The myriad hydrologic and biogeochemical processes taking place in watersheds occurring across space and time are integrated and reflected in the quantity and quality of water in streams and rivers. Collection of high‐frequency water quality data with sensors in surface waters provides new opportunities to disentangle these processes and quantify sources and transport of water and solutes in the coupled groundwater‐surface water system. A new approach for separating the streamflow hydrograph into three components was developed and coupled with high‐frequency nitrate data to estimate time‐variable nitrate loads from chemically dilute quick flow, chemically concentrated quick flow, and slowflow groundwater end‐member pathways for periods of up to 2 years in a groundwater‐dominated and a quick‐flow‐dominated stream in central Wisconsin, using only streamflow and in‐stream water quality data. The dilute and concentrated quick flow end‐members were distinguished using high‐frequency specific conductance data. Results indicate that dilute quick flow contributed less than 5% of the nitrate load at both sites, whereas 89 ± 8% of the nitrate load at the groundwater‐dominated stream was from slowflow groundwater, and 84 ± 25% of the nitrate load at the quick‐flow‐dominated stream was from concentrated quick flow. Concentrated quick flow nitrate concentrations varied seasonally at both sites, with peak concentrations in the winter that were 2–3 times greater than minimum concentrations during the growing season. Application of this approach provides an opportunity to assess stream vulnerability to nonpoint source nitrate loading and expected stream responses to current or changing conditions and practices in watersheds.
","language":"English ","publisher":"American Geophysical Union","doi":"10.1002/2017WR021654","usgsCitation":"Miller, M.P., Tesoriero, A.J., Hood, K., Terziotti, S., and Wolock, D.M., 2017, Estimating discharge and nonpoint source nitrate loading to streams from three end‐member pathways using high‐frequency water quality data: Water Resources Research, v. 53, no. 12, p. 10201-10216, https://doi.org/10.1002/2017WR021654.","productDescription":"16 p.","startPage":"10201","endPage":"10216","ipdsId":"IP-091844","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"links":[{"id":469303,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017wr021654","text":"Publisher Index Page"},{"id":357845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"12","noUsgsAuthors":false,"publicationDate":"2017-12-07","publicationStatus":"PW","scienceBaseUri":"5bc030a6e4b0fc368eb53a0a","contributors":{"authors":[{"text":"Miller, Matthew P. 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":3919,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746513,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746514,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hood, Krista","contributorId":208243,"corporation":false,"usgs":false,"family":"Hood","given":"Krista","email":"","affiliations":[],"preferred":false,"id":746521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Terziotti, Silvia 0000-0003-3559-5844 seterzio@usgs.gov","orcid":"https://orcid.org/0000-0003-3559-5844","contributorId":1613,"corporation":false,"usgs":true,"family":"Terziotti","given":"Silvia","email":"seterzio@usgs.gov","affiliations":[{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wolock, David M. 0000-0002-6209-938X dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":746523,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193922,"text":"ofr20171146 - 2017 - Timing of warm water refuge use in Crystal River National Wildlife Refuge by manatees—Results and insights from Global Positioning System telemetry data","interactions":[],"lastModifiedDate":"2017-11-21T15:53:56","indexId":"ofr20171146","displayToPublicDate":"2017-11-21T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1146","title":"Timing of warm water refuge use in Crystal River National Wildlife Refuge by manatees—Results and insights from Global Positioning System telemetry data","docAbstract":"Managers at the U.S. Fish and Wildlife Service Crystal River National Wildlife Refuge (CRNWR) desire to update their management plan regarding the operation of select springs including Three Sisters Springs. They wish to refine existing parameters used to predict the presence of federally threatened Trichechus manatus latirostris (Florida manatee) in the springs and thereby improve their manatee management options. The U.S. Geological Survey Sirenia Project has been tracking manatees in the CRNWR area since 2006 with floating Global Positioning System (GPS) satellite-monitored telemetry tags. Analyzing movements of these tagged manatees will provide valuable insight into their habitat use patterns.
A total of 136 GPS telemetry bouts were available for this project, representing 730,009 locations generated from 40 manatees tagged in the Gulf of Mexico north of Tampa, Florida. Dates from October through March were included to correspond to the times that cold ambient temperatures were expected, thus requiring a need for manatee thermoregulation and a physiologic need for warm water. Water level (tide) and water temperatures were obtained for the study from Salt River, Crystal River mouth, Bagley Cove, Kings Bay mouth, and Magnolia Spring. Polygons were drawn to subdivide the manatee locations into areas around the most-used springs (Three Sisters/Idiots Delight, House/Hunter/Jurassic, Magnolia and King), Kings Bay, Crystal/Salt Rivers and the Gulf of Mexico.
Manatees were found in the Crystal or Salt Rivers or in the Gulf of Mexico when ambient temperatures were warmer (>20 °C), while they were found in or near the springs (especially Three Sisters Springs) at colder ambient water temperatures. There was a trend of manatees entering springs early in the morning and leaving in the afternoon. There was a strong association of manatee movements in and out of the Three Sisters/Idiots Delight polygon with tide cycles: manatees were more likely to enter the Three Sisters/Idiots Delight polygon on an incoming tide, and leave the polygon on an outgoing tide. Both movement directions were associated with midtide. Future analysis will incorporate human activity and a finer spatial scale, including movements between Three Sisters Springs and Idiots Delight and nearby canals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171146","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Slone, D.H., Butler, S.M., Reid, J.P., and Haase, C.G., 2017, Timing of warm water refuge use in Crystal River National Wildlife Refuge by manatees—Results and insights from Global Positioning System telemetry data: U.S. Geological Survey Open-File Report 2017–1146, 17 p., https://doi.org/10.3133/ofr20171146.","productDescription":"Report: v, 17 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091745","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":349180,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1146/ofr20171146.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1146"},{"id":349181,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78P5ZGR","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water temperature in Three Sisters Springs, and water temperature and level in Magnolia Spring: Winter 2014–15"},{"id":349179,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1146/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Crystal River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.850,\n 28.830\n ],\n [\n -82.570,\n 28.830\n ],\n [\n -82.570,\n 28.955\n ],\n [\n -82.850,\n 28.955\n ],\n [\n -82.850,\n 28.830\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
7920 NW 71 Street
Gainesville, FL 32653
Annual peak streamflow (peak flow) at a streamgage is defined as the maximum instantaneous flow in a water year. A water year begins on October 1 and continues through September 30 of the following year; for example, water year 2015 extends from October 1, 2014, through September 30, 2015. The accuracy, characterization, and completeness of the peak streamflow data are critical in determining flood-frequency estimates that are used daily to design water and transportation infrastructure, delineate flood-plain boundaries, and regulate development and utilization of lands throughout the United States and are essential to understanding the implications of climate and land-use change on flooding and high-flow conditions.
As of November 14, 2016, peak-flow data existed for 27,240 unique streamgages in the United States and its territories. The data, collectively referred to as the “peak-flow file,” are available as part of the U.S. Geological Survey (USGS) public web interface, the National Water Information System, at https://nwis.waterdata.usgs.gov/usa/nwis/peak. Although the data have been routinely subjected to periodic review by the USGS Office of Surface Water and screening at the USGS Water Science Center level, these data were not reviewed in a national, systematic manner until 2008 when automated scripts were developed and applied to detect potential errors in peak-flow values and their associated dates, gage heights, and peak-flow qualification codes, as well as qualification codes associated with the gage heights. USGS scientists and hydrographers studied the resulting output, accessed basic records and field notes, and corrected observed errors or, more commonly, confirmed existing data as correct.
This report summarizes the changes in peak-flow file data at a national level, illustrates their nature and causation, and identifies the streamgages affected by these changes. Specifically, the peak-flow data were compared for streamgages with peak flow measured as of November 19, 2008 (before the automated scripts were widely applied) and on November 14, 2016 (after several rounds of corrections). There were 659,332 peak-flow values in the 2008 dataset and 731,965 peak-flow values in the 2016 dataset. When compared to the 2016 dataset, 5,179 (0.79 percent) peak-flow values had changed; 36,506 (5.54 percent) of the peak-flow qualification codes had changed; 1,938 (0.29 percent) peak-flow dates had changed; 18,599 (2.82 percent) of the peak-flow gage heights had changed; and 20,683 (3.14 percent) of the gage-height qualification codes had changed—most as a direct result of the peak-flow file data verification effort led by USGS personnel. The various types of changes are summarized and mapped in this report. In addition to this report, a corresponding USGS data release is provided to identify changes in peak flows at individual streamgages. The data release and the procedures to access the data release are described in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175119","usgsCitation":"Ryberg, K.R., Goree, B.B., Williams-Sether, Tara, and Mason, R.R., Jr., 2017, The U.S. Geological Survey Peak-Flow File Data Verification Project, 2008–16: U.S. Geological Survey Scientific Investigations Report 2017–5119, 61 p., https://doi.org/10.31333/sir20175119.","productDescription":"Report: vii, 63 p.; Appendixes 1-2; Data Release","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-068669","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":347884,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GH9G3P","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data documenting the U.S. Geological Survey peak-flow file data verification project, 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415 National Center
12201 Sunrise Valley Drive
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Subspecies relationships within the peregrine falcon (Falco peregrinus) have been long debated because of the polytypic nature of melanin-based plumage characteristics used in subspecies designations and potential differentiation of local subpopulations due to philopatry. In North America, understanding the evolutionary relationships among subspecies may have been further complicated by the introduction of captive bred peregrines originating from non-native stock, as part of recovery efforts associated with mid 20th century population declines resulting from organochloride pollution. Alaska hosts all three nominal subspecies of North American peregrine falcons–F. p. tundrius, anatum, and pealei–for which distributions in Alaska are broadly associated with nesting locales within Arctic, boreal, and south coastal maritime habitats, respectively. Unlike elsewhere, populations of peregrine falcon in Alaska were not augmented by captive-bred birds during the late 20th century recovery efforts. Population genetic differentiation analyses of peregrine populations in Alaska, based on sequence data from the mitochondrial DNA control region and fragment data from microsatellite loci, failed to uncover genetic distinction between populations of peregrines occupying Arctic and boreal Alaskan locales. However, the maritime subspecies, pealei, was genetically differentiated from Arctic and boreal populations, and substructured into eastern and western populations. Levels of interpopulational gene flow between anatum and tundrius were generally higher than between pealei and either anatum or tundrius. Estimates based on both marker types revealed gene flow between augmented Canadian populations and unaugmented Alaskan populations. While we make no attempt at formal taxonomic revision, our data suggest that peregrine falcons occupying habitats in Alaska and the North Pacific coast of North America belong to two distinct regional groupings–a coastal grouping (pealei) and a boreal/Arctic grouping (currently anatum and tundrius)–each comprised of discrete populations that are variously intra-regionally connected.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0188185","usgsCitation":"Talbot, S.L., Sage, G.K., Sonsthagen, S.A., Gravley, M.C., Swem, T., Williams, J.C., Longmire, J.L., Ambrose, S., Flamme, M.J., Lewis, S.B., Phillips, L.M., Anderson, C., and White, C., 2017, Intraspecific evolutionary relationships among peregrine falcons in western North American high latitudes: PLoS ONE, v. 12, no. 11, p. 1-25, https://doi.org/10.1371/journal.pone.0188185.","productDescription":"e0188185; 25 p.","startPage":"1","endPage":"25","ipdsId":"IP-070365","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":469305,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0188185","text":"Publisher Index Page"},{"id":438145,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7F18WV0","text":"USGS data release","linkHelpText":"Peregrine Falcon 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Article"},"seriesTitle":{"id":2315,"text":"Journal of Geophysical Research C: Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Persistent shoreline shape induced from offshore geologic framework: Effects of shoreface connected ridges","docAbstract":"Mechanisms relating offshore geologic framework to shoreline evolution are determined through geologic investigations, oceanographic deployments, and numerical modeling. Analysis of shoreline positions from the past 50 years along Fire Island, New York, a 50 km long barrier island, demonstrates a persistent undulating shape along the western half of the island. The shelf offshore of these persistent undulations is characterized with shoreface-connected sand ridges (SFCR) of a similar alongshore length scale, leading to a hypothesis that the ridges control the shoreline shape through the modification of flow. To evaluate this, a hydrodynamic model was configured to start with the US East Coast and scale down to resolve the Fire Island nearshore. The model was validated using observations along western Fire Island and buoy data, and used to compute waves, currents and sediment fluxes. To isolate the influence of the SFCR on the generation of the persistent shoreline shape, simulations were performed with a linearized nearshore bathymetry to remove alongshore transport gradients associated with shoreline shape. The model accurately predicts the scale and variation of the alongshore transport that would generate the persistent shoreline undulations. In one location, however, the ridge crest connects to the nearshore and leads to an offshore-directed transport that produces a difference in the shoreline shape. This qualitatively supports the hypothesized effect of cross-shore fluxes on coastal evolution. Alongshore flows in the nearshore during a representative storm are driven by wave breaking, vortex force, advection and pressure gradient, all of which are affected by the SFCR.
","language":"English","publisher":"AGU","doi":"10.1002/2017JC012808","usgsCitation":"Safak, I., List, J.H., Warner, J., and Schwab, W.C., 2017, Persistent shoreline shape induced from offshore geologic framework: Effects of shoreface connected ridges: Journal of Geophysical Research C: Oceans, v. 122, no. 11, p. 8721-8738, https://doi.org/10.1002/2017JC012808.","productDescription":"18 p.","startPage":"8721","endPage":"8738","ipdsId":"IP-082366","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469311,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://hdl.handle.net/1912/9472","text":"Publisher Index Page"},{"id":349010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.33099365234375,\n 40.588928169693745\n ],\n [\n -72.35321044921875,\n 40.588928169693745\n ],\n [\n -72.35321044921875,\n 40.865756786006806\n ],\n [\n -73.33099365234375,\n 40.865756786006806\n ],\n [\n -73.33099365234375,\n 40.588928169693745\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"11","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-15","publicationStatus":"PW","scienceBaseUri":"5a60fb0fe4b06e28e9c22b8e","contributors":{"authors":[{"text":"Safak, Ilgar 0000-0001-7675-0770 isafak@usgs.gov","orcid":"https://orcid.org/0000-0001-7675-0770","contributorId":5522,"corporation":false,"usgs":true,"family":"Safak","given":"Ilgar","email":"isafak@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":722349,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"List, Jeffrey H. 0000-0001-8594-2491 jlist@usgs.gov","orcid":"https://orcid.org/0000-0001-8594-2491","contributorId":174581,"corporation":false,"usgs":true,"family":"List","given":"Jeffrey","email":"jlist@usgs.gov","middleInitial":"H.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":722350,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":722351,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwab, William C. 0000-0001-9274-5154 bschwab@usgs.gov","orcid":"https://orcid.org/0000-0001-9274-5154","contributorId":417,"corporation":false,"usgs":true,"family":"Schwab","given":"William","email":"bschwab@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":722352,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70222533,"text":"70222533 - 2017 - Inflation leading to a Slow Slip Event and volcanic unrest at Mt. Etna in 2016: Insights from CGPS data","interactions":[],"lastModifiedDate":"2021-08-03T12:42:44.831853","indexId":"70222533","displayToPublicDate":"2017-11-15T07:40:54","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Inflation leading to a Slow Slip Event and volcanic unrest at Mt. Etna in 2016: Insights from CGPS data","docAbstract":"Global Positioning System (CGPS) data from Mount Etna between May 2015 and September 2016 show intense inflation and a concurrent Slow Slip Event (SSE) from 11 December 2015 to 17 May 2016. In May 2016, an eruptive phase started from the summit craters, temporarily stopping the ongoing inflation. The CGPS data presented here give us the opportunity to determine (1) the source of the inflating body, (2) the strain rate parameters highlighting shear strain rate accumulating along NE Rift and S Rift, (3) the magnitude of the SSE, and (4) possible interaction between modeled sources and other flank structures through stress calculations. By analytical inversion, we find an inflating source 5.5 km under the summit (4.4 km below sea level) and flank slip in a fragmented shallow structure accommodating displacements equivalent to a magnitude Mw6.1 earthquake. These large displacements reflect a complex mechanism of rotations indicated by the inversion of CGPS data for strain rate parameters. At the scale of the volcano, these processes can be considered precursors of seismic activity in the eastern flank of the volcano but concentrated mainly on the northern boundary of the mobile eastern flank along the Pernicana Fault and in the area of the Timpe Fault System.
The methodological procedures used in the geologic assessments of the 2008 Circum-Arctic Resource Appraisal (CARA) were based largely on the methodology developed for the 2000 U.S. Geological Survey World Petroleum Assessment. The main variables were probability distributions for numbers and sizes of undiscovered accumulations with an associated risk of occurrence. The CARA methodology expanded on the previous methodology in providing additional tools and procedures more applicable to the many Arctic basins that have little or no exploration history. Most importantly, geologic analogs from a database constructed for this study were used in many of the assessments to constrain numbers and sizes of undiscovered oil and gas accumulations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824B","usgsCitation":"Charpentier, R.R., 2017, Methodology for assessment of undiscovered oil and gas resources for the 2008 Circum-Arctic Resource Appraisal, chap. B of Moore, T.L., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 7 p., https://doi.org/10.3133/pp1824B.","productDescription":"Report: vii, 7 p.; 3 Appendixes","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-050993","costCenters":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"links":[{"id":348828,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/b/pp1824_chapterb_appendix3.xls","text":"Appendix 3","size":"42 KB xls","description":"PP 1824 Chapter B Appendix 3","linkHelpText":"Circum-Arctic Resource Appraisal Geologic Data Form for Conventional Assessment Units"},{"id":348827,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/b/pp1824_chapterb_appendix2.pdf","text":"Appendix 2","size":"445 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824 Chapter B Appendix 2","linkHelpText":"Basin Evolution Chart"},{"id":348826,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/b/pp1824_chapterb_appendix1.pdf","text":"Appendix 1","size":"51 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824 Chapter B Appendix 1","linkHelpText":"Monte Carlo Program for Estimating Oil/Gas Accumation Mix"},{"id":348825,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/b/pp1824b.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824 Chapter B"},{"id":348824,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/b/coverthb.jpg"}],"otherGeospatial":"Arctic Circle","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
A major issue affecting land-based, closed containment Atlantic salmon Salmo salar growout production in water recirculation aquaculture systems (RAS) is precocious male maturation, which can negatively impact factors such as feed conversion, fillet yield, and product quality. Along with other water quality parameters, elevated nitrate nitrogen (NO3-N) has been shown to influence the reproductive development and endogenous sex steroid production in a number of aquatic animal species, including Atlantic salmon. We sought to determine whether elevated NO3-N in RAS can influence early maturation in post-smolt Atlantic salmon in an 8-month trial in replicated freshwater RAS. Post-smolt Atlantic salmon (102 ± 1 g) were stocked into six RAS, with three RAS randomly selected for dosing with high NO3-N (99 ± 1 mg/L) and three RAS set for low NO3-N (10 ± 0 mg/L). At 2-, 4-, 6-, and 8-months post-stocking, 5 fish were randomly sampled from each RAS, gonadosomatic index(GSI) data were collected, and plasma was sampled for 11-ketotestosterone(11-KT) quantification. At 4- and 8-months post-stocking, samples of culture tank and spring water (used as “makeup” or replacement water) were collected and tested for a suite of 42 hormonally active compounds using liquid chromatography/mass spectrometry, as well as for estrogenicity using the bioluminescent yeast estrogen screen (BLYES) reporter system. Finally, at 8-months post-stocking 8–9 salmon were sampled from each RAS for blood gas and chemistry analyses, and multiple organ tissues were sampled for histopathology evaluation. Overall, sexually mature males were highly prevalent in both NO3-N treatment groups by study’s end, and there did not appear to be an effect of NO3-N on male maturation prevalence based on grilse identification, GSI, and 11-KT results, indicating that other culture parameters likely instigated early maturation. No important differences were noted between treatment groups for whole blood gas and chemistry parameters, and no significant tissue changes were noted on histopathology. No hormones, hormone conjugates, or mycotoxins were detected in any water samples; phytoestrogens were generally detected at low levels but were unrelated to NO3-N treatment. Finally, low-level estrogenicity was detected in RAS water, but a NO3-N treatment effect could not be determined. The major findings of this study are i) the NO3-N treatments did not appear to be related to the observed male maturation, and ii) the majority of hormonally active compounds were not detectable in RAS water.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.aquaeng.2016.09.003","usgsCitation":"Good, C., Davidson, J., Iwanowicz, L.R., Meyer, M.T., Dietze, J.E., Kolpin, D.W., Marancik, D., Birkett, J., Williams, C., and Summerfelt, S.T., 2017, Investigating the influence of nitrate nitrogen on post-smolt Atlantic salmon Salmo salar reproductive physiology in water recirculation aquaculture systems: Aquacultural Engineering, v. 78, no. Part A, p. 2-8, https://doi.org/10.1016/j.aquaeng.2016.09.003.","productDescription":"7 p.","startPage":"2","endPage":"8","ipdsId":"IP-076818","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":469312,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.aquaeng.2016.09.003","text":"Publisher Index Page"},{"id":348872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"78","issue":"Part A","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb11e4b06e28e9c22bb3","contributors":{"authors":[{"text":"Good, Christopher","contributorId":200359,"corporation":false,"usgs":false,"family":"Good","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":713569,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davidson, John","contributorId":197473,"corporation":false,"usgs":false,"family":"Davidson","given":"John","affiliations":[],"preferred":false,"id":713571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178 liwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":190787,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke","email":"liwanowicz@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":713575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyer, Michael T. 0000-0001-6006-7985 mmeyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6006-7985","contributorId":866,"corporation":false,"usgs":true,"family":"Meyer","given":"Michael","email":"mmeyer@usgs.gov","middleInitial":"T.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":713567,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dietze, Julie E. 0000-0002-5936-5739 juliec@usgs.gov","orcid":"https://orcid.org/0000-0002-5936-5739","contributorId":3939,"corporation":false,"usgs":true,"family":"Dietze","given":"Julie","email":"juliec@usgs.gov","middleInitial":"E.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":713568,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":713576,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marancik, David","contributorId":197474,"corporation":false,"usgs":false,"family":"Marancik","given":"David","email":"","affiliations":[],"preferred":false,"id":713572,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Birkett, Jill","contributorId":197477,"corporation":false,"usgs":false,"family":"Birkett","given":"Jill","email":"","affiliations":[],"preferred":false,"id":713577,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Williams, Christina","contributorId":197475,"corporation":false,"usgs":false,"family":"Williams","given":"Christina","email":"","affiliations":[],"preferred":false,"id":713573,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Summerfelt, Steven T.","contributorId":192709,"corporation":false,"usgs":false,"family":"Summerfelt","given":"Steven","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":713574,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70191844,"text":"70191844 - 2017 - Geoelectric monitoring at the Boulder magnetic observatory","interactions":[],"lastModifiedDate":"2017-11-15T10:37:40","indexId":"70191844","displayToPublicDate":"2017-11-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5518,"text":"Geoscientific Instrumentation, Methods and Data Systems","active":true,"publicationSubtype":{"id":10}},"title":"Geoelectric monitoring at the Boulder magnetic observatory","docAbstract":"Despite its importance to a range of applied and fundamental studies, and obvious parallels to a robust network of magnetic-field observatories, long-term geoelectric field monitoring is rarely performed. The installation of a new geoelectric monitoring system at the Boulder magnetic observatory of the US Geological Survey is summarized. Data from the system are expected, among other things, to be used for testing and validating algorithms for mapping North American geoelectric fields. An example time series of recorded electric and magnetic fields during a modest magnetic storm is presented. Based on our experience, we additionally present operational aspects of a successful geoelectric field monitoring system.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/gi-2017-27","usgsCitation":"Blum, C., White, T., Sauter, E.A., Stewart, D., Bedrosian, P.A., and Love, J.J., 2017, Geoelectric monitoring at the Boulder magnetic observatory: Geoscientific Instrumentation, Methods and Data Systems, v. 6, p. 447-452, https://doi.org/10.5194/gi-2017-27.","productDescription":"6 p.","startPage":"447","endPage":"452","ipdsId":"IP-088490","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":469316,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gi-2017-27","text":"Publisher Index Page"},{"id":348869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Boulder","volume":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb11e4b06e28e9c22bb8","contributors":{"authors":[{"text":"Blum, Cletus","contributorId":197377,"corporation":false,"usgs":false,"family":"Blum","given":"Cletus","affiliations":[],"preferred":false,"id":713359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Tim 0000-0002-3563-0649 ttwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-3563-0649","contributorId":2010,"corporation":false,"usgs":true,"family":"White","given":"Tim","email":"ttwhite@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":713360,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sauter, Edward A. 0000-0001-7541-8506 esauter@usgs.gov","orcid":"https://orcid.org/0000-0001-7541-8506","contributorId":3773,"corporation":false,"usgs":true,"family":"Sauter","given":"Edward","email":"esauter@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":713361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, Duff 0000-0001-6378-6600 dcstewart@usgs.gov","orcid":"https://orcid.org/0000-0001-6378-6600","contributorId":3787,"corporation":false,"usgs":true,"family":"Stewart","given":"Duff","email":"dcstewart@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":713362,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":713363,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":713364,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70194817,"text":"70194817 - 2017 - Monitoring Hawaiian biodiversity: Pilot study to assess changes to forest birds and their habitat","interactions":[],"lastModifiedDate":"2018-01-02T14:10:22","indexId":"70194817","displayToPublicDate":"2017-11-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":414,"text":"Technical Report","active":false,"publicationSubtype":{"id":9}},"seriesNumber":"HCSU-TR083","title":"Monitoring Hawaiian biodiversity: Pilot study to assess changes to forest birds and their habitat","docAbstract":"Biological diversity, or biodiversity, is the variety and abundance of species in a defined area, and is one of the oldest and most basic descriptions of biological communities. Understanding how populations and communities are structured and change over space and time in response to internal and external forces is a management priority. Effective management practices and conservation strategies depend on our understanding of the relationship between changes in biodiversity and ecological drivers such as invasive species, land use and climate change. To demonstrate how changes in biodiversity may be monitored over a large (400 km2) tract of native forest habitat, we compared bird and plant community composition and structure in an upper montane region of Hawai‘i Island originally surveyed in 1977 as part of the Hawai‘i Forest Bird Survey (Scott et al. 1986) with a comprehensive sample of the same region in 2015.
Our findings suggest that across a region spanning an elevation range of 600 to 2,000 m considerable changes occurred in the plant and bird communities between 1977 and 2015. Endemic and indigenous plants species richness (i.e., total number of species) decreased dramatically in the low and middle elevations below an invasive weed front, whereas naturalized plant species richness did not change between the two periods at any elevation. Endemic bird abundance decreased and two species were lost in the lower elevations (< 1,100 m) between 1977 and 2015, while naturalized bird abundance and the numbers of species increased in the same area. In addition to changes in community composition, the structure of the forest showed evidence of changes in dominant and sub-dominant tree canopy cover, shrub and herbaceous cover, dominant tree canopy height, and matted fern cover.
Biodiversity monitoring helps to define specific conservation targets and to measure progress towards reaching those targets. It is difficult to ascribe causative factors to a change in biodiversity without directly manipulating the environment. Forest habitat in a variety of settings (i.e., islands and regions with differing land-use histories and elevation ranges), however, can provide opportunities to evaluate the influence of ecological drivers. Declines in native bird biodiversity in low-elevation areas may be attributed to invasive species as land use and climate conditions have remained relatively similar over the 40-year period. Thus, the shift from an endemic-naturalized co-dominated community in 1977 to one dominated by naturalized, alien birds in 2015, and reduction in native bird abundance over that period, may reflect increasing dominance by naturalized plants within this forested area. Inferences drawn from analyses of region-wide surveys, especially with replicate datasets, will facilitate the identification of broad-scale changes in biodiversity, and provide a needed current datum in Hawaiian plant and bird biodiversity monitoring.
We quantified gas and heat emissions in an acid-sulfate, vapor-dominated area (0.04-km2) of Norris Geyser Basin, located just north of the 0.63 Ma Yellowstone Caldera and near an area of anomalous uplift. From 14 May to 3 October 2016, an eddy covariance system measured half-hourly CO2, H2O and sensible (H) and latent (LE) heat fluxes and a Multi-GAS instrument measured (1 Hz frequency) atmospheric H2O, CO2 and H2S volumetric mixing ratios. We also measured soil CO2 fluxes using the accumulation chamber method and temperature profiles on a grid and collected fumarole gas samples for geochemical analysis. Eddy covariance CO2 fluxes ranged from − 56 to 885 g m− 2 d− 1. Using wavelet analysis, average daily eddy covariance CO2 fluxes were locally correlated with average daily environmental parameters on several-day to monthly time scales. Estimates of CO2emission rate from the study area ranged from 8.6 t d− 1 based on eddy covariance measurements to 9.8 t d− 1 based on accumulation chamber measurements. Eddy covariance water vapor fluxes ranged from 1178 to 24,600 g m− 2 d− 1. Nighttime H and LEwere considered representative of hydrothermal heat fluxes and ranged from 4 to 183 and 38 to 504 W m− 2, respectively. The total hydrothermal heat emission rate (H + LE + radiant) estimated for the study area was 11.6 MW and LE contributed 69% of the output. The mean ± standard deviation of H2O, CO2 and H2S mixing ratios measured by the Multi-GAS system were 9.3 ± 3.1 parts per thousand, 467 ± 61 ppmv, and 0.5 ± 0.6 ppmv, respectively, and variations in the gas compositions were strongly correlated with diurnal variations in environmental parameters (wind speed and direction, atmospheric temperature). After removing ambient H2O and CO2, the observed variations in the Multi-GAS data could be explained by the mixing of relatively H2O-CO2-H2S-rich fumarole gases with CO2-rich and H2O-H2S-poor soil gases. The fumarole H2O/CO2 and CO2/H2S end member ratios (101.7 and 27.1, respectively, on average) were invariant during the measurement period and fell within the range of values measured in direct fumarole gas samples. The soil gas H2O/CO2end member ratios (~ 15–30) were variable and low relative to the fumarole end member, likely resulting from water vapor loss during cooling and condensation in the shallow subsurface, whereas the CO2/H2S end member ratio was high (~ 160), presumably related to transport of CO2-dominated soil gas emissions mixed with trace fumarolic emissions to the Multi-GAS station. Nighttime eddy covariance ratios of H2O to CO2 flux were typically between the soil gas and fumarole end member H2O/CO2 ratios defined by Multi-GAS measurements. Overall, the combined eddy covariance and Multi-GAS approach provides a powerful tool for quasi-continuous measurements of gas and heat emissions for improved volcano-hydrothermal monitoring.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2017.10.001","usgsCitation":"Lewicki, J.L., Kelly, P.J., Bergfeld, D., Vaughan, R., and Lowenstern, J.B., 2017, Monitoring gas and heat emissions at Norris Geyser Basin, Yellowstone National Park, USA based on a combined eddy covariance and Multi-GAS approach: Journal of Volcanology and Geothermal Research, v. 347, p. 312-326, https://doi.org/10.1016/j.jvolgeores.2017.10.001.","productDescription":"15 p.","startPage":"312","endPage":"326","ipdsId":"IP-088820","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":349628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Norris Geyser Basin, Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.72776794433592,\n 44.71642860567541\n ],\n [\n -110.6945514678955,\n 44.71642860567541\n ],\n [\n -110.6945514678955,\n 44.742222087511614\n ],\n [\n -110.72776794433592,\n 44.742222087511614\n ],\n [\n -110.72776794433592,\n 44.71642860567541\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"347","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb10e4b06e28e9c22b9e","contributors":{"authors":[{"text":"Lewicki, Jennifer L. 0000-0003-1994-9104 jlewicki@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-9104","contributorId":5071,"corporation":false,"usgs":true,"family":"Lewicki","given":"Jennifer","email":"jlewicki@usgs.gov","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":724170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":724171,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergfeld, Deborah 0000-0003-4570-7627 dbergfel@usgs.gov","orcid":"https://orcid.org/0000-0003-4570-7627","contributorId":152531,"corporation":false,"usgs":true,"family":"Bergfeld","given":"Deborah","email":"dbergfel@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":724172,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vaughan, R. Greg gvaughan@usgs.gov","contributorId":200796,"corporation":false,"usgs":true,"family":"Vaughan","given":"R. Greg","email":"gvaughan@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":false,"id":724173,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lowenstern, Jacob B. 0000-0003-0464-7779 jlwnstrn@usgs.gov","orcid":"https://orcid.org/0000-0003-0464-7779","contributorId":2755,"corporation":false,"usgs":true,"family":"Lowenstern","given":"Jacob","email":"jlwnstrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":724174,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189972,"text":"ofr20171088 - 2017 - Virginia flow-ecology modeling results—An initial assessment of flow reduction effects on aquatic biota","interactions":[],"lastModifiedDate":"2017-11-14T12:14:48","indexId":"ofr20171088","displayToPublicDate":"2017-11-14T10:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1088","title":"Virginia flow-ecology modeling results—An initial assessment of flow reduction effects on aquatic biota","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the Virginia Department of Environmental Quality (DEQ), reviewed a previously compiled set of linear regression models to assess their utility in defining the response of the aquatic biological community to streamflow depletion.
As part of the 2012 Virginia Healthy Watersheds Initiative (HWI) study conducted by Tetra Tech, Inc., for the U.S. Environmental Protection Agency (EPA) and Virginia DEQ, a database with computed values of 72 hydrologic metrics, or indicators of hydrologic alteration (IHA), 37 fish metrics, and 64 benthic invertebrate metrics was compiled and quality assured. Hydrologic alteration was represented by simulation of streamflow record for a pre-water-withdrawal condition (baseline) without dams or developed land, compared to the simulated recent-flow condition (2008 withdrawal simulation) including dams and altered landscape to calculate a percent alteration of flow. Biological samples representing the existing populations represent a range of alteration in the biological community today.
For this study, all 72 IHA metrics, which included more than 7,272 linear regression models, were considered. This extensive dataset provided the opportunity for hypothesis testing and prioritization of flow-ecology relations that have the potential to explain the effect(s) of hydrologic alteration on biological metrics in Virginia streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171088","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"Rapp, J.L., and Reilly, P.A., 2017, Virginia flow-ecology modeling results—An initial assessment of flow reduction effects on aquatic biota: U.S. Geological Survey Open-File Report 2017–1088, 68 p., https://doi.org/10.3133/ofr20171088.","productDescription":"68 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086496","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":438149,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZW1J42","text":"USGS data release","linkHelpText":"Fish and Benthic Macroinvertebrate Flow-Ecology Regression Summary Statistics for 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\"}}]}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond VA 23228
This report describes a compilation of existing water-quality data associated with groundwater resources originating from abandoned underground coal mines in West Virginia. Data were compiled from multiple sources for the purpose of understanding the suitability of groundwater from abandoned underground coal mines for public supply, industrial, agricultural, and other uses. This compilation includes data collected for multiple individual studies conducted from July 13, 1973 through September 7, 2016. Analytical methods varied by the time period of data collection and requirements of the independent studies.This project identified 770 water-quality samples from 294 sites that could be attributed to abandoned underground coal mine aquifers originating from multiple coal seams in West Virginia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1069","collaboration":"Prepared in cooperation with the West Virginia Department of Environmental Protection Division of Water and Waste Management","usgsCitation":"McAdoo, M.A., and Kozar M.D., 2017, Groundwater-quality data associated with abandoned underground coal mine aquifers in West Virginia, 1973-2016: Compilation of existing data from multiple sources: U.S. Geological Survey Data Series 1069, 7 p., https://doi.org/10.3133/ds1069.","productDescription":"Report: iv, 7 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088916","costCenters":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":438150,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TM78C5","text":"USGS data release","linkHelpText":"Site and Groundwater-Quality Sample Data for Abandoned 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Virginia\",\"nation\":\"USA \"}}]}","contact":"Director, West Virginia Water Science Center
U.S. Geological Survey
11 Dunbar Street
Charleston, WV 25301
The United States (U.S.) has faced major environmental changes in recent decades, including agricultural intensification and urban expansion, as well as changes in atmospheric deposition and climate—all of which may influence eutrophication of freshwaters. However, it is unclear whether or how water quality in lakes across diverse ecological settings has responded to environmental change. We quantified water quality trends in 2913 lakes using nutrient and chlorophyll (Chl) observations from the Lake Multi-Scaled Geospatial and Temporal Database of the Northeast U.S. (LAGOS-NE), a collection of preexisting lake data mostly from state agencies. LAGOS-NE was used to quantify whether lake water quality has changed from 1990 to 2013, and whether lake-specific or regional geophysical factors were related to the observed changes. We modeled change through time using hierarchical linear models for total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP), and Chl. Both the slopes (percent change per year) and intercepts (value in 1990) were allowed to vary by lake and region. Across all lakes, TN declined at a rate of 1.1% year−1, while TP, TN:TP, and Chl did not change. A minority (7%–16%) of individual lakes had changing nutrients, stoichiometry, or Chl. Of those lakes that changed, we found differences in the geospatial variables that were most related to the observed change in the response variables. For example, TN and TN:TP trends were related to region-level drivers associated with atmospheric deposition of N; TP trends were related to both lake and region-level drivers associated with climate and land use; and Chl trends were found in regions with high air temperature at the beginning of the study period. We conclude that despite large environmental change and management efforts over recent decades, water quality of lakes in the Midwest and Northeast U.S. has not overwhelmingly degraded or improved.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13810","usgsCitation":"Oliver, S., Collins, S.M., Soranno, P.A., Wagner, T., Stanley, E.H., Jones, J., Stow, C., and Lottig, N.R., 2017, Unexpected stasis in a changing world: Lake nutrient and chlorophyll trends since 1990: Global Change Biology, v. 23, no. 12, p. 5455-5467, https://doi.org/10.1111/gcb.13810.","productDescription":"13 p.","startPage":"5455","endPage":"5467","ipdsId":"IP-081858","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":469318,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.13810","text":"Publisher Index Page"},{"id":348839,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"23","issue":"12","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-23","publicationStatus":"PW","scienceBaseUri":"5a60fb13e4b06e28e9c22be5","contributors":{"authors":[{"text":"Oliver, Samantha K.","contributorId":169273,"corporation":false,"usgs":false,"family":"Oliver","given":"Samantha K.","affiliations":[],"preferred":false,"id":719279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collins, Sarah M.","contributorId":172181,"corporation":false,"usgs":false,"family":"Collins","given":"Sarah","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":719280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soranno, Patricia A.","contributorId":172104,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":719281,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":719278,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":719282,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, John R.","contributorId":48459,"corporation":false,"usgs":false,"family":"Jones","given":"John R.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":719283,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stow, Craig A.","contributorId":49733,"corporation":false,"usgs":true,"family":"Stow","given":"Craig A.","affiliations":[],"preferred":false,"id":719284,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lottig, Noah R.","contributorId":172031,"corporation":false,"usgs":false,"family":"Lottig","given":"Noah","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":719285,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70193548,"text":"70193548 - 2017 - Placement of intracoelomic radio transmitters and silicone passive sampling devices in northern leopard frogs (Lithobates pipiens)","interactions":[],"lastModifiedDate":"2017-12-11T13:04:48","indexId":"70193548","displayToPublicDate":"2017-11-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5551,"text":"Journal of Herpetological Medicine and Surgery","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Placement of intracoelomic radio transmitters and silicone passive sampling devices in northern leopard frogs (Lithobates pipiens)","title":"Placement of intracoelomic radio transmitters and silicone passive sampling devices in northern leopard frogs (Lithobates pipiens)","docAbstract":"Historically, wetland toxin exposure studies have relied on single time point samples from stationary sampling devices. Development of passive sampling devices (PSDs) that can be attached to individual animals within wetland habitats has greatly improved in recent years, presenting an innovative sampling technology that can potentially yield individual-specific, quantifiable data about chemical exposure. In this study, silicone based PSDs were attached to the ventral skin of 20 northern leopard frogs (Lithobates pipiens) with polypropylene sutures after radio transmitters had been surgically implanted into the coleomic cavity. After a recovery period frogs were released back into the wetland habitat where they were acquired. The animals were located daily using radio telemetry to assess how long PSDs would remain attached in the frogs' natural habitat. After one week, PSDs remained on 18 of the original 20 frogs. At 2 weeks 17 frogs were recovered and no PSDs remained attached. Although valuable data can be obtained over a short time period, more research will be necessary to demonstrate effectiveness of externally attaching silicone PSDs to northern leopard frogs for time periods longer than 1-2 weeks.
","language":"English","publisher":"ARAV","doi":"10.5818/16-10-090.1","usgsCitation":"Yaw, T., Swanson, J.E., Pierce, C., Muths, E.L., Smalling, K.L., Vandever, M.W., and Zaffarano, B.A., 2017, Placement of intracoelomic radio transmitters and silicone passive sampling devices in northern leopard frogs (Lithobates pipiens): Journal of Herpetological Medicine and Surgery, v. 27, no. 3-4, p. 111-115, https://doi.org/10.5818/16-10-090.1.","productDescription":"5 p.","startPage":"111","endPage":"115","ipdsId":"IP-079504","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":469317,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/vcs_pubs/21","text":"External Repository"},{"id":348798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"3-4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb13e4b06e28e9c22bde","contributors":{"authors":[{"text":"Yaw, Taylor","contributorId":200355,"corporation":false,"usgs":false,"family":"Yaw","given":"Taylor","affiliations":[],"preferred":false,"id":722030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swanson, Jennifer E.","contributorId":140894,"corporation":false,"usgs":false,"family":"Swanson","given":"Jennifer","email":"","middleInitial":"E.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":722031,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pierce, Clay 0000-0001-5088-5431 cpierce@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-5431","contributorId":150492,"corporation":false,"usgs":true,"family":"Pierce","given":"Clay","email":"cpierce@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719328,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":719331,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smalling, Kelly L. 0000-0002-1214-4920 ksmall@usgs.gov","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":190789,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","email":"ksmall@usgs.gov","middleInitial":"L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":719329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vandever, Mark W. 0000-0003-0247-2629 vandeverm@usgs.gov","orcid":"https://orcid.org/0000-0003-0247-2629","contributorId":197674,"corporation":false,"usgs":true,"family":"Vandever","given":"Mark","email":"vandeverm@usgs.gov","middleInitial":"W.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":719330,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zaffarano, Bianca Anne","contributorId":200354,"corporation":false,"usgs":false,"family":"Zaffarano","given":"Bianca","email":"","middleInitial":"Anne","affiliations":[],"preferred":false,"id":722029,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193557,"text":"70193557 - 2017 - Novel RAD sequence data reveal a lack of genomic divergence between dietary ecotypes in a landlocked salmonid population","interactions":[],"lastModifiedDate":"2018-06-19T10:01:26","indexId":"70193557","displayToPublicDate":"2017-11-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1325,"text":"Conservation Genetics Resources","active":true,"publicationSubtype":{"id":10}},"title":"Novel RAD sequence data reveal a lack of genomic divergence between dietary ecotypes in a landlocked salmonid population","docAbstract":"Preservation of heritable ecological diversity within species and populations is a key challenge for managing natural resources and wild populations. Salmonid fish are iconic and socio-economically important species for commercial, aquaculture, and recreational fisheries across the globe. Many salmonids are known to exhibit ecological divergence within species, including distinct feeding ecotypes within the same lakes. Here we used 5559 SNPs, derived from RAD sequencing, to perform population genetic comparisons between two dietary ecotypes of sockeye salmon (Oncorhynchus nerka) in Jo-Jo Lake, Alaska (USA). We tested the standing hypothesis that these two ecotypes are currently diverging as a result of adaptation to distinct dietary niches; results support earlier conclusions of a single panmictic population. The RAD sequence data revealed 40 new SNPs not previously detected in the species, and our sequence data can be used in future studies of ecotypic diversity in salmonid species.
","language":"English","publisher":"Springer","doi":"10.1007/s12686-017-0791-4","usgsCitation":"Limborg, M.T., Larson, W., Shedd, K., Seeb, L.W., and Seeb, J.E., 2017, Novel RAD sequence data reveal a lack of genomic divergence between dietary ecotypes in a landlocked salmonid population: Conservation Genetics Resources, v. 10, no. 2, p. 169-171, https://doi.org/10.1007/s12686-017-0791-4.","productDescription":"3 p.","startPage":"169","endPage":"171","ipdsId":"IP-084201","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348770,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Jo-Jo Lake","volume":"10","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-30","publicationStatus":"PW","scienceBaseUri":"5a60fb13e4b06e28e9c22bda","contributors":{"authors":[{"text":"Limborg, Morten T.","contributorId":199510,"corporation":false,"usgs":false,"family":"Limborg","given":"Morten","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":722018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719356,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shedd, Kyle","contributorId":200347,"corporation":false,"usgs":false,"family":"Shedd","given":"Kyle","email":"","affiliations":[],"preferred":false,"id":722019,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seeb, Lisa W.","contributorId":66008,"corporation":false,"usgs":false,"family":"Seeb","given":"Lisa","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":722020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seeb, James E.","contributorId":87003,"corporation":false,"usgs":true,"family":"Seeb","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":722021,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193727,"text":"fs20173083 - 2017 - Streamflow conditions along Soldier Creek, Northeast Kansas","interactions":[],"lastModifiedDate":"2017-11-14T11:08:04","indexId":"fs20173083","displayToPublicDate":"2017-11-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3083","title":"Streamflow conditions along Soldier Creek, Northeast Kansas","docAbstract":"The availability of adequate water to meet the present (2017) and future needs of humans, fish, and wildlife is a fundamental issue for the Prairie Band Potawatomi Nation in northeast Kansas. Because Soldier Creek flows through the Prairie Band Potawatomi Nation Reservation, it is an important tribal resource. An understanding of historical Soldier Creek streamflow conditions is required for the effective management of tribal water resources, including drought contingency planning. Historical data for six selected U.S. Geological Survey (USGS) streamgages along Soldier Creek were used in an assessment of streamflow characteristics and trends by Juracek (2017). Streamflow data for the period of record at each streamgage were used to compute annual mean streamflow, annual mean base flow, mean monthly flow, annual peak flow, and annual minimum flow. Results of the assessment are summarized in this fact sheet.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173083","usgsCitation":"Juracek, K.E., 2017, Streamflow conditions along Soldier Creek, Northeast Kansas: U.S. Geological Survey Fact Sheet 2017–3083, 4 p., https://doi.org/10.3133/fs20173083.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-091639","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":348318,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3083/coverthb.jpg"},{"id":348319,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3083/fs20173083.pdf","text":"Report","size":"3.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3083"},{"id":348320,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20175061","text":"SIR 2017–5061","description":"SIR 2017–5061","linkHelpText":"Streamflow characteristics and trends along Soldier Creek, Northeast Kansas"}],"country":"United States","state":"Kansas","otherGeospatial":"Soldier Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.0040283203125,\n 39.08210619353512\n ],\n [\n -95.60440063476562,\n 39.08210619353512\n ],\n [\n -95.60440063476562,\n 39.67019926771586\n ],\n [\n -96.0040283203125,\n 39.67019926771586\n ],\n [\n -96.0040283203125,\n 39.08210619353512\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
In this article we describe eDNAoccupancy, an R package for fitting Bayesian, multi-scale occupancy models. These models are appropriate for occupancy surveys that include three, nested levels of sampling: primary sample units within a study area, secondary sample units collected from each primary unit, and replicates of each secondary sample unit. This design is commonly used in occupancy surveys of environmental DNA (eDNA). eDNAoccupancy allows users to specify and fit multi-scale occupancy models with or without covariates, to estimate posterior summaries of occurrence and detection probabilities, and to compare different models using Bayesian model-selection criteria. We illustrate these features by analyzing two published data sets: eDNA surveys of a fungal pathogen of amphibians and eDNA surveys of an endangered fish species.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.12735","usgsCitation":"Dorazio, R., and Erickson, R.A., 2017, eDNAoccupancy: An R package for multi-scale occupancy modeling of environmental DNA data: Molecular Ecology Resources, v. 18, no. 2, p. 368-380, https://doi.org/10.1111/1755-0998.12735.","productDescription":"13 p.","startPage":"368","endPage":"380","ipdsId":"IP-087512","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469322,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1755-0998.12735","text":"Publisher Index Page"},{"id":438153,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q23Z67","text":"USGS data release","linkHelpText":"eDNAoccupancy"},{"id":348737,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350191,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://my.usgs.gov/bitbucket/projects/USGS_WARC/repos/ednaoccupancy/browse","description":"Software release"}],"volume":"18","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-02","publicationStatus":"PW","scienceBaseUri":"5a60fb13e4b06e28e9c22beb","contributors":{"authors":[{"text":"Dorazio, Robert 0000-0003-2663-0468 bob_dorazio@usgs.gov","orcid":"https://orcid.org/0000-0003-2663-0468","contributorId":172151,"corporation":false,"usgs":true,"family":"Dorazio","given":"Robert","email":"bob_dorazio@usgs.gov","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":721882,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":721883,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193073,"text":"sir20175127 - 2017 - Flood-inundation maps for North Fork Salt Creek at Nashville, Indiana","interactions":[],"lastModifiedDate":"2017-11-14T11:17:30","indexId":"sir20175127","displayToPublicDate":"2017-11-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5127","title":"Flood-inundation maps for North Fork Salt Creek at Nashville, Indiana","docAbstract":"Digital flood-inundation maps for a 3.2-mile reach of North Fork Salt Creek at Nashville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding that correspond to selected water levels (stages) at the North Fork Salt Creek at Nashville, Ind., streamgage (USGS station number 03371650). Real-time stages at this streamgage may be obtained from the USGS National Water Information System at http://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also shows observed USGS stages at the same site as the USGS streamgage (NWS site NFSI3).
Flood profiles were computed for the stream reach by means of a one-dimensional, step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated using the current (2015) stage-discharge rating at the USGS streamgage 03371650, North Fork Salt Creek at Nashville, Ind. The hydraulic model was then used to compute 12 water-surface profiles for flood stages at 1-foot (ft) intervals, except for the highest profile of 22.9 ft, referenced to the streamgage datum ranging from 12.0 ft (the NWS “action stage”) to 22.9 ft, which is the highest stage of the current (2015) USGS stage-discharge rating curve and 1.9 ft higher than the NWS “major flood stage.” The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.98-ft vertical accuracy and 4.9-ft horizontal resolution) to delineate the area flooded at each stage.
The availability of these maps, along with information regarding current stage from the USGS streamgage, will provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175127","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Martin, Z.W., 2017, Flood-inundation maps for North Fork Salt Creek at Nashville, Indiana: U.S. Geological Survey Scientific Investigations Report 2017–5127, 10 p., https://doi.org/10.3133/sir20175127.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087895","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":348732,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5127/sir20175127.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5127"},{"id":348733,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ316V","text":"USGS data release","description":"USGS Data Release","linkHelpText":"North Fork Salt Creek at Nashville, Indiana, flood-inundation model and GIS data"},{"id":348731,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5127/coverthb.jpg"}],"country":"United States","state":"Indiana","city":"Nashville","otherGeospatial":"North Fork Salt Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.26336097717285,\n 39.190954761524445\n ],\n [\n -86.22027397155762,\n 39.190954761524445\n ],\n [\n -86.22027397155762,\n 39.213036788153914\n ],\n [\n -86.26336097717285,\n 39.213036788153914\n ],\n [\n -86.26336097717285,\n 39.190954761524445\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard,
Indianapolis, IN 46278–1996
In 1964, E.H. Hammond proposed criteria for classifying and mapping physiographic regions of the United States. Hammond produced a map entitled “Classes of Land Surface Form in the Forty-Eight States, USA”, which is regarded as a pioneering and rigorous treatment of regional physiography. Several researchers automated Hammond?s model in GIS. However, these were local or regional in application, and resulted in inadequate characterization of tablelands. We used a global 250 m DEM to produce a new characterization of global Hammond landform regions. The improved algorithm we developed for the regional landform modeling: (1) incorporated a profile parameter for the delineation of tablelands; (2) accommodated negative elevation data values; (3) allowed neighborhood analysis window (NAW) size to vary between parameters; (4) more accurately bounded plains regions; and (5) mapped landform regions as opposed to discrete landform features. The new global Hammond landform regions product builds on an existing global Hammond landform features product developed by the U.S. Geological Survey, which, while globally comprehensive, did not include tablelands, used a fixed NAW size, and essentially classified pixels rather than regions. Our algorithm also permits the disaggregation of “mixed” Hammond types (e.g. plains with high mountains) into their component parts.
","language":"English","publisher":"Wiley","doi":"10.1111/tgis.12265","usgsCitation":"Karagulle, D., Frye, C., Sayre, R., Breyer, S.P., Aniello, P., Vaughan, R., and Wright, D.J., 2017, Modeling global Hammond landform regions from 250-m elevation data: Transactions in GIS, v. 21, no. 5, p. 1040-1060, https://doi.org/10.1111/tgis.12265.","productDescription":"21 p.","startPage":"1040","endPage":"1060","ipdsId":"IP-075528","costCenters":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"links":[{"id":348680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-12","publicationStatus":"PW","scienceBaseUri":"5a60fb14e4b06e28e9c22bf4","contributors":{"authors":[{"text":"Karagulle, Deniz","contributorId":200267,"corporation":false,"usgs":false,"family":"Karagulle","given":"Deniz","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frye, Charlie","contributorId":191631,"corporation":false,"usgs":false,"family":"Frye","given":"Charlie","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sayre, Roger 0000-0001-6703-7105 rsayre@usgs.gov","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":191629,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","email":"rsayre@usgs.gov","affiliations":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":721741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breyer, Sean P.","contributorId":191634,"corporation":false,"usgs":false,"family":"Breyer","given":"Sean","email":"","middleInitial":"P.","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721744,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aniello, Peter","contributorId":191633,"corporation":false,"usgs":false,"family":"Aniello","given":"Peter","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721745,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vaughan, Randy","contributorId":191632,"corporation":false,"usgs":false,"family":"Vaughan","given":"Randy","email":"","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721746,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wright, Dawn J.","contributorId":191639,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","email":"","middleInitial":"J.","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721778,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193967,"text":"70193967 - 2017 - A three-dimensional mapping of the ocean based on environmental data","interactions":[],"lastModifiedDate":"2018-01-19T17:59:46","indexId":"70193967","displayToPublicDate":"2017-11-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2929,"text":"Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"A three-dimensional mapping of the ocean based on environmental data","docAbstract":"The existence, sources, distribution, circulation, and physicochemical nature of macroscale oceanic water bodies have long been a focus of oceanographic inquiry. Building on that work, this paper describes an objectively derived and globally comprehensive set of 37 distinct volumetric region units, called ecological marine units (EMUs). They are constructed on a regularly spaced ocean point-mesh grid, from sea surface to seafloor, and attributed with data from the 2013 World Ocean Atlas version 2. The point attribute data are the means of the decadal averages from a 57-year climatology of six physical and chemical environment parameters (temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate). The database includes over 52 million points that depict the global ocean in x, y, and z dimensions. The point data were statistically clustered to define the 37 EMUs, which represent physically and chemically distinct water volumes based on spatial variation in the six marine environmental characteristics used. The aspatial clustering to produce the 37 EMUs did not include point location or depth as a determinant, yet strong geographic and vertical separation was observed. Twenty-two of the 37 EMUs are globally or regionally extensive, and account for 99% of the ocean volume, while the remaining 15 are smaller and shallower, and occur around coastal features. We assessed the vertical distribution of EMUs in the water column and placed them into classical depth zones representing epipelagic (0 m to 200 m), mesopelagic (200 m to 1,000 m), bathypelagic (1,000 m to 4,000 m) and abyssopelagic (>4,000 m) layers. The mapping and characterization of the EMUs represent a new spatial framework for organizing and understanding the physical, chemical, and ultimately biological properties and processes of oceanic water bodies. The EMUs are an initial objective partitioning of the ocean using long-term historical average data, and could be extended in the future by adding new classification variables and by introducing functionality to develop time-specific EMU distribution maps. The EMUs are an open-access resource, and as both a standardized geographic framework and a baseline physicochemical characterization of the oceanic environment, they are intended to be useful for disturbance assessments, ecosystem accounting exercises, conservation priority setting, and marine protected area network design, along with other research and management applications.
","language":"English","publisher":"The Oceanography Society","doi":"10.5670/oceanog.2017.116","usgsCitation":"Sayre, R., Wright, D.J., Breyer, S.P., Butler, K., Van Graafeiland, K., Costello, M.J., Harris, P.T., Goodin, K., Guinotte, J.M., Basher, Z., Kavanaugh, M.T., Halpin, P.N., Monaco, M.E., Cressie, N., Aniello, P., Frye, C., and Stephens, D., 2017, A three-dimensional mapping of the ocean based on environmental data: Oceanography, v. 30, no. 1, p. 90-103, https://doi.org/10.5670/oceanog.2017.116.","productDescription":"14 p.","startPage":"90","endPage":"103","ipdsId":"IP-082069","costCenters":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"links":[{"id":461345,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5670/oceanog.2017.116","text":"Publisher Index Page"},{"id":348684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb14e4b06e28e9c22bf1","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105 rsayre@usgs.gov","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":191629,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","email":"rsayre@usgs.gov","affiliations":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":721752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wright, Dawn J.","contributorId":191639,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","email":"","middleInitial":"J.","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breyer, Sean P.","contributorId":191634,"corporation":false,"usgs":false,"family":"Breyer","given":"Sean","email":"","middleInitial":"P.","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Butler, Kevin","contributorId":200270,"corporation":false,"usgs":false,"family":"Butler","given":"Kevin","email":"","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721755,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van Graafeiland, Keith","contributorId":200271,"corporation":false,"usgs":false,"family":"Van Graafeiland","given":"Keith","email":"","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721756,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":721757,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harris, Peter T.","contributorId":200273,"corporation":false,"usgs":false,"family":"Harris","given":"Peter","email":"","middleInitial":"T.","affiliations":[{"id":35732,"text":"GRID-Arendal","active":true,"usgs":false}],"preferred":false,"id":721758,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goodin, Kathleen","contributorId":200274,"corporation":false,"usgs":false,"family":"Goodin","given":"Kathleen","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":721759,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Guinotte, John M.","contributorId":200275,"corporation":false,"usgs":false,"family":"Guinotte","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":721760,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Basher, Zeenatul 0000-0002-6439-8324 zbasher@usgs.gov","orcid":"https://orcid.org/0000-0002-6439-8324","contributorId":48118,"corporation":false,"usgs":true,"family":"Basher","given":"Zeenatul","email":"zbasher@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":721761,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kavanaugh, Maria T.","contributorId":200277,"corporation":false,"usgs":false,"family":"Kavanaugh","given":"Maria","email":"","middleInitial":"T.","affiliations":[{"id":13294,"text":"Woods Hole Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":721762,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Halpin, Patrick N.","contributorId":200278,"corporation":false,"usgs":false,"family":"Halpin","given":"Patrick","email":"","middleInitial":"N.","affiliations":[{"id":12868,"text":"Nicholas School of the Environment, Duke University, Durham, NC, USA","active":true,"usgs":false}],"preferred":false,"id":721763,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Monaco, Mark E.","contributorId":200279,"corporation":false,"usgs":false,"family":"Monaco","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":12448,"text":"U.S. National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":721764,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Cressie, Noel","contributorId":200280,"corporation":false,"usgs":false,"family":"Cressie","given":"Noel","email":"","affiliations":[{"id":16754,"text":"University of Wollongong, Australia","active":true,"usgs":false}],"preferred":false,"id":721765,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Aniello, Peter","contributorId":200281,"corporation":false,"usgs":false,"family":"Aniello","given":"Peter","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":721766,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Frye, Charles","contributorId":200282,"corporation":false,"usgs":false,"family":"Frye","given":"Charles","email":"","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721767,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Stephens, Drew","contributorId":200283,"corporation":false,"usgs":false,"family":"Stephens","given":"Drew","email":"","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":721768,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70194130,"text":"70194130 - 2017 - No evidence of critical slowing down in two endangered Hawaiian honeycreepers","interactions":[],"lastModifiedDate":"2018-01-03T13:08:43","indexId":"70194130","displayToPublicDate":"2017-11-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"No evidence of critical slowing down in two endangered Hawaiian honeycreepers","docAbstract":"There is debate about the current population trends and predicted short-term fates of the endangered forest birds, Hawai`i Creeper (Loxops mana) and Hawai`i `Ākepa (L. coccineus). Using long-term population size estimates, some studies report forest bird populations as stable or increasing, while other studies report signs of population decline or impending extinction associated with introduced Japanese White-eye (Zosterops japonicus) increase. Reliable predictors of impending population collapse, well before the collapse begins, have been reported in simulations and microcosm experiments. In these studies, statistical indicators of critical slowing down, a phenomenon characterized by longer recovery rates after population size perturbation, are reported to be early warning signals of an impending regime shift observable prior to the tipping point. While the conservation applications of these metrics are commonly discussed, early warning signal detection methods are rarely applied to population size data from natural populations, so their efficacy and utility in species management remain unclear. We evaluated two time series of state-space abundance estimates (1987–2012) from Hakalau Forest National Wildlife Refuge, Hawai`i to test for evidence of early warning signals of impending population collapse for the Hawai`i Creeper and Hawai`i `Ākepa. We looked for signals throughout the time series, and prior to 2000, when white-eye abundance began increasing. We found no evidence for either species of increasing variance, autocorrelation, or skewness, which are commonly reported early warning signals. We calculated linear rather than ordinary skewness because the latter is biased, particularly for small sample sizes. Furthermore, we identified break-points in trends over time for both endangered species, indicating shifts in slopes away from strongly increasing trends, but they were only weakly supported by Bayesian change-point analyses (i.e., no step-wise changes in abundance). The break-point and change-point test results, in addition to the early warning signal analyses, support that the two populations do not appear to show signs of critical slowing down or decline.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0187518","usgsCitation":"Rozek, J.C., Camp, R.J., and Reed, J.M., 2017, No evidence of critical slowing down in two endangered Hawaiian honeycreepers: PLoS ONE, v. 12, no. 11, p. 1-18, https://doi.org/10.1371/journal.pone.0187518.","productDescription":"e0187518; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-079982","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":469324,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0187518","text":"Publisher Index Page"},{"id":349028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","city":"Hakalau","volume":"12","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-13","publicationStatus":"PW","scienceBaseUri":"5a60fb13e4b06e28e9c22be8","contributors":{"authors":[{"text":"Rozek, Jessica C. 0000-0002-8099-8557","orcid":"https://orcid.org/0000-0002-8099-8557","contributorId":200428,"corporation":false,"usgs":false,"family":"Rozek","given":"Jessica","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":722281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":116175,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":false,"id":722280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, J. Michael","contributorId":198605,"corporation":false,"usgs":false,"family":"Reed","given":"J.","email":"","middleInitial":"Michael","affiliations":[],"preferred":false,"id":722282,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193974,"text":"70193974 - 2017 - Benefits of the fire mitigation ecosystem service in the Great Dismal Swamp National Wildlife Refuge, Virginia, USA","interactions":[],"lastModifiedDate":"2017-11-14T11:35:53","indexId":"70193974","displayToPublicDate":"2017-11-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Benefits of the fire mitigation ecosystem service in the Great Dismal Swamp National Wildlife Refuge, Virginia, USA","docAbstract":" The Great Dismal Swamp (GDS) National Wildlife Refuge delivers multiple ecosystem services, including air quality and human health via fire mitigation. Our analysis estimates benefits of this service through its potential to reduce catastrophic wildfire related impacts on the health of nearby human populations. We used a combination of high-frequency satellite data, ground sensors, and air quality indices to determine periods of public exposure to dense emissions from a wildfire within the GDS. We examined emergency department (ED) visitation in seven Virginia counties during these periods, applied measures of cumulative Relative Risk to derive the effects of wildfire smoke exposure on ED visitation rates, and estimated economic losses using regional Cost of Illness values established within the US Environmental Protection Agency BenMAP framework. Our results estimated the value of one avoided catastrophic wildfire in the refuge to be \\$3.69 million (2015 USD), or \\$306 per hectare of burn. Reducing the frequency or severity of extensive, deep burning peatland wildfire events has additional benefits not included in this estimate, including avoided costs related to fire suppression during a burn, carbon dioxide emissions, impacts to wildlife, and negative outcomes associated with recreation and regional tourism. We suggest the societal value of the public health benefits alone provides a significant incentive for refuge mangers to implement strategies that will reduce the severity of catastrophic wildfires.