Lakes are dominant and diverse landscape features in the Arctic, but conventional land cover classification schemes typically map them as a single uniform class. Here, we present a detailed lake-centric geospatial database for an Arctic watershed in northern Alaska. We developed a GIS dataset consisting of 4362 lakes that provides information on lake morphometry, hydrologic connectivity, surface area dynamics, surrounding terrestrial ecotypes, and other important conditions describing Arctic lakes. Analyzing the geospatial database relative to fish and bird survey data shows relations to lake depth and hydrologic connectivity, which are being used to guide research and aid in the management of aquatic resources in the National Petroleum Reserve in Alaska. Further development of similar geospatial databases is needed to better understand and plan for the impacts of ongoing climate and land-use changes occurring across lake-rich landscapes in the Arctic.
","language":"English","publisher":"Springer","doi":"10.1007/s13280-017-0915-9","usgsCitation":"Jones, B.M., Arp, C.D., Whitman, M.S., Nigro, D.A., Nitze, I., Beaver, J., Gadeke, A., Zuck, C., Liljedahl, A.K., Daanen, R., Torvinen, E., Fritz, S., and Grosse, G., 2017, A lake-centric geospatial database to guide research and inform management decisions in an Arctic watershed in northern Alaska experiencing climate and land-use changes: Ambio, v. 46, no. 7, p. 769-786, https://doi.org/10.1007/s13280-017-0915-9.","productDescription":"18 p.","startPage":"769","endPage":"786","ipdsId":"IP-076338","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":469915,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13280-017-0915-9","text":"Publisher Index Page"},{"id":438369,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H70CXB","text":"USGS data release","linkHelpText":"Fish Creek Watershed Lake Classification; NPRA, Alaska, 2016"},{"id":339942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-25","publicationStatus":"PW","scienceBaseUri":"58f877ace4b0b7ea54521bfc","contributors":{"authors":[{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":691675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":691676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitman, Matthew S.","contributorId":67961,"corporation":false,"usgs":false,"family":"Whitman","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":691677,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nigro, Debora A.","contributorId":10628,"corporation":false,"usgs":false,"family":"Nigro","given":"Debora","email":"","middleInitial":"A.","affiliations":[{"id":12934,"text":"Bureau of Land Management, Arctic Field Office","active":true,"usgs":false}],"preferred":false,"id":691678,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nitze, Ingmar","contributorId":191057,"corporation":false,"usgs":false,"family":"Nitze","given":"Ingmar","affiliations":[],"preferred":false,"id":691679,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beaver, John","contributorId":191058,"corporation":false,"usgs":false,"family":"Beaver","given":"John","affiliations":[],"preferred":false,"id":691680,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gadeke, Anne","contributorId":191059,"corporation":false,"usgs":false,"family":"Gadeke","given":"Anne","email":"","affiliations":[],"preferred":false,"id":691681,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zuck, Callie 0000-0002-7040-6191 czuck@usgs.gov","orcid":"https://orcid.org/0000-0002-7040-6191","contributorId":175209,"corporation":false,"usgs":true,"family":"Zuck","given":"Callie","email":"czuck@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":691682,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Liljedahl, Anna K. 0000-0001-7114-6443","orcid":"https://orcid.org/0000-0001-7114-6443","contributorId":150135,"corporation":false,"usgs":false,"family":"Liljedahl","given":"Anna","email":"","middleInitial":"K.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":691683,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Daanen, Ronald","contributorId":191060,"corporation":false,"usgs":false,"family":"Daanen","given":"Ronald","email":"","affiliations":[],"preferred":false,"id":691684,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Torvinen, Eric","contributorId":191061,"corporation":false,"usgs":false,"family":"Torvinen","given":"Eric","email":"","affiliations":[],"preferred":false,"id":691685,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Fritz, Stacey","contributorId":176574,"corporation":false,"usgs":false,"family":"Fritz","given":"Stacey","email":"","affiliations":[],"preferred":false,"id":691686,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Grosse, Guido","contributorId":146182,"corporation":false,"usgs":false,"family":"Grosse","given":"Guido","email":"","affiliations":[{"id":12916,"text":"Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":691687,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70193799,"text":"70193799 - 2017 - Migratory connectivity of american woodcock using band return data","interactions":[],"lastModifiedDate":"2017-11-08T14:21:46","indexId":"70193799","displayToPublicDate":"2017-04-19T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Migratory connectivity of american woodcock using band return data","docAbstract":"American woodcock (Scolopax minor) are managed as a Central and an Eastern population in the United States and Canada based on band return data showing little crossover between populations or management regions. The observed proportion of crossover between management regions, however, depends on the criteria used to subset the band return data. We analyzed the amount of crossover between management regions using only band return records that represent complete migrations between the breeding and wintering grounds by using only band return records in which the capture took place during the breeding season and the band recovery took place during the wintering season or vice versa (n = 224). Additionally, we applied spatial statistics and a clustering algorithm to investigate woodcock migratory connectivity using this subset of migratory woodcock band return records. Using raw counts, 17.9% of records showed crossover between management regions, a higher proportion than the <5% crossover reported in studies that did not use only migratory band returns. Our results showed woodcock from the breeding grounds in the Central Region largely migrate to destinations within the Central Region, whereas woodcock from the breeding grounds in the Eastern Region migrate to destinations across the entire wintering range and mix with individuals from the Central Region. Using the division coefficient, we estimated that 54% of woodcock from the breeding grounds of the Eastern Region migrate to the Central Region wintering grounds. Our result that many woodcock from separate regions of the breeding grounds mix on the wintering grounds has implications for the 2-region basis for woodcock management. Elucidating finer scale movement patterns among regions provides a basis for reassessing the need for separate management regions to ensure optimal conservation and management of the species.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21269","usgsCitation":"Moore, J.D., and Krementz, D.G., 2017, Migratory connectivity of american woodcock using band return data: Journal of Wildlife Management, v. 81, no. 6, p. 1063-1072, https://doi.org/10.1002/jwmg.21269.","productDescription":"12 p.","startPage":"1063","endPage":"1072","ipdsId":"IP-080526","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348470,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -72.26806640624999,\n 45.1510532655634\n ],\n [\n -74.0478515625,\n 46.31658418182218\n ],\n [\n -77.1240234375,\n 47.54687159892238\n ],\n [\n -82.0458984375,\n 48.48748647988415\n ],\n [\n -86.0009765625,\n 48.86471476180277\n ],\n [\n -89.033203125,\n 48.60385760823255\n ],\n [\n -90.615234375,\n 48.019324184801185\n ],\n [\n -93.603515625,\n 46.98025235521883\n ],\n [\n -95.0537109375,\n 45.82879925192134\n ],\n [\n -95.2294921875,\n 43.77109381775651\n ],\n [\n -96.064453125,\n 39.40224434029275\n ],\n [\n -96.15234375,\n 32.0639555946604\n ],\n [\n -95.0537109375,\n 29.22889003019423\n ],\n [\n -87.802734375,\n 30.751277776257812\n ],\n [\n -85.62744140625,\n 34.77771580360469\n ],\n [\n -80.74951171875,\n 37.59682400108367\n ],\n [\n -74.02587890625,\n 41.32732632036622\n ],\n [\n -71.7626953125,\n 43.229195113965005\n ],\n [\n -72.26806640624999,\n 45.1510532655634\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-19","publicationStatus":"PW","scienceBaseUri":"5a0425b9e4b0dc0b45b4538e","contributors":{"authors":[{"text":"Moore, Joseph D.","contributorId":199996,"corporation":false,"usgs":false,"family":"Moore","given":"Joseph","email":"","middleInitial":"D.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":720543,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":720542,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188857,"text":"70188857 - 2017 - Long-term afterslip of the M6.0, 2004 Parkfield, California, earthquake—Implications for forecasting amount and duration of afterslip on other major creeping faults","interactions":[],"lastModifiedDate":"2017-06-26T14:45:27","indexId":"70188857","displayToPublicDate":"2017-04-18T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Long-term afterslip of the M6.0, 2004 Parkfield, California, earthquake—Implications for forecasting amount and duration of afterslip on other major creeping faults","docAbstract":"We present the longest record of surface afterslip on a continental strike‐slip fault for the 2004 M 6.0 Parkfield, California, earthquake, from which we can derive critical information about the duration and predictability of afterslip relevant to urban displacement hazard applications. Surface slip associated with this event occurred entirely postseismically along the interseismically creeping (0.6–1.5 cm/yr) main trace of the San Andreas fault. Using the first year of afterslip data, the program AFTER correctly predicted the cumulative surface afterslip (maximum ∼35 cm) eventually attained. By 1 yr postearthquake, observed afterslip had accumulated to only ∼74% of its modeled final value uf in units of length. The 6‐yr data suggested final slip would be reached everywhere by ∼6–12 yrs.
Parkfield’s afterslip lasted much longer (∼6–12 yrs) than afterslip following a 2014 M 6.0 event in Napa, California, where no interseismic creep was known, and its afterslip neared completion (∼97% of uf) by 1 yr. The uncertainty in uf for the Napa event fell to ≤2 cm in only three months, versus in 2 yrs for the Parkfield event, mostly because duration of the power‐law stage of afterslip at Parkfield is much longer, ∼1000 (493–1666) days versus ∼100 (35–421) days for Napa. Because the urban Hayward fault near San Francisco, California, like the Parkfield section, exhibits interseismic creep in a similar geological regime, significant afterslip might last for up to a decade following an anticipated M≥6.7 earthquake, potentially delaying postearthquake recovery.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160321","usgsCitation":"Lienkaemper, J.J., and McFarland, F.S., 2017, Long-term afterslip of the M6.0, 2004 Parkfield, California, earthquake—Implications for forecasting amount and duration of afterslip on other major creeping faults: Bulletin of the Seismological Society of America, v. 107, no. 3, p. 1082-1093, https://doi.org/10.1785/0120160321.","productDescription":"12 p.","startPage":"1082","endPage":"1093","ipdsId":"IP-075209","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":342910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Parkfield ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.91690063476562,\n 35.54116627999813\n ],\n [\n -120.16708374023438,\n 35.39912537474416\n ],\n [\n -120.16708374023438,\n 36.1312200154285\n ],\n [\n -121.11602783203124,\n 36.09682839442643\n ],\n [\n -120.91690063476562,\n 35.54116627999813\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-18","publicationStatus":"PW","scienceBaseUri":"59521d20e4b062508e3c366d","contributors":{"authors":[{"text":"Lienkaemper, James J. 0000-0002-7578-7042 jlienk@usgs.gov","orcid":"https://orcid.org/0000-0002-7578-7042","contributorId":1941,"corporation":false,"usgs":true,"family":"Lienkaemper","given":"James","email":"jlienk@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":700713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McFarland, Forrest S.","contributorId":192264,"corporation":false,"usgs":false,"family":"McFarland","given":"Forrest","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":700715,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70180073,"text":"ofr20161123 - 2017 - Shallow-depth location and geometry of the Piedmont Reverse splay of the Hayward Fault, Oakland, California","interactions":[],"lastModifiedDate":"2017-04-19T10:03:02","indexId":"ofr20161123","displayToPublicDate":"2017-04-18T00:00:00","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":"2016-1123","title":"Shallow-depth location and geometry of the Piedmont Reverse splay of the Hayward Fault, Oakland, California","docAbstract":"The Piedmont Thrust Fault, herein referred to as the Piedmont Reverse Fault (PRF), is a splay of the Hayward Fault that trends through a highly populated area of the City of Oakland, California (fig. 1A). Although the PRF is unlikely to generate a large-magnitude earthquake, slip on the PRF or high-amplitude seismic energy traveling along the PRF may cause considerable damage during a large earthquake on the Hayward Fault. Thus, it is important to determine the exact location, geometry (particularly dip), and lateral extent of the PRF within the densely populated Oakland area. In the near surface, the PRF juxtaposes Late Cretaceous sandstone (of the Franciscan Complex Novato Quarry terrane of Blake and others, 1984) and an older Pleistocene alluvial fan unit along much of its mapped length (fig. 1B; Graymer and others, 1995). The strata of the Novato Quarry unit vary greatly in strike (NW, NE, and E), dip direction (NE, SW, E, and NW), dip angle (15° to 85°), and lithology (shale and sandstone), and the unit has been intruded by quartz diorite in places. Thus, it is difficult to infer the structure of the fault, particularly at depth, with conventional seismic reflection imaging methods. To better determine the location and shallow-depth geometry of the PRF, we used high-resolution seismic imaging methods described by Catchings and others (2014). These methods involve the use of coincident P-wave (compressional wave) and S-wave (shear wave) refraction tomography and reflection data, from which tomographic models of P- and S-wave velocity and P-wave reflection images are developed. In addition, the coincident P-wave velocity (VP) and S-wave velocity (VS) data are used to develop tomographic models of VP/VS ratios and Poisson’s ratio, which are sensitive to shallow-depth faulting and groundwater. In this study, we also compare measurements of Swave velocities determined from surface waves with those determined from refraction tomography. We use the combination of seismic methods to infer the fault location, dip, and the National Earthquake Hazards Reduction Program (NEHRP) site classification along the seismic profile. Our seismic study is a smaller part of a larger study of the PRF by Trench and others (2016).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161123","usgsCitation":"Catchings, R.D., Goldman, M.R., Trench, David, Buga, Michael, Chan, J.H., Criley, C.J., and Strayer, L.M., 2017, Shallow-depth location and geometry of the Piedmont Reverse splay of the Hayward Fault, Oakland, California: U.S. Geological Survey Open-File Report 2016–1123, 22 p., https://dx.doi.org/10.3133/ofr20161123.","productDescription":"iii, 22 p.","onlineOnly":"Y","ipdsId":"IP-073235","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":339832,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1123/ofr20161123.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1123"},{"id":339831,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1123/coverthb.jpg"}],"country":"United States","state":"California","city":"Oakland","otherGeospatial":"Hayward Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.24727630615236,\n 37.784554114444994\n ],\n [\n -122.16590881347656,\n 37.784554114444994\n ],\n [\n -122.16590881347656,\n 37.83771661984569\n ],\n [\n -122.24727630615236,\n 37.83771661984569\n ],\n [\n -122.24727630615236,\n 37.784554114444994\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center—Menlo Park, Calif. Office
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
http://earthquake.usgs.gov/
The U.S. Geological Survey (USGS), in partnership with the Idaho Department of Water Resources (IDWR) and Idaho Water Resource Board (IWRB), will construct a numerical groundwater-flow model of the Treasure Valley and surrounding area. Resource managers will use the model to simulate potential anthropogenic and climatic effects on groundwater for water-supply planning and management. As part of model construction, the hydrogeologic understanding of the aquifer system will be updated with information collected during the last two decades, as well as new data collected for the study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173027","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Bartolino, J.R., and Vincent, Sean, 2017, A groundwater-flow model for the Treasure Valley and surrounding area, southwestern Idaho: U.S. Geological Survey Fact Sheet 2017-3027, 4 p., https://doi.org/10.3133/fs20173027.","productDescription":"4 p.","ipdsId":"IP-080721","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":339801,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3027/fs20173027.pdf","text":"Report","size":"4.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3027"},{"id":339800,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3027/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Treasure Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 43\n ],\n [\n -115.5,\n 43\n ],\n [\n -115.5,\n 44\n ],\n [\n -117,\n 44\n ],\n [\n -117,\n 43\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
F.H. Newell Federal building
230 Collins Road
Boise, ID 83702
http://id.water.usgs.gov
Precipitation samples have been collected by the National Atmospheric Deposition Program's (NADP) National Trends Network (NTN) using the Aerochem Metrics Model 301 (ACM) collector since 1978. Approximately one-third of the NTN ACM collectors have been replaced with N-CON Systems, Inc. Model ADS 00-120 (NCON) collectors. Concurrent data were collected over 6 years at 12 NTN sites using colocated ACM and NCON collectors in various precipitation regimes. Linear regression models of the colocated data were used to adjust for relative bias between the collectors. Replacement of ACM collectors with NCON collectors resulted in shifts in 10-year seasonal precipitation-weighted mean concentration (PWMC) trend slopes for: cations (−0.001 to −0.007 mgL−1yr−1), anions (−0.009 to −0.028 mgL−1yr−1), and hydrogen ion (+0.689 meqL-1yr−1). Larger shifts in NO3− and SO4−2 seasonal PWMC trend slopes were observed in the Midwest and Northeast US, where concentrations are generally higher than in other regions. Geospatial analysis of interpolated concentration rasters indicated regions of accentuated variability introduced by incorporation of NCON collectors into the NTN.
","language":"English","publisher":"Elsevier","publisherLocation":"London","doi":"10.1016/j.envpol.2016.12.036","usgsCitation":"Wetherbee, G.A., 2017, Precipitation collector bias and its effects on temporal trends and spatial variability in National Atmospheric Deposition Program/National Trends Network data: Environmental Pollution, v. 223, p. 90-101, https://doi.org/10.1016/j.envpol.2016.12.036.","productDescription":"12 p.","startPage":"90","endPage":"101","ipdsId":"IP-076925","costCenters":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"links":[{"id":461635,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2016.12.036","text":"Publisher Index Page"},{"id":339803,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}\n","volume":"223","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f5d43ae4b0f2e20545e3fd","contributors":{"authors":[{"text":"Wetherbee, Gregory A. 0000-0002-6720-2294 wetherbe@usgs.gov","orcid":"https://orcid.org/0000-0002-6720-2294","contributorId":1044,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory","email":"wetherbe@usgs.gov","middleInitial":"A.","affiliations":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"preferred":true,"id":691257,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70186921,"text":"70186921 - 2017 - The effects of drought and fire in the extirpation of an abundant semi-aquatic turtle from a lacustrine environment in the southwestern USA","interactions":[],"lastModifiedDate":"2017-04-14T13:02:13","indexId":"70186921","displayToPublicDate":"2017-04-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2585,"text":"Knowledge and Management of Aquatic Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"The effects of drought and fire in the extirpation of an abundant semi-aquatic turtle from a lacustrine environment in the southwestern USA","docAbstract":"We documented a significant mortality event affecting a southwestern pond turtle (Actinemys pallida) population living in a lake in southern California, USA. The area around the lake was impacted by a large wildland fire in 2013 that occurred during a protracted drought. As the mortality event was still unfolding, we collected data in 2014 on water quality, demographic structure, and short-term survivorship of the population. Water quality was poor with low levels of dissolved oxygen and high salinity of up to 45.90 ppt. Many turtles were severely emaciated and coated with a pale mineralized layer on their shells and skin. Estimated survival rate was low leading to a projected 90% decline in 134 days and a high probability of extirpation within a year. The lake was dry in September 2015 with no evidence of live turtles. Necropsies and low volumetric body condition indices suggested death by starvation. Although this semi-aquatic species has the ability to aestivate in upland habitats during periods of low water or move to other nearby water bodies, it is unlikely that many were able to do so because of their extremely poor condition and the severity of the drought conditions throughout the area.
","language":"English","publisher":"EDP Sciences","doi":"10.1051/kmae/2017008","usgsCitation":"Lovich, J.E., Quillman, M., Zitt, B., Schroeder, A., Green, D.E., Yackulic, C.B., Gibbons, P., and Goode, E., 2017, The effects of drought and fire in the extirpation of an abundant semi-aquatic turtle from a lacustrine environment in the southwestern USA: Knowledge and Management of Aquatic Ecosystems, v. 418, p. 1-11, https://doi.org/10.1051/kmae/2017008.","productDescription":"Article number 18; 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-071578","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":461639,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1051/kmae/2017008","text":"Publisher Index Page"},{"id":438371,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FN154F","text":"USGS data release","linkHelpText":"Southern Pacific Pond Turtle Data, Elizabeth Lake, Los Angeles County, California, USA"},{"id":339735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"418","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-10","publicationStatus":"PW","scienceBaseUri":"58f1e0c7e4b08144348b7de5","contributors":{"authors":[{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":691001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quillman, Mari","contributorId":190889,"corporation":false,"usgs":false,"family":"Quillman","given":"Mari","email":"","affiliations":[],"preferred":false,"id":691002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zitt, Brian","contributorId":190890,"corporation":false,"usgs":false,"family":"Zitt","given":"Brian","email":"","affiliations":[],"preferred":false,"id":691003,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schroeder, Adam","contributorId":190891,"corporation":false,"usgs":false,"family":"Schroeder","given":"Adam","email":"","affiliations":[],"preferred":false,"id":691004,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Green, David E. 0000-0002-7663-1832 degreen@usgs.gov","orcid":"https://orcid.org/0000-0002-7663-1832","contributorId":3715,"corporation":false,"usgs":true,"family":"Green","given":"David","email":"degreen@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":691005,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":691006,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gibbons, Paul","contributorId":190892,"corporation":false,"usgs":false,"family":"Gibbons","given":"Paul","affiliations":[],"preferred":false,"id":691007,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goode, Eric","contributorId":190893,"corporation":false,"usgs":false,"family":"Goode","given":"Eric","email":"","affiliations":[],"preferred":false,"id":691008,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70187414,"text":"70187414 - 2017 - Monitoring protocols: Options, approaches, implementation, benefits","interactions":[],"lastModifiedDate":"2017-11-22T16:19:52","indexId":"70187414","displayToPublicDate":"2017-04-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Monitoring protocols: Options, approaches, implementation, benefits","docAbstract":"Monitoring and adaptive management are fundamental concepts to rangeland management across land management agencies and embodied as best management practices for private landowners. Historically, rangeland monitoring was limited to determining impacts or maximizing the potential of specific land uses—typically grazing. Over the past several decades, though, the uses of and disturbances to rangelands have increased dramatically against a backdrop of global climate change that adds uncertainty to predictions of future rangeland conditions. Thus, today’s monitoring needs are more complex (or multidimensional) and yet still must be reconciled with the realities of costs to collect requisite data. However, \r\nconceptual advances in rangeland ecology and management and changes in natural resource policies and societal values over the past 25 years have facilitated new approaches to monitoring that can support rangeland management’s diverse information needs. Additionally, advances in sensor technologies and remote-sensing techniques have broadened the suite of rangeland attributes that can be monitored and the temporal and spatial scales at which they can be monitored. We review some of the conceptual and technological advancements and provide examples of how they have influenced rangeland monitoring. We then discuss implications of these developments for rangeland management and highlight what we see as challenges and opportunities for implementing effective rangeland monitoring. We conclude with a vision for how monitoring can contribute to rangeland information needs in the future.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Monitoring protocol: Options, approaches, implementation, benefits","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-46709-2_16","usgsCitation":"Karl, J.W., Herrick, J.E., and Pyke, D.A., 2017, Monitoring protocols: Options, approaches, implementation, benefits, chap. of Monitoring protocol: Options, approaches, implementation, benefits, p. 527-567, https://doi.org/10.1007/978-3-319-46709-2_16.","productDescription":"41 p.","startPage":"527","endPage":"567","ipdsId":"IP-066903","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":488627,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-319-46709-2_16","text":"Publisher Index Page"},{"id":340762,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-14","publicationStatus":"PW","scienceBaseUri":"590aec47e4b0fc4e4492aba3","contributors":{"authors":[{"text":"Karl, Jason W.","contributorId":191703,"corporation":false,"usgs":false,"family":"Karl","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":7045,"text":"USDA-ARS Jornada Experimental Range ","active":true,"usgs":false}],"preferred":false,"id":693921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herrick, Jeffrey E.","contributorId":26054,"corporation":false,"usgs":false,"family":"Herrick","given":"Jeffrey","email":"","middleInitial":"E.","affiliations":[{"id":12627,"text":"USDA-ARS Jornada Experimental Range, New Mexico State University, Las Cruces, NM 88003-8003, USA","active":true,"usgs":false}],"preferred":false,"id":693922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":693920,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70186759,"text":"sir20175024 - 2017 - Developing flood-inundation maps for Johnson Creek, Portland, Oregon","interactions":[],"lastModifiedDate":"2017-04-20T11:18:36","indexId":"sir20175024","displayToPublicDate":"2017-04-14T00:00:00","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":"2017-5024","title":"Developing flood-inundation maps for Johnson Creek, Portland, Oregon","docAbstract":"Digital flood-inundation maps were created for a 12.9‑mile reach of Johnson Creek by the U.S. Geological Survey (USGS). The flood-inundation maps depict estimates of water depth and areal extent of flooding from the mouth of Johnson Creek to just upstream of Southeast 174th Avenue in Portland, Oregon. Each flood-inundation map is based on a specific water level and associated streamflow at the USGS streamgage, Johnson Creek at Sycamore, Oregon (14211500), which is located near the upstream boundary of the maps. The maps produced by the USGS, and the forecasted flood hydrographs produced by National Weather Service River Forecast Center can be accessed through the USGS Flood Inundation Mapper Web site (http://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html).
Water-surface elevations were computed for Johnson Creek using a combined one-dimensional and two‑dimensional unsteady hydraulic flow model. The model was calibrated using data collected from the flood of December 2015 (including the calculated streamflows at two USGS streamgages on Johnson Creek) and validated with data from the flood of January 2009. Results were typically within 0.6 foot (ft) of recorded or measured water-surface elevations from the December 2015 flood, and within 0.8 ft from the January 2009 flood. Output from the hydraulic model was used to create eight flood inundation maps ranging in stage from 9 to 16 ft. Boundary condition hydrographs were identical in shape to those from the December 2015 flood event, but were scaled up or down to produce the amount of streamflow corresponding to a specific water-surface elevation at the Sycamore streamgage (14211500). Sensitivity analyses using other hydrograph shapes, and a version of the model in which the peak flow is maintained for an extended period of time, showed minimal variation, except for overbank areas near the Foster Floodplain Natural Area.
Simulated water-surface profiles were combined with light detection and ranging (lidar) data collected in 2014 to delineate water-surface extents for each of the eight modeled stages. The availability of flood-inundation maps in conjunction with real-time data from the USGS streamgages along Johnson Creek and forecasted hydrographs from the National Weather Service Northwest River Forecast Center will provide residents of the watershed and emergency management personnel with valuable information that may aid in flood response, including potential evacuations, road closures, and mitigation efforts. In addition, these maps may be used for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175024","collaboration":"Prepared in cooperation with the City of Portland Bureau of Environmental Services","usgsCitation":"Stonewall, A.J., and Beal, B.A., 2017, Developing flood-Inundation maps for Johnson Creek, Portland, Oregon: U.S. Geological Survey Scientific Investigations Report 2017–5024, 26 p., https://doi.org/10.3133/sir20175024.","productDescription":"v, 26 p.","onlineOnly":"Y","ipdsId":"IP-080503","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":339976,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75X273G","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood inundation mapping data for Johnson Creek near Sycamore, Oregon"},{"id":339738,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5024/coverthb.jpg"},{"id":339739,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5024/sir20175024.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5024"}],"country":"United States","state":"Oregon","city":"Portland","otherGeospatial":"Johnson Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.655556,\n 45.506944\n ],\n [\n -122.472222,\n 45.506944\n ],\n [\n -122.472222,\n 45.408333\n ],\n [\n -122.655556,\n 45.408333\n ],\n [\n -122.655556,\n 45.506944\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
Evidence indicates that competition with invasive barred owls (Strix varia) is causing rapid declines in populations of northern spotted owls (S. occidentalis caurina), and that the long-term persistence of spotted owls may be in question without additional management intervention. A pilot study in California showed that removal of barred owls in combination with habitat conservation may be able to slow or even reverse population declines of spotted owls at local scales, but it remains unknown whether similar results can be obtained in areas with different forest conditions and a greater density of barred owls. In 2015, we implemented a before-after-control-impact (BACI) experimental design on three study areas in Oregon and Washington with at least 20 years of pre-treatment demographic data on spotted owls to determine if removal of barred owls can improve localized population trends of spotted owls. Here, we report on research accomplishments and preliminary results from the first 21 months (March 2015–December 2016) of the planned 5-year experiment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171040","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, Bureau of Land Management, and U.S. Forest Service","usgsCitation":"Wiens, J.D., Dugger, K.M., Lewicki, K.E., and Simon, D.C., 2017, Effects of experimental removal of barred owls on population demography of northern spotted owls in Washington and Oregon—2016 progress report: U.S. Geological Survey Open-File Report 2017-1040, 23 p., https://doi.org/10.3133/ofr20171040.","productDescription":"iv, 23 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-084885","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":339711,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1040/coverthb.jpg"},{"id":339712,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1040/ofr20171040.pdf","text":"Report","size":"5.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1040"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.94775390625,\n 42.4234565179383\n ],\n [\n -120.10253906249999,\n 42.4234565179383\n ],\n [\n -120.10253906249999,\n 47.945786463687185\n ],\n [\n -123.94775390625,\n 47.945786463687185\n ],\n [\n -123.94775390625,\n 42.4234565179383\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov/
The ability to quantify the processes driving geomorphic change in river valley margins is vital to geomorphologists seeking to understand the relative role of transport mechanisms (e.g. fluvial, aeolian, and hillslope processes) in landscape dynamics. High-resolution, repeat topographic data are becoming readily available to geomorphologists. By contrasting digital elevation models derived from repeat surveys, the transport processes driving topographic changes can be inferred, a method termed ‘mechanistic segregation.’ Unfortunately, mechanistic segregation largely relies on subjective and time consuming manual classification, which has implications both for its reproducibility and the practical scale of its application. Here we present a novel computational workflow for the mechanistic segregation of geomorphic transport processes in geospatial datasets. We apply the workflow to seven sites along the Colorado River in the Grand Canyon, where geomorphic transport is driven by a diverse suite of mechanisms. The workflow performs well when compared to field observations, with an overall predictive accuracy of 84% across 113 validation points. The approach most accurately predicts changes due to fluvial processes (100% accuracy) and aeolian processes (96%), with reduced accuracy in predictions of alluvial and colluvial processes (64% and 73%, respectively). Our workflow is designed to be applicable to a diversity of river systems and will likely provide a rapid and objective understanding of the processes driving geomorphic change at the reach and network scales. We anticipate that such an understanding will allow insight into the response of geomorphic transport processes to external forcings, such as shifts in climate, land use, or river regulation, with implications for process-based river management and restoration.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4143","usgsCitation":"Kasprak, A., Caster, J.J., Bangen, S.G., and Sankey, J.B., 2017, Geomorphic process from topographic form: automating the interpretation of repeat survey data in river valleys: Earth Surface Processes and Landforms, v. 42, no. 12, p. 1872-1883, https://doi.org/10.1002/esp.4143.","productDescription":"12 p.","startPage":"1872","endPage":"1883","ipdsId":"IP-079655","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":438376,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73776X6","text":"USGS data release","linkHelpText":"Geomorphic Process from Topographic FormData & Models"},{"id":339687,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.8787841796875,\n 35.67068501330236\n ],\n [\n -111.258544921875,\n 35.67068501330236\n ],\n [\n -111.258544921875,\n 37.077093191754436\n ],\n [\n -113.8787841796875,\n 37.077093191754436\n ],\n [\n -113.8787841796875,\n 35.67068501330236\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-10","publicationStatus":"PW","scienceBaseUri":"58f08e5de4b06911a29fa83c","contributors":{"authors":[{"text":"Kasprak, Alan 0000-0001-8184-6128 akasprak@usgs.gov","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":190848,"corporation":false,"usgs":true,"family":"Kasprak","given":"Alan","email":"akasprak@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":690869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caster, Joshua J. 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":131114,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":690902,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bangen, Sara G.","contributorId":190858,"corporation":false,"usgs":false,"family":"Bangen","given":"Sara","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":690903,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":690904,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70186881,"text":"70186881 - 2017 - Cloud detection algorithm comparison and validation for operational Landsat data products","interactions":[],"lastModifiedDate":"2017-04-13T09:40:27","indexId":"70186881","displayToPublicDate":"2017-04-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Cloud detection algorithm comparison and validation for operational Landsat data products","docAbstract":"Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.03.026","usgsCitation":"Foga, S.C., Scaramuzza, P., Guo, S., Zhu, Z., Dilley, R., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M., and Laue, B., 2017, Cloud detection algorithm comparison and validation for operational Landsat data products: Remote Sensing of Environment, v. 194, p. 379-390, https://doi.org/10.1016/j.rse.2017.03.026.","productDescription":"12 p.","startPage":"379","endPage":"390","ipdsId":"IP-076780","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469926,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2017.03.026","text":"Publisher Index Page"},{"id":339659,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"194","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f08e5ee4b06911a29fa842","contributors":{"authors":[{"text":"Foga, Steven Curtis 0000-0003-1835-1987 sfoga@usgs.gov","orcid":"https://orcid.org/0000-0003-1835-1987","contributorId":5703,"corporation":false,"usgs":true,"family":"Foga","given":"Steven","email":"sfoga@usgs.gov","middleInitial":"Curtis","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scaramuzza, Pat 0000-0002-2616-8456 pscar@usgs.gov","orcid":"https://orcid.org/0000-0002-2616-8456","contributorId":3970,"corporation":false,"usgs":true,"family":"Scaramuzza","given":"Pat","email":"pscar@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guo, Song 0000-0001-8823-188X sguo@usgs.gov","orcid":"https://orcid.org/0000-0001-8823-188X","contributorId":5245,"corporation":false,"usgs":true,"family":"Guo","given":"Song","email":"sguo@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhe 0000-0001-8283-6407","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":190828,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[],"preferred":false,"id":690808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dilley, Ronald 0000-0002-6960-1125 ronald.dilley.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-6960-1125","contributorId":190829,"corporation":false,"usgs":true,"family":"Dilley","given":"Ronald","email":"ronald.dilley.ctr@usgs.gov","affiliations":[],"preferred":false,"id":690809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beckmann, Tim 0000-0002-2557-0638 tim.beckmann.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-2557-0638","contributorId":190830,"corporation":false,"usgs":true,"family":"Beckmann","given":"Tim","email":"tim.beckmann.ctr@usgs.gov","affiliations":[],"preferred":false,"id":690811,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmidt, Gail L. 0000-0002-9684-8158 gschmidt@usgs.gov","orcid":"https://orcid.org/0000-0002-9684-8158","contributorId":3475,"corporation":false,"usgs":true,"family":"Schmidt","given":"Gail","email":"gschmidt@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690810,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690812,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hughes, MJ","contributorId":190831,"corporation":false,"usgs":false,"family":"Hughes","given":"MJ","email":"","affiliations":[],"preferred":false,"id":690813,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Laue, Brady 0000-0002-4559-3618 brady.laue.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-4559-3618","contributorId":190832,"corporation":false,"usgs":true,"family":"Laue","given":"Brady","email":"brady.laue.ctr@usgs.gov","affiliations":[],"preferred":false,"id":690814,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70186886,"text":"70186886 - 2017 - Integrated species distribution models: combining presence-background data and site-occupancy data with imperfect detection","interactions":[],"lastModifiedDate":"2017-04-13T11:31:34","indexId":"70186886","displayToPublicDate":"2017-04-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Integrated species distribution models: combining presence-background data and site-occupancy data with imperfect detection","docAbstract":"The Deepwater Horizon explosion in April 2010 and subsequent oil spill released 3.19 × 106 barrels (5.07 × 108 L) of MC252 crude oil into important foraging areas of the endangered Kemp’s ridley sea turtle Lepidochelys kempii (Lk) in the northern Gulf of Mexico (GoM). We measured δ13C and δ15N in scute biopsy samples from 33 Lk nesting in Texas during 2010–-12. Of these, 27 were equipped with satellite transmitters and were tracked to traditional foraging areas in the northern GoM after the spill. Differences in δ13C between the oldest and newest scute layers from 2010 nesters were not significantly different, but δ13C in the newest layers from 2011 and 2012 nesters was significantly lower compared to 2010. δ15N differences were not statistically significant. Collectively, the stable isotope and tracking data indicate that the lower δ13C values reflect the incorporation of oil rather than changes in diet or foraging area. Discriminant analysis indicated that 51.5% of the turtles sampled had isotope signatures indicating oil exposure. Growth of the Lk population slowed in the years following the spill. The involvement of oil exposure in recent population trends is unknown, but long-term effects may not be evident for many years. Our results indicate that C isotope signatures in scutes may be useful biomarkers of sea turtle exposure to oil.
","language":"English","publisher":"Inter-Research","publisherLocation":"Oldendorf/Luhe","doi":"10.3354/esr00819","usgsCitation":"Reich, K.J., Lopez-Castro, M.C., Shaver, D.J., Iseton, C., Hart, K.M., Hooper, M.J., and Schmitt, C.J., 2017, δ13C and d15N in the endangered Kemp’s ridley sea turtle Lepidochelys kempii after the Deepwater Horizon oil spill: Endangered Species Research, v. 33, p. 281-289, https://doi.org/10.3354/esr00819.","productDescription":"9 p.","startPage":"281","endPage":"289","ipdsId":"IP-076594","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":469928,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr00819","text":"Publisher Index Page"},{"id":438375,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70C4SXJ","text":"USGS data release","linkHelpText":"Data tables in support of manuscript "​δ13C and δ15N in the Endangered Kemp's Ridley Sea Turtle Lepidochelys kempii After the Deepwater Horizon Oil Spill""},{"id":340065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98,\n 25\n ],\n [\n -84,\n 25\n ],\n [\n -84,\n 31\n ],\n [\n -98,\n 31\n ],\n [\n -98,\n 25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"33","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4be4b0c3010a8087b5","contributors":{"authors":[{"text":"Reich, Kimberly J.","contributorId":175452,"corporation":false,"usgs":false,"family":"Reich","given":"Kimberly","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":692331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lopez-Castro, Melania C.","contributorId":191185,"corporation":false,"usgs":false,"family":"Lopez-Castro","given":"Melania","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":692332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shaver, Donna J.","contributorId":191186,"corporation":false,"usgs":false,"family":"Shaver","given":"Donna","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":692333,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iseton, Claire","contributorId":191187,"corporation":false,"usgs":false,"family":"Iseton","given":"Claire","email":"","affiliations":[],"preferred":false,"id":692334,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Kristen M. 0000-0002-5257-7974 kristen_hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":1966,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","email":"kristen_hart@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":692335,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hooper, Michael J. 0000-0002-4161-8961 mhooper@usgs.gov","orcid":"https://orcid.org/0000-0002-4161-8961","contributorId":3251,"corporation":false,"usgs":true,"family":"Hooper","given":"Michael","email":"mhooper@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":692336,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmitt, Christopher J. 0000-0001-6804-2360 cjschmitt@usgs.gov","orcid":"https://orcid.org/0000-0001-6804-2360","contributorId":491,"corporation":false,"usgs":true,"family":"Schmitt","given":"Christopher","email":"cjschmitt@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":692330,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70190047,"text":"70190047 - 2017 - A new model for turbidity current behavior based on integration of flow monitoring and precision coring in a submarine canyon","interactions":[],"lastModifiedDate":"2017-08-07T17:09:12","indexId":"70190047","displayToPublicDate":"2017-04-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"A new model for turbidity current behavior based on integration of flow monitoring and precision coring in a submarine canyon","docAbstract":"Submarine turbidity currents create some of the largest sediment accumulations on Earth, yet there are few direct measurements of these flows. Instead, most of our understanding of turbidity currents results from analyzing their deposits in the sedimentary record. However, the lack of direct flow measurements means that there is considerable debate regarding how to interpret flow properties from ancient deposits. This novel study combines detailed flow monitoring with unusually precisely located cores at different heights, and multiple locations, within the Monterey submarine canyon, offshore California, USA. Dating demonstrates that the cores include the time interval that flows were monitored in the canyon, albeit individual layers cannot be tied to specific flows. There is good correlation between grain sizes collected by traps within the flow and grain sizes measured in cores from similar heights on the canyon walls. Synthesis of flow and deposit data suggests that turbidity currents sourced from the upper reaches of Monterey Canyon comprise three flow phases. Initially, a thin (38–50 m) powerful flow in the upper canyon can transport, tilt, and break the most proximal moorings and deposit chaotic sands and gravel on the canyon floor. The initially thin flow front then thickens and deposits interbedded sands and silty muds on the canyon walls as much as 62 m above the canyon floor. Finally, the flow thickens along its length, thus lofting silty mud and depositing it at greater altitudes than the previous deposits and in excess of 70 m altitude.","language":"English","publisher":"Geological Society of America","doi":"10.1130/G38764.1","usgsCitation":"Symons, W.O., Sumner, E.J., Paull, C.K., Cartigny, M.J., Xu, J., Maier, K., Lorenson, T., and Talling, P.J., 2017, A new model for turbidity current behavior based on integration of flow monitoring and precision coring in a submarine canyon: Geology, v. 45, no. 4, p. 367-370, https://doi.org/10.1130/G38764.1.","productDescription":"4 p.","startPage":"367","endPage":"370","ipdsId":"IP-075966","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469934,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/g38764.1","text":"Publisher Index Page"},{"id":344623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-01","publicationStatus":"PW","scienceBaseUri":"59897c15e4b09fa1cb0c2c04","contributors":{"authors":[{"text":"Symons, William O.","contributorId":195511,"corporation":false,"usgs":false,"family":"Symons","given":"William","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":707308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sumner, Esther J.","contributorId":195512,"corporation":false,"usgs":false,"family":"Sumner","given":"Esther","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":707309,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paull, Charles K. 0000-0001-5940-3443","orcid":"https://orcid.org/0000-0001-5940-3443","contributorId":55825,"corporation":false,"usgs":false,"family":"Paull","given":"Charles","email":"","middleInitial":"K.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":true,"id":707310,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cartigny, Matthieu J.B.","contributorId":195513,"corporation":false,"usgs":false,"family":"Cartigny","given":"Matthieu","email":"","middleInitial":"J.B.","affiliations":[],"preferred":false,"id":707311,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Jingping","contributorId":195514,"corporation":false,"usgs":false,"family":"Xu","given":"Jingping","affiliations":[],"preferred":false,"id":707312,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maier, Katherine L.","contributorId":91411,"corporation":false,"usgs":true,"family":"Maier","given":"Katherine L.","affiliations":[],"preferred":false,"id":707307,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lorenson, Thomas 0000-0001-7669-2873 tlorenson@usgs.gov","orcid":"https://orcid.org/0000-0001-7669-2873","contributorId":174599,"corporation":false,"usgs":true,"family":"Lorenson","given":"Thomas","email":"tlorenson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":707313,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Talling, Peter J.","contributorId":195515,"corporation":false,"usgs":false,"family":"Talling","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":707314,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70184176,"text":"70184176 - 2017 - Final data report for factors controlling DDE dechlorination rates on the Palos Verdes Shelf: A field and laboratory investigation","interactions":[],"lastModifiedDate":"2019-03-06T13:44:23","indexId":"70184176","displayToPublicDate":"2017-04-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Final data report for factors controlling DDE dechlorination rates on the Palos Verdes Shelf: A field and laboratory investigation","docAbstract":"This data report provides a compilation of information developed over the last 6+ years by a\nmulti-disciplinary, multi-institutional research team. The overall goal of this work has been to\nidentify the biological, chemical, and physical factors that control rates of reductive\ndechlorination of DDE and DDMU in sediments of the Palos Verdes Shelf (PVS). More specific\nquestions and objectives are delineated in the Scope of Work (section 12.1., Appendix 1).\nThe study was composed of two parts: 1) field characterization studies, and 2) laboratory\nmicrocosm experiments. The goal of the field characterization studies was to define the\nconditions under which reductive dechlorination of DDE (and DDMU) is occurring in PVS\nsediments. This involved two separate cruises (2009, 2010) during which sediment cores,\nbottom water and other real-time field measurements (e.g., conductivity, temperature, depth of\nthe water column) were acquired. The sediment cores were distributed among research team\nmembers for detailed chemical (R. Eganhouse, B. Orem, M. Reinhard), microbiological (A.\nSpormann), and physical (B. Edwards) analysis as well as for laboratory microcosm experiments\n(M. Reinhard). A team of collaborating USGS scientists generously contributed valuable\ninformation pertaining to geochronology (P. Swarzenski), the character of sedimentary\ngeosorbent phases (P. Hackley), mineralogy (D. Webster), and grain-size characteristics (C.\nSherwood) of PVS sediment samples.\nTogether, this information will serve as framework for a conceptual model of natural degradation\nprocesses in the DDT-contaminated sediments on the PVS. These findings will enable the\nUSEPA to gain a better understanding of the controls on reductive dechlorination and how\ndechlorination rates vary spatially and temporally. This, in turn, should facilitate decision\nmaking concerning the progress of natural attenuation and when monitoring at the site can be\nterminated. Toward that end, a brief Synthesis Report, summarizing and interpreting the\nacquired data, is being prepared and will be released in the coming year.","language":"English","publisher":"U.S. Environmental Protection Agency","usgsCitation":"Eganhouse, R., Pontolillo, J., Orem, W.H., Webster, D.M., Hackley, P.C., Edwards, B.D., Rosenberger, K.J., Dickhudt, P., Sherwood, C.R., Reinhard, M., Qin, S., Dougherty, J., Hopkins, G., Marshall, I., and Spormann, A., 2017, Final data report for factors controlling DDE dechlorination rates on the Palos Verdes Shelf: A field and laboratory investigation, Zip File.","productDescription":"Zip File","ipdsId":"IP-063652","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":339628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":336716,"type":{"id":15,"text":"Index Page"},"url":"https://cumulis.epa.gov/supercpad/cursites/cscdocument.cfm?id=0900993&doc=Y&colid=36797"}],"country":"United States","otherGeospatial":"Palos Verdes Shelf","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.44085693359375,\n 33.773439833797724\n ],\n [\n -118.47381591796875,\n 33.799691173251084\n ],\n [\n -118.5163879394531,\n 33.78827853625996\n ],\n [\n -118.52600097656249,\n 33.76773195605407\n ],\n [\n -118.5150146484375,\n 33.75174787568194\n ],\n [\n -118.50128173828125,\n 33.71291698851023\n ],\n [\n -118.48205566406249,\n 33.678639851675555\n ],\n [\n -118.44223022460938,\n 33.64434904445888\n ],\n [\n -118.4230041503906,\n 33.63062889539564\n ],\n [\n -118.3941650390625,\n 33.618050171974545\n ],\n [\n -118.34747314453125,\n 33.622624465698685\n ],\n [\n -118.31039428710936,\n 33.63634588982396\n ],\n [\n -118.28018188476561,\n 33.67406853374198\n ],\n [\n -118.29666137695311,\n 33.687781758439364\n ],\n [\n -118.32550048828124,\n 33.70377775573253\n ],\n [\n -118.36120605468747,\n 33.72205524868731\n ],\n [\n -118.39691162109375,\n 33.7243396617476\n ],\n [\n -118.41339111328125,\n 33.73119253613475\n ],\n [\n -118.42849731445312,\n 33.75060604160645\n ],\n [\n -118.44085693359375,\n 33.773439833797724\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ef3dabe4b0eed1ab8e3bdc","contributors":{"authors":[{"text":"Eganhouse, Robert P. eganhous@usgs.gov","contributorId":2031,"corporation":false,"usgs":true,"family":"Eganhouse","given":"Robert P.","email":"eganhous@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - 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Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":680346,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":680347,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, Brian D. bedwards@usgs.gov","contributorId":3161,"corporation":false,"usgs":true,"family":"Edwards","given":"Brian","email":"bedwards@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":680348,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rosenberger, Kurt J. 0000-0002-5185-5776 krosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5185-5776","contributorId":140453,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Kurt","email":"krosenberger@usgs.gov","middleInitial":"J.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":680349,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dickhudt, Patrick 0000-0001-8003-7089 pdickhudt@usgs.gov","orcid":"https://orcid.org/0000-0001-8003-7089","contributorId":187402,"corporation":false,"usgs":true,"family":"Dickhudt","given":"Patrick","email":"pdickhudt@usgs.gov","affiliations":[],"preferred":true,"id":680350,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":680351,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reinhard, Martin","contributorId":187403,"corporation":false,"usgs":false,"family":"Reinhard","given":"Martin","email":"","affiliations":[],"preferred":false,"id":680352,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Qin, Sujie","contributorId":187404,"corporation":false,"usgs":false,"family":"Qin","given":"Sujie","email":"","affiliations":[],"preferred":false,"id":680353,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Dougherty, Jennifer","contributorId":187405,"corporation":false,"usgs":false,"family":"Dougherty","given":"Jennifer","affiliations":[],"preferred":false,"id":680354,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hopkins, Gary","contributorId":187406,"corporation":false,"usgs":false,"family":"Hopkins","given":"Gary","affiliations":[],"preferred":false,"id":680355,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Marshall, Ian","contributorId":187407,"corporation":false,"usgs":false,"family":"Marshall","given":"Ian","email":"","affiliations":[],"preferred":false,"id":680356,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Spormann, Alfred","contributorId":187408,"corporation":false,"usgs":false,"family":"Spormann","given":"Alfred","email":"","affiliations":[],"preferred":false,"id":680357,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70186880,"text":"70186880 - 2017 - Quantifying the demographic cost of human-related mortality to a raptor population","interactions":[],"lastModifiedDate":"2017-11-22T16:58:07","indexId":"70186880","displayToPublicDate":"2017-04-12T00:00:00","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":"Quantifying the demographic cost of human-related mortality to a raptor population","docAbstract":"Raptors are exposed to a wide variety of human-related mortality agents, and yet population-level effects are rarely quantified. Doing so requires modeling vital rates in the context of species life-history, behavior, and population dynamics theory. In this paper, we explore the details of such an analysis by focusing on the demography of a resident, tree-nesting population of golden eagles (Aquila chrysaetos) in the vicinity of an extensive (142 km2) windfarm in California. During 1994–2000, we tracked the fates of >250 radio-marked individuals of four life-stages and conducted five annual surveys of territory occupancy and reproduction. Collisions with wind turbines accounted for 41% of 88 uncensored fatalities, most of which were subadults and nonbreeding adults (floaters). A consistent overall male preponderance in the population meant that females were the limiting sex in this territorial, monogamous species. Estimates of potential population growth rate and associated variance indicated a stable breeding population, but one for which any further decrease in vital rates would require immigrant floaters to fill territory vacancies. Occupancy surveys 5 and 13 years later (2005 and 2013) showed that the nesting population remained intact, and no upward trend was apparent in the proportion of subadult eagles as pair members, a condition that would have suggested a deficit of adult replacements. However, the number of golden eagle pairs required to support windfarm mortality was large. We estimated that the entire annual reproductive output of 216–255 breeding pairs would have been necessary to support published estimates of 55–65 turbine blade-strike fatalities per year. Although the vital rates forming the basis for these calculations may have changed since the data were collected, our approach should be useful for gaining a clearer understanding of how anthropogenic mortality affects the health of raptor populations, particularly those species with delayed maturity and naturally low reproductive rates.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0172232","usgsCitation":"Hunt, W.G., Wiens, D., Law, P.R., Fuller, M.R., Hunt, T.L., Driscoll, D.E., and Jackman, R.E., 2017, Quantifying the demographic cost of human-related mortality to a raptor population: PLoS ONE, v. 12, no. 2, e0172232; 22 p., https://doi.org/10.1371/journal.pone.0172232.","productDescription":"e0172232; 22 p.","ipdsId":"IP-077853","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469932,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0172232","text":"Publisher Index Page"},{"id":339649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-24","publicationStatus":"PW","scienceBaseUri":"58ef3da4e4b0eed1ab8e3bc2","contributors":{"authors":[{"text":"Hunt, W. Grainger","contributorId":139544,"corporation":false,"usgs":false,"family":"Hunt","given":"W.","email":"","middleInitial":"Grainger","affiliations":[{"id":12795,"text":"The Peregrine Fund, Inc.","active":true,"usgs":false}],"preferred":false,"id":690799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wiens, David 0000-0002-2020-038X jwiens@usgs.gov","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":167538,"corporation":false,"usgs":true,"family":"Wiens","given":"David","email":"jwiens@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":690800,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Law, Peter R.","contributorId":190824,"corporation":false,"usgs":false,"family":"Law","given":"Peter","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":690801,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fuller, Mark R. 0000-0001-7459-1729 mark_fuller@usgs.gov","orcid":"https://orcid.org/0000-0001-7459-1729","contributorId":2296,"corporation":false,"usgs":true,"family":"Fuller","given":"Mark","email":"mark_fuller@usgs.gov","middleInitial":"R.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":690798,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunt, Teresa L.","contributorId":190825,"corporation":false,"usgs":false,"family":"Hunt","given":"Teresa","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":690802,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Driscoll, Daniel E.","contributorId":190826,"corporation":false,"usgs":false,"family":"Driscoll","given":"Daniel","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":690803,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jackman, Ronald E.","contributorId":190827,"corporation":false,"usgs":false,"family":"Jackman","given":"Ronald","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":690804,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70185354,"text":"ds1045 - 2017 - Sediment lithology and radiochemistry from the back-barrier environments along the northern Chandeleur Islands, Louisiana—March 2012","interactions":[],"lastModifiedDate":"2025-05-13T16:44:15.712868","indexId":"ds1045","displayToPublicDate":"2017-04-11T16:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1045","title":"Sediment lithology and radiochemistry from the back-barrier environments along the northern Chandeleur Islands, Louisiana—March 2012","docAbstract":"Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center collected a set of 8 sediment cores from the back-barrier environments along the northern Chandeleur Islands, Louisiana, in March 2012. The sampling efforts were part of a larger USGS study to evaluate effects on the geomorphology of the Chandeleur Islands following the construction of an artificial sand berm to reduce oil transport onto federally managed lands. The objective of this study was to evaluate the response of the back-barrier tidal and wetland environments to the berm. This report serves as an archive for sedimentological and radiochemical data derived from the sediment cores. The data described in this report are available for download on the data downloads page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1045","usgsCitation":"Marot, M.E., Smith, C.G., Adams, C.S., and Richwine, K.A, 2017, Sediment lithology and radiochemistry from the back-barrier environments along the northern Chandeleur Islands, Louisiana—March 2012: U.S. Geological Survey Data Series 1045, https://doi.org/10.3133/ds1045.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078217","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":338203,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1045/index.html","text":"Report HTML"},{"id":338202,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1045/coverthb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.88626098632812,\n 29.859701442126756\n ],\n [\n -88.78326416015625,\n 29.859701442126756\n ],\n [\n -88.78326416015625,\n 30.022732549250424\n ],\n [\n -88.88626098632812,\n 30.022732549250424\n ],\n [\n -88.88626098632812,\n 29.859701442126756\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
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Sexual selection may act as a promotor of speciation since divergent mate choice and competition for mates can rapidly lead to reproductive isolation. Alternatively, sexual selection may also retard speciation since polygamous individuals can access additional mates by increased breeding dispersal. High breeding dispersal should hence increase gene flow and reduce diversification in polygamous species. Here, we test how polygamy predicts diversification in shorebirds using genetic differentiation and subspecies richness as proxies for population divergence. Examining microsatellite data from 79 populations in 10 plover species (Genus: Charadrius) we found that polygamous species display significantly less genetic structure and weaker isolation-by-distance effects than monogamous species. Consistent with this result, a comparative analysis including 136 shorebird species showed significantly fewer subspecies for polygamous than for monogamous species. By contrast, migratory behavior neither predicted genetic differentiation nor subspecies richness. Taken together, our results suggest that dispersal associated with polygamy may facilitate gene flow and limit population divergence. Therefore, intense sexual selection, as occurs in polygamous species, may act as a brake rather than an engine of speciation in shorebirds. We discuss alternative explanations for these results and call for further studies to understand the relationships between sexual selection, dispersal, and diversification.
","language":"English","publisher":"Wiley","doi":"10.1111/evo.13212","usgsCitation":"Jackson, J.D., dos Remedios, N., Maher, K., Zefania, S., Haig, S.M., Oyler-McCance, S.J., Blomqvist, D., Burke, T., Bruford, M.W., Szekely, T., and Kupper, C., 2017, Polygamy slows down population divergence in shorebirds: Evolution, v. 71, no. 5, p. 1313-1326, https://doi.org/10.1111/evo.13212.","productDescription":"14 p.","startPage":"1313","endPage":"1326","ipdsId":"IP-084178","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469935,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/evo.13212","text":"Publisher Index Page"},{"id":339578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-10","publicationStatus":"PW","scienceBaseUri":"58edb943e4b0eed1ab8c6eff","contributors":{"authors":[{"text":"Jackson, Josephine D’Urban","contributorId":190763,"corporation":false,"usgs":false,"family":"Jackson","given":"Josephine","email":"","middleInitial":"D’Urban","affiliations":[],"preferred":false,"id":690654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"dos Remedios, Natalie","contributorId":190764,"corporation":false,"usgs":false,"family":"dos Remedios","given":"Natalie","email":"","affiliations":[],"preferred":false,"id":690655,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maher, Kathryn","contributorId":190765,"corporation":false,"usgs":false,"family":"Maher","given":"Kathryn","email":"","affiliations":[],"preferred":false,"id":690656,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zefania, Sama","contributorId":190766,"corporation":false,"usgs":false,"family":"Zefania","given":"Sama","email":"","affiliations":[],"preferred":false,"id":690657,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":690653,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":690658,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blomqvist, Donald","contributorId":190767,"corporation":false,"usgs":false,"family":"Blomqvist","given":"Donald","email":"","affiliations":[],"preferred":false,"id":690659,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Burke, Terry","contributorId":190768,"corporation":false,"usgs":false,"family":"Burke","given":"Terry","email":"","affiliations":[],"preferred":false,"id":690660,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bruford, Michael W.","contributorId":190769,"corporation":false,"usgs":false,"family":"Bruford","given":"Michael","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":690661,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Szekely, Tamas","contributorId":190770,"corporation":false,"usgs":false,"family":"Szekely","given":"Tamas","email":"","affiliations":[],"preferred":false,"id":690662,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kupper, Clemens","contributorId":190771,"corporation":false,"usgs":false,"family":"Kupper","given":"Clemens","email":"","affiliations":[],"preferred":false,"id":690663,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70186820,"text":"70186820 - 2017 - Contrasting nest survival patterns for ducks and songbirds in northern mixed-grass prairie","interactions":[],"lastModifiedDate":"2017-05-02T15:24:33","indexId":"70186820","displayToPublicDate":"2017-04-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Contrasting nest survival patterns for ducks and songbirds in northern mixed-grass prairie","docAbstract":"Management actions intended to protect or improve habitat for ducks may benefit grassland-nesting passerines, but scant information is available to explore this assumption. During 1998–2003, we examined nest survival of ducks and songbirds to determine whether effects of prescribed fire and other habitat features (e.g., shrub cover and distance to habitat edges) were similar for ducks and passerines breeding in North Dakota. We used the logistic-exposure method to estimate survival of duck and songbird nests (n = 3,171). We used an information-theoretic approach to identify factors that most influenced nest survival. Patterns of nest survival were markedly different between taxonomic groups. For ducks, nest survival was greater during the first postfire nesting season (daily survival rate [DSR] = 0.957, 85% CI = 0.951–0.963), relative to later postfire nesting seasons (DSR = 0.946, 85% CI = 0.942–0.950). Furthermore duck nest survival and nest densities were inversely related. Duck nest survival also was greater as shrub cover decreased and as distance from cropland and wetland edges increased. Passerines had lower nest survival during the first postfire nesting season (DSR = 0.934, 85% CI = 0.924–0.944), when densities also were low compared to subsequent postfire nesting seasons (DSR = 0.947, 85% CI = 0.944–0.950). Parasitism by brown-headed cowbirds (Molothrus ater) reduced passerine nest survival and this effect was more pronounced during the first postfire nesting season compared to subsequent nesting seasons. Passerine nest survival was greater as shrub cover decreased and perhaps for more concealed nests. Duck and songbird nest survival rates were not correlated during this study and for associated studies that examined additional variables using the same dataset, suggesting that different mechanisms influenced their survival. Based on our results, ducks should not be considered direct surrogates for passerines when predicting effects of prescribed fire, shrub cover, and habitat edges on nest survival.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21224","usgsCitation":"Grant, T., Shaffer, T.L., Madden, E.M., and Nenneman, M.P., 2017, Contrasting nest survival patterns for ducks and songbirds in northern mixed-grass prairie: Journal of Wildlife Management, v. 81, no. 4, p. 641-651, https://doi.org/10.1002/jwmg.21224.","productDescription":"11 p.","startPage":"641","endPage":"651","ipdsId":"IP-073644","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":339585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","county":"Bottineau County","otherGeospatial":"J. Clark Salyer National Wildlife Refuge","volume":"81","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-07","publicationStatus":"PW","scienceBaseUri":"58edb943e4b0eed1ab8c6efd","contributors":{"authors":[{"text":"Grant, Todd","contributorId":190775,"corporation":false,"usgs":false,"family":"Grant","given":"Todd","affiliations":[],"preferred":false,"id":690673,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaffer, Terry L. 0000-0001-6950-8951 tshaffer@usgs.gov","orcid":"https://orcid.org/0000-0001-6950-8951","contributorId":3192,"corporation":false,"usgs":true,"family":"Shaffer","given":"Terry","email":"tshaffer@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":690672,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madden, Elizabeth M.","contributorId":190776,"corporation":false,"usgs":false,"family":"Madden","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":690674,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nenneman, Melvin P.","contributorId":190777,"corporation":false,"usgs":false,"family":"Nenneman","given":"Melvin","email":"","middleInitial":"P.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":690675,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70186797,"text":"70186797 - 2017 - Groundwater flow model for the Little Plover River basin in Wisconsin’s Central Sands","interactions":[],"lastModifiedDate":"2017-04-11T10:40:43","indexId":"70186797","displayToPublicDate":"2017-04-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":5368,"text":"Bulletin","active":true,"publicationSubtype":{"id":9}},"seriesNumber":"111","title":"Groundwater flow model for the Little Plover River basin in Wisconsin’s Central Sands","docAbstract":"The Little Plover River is a groundwater-fed stream in the sand plains region of central Wisconsin. In this region, sandy sediment deposited during or soon after the last glaciation forms an important unconfined sand and gravel aquifer. This aquifer supplies water for numerous high-capacity irrigation, municipal, and industrial wells that support a thriving agricultural industry. In recent years the addition of many new wells, combined with observed diminished flows in the Little Plover and other nearby rivers, has raised concerns about the impacts of the wells on groundwater levels and on water levels and flows in nearby lakes, streams, and wetlands. Diverse stakeholder groups, including well operators, Growers, environmentalists, local land owners, and regulatory and government officials have sought a better understanding of the local groundwater-surface water system and have a shared desire to balance the water needs of the he liagricultural, industrial, and urban users with the maintenance and protection of groundwater-dependent natural resources. To help address these issues, the Wisconsin Department of Natural Resources requested that the Wisconsin Geological and Natural History Survey and U.S. Geological Survey cooperatively develop a groundwater flow model that could be used to demonstrate the relationships among groundwater, surface water, and well withdrawals and also be a tool for testing and evaluating alternative water management strategies for the central sands region. Because of an abundance of previous studies, data availability, local interest, and existing regulatory constraints the model focuses on the Little Plover River watershed, but the modeling methodology developed during this study can apply to much of the larger central sands of Wisconsin. The
Little Plover River groundwater flow model simulates three-dimensional groundwater movement in and around the Little Plover River basin under steady-state and transient conditions. This model explicitly includes all high-capacity wells in the model domain and simulates seasonal variations in recharge and well pumping. The model represents the Little Plover River, and other significant streams and drainage ditches in the model domain, as fully connected to the groundwater system, computes stream base flow resulting from groundwater discharge, and routes the flow along the stream channel. A separate soil-water-balance (SWB) model was used to develop groundwater recharge arrays as input for the groundwater flow model. The SWB model uses topography, soils, land use, and climatic data to estimate recharge as deep drainage from the soil zone. The SWB model explicitly includes recharge originating as irrigation water, and computes irrigation using techniques similar to those used by local irrigation operators.
The groundwater flow model uses the U.S. Geological Survey’s MODFLOW modeling code which is freely available, widely accepted, and commonly used by the groundwater community. The groundwater flow model and the SWB model use identical high-resolution numerical grids having model cells 100 feet on a side, with physical properties assigned to each grid cell. This grid allows accurate geographic placement of wells, streams, and other model features. The 3-dimensional grid has three layers; layers 1 and 2 represent the sand and gravel aquifer and layer 3 represents the underlying sandstone. The distribution of material properties in the model (hydraulic conductivity, aquifer thickness, etc.) comes from previous published geologic studies of the region, updated by calibration to recent streamflow and groundwater level data. The SWB model operates on a daily time step. The groundwater flow model was calibrated to monthly stress periods with time steps ranging from 1 to 16 days. More detailed time discretization is possible.
The groundwater model was calibrated to water-level and streamflow data collected during 2013 and 2014 by adjusting model parameters (primarily hydraulic conductivity, storage, and recharge) until the model produced a conditionally optimal fit between field observations and model output, subject to consistency with previously published geologic studies. Calibration was performed under both steady and transient conditions, and used a sophisticated parameter-estimation procedure (PEST) for the calibration process and to identify important model parameters. For the Little Plover River, the two most important parameters are the global recharge multiplier and the hydraulic conductivity of the stream bed. The calibrated model produces water-level and mass-balance results that are consistent with field observations and previous studies of the area.
The completed model is a powerful tool for testing and demonstrating alternative water-management scenarios. Example model applications described in this report include simulating how the cumulative impacts of pumping and land-use change have affected average baseflow in the Little Plover River. Depletion-potential mapping represents a method for predicting which wells and well locations have the greatest impact on nearby surface-water resources.
The completed model is publicly available, along with a companion user’s guide to assist with its operation, at http://wgnhs.org/littleplover- river-groundwater-model.
","language":"English","publisher":"Wisconsin Geological and Natural History Survey","publisherLocation":"Madison, WI","usgsCitation":"Bradbury, K., Fienen, M., Kniffin, M., Jacob Krause, Westenbroek, S.M., Leaf, A.T., and Barlow, P.M., 2017, Groundwater flow model for the Little Plover River basin in Wisconsin’s Central Sands: Bulletin 111, Zip file: Report: x, 82 p., Appendixes 1-8.","productDescription":"Zip file: Report: x, 82 p., Appendixes 1-8","ipdsId":"IP-080836","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":339550,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.org/little-plover-river-groundwater-model/"},{"id":339556,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Little Plover River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": 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44.17826452922573\n ],\n [\n -90.02471923828125,\n 44.081666311450526\n ],\n [\n -89.99450683593749,\n 43.92361542884112\n ],\n [\n -89.97528076171874,\n 43.84839376489152\n ],\n [\n -89.74731445312499,\n 43.61221676817573\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58edb944e4b0eed1ab8c6f05","contributors":{"authors":[{"text":"Bradbury, Ken","contributorId":190742,"corporation":false,"usgs":false,"family":"Bradbury","given":"Ken","email":"","affiliations":[],"preferred":false,"id":690592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":177065,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","email":"mnfienen@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":690591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kniffin, Maribeth","contributorId":190743,"corporation":false,"usgs":false,"family":"Kniffin","given":"Maribeth","email":"","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":690593,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacob Krause","contributorId":190744,"corporation":false,"usgs":false,"family":"Jacob Krause","affiliations":[],"preferred":false,"id":690594,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Westenbroek, Stephen M. 0000-0002-6284-8643 smwesten@usgs.gov","orcid":"https://orcid.org/0000-0002-6284-8643","contributorId":2210,"corporation":false,"usgs":true,"family":"Westenbroek","given":"Stephen","email":"smwesten@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":690595,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leaf, Andrew T. 0000-0001-8784-4924 aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":690596,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barlow, Paul M. 0000-0003-4247-6456 pbarlow@usgs.gov","orcid":"https://orcid.org/0000-0003-4247-6456","contributorId":1200,"corporation":false,"usgs":true,"family":"Barlow","given":"Paul","email":"pbarlow@usgs.gov","middleInitial":"M.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":690597,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70180400,"text":"ds1035 - 2017 - USGS Spectral Library Version 7","interactions":[{"subject":{"id":80486,"text":"ds231 - 2007 - USGS Digital Spectral Library splib06a","indexId":"ds231","publicationYear":"2007","noYear":false,"title":"USGS Digital Spectral Library splib06a"},"predicate":"SUPERSEDED_BY","object":{"id":70180400,"text":"ds1035 - 2017 - USGS Spectral Library Version 7","indexId":"ds1035","publicationYear":"2017","noYear":false,"title":"USGS Spectral Library Version 7"},"id":1}],"lastModifiedDate":"2025-01-31T18:24:46.250139","indexId":"ds1035","displayToPublicDate":"2017-04-10T13:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1035","title":"USGS Spectral Library Version 7","docAbstract":"We have assembled a library of spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns [μm]). Laboratory samples of specific minerals, plants, chemical compounds, and manmade materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically constructed as well as mathematically computed mixtures. Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of Analytical Spectral Devices (ASD) field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Measurements of rocks, soils, and natural mixtures of minerals were made in laboratory and field settings. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. This report describes the instruments used, the organization of materials into chapters, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements. To facilitate greater application of the spectra, the library has also been convolved to selected spectrometer and imaging spectrometers sampling and bandpasses, and resampled to selected broadband multispectral sensors. The native file format of the library is the SPECtrum Processing Routines (SPECPR) data format. This report describes how to access freely available software to read the SPECPR format. To facilitate broader access to the library, we produced generic formats of the spectra and metadata in text files. The library is provided on digital media and online at https://speclab.cr.usgs.gov/spectral-lib.html. A long-term archive of these data are stored on the USGS ScienceBase data server (https://dx.doi.org/10.5066/F7RR1WDJ).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1035","usgsCitation":"Kokaly, R.F., Clark, R.N., Swayze, G.A., Livo, K.E., Hoefen, T.M., Pearson, N.C., Wise, R.A., Benzel, W.M., Lowers, H.A., Driscoll, R.L., and Klein, A.J., 2017, USGS Spectral Library Version 7: U.S. Geological Survey Data Series 1035, 61 p., https://doi.org/10.3133/ds1035.","productDescription":"Report: iv, 61 p.; Dataset; Data Release","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-075936","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":336935,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1035/coverthb.jpg"},{"id":336936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1035/ds1035.pdf","text":"Report","size":"4.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1035"},{"id":438380,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RR1WDJ","text":"USGS data release","linkHelpText":"USGS Spectral Library Version 7 Data"}],"contact":"Center Director, USGS Crustal Geophysics and Geochemistry Science Center
Box 25046, Mail Stop 964
Denver, CO 80225
Coral skeletons are valuable archives of past ocean conditions. However, interpretation of coral paleotemperature records is confounded by uncertainties associated with single-element ratio thermometers, including Sr/Ca. A new approach, Sr-U, uses U/Ca to constrain the influence of Rayleigh fractionation on Sr/Ca. Here we build on the initial Pacific Porites Sr-U calibration to include multiple Atlantic and Pacific coral genera from multiple coral reef locations spanning a temperature range of 23.15–30.12°C. Accounting for the wintertime growth cessation of one Bermuda coral, we show that Sr-U is strongly correlated with the average water temperature at each location (r2 = 0.91, P < 0.001, n = 19). We applied the multispecies spatial calibration between Sr-U and temperature to reconstruct a 96 year long temperature record at Mona Island, Puerto Rico, using a coral not included in the calibration. Average Sr-U derived temperature for the period 1900–1996 is within 0.12°C of the average instrumental temperature at this site and captures the twentieth century warming trend of 0.06°C per decade. Sr-U also captures the timing of multiyear variability but with higher amplitude than implied by the instrumental data. Mean Sr-U temperatures and patterns of multiyear variability were replicated in a second coral in the same grid box. Conversely, Sr/Ca records from the same two corals were inconsistent with each other and failed to capture absolute sea temperatures, timing of multiyear variability, or the twentieth century warming trend. Our results suggest that coral Sr-U paleothermometry is a promising new tool for reconstruction of past ocean temperatures.
","language":"English","publisher":"Wiley","doi":"10.1002/2016PA002976","usgsCitation":"Alpert, A.E., Cohen, A.L., Oppo, D.W., DeCarlo, T.M., Gaetani, G.A., Hernandez-Delgado, E.A., Winter, A., and Gonneea Eagle, M., 2017, Twentieth century warming of the tropical Atlantic captured by Sr-U paleothermometry: Paleoceanography, v. 32, no. 2, p. 146-160, https://doi.org/10.1002/2016PA002976.","productDescription":"15 p.","startPage":"146","endPage":"160","ipdsId":"IP-079454","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016pa002976","text":"Publisher Index Page"},{"id":339514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"Mona Island","volume":"32","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-16","publicationStatus":"PW","scienceBaseUri":"58ec99d9e4b0b4d95d335259","contributors":{"authors":[{"text":"Alpert, Alice E.","contributorId":190715,"corporation":false,"usgs":false,"family":"Alpert","given":"Alice","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":690471,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cohen, Anne L.","contributorId":190716,"corporation":false,"usgs":false,"family":"Cohen","given":"Anne","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":690472,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oppo, Delia W.","contributorId":190717,"corporation":false,"usgs":false,"family":"Oppo","given":"Delia","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":690473,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeCarlo, Thomas M.","contributorId":190720,"corporation":false,"usgs":false,"family":"DeCarlo","given":"Thomas","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":690474,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gaetani, Glenn A.","contributorId":190718,"corporation":false,"usgs":false,"family":"Gaetani","given":"Glenn","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":690475,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hernandez-Delgado, Edwin A.","contributorId":190719,"corporation":false,"usgs":false,"family":"Hernandez-Delgado","given":"Edwin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":690476,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Winter, Amos","contributorId":72271,"corporation":false,"usgs":false,"family":"Winter","given":"Amos","email":"","affiliations":[],"preferred":false,"id":690477,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gonneea Eagle, Meagan 0000-0001-5072-2755 mgonneea@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":174590,"corporation":false,"usgs":true,"family":"Gonneea Eagle","given":"Meagan","email":"mgonneea@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":690470,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70185332,"text":"sir20175021 - 2017 - An update of hydrologic conditions and distribution of selected constituents in water, eastern Snake River Plain aquifer and perched groundwater zones, Idaho National Laboratory, Idaho, emphasis 2012-15","interactions":[],"lastModifiedDate":"2017-04-11T15:16:36","indexId":"sir20175021","displayToPublicDate":"2017-04-10T00:00:00","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":"2017-5021","title":"An update of hydrologic conditions and distribution of selected constituents in water, eastern Snake River Plain aquifer and perched groundwater zones, Idaho National Laboratory, Idaho, emphasis 2012-15","docAbstract":"Since 1952, wastewater discharged to in ltration ponds (also called percolation ponds) and disposal wells at the Idaho National Laboratory (INL) has affected water quality in the eastern Snake River Plain (ESRP) aquifer and perched groundwater zones underlying the INL. The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy, maintains groundwater-monitoring networks at the INL to determine hydrologic trends and to delineate the movement of radiochemical and chemical wastes in the aquifer and in perched groundwater zones. This report presents an analysis of water-level and water-quality data collected from the ESRP aquifer, multilevel monitoring system (MLMS) wells in the ESRP aquifer, and perched groundwater wells in the USGS groundwater monitoring networks during 2012-15.
Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
https://id.water.usgs.gov
Topographic elevation data collected with airborne light detection and ranging (lidar) can be used to analyze short- and long-term changes to beach and dune systems. Analysis of multiple lidar datasets at Dauphin Island, Alabama, revealed systematic, island-wide elevation differences on the order of 10s of centimeters (cm) that were not attributable to real-world change and, therefore, were likely to represent systematic sampling offsets. These offsets vary between the datasets, but appear spatially consistent within a given survey. This report describes a method that was developed to identify and correct offsets between lidar datasets collected over the same site at different times so that true elevation changes over time, associated with sediment accumulation or erosion, can be analyzed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171031","usgsCitation":"Thompson, D.M., Dalyander, P.S., Long, J.W., and Plant, N.G., 2017, Correction of elevation offsets in multiple co-located lidar datasets: U.S. Geological Survey Open-File Report 2017–1031, 10 p., https://doi.org/10.3133/ofr20171031. ","productDescription":"iv, 10 p.","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-079032","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":339143,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1031/ofr20171031.pdf","text":"Report","size":"546 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1031"},{"id":339142,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1031/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.21369171142578,\n 30.217541849095714\n ],\n [\n -88.06949615478516,\n 30.217541849095714\n ],\n [\n -88.06949615478516,\n 30.28575280701959\n ],\n [\n -88.21369171142578,\n 30.28575280701959\n ],\n [\n -88.21369171142578,\n 30.217541849095714\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
http://coastal.er.usgs.gov/