Larval fish ecology is poorly characterized because sampling is difficult and tools for phenotypically identifying larvae are poorly developed. While DNA barcoding can help address the latter problem, ‘universal’ primers do not work for all fish species. The Roanoke River in the southeastern United States includes seven darters (Family Percide: Tribe Etheostomatini). We made 393 collections of larval fishes in 2015 and 2018, examined darter larvae for morphometric and pigmentation traits, developed PCR primers amplifying darter DNA, and evaluated three gear types for collecting larval darters. Amplified DNA sequences for 1351 larvae matched archived mitochondrial cytochrome oxidase I sequences for darters occurring in the ecosystem. Larval darters were classified to genus with 100% accuracy using the ratio of pectoral fin length to body length; however, identification to species using morphometrics alone was subject to a misclassification rate of 11.8%, which can be resolved by considering pigmentation patterns. Gear-types varied considerably in their capture efficacy for larval darters; most Percina larvae were collected in drift nets. Larval Percina species appeared in the drift before Etheostoma species in both study years. Application of molecular genetic and phenotypic tools to larval fish identification can advance understanding of larval darter ecology.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes4040059","usgsCitation":"Buckwalter, J., Angermeier, P.L., Argentina, J., Wolf, S., Floyd, S., and Hallerman, E., 2019, Drift of larval darters (Family Percidae) in the upper Roanoke River basin, USA, characterized using phenotypic and DNA barcoding markers: Fishes, v. 4, no. 4, 59, 16 p., https://doi.org/10.3390/fishes4040059.","productDescription":"59, 16 p.","ipdsId":"IP-113480","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":459002,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes4040059","text":"Publisher Index Page"},{"id":396021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"Roanoke River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.2279052734375,\n 36.09349937380574\n ],\n [\n -77.1075439453125,\n 36.09349937380574\n ],\n [\n -77.1075439453125,\n 37.274052809979054\n ],\n [\n -79.2279052734375,\n 37.274052809979054\n ],\n [\n -79.2279052734375,\n 36.09349937380574\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Buckwalter, Joseph","contributorId":279470,"corporation":false,"usgs":false,"family":"Buckwalter","given":"Joseph","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":834962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Angermeier, Paul L. 0000-0003-2864-170X biota@usgs.gov","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":166679,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul","email":"biota@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834961,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Argentina, Jane","contributorId":279471,"corporation":false,"usgs":false,"family":"Argentina","given":"Jane","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":834963,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wolf, Skylar","contributorId":279472,"corporation":false,"usgs":false,"family":"Wolf","given":"Skylar","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":834964,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Floyd, Stephen","contributorId":279473,"corporation":false,"usgs":false,"family":"Floyd","given":"Stephen","email":"","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":834965,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hallerman, Eric M.","contributorId":279474,"corporation":false,"usgs":false,"family":"Hallerman","given":"Eric M.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":834966,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212557,"text":"70212557 - 2019 - Using incidental mark-encounter data to improve survival estimation","interactions":[],"lastModifiedDate":"2020-08-20T13:31:25.026173","indexId":"70212557","displayToPublicDate":"2019-12-08T08:26:08","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Using incidental mark-encounter data to improve survival estimation","docAbstract":"No abstract available.
","language":"English","publisher":"AAAS","doi":"10.1126/science.aaz9989","usgsCitation":"Katzner, T., Nelson, D.M., Diffendorfer, J.E., Duerr, A.E., Campbell, C.J., Leslie, D., Vander Zanden, H.B., Yee, J.L., Sur, M., Huso, M.M., Braham, M.A., Morrison, M.L., Loss, S., Poessel, S.A., Conkling, T., and Miller, T., 2019, Wind energy: An ecological challenge: Science, v. 366, no. 6470, p. 1206-1207, https://doi.org/10.1126/science.aaz9989.","productDescription":"2 p.","startPage":"1206","endPage":"1207","ipdsId":"IP-114022","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research 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Glacier retreat in Glacier National Park, Montana, U.S.A., is one example of land ice loss and glacier change. The U.S. Geological Survey Benchmark Glacier Project conducts glaciological research and collects field measurements across select North American glaciers, including Sperry Glacier located in Glacier National Park. These records provide direct evidence of glacier change. The amount and timing of Earth’s land ice that will be lost in the future depends on the future trajectory of greenhouse gas emissions. Ongoing glaciological research will enhance physical understanding and forecasting capabilities; however, the continued retreat and ice mass loss of small glaciers in Glacier National Park is certain.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193068","usgsCitation":"Florentine, C., 2019, Glacier retreat in Glacier National Park, Montana (ver. 1.1, December 2019): U.S. Geological Survey Fact Sheet 2019–3068, 2 p., https://doi.org/10.3133/fs20193068.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-110825","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":399143,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109486.htm"},{"id":370057,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2019/3068/versionHist.txt","text":"Version History","linkFileType":{"id":2,"text":"txt"},"description":"FS 2019-3068 Version History"},{"id":369842,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3068/coverthb2.jpg"},{"id":369843,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3068/fs20193068.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3068"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.01586914062499,\n 48.070738264258296\n ],\n [\n -112.54394531249999,\n 48.070738264258296\n ],\n [\n -112.54394531249999,\n 48.97661158387714\n ],\n [\n -115.01586914062499,\n 48.97661158387714\n ],\n [\n -115.01586914062499,\n 48.070738264258296\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1; December 6, 2019","contact":"Director, Northern Rocky Mountain Science Center
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2327 University Way, Suite 2
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The Arctic Alaska Province encompasses all lands and adjacent continental shelf areas north of the Brooks Range-Herald Arch tectonic belts and south of the northern (outboard) margin of the Alaska rift shoulder. Even though only a small part is thoroughly explored, it is one of the most prolific petroleum provinces in North America, with total known resources (cumulative production plus proved reserves) of about 28 billion barrels of oil equivalent.
For assessment purposes, the province is divided into a platform assessment unit, comprising the Alaska rift shoulder and its relatively undeformed flanks, and a fold-and-thrust belt assessment unit, comprising the deformed area north of the Brooks Range and Herald Arch tectonic belts. Mean estimates of undiscovered, technically recoverable resources include nearly 28 billion barrels of oil and 122 trillion cubic feet of nonassociated gas in the platform assessment unit and 2 billion barrels of oil and 59 trillion cubic feet of nonassociated gas in the fold-and-thrust belt assessment unit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824E","usgsCitation":"Houseknecht, D.W., Bird, K.J., and Garrity, C.P., 2019, Geology and assessment of undiscovered oil and gas resources of the Arctic Alaska Province, 2008, chap. E of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 25 p., https://doi.org/10.3133/pp1824E. [Supersedes USGS Scientific Investigations Report 2012–5147.]","productDescription":"Report: viii, 25 p.; 2 Appendixes","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114210","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":370053,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/e/pp1824e_appendix1.xlsx","text":"Appendix 1","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter E Appendix 1"},{"id":370052,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/e/pp1824e.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824 Chapter E"},{"id":370051,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/e/coverthb.jpg"},{"id":399506,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109507.htm"},{"id":370054,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/e/pp1824e_appendix2.xlsx","text":"Appendix 2","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter E Appendix 2"}],"country":"Canada, United States","state":"Alaska, Yukon","otherGeospatial":"Arctic Alaska Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -136.0986328125,\n 68.89518688943544\n ],\n [\n -137.3291015625,\n 69.54987728327795\n ],\n [\n -156.7529296875,\n 71.37110941823617\n ],\n [\n -159.5654296875,\n 70.85908719717143\n ],\n [\n -161.015625,\n 70.27428967614655\n ],\n [\n -162.1142578125,\n 70.28911664330674\n ],\n [\n -164.3994140625,\n 68.98992503056704\n ],\n [\n -166.11328125,\n 68.942606818121\n ],\n [\n -166.5087890625,\n 68.38299634059615\n ],\n [\n -162.7294921875,\n 66.80922097449334\n ],\n [\n -161.19140625,\n 66.73990169639414\n ],\n [\n -154.599609375,\n 66.75724984139227\n ],\n [\n -149.4140625,\n 66.7745857647255\n ],\n [\n -143.61328125,\n 68.15520923883976\n ],\n [\n -141.1083984375,\n 68.13885164925573\n ],\n [\n -136.6259765625,\n 67.69277095059344\n ],\n [\n -135.3076171875,\n 68.15520923883976\n ],\n [\n -136.0986328125,\n 68.89518688943544\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
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345 Middlefield Road
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Five integrator sites on streams draining mixed land-use watersheds were sampled monthly from July 2016 to June 2017. Two sites were sampled in the San Joaquin River watershed and one site was sampled in each of the Mokelumne River, Sacramento River, and Ulatis Creek watersheds.
A total of 53 out of 154 pesticides (18 herbicides, 14 insecticides, 13 fungicides, 7 breakdown products, and 1 synergist) were detected in surface-water samples and 95 percent of samples contained mixtures of 2 or more pesticides. The most frequently detected pesticides were the herbicides hexazinone, metolachlor, and diuron (present in 83 percent, 72 percent, and 67 percent of water samples, respectively), the insecticide methoxyfenozide (present in 83 percent of samples), and the fungicides boscalid and azoxystrobin (present in 67 percent and 58 percent of samples, respectively). Pesticide concentrations detected in water samples ranged from below method detection limits to 1,300 nanograms per liter (ng/L) for the insecticide chlorantraniliprole. A total of 4 pesticides (2 herbicides and 2 insecticides) were detected in suspended-sediment samples and 13 percent of suspended-sediment samples contained at least 1 pesticide. Pesticide concentrations detected in suspended-sediment samples ranged from 4.1 to 750 ng/L, both for the herbicide pendimethalin.
Six samples contained the insecticide imidacloprid at concentrations above the U.S. Environmental Protection Agency (EPA) Aquatic Life Benchmark (10 ng/L) for chronic toxicity to aquatic invertebrates. Three samples contained bifenthrin at concentrations above the EPA Aquatic Life Benchmark (1.3 ng/L) for chronic toxicity to invertebrates. One sample contained cyhalothrin at a concentration above the U.S. Aquatic Life Benchmark (3.5 ng/L) for acute toxicity to invertebrates.
Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The largest earthquakes recorded in northern Alaska (Mw 6.4 and Mw 6.0) occurred ~6 hours apart on August 12, 2018 in the northeastern Brooks Range. The earthquakes were captured by Sentinel-1 InSAR satellites and Earthscope Transportable Array seismic data, giving insight into the little-known active tectonic processes of Arctic Alaska, obscured until recently by sparse data availability. In this study, InSAR modelling, teleseismic back projections, calibrated hypocentral relocations and regional moment tensor solutions resolve two previously unknown, SSW-dipping right-lateral fault segments. These are the first active faults identified as conjugate to the NE-trending sinistral Canning Displacement Zone directly to the west, which is therefore a more complex zone of diffuse faulting than previously thought. The northeastern Brooks Range has been characterized as an area of low to moderate seismic hazard, but these earthquakes illustrate the potential for larger, possibly destructive events in a region earmarked for rapid resource development.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL085651","usgsCitation":"Gaudreau, E., Nissen, E., Bergman, E.A., Benz, H.M., Tan, F., and Karasözen, E., 2019, The August 2018 Kaktovik earthquakes: Active tectonics in northeastern Alaska revealed With InSAR and seismology: Geophysical Research Letters, v. 46, no. 24, p. 14412-14420, https://doi.org/10.1029/2019GL085651.","productDescription":"9 p.","startPage":"14412","endPage":"14420","ipdsId":"IP-113641","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":370590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -167.51953124999997,\n 64.92354174306496\n ],\n [\n -140.9765625,\n 64.92354174306496\n ],\n [\n -140.9765625,\n 71.35706654962706\n ],\n [\n -167.51953124999997,\n 71.35706654962706\n ],\n [\n -167.51953124999997,\n 64.92354174306496\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"24","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Gaudreau, E.","contributorId":221460,"corporation":false,"usgs":false,"family":"Gaudreau","given":"E.","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":778301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nissen, E.K.","contributorId":221461,"corporation":false,"usgs":false,"family":"Nissen","given":"E.K.","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":778302,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergman, Eric A. 0000-0002-7069-8286","orcid":"https://orcid.org/0000-0002-7069-8286","contributorId":84513,"corporation":false,"usgs":false,"family":"Bergman","given":"Eric","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":778303,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":778304,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tan, F.","contributorId":221462,"corporation":false,"usgs":false,"family":"Tan","given":"F.","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":778305,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Karasözen, E.","contributorId":221463,"corporation":false,"usgs":false,"family":"Karasözen","given":"E.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":778306,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70206449,"text":"sir20195131 - 2019 - Flood-frequency comparison from 1995 to 2016 and trends in peak streamflow in Arkansas, water years 1930–2016","interactions":[],"lastModifiedDate":"2022-04-25T19:34:30.278239","indexId":"sir20195131","displayToPublicDate":"2019-12-05T14:04:12","publicationYear":"2019","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":"2019-5131","displayTitle":"Flood-Frequency Comparison from 1995 to 2016 and Trends in Peak Streamflow in Arkansas, Water Years 1930–2016","title":"Flood-frequency comparison from 1995 to 2016 and trends in peak streamflow in Arkansas, water years 1930–2016","docAbstract":"In 2016, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers and the Federal Emergency Management Agency, began a study in Arkansas to investigate possible increasing trends in annual peak streamflow data and the possible resulting increase in the annual exceedance probability flood (AEPF) predictions. Temporal trends of peak streamflow were investigated at 15 selected streamgages on unregulated streams in Arkansas having 30 or more years of peak streamflow data through the 2016 water year. For the period of record at each streamgage, the Mann-Kendall trend test indicated that 14 of the 15 streamgages had no statistically significant peak streamflow trends and 1 streamgage had a statistically significant decreasing peak streamflow trend. Visual examination of the locally estimated scatterplot smoothing technique trend lines of the peak streamflow data indicated a possible increasing peak streamflow trend at 8 of the 15 streamgages since the 1990s.
A sequential series analysis of the 1-percent AEPF at each of the 15 selected streamgages was completed by selecting an initial subset of the oldest peak streamflow data from each site to estimate the initial 1-percent AEPF. This initial peak streamflow data subset was subsequently appended with 10-year increments of additional peak streamflow data until the full period of peak streamflow data was analyzed. The maximum increase in the 1-percent AEPF was 113 percent, and the maximum decrease was 31.9 percent.
Percentage differences between the AEPFs derived from regional regression equations presented in the 1995 and 2016 Arkansas flood-frequency reports were compared. The average percentage differences for the 74 selected locations indicate that the 4-, 2-, 1-, and 0.2-percent AEPFs computed using the 2016 regional regression equations were higher by 3.52, 5.10, 8.59, and 13.31 percent, respectively (25-, 50-, 100-, and 500-year recurrence interval floods), than the same percentage AEPFs computed using the 1995 regional regression equations. The average percentage differences between the 1995 and 2016 AEPFs for the 10-percent AEPF (10-year recurrence interval flood) resulted in 2016 AEPF predictions being 0.41 percent higher. For the 50- and 20-percent AEPFs (2- and 5-year recurrence interval floods), the 2016 AEPFs were less than the 1995 AEPFs by 2.53 and 0.31 percent, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195131","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and Federal Emergency Management Agency","usgsCitation":"Ensminger, P.A., and Breaker, B.K., 2019, Flood-frequency comparison from 1995 to 2016 and trends in peak streamflow in Arkansas, water years 1930–2016: U.S. Geological Survey Scientific Investigations Report 2019–5131, 20 p., https://doi.org/10.3133/sir20195131.","productDescription":"vi, 20 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-087618","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":399610,"rank":3,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
640 Grassmere Park, Ste 100
Nashville, TN 37211
The Yorktown-Eastover aquifer system of the Virginia Eastern Shore consists of upper, middle, and lower confined aquifers overlain by correspondingly named confining units and underlain by the Saint Marys confining unit. Miocene- to Pliocene-age marine-shelf sediments observed in 205 boreholes include medium- to coarse-grained sand and shells that compose the aquifers and fine-grained sand, silt, and clay that compose the confining units. The upper confining unit also includes fine-grained and organic-rich back-barrier and estuarine sediments of Pleistocene age. An overlying surficial aquifer is composed mostly of Pleistocene-age nearshore sand and gravel with smaller amounts of cobbles and boulders.
In addition, Pleistocene-age sediments that fill three buried paleochannels are for the first time explicitly delineated here as distinct hydrogeologic units. Two aquifers are composed of medium- to coarse-grained fluvial sand and gravel, and an intervening confining unit is composed of fine-grained estuarine sand, silt, clay, and organic material. Aquifer and confining-unit sediments are also mixed with reworked marine-shelf sediments eroded from the sides of the paleochannels.
Hydrogeologic units of the Yorktown-Eastover aquifer system generally dip eastward, are as much as several tens of feet thick, and have an undulating configuration possibly resulting from the underlying Chesapeake Bay impact crater. Aquifers and confining units are incised by the three paleochannels along an upward-widening and eastward-lengthening series of structural “windows.” Hydrogeologic units within mainstems and branching tributaries of the paleochannels dip southeastward parallel to slopes of the paleochannels, are as much as several tens of feet thick, and laterally abut the Yorktown-Eastover aquifer system along paleochannel sidewalls. The Yorktown-Eastover aquifer system is thereby hydraulically breached by the paleochannels to alternately create barriers to or conduits for groundwater flow.
Results of previously documented aquifer tests at 58 wells indicate that transmissivity is generally greatest in young, shallow, and coarse-grained nearshore and fluvial sediments of the surficial aquifer and paleochannels. Transmissivity progressively decreases with depth in older, deeper, and finer grained marine-shelf sediments of the Yorktown-Eastover aquifer system, probably because they have undergone compaction as a result of greater overburden pressure over longer periods of time.
Compiled chloride concentrations in samples from 330 wells generally increase downward, with most of the samples collected at altitudes above −300 feet and with most concentrations less than 250 milligrams per liter. The saltwater-transition zone has a broad trough-like shape aligned with the peninsula, being relatively shallow along the coastline and deeper along the central “spine.” Because movement of the saltwater is slow, the configuration largely reflects groundwater flow prior to widespread groundwater withdrawals. Fresh groundwater has leaked downward along deep parts of the saltwater-transition zone and leaked upward along shallower parts to discharge at the coast.
The saltwater-transition zone also exhibits an anomalous ridge across the center of the peninsula. Groundwater levels indicate that the saltwater ridge formed primarily by the Exmore paleochannel acting as a large lateral collector drain. Groundwater levels were lowered, and the position of saltwater-transition zone was elevated, by a flow conduit that intercepted groundwater that otherwise would have flowed toward and discharged along the coastline.
Nearly all freshwater on the Virginia Eastern Shore is supplied by groundwater withdrawals, which have lowered water levels, altered hydraulic gradients, and created a concern for saltwater intrusion. Previous characterizations of groundwater conditions that are relied on to manage groundwater development have been limited by a lack of hydrogeologic information, particularly data on buried paleochannels that are critical to safeguarding the groundwater supply. Using recently available expanded information, the U.S. Geological Survey undertook a study in cooperation with the Virginia Department of Environmental Quality during 2016–19 to develop an improved description of the groundwater system called a “hydrogeologic framework.”
The hydrogeologic framework can aid water-supply planning and development by providing information on broad trends in aquifer configurations, hydraulic properties, and proximity to saltwater to avoid chloride contamination. Digital models to evaluate effects of groundwater withdrawals can also be improved with expanded data and capabilities to evaluate paleochannel hydraulic connections and the potential for saltwater movement.
The hydrogeologic framework is limited by the nonuniform distribution of boreholes and the subjective delineation of aquifers and confining units, including those within paleochannels that are regarded as preliminary. The configuration of the saltwater-transition zone is also regarded as preliminary because of the nonuniform distribution of groundwater samples. Low well-sampling frequency precludes characterizing movement of the saltwater-transition zone. A monitoring strategy of sampling and possibly electromagnetic-induction well logging could be used to detect saltwater movement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195093","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"McFarland, E.R., and Beach, T.A., 2019, Hydrogeologic framework of the Virginia Eastern Shore: U.S. Geological Survey Scientific Investigations Report 2019–5093, 26 p., 13 pl., https://doi.org/10.3133/sir20195093.","productDescription":"Report: viii, 26 p.; 13 Plates: 11.00 x 17.00 inches or smaller; Data Release","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-108409","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":369803,"rank":16,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093.pdf","text":"Report","size":"3.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5093"},{"id":369731,"rank":15,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate13.pdf","text":"Plate 13","size":"352 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Locations and Numbers of Sampled Wells and Altitude of the 250-Milligram-Per-Liter Chloride-Concentration Surface on the Virginia Eastern Shore"},{"id":369730,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate12.pdf","text":"Plate 12","size":"332 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Upper Confining Unit on the Virginia Eastern Shore"},{"id":369725,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate07.pdf","text":"Plate 7","size":"346 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Middle Confining Unit on the Virginia Eastern Shore"},{"id":369724,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate06.pdf","text":"Plate 6","size":"340 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Middle Aquifer on the Virginia Eastern Shore"},{"id":369723,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate05.pdf","text":"Plate 5","size":"332 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Lower Confining Unit on the Virginia Eastern Shore"},{"id":399542,"rank":17,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109487.htm"},{"id":369721,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate03.pdf","text":"Plate 3","size":"323 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Saint Marys Confining Unit on the Virginia Eastern Shore"},{"id":369720,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate02.pdf","text":"Plate 2","size":"336 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Hydrogeologic Section through the Virginia Eastern Shore"},{"id":369719,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate01.pdf","text":"Plate 1","size":"339 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Locations and Numbers of Boreholes on the Virginia Eastern Shore"},{"id":369714,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MPE5SD","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Borehole hydrogeologic-unit top-surface altitudes, aquifer hydraulic properties, and groundwater-sample chloride-concentration data from 1906 through 2016 for the Virginia Eastern Shore"},{"id":369728,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate10.pdf","text":"Plate 10","size":"319 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Confining Unit on the Virginia Eastern Shore"},{"id":369727,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate09.pdf","text":"Plate 9","size":"316 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Lower Aquifer on the Virginia Eastern Shore"},{"id":369726,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate08.pdf","text":"Plate 8","size":"350 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Upper Aquifer on the Virginia Eastern 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U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
The Cryptotis goldmani group of small-eared shrews (Eulipotyphla: Soricidae: Cryptotis Pomel, 1848) is a clade of semifossorially adapted species discontinuously distributed in moist highlands from central Mexico to western Panama. Inspection of a recent collection of small mammals resulting from field work in Guatemala provided the impetus for a re-evaluation of one member of that group, Goodwin’s small-eared shrew, Cryptotis goodwini Jackson, 1933. On the basis of the results of that study, three new species of small-eared shrews are described from (1) disturbed cloud forest on Cerro Cucurucho, Sacatepéquez Department, Guatemala; (2) highlands east of Mataquescuintla, Jalapa Department, Guatemala; and (3) Cerro Montecristo, a massif at the mutual borders of northern El Salvador, eastern Guatemala, and western Honduras. The new species are most similar morphologically to the Guatemalan endemic species C. goodwini and C. mam Woodman, 2010, from which they are distinguished by a variety of external and cranial characters. Recognition of the three new species more narrowly defines the distribution of C. goodwini (sensu stricto), which is now known only from portions of the western Sierra Madre of Guatemala.
","language":"English","publisher":"Texas Tech University Press","usgsCitation":"Woodman, N., 2019, Three new species of small-eared shrews, genus Cryptotis, from El Salvador and Guatemala (Mammalia: Eulipotyphla: Soricidae), no. 72, 46 p.","productDescription":"46 p.","ipdsId":"IP-107609","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":370301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":370300,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.depts.ttu.edu/nsrl/publications/special-publications.php"}],"country":"El Salvador, Guatamala, Honduras","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.14453125,\n 15.093339268117337\n ],\n [\n -84.8583984375,\n 16.130262012034756\n ],\n [\n -87.42919921875,\n 16.109153239219467\n ],\n [\n -88.76953125,\n 15.961329081596647\n ],\n [\n -89.20898437499999,\n 15.940202412387029\n ],\n [\n -89.1650390625,\n 17.895114303749143\n ],\n [\n -91.01074218749999,\n 17.832374329567518\n ],\n [\n -91.01074218749999,\n 17.22475820662464\n ],\n [\n -91.4501953125,\n 17.24574420800713\n ],\n [\n -90.37353515625,\n 16.36230951024085\n ],\n [\n -90.50537109375,\n 16.04581345375217\n ],\n [\n -91.69189453125,\n 16.088042220148818\n ],\n [\n -92.197265625,\n 15.368949896534705\n ],\n [\n -92.197265625,\n 14.477234210156519\n ],\n [\n -91.0546875,\n 13.774066408054827\n ],\n [\n -87.82470703125,\n 13.004557745339769\n ],\n [\n -87.4072265625,\n 13.047372256948787\n ],\n [\n -86.81396484375,\n 13.090179355733738\n ],\n [\n -86.72607421875,\n 13.304102866767076\n ],\n [\n -86.72607421875,\n 13.774066408054827\n ],\n [\n -86.30859375,\n 13.774066408054827\n ],\n [\n -86.0888671875,\n 14.051330743518175\n ],\n [\n -85.80322265625,\n 13.880745842025602\n ],\n [\n -85.10009765625,\n 14.28567730018259\n ],\n [\n -84.8583984375,\n 14.796127603627053\n ],\n [\n -84.48486328124999,\n 14.668625907385914\n ],\n [\n -83.14453125,\n 15.093339268117337\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"72","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Woodman, Neal 0000-0003-2689-7373 nwoodman@usgs.gov","orcid":"https://orcid.org/0000-0003-2689-7373","contributorId":3547,"corporation":false,"usgs":true,"family":"Woodman","given":"Neal","email":"nwoodman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":777543,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70227253,"text":"70227253 - 2019 - Identifying and characterizing extrapolation in multivariate response data","interactions":[],"lastModifiedDate":"2022-01-05T14:32:01.511268","indexId":"70227253","displayToPublicDate":"2019-12-05T08:19:48","publicationYear":"2019","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":"Identifying and characterizing extrapolation in multivariate response data","docAbstract":"Faced with limitations in data availability, funding, and time constraints, ecologists are often tasked with making predictions beyond the range of their data. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We propose using the trace or determinant of the predictive variance matrix to obtain a scalar value measure that, when paired with a selected cutoff value, allows for delineation between prediction and extrapolation. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we outline novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees. The use of our Multivariate Predictive Variance (MVPV) measures and multiple cutoff values when exploring the validity of predictions made from multivariate statistical models can help guide ecological inferences.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0225715","usgsCitation":"Bartley, M., Hanks, E.M., Schliep, E.M., Soranno, P.A., and Wagner, T., 2019, Identifying and characterizing extrapolation in multivariate response data: PLoS ONE, v. 14, no. 12, e0225715, 20 p., https://doi.org/10.1371/journal.pone.0225715.","productDescription":"e0225715, 20 p.","ipdsId":"IP-107783","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":459016,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0225715","text":"Publisher Index Page"},{"id":393911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Bartley, Meridith L.","contributorId":270913,"corporation":false,"usgs":false,"family":"Bartley","given":"Meridith L.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":830122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanks, Ephraim M.","contributorId":178093,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":830123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schliep, Erin M.","contributorId":171525,"corporation":false,"usgs":false,"family":"Schliep","given":"Erin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":830124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soranno, Patricia A.","contributorId":172104,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":830125,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":830121,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70207037,"text":"70207037 - 2019 - Evaluation of stormwater treatment vault with Coanda-effect screen for removal of solids and phosphorus in urban runoff","interactions":[],"lastModifiedDate":"2019-12-04T15:38:49","indexId":"70207037","displayToPublicDate":"2019-12-04T15:37:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5896,"text":"Journal of Sustainable Water in the Built Environment","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of stormwater treatment vault with Coanda-effect screen for removal of solids and phosphorus in urban runoff","docAbstract":"Catch basins commonly are used by cities as part of a stormwater management plan to remove sediment and associated contaminants from stormwater, keeping them in compliance with regulations. Recently, the city of Madison, Wisconsin modified traditional catch basins by incorporating a fine-mesh (1-mm) Coanda-effect screen into the design with the goal of increasing removal of sediment and organic matter from stormwater. The US Geological Survey (USGS), in cooperation with the City of Madison, installed a water-quality monitoring station at such a catch basin to quantify reductions in total suspended solids (TSS), volatile suspended solids (VSS), suspended sediment concentration (SSC), total phosphorus (TP), and dissolved phosphorus (DP) from urban stormwater before entering Lake Monona. A comparison of the cumulative load from 33 samples collected during the summers of 2016 and 2017 showed 23% and 45% reductions in TSS and SSC, respectively. A smaller reduction was observed for TP, 16%, whereas DP remained unchanged. Reported traditional catch basin sediment removal varies greatly, although typical removal rates are similar. Results from this study will help regulated municipalities determine whether the use of screened catch basins can help meet water-quality goals.","language":"English","publisher":"ASCE","doi":"10.1061/JSWBAY.0000892","usgsCitation":"Buer, N., and Selbig, W.R., 2019, Evaluation of stormwater treatment vault with Coanda-effect screen for removal of solids and phosphorus in urban runoff: Journal of Sustainable Water in the Built Environment, v. 6, no. 1, 04019013, 10 p., https://doi.org/10.1061/JSWBAY.0000892.","productDescription":"04019013, 10 p.","ipdsId":"IP-096348","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":459017,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/jswbay.0000892","text":"Publisher Index Page"},{"id":369921,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Dane County","city":"Madison","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.0094,43.286],[-89.0084,43.2555],[-89.0094,43.2],[-89.01,43.1131],[-89.0109,43.0849],[-89.0107,43.0271],[-89.0132,42.9353],[-89.013,42.8762],[-89.0119,42.8471],[-89.132,42.8479],[-89.2488,42.8478],[-89.3689,42.8484],[-89.3688,42.8575],[-89.4832,42.858],[-89.6026,42.8575],[-89.7196,42.8587],[-89.8377,42.8598],[-89.8375,42.9471],[-89.8386,43.0317],[-89.8384,43.1181],[-89.8394,43.205],[-89.8325,43.2123],[-89.825,43.2187],[-89.8175,43.226],[-89.8125,43.2342],[-89.8088,43.2369],[-89.8012,43.2365],[-89.7874,43.2356],[-89.771,43.237],[-89.7579,43.2379],[-89.7529,43.2443],[-89.7485,43.2507],[-89.7391,43.2548],[-89.7259,43.2644],[-89.7171,43.2739],[-89.714,43.2821],[-89.7165,43.2867],[-89.7235,43.2935],[-89.7209,43.2935],[-89.6008,43.2932],[-89.4819,43.2942],[-89.3617,43.2954],[-89.3624,43.2832],[-89.246,43.2834],[-89.1271,43.2827],[-89.0094,43.286]]]},\"properties\":{\"name\":\"Dane\",\"state\":\"WI\"}}]}","volume":"6","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buer, Nicolas 0000-0002-4369-8715","orcid":"https://orcid.org/0000-0002-4369-8715","contributorId":204808,"corporation":false,"usgs":true,"family":"Buer","given":"Nicolas","email":"","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776593,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217534,"text":"70217534 - 2019 - BbsAssistant: An R package for downloading and handling data and information from the North American Breeding Bird Survey","interactions":[],"lastModifiedDate":"2021-01-21T21:21:24.573822","indexId":"70217534","displayToPublicDate":"2019-12-04T15:18:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5929,"text":"Journal of Open Source Software","active":true,"publicationSubtype":{"id":10}},"title":"BbsAssistant: An R package for downloading and handling data and information from the North American Breeding Bird Survey","docAbstract":"This R package contains functions for downloading and munging data from the U.S. Geological Surveys North American Breeding Bird Survey (BBS) via file transfer protocol (FTP)\n(Pardieck, Ziolkowski Jr, Lutmerding, & Hudson, 2018; J. R. Sauer et al., 2017). This package was created to allow the user to bulk-download the BBS point count and related (e.g.,\nroute-level conditions) via FTP, and to quickly subset the data by taxonomic classifications\nand/or geographical locations. This package also maintains data containing the trend and\nannual indices from the most recent (1996-2017) hierarchical population analyses (J. Sauer\net al., 2017).","language":"English","publisher":"JOSS","doi":"10.21105/joss.01768","usgsCitation":"Burnett, J.L., Wszola, L.S., and Palomo-Munoz, G., 2019, BbsAssistant: An R package for downloading and handling data and information from the North American Breeding Bird Survey: Journal of Open Source Software, v. 4, no. 44, https://doi.org/10.21105/joss.01768.","productDescription":"1768, 2 p.","startPage":"1768","ipdsId":"IP-111972","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":459020,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21105/joss.01768","text":"Publisher Index Page"},{"id":437264,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93W0EAW","text":"USGS data release","linkHelpText":"bbsAssistant: An R package for downloading and handling data and information from the North American Breeding Bird Survey."},{"id":382443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"44","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burnett, Jessica Leigh 0000-0002-0896-5099","orcid":"https://orcid.org/0000-0002-0896-5099","contributorId":248195,"corporation":false,"usgs":true,"family":"Burnett","given":"Jessica","email":"","middleInitial":"Leigh","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":808599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wszola, Lyndsie S.","contributorId":205135,"corporation":false,"usgs":false,"family":"Wszola","given":"Lyndsie","email":"","middleInitial":"S.","affiliations":[{"id":37031,"text":"Nebraska Cooperative Fish & Wildlife Research Unit, University of Nebraska-Lincoln, Lincoln, Nebraska","active":true,"usgs":false}],"preferred":false,"id":808600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palomo-Munoz, Gabriela","contributorId":248196,"corporation":false,"usgs":false,"family":"Palomo-Munoz","given":"Gabriela","email":"","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":808601,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215094,"text":"70215094 - 2019 - Improving predictions of fine particle immobilization in streams","interactions":[],"lastModifiedDate":"2020-10-07T20:12:43.002013","indexId":"70215094","displayToPublicDate":"2019-12-04T15:06:29","publicationYear":"2019","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":"Improving predictions of fine particle immobilization in streams","docAbstract":"Fine particles are critical to stream ecosystem functioning, influencing in-stream processes from pathogen transmission to carbon cycling, all of which depend on particle immobilization. However, our ability to predict particle immobilization is limited by: (1) availability of combined solute and particle tracer data and (2) identifying parameters that appropriately represent fine particle immobilization, due to the myriad of objective functions and model formulations. We found that improved predictions of the full distribution of possible fine particle residence times requires using an objective function that assesses both the peak and tailing together with solute tracers to constrain in-stream transport processes. The representation of immobilization processes was significantly improved when solute tracer data were combined with a particle model, starkly contrasting the common assumption that fine particles transport as washload. We develop a clear strategy for improving fine particle transport predictions, reshaping the potential role of fine particles in water quality management.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL085849","usgsCitation":"Drummond, J.D., Schmadel, N., Kelleher, C., Packman, A.I., and Ward, A.S., 2019, Improving predictions of fine particle immobilization in streams: Geophysical Research Letters, v. 46, no. 23, p. 13,853-13,861, https://doi.org/10.1029/2019GL085849.","productDescription":"9 p.","startPage":"13,853","endPage":"13,861","ipdsId":"IP-114105","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":459021,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gl085849","text":"Publisher Index Page"},{"id":379197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"23","noUsgsAuthors":false,"publicationDate":"2019-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Drummond, Jennifer D.","contributorId":191390,"corporation":false,"usgs":false,"family":"Drummond","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":800820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmadel, Noah 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":800821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelleher, Christa","contributorId":242798,"corporation":false,"usgs":false,"family":"Kelleher","given":"Christa","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":800822,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Packman, Aaron I.","contributorId":124517,"corporation":false,"usgs":false,"family":"Packman","given":"Aaron","email":"","middleInitial":"I.","affiliations":[{"id":5041,"text":"Department of Civil and Environmental Engineering, Northwestern University, Evanston, Illinois, USA","active":true,"usgs":false}],"preferred":false,"id":800823,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ward, Adam S","contributorId":191363,"corporation":false,"usgs":false,"family":"Ward","given":"Adam","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":800824,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212499,"text":"70212499 - 2019 - Offset channels may not accurately record strike-slip fault displacement: Evidence from landscape evolution models","interactions":[],"lastModifiedDate":"2020-12-18T21:19:35.5406","indexId":"70212499","displayToPublicDate":"2019-12-04T09:25:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6453,"text":"Journal of Geophysical Research Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Offset channels may not accurately record strike-slip fault displacement: Evidence from landscape evolution models","docAbstract":"Slip distribution, slip rate, and slip per event for strike‐slip faults are commonly determined by correlating offset stream channels—under the assumption that they record seismic slip—but offset channels are formed by the interplay of tectonic and geomorphic processes. To constrain offset channel development under known tectonic and geomorphic conditions, we use numerical landscape evolution simulations along a theoretical strike‐slip fault with uniform and steady uplift, erosion, and diffusion. We investigate the influence of four tectonic parameters (fault zone width, earthquake recurrence interval, variance of the recurrence interval, and total slip relative to channel spacing) on offset channel development through multiple earthquake cycles. Analysis of >3,000 automatically measured offsets from >135 simulations suggests ~30% variability in individual measurements, but modeled displacement is recovered by averaging multiple measurements. However, the average of multiple offset measurements systematically underestimates modeled slip except when the fault zone is less than ~5 m wide, total slip is less than channel spacing, and offsets are measured shortly after an earthquake. In these simulations, postearthquake landscape evolution widens the geomorphic expression of the fault zone and modifies apparent channel offsets. We distinguish this “geomorphic fault zone” from the tectonic fault zone (zone of coseismic distributed deformation). This study highlights the capability of landscape evolution models to explore a range of conditions not easily defined in natural examples and the importance of averaging multiple measurements. Our results verify that paleoseismic studies must consider how geomorphic change has modified offset markers and use caution interpreting slip histories with multiple earthquakes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB018596","usgsCitation":"Reitman, N.G., Mueller, K.J., Tucker, G.E., Gold, R.D., Briggs, R.W., and Barnhart, K., 2019, Offset channels may not accurately record strike-slip fault displacement: Evidence from landscape evolution models: Journal of Geophysical Research Solid Earth, v. 124, no. 12, p. 13427-13451, https://doi.org/10.1029/2019JB018596.","productDescription":"25 p.","startPage":"13427","endPage":"13451","ipdsId":"IP-113549","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"124","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796591,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mueller, Karl J.","contributorId":238813,"corporation":false,"usgs":false,"family":"Mueller","given":"Karl","email":"","middleInitial":"J.","affiliations":[{"id":47791,"text":"Department of Geological Sciences, University of Colorado Boulder, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":796592,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, Gregory E.","contributorId":177811,"corporation":false,"usgs":false,"family":"Tucker","given":"Gregory","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":796593,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796594,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":139002,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796595,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnhart, Katherine R.","contributorId":238814,"corporation":false,"usgs":false,"family":"Barnhart","given":"Katherine R.","affiliations":[{"id":47792,"text":"Department of Geological Sciences, University of Colorado Boulder, Boulder, Colorado, USA; Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":796596,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208455,"text":"70208455 - 2019 - Measurement of cyanobacteria bloom magnitude using satellite remote sensing","interactions":[],"lastModifiedDate":"2020-02-11T07:47:17","indexId":"70208455","displayToPublicDate":"2019-12-04T07:43:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Measurement of cyanobacteria bloom magnitude using satellite remote sensing","docAbstract":"Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatio-temporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape-based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Ocean Land Color Imager (OLCI) on Sentinel-3 data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision-making for recreational and drinking water sources.","language":"English","publisher":"Nature","doi":"10.1038/s41598-019-54453-y","usgsCitation":"Mishra, S., Stumpf, R.P., Schaeffer, B., Werdell, P.J., Loftin, K., and Meredith, A., 2019, Measurement of cyanobacteria bloom magnitude using satellite remote sensing: Scientific Reports, no. 1, 18310, 17 p., https://doi.org/10.1038/s41598-019-54453-y.","productDescription":"18310, 17 p.","ipdsId":"IP-111006","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":459026,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-54453-y","text":"Publisher Index Page"},{"id":372207,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, 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Pooled-Petersen abundance estimates had a positive bias when datasets were generated from simulated populations with empirical relationships between environmental characteristics and catchability. The degree of bias depended upon changes in stream warming patterns and was not consistent among trapping locations. Jolly-Seber models using data from either weekly-batch-marked or uniquely-marked individuals generated abundance estimate with low bias when data quality was high, but performed poorly in scenarios with few recaptured Sea Lamprey. 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U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The American woodcock (Scolopax minor; hereafter, woodcock) Singing-Ground Survey (SGS) was developed to inform management decisions by monitoring changes in the relative abundance of woodcock. The timing of the designated survey windows was designed to count resident woodcock while minimizing counting of migrating woodcock. Since the implementation of the SGS in 1968, concerns over survey protocols that may bias data have been raised and investigated; however, the extent of survey coverage and the timing of the survey window zones have not been critically investigated. We used 3 years of data collected from male and female woodcock marked with satellite tags to assess the extent of survey coverage and the timing of the SGS survey windows relative to presence of woodcock. SGS coverage encompassed the majority of woodcock breeding-period sites (locations where marked woodcock returned to in spring) within the U.S. (n = 17, 92%) and approximately half of the breeding-period sites in Canada (n = 6, 43%). Thirteen of the 37 monitored woodcock with known breeding-period site arrival dates (35%) were migrating through a survey zone during an active survey window, all in the northernmost 4 of 5 SGS zones. Thirteen woodcock arrived at breeding-period sites after the start of surveys, and all but one of these was located in the northernmost 2 zones. The combination of migration through a SGS zone during the survey window and arrival at breeding-period sites after the beginning of the survey window in northern zones may result in the SGS weighing too heavily the contribution of routes in the southern portion of the primary breeding range, while weighing too lightly the routes in the northern portion of the primary breeding range. We suggest that additional information is necessary to evaluate whether current survey windows are sufficient, or whether they need to be changed.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the eleventh American woodcock symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Eleventh American Woodcock Symposium","conferenceDate":"Oct 24-27, 2017","conferenceLocation":"Roscommon, MI","language":"English","publisher":"University of Minnesota Libraries Publishing","usgsCitation":"Moore, J., Cooper, T.R., Rau, R.D., Andersen, D.E., Duguay, J., Stewart, C.A., and Krementz, D.G., 2019, Assessment of the American woodcock singing-ground survey zone timing and coverage, in Proceedings of the eleventh American woodcock symposium, v. 11, Roscommon, MI, Oct 24-27, 2017.","productDescription":"12 p.","endPage":"181","numberOfPages":"192","ipdsId":"IP-096403","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395367,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.lib.umn.edu/index.php/aws/article/view/2385"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -63.896484375,\n 43.51668853502906\n ],\n [\n -59.0625,\n 46.13417004624326\n ],\n [\n -66.181640625,\n 49.38237278700955\n ],\n [\n -71.54296874999999,\n 51.12421275782688\n ],\n [\n -91.318359375,\n 51.6180165487737\n ],\n [\n -98.4375,\n 51.508742458803326\n ],\n [\n -96.240234375,\n 45.767522962149876\n ],\n [\n -94.306640625,\n 36.59788913307022\n ],\n [\n -80.595703125,\n 39.70718665682654\n ],\n [\n -63.896484375,\n 43.51668853502906\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, J. D.","contributorId":274309,"corporation":false,"usgs":false,"family":"Moore","given":"J. D.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":832894,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cooper, Thomas R.","contributorId":191468,"corporation":false,"usgs":false,"family":"Cooper","given":"Thomas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":832895,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rau, Rebecca D.","contributorId":256726,"corporation":false,"usgs":false,"family":"Rau","given":"Rebecca","email":"","middleInitial":"D.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":832896,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832897,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duguay, J. P","contributorId":274311,"corporation":false,"usgs":false,"family":"Duguay","given":"J. P","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":832898,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stewart, C. Alan","contributorId":274312,"corporation":false,"usgs":false,"family":"Stewart","given":"C.","email":"","middleInitial":"Alan","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":832899,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832900,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227671,"text":"70227671 - 2019 - Estimating density and effective area surveyed for American woodcock","interactions":[],"lastModifiedDate":"2022-01-26T16:08:30.891006","indexId":"70227671","displayToPublicDate":"2019-12-03T10:03:59","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Estimating density and effective area surveyed for American woodcock","docAbstract":"The American Woodcock (Scolopax minor; hereafter, woodcock) Singing-ground Survey (SGS) is conducted annually during the woodcock breeding season, and survey points along survey routes are set 0.4 mile (0.65 km) apart to avoid counting individual birds from >1 listening location. The effective area surveyed (EAS) at a listening point is not known, and may vary as a function of land-cover type or other factors. To define the relationship describing distance between vocalizing woodcock and an observer and how cover types influence that relationship, we broadcast a recording of woodcock vocalizations in 2 land-cover types (forest and field) at varying distance. We evaluated the proportion of call broadcasts detected as a function of distance and fit regression curves to detection data to estimate a distance (r*) where the area above the curve at distances <r* was equal to the area under the curve at distances >r*, which allowed determination of the radius of an area where detection probability was effectively 1.0. This EAS had a radius (r*) of 198 m for forest, 384 m for field, and 309 m for both of these land-cover types combined, and an estimated size of 12.3 ha for forest, 46.3 ha for field, and 30.0 ha for both land-cover types combined. We used this information to estimate density of displaying male woodcock based on counts from the SGS in east-central Minnesota that incorporated variation in EAS, probability of detection, survey date, and survey route. Our density estimates (5.0 birds/100 ha in 2009 and 7.1 birds/100 ha in 2010) represent the highest density of singing male American woodcock yet reported, and indicated a substantive increase in density between years.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the eleventh American woodcock symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Eleventh American Woodcock Symposium","conferenceDate":"Oct 24-27, 2017","conferenceLocation":"Roscommon, MI","language":"English","publisher":"University of Minnesota Libraries Publishing","doi":"10.24926/AWS.0125","usgsCitation":"Bergh, S.M., and Andersen, D.E., 2019, Estimating density and effective area surveyed for American woodcock, in Proceedings of the eleventh American woodcock symposium, v. 11, Roscommon, MI, Oct 24-27, 2017, p. 193-199, https://doi.org/10.24926/AWS.0125.","productDescription":"7 p.","startPage":"193","endPage":"199","ipdsId":"IP-057491","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":459030,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Stefanie M.","contributorId":272056,"corporation":false,"usgs":false,"family":"Bergh","given":"Stefanie","email":"","middleInitial":"M.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":831784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831678,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227628,"text":"70227628 - 2019 - Detection probability and occupancy of American woodcock during Singing-ground surveys","interactions":[],"lastModifiedDate":"2022-01-21T15:17:00.679985","indexId":"70227628","displayToPublicDate":"2019-12-03T09:08:52","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Detection probability and occupancy of American woodcock during Singing-ground surveys","docAbstract":"The Singing-ground Survey (SGS) was designed to exploit the conspicuous breeding-season display of male American woodcock (Scolopax minor; hereafter, woodcock) to monitor these otherwise inconspicuous birds. The SGS was standardized in 1968 and has since been conducted annually to derive an index of abundance and population trend. Counts of singing male woodcock on the SGS have generally declined through time, but without knowledge of the relationship among counts, woodcock abundance, and the factors affecting detection, considerable uncertainty remains in interpretation of SGS data. Using modified SGS protocols, we surveyed SGS routes in Pine County, Minnesota, in 2009 and 2010 and developed models to assess factors associated with detection probability and estimated occupancy. The intercept-only model (i.e., constant detection and occupancy probabilities across sites and no covariates) included overall detection probability of 0.59 (SE = 0.018) in 2009 and 0.66 (SE = 0.017) in 2010 with an occupancy estimate of 0.74 (SE = 0.049) in 2009 and 0.81 (SE = 0.044) in 2010. The best-supported model of detection probability for both years combined included detection as a function of woodcock abundance, observer, date, disturbance level (i.e., ambient noise that interfered with detecting woodcock), and wind speed. High wind speeds were negatively related to detection, different observers had different detection probabilities, date was quadratically related to detection (indicating a mid-period peak in detection), and high woodcock abundance and low disturbance levels were positively related to detection. We provide suggestions for incorporating these resulting into SGS protocol and analyses.
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