \\$5.5 billion in economic output directly contributed to local gateway economies nationally. The sector with the next greatest direct contributions was the restaurants and bars sector, with \\$3.7 billion in economic output directly contributed to local gateway economies nationally. Results from the Visitor Spending Effects report series are available online via an interactive tool. Users can view year-by-year trend data and explore current year visitor spending, jobs, labor income, value added, and economic output effects by sector for national, state, and local economies. This interactive tool is available at https://www.nps.gov/subjects/socialscience/vse.htm.
","language":"English","publisher":"National Park Service","publisherLocation":"Fort Collins, CO","usgsCitation":"Cullinane Thomas, C., Koontz, L., and Cornachione, E., 2018, 2017 National Park visitor spending effects : Economic contributions to local communities, states, and the Nation : Natural Resource Technical Report NPS/NRSS/EQD/NRR—2018/1616 , iv, 56 p.","productDescription":"iv, 56 p.","ipdsId":"IP-096074","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":354886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354889,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://www.nps.gov/subjects/socialscience/vse.htm"},{"id":354887,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://www.nps.gov/nature/customcf/NPS_Data_Visualization/docs/NPS_2017_Visitor_Spending_Effects.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e56ee4b060350a15d155","contributors":{"authors":[{"text":"Cullinane Thomas, Catherine M. 0000-0001-8168-1271 ccullinanethomas@usgs.gov","orcid":"https://orcid.org/0000-0001-8168-1271","contributorId":5281,"corporation":false,"usgs":true,"family":"Cullinane Thomas","given":"Catherine M.","email":"ccullinanethomas@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":737590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koontz, Lynne koontzl@usgs.gov","contributorId":131112,"corporation":false,"usgs":true,"family":"Koontz","given":"Lynne","email":"koontzl@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":737591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornachione, Egan 0000-0001-9248-4118","orcid":"https://orcid.org/0000-0001-9248-4118","contributorId":205507,"corporation":false,"usgs":true,"family":"Cornachione","given":"Egan","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":737592,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195401,"text":"sim3398 - 2018 - Water-table and potentiometric-surface altitudes in the upper glacial, Magothy, and Lloyd aquifers of Long Island, New York, April–May 2016","interactions":[],"lastModifiedDate":"2018-06-07T10:40:26","indexId":"sim3398","displayToPublicDate":"2018-06-06T12:00:00","publicationYear":"2018","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":"3398","title":"Water-table and potentiometric-surface altitudes in the upper glacial, Magothy, and Lloyd aquifers of Long Island, New York, April–May 2016","docAbstract":"The U.S. Geological Survey, in cooperation with State and local agencies, systematically collects groundwater data at varying measurement frequencies to monitor the hydrologic conditions on Long Island, New York. Each year during April and May, the U.S. Geological Survey completes a synoptic survey of water levels to define the spatial distribution of the water table and potentiometric surfaces within the three main water-bearing units underlying Long Island—the upper glacial, Magothy, and Lloyd aquifers—and the hydraulically connected Jameco and North Shore aquifers. These data and the maps constructed from them are commonly used in studies of the hydrology of Long Island and are used by water managers and suppliers for aquifer management and planning purposes.
Water-level measurements made in 424 monitoring wells (observation and supply wells), 13 streamgages, and 2 lake gages across Long Island during April–May 2016 were used to prepare the maps in this report. Groundwater measurements were made by the wetted-tape or electric-tape method to the nearest hundredth of a foot. Contours of water-table and potentiometric-surface altitudes were created using the groundwater measurements. The water-table contours were interpreted using water-level data collected from 275 observation wells and 1 supply well screened in the upper glacial aquifer and the shallow Magothy aquifer and 13 streamgages and 2 lake gages. The potentiometric-surface contours of the Magothy aquifer were interpreted from measurements at 88 wells (61 observation wells and 27 supply wells) screened in the middle to deep Magothy aquifer and the contiguous and hydraulically connected Jameco aquifer. The potentiometric-surface contours of the Lloyd aquifer were interpreted from measurements at 60 wells (55 observation wells and 5 supply wells) screened in the Lloyd aquifer and the contiguous and hydraulically connected North Shore aquifer. Many of the supply wells are in continuous operation and, therefore, were turned off for a minimum of 24 hours before measurements were made to allow the water levels in the wells to recover to ambient (nonpumping) conditions. Full recovery time at some of these supply wells can exceed 24 hours; therefore, water levels measured at these wells are assumed to be less accurate than those measured at observation wells, which are not pumped. In addition to pumping stresses, density differences (saline water) also lower the water levels measured in certain wells. Recent water-quality data are lacking in these wells; therefore, a conversion to freshwater head could not be performed accurately and was not attempted. In this report, all water-level altitudes are referenced to the National Geodetic Vertical Datum of 1929 (NGVD 29).
The land surface altitude, or topography, was obtained from the National Oceanic and Atmospheric Administration. The data were collected using light detection and ranging (lidar) and were used to produce a three-dimensional digital elevation model. The lidar data have a horizontal accuracy of 1.38 feet and a vertical accuracy of 0.40 foot at a 95-percent confidence level for the “open terrain” land-cover category. The digital elevation model was developed jointly by the National Oceanic and Atmospheric Administration and the U.S. Geological Survey as part of the Disaster Relief Appropriations Act of 2013. Land surface altitude is referenced to the North American Vertical Datum of 1988 (NAVD 88). On Long Island, NAVD 88 is approximately 1 foot higher than NGVD 29.
Hydrographs are included on these maps for selected wells that have continuous digital recording equipment, and each hydrograph includes the water level measured during the synoptic survey. These hydrographs are representative of the 2016 water year and show the changes throughout that period; a water year is the 12-month period from October 1 to September 30 and is designated by the year in which it ends.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3398","collaboration":"Prepared in cooperation with the Manhasset-Lakeville Water District, Nassau County Department of Public Works, New York State Department of Environmental Conservation, Port Washington Water District, Sands Point Water Department, Suffolk County Department of Health Services, Suffolk County Water Authority, Town of North Hempstead, Town of Shelter Island, and Water Authority of Great Neck North","usgsCitation":"Como, M.D., Finkelstein, J.S., Rivera, S.L., Monti, Jack, Jr., and Busciolano, Ronald, 2018, Water-table and potentiometric-surface altitudes in the upper glacial, Magothy, and Lloyd aquifers of Long Island, New York, April–May 2016: U.S. Geological Survey Scientific Investigations Map 3398, 4 sheets, scale 1:125,000, 5-p. pamphlet, https://doi.org/10.3133/sim3398.","productDescription":"Pamphlet: iii, 5 p.; 8 Sheets: 69.00 x 24.11 inches; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-085602","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":354435,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3398/sim3398_sheet03.pdf","text":"Sheet 3 - (Full size)","size":"107 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric Surface in the Lloyd and North Shore Aquifers"},{"id":354436,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3398/sim3398_sheet04.pdf","text":"Sheet 4 - (Full size)","size":"90 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Depth to Water Table"},{"id":354438,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7G15Z9T","text":"USGS data release","description":"USGS data release","linkHelpText":"USGS data release—Geospatial dataset of water-table and potentiometric-surface altitudes in the upper glacial, Magothy, and Lloyd aquifers of Long Island, New York, April–May 2016 "},{"id":354439,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3398/sim3398_sheet01w.pdf","text":"Sheet 1 - (Reduced size)","size":"76.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":354440,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3398/sim3398_sheet02w.pdf","text":"Sheet 2 - (Reduced size)","size":"71.6 MB"},{"id":354441,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3398/sim3398_sheet03w.pdf","text":"Sheet 3 - (Reduced size)","size":"71.6 MB"},{"id":354442,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3398/sim3398_sheet04w.pdf","text":"Sheet 4 - (Reduced size)","size":"69.6 MB"},{"id":354431,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3398/coverthb.jpg"},{"id":354432,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3398/sim3398.pdf","text":"Report (Pamphlet)","description":"SIM 3398"},{"id":354433,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3398/sim3398_sheet01.pdf","text":"Sheet 1 - (Full size)","size":"103 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Upper Glacial and Shallow Magothy Aquifers (Water Table)"},{"id":354434,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3398/sim3398_sheet02.pdf","text":"Sheet 2 - (Full size)","size":"107 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric Surface in the Magothy and Jameco Aquifers"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0423583984375,\n 40.51797520038851\n ],\n [\n -71.8011474609375,\n 40.51797520038851\n ],\n [\n -71.8011474609375,\n 41.21585377825921\n ],\n [\n -74.0423583984375,\n 41.21585377825921\n ],\n [\n -74.0423583984375,\n 40.51797520038851\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
2045 Route 112, Building 4
Coram, NY 11727
We investigated the benthic foraminiferal faunal and stable carbon and oxygen isotopic composition of a 15-cm push core (NA075-092b) obtained on a Telepresence-Enabled cruise to the Southeast Seep on Kimki Ridge offshore southern California. The seep core was taken at a depth of 973 m in the vicinity of a Beggiatoa bacterial mat and vesicomyid clams (Calyptogena) and compared to previously published data of living assemblages from ~ 714 m, four reference cores obtained at ~ 1030 m, and another one at 739 m. All of the reference sites are also from the Inner Continental Borderland but with no evidence of methane seepage.
No endemic species were found at the seep site and most of the taxa recovered there have been reported previously from other seep or low oxygen environments. Q- and R-mode cluster analyses clearly illustrated differences in the faunal assemblages of the seep and non-seep sites. The living assemblage at Southeast Seep was characterized by abundant Takayanagia delicata, Cassidulina translucens, and Spiroplectammina biformis, whereas the non-seep San Pedro Basin reference assemblage was comprised primarily of Chilostomella oolina and Globobulimina pacifica. Density and species richness were lower at the seep site compared to the non-seep site, reflecting the harsher living conditions there. The dead assemblage at the seep site was dominated by Gyroidina turgida compared to Cassidulina translucens at the ~ 1030 m non-seep site and Cassidulina translucens, Pseudoparrella pacifica, and Takayanagia delicata at the 739 m non-seep site. Density was three times lower at Southeast Seep than at the non-seep sites of comparable water depth but species richness was ~ 30% higher. Stable carbon isotopic values were considerably depleted in the seep samples compared to the non-seep samples, with a progression from lightest to heaviest average δ13C values evident at the seep site reflecting microhabitat preference and vital effect: the deep infaunal species of Globobulimina, the shallow infaunal species Uvigerina peregrina, the epifaunal species Cibicidoides wuellerstorfi, and the shallow infaunal but aragonite-shelled species Hoeglundina elegans. The δ13C values downcore among each benthic species indicates ongoing fluid seepage through at least the last 3800 cal yr B.P. at Southeast Seep. Besides the continual local seepage, evidence from δ13C values of planktic foraminifera in the seep core suggest two pulses of methane (at 3000 and 3700 cal yr B.P.) were released that were large enough to influence much of the water column. Paired benthic and planktic foraminiferal stable oxygen isotope records provide evidence that there were no paleoenvironmental changes such as increased bottom-water temperature or changes in oxygen isotopic composition of bottom and pore waters during this 3800-year record to induce the methane releases. Instead, Southeast Seep appears to be the result of local faulting providing pathways for fluid to flow to the seafloor at a fault stepover or transpressional bend in the regional strike-slip system.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2018.01.011","usgsCitation":"McGann, M., and Conrad, J.E., 2018, Faunal and stable isotopic analyses of benthic foraminifera from the Southeast Seep on Kimki Ridge offshore southern California, USA: Deep Sea Research Part II: Topical Studies in Oceanography, v. 150, p. 92-117, https://doi.org/10.1016/j.dsr2.2018.01.011.","productDescription":"26 p.","startPage":"92","endPage":"117","ipdsId":"IP-091301","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":460901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2018.01.011","text":"Publisher Index Page"},{"id":354758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kimki Ridge","volume":"150","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e571e4b060350a15d16d","contributors":{"authors":[{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":737319,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conrad, James E. 0000-0001-6655-694X jconrad@usgs.gov","orcid":"https://orcid.org/0000-0001-6655-694X","contributorId":2316,"corporation":false,"usgs":true,"family":"Conrad","given":"James","email":"jconrad@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":737320,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70197473,"text":"70197473 - 2018 - Minimum energy requirements for desalination of brackish groundwater in the United States with comparison to international datasets","interactions":[],"lastModifiedDate":"2018-06-06T11:06:03","indexId":"70197473","displayToPublicDate":"2018-06-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Minimum energy requirements for desalination of brackish groundwater in the United States with comparison to international datasets","docAbstract":"This paper uses chemical and physical data from a large 2017 U.S. Geological Surveygroundwater dataset with wells in the U.S. and three smaller international groundwater datasets with wells primarily in Australia and Spain to carry out a comprehensive investigation of brackish groundwater composition in relation to minimum desalinationenergy costs. First, we compute the site-specific least work required for groundwater desalination. Least work of separation represents a baseline for specific energy consumptionof desalination systems. We develop simplified equations based on the U.S. data for least work as a function of water recovery ratio and a proxy variable for composition, either total dissolved solids, specific conductance, molality or ionic strength. We show that the U.S. correlations for total dissolved solids and molality may be applied to the international datasets. We find that total molality can be used to calculate the least work of dilute solutions with very high accuracy. Then, we examine the effects of groundwater solute composition on minimum energy requirements, showing that separation requirements increase from calcium to sodium for cations and from sulfate to bicarbonate to chloride for anions, for any given TDS concentration. We study the geographic distribution of least work, total dissolved solids, and major ions concentration across the U.S. We determine areas with both low least work and high water stress in order to highlight regions holding potential for desalination to decrease the disparity between high water demand and low water supply. Finally, we discuss the implications of the USGS results on water resource planning, by comparing least work to the specific energy consumption of brackish water reverse osmosisplants and showing the scaling propensity of major electrolytes and silica in the U.S. groundwater samples.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2018.04.015","usgsCitation":"Ahdab, Y.D., Thiel, G.P., Bohlke, J., Stanton, J.S., and Lienhard, J.H., 2018, Minimum energy requirements for desalination of brackish groundwater in the United States with comparison to international datasets: Water Research, v. 141, p. 387-404, https://doi.org/10.1016/j.watres.2018.04.015.","productDescription":"18 p.","startPage":"387","endPage":"404","ipdsId":"IP-091074","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":468679,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2018.04.015","text":"Publisher Index Page"},{"id":354757,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"141","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e571e4b060350a15d16b","contributors":{"authors":[{"text":"Ahdab, Yvana D.","contributorId":205444,"corporation":false,"usgs":false,"family":"Ahdab","given":"Yvana","email":"","middleInitial":"D.","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":737325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thiel, Gregory P.","contributorId":205445,"corporation":false,"usgs":false,"family":"Thiel","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":737326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":737324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanton, Jennifer S. 0000-0002-2520-753X jstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-2520-753X","contributorId":830,"corporation":false,"usgs":true,"family":"Stanton","given":"Jennifer","email":"jstanton@usgs.gov","middleInitial":"S.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":737327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lienhard, John H.","contributorId":205447,"corporation":false,"usgs":false,"family":"Lienhard","given":"John","email":"","middleInitial":"H.","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":737328,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191220,"text":"cir1437 - 2018 - Understanding the influence of nutrients on stream ecosystems in agricultural landscapes","interactions":[],"lastModifiedDate":"2018-06-07T10:11:38","indexId":"cir1437","displayToPublicDate":"2018-06-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1437","title":"Understanding the influence of nutrients on stream ecosystems in agricultural landscapes","docAbstract":"Sustaining the quality of the Nation’s water resources and the health of our diverse ecosystems depends on the availability of sound water-resources data and information to develop effective, science-based policies. Effective management of water resources also brings more certainty and efficiency to important economic sectors. Taken together, these actions lead to immediate and long-term economic, social, and environmental benefits that make a difference to the lives of the almost 400 million people projected to live in the United States by 2050.
In 1991, Congress established the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) to address where, when, why, and how the Nation’s water quality has changed, or is likely to change in the future, in response to human activities and natural factors. Since then, NAWQA has been a leading source of scientific data and knowledge used by national, regional, State, and local agencies to develop science-based policies and management strategies to improve and protect water resources used for drinking water, recreation, irrigation, energy development, and ecosystem needs (https://water.usgs.gov/nawqa/applications/). Plans for the third decade of NAWQA (2013–23) address priority water-quality issues and science needs identified by NAWQA stakeholders, such as the Advisory Committee on Water Information and the National Research Council, and are designed to meet increasing challenges related to population growth, increasing needs for clean water, and changing land-use and weather patterns.
Excess nutrients are a pervasive problem of streams, lakes, and coastal waters. The current report, “The Quality of Our Nation’s Waters—Understanding the Effects of Nutrients on Stream Ecosystems in Agricultural Landscapes,” presents a summary of results from USGS investigations conducted from 2003 to 2011 on processes that influence nutrients and how nutrient enrichment can alter biological components of agricultural streams. This study included collecting data from 232 sites distributed among eight study areas. This report summarizes findings on processes that influence nutrients and how nutrient enrichment can alter biological communities in agricultural streams. These findings are relevant to local, State, regional, and national decision-makers involved in efforts to (1) better understand the influence of nutrients on agricultural streams, (2) develop nutrient criteria for streams and rivers, (3) reduce nutrients to streams and downstream receiving waters, and (4) develop tools for tracking nutrient and biological conditions following nutrient reduction strategies. All NAWQA reports are available online at https://water.usgs.gov/nawqa/bib/.
We hope this publication will provide you with insights and information to meet your water-resource needs and will foster increased citizen awareness and involvement in the protection and restoration of our Nation’s waters. The information in this report is intended primarily for those interested or involved in resource management and protection, conservation, regulation, and policymaking at the regional and national levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1437","collaboration":"National Water-Quality ProgramNational Water-Quality Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Sustaining the quality of the Nation’s water resources and the health of our diverse ecosystems depends on the availability of sound water-resources data and information to develop effective, science-based policies. Effective management of water resources also brings more certainty and efficiency to important economic sectors. Taken together, these actions lead to immediate and longterm economic, social, and environmental benefits that make a difference to the lives of the almost 400 million people projected to live in the United States by 2050.
In 1991, Congress established the U.S. Geological Survey National Water-Quality Assessment (NAWQA) to address where, when, why, and how the Nation’s water quality has changed, or is likely to change in the future, in response to human activities and natural factors. Since then, NAWQA has been a leading source of scientific data and knowledge used by national, regional, state, and local agencies to develop science-based policies and management strategies to improve and protect water resources used for drinking water, recreation, irrigation, energy development, and ecosystem needs. Plans for the third decade of NAWQA (2013–23) address priority water-quality issues and science needs identified by NAWQA stakeholders, such as the Advisory Committee on Water Information and the National Research Council, and are designed to meet increasing challenges related to population growth, increasing needs for clean water, and changing land-use and weather patterns.
This report is one of a series of publications, The Quality of Our Nation’s Waters, which describes major findings of the NAWQA Project on water-quality issues of regional and national concern and provides science-based information for assessing and managing the quality of our groundwater resources. Other reports in this series focus on occurrence and distribution of nutrients, pesticides, and volatile organic compounds in streams and groundwater, the effects of contaminants and stream-flow alteration on the condition of aquatic communities in streams, and on the quality of groundwater from private domestic and public supply wells. Each reports builds toward a more comprehensive understanding of the quality of regional and national water resources. All NAWQA reports are available online (https://water.usgs.gov/nawqa/bib/).
We hope this publication will provide you with insights and information to meet your water-resource needs and will foster increased citizen awareness and involvement in the protection and restoration of our Nation’s waters. The information in this report is intended primarily for those interested or involved in resource management and protection, conservation, regulation, and policymaking at the regional and national levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1433","collaboration":"National Water-Quality ProgramNational Water-Quality Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
To address actions identified in the Department of Interior’s “Safeguarding the West” strategy, the Pacific States Marine Fisheries Commission (PSMFC) and US Geological Survey (USGS) convened 43 invasive species coordinators and scientific experts June 5-6, 2018 in Portland, Oregon to assess the status of dreissenid monitoring efforts in the Columbia River Basin (CRB); identify key strengths and weaknesses of existing collaborative efforts to monitor for dreissenids; identify priority monitoring gaps foundational to dreissenid prevention efforts; and achieve consensus on a set of strategies to address those gaps and maintain a strong monitoring collaborative and framework moving forward. Prior to the workshop, two webinars were conducted to summarize how states and provinces are estimating and using metrics that describe dreissenid mussel introduction (i.e., the risk of mussels being introduced into a waterbody) and establishment (i.e., the risk of a mussel population becoming established after an introduction event).
The goal of the webinars was to provide context to discussions at the Columbia River Basin Dreissenid Mussel Monitoring Forum.
As part of the webinars expert practitioners, gave presentations that summarized the origins and basis for metrics typically used to characterize invasive species invasion risk. During the first webinar which was held on May 7, 2018, Dr. Bob McMahon, University of Texas at Arlington gave a presentation discussing factors affecting the establishment of dreissenid mussels. During the second webinar which was held on May 30, 2018, Samuel Fischer and Mark Lewis from the University of Alberta presented information on factors affecting the introduction of mussels.
Presentations at the workshop were given to update participants on the status of dreissenid mussel monitoring in the Columbia River Basin. Staff from the US Army Corps of Engineers (ACOE), Bureau of Reclamation (BOR), USGS, Bureau of Indian Affairs (BIA), and National Park Service (NPS) gave presentations that described the status of dreissenid mussel monitoring efforts conducted by federal agencies. Participants also heard presentations on the status of other monitoring related efforts. Representatives from the Western Regional Panel and Montana Fish Wildlife and Parks (MFWP) presented information about method and protocol standardization coordination activities. Staff from the British Columbia Ministry of Environment and Climate Change Strategy also presented an update on their sampling and resource allocation protocols.
The USGS then presented an overview of the evolution and status of dreissenid mussel monitoring in the CRB as well as summary of the results of webinars that addressed facets of dreissenid mussel introduction and establishment risk estimation. To facilitate learning from ongoing efforts that address similar invasive species coordination, monitoring, and research activities, Kelly Baerwaldt, US Fish and Wildlife Service (USFWS) presented remotely on the activities of the Asian Carp Coordinating Committee, informing workshop participants of the how the effort formed and is funded, priority goals and activities, as well as key successes and challenges.
The meeting culminated with workshop attendees participating in four breakout groups (risk assessment and research, data/lab analysis, monitoring/coordination, and funding) to identify priority key gaps or weaknesses to existing monitoring/coordination efforts as well as identify priority actions or strategies could help address those gaps or weaknesses. The recommended priority actions from each breakout group were compiled into one overall recommendation to build on existing strengths and address weaknesses associated with monitoring for dreissenids in the Columbia River Basin: Using existing infrastructure and datasets, develop a transboundary, interagency, adaptive, coordinated, regional monitoring framework/partnership to ensure optimal resource allocation.
","conferenceTitle":"Columbia River Basin Dreissenid Mussel Monitoring Form Workshop.","conferenceDate":"June 5-6, 2018 i","conferenceLocation":"Portland, Oregon ","language":"English","publisher":"Aquatic Invasive Species Network. ","usgsCitation":"DeBruyckere, L., Counihan, T., and Phillips, S., 2018, Columbia River Basin dreissenid mussel monitoring forum workshop, Columbia River Basin Dreissenid Mussel Monitoring Form Workshop., Portland, Oregon , June 5-6, 2018 i, p. 1-18.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-100594","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":357716,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":357715,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.westernais.org/monitoring"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02fe4e4b0fc368eb5399b","contributors":{"authors":[{"text":"DeBruyckere, Lisa","contributorId":207531,"corporation":false,"usgs":false,"family":"DeBruyckere","given":"Lisa","email":"","affiliations":[{"id":37555,"text":"Creative Resource Strategies, LLC","active":true,"usgs":false}],"preferred":false,"id":744086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Counihan, Timothy D. 0000-0003-4967-6514","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":207532,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":744087,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Phillips, Stephen","contributorId":156280,"corporation":false,"usgs":false,"family":"Phillips","given":"Stephen","affiliations":[{"id":20304,"text":"Pacific States Marine Fisheries Commission","active":true,"usgs":false}],"preferred":false,"id":744088,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197452,"text":"70197452 - 2018 - Spatial variability and macro‐scale drivers of growth for native and introduced Flathead Catfish populations","interactions":[],"lastModifiedDate":"2018-06-05T10:41:28","indexId":"70197452","displayToPublicDate":"2018-06-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Spatial variability and macro‐scale drivers of growth for native and introduced Flathead Catfish populations","docAbstract":"Quantifying spatial variability in fish growth and identifying large‐scale drivers of growth are fundamental to many conservation and management decisions. Although fish growth studies often focus on a single population, it is becoming increasingly clear that large‐scale studies are likely needed for addressing transboundary management needs. This is particularly true for species with high recreational value and for those with negative ecological consequences when introduced outside of their native range, such as the Flathead Catfish Pylodictis olivaris. This study quantified growth variability of the Flathead Catfish across a large portion of its contemporary range to determine whether growth differences existed between habitat types (i.e., reservoirs and rivers) and between native and introduced populations. Additionally, we investigated whether growth parameters varied as a function of latitude and time since introduction (for introduced populations). Length‐at‐age data from 26 populations across 11 states in the USA were modeled using a Bayesian hierarchical von Bertalanffy growth model. Population‐specific growth trajectories revealed large variation in Flathead Catfish growth and relatively high uncertainty in growth parameters for some populations. Relatively high uncertainty was also evident when comparing populations and when quantifying large‐scale patterns. Growth parameters (Brody growth coefficient [K] and theoretical maximum average length [L∞]) were not different (based on overlapping 90% credible intervals) between habitat types or between native and introduced populations. For populations within the introduced range of Flathead Catfish, latitude was negatively correlated with K. For native populations, we estimated an 85% probability that L∞ estimates were negatively correlated with latitude. Contrary to predictions, time since introduction was not correlated with growth parameters in introduced populations of Flathead Catfish. Results of this study suggest that Flathead Catfish growth patterns are likely shaped more strongly by finer‐scale processes (e.g., exploitation or prey abundances) as opposed to macro‐scale drivers.
","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10055","usgsCitation":"Massie, D.L., Smith, G., Bonvechio, T.F., Bunch, A.J., Lucchesi, D.O., and Wagner, T., 2018, Spatial variability and macro‐scale drivers of growth for native and introduced Flathead Catfish populations: Transactions of the American Fisheries Society, v. 147, no. 3, p. 554-565, https://doi.org/10.1002/tafs.10055.","productDescription":"12 p.","startPage":"554","endPage":"565","ipdsId":"IP-090614","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":354719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"147","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-22","publicationStatus":"PW","scienceBaseUri":"5b46e574e4b060350a15d17f","contributors":{"authors":[{"text":"Massie, Danielle L.","contributorId":196717,"corporation":false,"usgs":false,"family":"Massie","given":"Danielle","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":737234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Geoffrey","contributorId":115958,"corporation":false,"usgs":true,"family":"Smith","given":"Geoffrey","affiliations":[],"preferred":false,"id":737235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonvechio, Timothy F.","contributorId":174468,"corporation":false,"usgs":false,"family":"Bonvechio","given":"Timothy","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":737236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunch, Aaron J.","contributorId":90262,"corporation":false,"usgs":true,"family":"Bunch","given":"Aaron","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":737237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lucchesi, David O.","contributorId":176629,"corporation":false,"usgs":false,"family":"Lucchesi","given":"David","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":737238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":737202,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197457,"text":"70197457 - 2018 - Groundwater flux estimation in streams: A thermal equilibrium approach","interactions":[],"lastModifiedDate":"2018-06-05T11:11:51","indexId":"70197457","displayToPublicDate":"2018-06-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater flux estimation in streams: A thermal equilibrium approach","docAbstract":"Stream and groundwater interactions play an essential role in regulating flow, temperature, and water quality for stream ecosystems. Temperature gradients have been used to quantify vertical water movement in the streambed since the 1960s, but advancements in thermal methods are still possible. Seepage runs are a method commonly used to quantify exchange rates through a series of streamflow measurements but can be labor and time intensive. The objective of this study was to develop and evaluate a thermal equilibrium method as a technique for quantifying groundwater flux using monitored stream water temperature at a single point and readily available hydrological and atmospheric data. Our primary assumption was that stream water temperature at the monitored point was at thermal equilibrium with the combination of all heat transfer processes, including mixing with groundwater. By expanding the monitored stream point into a hypothetical, horizontal one-dimensional thermal modeling domain, we were able to simulate the thermal equilibrium achieved with known atmospheric variables at the point and quantify unknown groundwater flux by calibrating the model to the resulting temperature signature. Stream water temperatures were monitored at single points at nine streams in the Ozark Highland ecoregion and five reaches of the Kiamichi River to estimate groundwater fluxes using the thermal equilibrium method. When validated by comparison with seepage runs performed at the same time and reach, estimates from the two methods agreed with each other with an R2 of 0.94, a root mean squared error (RMSE) of 0.08 (m/d) and a Nash–Sutcliffe efficiency (NSE) of 0.93. In conclusion, the thermal equilibrium method was a suitable technique for quantifying groundwater flux with minimal cost and simple field installation given that suitable atmospheric and hydrological data were readily available.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.04.001","usgsCitation":"Zhou, Y., Fox, G.A., Miller, R.B., Mollenhauer, R., and Brewer, S.K., 2018, Groundwater flux estimation in streams: A thermal equilibrium approach: Journal of Hydrology, v. 561, p. 822-832, https://doi.org/10.1016/j.jhydrol.2018.04.001.","productDescription":"11 p.","startPage":"822","endPage":"832","ipdsId":"IP-091649","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468683,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2018.04.001","text":"Publisher Index Page"},{"id":354724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Kiamichi River, Ozark Highland Ecoregin","volume":"561","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e573e4b060350a15d17b","contributors":{"authors":[{"text":"Zhou, Yan","contributorId":205427,"corporation":false,"usgs":false,"family":"Zhou","given":"Yan","email":"","affiliations":[],"preferred":false,"id":737268,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, Garey A.","contributorId":205428,"corporation":false,"usgs":false,"family":"Fox","given":"Garey","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":737269,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Ron B.","contributorId":205429,"corporation":false,"usgs":false,"family":"Miller","given":"Ron","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":737270,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mollenhauer, Robert","contributorId":205275,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":737271,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":737239,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197454,"text":"70197454 - 2018 - Genetic population structure of Shoal Bass within their native range","interactions":[],"lastModifiedDate":"2018-07-03T11:09:19","indexId":"70197454","displayToPublicDate":"2018-06-05T00:00:00","publicationYear":"2018","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}},"title":"Genetic population structure of Shoal Bass within their native range","docAbstract":"Endemic to the Apalachicola River basin of the southeastern USA, the Shoal Bass Micropterus cataractae is a fluvial‐specialist sport fish that is imperiled because of anthropogenic habitat alteration. To counter population declines, restorative stocking efforts are becoming an increasingly relevant management strategy. However, population genetic structure within the species is currently unknown, but it could influence management decisions, such as brood source location. Leveraging a collaborative effort to collect and genotype specimens with 16 microsatellite loci, our objective was to characterize hierarchical population structure and genetic differentiation of the Shoal Bass across its native range, including an examination of structuring mechanisms, such as relatedness and inbreeding levels. Specimens identified as Shoal Bass were collected from 13 distinct sites (N ranged from 17 to 209 per location) and were then taxonomically screened to remove nonnative congeners and hybrids (pure Shoal Bass N ranged from 13 to 183 per location). Our results revealed appreciable population structure, with five distinct Shoal Bass populations identifiable at the uppermost hierarchical level that generally corresponded with natural geographic features and anthropogenic barriers. Substructure was recovered within several of these populations, wherein differences appeared related to spatial isolation and local population dynamics. An analysis of molecular variance revealed that 3.6% of the variation in our data set was accounted for among three larger river drainages, but substructure within each river drainage also explained an additional 8.9% of genetic variation, demonstrating that management at a scale lower than the river drainage level would likely best conserve genetic diversity. Results provide a population genetic framework that can inform future management decisions, such as brood source location, so that genetic diversity within and among populations is conserved and overall adaptability of the species is maintained.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10048","usgsCitation":"Taylor, A.T., Tringali, M.D., Sammons, S.M., Ingram, T.R., O’Rouke, P.M., Peterson, D.L., and Long, J.M., 2018, Genetic population structure of Shoal Bass within their native range: North American Journal of Fisheries Management, v. 38, no. 3, p. 549-564, https://doi.org/10.1002/nafm.10048.","productDescription":"16 p.","startPage":"549","endPage":"564","ipdsId":"IP-090674","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354720,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Apalachicola–Chattahoochee–Flint River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.572509765625,\n 29.563901551414418\n ],\n [\n -83.770751953125,\n 29.563901551414418\n ],\n [\n -83.770751953125,\n 34.49750272138159\n ],\n [\n -85.572509765625,\n 34.49750272138159\n ],\n [\n -85.572509765625,\n 29.563901551414418\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-01","publicationStatus":"PW","scienceBaseUri":"5b46e573e4b060350a15d17d","contributors":{"authors":[{"text":"Taylor, Andrew T.","contributorId":177197,"corporation":false,"usgs":false,"family":"Taylor","given":"Andrew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":737254,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tringali, Michael D.","contributorId":191189,"corporation":false,"usgs":false,"family":"Tringali","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":737255,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sammons, Steven M.","contributorId":205417,"corporation":false,"usgs":false,"family":"Sammons","given":"Steven","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":737256,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ingram, Travis R.","contributorId":205418,"corporation":false,"usgs":false,"family":"Ingram","given":"Travis","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":737257,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Rouke, Patrick M.","contributorId":205426,"corporation":false,"usgs":false,"family":"O’Rouke","given":"Patrick","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":737258,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peterson, Douglas L.","contributorId":38911,"corporation":false,"usgs":true,"family":"Peterson","given":"Douglas","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":737259,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":737207,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70197435,"text":"tm2A14 - 2018 - Monitoring riparian-vegetation composition and cover along the Colorado River downstream of Glen Canyon Dam, Arizona","interactions":[],"lastModifiedDate":"2018-06-06T10:52:17","indexId":"tm2A14","displayToPublicDate":"2018-06-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2-A14","title":"Monitoring riparian-vegetation composition and cover along the Colorado River downstream of Glen Canyon Dam, Arizona","docAbstract":"Vegetation in the riparian zone (the area immediately adjacent to streams, such as stream banks) along the Colorado River downstream of Glen Canyon Dam, Arizona, supports many ecosystem and societal functions. In both Glen Canyon and Grand Canyon, this ecosystem has changed over time in response to flow alterations, invasive species, and recreational use. Riparian-vegetation cover and composition are likely to continue to change as these pressures persist and new ones emerge. Because this system is a valuable resource that is known to change in response to flow regime and other disturbances, a long-term monitoring protocol has been designed with three primary objectives:
A protocol for the long-term monitoring of riparian vegetation is described in detail and standard operating procedures are included herein for all tasks. Visual estimates of foliar and ground covers are collected in conjunction with environmental measurements to assess correlations of foliar cover with abiotic and flow variables. Sample quadrats are stratified by frequency of inundation, geomorphic feature, and by river segment to account for differences in vegetation type. Photographs of sites are also taken to illustrate qualitative characteristics of the site at the time of sampling. Procedures for field preparation, generating random samples, data collection, data management, collecting and managing unknown species collections, and reporting are also described. Although this protocol is intended to be consistent over the long-term, procedures for minor and major revisions to the protocol are also outlined.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Biological science in Book 2: Collection of environmental data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2A14","collaboration":"Prepared in cooperation with the Bureau of Reclamation Glen Canyon Adaptive Management Program","usgsCitation":"Palmquist, E.C., Ralston, B.E., Sarr, D.A., and Johnson, T.C., 2018, Monitoring riparian-vegetation composition and cover along the Colorado River downstream of Glen Canyon Dam, Arizona: U.S. Geological Survey Techniques and Methods, book 2, chap. A14, 65 p., https://doi.org/10.3133/tm2A14.","productDescription":"ix, 65 p.","numberOfPages":"79","onlineOnly":"Y","ipdsId":"IP-071203","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":354697,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a14/tm2a14.pdf","text":"Report","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods 2-A14"},{"id":354696,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a14/coverthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114,\n 35.5\n ],\n [\n -111.5,\n 35.5\n ],\n [\n -111.5,\n 37\n ],\n [\n -114,\n 37\n ],\n [\n -114,\n 35.5\n ]\n ]\n ]\n }\n }\n ]\n}","publicComments":"This report is Chapter 14 of Section A: Biological science in Book 2: Collection of environmental data.","contact":"SBSC Staff,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Compiling data from disparate sources to address pressing ecological issues is increasingly common. Many ecological datasets contain left-censored data – observations below an analytical detection limit. Studies from single and typically small datasets show that common approaches for handling censored data — e.g., deletion or substituting fixed values — result in systematic biases. However, no studies have explored the degree to which the documentation and presence of censored data influence outcomes from large, multi-sourced datasets. We describe left-censored data in a lake water quality database assembled from 74 sources and illustrate the challenges of dealing with small values in big data, including detection limits that are absent, range widely, and show trends over time. We show that substitutions of censored data can also bias analyses using ‘big data’ datasets, that censored data can be effectively handled with modern quantitative approaches, but that such approaches rely on accurate metadata that describe treatment of censored data from each source.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoinf.2018.03.002","usgsCitation":"Stow, C.A., Webster, K.E., Wagner, T., Lottig, N.R., Soranno, P.A., and Cha, Y., 2018, Small values in big data: The continuing need for appropriate metadata: Ecological Informatics, v. 45, p. 26-30, https://doi.org/10.1016/j.ecoinf.2018.03.002.","productDescription":"5 p.","startPage":"26","endPage":"30","ipdsId":"IP-087729","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468788,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoinf.2018.03.002","text":"Publisher Index Page"},{"id":354712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e574e4b060350a15d189","contributors":{"authors":[{"text":"Stow, Craig A.","contributorId":204103,"corporation":false,"usgs":false,"family":"Stow","given":"Craig","email":"","middleInitial":"A.","affiliations":[{"id":36843,"text":"NOAA, Great Lakes Environmental Research Lab","active":true,"usgs":false}],"preferred":false,"id":737218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webster, Katherine E.","contributorId":147903,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":737219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":737163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lottig, Noah R.","contributorId":172031,"corporation":false,"usgs":false,"family":"Lottig","given":"Noah","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":737220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soranno, Patricia A.","contributorId":172104,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":737221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cha, YoonKyung","contributorId":9741,"corporation":false,"usgs":true,"family":"Cha","given":"YoonKyung","email":"","affiliations":[],"preferred":false,"id":737222,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196754,"text":"ofr20181074 - 2018 - Freshwater mussel survey for the Columbia Dam removal, Paulins Kill, New Jersey","interactions":[],"lastModifiedDate":"2024-03-04T19:07:50.505204","indexId":"ofr20181074","displayToPublicDate":"2018-06-04T14:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1074","title":"Freshwater mussel survey for the Columbia Dam removal, Paulins Kill, New Jersey","docAbstract":"Semi-quantitative mussel surveys, conducted by the U.S. Geological Survey and the Delaware Riverkeeper Network in cooperation with The Nature Conservancy, were completed in the vicinity of the Columbia Dam, on the Paulins Kill, New Jersey, in August 2017 in order to document the mussel species composition and relative abundance prior to removal of the dam. Surveys were conducted from the Brugler Road Bridge downriver approximately 2,000 meters (m) to the Columbia Dam and downriver from the dam about 300 m to 75 m upriver from the confluence of the Paulins Kill with the Delaware River. Sixteen sections (average length=175 m) were surveyed by personnel snorkeling or SCUBA diving; 13 sections were upriver from the dam, and 3 were downriver from the dam. Mussels, as they were encountered by surveyors, were removed from the sediment, immediately identified to species, and replaced in their original collection locations. Habitat data were collected for each surveyed section. Upriver and downriver from the dam, river margins with dense vegetation were examined for mussels by personnel using snorkels in transects (approximately 25 meters) perpendicular to river flow every 50 m on both sides of the river. Only two species were found upriver from the dam, and those were present in relatively low numbers. Catch per unit effort is reported here within parentheses as the average across upriver sections in number of mussels per person hour of survey time: 42 Elliptio complanata (2.6) and 1 Pyganodon cataracta (0.1) were found upriver from the dam. No mussels were found in the dense vegetation either upriver or downriver of the dam by surveyors using snorkels. Significantly higher species richness and mussel catch per unit effort were found downriver from the dam than upriver, including 106 E. complanta (32.5), 27 Utterbackiana implicata (8.2), 1 Alasmidonta undulata (0.4), 2 Lampsilis cariosa (0.5), 6 Lampsilis radiata (2.1), 4 P. cataracta (1.1), and 1 Strophitus undulatus (0.4). The average habitat assessment score did not differ upriver and downriver from the dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181074","collaboration":"Prepared in cooperation with The Nature Conservancy","usgsCitation":"Galbraith, H.S., Blakeslee, C.J., Cole, J.C., and Silldorff, E.L., 2018, Freshwater mussel survey for the Columbia Dam removal, Paulins Kill, New Jersey: U.S. Geological Survey Open-File Report 2018–1074, 7 p., https://doi.org/10.3133/ofr20181074.","productDescription":"v, 7 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094047","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":354676,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1074/ofr20181074.pdf","text":"Report","size":"9.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1074"},{"id":354675,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1074/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Columbia Dam, Paulins Kill","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.0889778137207,\n 40.9203876084737\n ],\n [\n -75.06837844848633,\n 40.9203876084737\n ],\n [\n -75.06837844848633,\n 40.937896253014145\n ],\n [\n -75.0889778137207,\n 40.937896253014145\n ],\n [\n -75.0889778137207,\n 40.9203876084737\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Use of agricultural subsurface drainage systems in the Prairie Pothole Region of North America continues to increase, prompting concerns over potential negative effects to the Region's vital wetlands. The U.S. Fish and Wildlife Service protects a large number of wetlands through conservation easements that often utilize standard lateral setback distances to provide buffers between wetlands and drainage systems. Because of a lack of information pertaining to the efficacy of these setback distances for protecting wetlands, information is required to support the decision making for placement of subsurface drainage systems adjacent to wetlands. We used qualitative graphical analyses and data comparisons to identify characteristics of subsurface drainage systems and wetland catchments that could be considered when assessing setback distances. We also compared setback distances with catchment slope lengths to determine if they typically exclude drainage systems from the catchment. We demonstrated that depth of a subsurface drainage system is a key factor for determining drainage setback distances. Drainage systems located closer to the surface (shallow) typically could be associated with shorter lateral setback distances compared with deeper systems. Subsurface drainage systems would be allowed within a wetland's catchment for 44–59% of catchments associated with wetland conservation easements in North Dakota. More specifically, results suggest that drainage setback distances generally would exclude drainage systems from catchments of the smaller wetlands that typically have shorter slopes in the adjacent upland contributing area. For larger wetlands, however, considerable areas of the catchment would be vulnerable to drainage that may affect wetland hydrology. U.S. Fish and Wildlife Service easements are associated with > 2,000 km2 of wetlands in North Dakota, demonstrating great potential to protect these systems from drainage depending on policies for installing subsurface drainage systems on these lands. The length of slope of individual catchments and depth of subsurface drainage systems could be considered when prescribing drainage setback distances and assessing potential effects to wetland hydrology. Moreover, because of uncertainties associated with the efficacy of standard drainage setback distances, exclusion of subsurface drainage systems from wetland catchments would be ideal when the goal is to protect wetlands.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/092017-JFWM-076","usgsCitation":"Tangen, B., and Wiltermuth, M.T., 2018, Prairie Pothole Region wetlands and subsurface drainage systems: Key factors for determining drainage setback distances: Journal of Fish and Wildlife Management, v. 9, no. 1, p. 274-284, https://doi.org/10.3996/092017-JFWM-076.","productDescription":"11 p.","startPage":"274","endPage":"284","ipdsId":"IP-090587","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":460905,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/092017-jfwm-076","text":"Publisher Index Page"},{"id":437879,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72806H6","text":"USGS data release","linkHelpText":"Conservation easements in the Prairie Pothole Region of North Dakota: characteristics of wetland catchments and key factors for determination of drainage setback distances"},{"id":354683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-22","publicationStatus":"PW","scienceBaseUri":"5b155d6ce4b092d9651e1ad8","contributors":{"authors":[{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":737114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":737115,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198525,"text":"70198525 - 2018 - An objective road risk assessment method for multiple species: ranking 166 reptiles and amphibians in California","interactions":[],"lastModifiedDate":"2018-08-06T16:51:31","indexId":"70198525","displayToPublicDate":"2018-06-01T16:51:25","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"An objective road risk assessment method for multiple species: ranking 166 reptiles and amphibians in California","docAbstract":"Context
Transportation and wildlife agencies may consider the need for barrier structures and safe wildlife road-crossings to maintain the long-term viability of wildlife populations. In order to prioritize these efforts, it is important to identify species that are most at risk of extirpation from road-related impacts.
Purpose
Our goal was to identify reptiles and amphibians in California most susceptible to road mortality and fragmentation. With over 160 species and a lack of species-specific research data, we developed an objective risk assessment method based upon road ecology science.
Methods
Risk scoring was based upon a suite of life history and space-use characteristics associated with negative road effects applied in a hierarchical manner from individuals to species. We evaluated risk to both aquatic and terrestrial connectivity and calculated buffer distances to encompass 95% of population-level movements. We ranked species into five relative categories of road-related risk (very-high to very-low) based upon 20% increments of all species scores.
Results
All chelonids, 72% of snakes, 50% of anurans, 18% of lizards and 17% of salamander species in California were ranked at high or very-high risk from negative road impacts. Results were largely consistent with local and global scientific literature in identifying high risk species and groups.
Conclusions
This comparative risk assessment method provides a science-based framework to identify species most susceptible to negative road impacts. The results can inform regional-scale road mitigation planning and prioritization efforts and threat assessments for special-status species. We believe this approach is applicable to numerous landscapes and taxonomic groups.
This paper examines the concept and implementation of a map as a knowledge base. A map as a knowledge base means that the visual map is not only the descriptive compilation of data and design principles, but also involves a compilation of semantic propositions and logical predicates that create a body of knowledge organized as a map. The digital product of a map as knowledge base can be interpreted by machines, as well as humans, and can provide access to the knowledge base through interfaces to select features and other information from the map. The design of maps as a knowledge base involves technical approaches and a system architecture to support a knowledge base. This paper clarifies how a map as a knowledge base differs from earlier map theory models by investigating the knowledge-based concepts of implementation through logical modelling, a knowledge repository, user interfaces for information access, and cartographic visualization. The paper ends with proof of concepts for two types of cartographic data query.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/23729333.2017.1421004","usgsCitation":"Varanka, D.E., and Usery, E., 2018, The map as knowledge base: International Journal of Cartography, v. 4, no. 2, p. 201-223, https://doi.org/10.1080/23729333.2017.1421004.","productDescription":"23 p.","startPage":"201","endPage":"223","ipdsId":"IP-086226","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":360842,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":734678,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Usery, E. Lynn 0000-0002-2766-2173","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":204684,"corporation":false,"usgs":true,"family":"Usery","given":"E. Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":734679,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198383,"text":"70198383 - 2018 - Advancing marine biological observations and data requirements of the complementary Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs) Frameworks","interactions":[],"lastModifiedDate":"2018-08-10T16:40:31","indexId":"70198383","displayToPublicDate":"2018-06-01T15:30:10","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Advancing marine biological observations and data requirements of the complementary Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs) Frameworks","docAbstract":"Measurements of the status and trends of key indicators for the ocean and marine life are required to inform policy and management in the context of growing human uses of marine resources, coastal development, and climate change. Two synergistic efforts identify specific priority variables for monitoring: Essential Ocean Variables (EOVs) through the Global Ocean Observing System (GOOS), and Essential Biodiversity Variables (EBVs) from the Group on Earth Observations Biodiversity Observation Network (GEO BON). Both systems support reporting against internationally agreed conventions and treaties. GOOS, established under the auspices of the Intergovernmental Oceanographic Commission (IOC), plays a leading role in coordinating global monitoring of the ocean and in the definition of EOVs. GEO BON is a global biodiversity observation network that coordinates observations to enhance management of the world’s biodiversity and promote both the awareness and accounting of ecosystem services. Convergence and agreement between these two efforts are required to streamline existing and new marine observation programs to advance scientific knowledge effectively and to support the sustainable use and management of ocean spaces and resources. In this context, the Marine Biodiversity Observation Network (MBON), a thematic component of GEO BON, is collaborating with GOOS, the Ocean Biogeographic Information System (OBIS), and the Integrated Marine Biosphere Research (IMBeR) project to ensure that EBVs and EOVs are complementary, representing alternative uses of a common set of scientific measurements. This work is informed by the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM), an intergovernmental body of technical experts that helps international coordination on best practices for observing, data management and services, combined with capacity development expertise. Characterizing biodiversity and understanding its drivers will require incorporation of observations from traditional and molecular taxonomy, animal tagging and tracking efforts, ocean biogeochemistry, and ocean observatory initiatives including deep ocean and seafloor. The partnership between large-scale ocean observing and product distribution initiatives (MBON, OBIS, JCOMM, and GOOS) is an expedited, effective way to support international policy-level assessments (e.g., the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services or IPBES), along with the implementation of international development goals (e.g., the United Nations Sustainable Development Goals).","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2018.00211","usgsCitation":"Muller-Karger, F.E., Miloslavich, P., Bax, N., Simmons, S.E., Costello, M.J., Sousa Pinto, I., Canonico, G., Turner, W., Gill, M.J., Montes, E., Best, B.D., Pearlman, J., Halpin, P.N., Dunn, D., Benson, A.L., Martin, C.S., Weatherdon, L., Appeltans, W., Provoost, P., Klein, E., Kelble, C.R., Miller, R.J., Chavez, F.P., Iken, K., Chiba, S., Obura, D., Navarro, L.M., Pereira, H.M., Allain, V., Batten, S., Benedetti-Checchi, L., Duffy, J.E., Kudela, R.M., Rebelo, L., Shin, Y., and Geller, G., 2018, Advancing marine biological observations and data requirements of the complementary Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs) Frameworks: Frontiers in Marine Science, v. 5, p. 1-15, https://doi.org/10.3389/fmars.2018.00211.","productDescription":"Article 211; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-096456","costCenters":[{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":468693,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2018.00211","text":"Publisher Index Page"},{"id":356118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-27","publicationStatus":"PW","scienceBaseUri":"5b6fc442e4b0f5d57878ea2d","contributors":{"authors":[{"text":"Muller-Karger, Frank E.","contributorId":206626,"corporation":false,"usgs":false,"family":"Muller-Karger","given":"Frank","email":"","middleInitial":"E.","affiliations":[{"id":37356,"text":"University of South Florida, Saint Petersburg, FL","active":true,"usgs":false}],"preferred":false,"id":741418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miloslavich, Patricia","contributorId":206627,"corporation":false,"usgs":false,"family":"Miloslavich","given":"Patricia","email":"","affiliations":[{"id":37357,"text":"University of Tasmania, Hobart, Tasmania, Australia","active":true,"usgs":false}],"preferred":false,"id":741419,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bax, Nicholas","contributorId":206628,"corporation":false,"usgs":false,"family":"Bax","given":"Nicholas","affiliations":[{"id":37358,"text":"Universidad Simon Bolivar, Caracas, Venezuela","active":true,"usgs":false}],"preferred":false,"id":741420,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simmons, Samantha E.","contributorId":156320,"corporation":false,"usgs":false,"family":"Simmons","given":"Samantha","email":"","middleInitial":"E.","affiliations":[{"id":20313,"text":"Marine Mammal Commission","active":true,"usgs":false}],"preferred":false,"id":741421,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Costello, Mark John","contributorId":146661,"corporation":false,"usgs":false,"family":"Costello","given":"Mark","email":"","middleInitial":"John","affiliations":[{"id":13376,"text":"The University of Auckland","active":true,"usgs":false}],"preferred":false,"id":741422,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sousa Pinto, 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Emmett","contributorId":78186,"corporation":false,"usgs":true,"family":"Duffy","given":"J.","email":"","middleInitial":"Emmett","affiliations":[],"preferred":false,"id":741449,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Kudela, Raphael M.","contributorId":205181,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":741450,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Rebelo, Lisa-Maria","contributorId":192423,"corporation":false,"usgs":false,"family":"Rebelo","given":"Lisa-Maria","email":"","affiliations":[],"preferred":false,"id":741451,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Shin, Yunne","contributorId":206683,"corporation":false,"usgs":false,"family":"Shin","given":"Yunne","email":"","affiliations":[],"preferred":false,"id":741452,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Geller, Gary","contributorId":81395,"corporation":false,"usgs":true,"family":"Geller","given":"Gary","affiliations":[],"preferred":false,"id":741453,"contributorType":{"id":1,"text":"Authors"},"rank":36}]}} ,{"id":70199947,"text":"70199947 - 2018 - Automated extraction of surface water extent from Sentinel-1 data","interactions":[],"lastModifiedDate":"2018-10-05T14:32:53","indexId":"70199947","displayToPublicDate":"2018-06-01T14:32:45","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Automated extraction of surface water extent from Sentinel-1 data","docAbstract":"Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional- to global-scale surface water products lack the spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic classification tree approach to classify surface water extent using Sentinel-1 synthetic aperture radar (SAR) data and training datasets derived from prior class masks. Prior classes of water and non-water were generated from the Shuttle Radar Topography Mission (SRTM) water body dataset (SWBD) or composited dynamic surface water extent (cDSWE) class probabilities. Classification maps of water and non-water were derived over two distinct wetlandscapes: the Delmarva Peninsula and the Prairie Pothole Region. Overall classification accuracy ranged from 79% to 93% when compared to high-resolution images in the Prairie Pothole Region site. Using cDSWE class probabilities reduced omission errors among water bodies by 10% and commission errors among non-water class by 4% when compared with results generated by using the SWBD water mask. These findings indicate that including prior water masks that reflect the dynamics in surface water extent (i.e., cDSWE) is important for the accurate mapping of water bodies using SAR data.
","language":"English","publisher":"MDPI","doi":"10.3390/rs10050797","usgsCitation":"Huang, W., DeVries, B., Huang, C., Lang, M.W., Jones, J., Creed, I., and Carroll, M.L., 2018, Automated extraction of surface water extent from Sentinel-1 data: Remote Sensing, v. 10, no. 5, p. 1-18, https://doi.org/10.3390/rs10050797.","productDescription":"Article 797; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-095856","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468694,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10050797","text":"Publisher Index Page"},{"id":358186,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-21","publicationStatus":"PW","scienceBaseUri":"5bc02fe4e4b0fc368eb5399d","contributors":{"authors":[{"text":"Huang, Wenli 0000-0001-9608-1690","orcid":"https://orcid.org/0000-0001-9608-1690","contributorId":198973,"corporation":false,"usgs":false,"family":"Huang","given":"Wenli","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":747418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeVries, Ben 0000-0003-2136-3401","orcid":"https://orcid.org/0000-0003-2136-3401","contributorId":198971,"corporation":false,"usgs":false,"family":"DeVries","given":"Ben","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":747419,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huang, Chengquan 0000-0003-0055-9798","orcid":"https://orcid.org/0000-0003-0055-9798","contributorId":198972,"corporation":false,"usgs":false,"family":"Huang","given":"Chengquan","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":747420,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lang, Megan W.","contributorId":196284,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":747421,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, John 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":747417,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Creed, Irena F.","contributorId":81209,"corporation":false,"usgs":false,"family":"Creed","given":"Irena F.","affiliations":[{"id":27655,"text":"Department of Biology, University of Western Ontario, London, ON Canada","active":true,"usgs":false}],"preferred":false,"id":747422,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carroll, Mark L.","contributorId":145826,"corporation":false,"usgs":false,"family":"Carroll","given":"Mark","email":"","middleInitial":"L.","affiliations":[{"id":7239,"text":"Science Systems and Applications, Inc.","active":true,"usgs":false},{"id":16246,"text":"Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA","active":true,"usgs":false},{"id":16247,"text":"Sigma Space Corp, NASA Goddard Space Flight Center, Greenbelt, MD, USA","active":true,"usgs":false}],"preferred":false,"id":747423,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70198414,"text":"70198414 - 2018 - Spatial and temporal patterns in population trends and burrow usage of burrowing owls in North America","interactions":[],"lastModifiedDate":"2018-08-06T10:45:33","indexId":"70198414","displayToPublicDate":"2018-06-01T13:46:23","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal patterns in population trends and burrow usage of burrowing owls in North America","docAbstract":"Many researchers have suggested that abundance of Burrowing Owls (Athene cunicularia) has declined in many portions of their breeding range, but a thorough review of their population trends over time is lacking. Published population trends from the North American Breeding Bird Survey program suggested that Burrowing Owl populations in the US have declined over the past 60 yr, but the declines were not considered significant until 2014. However, accurate trend estimates and the statistical significance of those estimates were hampered by low relative abundance of owls. Moreover, many authors have suggested that eradication of burrowing animals is a major cause of Burrowing Owl declines, because burrows dug by burrowing animals are a critical resource for Western Burrowing Owls (A. cunicularia hypugaea). Despite this, we currently lack a range-wide summary of the burrowing animals on which Western Burrowing Owls depend. To help fill these two information gaps, my objectives were to: (1) use Breeding Bird Survey (BBS) data to examine geographic patterns in population trends of Burrowing Owls throughout their breeding range in the USA, and (2) use past studies to provide the first summary of the spatial extent to which Western Burrowing Owls rely on the suite of burrowing animals throughout their breeding range. Significantly more BBS routes in the US show declining counts of owls than show increasing or stable counts, and the declines were most apparent prior to 1995. Counts of Burrowing Owls declined most precipitously on the northern edge and southern edge of the owl's US breeding range. Western Burrowing Owls primarily use black-tailed prairie dog (Cynomys ludovicianus) burrows in the eastern portion of their breeding range, whereas the diversity of burrowing species on which the owls depend is much greater in the western and central portions of their breeding range. Burrowing owl declines have been most apparent in portions of their range where they rely primarily on Richardson's ground squirrels (Urocitellus richardsonii), California ground squirrels (Otospermophilus beecheyi), black-tailed prairie dogs, and American badgers (Taxidea taxus).
","language":"English","publisher":"The Raptor Research Foundation","doi":"10.3356/JRR-16-109.1","collaboration":"University of Idaho","usgsCitation":"Conway, C.J., 2018, Spatial and temporal patterns in population trends and burrow usage of burrowing owls in North America: Journal of Raptor Research, v. 52, no. 2, p. 129-142, https://doi.org/10.3356/JRR-16-109.1.","productDescription":"14 p.","startPage":"129","endPage":"142","ipdsId":"IP-082315","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468699,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr-16-109.1","text":"Publisher Index Page"},{"id":356147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc442e4b0f5d57878ea31","contributors":{"authors":[{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":741366,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70200925,"text":"70200925 - 2018 - Mapping cropland waterway buffers for switchgrass development in the eastern Great Plains, USA","interactions":[],"lastModifiedDate":"2018-11-15T12:07:28","indexId":"70200925","displayToPublicDate":"2018-06-01T12:07:21","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5777,"text":"Global Change Biology Bioenergy","active":true,"publicationSubtype":{"id":10}},"title":"Mapping cropland waterway buffers for switchgrass development in the eastern Great Plains, USA","docAbstract":"Switchgrass (Panicum virgatum L.), a highly productive perennial grass, has been recommended as one potential source for cellulosic biofuel feedstocks. Previous studies indicate that planting perennial grasses (e.g., switchgrass) in high‐topographic‐relief cropland waterway buffers can improve local environmental conditions and sustainability. The main advantages of this land management practice include (i) reducing soil erosion and improving water quality because switchgrass requires less tillage, fertilizers, and pesticides; and (ii) improving regional ecosystem services (e.g., improving water infiltration, minimizing drought and flood impacts on production, and serving as carbon sinks). In this study, we mapped high‐topographic‐relief cropland waterway buffers with high switchgrass productivity potential that may be suitable for switchgrass development in the eastern Great Plains (EGP). The US Geological Survey (USGS) Compound Topographic Index map, National Land Cover Database 2011, USGS irrigation map, and a switchgrass biomass productivity map derived from a previous study were used to identify the switchgrass potential areas. Results show that about 16 342 km2(c. 1.3% of the total study area) of cropland waterway buffers in the EGP are potentially suitable for switchgrass development. The total annual estimated switchgrass biomass production for these suitable areas is approximately 15 million metric tons. Results from this study provide useful information on EGP areas with good cellulosic switchgrass biomass production potential and synergistic substantial potential for improvement of ecosystem services.
","language":"English","publisher":"Wiley","doi":"10.1111/gcbb.12511","usgsCitation":"Gu, Y., and Wylie, B.K., 2018, Mapping cropland waterway buffers for switchgrass development in the eastern Great Plains, USA: Global Change Biology Bioenergy, v. 10, no. 6, p. 415-424, https://doi.org/10.1111/gcbb.12511.","productDescription":"10 p.","startPage":"415","endPage":"424","ipdsId":"IP-093012","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468703,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcbb.12511","text":"Publisher Index Page"},{"id":359460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Plains","volume":"10","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-17","publicationStatus":"PW","scienceBaseUri":"5bee93e6e4b08f163c24a1c3","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":751324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":751325,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198520,"text":"70198520 - 2018 - burnr: Fire history analysis and graphics in R","interactions":[],"lastModifiedDate":"2018-08-07T11:41:28","indexId":"70198520","displayToPublicDate":"2018-06-01T11:41:22","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1377,"text":"Dendrochronologia","active":true,"publicationSubtype":{"id":10}},"title":"burnr: Fire history analysis and graphics in R","docAbstract":"We developed a new software package, burnr, for fire history analysis and plotting in the Rstatistical programming environment. It was developed for tree-ring fire-scar analysis, but is broadly applicable to other event analyses (e.g., avalanches, frost rings, or culturally modified trees). Our new package can read, write, and manipulate standard tree-ring fire history FHX files, produce fire—demography charts, calculate fire frequency and seasonality statistics, and run superposed epoch analysis (SEA). A key benefit of burnr is that it enables automation of analyses and plotting, especially for large data sets. The package also facilitates creative plotting, mapping, and analyses when combined with the thousands of packages available in R. In this paper, we describe the basic functionality of burnr and introduce users to fire history analyses in R.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dendro.2018.02.005","usgsCitation":"Malevich, S.B., Guiterman, C.H., and Margolis, E.Q., 2018, burnr: Fire history analysis and graphics in R: Dendrochronologia, v. 49, p. 9-15, https://doi.org/10.1016/j.dendro.2018.02.005.","productDescription":"7 p.","startPage":"9","endPage":"15","ipdsId":"IP-090900","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468704,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1016/j.dendro.2018.02.005","text":"External Repository"},{"id":356274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"49","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc443e4b0f5d57878ea35","contributors":{"authors":[{"text":"Malevich, Steven B.","contributorId":173544,"corporation":false,"usgs":false,"family":"Malevich","given":"Steven","email":"","middleInitial":"B.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":741767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guiterman, Christopher H.","contributorId":190553,"corporation":false,"usgs":false,"family":"Guiterman","given":"Christopher","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":741768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":741766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70200460,"text":"70200460 - 2018 - Using the NHD, WBD, and NHDPlus to solve problems","interactions":[],"lastModifiedDate":"2018-11-27T11:28:39","indexId":"70200460","displayToPublicDate":"2018-06-01T11:28:31","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Using the NHD, WBD, and NHDPlus to solve problems","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"GIS for surface water: Using the National Hydrography Dataset","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"ESRI Press","isbn":"9781589484795","usgsCitation":"Ries, K., and Steeves, P.A., 2018, Using the NHD, WBD, and NHDPlus to solve problems, chap. of GIS for surface water: Using the National Hydrography Dataset.","ipdsId":"IP-082120","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":359713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":359712,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://esripress.esri.com/display/index.cfm?fuseaction=display&websiteID=357&moduleID=1"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bfe65e3e4b0815414ca60fc","contributors":{"authors":[{"text":"Ries, Kernell G. III 0000-0003-1690-5499 kries@usgs.gov","orcid":"https://orcid.org/0000-0003-1690-5499","contributorId":192960,"corporation":false,"usgs":true,"family":"Ries","given":"Kernell G.","suffix":"III","email":"kries@usgs.gov","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":748977,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steeves, Peter A. 0000-0001-7558-9719 psteeves@usgs.gov","orcid":"https://orcid.org/0000-0001-7558-9719","contributorId":1873,"corporation":false,"usgs":true,"family":"Steeves","given":"Peter","email":"psteeves@usgs.gov","middleInitial":"A.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":748978,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70199839,"text":"70199839 - 2018 - Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification","interactions":[],"lastModifiedDate":"2018-10-02T11:12:49","indexId":"70199839","displayToPublicDate":"2018-06-01T11:12:42","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification","docAbstract":"Acoustic recording units (ARUs) enable geographically extensive surveys of sensitive and elusive species. However, a hidden cost of using ARU data for modeling species occupancy is that prohibitive amounts of human verification may be required to correct species identifications made from automated software. Bat acoustic studies exemplify this challenge because large volumes of echolocation calls could be recorded and automatically classified to species. The standard occupancy model requires aggregating verified recordings to construct confirmed detection/non‐detection datasets. The multistep data processing workflow is not necessarily transparent nor consistent among studies. We share a workflow diagramming strategy that could provide coherency among practitioners. A false‐positive occupancy model is explored that accounts for misclassification errors and enables potential reduction in the number of confirmed detections. Simulations informed by real data were used to evaluate how much confirmation effort could be reduced without sacrificing site occupancy and detection error estimator bias and precision. We found even under a 50% reduction in total confirmation effort, estimator properties were reasonable for our assumed survey design, species‐specific parameter values, and desired precision. For transferability, a fully documented r package, OCacoustic, for implementing a false‐positive occupancy model is provided. Practitioners can apply OCacoustic to optimize their own study design (required sample sizes, number of visits, and confirmation scenarios) for properly implementing a false‐positive occupancy model with bat or other wildlife acoustic data. Additionally, our work highlights the importance of clearly defining research objectives and data processing strategies at the outset to align the study design with desired statistical inferences.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4162","usgsCitation":"Banner, K.M., Irvine, K.M., Rodhouse, T., Wright, W.J., Rodriguez, R., and Litt, A.R., 2018, Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification: Ecology and Evolution, v. 8, no. 12, p. 6144-6156, https://doi.org/10.1002/ece3.4162.","productDescription":"13 p.","startPage":"6144","endPage":"6156","ipdsId":"IP-092219","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468705,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4162","text":"Publisher Index Page"},{"id":437884,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JD4W00","text":"USGS data release","linkHelpText":"Online supporting information for "Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification""},{"id":358014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-20","publicationStatus":"PW","scienceBaseUri":"5bc02fe4e4b0fc368eb539a3","contributors":{"authors":[{"text":"Banner, Katharine M.","contributorId":208354,"corporation":false,"usgs":false,"family":"Banner","given":"Katharine","email":"","middleInitial":"M.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":746852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":746851,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodhouse, Thomas J.","contributorId":127378,"corporation":false,"usgs":false,"family":"Rodhouse","given":"Thomas J.","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":746853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wright, Wilson J. 0000-0003-4276-3850 wjwright@usgs.gov","orcid":"https://orcid.org/0000-0003-4276-3850","contributorId":198317,"corporation":false,"usgs":true,"family":"Wright","given":"Wilson","email":"wjwright@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":746854,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rodriguez, Rogelio M.","contributorId":208357,"corporation":false,"usgs":false,"family":"Rodriguez","given":"Rogelio M.","affiliations":[{"id":37789,"text":"Zots Ecological Solutions","active":true,"usgs":false}],"preferred":false,"id":746857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Litt, Andrea R.","contributorId":208358,"corporation":false,"usgs":false,"family":"Litt","given":"Andrea","email":"","middleInitial":"R.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":746858,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202651,"text":"70202651 - 2018 - A multi-species synthesis of satellite telemetry data in the Pacific Arctic (1987–2015): Overlap of marine mammal distributions and core use areas","interactions":[],"lastModifiedDate":"2019-03-15T10:55:23","indexId":"70202651","displayToPublicDate":"2018-06-01T10:55:17","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5536,"text":"Deep Sea Research Part II: Topical Studies in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"A multi-species synthesis of satellite telemetry data in the Pacific Arctic (1987–2015): Overlap of marine mammal distributions and core use areas","docAbstract":"We collated available satellite telemetry data for six species of ice-associated marine mammals in the Pacific Arctic: ringed seals (Pusa hispida; n = 118), bearded seals(Erignathus barbatus, n = 51), spotted seals (Phoca largha, n = 72), Pacific walruses (Odobenus rosmarus divergens, n = 389); bowhead whales (Balaena mysticetus, n = 46), and five Arctic and sub-arctic stocks of beluga whales (Delphinapterus leucas, n = 103). We also included one seasonal resident, eastern North Pacific gray whales (Eschrichtius robustus, n = 12). This review summarized the distribution of daily locations from satellite-linked transmitters during two analysis periods, summer (May–November) and winter (December–April), and then examined the overlap among species. Six multi-species core use areas were identified during the summer period: 1) Chukotka/Bering Strait; 2) Norton Sound; 3) Kotzebue Sound; 4) the northeastern Chukchi Sea; 5) Mackenzie River Delta/Amundsen Gulf; and 6) Viscount Melville Sound. During the winter period, we identified four multi-species core use areas: 1) Anadyr Gulf/Strait; 2) central Bering Sea; 3) Nunivak Island; and 4) Bristol Bay. During the summer period, four of the six areas were centered on the greater Bering Strait region and the northwestern coast of Alaska and included most of the species we examined. The two remaining summer areas were in the western Canadian Arctic and were largely defined by the seasonal presence of Bering-Chukchi-Beaufort stock bowhead whales and Eastern Beaufort Sea stock beluga whales, whose distribution overlapped during both summer and winter periods. During the winter period, the main multi-species core use area was located near the Gulf of Anadyr and extended northwards through Anadyr and Bering Straits. This area is contained within the Bering Sea “green belt”, an area of enhanced primary and secondary productivity in the Bering Sea. We also described available telemetry data and where they can be found as of 2017. These data are important for understanding ice-associated marine mammal movements and habitat use in the Pacific Arctic and should be archived, with appropriate metadata, to ensure they are available for future retrospective analyses.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2018.02.006","usgsCitation":"Citta, J.J., Lowry, L., Quakenbush, L.T., Kelly, B.P., Fischbach, A., London, J.M., Jay, C.V., Frost, K.J., Crowe, G.O., Crawford, J.A., Boveng, P.L., Cameron, M., Von Duyke, A.L., Nelson, M., Harwood, L.A., Richard, P., Suydam, R., Heide-Jorgensen, M.P., Hobbs, R.C., Litovka, D.I., Marcoux, M., Whiting, A., Kennedy, A.S., George, J., Orr, J., and Gray, T., 2018, A multi-species synthesis of satellite telemetry data in the Pacific Arctic (1987–2015): Overlap of marine mammal distributions and core use areas: Deep Sea Research Part II: Topical Studies in Oceanography, v. 152, p. 132-153, https://doi.org/10.1016/j.dsr2.2018.02.006.","productDescription":"22 p.","startPage":"132","endPage":"153","ipdsId":"IP-086975","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":468707,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index 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of Alaska, School of Fisheries and Ocean Science","active":true,"usgs":false}],"preferred":false,"id":759349,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Quakenbush, Lori T.","contributorId":192737,"corporation":false,"usgs":false,"family":"Quakenbush","given":"Lori","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":759350,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelly, Brendan P.","contributorId":214203,"corporation":false,"usgs":false,"family":"Kelly","given":"Brendan","email":"","middleInitial":"P.","affiliations":[{"id":38992,"text":"International Arctic Research Center, University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":759351,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":200780,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony S.","email":"afischbach@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":759347,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"London, Josh M.","contributorId":214204,"corporation":false,"usgs":false,"family":"London","given":"Josh","email":"","middleInitial":"M.","affiliations":[{"id":38993,"text":"Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":759352,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jay, Chadwick V. 0000-0002-9559-2189 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facies relationships in a Lower(?) Devonian rocky shoreline depositional environment, East Lime Creek Conglomerate, south‐western Colorado, USA","interactions":[],"lastModifiedDate":"2018-11-20T10:50:59","indexId":"70200979","displayToPublicDate":"2018-06-01T10:50:47","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5781,"text":"The Depositional Record","active":true,"publicationSubtype":{"id":10}},"title":"Processes and facies relationships in a Lower(?) Devonian rocky shoreline depositional environment, East Lime Creek Conglomerate, south‐western Colorado, USA","docAbstract":"Rocky shorelines are relatively common features along modern coastlines, but few have been recognized in the geological record. The hard substrates of rocky shorelines telescope the width of offshore marine environments, thus the diagnostic deposits observed in such settings today have a low preservation potential due to small accommodation space and high‐energy conditions. This study recognized previously overlooked, laterally extensive Lower(?) Devonian rocky shoreline deposits in the San Juan Mountains of south‐western Colorado. The newly defined lithostratigraphic unit, the East Lime Creek Conglomerate (ELCC), is 0–23 m thick, unconformably overlying Proterozoic crystalline rocks and unconformably overlain by the Upper Devonian Ignacio Formation and/or Elbert Formation. The unit mostly consists of clast‐supported cobble‐boulder conglomerate with rounded quartzite clasts up to 1.4 m in length interbedded with thin sandstone layers and lenses. Sandstones in the ELCC are distinguished from unconformably overlying Upper Devonian sedimentary rocks because they have sericite cements. Most importantly, there are buttressing relationships between the ELCC and underlying Proterozoic crystalline rocks interpreted as palaeo‐sea cliffs, palaeo‐wave‐cut platforms and palaeo‐tombolos. A proposed rocky shoreline facies model includes headlands with upper shoreface‐beachface tabular cobble‐boulder gravels sourced from rock fall talus, nearshore subaqueous debris‐flow deposits and intervening pocket beaches with imbricated, stratified pebble‐cobble gravel sheets. Palaeocurrent data (n = 338) from clast long‐axis orientations, imbrication and cross‐bedding indicate south‐to‐north transport roughly onshore‐offshore to a coastline consisting of alternating rocky headlands and pocket beaches. This Lower(?) Devonian unit documents a previously unrecognized episode in the geological history of south‐western Colorado.
","language":"English","publisher":"Wiley","doi":"10.1002/dep2.41","usgsCitation":"Evans, J.E., and Holm-Denoma, C.S., 2018, Processes and facies relationships in a Lower(?) Devonian rocky shoreline depositional environment, East Lime Creek Conglomerate, south‐western Colorado, USA: The Depositional Record, v. 4, no. 1, p. 133-156, https://doi.org/10.1002/dep2.41.","productDescription":"24 p.","startPage":"133","endPage":"156","ipdsId":"IP-090285","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":468708,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/dep2.41","text":"Publisher Index Page"},{"id":359601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108,\n 37.25\n ],\n [\n -107.5,\n 37.25\n ],\n [\n -107.5,\n 37.88027325525864\n ],\n [\n -108,\n 37.88027325525864\n ],\n [\n -108,\n 37.25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-14","publicationStatus":"PW","scienceBaseUri":"5bf52b69e4b045bfcae2800c","contributors":{"authors":[{"text":"Evans, James E.","contributorId":194435,"corporation":false,"usgs":false,"family":"Evans","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":751544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440 cholm-denoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":2442,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher","email":"cholm-denoma@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":751543,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}